Low-variance RNAs identify Parkinson's disease molecular signature in blood.
Identifieur interne : 000215 ( PubMed/Corpus ); précédent : 000214; suivant : 000216Low-variance RNAs identify Parkinson's disease molecular signature in blood.
Auteurs : Maria D. Chikina ; Christophe P. Gerald ; Xianting Li ; Yongchao Ge ; Hanna Pincas ; Venugopalan D. Nair ; Aaron K. Wong ; Arjun Krishnan ; Olga G. Troyanskaya ; Deborah Raymond ; Rachel Saunders-Pullman ; Susan B. Bressman ; Zhenyu Yue ; Stuart C. SealfonSource :
- Movement disorders : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2015.
Abstract
The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.
DOI: 10.1002/mds.26205
PubMed: 25786808
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pubmed:25786808Le document en format XML
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<front><div type="abstract" xml:lang="en">The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.</div>
</front>
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<Abstract><AbstractText>The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers. Using a digital gene expression platform to quantify 175 messenger RNA (mRNA) markers with low coefficients of variation (CV), we first compared whole-blood transcript levels in mouse models (1) overexpressing wild-type (WT) LRRK2, (2) overexpressing G2019S LRRK2, (3) lacking LRRK2 (knockout), and (4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. The expression profiles distinguished the four mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in levodopa-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. We identify whole-blood mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.</AbstractText>
<CopyrightInformation>© 2015 International Parkinson and Movement Disorder Society.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Chikina</LastName>
<ForeName>Maria D</ForeName>
<Initials>MD</Initials>
<AffiliationInfo><Affiliation>Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Gerald</LastName>
<ForeName>Christophe P</ForeName>
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<ForeName>Xianting</ForeName>
<Initials>X</Initials>
<AffiliationInfo><Affiliation>Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Ge</LastName>
<ForeName>Yongchao</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.</Affiliation>
</AffiliationInfo>
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<ForeName>Venugopalan D</ForeName>
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<Initials>AK</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Krishnan</LastName>
<ForeName>Arjun</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Troyanskaya</LastName>
<ForeName>Olga G</ForeName>
<Initials>OG</Initials>
<AffiliationInfo><Affiliation>Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Raymond</LastName>
<ForeName>Deborah</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Neurology, Mount Sinai Beth Israel, New York, New York, USA.</Affiliation>
</AffiliationInfo>
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</AffiliationInfo>
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</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Yue</LastName>
<ForeName>Zhenyu</ForeName>
<Initials>Z</Initials>
<AffiliationInfo><Affiliation>Departments of Neurology and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Sealfon</LastName>
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