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A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes.

ABSTRACT: Songbirds have one of the most accessible neural systems for the study of brain mechanisms of behavior. However, neuroethological studies in songbirds have been limited by the lack of high-throughput molecular resources and gene-manipulation tools. To overcome these limitations, we constructed 21 regular, normalized, and subtracted full-length cDNA libraries from brains of zebra finches in 57 developmental and behavioral conditions in an attempt to clone as much of the brain transcriptome as possible. From these libraries, approximately 14,000 transcripts were isolated, representing an estimated 4,738 genes. With the cDNAs, we created a hierarchically organized transcriptome database and a large-scale songbird brain cDNA microarray. We used the arrays to reveal a set of 33 genes that are regulated in forebrain vocal nuclei by singing behavior. These genes clustered into four anatomical and six temporal expression patterns. Their functions spanned a large range of cellular and molecular categories, from signal transduction, trafficking, and structural, to synaptically released molecules. With the full-length cDNAs and a lentiviral vector system, we were able to overexpress, in vocal nuclei, proteins of representative singing-regulated genes in the absence of singing. This publicly accessible resource http://songbirdtranscriptome.net can now be used to study molecular neuroethological mechanisms of behavior.

SUBMITTER: Wada K

PROVIDER: S-EPMC1622802 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes.

Wada Kazuhiro K Howard Jason T JT McConnell Patrick P Whitney Osceola O Lints Thierry T Rivas Miriam V MV Horita Haruhito H Patterson Michael A MA White Stephanie A SA Scharff Constance C Haesler Sebastian S Zhao Shengli S Sakaguchi Hironobu H Hagiwara Masatoshi M Shiraki Toshiyuki T Hirozane-Kishikawa Tomoko T Skene Pate P Hayashizaki Yoshihide Y Carninci Piero P Jarvis Erich D ED

Proceedings of the National Academy of Sciences of the United States of America 20061003 41

Songbirds have one of the most accessible neural systems for the study of brain mechanisms of behavior. However, neuroethological studies in songbirds have been limited by the lack of high-throughput molecular resources and gene-manipulation tools. To overcome these limitations, we constructed 21 regular, normalized, and subtracted full-length cDNA libraries from brains of zebra finches in 57 developmental and behavioral conditions in an attempt to clone as much of the brain transcriptome as pos ...[more]

PMID: 17018643

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Project description:MotivationA growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes.ResultsWe propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos.Availability and implementationThe R package containing the implementation of the proposed method in C ++ is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/Contactjinbo@engr.uconn.edu.

Dataset Information

A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes.

Publications

A molecular neuroethological approach for identifying and characterizing a cascade of behaviorally regulated genes.

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