Jonathan D. Pollock

The Knockout Mouse Project

Mouse knockout technology provides a powerful means of elucidating gene function in vivo, and a publicly available genome-wide collection of mouse knockouts would be significantly enabling for biomedical discovery. To date, published knockouts exist for only about 10% of mouse genes. Furthermore, many of these are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available to researchers. It is time to harness new technologies and efficiencies of production to mount a high-throughput international effort to produce and phenotype knockouts for all mouse genes, and place these resources into the public domain.Mouse knockout technology provides a powerful means of elucidating gene function in vivo, and a publicly available genome-wide collection of mouse knockouts would be significantly enabling for biomedical discovery. To date, published knockouts exist for only about 10% of mouse genes. Furthermore, many of these are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available to researchers. It is time to harness new technologies and efficiencies of production to mount a high-throughput international effort to produce and phenotype knockouts for all mouse genes, and place these resources into the public domain.

Epigenetic Mechanisms and Gene Networks in the Nervous System

Adaptation to the environment is one of the fundamental regulatory processes in biology and is found among both simple and complex organisms. In a changing environment, simple organisms enhance species survival by high rates of spontaneous mutation achieved by several means: short maturation rates,

Gene expression profiling: methodological challenges, results, and prospects for addiction research.

This review describes the current methods used to profile gene expression. These methods include microarrays, spotted arrays, serial analysis of gene expression (SAGE), and massive parallel signature sequencing (MPSS). Methodological and statistical problems in interpreting microarray and spotted array experiments are also discussed. Methods and formats such as minimum information about microarray experiments (MIAME) needed to share gene expression data are described. The last part of the review provides an overview of the application of gene-expression profiling technology to substance abuse research and discusses future directions.

Molecular Neuroanatomy: A Generation of Progress

The neuroscience research landscape has changed dramatically over the past decade. Specifically, an impressive array of new tools and technologies have been generated, including but not limited to: brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity, and new methods for imaging and mapping circuits. However, despite these technological advances, several significant challenges must be overcome to enable a better understanding of brain function and to develop cell type-targeted therapeutics to treat brain disorders. This review provides an overview of some of the tools and technologies currently being used to advance the field of molecular neuroanatomy, and also discusses emerging technologies that may enable neuroscientists to address these crucial scientific challenges over the coming decade.

Novel RNA Modifications in the Nervous System: Form and Function

Modified RNA molecules have recently been shown to regulate nervous system functions. This mini-review and associated mini-symposium provide an overview of the types and known functions of novel modified RNAs in the nervous system, including covalently modified RNAs, edited RNAs, and circular RNAs. We discuss basic molecular mechanisms involving RNA modifications as well as the impact of modified RNAs and their regulation on neuronal processes and disorders, including neural fate specification, intellectual disability, neurodegeneration, dopamine neuron function, and substance use disorders.

Bioinformatic challenges of big data in non-coding RNA research.

