Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

<div><p>We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F₂ intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.</p> </div

<i>Bcl11b</i> connectivity and allelic differences between High and Low selected lines – HS-CC founders.

<p>A: <i>Bcl11b</i> connectivity patterns in the High network. For visual clarity, only edges involving <i>Bcl11b</i> are represented. Edge thickness and opacity are proportional with the edge weight (adjacency). Node size (except <i>Bcl11b</i>) is proportional with modular connectivity. B: Low network <i>Bcl11b</i> connectivity pattern. C: Allele distribution for <i>Bcl11b</i> in the naïve HS-CC animals (“Green”, top) and in the High and Low selected lines (red and blue, bottom). NOD and A/J alleles are more prevalent in the High group (blue) while NZO, B6 and A129 are more prevalent in the Low group (red). Strains: C57BL/6J (B6); A/J (A); 129S1/SvImJ (129); NOD/LtJ (NOD); NZO/HILtJ (NZO). CAST/EiJ (CAST). PWK/PhJ (PWK), WSB/EiJ (WSB).</p

List of disruption Z scores.

1<p>Three modules (Green, Grey60 and Pink) displayed significant disruption (cor.kIM absolute value z scores above 2) in all three datasets.</p

Hierarchical clustering of gene modules and module color assignments.

<p>Top: clustering tree. Bottom: initial unmerged colors and subsequent merged (final) module color assignments.</p

Phenotypic and genetic differences between naïve animals and selected lines.

<p>“Green”, naïve animals; blue, High line; red, Low line. A–C: Top, distribution of catalepsy responses in the naïve populations—high scores denote responders. Bottom, distribution of catalepsy responses in the selected lines. The three selected populations display differences in scores, showing successful selection. D–F: Multidimensional scaling of the genetic distances between individuals. The selected populations appear distinct from each other and closer together due to allele loss/fixation.</p

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays.

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, 'digital mRNA counting' is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation

Genes with connectivity and allele origin differences.

2<p>For the three modules affected by selection, a number of genes change connectivity significantly, as indicated by change in connectivity rank and z Score. The same genes fall within genomic regions that segregate between High and Low populations.</p