scholarly journals A classifier driven approach to find biomarkers for affective disorders from transcription profiles in blood

2016 ◽  
Vol 1 (1) ◽  
pp. 34 ◽  
Author(s):  
Wiktor Mazin ◽  
Joseph A Tamm ◽  
Irina A Antonijevic ◽  
Aicha Abdourahman ◽  
Munish Das ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Affective Disorders ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Levels ◽  
Control Subjects ◽  
Transcription Profiles

Gene expression profiles in blood are increasingly being used to identify biomarkers for different affective disorders. We have selected a set of 29 genes to generate expression profiles for healthy control subjects as well as for patients diagnosed with acute post-traumatic stress disorder (PTSD) and with borderline personality disorder (BPD). Measurements were performed by quantitative polymerase chain reaction (qPCR). Using the actual data in an anonym-ous form we constructed a series of artificial data sets with known gene expression profiles. These sets were used to test 14 classification algorithms and feature selection methods for their ability to identify the correct expression patterns. Application of the three most effective algorithms to the actual expression data showed that control subjects can be dis-tinguished from BPD patients based on differential expression levels of the gene transcripts Gi2, GR and MAPK14, targets that may have links to stress related diseases. Controls can also be distinguished from acute PTSD patients by differential expression levels of the transcripts for ERK2 and RGS2 that are known to be associated with mood disord-ers and social anxiety. We conclude that it is possible to identify informative transcription profiles in blood samples from individuals with affective disorders.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

scholarly journals Transcriptional Profiling of Leucocyte Count Variation from Porcine Peripheral Blood Reveals Differential Gene Expression

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Adeyinka Abiola Adetula ◽  
Xiangdong Liu ◽  
Thuy Nhien Tran Thi ◽  
Ali Akbar Bhuiyan ◽  
Xiaoyong Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Differential Expression ◽  
Leucocyte Count ◽  
Expression Profiles ◽  
Transcriptional Profiling ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Poly I:C ◽  
Biological Processes

Leucocytes have tremendous health-check importance related to the individual antiviral capacity of pigs and other mammals. However, the molecular mechanism of the immune response of blood leucocytes in pigs is not completely known. This study investigated the leucocyte-count variation before and after poly I:C stimulation in a Duroc–Erhualian F2 population. Pigs with increased and decreased differences in leucocyte counts were coded as increased responder (IR) and decreased responder (DR), respectively. Then, we used microarray technology to compare the gene-expression profiles of both groups of pigs. Transcriptomic analysis identified 129 differentially expressed genes (DEGs) in IR pigs and 136 DEGs in DR pigs. Forty-one common DEGs showed that both groups had similar expression patterns of immune responses. These results illustrated a differential expression in both groups. Furthermore, qPCR experiment was performed to verify the differential-expression profile. Functional annotation of the DEGs indicated that both IR and DR pigs were similar in several biological processes, including innate immune response, and also exhibited distinct differences in biological processes, molecular function, and pathways. These results provided insights into the mechanism underlying the antiviral capacity of pigs.Trial registration numberis CAS Registry Number24939-03-5.

scholarly journals Identification of reference genes for gene expression studies among different developmental stages of murine hearts

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jie Ren ◽  
Ningning Zhang ◽  
Xiangjie Li ◽  
Xiaogang Sun ◽  
Jiangping Song
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Heart Development ◽  
Reference Genes ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Qpcr Data

