Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosus

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

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The effect of colonoscopy on whole blood gene expression profile: an experimental investigation for colorectal cancer biomarker discovery

Journal of Cancer Research and Clinical Oncology ◽

10.1007/s00432-014-1837-6 ◽

2014 ◽

Vol 141 (4) ◽

pp. 591-599

Author(s):

Ye Xu ◽

Qinghua Xu ◽

Li Yang ◽

Fang Liu ◽

Xun Ye ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Experimental Investigation ◽

Gene Expression Profile ◽

Expression Profile ◽

Whole Blood ◽

Biomarker Discovery ◽

Cancer Biomarker ◽

Blood Gene Expression

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Whole Blood Gene Expression Profile Associated with Spontaneous Preterm Birth in Women with Threatened Preterm Labor

PLoS ONE ◽

10.1371/journal.pone.0096901 ◽

2014 ◽

Vol 9 (5) ◽

pp. e96901 ◽

Cited By ~ 40

Author(s):

Yujing Jan Heng ◽

Craig Edward Pennell ◽

Hon Nian Chua ◽

Jonathan Edward Perkins ◽

Stephen James Lye

Keyword(s):

Gene Expression ◽

Preterm Birth ◽

Gene Expression Profile ◽

Expression Profile ◽

Whole Blood ◽

Preterm Labor ◽

Spontaneous Preterm Birth ◽

Blood Gene Expression ◽

Threatened Preterm Labor

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Blood Genomic Expression Profile for Neuronal Injury

Journal of Cerebral Blood Flow & Metabolism ◽

10.1097/01.wcb.0000048518.34839.de ◽

2003 ◽

Vol 23 (3) ◽

pp. 310-319 ◽

Cited By ~ 33

Author(s):

Yang Tang ◽

Alex C. Nee ◽

Aigang Lu ◽

Ruiqiong Ran ◽

Frank R. Sharp

Keyword(s):

Gene Expression ◽

Brain Injury ◽

Gene Expression Profile ◽

Expression Profile ◽

Whole Blood ◽

Neuronal Injury ◽

Adult Rats ◽

Mild Brain Injury ◽

Microarray Analyses ◽

Genomic Response

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.

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Genomic dissection of Systemic Lupus Erythematosus: Distinct Susceptibility, Activity and Severity Signatures

10.1101/255109 ◽

2018 ◽

Author(s):

Nikolaos I. Panousis ◽

George Bertsias ◽

Halit Ongen ◽

Irini Gergianaki ◽

Maria Tektonidou ◽

...

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Disease Activity ◽

Lupus Erythematosus ◽

Whole Blood ◽

Personalized Therapy ◽

Flare Activity ◽

Rna Seq ◽

Healthy Individuals ◽

Systemic Lupus

AbstractRecent genetic and genomics approaches have yielded novel insights in the pathogenesis of Systemic Lupus Erythematosus (SLE) but the diagnosis, monitoring and treatment still remain largely empirical1,2. We reasoned that molecular characterization of SLE by whole blood transcriptomics may facilitate early diagnosis and personalized therapy. To this end, we analyzed genotypes and RNA-seq in 142 patients and 58 matched healthy individuals to define the global transcriptional signature of SLE. By controlling for the estimated proportions of circulating immune cell types, we show that the Interferon (IFN) and p53 pathways are robustly expressed. We also report cell-specific, disease-dependent regulation of gene expression and define a core/susceptibility and a flare/activity disease expression signature, with oxidative phosphorylation, ribosome regulation and cell cycle pathways being enriched in lupus flares. Using these data, we define a novel index of disease activity/severity by combining the validated Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)1 with a new variable derived from principal component analysis (PCA) of RNA-seq data. We also delineate unique signatures across disease endo-phenotypes whereby active nephritis exhibits the most extensive changes in transcriptome, including prominent drugable signatures such as granulocyte and plasmablast/plasma cell activation. The substantial differences in gene expression between SLE and healthy individuals enables the classification of disease versus healthy status with median sensitivity and specificity of 83% and 100%, respectively. We explored the genetic regulation of blood transcriptome in SLE and found 3142 cis-expression quantitative trait loci (eQTLs). By integration of SLE genome-wide association study (GWAS) signals and eQTLs from 44 tissues from the Genotype-Tissue Expression (GTEx) consortium, we demonstrate that the genetic causality of SLE arises from multiple tissues with the top causal tissue being the liver, followed by brain basal ganglia, adrenal gland and whole blood. Collectively, our study defines distinct susceptibility and activity/severity signatures in SLE that may facilitate diagnosis, monitoring, and personalized therapy.

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Integrating the analysis of transcriptome-wide association study and gene expression profile to identify genes associated with spondyloarthritis

10.21203/rs.3.rs-936677/v1 ◽

2021 ◽

Author(s):

Jiawen Xu ◽

Haibo Si ◽

Yi Zeng ◽

Yuangang Wu ◽

Shaoyun Zhang ◽

...

Keyword(s):

Gene Expression ◽

Skeletal Muscle ◽

Association Study ◽

Candidate Genes ◽

Gene Expression Profile ◽

Expression Profile ◽

Whole Blood ◽

Genome Wide Association Study ◽

Bone Diseases ◽

Functional Enrichment

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.

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A study on linking of clinical phenotype and gene expression profile in systemic lupus erythematosus

10.21203/rs.3.rs-16323/v1 ◽

2020 ◽

Author(s):

Jian-Ruei Ciou ◽

Pu-Wei Ho ◽

Po-Chang Wu ◽

Shu-I Chen ◽

Ching-Mao Chang ◽

...

Keyword(s):

Gene Expression ◽

Systemic Lupus Erythematosus ◽

Gene Expression Profile ◽

Expression Profile ◽

Lupus Erythematosus ◽

Transcriptome Sequencing ◽

Clinical Phenotype ◽

Differentially Expressed ◽

Systemic Lupus ◽

Malar Rash

Abstract Objectives: Malar rash is one of clinical phenotypes seen in systemic lupus erythematosus (SLE). However, the pathogenesis of malar rash is not clear for each case of SLE patients. In this paper we endeavored to investigate the linking of clinical phenotype from the gene expression profiles between both patients with malar rash and without malar rash. Therefore we might perform better evaluation of the possible prognosis for different SLE patients in the future.Methods: This study utilizes transcriptome sequencing (RNA-Seq) technologies to discover underlying gene expression profile for systemic lupus erythematosus patients. We performed transcriptome sequencing experiments and analyzed differentially expressed genes (DEGs) and associated pathways.Results: From the analysis of gene expression profiling, we identified the gene DAAM2 is the most differentially expressed gene for patients with malar rash. Using a gene set enrichment analysis, we discuss the linkage between DAAM2 and the possible pathways for systemic lupus erythematosus with malar rash. Conclusions: We identified DAAM2 as a candidate biomarker for the clinical phenotype of malar rash for systemic lupus erythematosus.

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Blood-based multi-tissue gene expression inference with Bayesian ridge regression

Bioinformatics ◽

10.1093/bioinformatics/btaa239 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3788-3794

Author(s):

Wenjian Xu ◽

Xuanshi Liu ◽

Fei Leng ◽

Wei Li

Keyword(s):

Gene Expression ◽

Gene Expression Profile ◽

Expression Profile ◽

Ridge Regression ◽

Whole Blood ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Blood Gene Expression ◽

Tissue Specific ◽

Tissue Gene Expression

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.

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