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Data Set Group: INIA LCM (11 Regions) RNA-seq Transcript Level (Dec15) modify this page

Data Set: INIA LCM (11 Regions) CIE/AIR/BAS RNA-seq Transcript Level (Dec15) modify this page
GN Accession: GN776
GEO Series: No Geo series yet
Organism: Mouse (mm10)
Group: BXD
Tissue: LCM Brain Regions mRNA
Dataset Status: Private
Platforms: ABI 550XL Wildfire
Normalization: RNA-seq
Contact Information
Megan Mulligan
University of Tennessee Health Science Center
855 Monroe Ave Suite 515
Memphis, Tennessee 38163 United States
Tel. 5126802386
Download datasets and supplementary data files

Specifics of this Data Set:

Published in Alcohol 2017:


 2017 Feb;58:61-72. doi: 10.1016/j.alcohol.2016.09.001. Epub 2016 Oct 15.

Genetic divergence in the transcriptional engram of chronic alcohol abuse: A laser-capture RNA-seq study of the mouse mesocorticolimbic system.

Mulligan MK1, Mozhui K2, Pandey AK2, Smith ML3, Gong S2, Ingels J2, Miles MF3, Lopez MF4, Lu L2, Williams RW2.


Genetic factors that influence the transition from initial drinking to dependence remain enigmatic. Recent studies have leveraged chronic intermittent ethanol (CIE) paradigms to measure changes in brain gene expression in a single strain at 0, 8, 72 h, and even 7 days following CIE. We extend these findings using LCM RNA-seq to profile expression in 11 brain regions in two inbred strains - C57BL/6J (B6) and DBA/2J (D2) - 72 h following multiple cycles of ethanol self-administration and CIE. Linear models identified differential expression based on treatment, region, strain, or interactions with treatment. Nearly 40% of genes showed a robust effect (FDR < 0.01) of region, and hippocampus CA1, cortex, bed nucleus stria terminalis, and nucleus accumbens core had the highest number of differentially expressed genes after treatment. Another 8% of differentially expressed genes demonstrated a robust effect of strain. As expected, based on similar studies in B6, treatment had a much smaller impact on expression; only 72 genes (p < 0.01) are modulated by treatment (independent of region or strain). Strikingly, many more genes (415) show a strain-specific and largely opposite response to treatment and are enriched in processes related to RNA metabolism, transcription factor activity, and mitochondrial function. Over 3 times as many changes in gene expression were detected in D2 compared to B6, and weighted gene co-expression network analysis (WGCNA) module comparison identified more modules enriched for treatment effects in D2. Substantial strain differences exist in the temporal pattern of transcriptional neuroadaptation to CIE, and these may drive individual differences in risk of addiction following excessive alcohol consumption.

PMID:27894806;  PMCID:PMC5450909; DOI:10.1016/j.alcohol.2016.09.001


This dataset is open. Please contact Dr. Megan K. Mulligan (mmulliga@uthsc.edu) or Dr. Robert W. Williams (rwilliams@uthsc.edu) if you need more information or low-level data access.

About the cases used to generate this set of data:

Male C57BL/6J and DBA/2J mice (10 weeks old upon arrival) were purchased from the Jackson Laboratory and assigned to either baseline untreated control (BAS), the air control (AIR), or CIE group. Mice were individually housed with free access to food (Harland Teklad, Madison, WI) and water throughout all phases of the experiments. Body weights were recorded weekly during ethanol-drinking weeks or daily during chronic intermittent ethanol (CIE) or air exposure (detailed below). Mice were housed in a temperature- and humidity-controlled an- imal facility under a reversed 12-h light/dark cycle (lights on at 0200 h). Mice were not food- or water-deprived at any time during the study. All procedures were approved by the Institutional Ani- mal Care and Use Committee at the Medical University of South Carolina (MUSC). Brain tissue was removed at MUSC and shipped to UTHSC for laser capture microdissection (LCM).

About the tissue used to generate this set of data:

Whole brain tissue was sectioned at 10 mm using a Leica cryostat and mounted in series with 6e8 sections per slide onto uncharged and uncoated glass slides. Mounted sections were dehydrated in 100% methanol (90 s), 70% ethanol (1 min), 95% ethanol (1 min), 100% ethanol (1 min  2), xylene (5 min). Slides were then allowed to air dry for 10 min under a fume hood.

Series were created from distinct coronal sections (bregma po- sitions based on a C57BL/6J reference brain atlas) and individual regions were matched across section and harvested by LCM (Supplemental Fig. 2). Prelimbic (PrL) and infralimbic (ILC) cortex included a series spanning from bregma 1.98 to 1.54 mm. The accumbens core (NAc) and shell (NAs) series were collected from bregma 1.54 to 0.98 mm, and dorsolateral (DLS) and dorsomedial (DMS) striatum and bed nucleus stria terminalis (BST) were collected from bregma 0.38 to 0.10 mm. Basolateral (BLA) and central nucleus (CeA) of the amygdala series were collected from bregma 0.58 to 1.22 mm and hippocampus (CA1 and CA3) was collected from bregma 1.46 to 2.46 mm. Finally, the ventral tegmental area (VTA) and primary visual cortex (VCX) series were collected from bregma 3.28 to 3.80 mm.

