Limits...
Huntington's disease biomarker progression profile identified by transcriptome sequencing in peripheral blood.

Mastrokolias A, Ariyurek Y, Goeman JJ, van Duijn E, Roos RA, van der Mast RC, van Ommen GB, den Dunnen JT, 't Hoen PA, van Roon-Mom WM - Eur. J. Hum. Genet. (2015)

Bottom Line: Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past.Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort.Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

ABSTRACT
With several therapeutic approaches in development for Huntington's disease, there is a need for easily accessible biomarkers to monitor disease progression and therapy response. We performed next-generation sequencing-based transcriptome analysis of total RNA from peripheral blood of 91 mutation carriers (27 presymptomatic and, 64 symptomatic) and 33 controls. Transcriptome analysis by DeepSAGE identified 167 genes significantly associated with clinical total motor score in Huntington's disease patients. Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past. Pathway analysis showed enrichment of genes of the immune system and target genes of miRNAs, which are downregulated in Huntington's disease models. Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort. The five genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) that were validated independently in both cohorts present a candidate biomarker panel for stage determination and therapeutic readout in Huntington's disease. Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel. Our data support the view that peripheral blood is a useful source to identify biomarkers for Huntington's disease and monitor disease progression in future clinical trials.

No MeSH data available.


Related in: MedlinePlus

Relative expression of the most significant Fluidigm RT-qPCR genes across the two independent cohorts for controls and HD patients. Asterisks represent statistical significance from a Student's t-test (*P<0.05, **P<0.01). Error bars represent SEM values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4592077&req=5

fig2: Relative expression of the most significant Fluidigm RT-qPCR genes across the two independent cohorts for controls and HD patients. Asterisks represent statistical significance from a Student's t-test (*P<0.05, **P<0.01). Error bars represent SEM values.

Mentions: To validate the DeepSAGE gene expression results, we performed nanoliter RT-qPCR using the Fluidigm Biomark microfluidics chip28 using 25 samples from the original discovery cohort as technical validation, supplemented with 23 patient and control samples as a biological validation in an independent cohort. Twenty genes in total, all from our DeepSAGE list of 167 significantly differentially expressed genes, were examined; the top 12 based on P-value, 6 further down the 167 gene list based on differential expression in previous HD studies (H2AFY, AQP9, ANXA3, RGS14, ZNF238, NOL3) and another 2 genes from the same list based on possible involvement in HD pathology (CEBPA, TAF15).3, 8, 9, 19, 29, 30, 31 Fluidigm data were analyzed using a linear model as a function of TMS, while accounting for gender and age. In the basic validation cohort, 12 out of the 20 genes tested were significantly associated with TMS, while, in the independent validation cohort, 7 out of the 20 genes were significant (see Table 2). Most other genes, while not reaching significance, showed trends in the same direction as in the discovery cohort. Five of the 20 genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) were the most robust and significantly associated with TMS in both the discovery and the independent cohort. The intergroup relative expression levels of these five genes across HD versus control samples, irrespective of TMS, can also be seen in Figure 2. Finally, when the linear modeling analysis was performed on all Fluidigm samples (n=48), we were able to validate 12 of the 20 genes tested (see rightmost column of Table 2).


Huntington's disease biomarker progression profile identified by transcriptome sequencing in peripheral blood.

Mastrokolias A, Ariyurek Y, Goeman JJ, van Duijn E, Roos RA, van der Mast RC, van Ommen GB, den Dunnen JT, 't Hoen PA, van Roon-Mom WM - Eur. J. Hum. Genet. (2015)

Relative expression of the most significant Fluidigm RT-qPCR genes across the two independent cohorts for controls and HD patients. Asterisks represent statistical significance from a Student's t-test (*P<0.05, **P<0.01). Error bars represent SEM values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4592077&req=5

fig2: Relative expression of the most significant Fluidigm RT-qPCR genes across the two independent cohorts for controls and HD patients. Asterisks represent statistical significance from a Student's t-test (*P<0.05, **P<0.01). Error bars represent SEM values.
Mentions: To validate the DeepSAGE gene expression results, we performed nanoliter RT-qPCR using the Fluidigm Biomark microfluidics chip28 using 25 samples from the original discovery cohort as technical validation, supplemented with 23 patient and control samples as a biological validation in an independent cohort. Twenty genes in total, all from our DeepSAGE list of 167 significantly differentially expressed genes, were examined; the top 12 based on P-value, 6 further down the 167 gene list based on differential expression in previous HD studies (H2AFY, AQP9, ANXA3, RGS14, ZNF238, NOL3) and another 2 genes from the same list based on possible involvement in HD pathology (CEBPA, TAF15).3, 8, 9, 19, 29, 30, 31 Fluidigm data were analyzed using a linear model as a function of TMS, while accounting for gender and age. In the basic validation cohort, 12 out of the 20 genes tested were significantly associated with TMS, while, in the independent validation cohort, 7 out of the 20 genes were significant (see Table 2). Most other genes, while not reaching significance, showed trends in the same direction as in the discovery cohort. Five of the 20 genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) were the most robust and significantly associated with TMS in both the discovery and the independent cohort. The intergroup relative expression levels of these five genes across HD versus control samples, irrespective of TMS, can also be seen in Figure 2. Finally, when the linear modeling analysis was performed on all Fluidigm samples (n=48), we were able to validate 12 of the 20 genes tested (see rightmost column of Table 2).

Bottom Line: Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past.Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort.Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

ABSTRACT
With several therapeutic approaches in development for Huntington's disease, there is a need for easily accessible biomarkers to monitor disease progression and therapy response. We performed next-generation sequencing-based transcriptome analysis of total RNA from peripheral blood of 91 mutation carriers (27 presymptomatic and, 64 symptomatic) and 33 controls. Transcriptome analysis by DeepSAGE identified 167 genes significantly associated with clinical total motor score in Huntington's disease patients. Relative to previous studies, this yielded novel genes and confirmed previously identified genes, such as H2AFY, an overlap in results that has proven difficult in the past. Pathway analysis showed enrichment of genes of the immune system and target genes of miRNAs, which are downregulated in Huntington's disease models. Using a highly parallelized microfluidics array chip (Fluidigm), we validated 12 of the top 20 significant genes in our discovery cohort and 7 in a second independent cohort. The five genes (PROK2, ZNF238, AQP9, CYSTM1 and ANXA3) that were validated independently in both cohorts present a candidate biomarker panel for stage determination and therapeutic readout in Huntington's disease. Finally we suggest a first empiric formula predicting total motor score from the expression levels of our biomarker panel. Our data support the view that peripheral blood is a useful source to identify biomarkers for Huntington's disease and monitor disease progression in future clinical trials.

No MeSH data available.


Related in: MedlinePlus