Recent technological developments have brought forth a new era of RNA research in which large sets of data are collected rapidly using the high-throughput next generation sequencing technology. Growing evidence suggests that only around 5% of nucleotides in the mammalian genomes are transcribed into protein-coding RNA, and large amount of transcripts are non-protein-coding RNA (ncRNA). During the last decade, much information has been generated from the studies of one type of ncRNA, namely microRNA (miRNA, the ncRNA of 19–25 nucleotides). miRNA modulates the expression of target genes through repression of mRNA translation or mRNA degradation. Its dysregulation has been implicated in various biological disorders and human diseases. Meanwhile, the long-non-coding RNA (lncRNA, the ncRNA that have 200 or more nucleotides) has recently emerged to catch significant attention. lncRNA is involved in chromatin modification, epigenetic regulation, transcription control, and pre- and post-translational mRNA processing. The functions of lncRNA are believed to be associated with development, imprinting, mental and psychiatric disorders, and tumor growth. Bioinformatics is a pivotal component of this new RNA research revolution. It utilizes mathematical models and computer simulations to form, extract and analyze RNA data, and to search new ncRNA gene sequences and predict their targets. Assumptions in this computational modeling are derived from the observations that ncRNAs are produced following step-wise processes from precursors to functional end products. Based on miRNA biogenesis, criteria in searching for new miRNAs from sequencing data include that the precursors fold into a stable stem-loop structure, mature miRNAs are found on one arm of the stem, and these sequences are usually evolutionarily conserved (Lim et al., 2003). Target prediction algorithms take into considerations stability of miRNA-mRNA duplex, accessibility of secondary structure, nucleotide content in and around the putative target sites, and position of seed-complementary sites within the mRNA transcript. Prior to the high-throughput sequencing techniques, computational programs were developed to search for new miRNAs based on attainable sequence data. These methods used one of the following approaches (Mendes et al., 2009): filter-based approaches, which identified small high-quality sets of conserved miRNA candidates; machine learning methods, which determined initial set of candidates with stem-loops structures, and target-centered approaches, which identify short conserved motifs in the 3′UTRs of protein-coding genes (Xie et al., 2005). Even though these algorithms were developed before the high-throughput sequencing era, they establish strong bases for bioinformatic analyses of big sequencing data; new ncRNAs and targets continue to be cataloged into many databases with sufficient annotations available to the public. High-throughput sequencing techniques and deep sequencing (or RNA-Seq) have offered much improved avenue for ncRNA discovery (Lu et al., 2005), by searching genomic sequences for evidence of hairpin structures and then determine if sequencing read aligned to these structures mimic miRNA processing byproducts (Friedlander et al., 2008), or using a regularized least-squares classification algorithm to mine miRNAs from smRNA-seq data (Lu et al., 2009) to perform genome-wide multiple sequence alignments (MSAs). At the same time, through adaptation of the latest biochemical approaches to miRNA target finding, it is possible to identify miRSNPs with greater accuracy and explain the association of certain miRNA-affecting polymorphisms with disease phenotypes (Wilbert and Yeo, 2011). Even though bioinformatic-based methods for the identification of new ncRNA and their targets have become more sophisticated and required less CPU time, there are gaps and challenges that need to be addressed to justify their biological relevancy: cross-platform validation of genomic and transcriptional sequence data, cross-algorithm validation of search engines, and development of more accurate models for ncRNA function in regard to biological environment and diseases. For example, high-throughput sequencing of small RNA results in an output file of short sequence (often termed short-reads or reads) accompanied by a quality score for each nucleotide in each sequence. Because of the high sensitivity of the technique, the “raw” data will also contain sequencing primers and contaminants which can potentially produce sequence bias that requires more sophisticated computational approaches to sieve out miRNA transcripts (Mendes et al., 2009) and cross-platform validations. There are currently at least 45 sequence formats; the most widespread data formats being those used by the major sequence database: EMBL, GenBank, SwissProt, and PIR. The lack of standardization in sequence formats not only hampers the feasibility for cross-platform comparison of existing data (Farazi et al., 2011), but also discourages the expansion of sequence data sharing for initial and value-added secondary analysis. In addition, currently available algorithms have employed different approaches dictated by the algorithm developers and may or may not be reproducible using a different approach. Cross-examination between the solutions derived from different algorithms is needed. Another complexity in ncRNA data analysis is that most of the software is primarily at a command-line level and not user-friendly to the end-users. Computational approaches developed so far make extensive use of evolutionary conservation information either to predict ncRNA genes or ncRNA-target associations, sometimes ignoring the subtle rules presiding ncRNA biogenesis and target specificity. Thus, approaches combining high-throughput sequencing biochemical techniques and bioinformatic analyses that emphasizes the synergy of genome-wide approaches are essential (Mendes et al., 2009). Furthermore, most lncRNA are under lower sequence constraints than protein-coding genes and lack conserved secondary structures like the pre-miRNAs, making it hard to predict computationally. In addition, since complex diseases can be affected by a number of ncRNAs rather than a single ncRNA, and ncRNA often operates in highly complex regulatory networks (Kargul and Laurent, 2011), it is a multi-dimensional challenge to identify ncRNA interactions at a system-wide level, and analyze the roles of ncRNA in disease and disorders in the ncRNA–ncRNA synergistic network (Xu et al., 2011). Lastly, careful interpretations of data with molecular validations are critical for ensuring acceptance of bioinformatic methods in the ncRNA research community. With knowledge gained from bioinformatic analyses of exponentially increasing massive ncRNA data, many issues remain to be addressed on the functional significance and how genetic variations of ncRNA plays important roles in disease processes.

