Large-Scale Gene Expression Differences Across Brain - Genetics

Copyright � 2006 by the Genetics Society of AmericaDOI: 10.1534/genetics.106.061481

Large-Scale Gene Expression Differences Across Brain Regions andInbred Strains Correlate With a Behavioral Phenotype

Jessica J. Nadler,*,† Fei Zou,‡,§ Hanwen Huang,§ Sheryl S. Moy,†,**,†† Jean Lauder,†,††,‡‡

Jacqueline N. Crawley,†,††,§§ David W. Threadgill,*,†,‡ Fred A. Wright‡,§,†† andTerry R. Magnuson*,†,‡,1

*Department of Genetics, ‡Carolina Center for Genome Sciences, §Department of Biostatistics, **Department of Psychiatry, ‡‡Department of Celland Developmental Biology, ††Neurodevelopmental Disorders Research Center and †North Carolina STAART Center, University of

North Carolina, Chapel Hill, North Carolina 27599 and §§Laboratory of Behavioral Neuroscience, IntramuralResearch Program, National Institute of Mental Health, Bethesda, Maryland 20892-3730

Manuscript received August 24, 2006Accepted for publication September 6, 2006

ABSTRACT

Behaviors are often highly heritable, polygenic traits. To investigate molecular mediators of behavior, weanalyzed gene expression patterns across seven brain regions (amygdala, basal ganglia, cerebellum, frontalcortex, hippocampus, cingulate cortex, and olfactory bulb) of 10 different inbred mouse strains (129S1/SvImJ,A/J, AKR/J, BALB/cByJ, BTBR T1 tf/J, C3H/HeJ, C57BL/6J, C57L/J, DBA/2J, and FVB/NJ). Extensivevariation was observed across both strain and brain region. These data provide potential transcriptionalintermediates linking polygenic variation to differences in behavior. For example, mice from different strainshad variable performance on the rotarod task, which correlated with the expression of .2000 transcripts in thecerebellum. Correlation with this task was also found in the amygdala and hippocampus, but not in otherregions examined, indicating the potential complexity of motor coordination. Thus we can begin to identifyexpression profiles contributing to behavioral phenotypes through variation in gene expression.

INVESTIGATIONS into the genetics of behavioraltraits, from alcohol preference to depression to cog-

nitive ability, have revealed that behavior is highly her-itable and likely influenced by many genes (Winterer

and Goldman 2003; Oroszi and Goldman 2004;Hamet and Tremblay 2005). This genetic complexityhas led to difficulty in identifying genes involved inpsychiatric disorders as well as those contributing togeneral behavioral characteristics. To understand bet-ter how genotype influences behavioral phenotype, weperformed a detailed analysis of expression profilesthroughout the brain to determine which transcriptsvary by genetic background and correlate with behav-ior. Recent catalogs of the mouse transcriptome indi-cate that there may be ,30,000 protein-coding genes,but that alternate splicing, alternative start and stopsites, and microRNAs can add substantially to geneticcomplexity (Carninci et al. 2005). This makes thedissection of gene expression, an intermediate betweenpolymorphic DNA sequence and variable phenotype, alogical choice to investigate relationships connectinggenotype to complex phenotypes like behavior.

Previous studies have examined gene expression pro-files in the brain by microarray analysis. Zapala et al.(2005) have shown that regional differences in gene

expression in the adult brain are largely reflective ofthe developmental origin of a particular region. Inves-tigations into strain-related differences have led toestimates that 1–2% of the genes may vary in expressionbetween six brain regions of C57BL/6 and 129SvEvmice (Sandberg et al. 2000; Pavlidis and Noble 2001).

To extend previous studies and gain a more accuratepicture of transcriptional variation, we measured geneexpression in seven different regions of the mousebrain: amygdala, basal ganglia, cerebellum, frontal cor-tex, hippocampus, cingulate cortex, and olfactory bulb.These regions all play roles in behavior, and they en-compass a range of neurocognitive functions, includinglocomotion, emotion, sensation, learning, and memory.Furthermore, the gene expression profile from eachregion was examined in 10 different inbred mousestrains: 129S1/SvImJ, A/J, AKR/J, BALB/cByJ, BTBRT1 tf/J, C3H/HeJ, C57BL/6J, C57L/J, DBA/2J, andFVB/NJ. Taking advantage of the diversity of both brainregion and strain, we found that 57% of all transcriptsassayed show variation across region and/or geneticbackground, a marked increase over previous reports(Sandberg et al. 2000; Pavlidis and Noble 2001; Zapala

et al. 2005). This diversity is due to the inclusion of moredistantly related strains and is a tool to focus on themolecular causes for the phenotypic diversity observedamong these strains.

