scholarly journals Blood gene expression studies in migraine: Potential and caveats

Cephalalgia ◽  
2016 ◽  
Vol 36 (7) ◽  
pp. 669-678 ◽  
Author(s):  
Zachary Gerring ◽  
Astrid J Rodriguez-Acevedo ◽  
Joseph E Powell ◽  
Lyn R Griffiths ◽  
Grant W Montgomery ◽  
...  
Keyword(s):  
Gene Expression ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Global Gene Expression ◽  
Current Evidence ◽  
Expression Data ◽  
Tissue Samples ◽  
Blood Gene Expression ◽  
Post Mortem Brain ◽  
Gene Expression Studies

Background Global gene expression analysis may be used to obtain insights into the functional processes underlying migraine. However, there is a shortage of high-quality post-mortem brain tissue samples for genetic analysis. One approach is to use a more accessible tissue as a surrogate, such as peripheral blood. Purpose Discuss the benefits and caveats of blood genomic profiling in migraine and its potential application in the development of biomarkers of migraine susceptibility and outcome. Demonstrate the utility of blood-based expression profiles in migraine by analysing pilot Illumina HT-12 expression data from 76 (38 case, 38 control) whole-blood samples. Conclusion Current evidence suggests peripheral blood is a biologically valid substrate for genetic studies of migraine, and may be used to identify biomarkers and therapeutic pathways. Pilot blood gene expression data confirm that expression profiles significantly differ between migraine case and non-migraine control individuals.

scholarly journals Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data

2007 ◽  
Vol 5 ◽  
pp. 117693510700500
Author(s):  
K-A. Do ◽  
G.J. McLachlan ◽  
R. Bean ◽  
S. Wen
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Clusters ◽  
Gene Clustering ◽  
Expression Data ◽  
Colon Tissue ◽  
Data Set ◽  
Tissue Samples ◽  
Gene Shaving

Researchers are frequently faced with the analysis of microarray data of a relatively large number of genes using a small number of tissue samples. We examine the application of two statistical methods for clustering such microarray expression data: EMMIX-GENE and GeneClust. EMMIX-GENE is a mixture-model based clustering approach, designed primarily to cluster tissue samples on the basis of the genes. GeneClust is an implementation of the gene shaving methodology, motivated by research to identify distinct sets of genes for which variation in expression could be related to a biological property of the tissue samples. We illustrate the use of these two methods in the analysis of Affymetrix oligonucleotide arrays of well-known data sets from colon tissue samples with and without tumors, and of tumor tissue samples from patients with leukemia. Although the two approaches have been developed from different perspectives, the results demonstrate a clear correspondence between gene clusters produced by GeneClust and EMMIX-GENE for the colon tissue data. It is demonstrated, for the case of ribosomal proteins and smooth muscle genes in the colon data set, that both methods can classify genes into co-regulated families. It is further demonstrated that tissue types (tumor and normal) can be separated on the basis of subtle distributed patterns of genes. Application to the leukemia tissue data produces a division of tissues corresponding closely to the external classification, acute myeloid meukemia (AML) and acute lymphoblastic leukemia (ALL), for both methods. In addition, we also identify genes specific for the subgroup of ALL-Tcell samples. Overall, we find that the gene shaving method produces gene clusters at great speed; allows variable cluster sizes and can incorporate partial or full supervision; and finds clusters of genes in which the gene expression varies greatly over the tissue samples while maintaining a high level of coherence between the gene expression profiles. The intent of the EMMIX-GENE method is to cluster the tissue samples. It performs a filtering step that results in a subset of relevant genes, followed by gene clustering, and then tissue clustering, and is favorable in its accuracy of ranking the clusters produced.

scholarly journals Peripheral blood gene expression profiles in COPD subjects

2011 ◽  
Vol 1 (1) ◽  
pp. 12 ◽  
Author(s):  
Soumyaroop Bhattacharya ◽  
Shivraj Tyagi ◽  
Sorachai Srisuma ◽  
Dawn L DeMeo ◽  
Steven D Shapiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Peripheral Blood Gene Expression

scholarly journals Biomarkers in Parkinson Disease: global gene expression analysis in peripheral blood from patients with and without mutations in PARK2 and PARK8