Abstract Background Real-time quantitative polymerase chain reaction (RT-qPCR) is a widely-used standard assay for assessing gene expression. RT-qPCR data requires reference genes for normalization to make the results comparable. Therefore, the selected reference gene should be highly stable in its expression throughout the experimental datasets. So far, reports about the optimal set of reference genes in murine left ventricle (LV) across embryonic and postnatal stages are few. The objective of our research was to identify the appropriate reference genes in murine LV among different developmental stages. Methods We investigated the gene expression profiles of 21 widely used housekeeping genes in murine LV from 7 different developmental stages (almost throughout the whole period of the mouse lifespan). The stabilities of the potential reference genes were evaluated by five methods: GeNorm, NormFinder, BestKeeper, Delta-Ct and RefFinder. Results We proposed a set of reliable reference genes for normalization of RT-qPCR experimental data in different conditions. Furthermore, our results showed that 6 genes (18S, Hmbs, Ubc, Psmb4, Tfrc and Actb) are not recommended to be used as reference genes in murine LV development studies. The data also suggested that the Rplp0 gene might serve as an optimal reference gene in gene expression analysis. Conclusions Our study investigated the expression stability of the commonly used reference genes in process of LV development and maturation. We proposed a set of optimal reference genes that are suitable for accurate normalization of RT-qPCR data in specific conditions. Our findings may be helpful in future studies for investigating the gene expression patterns and mechanism of mammalian heart development.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

CFTR ΔF508 mutation has minimal effect on the gene expression profile of differentiated human airway epithelia

2005 ◽  
Vol 289 (4) ◽  
pp. L545-L553 ◽  
Author(s):  
Joseph Zabner ◽  
Todd E. Scheetz ◽  
Hakeem G. Almabrazi ◽  
Thomas L. Casavant ◽  
Jian Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cystic Fibrosis ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Primary Cultures ◽  
Gene Expression Profiles ◽  
Filter Method ◽  
Tissue Destruction ◽  
Airway Epithelia

Cystic fibrosis (CF) is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial chloride channel regulated by phosphorylation. Most of the disease-associated morbidity is the consequence of chronic lung infection with progressive tissue destruction. As an approach to investigate the cellular effects of CFTR mutations, we used large-scale microarray hybridization to contrast the gene expression profiles of well-differentiated primary cultures of human CF and non-CF airway epithelia grown under resting culture conditions. We surveyed the expression profiles for 10 non-CF and 10 ΔF508 homozygote samples. Of the 22,283 genes represented on the Affymetrix U133A GeneChip, we found evidence of significant changes in expression in 24 genes by two-sample t-test ( P < 0.00001). A second, three-filter method of comparative analysis found no significant differences between the groups. The levels of CFTR mRNA were comparable in both groups. There were no significant differences in the gene expression patterns between male and female CF specimens. There were 18 genes with significant increases and 6 genes with decreases in CF relative to non-CF samples. Although the function of many of the differentially expressed genes is unknown, one transcript that was elevated in CF, the KCl cotransporter (KCC4), is a candidate for further study. Overall, the results indicate that CFTR dysfunction has little direct impact on airway epithelial gene expression in samples grown under these conditions.

scholarly journals Comparative transcriptomics identifies differences in the regulation of the floral transition between Arabidopsis and Brassica rapa cultivars

2020 ◽  
Author(s):  
Alexander Calderwood ◽  
Jo Hepworth ◽  
Shannon Woodhouse ◽  
Lorelei Bilham ◽  
D. Marc Jones ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brassica Rapa ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Developmental Time ◽  
Floral Transition ◽  
Gene Regulatory

AbstractThe timing of the floral transition affects reproduction and yield, however its regulation in crops remains poorly understood. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. A direct comparison of gene expression over time between species shows little similarity, which could lead to the inference that different gene regulatory networks are at play. However, these differences can be largely resolved by synchronisation, through curve registration, of gene expression profiles. We find that different registration functions are required for different genes, indicating that there is no common ‘developmental time’ to which Arabidopsis and B. rapa can be mapped through gene expression. Instead, the expression patterns of different genes progress at different rates. We find that co-regulated genes show similar changes in synchronisation between species, suggesting that similar gene regulatory sub-network structures may be active with different wiring between them. A detailed comparison of the regulation of the floral transition between Arabidopsis and B. rapa, and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways, even when grown under the same environmental conditions. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa under long days and highlights the importance of registration methods for the comparison of developmental gene expression data.