Performance on the accelerating rotarod is a com-mon motor coordination task, utilized with genetically

1Corresponding author: Department of Genetics, University of NorthCarolina, CB 7264, 103 Mason Farm Rd., Chapel Hill, NC 27599-7264.E-mail: [email protected]

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and pharmacologically modified mouse models. Com-parison of this strain-specific phenotype to gene expres-sion serves as a clear proof of principle for our approachrelating expression to strain-specific phenotypes. Strik-ing correlation was found between this task and geneexpression in the cerebellum, a region involved withmotor coordination. Surprisingly, correlation was alsofound to a lesser extent in the amygdala and hippocam-pus, suggesting the involvement of fear response andlearning and memory in this task. No significant correla-tions were found in the other brain regions. These find-ings demonstrate the power of using gene expressionprofiles as an intermediate molecular phenotype to linkunderlying genetic variation to a behavioral phenotype.

MATERIALS AND METHODS

Mice: Male mice, 3–4 weeks of age, were purchased from theJackson Laboratories. Upon arrival at the University of NorthCarolina, mice were housed four to five per cage understandard specific pathogen free conditions. After acclimatingfor 1 week, mice were killed by cervical dislocation and specificbrain regions were dissected. Animals used for the rotarod taskwere as previously described (Nadler et al. 2004).

Tissue samples: Brains were placed in RNAlater (Ambion,Austin, TX) immediately upon removal and remained in thesolution during dissection under low magnification (see sup-plemental Figure 1 at http://www.genetics.org/supplemental/).After removing the cerebellum and the olfactory bulb, thefrontal cortex region was taken from the most anterior corticalarea, avoiding any remaining olfactory bulb. At this point, a cutwas made at the level of the optic chiasm to reveal the basalganglia region anterior to the cut and leaving the amygdala,cingulate cortex, and hippocampus in the posterior section.The basal ganglia region was removed from the anterior por-tion, avoiding any cortical tissue. From the posterior portion,the cingulate cortex region was removed from both sides of themidline, above the hippocampus, which was visible from theoriginal cut. The amygdala region was taken from both sides ofthe brain. Finally, the hippocampus was blunt dissected fromthe remaining posterior portion.

Tissues from three mice were pooled by region, such thatthe tissue sample contained representatives from each homecage. Tissues were stored overnight in RNAlater at 4� beforetransferring into TRIzol Reagent (Invitrogen, San Diego)for homogenization with a Kinematica AG (Brinkmann,Westbury, NY). The homogenate was stored at�80� until sam-ple preparation.

RNA samples: RNA was prepared from the pooled samplesusing the TRIzol protocol. Following extraction, the RNeasyminiprep cleanup protocol (QIAGEN, Valencia, CA) was used.RNA was quantitated by a spectrophotometer and its qualityvisualized using the Bioanalyzer Lab-on-a-chip (Agilent).

Microarray: The strategy for microarray hybridization was asfollows. Each tissue preparation consisted of a pooled samplefrom three animals, hybridized to a single array. Three poolswere prepared for each strain-by-region condition, exceptC57BL/6J 3 frontal cortex, C3H/HeJ 3 cerebellum, andC3H/HeJ 3 hippocampus, which had four pools each. Poolswere derived from independent sets of mice. Each pool washybridized to a single array. Therefore, each strain-by-regioncondition had three biological replicates from which to cal-culate mean expression and variation. Furthermore, no twobiological replicate samples were hybridized concurrently.

Arrays were hybridized in batches of six to eight. Microarrayanalysis was performed using the Agilent mouse platform(G4121A). Fifteen micrograms of total RNA from each samplewas labeled with Cy3 using the Agilent fluorescent direct labelkit. Similarly, 15 mg of Stratagene mouse universal referenceRNA (740100) was labeled with Cy5. Differentially labeledRNAs were cohybridized using the Agilent protocol overnightat 60�. Microarrays were washed using the Agilent SSPE/solu-tion 3 protocol and scanned using an Agilent scanner. Raw datawere collected using feature extraction software (Agilent).

