Hitzemann / Mcweeney Brain Transcriptome

1. Auflage 2014
ISBN: 978-0-12-801319-9
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band 116, 358 Seiten

Reihe: International Review of Neurobiology

Published since 1959, International Review of Neurobiology is a well-known series appealing to neuroscientists, clinicians, psychologists, physiologists, and pharmacologists. Led by an internationally renowned editorial board, this important serial publishes both eclectic volumes made up of timely reviews and thematic volumes that focus on recent progress in a specific area of neurobiology research. This volume, concentrates on the brain transcriptome. - Brings together cutting-edge research on the brain transcriptome

Autoren/Hrsg.

Hitzemann, Robert

Mcweeney, Shannon

Inhaltsverzeichnis

1;Front Cover;1
2;Brain Transcriptome;4
3;Copyright;5
4;Contents;6
5;Contributors;10
6;Chapter One: Introduction to Sequencing the Brain Transcriptome;14
6.1;1. Introduction;15
6.2;2. From Microarrays to RNA-Seq;15
6.3;3. NGS Platforms;18
6.4;4. RNA-Seq Overview;18
6.5;5. RNA-Seq and Data Analysis;22
6.6;6. Sequencing the Brain Transcriptome;25
6.7;7. Conclusions;27
6.8;Acknowledgments;27
6.9;References;28
7;Chapter Two: Analysis Considerations for Utilizing RNA-Seq to Characterize the Brain Transcriptome;34
7.1;1. Introduction;35
7.2;2. Defining and Quantifying Transcript/Gene Expression;36
7.2.1;2.1. Step 1: Alignment of RNA-Seq reads to a reference sequence;37
7.2.1.1;2.1.1. Splice-aware aligners;38
7.2.1.2;2.1.2. Sequence variations between the short read and reference sequence;39
7.2.1.3;2.1.3. Uniquely mapping or multimapping reads;40
7.2.2;2.2. Step 2: Transcriptome reconstruction;40
7.2.2.1;2.2.1. Genome guided;40
7.2.2.2;2.2.2. Genome independent;41
7.2.3;2.3. Step 3: Quantification of expression levels;42
7.3;3. Detecting Differential Expression;43
7.3.1;3.1. The need for normalization;43
7.3.2;3.2. Inferring putative DE;46
7.3.3;3.3. Outliers, subgroups, and individual expression;50
7.3.4;3.4. Isoform-specific DE;52
7.4;4. Frameworks for Interpretation;52
7.4.1;4.1. RNA-Seq library construction;52
7.4.2;4.2. Gene-model databases;53
7.4.3;4.3. Functional annotation databases;54
7.5;5. Summary;56
7.6;References;56
7.7;Further Reading;63
8;Chapter Three: Data Integration and Reproducibility for High-Throughput Transcriptomics;68
8.1;1. Opportunities for Secondary Use of Data and Meta-Anlaysis in Transcriptomics;68
8.1.1;1.1. Transcriptomics platforms;69
8.2;2. Selecting the Unit of Comparison;72
8.3;3. Metrics for Agreement;73
8.4;4. Studies on Reproducibility and Validation;74
8.5;5. Guidelines for Cross-Platform Studies;75
8.6;6. Other Data Integration Considerations;77
8.6.1;6.1. Multi-omic data integration;77
8.6.2;6.2. Cross-species comparisons;79
8.7;7. Summary;80
8.8;References;81
9;Chapter Four: Coexpression and Cosplicing Network Approaches for the Study of Mammalian Brain Transcriptomes;86
9.1;1. Introduction;87
9.2;2. Construction of Coexpression and Cosplicing Networks;88
9.3;3. Cosplicing Network Construction;91
9.4;4. Biological Annotation of Coexpression and Cosplicing Networks;95
9.5;5. Effects of Genetic Selection on Gene Networks;98
9.6;6. Module Preservation Across Subpopulations and Species;99
9.7;7. Module Disruption Related to Behavioral Changes;100
9.8;8. Summary and Future Directions;103
9.9;References;104
10;Chapter Five: Splicing in the Human Brain;108
10.1;1. Pre-mRNA Splicing in Human Cells;109
10.2;2. Alternative Pre-mRNA Splicing;109
10.3;3. Tissue-Specific Alternative Splicing;113
10.4;4. Alternative Splicing in the Brain;113
10.5;5. Brain-Specific Splicing Regulation;116
10.6;6. Transcription-Coupled Regulation of Alternative Splicing;118
10.7;7. Cotranscriptional and Posttranscriptional Splicing;120
10.8;8. Global Analysis of Pre-mRNA Splicing;122
10.9;9. The Influence of RNA Extraction Methods on Transcriptome Analysis;123
10.10;10. Computational Methods to Study Splicing Dynamics;127
10.11;References;130
11;Chapter Six: Understanding Complex Transcriptome Dynamics in Schizophrenia and Other Neurological Diseases Using RNA Seque ...;140
11.1;1. Introduction;141
11.2;2. RNA-Seq Studies on Neurological Disorders;143
11.2.1;2.1. Study design;143
11.2.2;2.2. Sequencing platforms and strategies;146
11.2.3;2.3. Data analysis;146
11.3;3. Quantifying Transcriptome Dynamics in Neurological Disorders;149
11.3.1;3.1. Gene/transcript expression;149
11.3.1.1;3.1.1. Synaptic plasticity and neurotransmission;149
