Gene Expression Profiling of Multiple Patients with Cyclic Thrombocytopenia Reveals Consistent Profile.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 219-219 ◽  
Author(s):  
Ruchira Sood ◽  
Erin Gourley ◽  
Stanley L. Schrier ◽  
Ronald Go ◽  
James L. Zehnder

Abstract Cyclic thrombocytopenia (CTP) is a rare disorder characterized by periodic changes in platelet count. While some previous reports suggest an association with several cytokines, the etiology of this disorder remains poorly characterized. Using DNA microarrays, we examined the gene expression profile in peripheral whole blood at multiple time points encompassing a cycle of platelet counts from two CTP patients. We hypothesized that the variation in gene expression program in whole blood would reflect on the transcriptional changes associated with or perhaps even underlying this disease. Genome-wide cDNA microarray analysis was performed using amplified RNA obtained from 11 and 8 whole blood samples from each patient. The first patient is a 41-year old male with a 2-year history of CTP while the second patient is a 54-year old male with a 3-year history of CTP. The period of both patients’ cycles is roughly 3 weeks. No associated underlying disease has been found in both patients. With a focus on 1500 genes that change 3 fold within each group of samples we observed clusters of gene expression in whole blood that correlate with changing platelet numbers in both patients. Significant variation in expression of a cluster of interferon responsive genes during the platelet count cycle was particularly striking in both samples. Interferon (IFN) therapy is known to suppress platelet counts, and this observation suggests that aberrant IFN levels and signalling could be in part responsible for CTP. At high platelet counts, platelet transcripts were detected in whole blood RNA as inferred by high expression of previously described platelet genes including TBXAS1, TUBB1, OAZ1, SEPT5, several mitochondrial genes, NRGN and F13A1. In addition, gene clusters including known genes as well as previously uncharacterized genes were found to correlate with the peak, increasing or decreasing trends of platelet counts. Briefly, GATA2 and NFE2 expression coincided with the platelet count peak, while Tyk2 and SOCS5 expression was consistent with a rising trend of platelet counts and GATA3 and JAK2 coincided with decreasing trend of platelet counts. These results show gene expression changes associated with CTP in all cell types in whole blood and pave the way for new investigation into regulation of platelet number in a rare and fascinating disease. Gene expression profile of whole blood of two CTP patients with platelet counts ranging from high to low and then increasing again from left to right of each panel Gene expression profile of whole blood of two CTP patients with platelet counts ranging from high to low and then increasing again from left to right of each panel

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96901 ◽  
Author(s):  
Yujing Jan Heng ◽  
Craig Edward Pennell ◽  
Hon Nian Chua ◽  
Jonathan Edward Perkins ◽  
Stephen James Lye

2003 ◽  
Vol 23 (3) ◽  
pp. 310-319 ◽  
Author(s):  
Yang Tang ◽  
Alex C. Nee ◽  
Aigang Lu ◽  
Ruiqiong Ran ◽  
Frank R. Sharp

This study determined whether stroke and other types of insults produced a gene expression profile in blood that correlated with the presence of neuronal injury. Adult rats were subjected to ischemic stroke, intracerebral hemorrhage, status epilepticus, and insulin-induced hypoglycemia and compared with untouched, sham surgery, and hypoxia animals that had no brain injury. One day later, microarray analyses showed that 117 genes were upregulated and 80 genes were downregulated in mononuclear blood cells of the “injury” (n = 12) compared with the “no injury” (n = 9) animals. A second experiment examined the whole blood genomic response of adult rats after global ischemia and kainate seizures. Animals with no brain injury were compared with those with brain injury documented by TUNEL and PANT staining. One day later, microarray analyses showed that 37 genes were upregulated and 67 genes were downregulated in whole blood of the injury (n = 4) animals compared with the no-injury (n = 4) animals. Quantitative reverse transcription–polymerase chain reaction confirmed that the vesicular monoamine transporter-2 increased 2.3- and 1.6-fold in animals with severe and mild brain injury, respectively, compared with no-injury animals. Vascular tyrosine phosphatase-1 increased 2.0-fold after severe injury compared with no injury. The data support the hypothesis that there is a peripheral blood genomic response to neuronal injury, and that this blood response is associated with a specific blood mRNA gene expression profile that can be used as a marker of the neuronal damage.


