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Post-mortem molecular profiling of three psychiatric disorders

Overview of attention for article published in Genome Medicine, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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16 X users
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1 patent
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Title
Post-mortem molecular profiling of three psychiatric disorders
Published in
Genome Medicine, July 2017
DOI 10.1186/s13073-017-0458-5
Pubmed ID
Authors

Ryne C. Ramaker, Kevin M. Bowling, Brittany N. Lasseigne, Megan H. Hagenauer, Andrew A. Hardigan, Nicholas S. Davis, Jason Gertz, Preston M. Cartagena, David M. Walsh, Marquis P. Vawter, Edward G. Jones, Alan F. Schatzberg, Jack D. Barchas, Stanley J. Watson, Blynn G. Bunney, Huda Akil, William E. Bunney, Jun Z. Li, Sara J. Cooper, Richard M. Myers

Abstract

Psychiatric disorders are multigenic diseases with complex etiology that contribute significantly to human morbidity and mortality. Although clinically distinct, several disorders share many symptoms, suggesting common underlying molecular changes exist that may implicate important regulators of pathogenesis and provide new therapeutic targets. We performed RNA sequencing on tissue from the anterior cingulate cortex, dorsolateral prefrontal cortex, and nucleus accumbens from three groups of 24 patients each diagnosed with schizophrenia, bipolar disorder, or major depressive disorder, and from 24 control subjects. We identified differentially expressed genes and validated the results in an independent cohort. Anterior cingulate cortex samples were also subjected to metabolomic analysis. ChIP-seq data were used to characterize binding of the transcription factor EGR1. We compared molecular signatures across the three brain regions and disorders in the transcriptomes of post-mortem human brain samples. The most significant disease-related differences were in the anterior cingulate cortex of schizophrenia samples compared to controls. Transcriptional changes were assessed in an independent cohort, revealing the transcription factor EGR1 as significantly down-regulated in both cohorts and as a potential regulator of broader transcription changes observed in schizophrenia patients. Additionally, broad down-regulation of genes specific to neurons and concordant up-regulation of genes specific to astrocytes was observed in schizophrenia and bipolar disorder patients relative to controls. Metabolomic profiling identified disruption of GABA levels in schizophrenia patients. We provide a comprehensive post-mortem transcriptome profile of three psychiatric disorders across three brain regions. We highlight a high-confidence set of independently validated genes differentially expressed between schizophrenia and control patients in the anterior cingulate cortex and integrate transcriptional changes with untargeted metabolite profiling.

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The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 203 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 18%
Researcher 35 17%
Student > Master 21 10%
Student > Bachelor 17 8%
Student > Postgraduate 12 6%
Other 24 12%
Unknown 58 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 19%
Neuroscience 30 15%
Medicine and Dentistry 25 12%
Agricultural and Biological Sciences 22 11%
Psychology 6 3%
Other 18 9%
Unknown 64 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2022.
All research outputs
#2,822,635
of 24,293,076 outputs
Outputs from Genome Medicine
#643
of 1,500 outputs
Outputs of similar age
#51,336
of 320,073 outputs
Outputs of similar age from Genome Medicine
#15
of 26 outputs
Altmetric has tracked 24,293,076 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,500 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has gotten more attention than average, scoring higher than 57% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 320,073 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.