Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect

Bipolar disorder (BD) is a highly heritable mood disorder with complex genetic architecture and poorly understood etiology. We performed a whole blood transcriptome analysis in a BD case-control sample (Nsubjects = 480) by RNA sequencing. While we observed widespread differential gene expression patterns between affected and unaffected individuals, these effects were largely linked to lithium treatment at the time of blood draw (FDR < 0.05, Ngenes = 976) rather than BD diagnosis itself (FDR < 0.05, Ngenes = 6). These lithium-associated genes were enriched for cell signaling and immune response functional annotations, among others, and were associated with neutrophil cell-type proportions, which were elevated in lithium users. Neither genes with altered expression in cases nor in lithium users were enriched for BD, schizophrenia, and depression genetic risk based on information from genome-wide association studies, nor was gene expression associated with polygenic risk scores for BD. Our findings suggest that BD is associated with minimal changes in whole blood gene expression independent of medication use but underline the importance of accounting for medication use and cell type heterogeneity in psychiatric transcriptomic studies. The results of our study add to mounting evidence of lithium's cell signaling and immune-related mechanisms.

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Abstract W P73: Feasibility and Preliminary Results of Whole Blood RNA-Sequencing Analysis in Patients With Intracranial Aneurysms

Stroke ◽

10.1161/str.45.suppl_1.wp73 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Blake Haas ◽

Nestor R Gonzalez ◽

Elina Nikkola ◽

Mark Connolly ◽

William Hsu ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Rna Sequencing ◽

Whole Blood ◽

Intracranial Aneurysms ◽

Small Sample Size ◽

Expression Patterns ◽

Small Sample ◽

Cellular Respiration ◽

Sequencing Analysis

Introduction: Intracranial aneurysms (IA) growth and rupture have been associated with chronic remodeling of the arterial wall. However, the pathobiology of this process remains poorly understood. The objective of the present study was to evaluate the feasibility of analyzing gene expression patterns in peripheral blood of patients with ruptured and unruptured saccular IAs. Materials and Methods: We analyzed human whole blood transcriptomes by performing paired-end, 100 bp RNA-sequencing (RNAseq) using the Illumina platform. We used STAR to align reads to the genome, HTSeq to count reads, and DESeq to normalize counts across samples. Self-reported patient information was used to correct expression values for ancestry, age, and sex. We utilized weighted gene co-expression network analysis (WGCNA) to identify gene expression network modules associated with IA size and rupture. The DAVID tool was employed to search for Gene Ontology enrichment in relevant modules. Results: Samples from 12 patients (9 females, age 57.6 +/-12) with IAs were analyzed. Four had ruptured aneurysms. RNA isolation and application of the methodology described above was successful in all samples. Although the small sample size prevents us from drawing definite conclusions, we observed promising novel co-expression networks for IAs: WCGNA analysis showed down-regulation of two transcript modules associated with ruptured IA status (r=-0.78, p=0.008 and r=-0.77, p=0.009), and up-regulation of two modules associated with aneurysm size (r=0.86, p=0.002 and r=0.9, p=4e-04), respectively. DAVID analyses showed that genes upregulated in an IA size-associated module were enriched with genes involved in cellular respiration and translation, while genes involved in transcription were down-regulated in a module associated with ruptured IAs. Conclusions: Whole blood RNAseq analysis is a feasible tool to capture transcriptome dynamics and achieve a better understanding of the pathophysiology of IAs. Further longitudinal studies of patients with IAs using network analysis are justified.

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Seasonal changes in gene expression represent cell-type composition in whole blood

Human Molecular Genetics ◽

10.1093/hmg/ddt665 ◽

2014 ◽

Vol 23 (10) ◽

pp. 2721-2728 ◽

Cited By ~ 27

Author(s):

S. De Jong ◽

M. Neeleman ◽

J. J. Luykx ◽

M. J. Ten Berg ◽

E. Strengman ◽

...

Keyword(s):

Gene Expression ◽

Whole Blood ◽

Seasonal Changes ◽

Cell Type ◽

Cell Type Composition ◽

Type Composition

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Whole Blood Gene Expression And Serum Biomarker Profiling In A COPD Cohort Identifies Markers Related To Neutrophilic Inflammation, Wound Healing And Altered B Cell Signaling

10.1164/ajrccm-conference.2011.183.1_meetingabstracts.a4091 ◽

2011 ◽

Author(s):

Debra A. Cockayne ◽

Donavan T. Cheng ◽

Benjamin Waschki ◽

Holly Hilton ◽

Galina Kourteva ◽

...