Rotarod task: Twenty mice per strain were tested on anaccelerating rotarod (Ugo Basile) to assess motor coordina-tion. Each subject was given two trials, with 45 sec betweentrials. Revolutions per minute (rpm) were initially 3 rpm, witha progressive increase to a maximum of 30 rpm across 5 min(maximum trial length). Measures were taken of latency to fallfrom the top of the rotating barrel.

Statistical analysis: Data were normalized by the lowess func-tion using the SMA package in R (http://stat-www.berkeley.edu/users/terry/zarray/Software/smacode.html) and fur-ther scale normalized. The log ratio of background-subtractedCy3 signal to background-subtracted Cy5 signal was calculatedfor each spot and used for subsequent analyses. The Excelplug-in of the SAM package (recoded in R to handle large datasets) was used to identify significant changes in gene expres-sion and to correlate expression in each region with the strainvalues on the rotarod task as described below (Tusher et al.2001). The false discovery rate was controlled at 0.01 for differ-ential expression hypotheses (2000 permutations) and at 0.05for correlation with rotarod performance (500 permutations).

Further, two-way analyses of variance were performed foreach transcript, to evaluate main effects of strain, region, andstrain-by-region interactions. To assess the relative contribu-tion of strain and region effects, we performed multiple re-gression analysis of expression values with strain and region asmain-effect predictors, using the glm function in R. Contri-butions of strain and tissue to the overall multiple R2 weredetermined for each gene separately.

Hierarchical cluster analysis was performed using the hclustroutine in R (http://www.r-project.org/), using the Pearsoncorrelation metric. Separate region clustering was performedwithin each strain, and strains were clustered within eachregion. In addition, for each spot, an average expression valuefor each strain was obtained across all brain regions. Theseaverage values were used for an overall averaged cluster anal-ysis of the strains. Similarly, an averaged cluster analysis wasperformed for brain regions, where averages were taken acrossstrains for each brain region.

Pairwise comparisons between individual regions withinstrains involved too few arrays for SAM analysis, and for thesecomparisons we used two-sample t-tests assuming unequalvariances and the Benjamini–Hochberg FDR-controlling pro-cedure (Benjamini and Hochberg 1995).

For the table showing transcripts with expression levelsspecific to a single strain-by-region combination (Figure 3a),the independence of strain and region was assessed via a stan-dard x2-contingency table statistic. We subjected the entries to10,000 random permutations to obtain an empirical P-value.

RESULTS

Gene expression profiles were generated for sevenbrain regions in 10 inbred strains of mice. Three arrayswere hybridized for each strain-by-region condition. Eacharray consisted of pooled RNA from three individualanimals, for a total of nine animals per strain-by-regioncondition. Samples were hybridized against a reference

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RNA (740100; Stratagene, La Jolla, CA) on the Agilentmouse oligo platform (G4121A, Agilent), which containsfeatures corresponding to 20,871 transcripts. Of these,14,003 are annotated genes, 4974 are ESTs or hypothet-ical genes, and 1893 are unknown. These microarray dataare publicly available at Gene Expression Omnibus(http://www.ncbi.nlm.nih.gov/geo/). A total of 11,884transcripts showed variation across brain region and/orstrain. The effects of strain and region on expressionwere analyzed using the significance analysis of micro-arrays (SAM) (Tusher et al. 2001) and supported byanalogous two-way ANOVA analyses including strain-by-region interaction effects (supplemental data 1 at http://www.genetics.org/supplemental/).

Widespread gene expression differences acrossseven mouse brain regions: SAM analyses were per-formed within each strain to identify transcripts exhib-iting significant regional variation with a false discoveryrate (FDR) ,0.01. A total of 9949 transcripts showedstatistically significant regional expression differences in$1 of the 10 strains. These transcripts were grouped bythe number of strains in which they exhibited regionalvariation (Figure 1a, supplemental data 2 at http://www.genetics.org/supplemental/). For example, 4806 tran-scripts showed regional variation in a single strain, themajority of which appeared in AKR/J or FVB/NJ. A totalof 236 transcripts exhibited regional variation in all 10strains. Expression profiles of these 236 transcripts show,with few exceptions, that the profile of gene expressionacross regions is similar in all strains (supplemental data3 at http://www.genetics.org/supplemental/). For ex-ample, Nts expression is higher in the amygdala, basalganglia, and hippocampus of all 10 strains.