11.3.1.2;3.1.2. Inflammatory/immune pathways;151
11.3.2;3.2. Alternative splicing;153
11.3.3;3.3. Allele-specific expression;154
11.3.4;3.4. RNA editing;154
11.3.5;3.5. Integrative analysis;155
11.3.6;3.6. Noncoding RNA alterations in neurological disorders;155
11.4;4. Discussion and Perspectives;157
11.5;References;160
12;Chapter Seven: The Central Role of Noncoding RNA in the Brain;166
12.1;1. Introduction;167
12.2;2. The Long and Short of Noncoding RNAs;169
12.2.1;2.1. Short noncoding RNAs;170
12.2.2;2.2. Long noncoding RNAs;174
12.3;3. Types and Function of lncRNAs;175
12.3.1;3.1. Current lncRNA classification according to origin and function;176
12.4;4. RNA Structure;178
12.5;5. Splicing;179
12.6;6. NcRNA Editing;180
12.7;7. Epigenetic Modifications;182
12.8;8. ncRNAs Are Involved in Neuronal Development, Maintenance, and Plasticity;183
12.8.1;8.1. Stimuli depend on expression, specificity, and memory;185
12.9;9. Evolutionary Role of ncRNAs and Primate Specificity;186
12.10;10. The Role of (Retro)transposons and Pseudogenes in ncRNA Evolution;188
12.11;11. ncRNAs and Disease;189
12.11.1;11.1. Alzheimer´s disease;189
12.11.2;11.2. Schizophrenia;190
12.11.3;11.3. Autism spectrum disorder;191
12.11.4;11.4. Parkinson´s disease;191
12.11.5;11.5. Angelman syndrome;192
12.11.6;11.6. Huntington´s disease;192
12.11.7;11.7. ncRNA as biomarkers;192
12.12;12. Perspectives and Outlook;193
12.13;Acknowledgments;194
12.14;References;194
13;Chapter Eight: Genetics of Gene Expression in CNS;208
13.1;1. Introduction;209
13.1.1;1.1. The history;210
13.1.2;1.2. How much variation is there in gene expression in brain?;212
13.1.3;1.3. Brain gene expression studies-A summary;213
13.1.4;1.4. Missing pieces;215
13.1.5;1.5. Genetic architecture of expression traits;217
13.1.6;1.6. RNA-seq to the rescue?;220
13.1.7;1.7. RNA-seq data generation;221
13.2;2. Genetic Resources for eQTL Analysis in Mice;221
13.2.1;2.1. Intercross progeny;222
13.2.2;2.2. RI strains;222
13.2.3;2.3. The BXD family;223
13.2.4;2.4. Heterogeneous stock;224
13.2.5;2.5. The Collaborative Cross;225
13.3;3. Genetic Mapping Methods;225
13.3.1;3.1. Single marker test;226
13.3.2;3.2. Interval mapping;226
13.3.3;3.3. Composite interval mapping;226
13.3.4;3.4. Evaluation of mapping precision;227
13.4;4. RNA-seq eQTL Studies;227
13.5;5. Pros and Cons of Arrays and RNA-seq for eQTL Studies;228
13.5.1;5.1. Advantages of arrays;228
13.5.2;5.2. Advantages of RNA-seq;228
13.6;6. RNA-seq Read Alignment and Normalization;231
13.6.1;6.1. Allelic bias in read mapping;231
13.6.2;6.2. Correct normalization of RNA-seq counts;232
13.7;7. eQTL Mapping of Alternative Splicing and Polyadenylation;233
13.8;8. RNA-seq for Allele-Specific Expression;234
13.8.1;8.1. Key factors in design of genomewide ASE;234
13.8.2;8.2. Advantages and disadvantages of ASE;235
13.9;9. Conclusions;235
13.10;Acknowledgments;236
13.11;References;236
14;Chapter Nine: Transcriptomic Changes in Brain Development;246
14.1;1. Introduction;246
14.2;2. Gene Expression;247
14.3;3. DNA Sequence Variation and Epigenetic Modification in Brain Development;251
14.4;4. Alternative Splicing;253
14.5;5. RNA Editing;255
14.6;6. Noncoding RNA;256
14.7;7. Summary;258
14.8;References;258
15;Chapter Ten: Gene Expression in the Addicted Brain;264
15.1;1. Introduction;265
15.2;2. Molecular Adaptations Accompanying Early Response and Long-Term Adaptations in the Addicted Brain;266
15.3;3. Substance-Specific and Shared Gene Expression Changes in Addicted Brain;270
15.4;4. Region-Specific Gene Expression Changes in Addicted Brain;272
15.5;5. Perturbation of the Glutamatergic System in Addicted Brain;274
15.6;6. Epigenetic Regulation of Gene Expression in Addicted Brain;277
15.7;7. Conclusion;280
15.8;References;280
16;Chapter Eleven: RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain;288
16.1;1. Overview;289
16.2;2. RNA-Seq of Postmortem Brain Tissue;289
16.3;3. Detection of Technical Biases in RNA-Seq Data;291
16.4;4. Normalization of RNA-Seq Data;297
16.5;5. Alternative Splicing and Differential Expression;297
16.6;6. Long Noncoding RNA;300
16.7;7. Novel Three Prime Untranslated Regions;300
16.8;8. Genetic Variation and Alcohol Dependence;302
16.9;9. Biological Coexpression Networks;304
16.10;10. Future Directions;305
16.11;References;307
17;Index;314
18;Contents of Recent Volumes;324

Leseproben

Chapter One

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: [e-mail]

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...