Spatial patterns of gene expression in the olfactory bulb.

Department of Molecular and Cell Biology, Functional Genomics Laboratory, Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 09/2004; 101(34):12718-23. DOI: 10.1073/pnas.0404872101
Source: PubMed

ABSTRACT How olfactory sensory neurons converge on spatially invariant glomeruli in the olfactory bulb is largely unknown. In one model, olfactory sensory neurons interact with spatially restricted guidance cues in the bulb that orient and guide them to their target. Identifying differentially expressed molecules in the olfactory bulb has been extremely difficult, however, hindering a molecular analysis of convergence. Here, we describe several such genes that have been identified in a screen that compiled microarray data to create a three-dimensional model of gene expression within the mouse olfactory bulb. The expression patterns of these identified genes form the basis of a nascent spatial map of differential gene expression in the bulb.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A challenge in gene expression studies is the reliable identification of differentially expressed genes. In many high-throughput studies, genes are accepted as differentially expressed only if they satisfy simultaneously a p value criterion and a fold change criterion. A statistical method, TREAT, has been developed for microarray data to assess formally if fold changes are significantly higher than a predefined threshold. We have recently applied the NanoString digital platform to study expression of mouse odorant receptor genes, which form with 1,200 members the largest gene family in the mouse genome. Our objectives are, on these data, to decrease false discoveries when formally assessing the genes relative to a fold change threshold, and to provide a guided selection in the choice of this threshold. Statistical tests have been developed for microarray data to identify genes that are differentially expressed relative to a fold change threshold. Here we report that another approach, which we refer to as tTREAT, is more appropriate for our NanoString data, where false discoveries lead to costly and time-consuming follow-up experiments. Methods that we refer to as tTREAT2 and the running fold change model improve the performance of the statistical tests by protecting or selecting the fold change threshold more objectively. We show the benefits on simulated and real data. Gene-wise statistical analyses of gene expression data, for which the significance relative to a fold change threshold is important, give reproducible and reliable results on NanoString data of mouse odorant receptor genes. Because it can be difficult to set in advance a fold change threshold that is meaningful for the available data, we developed methods that enable a better choice (thus reducing false discoveries and/or missed genes) or avoid this choice altogether. This set of tools may be useful for the analysis of other types of gene expression data.
    BMC Bioinformatics 02/2014; 15(1):39. DOI:10.1186/1471-2105-15-39 · 2.67 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Neural crest development is orchestrated by a complex and still poorly understood gene regulatory network. Premigratory neural crest is induced at the lateral border of the neural plate by the combined action of signaling molecules and transcription factors such as AP2, Gbx2, Pax3 and Zic1. Among them, Pax3 and Zic1 are both necessary and sufficient to trigger a complete neural crest developmental program. However, their gene targets in the neural crest regulatory network remain unknown. Here, through a transcriptome analysis of frog microdissected neural border, we identified an extended gene signature for the premigratory neural crest, and we defined novel potential members of the regulatory network. This signature includes 34 novel genes, as well as 44 known genes expressed at the neural border. Using another microarray analysis which combined Pax3 and Zic1 gain-of-function and protein translation blockade, we uncovered 25 Pax3 and Zic1 direct targets within this signature. We demonstrated that the neural border specifiers Pax3 and Zic1 are direct upstream regulators of neural crest specifiers Snail1/2, Foxd3, Twist1, and Tfap2b. In addition, they may modulate the transcriptional output of multiple signaling pathways involved in neural crest development (Wnt, Retinoic Acid) through the induction of key pathway regulators (Axin2 and Cyp26c1). We also found that Pax3 could maintain its own expression through a positive autoregulatory feedback loop. These hierarchical inductions, feedback loops, and pathway modulations provide novel tools to understand the neural crest induction network.
    Developmental Biology 12/2013; 386(2). DOI:10.1016/j.ydbio.2013.12.010 · 3.64 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The human olfactory bulb displays high morphologic dynamics changing its volume with olfactory function, which has been explained by active neurogenetic processes. Discussion continues whether the human olfactory bulb hosts a continuous turnover of neurons. We analyzed the transcriptome via RNA quantification of adult human olfactory bulbs and intersected the set of expressed transcriptomic genes with independently available proteomic expression data. To obtain a functional genomic perspective, this intersection was analyzed for higher-level organization of gene products into biological pathways established in the gene ontology database. We report that a fifth of genes expressed in adult human olfactory bulbs serve functions of nervous system or neuron development, half of them functionally converging to axonogenesis but no other non-neurogenetic biological processes. Other genes were expectedly involved in signal transmission and response to chemical stimuli. This provides a novel, functional genomics perspective supporting the existence of neurogenesis in the adult human olfactory bulb.
    Brain Structure and Function 08/2013; 219(6). DOI:10.1007/s00429-013-0618-3 · 4.57 Impact Factor

Full-text (2 Sources)

Available from
May 23, 2014