2004 ◽  
Vol 20 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Rafaela da Silva ◽  
Eliana Lucchinetti ◽  
Thomas Pasch ◽  
Marcus C. Schaub ◽  
Michael Zaugg

Pharmacological (PPC) and ischemic preconditioning (IschPC) provide comparable protection against ischemia in the heart. However, the genomic phenotype may depend on the type of preconditioning. Isolated perfused rat hearts were used to evaluate transcriptional responses to PPC and IschPC in the presence (mediator/effector response) or absence (trigger response) of 40 min of test ischemia using oligonucleotide microarrays. IschPC was induced by 3 cycles of 5 min of ischemia, and PPC by 15 min of 2.1 vol% isoflurane. Unsupervised analysis methods were used to identify gene expression patterns. PPC and IschPC were accompanied by marked alterations in gene expression. PPC and IschPC shared only ∼25% of significantly up- and downregulated genes after triggering. The two types of preconditioning induced a more uniform genomic response after ischemia/reperfusion. Numerous genes separated preconditioned from unprotected ischemic hearts. Three stable gene clusters were identified in the trigger response to preconditioning, while eight stable clusters related to cytoprotection, inflammation, remodeling, and long interspersed nucleotide elements (LINEs) were delineated after prolonged ischemia. A single stable sample cluster emerged from cluster analysis for both IschPC and unprotected myocardium, indicating a close molecular relationship between these two treatments. Principal component analysis revealed differences between PPC vs. IschPC, and trigger vs. mediator/effector responses in transcripts predominantly related to biosynthesis and apoptosis. IschPC and PPC similarly but distinctly reprogram the genetic response to ischemic injury. IschPC elicits a postischemic gene expression profile closer to unprotected myocardium than PPC, which may be therefore more advantageous as therapeutic strategy in cardioprotection.


Genomics ◽  
2017 ◽  
Vol 109 (1) ◽  
pp. 1-8
Author(s):  
Anna Landsman ◽  
Rafael Aidelman ◽  
Yoav Smith ◽  
Matthew Boyko ◽  
Chaya Greenberger

2021 ◽  
Author(s):  
Jiawen Xu ◽  
Haibo Si ◽  
Yi Zeng ◽  
Yuangang Wu ◽  
Shaoyun Zhang ◽  
...  

Abstract Background Spondyloarthritis(SpA) is a group of multi-factorial bone diseases influenced by genetic factors, environment and lifestyles. However, the genetic and pathogenic mechanism of SpA is still elusive. Methods Firstly, the tissue-specific transcriptome-wide association study (TWAS) of SpA was performed by utilizing the genome-wide association study (GWAS, including 3966 SpA patients and 452264 controls) summary data and gene expression weights of the whole blood and skeletal muscle. Secondly, the SpA-associated genes identified by TWAS were further compared with the differentially expressed genes(DEGs) detected by gene expression profile of SpA acquired from the Gene Expression Omnibus database (GEO, accession number:GSE58667). Finally, FUMA and Metascape tools were used to conduct gene functional enrichment and annotation analysis. Results TWAS detected 28 significant genes associated with SpA both in the whole blood and skeletal muscle, such as CTNNAL1 (PSM=0.0304, PWB=0.0096). Further comparing with gene expression profile of SpA, we identified 20 candidate genes which overlapped in TWAS, such as MCM4 (PTWAS=0.0132, PDEG=0.0275), KIAA1109 (PTWAS=0.0371,PDEG=0.0467). The enrichment analysis of the genes identified by TWAS detected 93 significant GO terms 33 and KEGG pathways, such as mitochondrion organization (GO:0007005, log10(P)= -4.29) and axon guidance(hsa04360, log10(P)= -4.26). Conclusion We identified multiple candidate genes genetically related to SpA. Our study may provide some novel clues for the further study of the genetic mechanism, diagnosis and treatment of SpA.


2020 ◽  
Vol 36 (12) ◽  
pp. 3788-3794
Author(s):  
Wenjian Xu ◽  
Xuanshi Liu ◽  
Fei Leng ◽  
Wei Li

Abstract Motivation Gene expression profiling is widely used in basic and cancer research but still not feasible in many clinical applications because tissues, such as brain samples, are difficult and not ethnical to collect. Gene expression in uncollected tissues can be computationally inferred using genotype and expression quantitative trait loci. No methods can infer unmeasured gene expression of multiple tissues with single tissue gene expression profile as input. Results Here, we present a Bayesian ridge regression-based method (B-GEX) to infer gene expression profiles of multiple tissues from blood gene expression profile. For each gene in a tissue, a low-dimensional feature vector was extracted from whole blood gene expression profile by feature selection. We used GTEx RNAseq data of 16 tissues to train inference models to capture the cross-tissue expression correlations between each target gene in a tissue and its preselected feature genes in peripheral blood. We compared B-GEX with least square regression, LASSO regression and ridge regression. B-GEX outperforms the other three models in most tissues in terms of mean absolute error, Pearson correlation coefficient and root-mean-squared error. Moreover, B-GEX infers expression level of tissue-specific genes as well as those of non-tissue-specific genes in all tissues. Unlike previous methods, which require genomic features or gene expression profiles of multiple tissues, our model only requires whole blood expression profile as input. B-GEX helps gain insights into gene expressions of uncollected tissues from more accessible data of blood. Availability and implementation B-GEX is available at https://github.com/xuwenjian85/B-GEX. Supplementary information Supplementary data are available at Bioinformatics online.


2016 ◽  
Vol 4 (1) ◽  
pp. e000273 ◽  
Author(s):  
Rafael B Gelaleti ◽  
Débora C Damasceno ◽  
Daisy M F Salvadori ◽  
Iracema M P Calderon ◽  
Roberto A A Costa ◽  
...  

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