Keyword(s):

Gene Expression ◽

Wound Healing ◽

Cell Signaling ◽

B Cell ◽

Whole Blood ◽

Serum Biomarker ◽

Blood Gene Expression ◽

Neutrophilic Inflammation ◽

B Cell Signaling

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Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants

10.1101/2020.04.22.055335 ◽

2020 ◽

Cited By ~ 2

Author(s):

Liis Kolberg ◽

Nurlan Kerimov ◽

Hedi Peterson ◽

Kaur Alasoo

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Expression Analysis ◽

Complex Disease ◽

Target Genes ◽

Disease Onset ◽

Functional Enrichment ◽

Eqtl Analysis ◽

Cell Type ◽

Motif Analysis

AbstractBackgroundDeveloping novel therapies for complex disease requires better understanding of the causal processes that contribute to disease onset and progression. Although trans-acting gene expression quantitative trait loci (trans-eQTLs) can be a powerful approach to directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes and large number of genes tested. However, if a single trans-eQTL controls a group of co-regulated genes, then multiple testing burden can be greatly reduced by summarising gene expression at the level of co-expression modules prior to trans-eQTL analysis.ResultsWe analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We inferred gene co-expression modules with five methods on the full dataset, as well as in each cell type separately. We detected a number of established co-expression module trans-eQTLs, such as the monocyte-specific associations at the IFNB1 and LYZ loci, as well as a platelet-specific ARHGEF3 locus associated with mean platelet volume. We also discovered a novel trans association near the SLC39A8 gene in LPS-stimulated monocytes. Here, we linked an early-response cis-eQTL of the SLC39A8 gene to a module of co-expressed metallothionein genes upregulated more than 20 hours later and used motif analysis to identify zinc-induced activation of the MTF1 transcription factor as a likely mediator of this effect.ConclusionsOur analysis provides a rare detailed characterisation of a trans-eQTL effect cascade from a proximal cis effect to the affected signalling pathway, transcription factor, and target genes. This highlights how co-expression analysis combined with functional enrichment analysis can greatly improve the identification and prioritisation of trans-eQTLs when applied to emerging cell-type specific datasets.

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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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Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer's Disease

10.21203/rs.3.rs-88940/v1 ◽

2020 ◽

Author(s):

Manish D Paranjpe ◽

Stella Belonwu ◽

Jason K Wang ◽

Tomiko Oskotsky ◽

Aarzu Gupta ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

B Cells ◽

Whole Blood ◽

Meta Analysis ◽

The United States ◽

Support Vector ◽

Cell Type ◽

Study Gene Expression

Abstract Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the apolipoprotein ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD signature. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusions: These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

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Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer's Disease

10.21203/rs.3.rs-29773/v1 ◽

2020 ◽

Author(s):

Manish Paranjpe ◽

Stella Belonwu ◽

Jason K Wang ◽

Tomiko Oskotsky ◽

Aarzu Gupta ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

B Cells ◽

Whole Blood ◽

Meta Analysis ◽

The United States ◽

Support Vector ◽

Cell Type ◽

Study Gene Expression

Abstract Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer’s disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD.Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the apolipoprotein ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD signature. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusions: These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

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Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women with Alzheimer’s Disease

10.1101/2020.04.24.060558 ◽

2020 ◽

Author(s):

Manish D Paranjpe ◽

Stella Belonwu ◽

Jason K Wang ◽

Tomiko Oskotsky ◽

Aarzu Gupta ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Whole Blood ◽

Meta Analysis ◽

Cell Type ◽

Immune Signature ◽

Study Gene Expression ◽

The Brain

ABSTRACTIn spite of evidence of females having a greater lifetime risk of developing Alzheimer’s Disease (AD) and greater apolipoprotein E4-related (apoE4) AD risk compared to males, molecular signatures underlying these findings remain elusive. We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls. Gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features revealed an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. These results help identify sex and apoE4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

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Correction for both common and rare cell types in blood is important to identify genes that correlate with age

BMC Genomics ◽

10.1186/s12864-020-07344-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Damiano Pellegrino-Coppola ◽

◽

Annique Claringbould ◽

Maartje Stutvoet ◽

Dorret I. Boomsma ◽

...

Keyword(s):

Gene Expression ◽

Blood Cell ◽

Whole Blood ◽

Cell Types ◽

Functional Enrichment ◽

Extended Model ◽

Initial Model ◽

Platelet Activity ◽

Blood Gene Expression ◽

New Model

Abstract Background Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. Results Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18–81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10−6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. Conclusions We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

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