Evidence map›Paper›PMID 26597164›Full record

ArticleBMC genomics2015

RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles.

Amy Webb, Audrey C Papp, Amanda Curtis, Leslie C Newman, Maciej Pietrzak, Michal Seweryn, Samuel K Handelman, Grzegorz A Rempala, Daqing Wang, Erica Graziosa and 5 more

Abstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Publicly availableFrontiers in neuroscience · 2023
    Review
  6. Circular RNAs: the next level of gene regulation.American journal of translational research · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. ThePharmacogenomics · 2019
    Review
  16. Article
  17. Noncoding RNAs in neurodegeneration.Nature reviews. Neuroscience · 2017
    Review
  18. Gene expression profiling of brain samples from patients with Lewy body dementia.Biochemical and biophysical research communications · 2016
    Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Amy WebbCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. Amy.Webb@osumc.edu.
Audrey C PappCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. papp.2@osu.edu.
Amanda CurtisCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. Curtis.164@osu.edu.
Leslie C NewmanCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. Leslie.Newman@osumc.edu.
Maciej PietrzakCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. pietrzak.20@osu.edu.
Michal SewerynDivision of Biostatistics, College of Public Health, and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA. panpit@gmail.com.
Samuel K HandelmanCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. handelman.9@osu.edu.
Grzegorz A RempalaDivision of Biostatistics, College of Public Health, and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH, USA. rempala.3@osu.edu.
Daqing WangThermo Fisher Scientific, South San Francisco, CA, 94080, USA. Daqing.wang@thermofisher.com.
Erica GraziosaThermo Fisher Scientific, South San Francisco, CA, 94080, USA. Erica.Graziosa@thermofisher.com.
Rachel F TyndaleCenter for Addiction and Mental Health and Departments of Psychiatry and Pharmacology & Toxicology, University of Toronto, Toronto, Ontario, Canada. r.tyndale@utoronto.ca.
Caryn LermanDepartment of Psychiatry, Annenberg School for Communication, and Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA. clerman@mail.med.upenn.edu.
John R KelsoeDepartment of Psychiatry, Laboratory of Psychiatric Genomics, University of California, San Diego, USA. jkelsoe@ucsd.edu.
Deborah C MashDepartment of Neurology, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA. DMash@med.miami.edu.
Wolfgang SadeeCenter for Pharmacogenomics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA. Wolfgang.Sadee@osumc.edu.

Funding

PHARMACOGENETICS OF MEMBRANE TRANSPORTERSU01GM061390 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GIACOMINI, KATHLEEN M · 2000 to 2009
$29.2M
Pharmacogenomics of Membrane TransportersU19GM061390 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2010 to 2014
$16.3M
Expression Genetics in Drug TherapyU01GM092655 · NIGMS · OHIO STATE UNIVERSITY · PI SADEE, WOLFGANG · 2010 to 2014
$8.6M
LIMBIC &MEDULLARY MECH. IN COCAINE-RELATED SUDDEN DEATHR01DA006227 · NIDA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MASH, DEBORAH C. · 1990 to 2012
$3.7M
NIDA NIH HHS DA06227NIDA NIH HHS R01 DA006227NIGMS NIH HHS GM61390NIGMS NIH HHS U01 GM061390NIGMS NIH HHS U01 GM092655NIGMS NIH HHS U01GM092655NIGMS NIH HHS U19 GM061390
6 · The paper itself

Abstract

backgroundWe used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants.

resultsOf 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in > 400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure.

conclusionsRNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance.

Indexed as

AllelesGene Expression ProfilingSequence Analysis, RNABrainHumansMalePolymorphism, Single NucleotideRNA IsoformsRNA, UntranslatedSmokingRNA IsoformsRNA, Untranslated

Identifiers

PMID26597164
PMCPMC4657279

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.