Defining Substance Use Disorders: The Need for Peripheral Biomarkers

Addiction is a brain disease, and current diagnostic criteria for substance use disorders (SUDs) are qualitative. Nevertheless, scientific advances are beginning to characterize neurobiological domains. Combining multiple units of measure may provide an opportunity to deconstruct the heterogeneities of a SUD and define endophenotypes by using peripheral biospecimens. There are several recent examples of potential biomarker types that can be examined, together with their categorical applications for SUDs. We propose that, in conjunction with rapidly advancing statistical and mathematical modeling techniques, there is now a unique opportunity for the discovery of composite biomarkers within specific domains of addiction; these may lay the foundation for future biomarker qualification, with important implications for drug development and medical care.

Preface: The Genetics and Epigenetics of Addiction

This Special Issue of the Journal of Neurogenetics is focused on the genetics of addiction for two key reasons: first, to highlight the fact that addiction is a process that can be addressed by classical and quantitative genetics, as well as by a variety of mechanistic studies and second, to communicate, to a wider audience, key insights that were communicated in a short course on the genetics and epigenetics of addiction held by the National Institute on Drug Abuse in Bethesda, Maryland, USA, March 31 April 4, 2008. Evidence from adoption and twin studies and from animal models suggests that vulnerability to addiction has a moderate to high heritable component. Like many other psychiatric illnesses, drug abuse and dependence comprise a complex set of genetic disorders lacking a simple pattern of Mendelian inheritance. Over the past 10 years, there have been rapid developments in the field of addiction genetics that has begun to illuminate the genetic architecture of addiction and identify the genetic variants associated with addiction and associated phenotypes. Equally important is the emergence of the field of pharmacogenomics that may improve treatment strategies for treating addiction. In response to these rapid developments in the field, the NIDAs Genetics Workgroup organized the course with a goal to provide an introduction to approaches for finding genes that confer vulnerability to addiction and individual differences in responses to treatments. The course was targeted at investigators who are new to the field of addiction genetics. A number of the instructors in the course are contributors to this special issue of the Journal of Neurogenetics. These contributors describe some of the conceptual and practical approaches to complex disorders, with appropriate statistical approaches, with the current status of identification of gene variants for addictions and treatment responses. In this issue, Nigel Atkinson, Ph.D., discusses the use of the fruit fly with highly tractable genetics to identify genes involved in tolerance to ethanol and inhalants. He suggests that there are homologous genes in humans that mediate tolerance. George Uhl, M.D. Ph.D., shows that common haplotypes making polygenic contributions underlie the phenotype of addiction and show pleiotropy with cognitive abilities, brain volume, and personality characteristics. This hypothesis stands in contrast to the rare variant hypothesis where there exists a large amount of genetic heterogeneity in which a rare genetic variant makes a large contribution to the observed phenotype. Marco Ramoni, Ph.D., and his colleagues show that while no single nucleotide polymorphism (SNP) identified in a whole genomewide association scan predicts nicotine dependence, in agreement with the model put forward by George Uhl, Baysian network analysis can be used to generate a multivariate predictive model that predicts a 75% risk for developing nicotine dependence, using GWAS data. Ray et al. and Swan and Lessov-Schlaggar review the pharmacogenomics of metabolizing enzymes in nicotine dependence and its treatment. A major nicotinemetabolizing enzyme is CYP2A6, that converts nicotine to cotinine. Individuals carrying genetic variants that reduce the enzyme activity of CYP2A6 metabolize nicotine more slowly, are less likely to be dependent, and may quit more easily. In contrast, individuals carrying alleles of CYP2A6 are less likely to respond to nicotine replacement therapy and respond better to bupropion, a non-nicotine replacement therapy. The response to bupropion is also affected by another metabolizing enzyme, CYP2B6. In conclusion, the rapid advances in the genetics of addiction hold great promise for developing treatments for addiction and reducing the enormous health burden of addiction. Addiction genetics will help to refine the phenotypes in addiction and hold promise for understanding its etiology. By defining genotypes, the environmental factors contributing to addiction can be better understood. By defining and analyzing functions of individual genes or alleles that contribute to quantitative traits, addiction mechanisms as well as potential sites for intervention may be identified. Important future studies will ask how epigenetic factors interact with genetics to produce the addicted state.