2010 ◽  
Vol 8 (3) ◽  
pp. 291-297 ◽  
Author(s):  
Patricia Maria de Carvalho Aguiar ◽  
Patricia Severino
Keyword(s):  
Gene Expression ◽  
Parkinson Disease ◽  
Expression Analysis ◽  
Peripheral Blood ◽  
Gene Expression Analysis ◽  
Small Sample Size ◽  
Expression Profiles ◽  
Global Gene Expression ◽  
Small Sample ◽  
Global Gene Expression Analysis

ABSTRACT Objective: To evaluate the performance of gene expression analysis in the peripheral blood of Parkinson disease patients with different genetic profiles using microarray as a tool to identify possible diseases related biomarkers which could contribute to the elucidation of the pathological process, as well as be useful in diagnosis. Methods: Global gene expression analysis by means of DNA microarrays was performed in peripheral blood of Parkinson disease patients with previously identified mutations in PARK2 or PARK8 genes, Parkinson disease patients without known mutations in these genes and normal controls. Each group consisted of five individuals. Results: Global gene expression profiles were heterogeneous among patients and controls, and it was not possible to detect a consistent pattern between groups. However, analyzing genes with differential expression of p < 0.005 and fold change ≥ 1.2, we were able to identify a small group of well-annotated genes. Conclusions: Despite the small sample size, the identification of differentially expressed genes suggests that the microarray technique may be useful in identifying potential biomarkers in the peripheral blood of Parkinson disease patients or in people at risk of developing the disease. This will be important once neuroprotective therapies become available, and may contribute to the identification of new pathways involved in the disease physiopathology. Results presented here should be further validated in larger groups of patients.

scholarly journals Pathway-Level Information ExtractoR (PLIER) for gene expression data

2017 ◽  
Author(s):  
Weiguang Mao ◽  
Elena Zaslavsky ◽  
Boris M. Hartmann ◽  
Stuart C. Sealfon ◽  
Maria Chikina
Keyword(s):  
Gene Expression ◽  
Global Gene Expression ◽  
Expression Data ◽  
Regulate Gene Expression ◽  
Expression Studies ◽  
Inference Algorithms ◽  
Biological Insight ◽  
Level Information ◽  
Gene Expression Studies ◽  
Cell Proportion

AbstractA major challenge in gene expression analysis is to accurately infer relevant biological insight, such as regulation of cell type proportion or pathways, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification.

scholarly journals Effects of Deoxynivalenol and Fumonisins Fed in Combination to Beef Cattle: Immunotoxicity and Gene Expression

Toxins ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 714
Author(s):  
Heaven L. Roberts ◽  
Massimo Bionaz ◽  
Duo Jiang ◽  
Barbara Doupovec ◽  
Johannes Faas ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Beef Cattle ◽  
Cd8 T Cells ◽  
Washout Period ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Blood Gene Expression ◽  
Bactericidal Capacity ◽  
Peripheral Blood Gene Expression

We evaluated the effects of a treatment diet contaminated with 1.7 mg deoxynivalenol and 3.5 mg fumonisins (B1, B2 and B3) per kg ration on immune status and peripheral blood gene expression profiles in finishing-stage Angus steers. The mycotoxin treatment diet was fed for a period of 21 days followed by a two-week washout period during which time all animals consumed the control diet. Whole-blood leukocyte differentials were performed weekly throughout the experimental and washout period. Comparative profiles of CD4+ and CD8+ T cells, along with bactericidal capacity of circulating neutrophils and monocytes were evaluated at 0, 7, 14, 21 and 35 days. Peripheral blood gene expression was measured at 0, 7, 21 and 35 days via RNA sequencing. Significant increases in the percentage of CD4-CD8+ T cells were observed in treatment-fed steers after two weeks of treatment and were associated with decreased CD4:CD8 T-cell ratios at this same timepoint (p ≤ 0.10). No significant differences were observed as an effect of treatment in terms of bactericidal capacity at any timepoint. Dietary treatments induced major changes in transcripts associated with endocrine, metabolic and infectious diseases; protein digestion and absorption; and environmental information processing (inhibition of signaling and processing), as evaluated by dynamic impact analysis. DAVID analysis also suggested treatment effects on oxygen transport, extra-cellular signaling, cell membrane structure and immune system function. These results indicate that finishing-stage beef cattle are susceptible to the immunotoxic and transcript-inhibitory effects of deoxynivalenol and fumonisins at levels which may be realistically encountered in feedlot situations.