For each transcript showing significant regional var-iation within a strain, we calculated the range of expres-sion across the regions. The ratio of extreme values(highest expression among all regions divided by low-est expression among regions) was averaged across allstrains in which the transcript showed variation. In-terestingly, the distribution of these average expressionratios did not seem to depend greatly on the number ofstrains showing regional variation, with most valuesfalling in the range of two to three (Figure 1b).

The relationship between brain regions is illustratedby clustering the regions using strain-averaged gene ex-pression (Figure 1c). The cerebellum is the most uniqueand clusters away from the telencephalic regions. In thetelencephalon, olfactory bulb and basal ganglia are dis-tinctive with amygdala and hippocampus more closelyrelated to each other. The frontal cortex and cingulatecortex are the most closely related regions examined.These data are consistent with those of Zapala et al.(2005), who found that regional cluster analysis re-capitulated the development of the embryonic brain.Expression data from each individual strain were alsoclustered separately to examine the differences in re-lationship between brain regions from strain to strain

(supplemental data 4 at http://www.genetics.org/supplemental/). Two pairs of strains, A/J-FVB/NJ and129/SvImJ-C57L/J, showed identical region clusters,the latter of which is very similar to the strain-averagedcluster. The brain region clusters of BTBR, BALB/cByJ,and C57BL/6J differ only in the placement of a singlebranch. In 8 of the 10 strains, frontal and cingulate cor-tex cluster most closely together. Within each strain weperformed all pairwise comparisons of the seven regionsand report transcripts with FDR ,0.05 (supplementaldata 9 at http://www.genetics.org/supplemental/).

Figure 1.—Gene expression varies with brain region. (a)Number of genes with variable expression across brain re-gions in one or more strains. Column 1, genes with variableexpression across brain regions in a single strain, is dividedby which strain has variation (supplemental data 2 at http://www.genetics.org/supplemental/). (b) Range of expressionlevel as a ratio of maximum expression to minimum expres-sion. Columns correspond to a. (c) Cluster of brain regionsusing strain-averaged gene expression profiles.

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Extensive variation in gene expression among inbredmouse strains: SAM analyses were performed withineach brain region to identify transcripts exhibiting sig-nificant strain variation at FDR ,0.01. A total of 6371transcripts showed statistically significant expressiondifferences by strain in one or more brain regions, withthe majority being specific to a single brain region (Figure2a). Transcripts showing strain variation in only onebrain region are variably expressed most often in thecerebellum (supplemental data 5 at http://www.genetics.org/supplemental/). For the 25 transcripts showing sig-nificantly variable expression across strains in all brainregions, variation is due to expression differences in thesame strains in all regions (supplemental data 6 at http://

www.genetics.org/supplemental/). For example, Chi3l3is expressed at a higher level in C3H/HeJ than in theother strains in all seven regions.

For each transcript showing significant variationwithin a region, we calculated the range of expressionacross the strains. The ratio of extreme values was av-eraged across all regions in which the transcript showedvariation. Similar to the results for region, the distribu-tion of these ratios does not depend upon the numberof regions showing strain variation, with most valuesaround two to three (Figure 2b).

The relationship between the strains examined isillustrated by cluster analysis using expression levelsaveraged across brain region (Figure 2c). DBA/2J showsthe most distinctive pattern of expression of the 10strains. BALB/cByJ and BTBR form a group more dis-tinct from the rest of the strains than from each other.Among the remaining strains, AKR/J forms a branch byitself, while C57L/J and C57BL/6J are more similar toeach other. C3H/HeJ and FVB/NJ also cluster together.When the strains are clustered using each brain regionindividually, the structure of the clusters is markedlydifferent (supplemental data 7 at http://www.genetics.org/supplemental/). DBA/2J remains the outlier inthree of the seven regions. A/J and AKR/J are closelyrelated in four of the seven regions, consistent with arecent single-nucleotide polymorphism (SNP) analysis(Petkov et al. 2005). None of the regional clustersrecapitulates the region-averaged cluster structure.

