Introduction to Sequencing the Brain Transcriptome

Robert Hitzemann*,†,1; Priscila Darakjian*; Nikki Walter*,†; Ovidiu Dan Iancu*; Robert Searles‡; Shannon McWeeney§,¶    * Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
† Research Service, Veterans Affairs Medical Center, Portland, Oregon, USA
‡ Integrative Genomics Laboratory, Oregon Health & Science University, Portland, Oregon, USA
§ Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon, USA
¶ Division of Biostatistics, Public Health & Preventative Medicine, Oregon Health & Science University, Portland, Oregon, USA
1 Corresponding author: email address: hitzeman@ohsu.edu

Abstract

High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain–behavior–disease relationships is substantial.

Keywords

Transcriptome

RNA-seq

Brain

Next-generation sequencing

Behavior

1 Introduction

Next-generation sequencing (NGS) refers to a variety of related technologies, often termed massively parallel sequencing. The first NGS platform (Roche 454) was introduced in 2004. Subsequently, other platforms were released by several manufacturers: Illumina (Solexa), Helicos, Pacific Biosciences, and Life Technologies (ABI). Although the instruments differ in the underlying chemistry and technical approach, the platforms are similar in their capability of producing very large numbers of simultaneous reads relative to traditional methods. Thus, it is now possible to sequence whole genomes, exomes, and transcriptomes for a reasonable cost and effort. The technology of transcriptome sequencing, also known as RNA-Seq, has matured to the point that it is reasonable to propose substituting RNA-Seq for microarray-based assessments of global gene expression. Of particular importance to our laboratories are the advantages RNA-Seq has over microarray platforms when analyzing complex rodent crosses, e.g., heterogeneous stocks (HSs). However, the same argument can be made when analyzing any outbred population, including humans. Of particular relevance to the brain transcriptome are the advantages RNA-Seq has over microarrays in analyzing alternative splicing. This chapter provides a starting point for understanding the emergence of RNA-Seq and emphasizes transcriptome/behavior relationships.

2 From Microarrays to RNA-Seq

Cirelli and Tononi (1999) were among the first to report genome-wide brain gene expression profiling associated with a behavioral phenotype; both mRNA differential display and cDNA arrays were used to examine the effects of sleep deprivation on rat prefrontal cortex gene expression. Sandberg et al. (2000) used Affymetrix microarrays to detect differences in brain gene expression between two inbred mouse strains (C57BL/6J [B6] and 129SvEv [129; now 129S6/SvEvTac]). Importantly, these authors observed that some differentially expressed (DE) genes were found in chromosomal regions with known behavioral quantitative trait loci (QTLs). For example, Kcnj9 that encodes for GIRK3, an inwardly rectifying potassium channel, was DE (higher expression in the 129 strain) and is located on distal chromosome 1 in a region where QTLs had been identified for locomotor activity, alcohol and pentobarbital withdrawal, open-field emotionality, and certain aspects of fear-conditioned behavior (see Sandberg et al., 2000). Subsequently, Buck and colleagues (Buck, Milner, Denmark, Grant, & Kozell, 2012; Kozell, Walter, Milner, Wickman, & Buck, 2009) have shown that Kcnj9 is a quantitative trait gene (QTG) for the withdrawal phenotypes. Over the past decade, this alignment of global brain gene expression data and behavioral QTLs has been reported in numerous publications and discussed in numerous symposia and reviews (e.g., Bergeson et al., 2005; Farris & Miles, 2012; Hoffman et al., 2003; Matthews et al., 2005; Mcbride et al., 2005; Saba et al., 2011; Sikela et al., 2006; Tabakoff et al., 2009). The association gained further support as the focus turned to genes whose expression appeared to be regulated by a factor or factors within the behavioral QTL interval. Web tools have been developed to facilitate integrating behavioral and brain microarray data (e.g., www.genenetwork.org and http://phenogen.ucdenver.edu/PhenoGen/index.jsp; Chapter 8). This integration has been successful in detecting several candidate QTGs for behavioral phenotypes (see, e.g., Hitzemann et al., 2004; Hofstetter et al., 2008; Mulligan et al., 2006; Saba et al., 2011; Tabakoff et al., 2009).

The alignment of DE genes with a behavioral phenotype can be further examined using a variety of secondary analyses, e.g., examining if the DE genes cluster within known gene ontology categories (Pavlidis, Qin, Arango, Mann, & Sibille, 2004) or are part of a known protein–protein interaction network (Bebek & Yang, 2007; Feng, Shaw, Rosen, Lin, & Kibbe, 2012). DE genes can also be grouped on the basis of common transcription factors and other regulatory elements (e.g., Vadigepalli, Chakravarthula, Zak, Schwaber, & Gonye, 2003). In addition to DE genes, microarrays have also facilitated gene coexpression-based analyses, such as the Weighted Gene Coexpression Network Analysis (WGCNA; Horvath et al., 2006; Zhang & Horvath, 2005). The rationale behind these approaches is that coexpressed genes frequently code for interacting proteins, which in turn leads to new insights into protein function(s) and in some cases leads to discovery of protein function (Zhao et al., 2010). Coexpression analysis has been used to analyze differences in functional brain organization between nonhuman primates and humans (Oldham, Horvath, & Geschwind, 2006), regional differences in the functional organization of the human brain (Oldham et al., 2008), and the molecular pathology of autism (Voineagu et al., 2011) and alcoholism (see Chapter 11).

Despite these successes, microarray-based approaches are not without problems. First, differences in brain gene expression among genetically unique individuals or lines selected for behavioral traits are generally small; reported differences of 15–25% are not uncommon. To some extent, these small variations occur because hybridization isotherms for oligonucleotide arrays are frequently not linear due to probe saturation (Pozhitkov, Boube, Brouwer, & Noble, 2010).

A second problem with oligonucleotide arrays is the effect of single nucleotide polymorphisms (SNPs; Duan, Pauley, Spindel, Zhang, & Norgren, 2010; Peirce et al., 2006; Sliwerska et al., 2007; Walter et al., 2009, 2007). Rodent oligonucleotide arrays are based upon the sequence of the B6 mouse or Brown-Norway (BN) rat. Even inbred strains closely related to the B6 or BN strains may differ by several million SNPs (see, e.g., Keane et al., 2011), which in turn can cause significant hybridization artifacts (Walter et al., 2009, 2007). Masking for SNPs can improve this situation but results in deleting probes or even an entire probe set from the analysis. Walter et al. (2009) used NGS to address the SNP problem, building upon the repeated observation that, when comparing gene expression in the B6 and DBA/2J (D2) inbred mouse strains (or crosses and selected lines formed from these strains) and after masking for known SNPs in the D2 strain, there remained an excess of genes showing higher expression...