scholarly journals Diagnosis of Partial Body Radiation Exposure in Mice Using Peripheral Blood Gene Expression Profiles

PLoS ONE ◽  
2010 ◽  
Vol 5 (7) ◽  
pp. e11535 ◽  
Author(s):  
Sarah K. Meadows ◽  
Holly K. Dressman ◽  
Pamela Daher ◽  
Heather Himburg ◽  
J. Lauren Russell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Radiation Exposure ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Peripheral Blood Gene Expression ◽  
Partial Body

scholarly journals Correlation between peripheral blood cell transcriptomic profile and clinical parameters of chronic mitral valve disease in Dachshunds

2016 ◽  
Vol 19 (4) ◽  
pp. 849-857 ◽  
Author(s):  
M. Garncarz ◽  
M. Hulanicka ◽  
H. Maciejewski ◽  
M. Parzeniecka-Jaworska ◽  
M. Jank
Keyword(s):  
Gene Expression ◽  
Heart Disease ◽  
Mitral Valve ◽  
Peripheral Blood ◽  
Mitral Valve Disease ◽  
Expression Profiles ◽  
Valve Disease ◽  
Microarray Data Analysis ◽  
Future Research ◽  
Tissue Samples

Abstract Studies identifying specific pathologically expressed genes have been performed on diseased myocardial tissue samples, however less invasive studies on gene expression of peripheral blood mononucleated cells give promising results. This study assessed transcriptomic data that may be used to evaluate Dachshunds with chronic mitral valve disease. Dachshunds with different stages of heart disease were compared to a control, healthy group. Microarray data analysis revealed clusters of patients with similar expression profiles. The clusters were compared to the clinical classification scheme. Unsupervised classification of the studied groups showed three clusters. Clinical and laboratory parameters of patients from the cluster 1 were in accordance with those found in patients without heart disease. Data obtained from patients from the cluster 3 were typical of advanced heart failure patients. Comparison of the cluster 1 and 3 groups revealed 1133 differentially expressed probes, 7 significantly regulated process pathways and 2 significantly regulated Ariadne Metabolic Pathways. This study may serve as a guideline for directing future research on gene expression in chronic mitral valve disease.

scholarly journals Subtype-specific peripheral blood gene expression profiles in recent-onset juvenile idiopathic arthritis

2009 ◽  
Vol 60 (7) ◽  
pp. 2102-2112 ◽  
Author(s):  
Michael G. Barnes ◽  
Alexei A. Grom ◽  
Susan D. Thompson ◽  
Thomas A. Griffin ◽  
Paul Pavlidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Juvenile Idiopathic Arthritis ◽  
Peripheral Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Blood Gene Expression ◽  
Recent Onset ◽  
Peripheral Blood Gene Expression

scholarly journals Marker Identification and Classification of Cancer Types Using Gene Expression Data and SIMCA

2004 ◽  
Vol 43 (01) ◽  
pp. 4-8 ◽  
Author(s):  
A. Luchini ◽  
C. Di Bello ◽  
S. Bicciato
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Computational Procedure ◽  
Diagnostic Tools ◽  
Human Leukemia ◽  
Expression Data ◽  
Tissue Samples ◽  
Multiple Tumor

Summary Objectives: High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA micro-arrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data. Methods: The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types. Results: The proposed method has been tested on two different microarray data sets, namely Golub’s analysis of acute human leukemia [1] and the small round blue cell tumors study presented by Khan et al. [2]. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states. Conclusions: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the same time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

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