Interaction between strain- and region-specific geneexpression profiles: Transcripts appearing in the lastcolumn of Figure 1a exhibit regional variation in all10 strains. Similarly, the transcripts in the last columnof Figure 2a exhibit characteristic variation in strain-specific expression in all seven brain regions. We nextconsidered transcripts appearing in the first columns ofFigures 1a and 2a. Such transcripts show regional var-iation in only 1 strain and strain variation in only oneregion and thus exhibit expression levels largely specificto a single strain-by-region combination. In Figure 3a,the table of transcripts with expression levels specificto strain-by-region combinations shows that certaincombinations appear to be over- or underrepresented(x2

70 ¼ 4428, permutation P ¼ 0.0033). Strain-by-regioncombinations AKR/J 3 cerebellum, AKR/J 3 hippo-campus, BALB/cByJ 3 cerebellum, and C3H/HeJ 3

olfactory bulb have an overrepresentation of variabletranscripts, as do multiple regions of BTBR and FVB/NJ. Strain-by-region combinations involving the cere-bellum, hippocampus, and olfactory bulb of multiplestrains, C57L/J 3 basal ganglia, and multiple regionsof AKR/J have an underrepresentation of variabletranscripts.

The relative contribution of strain or region to thevariation in expression of each transcript can be de-scribed by the portion of the multiple regression R2 at-tributable to strain and region as main effects. A slightly

Figure 2.—Gene expression is quantifiably different be-tween strains. (a) Number of genes with variable expressionacross strains in one or more brain regions. Column 1, geneswith variable expression across strains in a single region, is di-vided by which region has variation (supplemental data 5 athttp://www.genetics.org/supplemental/). (b) Range of ex-pression level as a ratio of maximum expression to minimumexpression. Columns correspond to a. (c) Cluster of strainsusing region-averaged gene expression profiles.


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larger average R2-value is observed for brain regioneffect than for strain effect (Figure 3b). This tendency ismore pronounced for higher R2-values. For example,ninefold more transcripts exhibit R2 . 0.6 for brainregion effect (716) than for strain effect (78). However,there are some transcripts whose explained variation isdue more to strain than to brain region. The transcriptsat the extremes are often those from the last columns ofFigures 1a and 2a.

Gene expression is correlated with motor task per-formance in the cerebellum, amygdala, and hippocam-pus: A further goal of quantifying expression variation isto investigate the genetic contribution to biologicalfunction. Inbred mouse strains differ in many physio-logical and behavioral phenotypes (Bogue and Grubb

2004). One behavioral phenotype showing strain-specific variation is performance on the rotarod task(Figure 4a). This task assays motor coordination byrequiring a mouse to walk on a rotating rod as its rota-tion increases; latency to fall is a measure of motor per-formance. In the cerebellum, transcripts were testedfor correlation with rotarod performance using linearregression analysis in the SAM package. At an FDR of

0.05, 1409 transcripts showed a positive correlation withrotarod performance, while 686 showed a negativecorrelation (Figure 4b, supplemental data 8 at http://www.genetics.org/supplemental/). Of the 500 tran-scripts most significantly positively correlated with anincrease in rotarod performance, several gene ontology(GO) term categories were overrepresented (Zhang et al.2004). These included signal transducer activity, theextracellular region, and detection of external stimuli.Of the 500 most negatively correlated transcripts, signi-ficant GO term categories included the organelle, nu-cleic acid binding proteins, and the cell cycle (Figure 4c).

Significant gene expression correlation was also foundin the amygdala and hippocampus. In the amygdala, 3genes were positively correlated and 38 were negativelycorrelated with rotarod performance. In the hippocam-pus, 529 genes showed a positive correlation betweenexpression and rotarod performance (Figure 4b, supple-mental data8 at http://www.genetics.org/supplemental/).GO term analysis of these genes shows an overrepresen-tation of signal transduction activity, response to stimu-lus, and sensory perception (Figure 4c). When similarSAM analyses were performed using gene expression

Figure 3.—Gene expres-sion is influenced by strainand region. (a) The numberof genes whose variation isattributed to a particularbrain region-by-strain iden-tity. This is the intersectionof genes in the first columnof Figures 1a and 2a. Cells inred indicate significant de-parture from that expected.A, amygdala. BG, basal gan-glia. Cer, cerebellum. Cing,cingulate cortex. FC, frontalcortex. H, hippocampus.OB,olfactorybulb.Observed(expected)valuesareshown.(b) Each point represents asingle gene with variable ex-pression across strain or re-gion. The x-axis indicatesthe proportion of expres-sion variation due to strain,the y-axis indicates variationdue to brain region. Linesindicate the mean R2-valuefor each axis. Genes fromthe last bar of Figure 1a arecircled in red. Genes fromthe last bar of Figure 2a arecircled in green.


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from olfactory bulb, basal ganglia, and cingulateand frontal cortices, no significant correlations werefound.