Arcturus XT (Life Technologies) was used to capture 13 brain areas. The infrared laser was then used to capture the tissue onto CapSure LCM caps (Life Technologies, laser spot power set to 70 mV with a duration of 25 msec).

About the array platform:



(Updated Dec 9, 2015 by AC and RW)

Annotation data for transcripts and genes were downloaded from ENSEMBL by Arthur Centeno, December 2015. We downloaded the entire transcript database at http://useast.ensembl.org/Mus_musculus/Info/Index.

The positions of transcripts and genes on the mouse assembly are version GRCm38.p4 (mm10 equivalent). However, we converted all chromosome coordinates to the mm9 assembly to be consistent with all other GN1 data sets. However, for some sequences, mostly on Chr Y and the mitochondrial genome, the values are mm10 equivalent (we had no corresponding mm9 values).

We also extracted sequence data corresponding to the transcripts whenever these data were available. However in some cases we do not have sequence data at all.

About data values and data processing:

RNA from tissue trapped in the CapSure LCM caps was extracted using the PicoPure RNA isolation kit (Life Technologies) according to the manufacturer's instructions (RNA was eluted from provided capture columns in 13.5-mL nuclease-free water). RNA quality was analyzed using a Bioanalyzer (Model 100, Agilent, Foster City, CA). Samples with an associated RNA integrity number (RIN) greater than 6 were subsequently used for RNA sequencing.


Poly-A enriched mRNA was sequenced on two platforms, ABI SOLID 550XL Wildfire (65 samples) and Ion Proton (39 samples). Read length was 50 nt for the SOLID system and the average read length for the Proton system was ~180 nt. Reads generated on the SOLID system were aligned to the mm10 reference genome using the LifeScope aligner, and BAM files were subsequently generated using custom scripts for third-party downstream analysis. For the Proton system, reads were also aligned to the mm10 (Ensemble GRCm38) reference genome using TopHat2. Settings for TopHat2 are as follows: “-p 15 -N 4 –read-gap-length 6 –read-edit-dist 8 –max-insertion-length 6 –max-deletion-length 6 –max-intron-length 300000 –b2-very-sensitive”. Alignments on both platforms were splice-aware. RSeQC-2.6.1 (RPKM_count.py) was used to generate count data based on mm10 GENECODE Basic transcript annotation (43,320 transcripts detected). We selected this annotation for greater reproducibility with existing microarray data sets and to simplify downstream analysis by limiting the number of transcript models for each gene. On average, 2.5 million and 7.8 million reads uniquely aligned to transcript models on the SOLID and Proton platforms, respectively. Data were further filtered to remove tran- scripts that had less than 1 count in 90% or more samples. After filtering, 24,597 transcripts representing 12,011 unique genes remained (Supplemental Table 1). The variance stabilizing trans- form (R package DESeq2, FitType 1⁄4 local) was applied to the count data, and transformed data were corrected by dividing by transcript length to generate log2 reads per kilobase gene model (RPK) values. The use of two different sequencing platforms was corrected using batch correction (ComBat, Supplemental Fig. 3). All data filtering, transformations, and batch correction were performed using custom R scripts. Batch-corrected and transformed log2 RPK and log2 count values are available in Supplemental Tables 2 and 3, and log2 RPK data are also available at GeneNetwork [http://www. genenetwork.org/webqtl/main.py?FormID1⁄4sharinginfo&GN_ AccessionId1⁄4772; Group 1⁄4 Chronic Intermittent Ethanol, Type 1⁄4 LCM Brain Regions mRNA, Data set 1⁄4 INIA LCM (11 Regions) CIE/AIR RNA-seq Transcript Level (Dec15)].


Experiment Type:


Megan K. Mulligan, Khyobeni Mozhui, Ashutosh K. Pandey, Maren L. Smith, Suzhen Gong, Jesse Ingels, Michael F. Miles, Marcelo F. Lopez, Lu Lu, Robert W. Williams


Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.alcohol.2016.09.001.


Data source acknowledgment:

Thanks to NIH NIAAA and funding support was provided by INIA grants U01AA013499 and U01AA06662.

Study Id:

CITG Web services initiated January, 1994 as Portable Dictionary of the Mouse Genome; June 15, 2001 as WebQTL; and Jan 5, 2005 as GeneNetwork. This site is currently operated by Rob Williams, Pjotr Prins, Zachary Sloan, Arthur Centeno. Design and code by Pjotr Prins, Zach Sloan, Arthur Centeno, Danny Arends, Christian Fischer, Sam Ockman, Lei Yan, Xiaodong Zhou, Christian Fernandez, Ning Liu, Rudi Alberts, Elissa Chesler, Sujoy Roy, Evan G. Williams, Alexander G. Williams, Kenneth Manly, Jintao Wang, and Robert W. Williams, colleagues. Python Powered Registered with Nif
GeneNetwork support from:
  • The UT Center for Integrative and Translational Genomics
  • NIGMS Systems Genetics and Precision Medicine project (R01 GM123489, 2017-2021)
  • NIDA NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017-2022)
  • NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013-2018)
  • NIAAA Integrative Neuroscience Initiative on Alcoholism (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017)
  • NIDA, NIMH, and NIAAA (P20-DA 21131, 2001-2012)
  • NCI MMHCC (U01CA105417), NCRR, BIRN, (U24 RR021760)
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