Spatial Transcriptomics analysis of uterine gene expression in enhancer of Zeste homolog 2 (Ezh2) conditional knockout mice

2021 ◽  
Author(s):  
Ana M Mesa ◽  
Jiude Mao ◽  
Theresa I Medrano ◽  
Nathan J Bivens ◽  
Alexander Jurkevich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Reproductive Organs ◽  
Conditional Knockout ◽  
Estrogen Signaling ◽  
Uterine Epithelial Cell ◽  
Stromal Tissue

Abstract Histone proteins undergo various modifications that alter chromatin structure, including addition of methyl groups. Enhancer of homolog 2 (EZH2), is a histone methyltransferase that methylates lysine residue 27, and thereby, suppresses gene expression. EZH2 plays integral role in the uterus and other reproductive organs. We have previously shown that conditional deletion of uterine EZH2 results in increased proliferation of luminal and glandular epithelial cells, and RNAseq analyses reveal several uterine transcriptomic changes in Ezh2 conditional (c) knockout (KO) mice that can affect estrogen signaling pathways. To pinpoint the origin of such gene expression changes, we used the recently developed spatial transcriptomics (ST) method with the hypotheses that Ezh2cKO mice would predominantly demonstrate changes in epithelial cells and/or ablation of this gene would disrupt normal epithelial/stromal gene expression patterns. Uteri were collected from ovariectomized adult WT and Ezh2cKO mice and analyzed by ST. Asb4, Cxcl14, Dio2, and Igfbp5 were increased, Sult1d1, Mt3, and Lcn2 were reduced in Ezh2cKO uterine epithelium vs. WT epithelium. For Ezh2cKO uterine stroma, differentially expressed key hub genes included Cald1, Fbln1, Myh11, Acta2, and Tagln. Conditional loss of uterine Ezh2 also appears to shift the balance of gene expression profiles in epithelial vs. stromal tissue toward uterine epithelial cell and gland development and proliferation, consistent with uterine gland hyperplasia in these mice. Current findings provide further insight into how EZH2 may selectively affect uterine epithelial and stromal compartments. Additionally, these transcriptome data might provide the mechanistic understanding and valuable biomarkers for human endometrial disorders with epigenetic underpinnings.

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

scholarly journals Genome-Wide Gene Expression Profiles Analysis Reveal Novel Insights into Drought Stress in Foxtail Millet (Setaria italica L.)

2020 ◽  
Vol 21 (22) ◽  
pp. 8520
Author(s):  
Ling Qin ◽  
Erying Chen ◽  
Feifei Li ◽  
Xiao Yu ◽  
Zhenyu Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Drought Stress ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Foxtail Millet ◽  
Gene Expression Profiles ◽  
Sugar Metabolism ◽  
Setaria Italica ◽  
Phenylpropanoid Biosynthesis

Foxtail millet (Setaria italica (L.) P. Beauv) is an important food and forage crop because of its health benefits and adaptation to drought stress; however, reports of transcriptomic analysis of genes responding to re-watering after drought stress in foxtail millet are rare. The present study evaluated physiological parameters, such as proline content, p5cs enzyme activity, anti-oxidation enzyme activities, and investigated gene expression patterns using RNA sequencing of the drought-tolerant foxtail millet variety (Jigu 16) treated with drought stress and rehydration. The results indicated that drought stress-responsive genes were related to many multiple metabolic processes, such as photosynthesis, signal transduction, phenylpropanoid biosynthesis, starch and sucrose metabolism, and osmotic adjustment. Furthermore, the Δ1-pyrroline-5-carboxylate synthetase genes, SiP5CS1 and SiP5CS2, were remarkably upregulated in foxtail millet under drought stress conditions. Foxtail millet can also recover well on rehydration after drought stress through gene regulation. Our data demonstrate that recovery on rehydration primarily involves proline metabolism, sugar metabolism, hormone signal transduction, water transport, and detoxification, plus reversal of the expression direction of most drought-responsive genes. Our results provided a detailed description of the comparative transcriptome response of foxtail millet variety Jigu 16 under drought and rehydration environments. Furthermore, we identify SiP5CS2 as an important gene likely involved in the drought tolerance of foxtail millet.

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