DISCUSSION

Widespread gene expression variation across bothbrain region and strain: Earlier studies estimated that

1% of genes show variable expression in a comparisonof C57BL6 and 129SvEv (Sandberg et al. 2000). Re-analysis of these data with a refined statistical methoddoubled this estimate (Pavlidis and Noble 2001).Using our data, comparison of only these two strainsyielded similarly low estimates (data not shown). How-ever, using 10 inbred strains, we found that nearly 30%of transcripts exhibit strain-specific expression variation

Figure 4.—Gene expression as a functionof motor coordination. (a) Performance onrotarod as measured by latency to fall in sec-onds. (b) Number of genes correlated to ro-tarod performance. Positive and negativecorrelations divided by known genes, ESTs,and unknown genes as listed on the Agilentgene list are shown. (c) OverrepresentedGO term categories in the 500 most highly cor-related sequences for both positive and nega-tive correlations. O, observed. E, expected. R,enrichment. P, P-value.


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in at least one region of the brain. This increase is due,in large part, to inclusion of more distantly relatedstrains, such as AKR/J and FVB/NJ, providing a greateropportunity to relate expression to phenotypic dif-ferences among strains. The majority of differentiallyexpressed transcripts identified in earlier work are pres-ent in our gene lists (Sandberg et al. 2000; Pavlidis

and Noble 2001). We hypothesize that many of thesegenes contribute directly to neurological and behavioralphenotypes, while others are influenced by geneticvariation among strains without direct behavioral con-sequences. Therefore, the list of differentially expressedgenes generated by this approach is a rich resource forthe genetic dissection of behavior.

These data reveal how genetic background interactswith gene expression across brain regions. Strain-averaged cluster analysis of the relatedness of brain re-gions supports the finding that regional expressionprofile is reflective of brain development (Zapala et al.2005). When brain regions are clustered by expressionprofile for each strain, different relationships emerge.Cluster analysis of region-averaged strain relatednessdoes not recapitulate the derivation of the strains ortheir relatedness based on SNP analyses (Petkov et al.2005). In fact, the region-averaged strain cluster differsfrom all individual brain region clusters. The differentialrelatedness of strains on each brain region may be anindication of why certain strains behave similarly on sometasks, but not others. The difference between averagedand individual clusters is yet another indication of thecomplex relationship between the influence of geneticbackground and brain region on gene expression.

Prior to this experiment, it was often assumed thatdifferences between strains would be much smaller thanbetween regions (Zapala et al. 2005). The present anal-ysis shows that there are more transcripts with an R2 .

0.6 for region than there are for strain. For the bulk ofthe transcripts, however, the mean R2 is very similar forregion or strain. Furthermore, the mean range of tran-script expression is similar across regions and strains.We also observed that transcripts exhibiting significantregional variation in all 10 strains tend to show remark-ably similar expression profiles across different strains.These genes are likely to perform functions specific tothe regions in which they are highly expressed, since thisexpression has been conserved in all strains. Similarly,transcripts exhibiting strain variation in all seven re-gions show similar profiles for different regions. Theexpression of these genes is dependent upon the gen-etic background and may be involved strain-specific phe-notypic changes in multiple organ systems. The range ofexpression changes in these two sets of genes, however,is still quite similar. So even though the expression ofa transcript varies by region in all 10 strains, the changein expression observed is not significantly larger thanwhat is seen in a transcript with variable expression in asingle strain.

Some transcripts exhibit expression levels largely spe-cific to single strain-by-region combinations, and thepattern of strain-by-region specificity is complex. Tran-scripts with this property may be of particular interestfor phenotypes likely to involve specific brain regions.For example, the cerebellum of an AKR/J mouse hasmore variable transcripts than can be accounted for bytranscripts differentially regulated in all AKR/J tissues.This indicates that AKR/J may perform significantly dif-ferently on a motor task. Similarly, overrepresentationof variable genes in the AKR/J hippocampus may contrib-ute to a differential learning and memory phenotype.

Gene expression is correlated with behavior: Al-though region-specific expression in the brain mayprovide clues to function, genetic background-specificexpression is required to explain behavioral differencesamong inbred strains. A gene whose expression doesnot vary between strains cannot contribute to a differ-ence in behavioral phenotype. Gene expression profilescan be used as an intermediary in efforts to understandphenotypic variation. To demonstrate the feasibility ofsuch an approach, we correlated gene expression datawith a behavioral task showing strain-specific variation.In the cerebellum, 2095 transcripts (10%) showed signi-ficant correlation between expression level and perfor-mance on the rotarod motor performance task acrossinbred strains. This is indicative of the complexity ofmotor coordination, as well as of the central role of thecerebellum in motor activity. Many known genes wereidentified in this analysis, several of which have beenshown to affect rotarod performance, such as Rac3(Corbetta et al. 2005). Furthermore, a proportion ofthe genes identified are of unknown function, pro-viding a rich resource for investigation of the geneticcontrol of motor coordination.

Examination of GO terms in the correlation lists re-vealed functions associated with transcription, such assignal transduction activity and nucleic acid-bindingproteins (Zhang et al. 2004). GO analysis also shows un-expected results, such as an overrepresentation of tran-scripts annotated as olfactory receptors in regions otherthan olfactory bulb and cell cycle genes in the post-mitotic cerebellum. These unexpected results couldreflect the incompleteness of GO annotation, regionalspecificity in gene function, or the indirect coexpres-sion of genes involved in motor function with othergenes in these brain regions. Additional clues to bio-logical function may be revealed by analysis of genesthat are targeted by known transcription factors.

While the cerebellum is thought to be involved inmotor tasks, regions such as olfactory bulb have notbeen shown to exhibit similar involvement (Mauk et al.2000; Nixon 2003; Ohyama et al. 2003). Analysis of geneexpression in the olfactory bulb yielded no significantcorrelations with rotarod performance, supporting ourgeneral approach. Furthermore, no significant correla-tions were found in the basal ganglia or cingulate or


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frontal cortices with this task, whereas significant cor-relations were observed in the amygdala and hippocam-pus. Relatively few genes were found to be correlatedwith motor function in these regions, perhaps indicat-ing that the major influence on motor coordination isfrom the cerebellum, with some level of mediation bythe other two brain regions, even though the basal gan-glia are involved with other aspects of motor function,such as motor planning (Glover 2004). Furthermore,transcripts with significant correlations in the amygdalaand hippocampus are not merely a subset of transcriptsidentified in the cerebellum. This indicates that differ-ent mechanisms function in each brain region andsuggests that the experiment is not merely identifyingthe gene expression signature of a particular geneticbackground in each region examined. There is evi-dence for a hippocampal role in locomotion (Bast andFeldon 2003), and the marked improvement with re-peated trials typically observed in this procedure sug-gests a contribution of learning and memory to rotarodperformance. The amygdala is not immediately impli-cated in the rotarod task, but there is potential that dif-ferential fear response could be a factor in the strengthof the motivation to remain on the rotarod and couldvary with gene expression in the amygdala. These resultsindicate that performance on the rotarod is a combina-tion of neurocognitive processes involving more thansimply cerebellar function.

It is clear that regions of the brain, having specificbiological functions, express a unique suite of genes toperform these functions. Many of the genes showingsignificant variation in specific brain regions in this dataset have also been identified in other published reportson regional specificity. For example, we found signifi-cant variation in Foxp1, Actn2, and Rgs9 in the basalganglia, in accordance with previous studies (Dunah

et al. 2000; Ferland et al. 2003; Rahman et al. 2003;Tamura et al. 2003, 2004; Zapala et al. 2005). However,our data also expand previous studies and show thatvariation in gene expression in the brain is substantiallyhigher than previously reported.

This catalog of gene expression is a useful toolfor generating hypotheses about the genetic basis ofany phenotype showing variation across inbred mousestrains, encompassing behavioral, physiological, or phar-macological studies. These data can also be informativein choosing strains likely to be sensitive to manipula-tions of certain pathways and thus suitable for a particu-lar experiment. By defining the expression differencesbetween inbred mouse strains, we can examine the ef-fect of genotype on molecular phenotype and ultimatelythe effect of genetic background on behavior.

We thank Antonio Perez and Nancy Young for their work on therotarod phenotyping. This work was supported by the University ofNorth Carolina Studies to Advance Autism Research and TreatmentU54 MH66418, 5P30HD003110, and EPA RD83272001.

LITERATURE CITED

Bast, T., and J. Feldon, 2003 Hippocampal modulation of sensori-motor processes. Prog. Neurobiol. 70: 319–345.

Benjamini, Y., and Y. Hochberg, 1995 Controlling the false discov-ery rate / a practical and powerful approach to multiple testing.J. R. Stat. Soc. 57: 289–300.

Bogue, M. A., and S. C. Grubb, 2004 The Mouse Phenome Project.Genetica 122: 71–74.

Carninci, P., T. Kasukawa, S. Katayama, J. Gough, M. C. Frith

et al., 2005 The transcriptional landscape of the mammalian ge-nome. Science 309: 1559–1563.

Corbetta, S., S. Gualdoni, C. Albertinazzi, S. Paris, L. Croci et al.,2005 Generation and characterization of Rac3 knockout mice.Mol. Cell. Biol. 25: 5763–5776.

Dunah, A. W., M. Wyszynski, D. M. Martin, M. Sheng and D. G.Standaert, 2000 alpha-actinin-2 in rat striatum: localizationand interaction with NMDA glutamate receptor subunits. BrainRes. Mol. Brain Res. 79: 77–87.

Ferland, R. J., T. J. Cherry, P. O. Preware, E. E. Morrisey and C. A.Walsh, 2003 Characterization of Foxp2 and Foxp1 mRNA andprotein in the developing and mature brain. J. Comp. Neurol.460: 266–279.

Glover, S., 2004 Separate visual representations in the planningand control of action. Behav. Brain Sci. 27: 3–78.

Hamet, P., and J. Tremblay, 2005 Genetics and genomics of depres-sion. Metabolism 54: 10–15.

Mauk, M. D., J. F. Medina, W. L. Nores and T. Ohyama, 2000 Cere-bellar function: Coordination, learning or timing? Curr. Biol. 10:R522–R525.

Nadler, J. J., S. S. Moy, G. Dold, D. Trang, N. Simmons et al.,2004 Automated apparatus for quantitation of social approachbehaviors in mice. Genes Brain Behav. 3: 303–314.

Nixon, P. D., 2003 The role of the cerebellum in preparing re-sponses to predictable sensory events. Cerebellum 2: 114–122.

Ohyama, T., W. L. Nores, M. Murphy and M. D. Mauk, 2003 Whatthe cerebellum computes. Trends Neurosci. 26: 222–227.

Oroszi, G., and D. Goldman, 2004 Alcoholism: genes and mecha-nisms. Pharmacogenomics 5: 1037–1048.

Pavlidis, P., and W. S. Noble, 2001 Analysis of strain and regionalvariation in gene expression in mouse brain. Genome Biol. 2:RESEARCH0042.

Petkov, P. M., J. H. Graber, G. A. Churchill, K. DiPetrillo, B. L.King et al., 2005 Evidence of a large-scale functional organiza-tion of mammalian chromosomes. PLoS Genet. 1: e33.

Rahman, Z., J. Schwarz, S. J. Gold, V. Zachariou, M. N. Wein et al.,2003 RGS9 modulates dopamine signaling in the basal ganglia.Neuron 38: 941–952.

Sandberg, R., R. Yasuda, D. G. Pankratz, T. A. Carter, J. A. Del Rio

et al., 2000 Regional and strain-specific gene expression map-ping in the adult mouse brain. Proc. Natl. Acad. Sci. USA 97:11038–11043.

Tamura, S., Y. Morikawa, H. Iwanishi, T. Hisaoka and E. Senba,2003 Expression pattern of the winged-helix/forkhead tran-scription factor Foxp1 in the developing central nervous system.Gene. Expr. Patterns 3: 193–197.

Tamura, S., Y. Morikawa, H. Iwanishi, T. Hisaoka and E. Senba,2004 Foxp1 gene expression in projection neurons of themouse striatum. Neuroscience 124: 261–267.

Tusher, V. G., R. Tibshiraniand G. Chu, 2001 Significance analysisof microarrays applied to the ionizing radiation response. Proc.Natl. Acad. Sci. USA 98: 5116–5121.

Winterer, G., and D. Goldman, 2003 Genetics of human prefron-tal function. Brain Res. Brain Res. Rev. 43: 134–163.

Zapala, M. A., I. Hovatta, J. A. Ellison, L. Wodicka, J. A. Del Rio

et al., 2005 Adult mouse brain gene expression patterns bearan embryologic imprint. Proc. Natl. Acad. Sci. USA 102: 10357–10362.

Zhang, B., D. Schmoyer, S. Kirov and J. Snoddy, 2004 GOTreeMachine (GOTM): a web-based platform for interpreting setsof interesting genes using gene ontology hierarchies. BMC Bio-informatics 5: 16.

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Large-Scale Gene Expression Differences Across Brain - Genetics

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