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      Analysis of gene expression during neurite outgrowth and regeneration

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          Abstract

          Background

          The ability of a neuron to regenerate functional connections after injury is influenced by both its intrinsic state and also by extrinsic cues in its surroundings. Investigations of the transcriptional changes undergone by neurons during in vivo models of injury and regeneration have revealed many transcripts associated with these processes. Because of the complex milieu of interactions in vivo, these results include not only expression changes directly related to regenerative outgrowth and but also unrelated responses to surrounding cells and signals. In vitro models of neurite outgrowth provide a means to study the intrinsic transcriptional patterns of neurite outgrowth in the absence of extensive extrinsic cues from nearby cells and tissues.

          Results

          We have undertaken a genome-wide study of transcriptional activity in embryonic superior cervical ganglia (SCG) and dorsal root ganglia (DRG) during a time course of neurite outgrowth in vitro. Gene expression observed in these models likely includes both developmental gene expression patterns and regenerative responses to axotomy, which occurs as the result of tissue dissection. Comparison across both models revealed many genes with similar gene expression patterns during neurite outgrowth. These patterns were minimally affected by exposure to the potent inhibitory cue Semaphorin3A, indicating that this extrinsic cue does not exert major effects at the level of nuclear transcription. We also compared our data to several published studies of DRG and SCG gene expression in animal models of regeneration, and found the expression of a large number of genes in common between neurite outgrowth in vitro and regeneration in vivo.

          Conclusion

          Many gene expression changes undergone by SCG and DRG during in vitro outgrowth are shared between these two tissue types and in common with in vivo regeneration models. This suggests that the genes identified in this in vitro study may represent new candidates worthy of further study for potential roles in the therapeutic regrowth of neuronal connections.

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          Most cited references88

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            The molecular biology of axon guidance.

            Neuronal growth cones navigate over long distances along specific pathways to find their correct targets. The mechanisms and molecules that direct this pathfinding are the topics of this review. Growth cones appear to be guided by at least four different mechanisms: contact attraction, chemoattraction, contact repulsion, and chemorepulsion. Evidence is accumulating that these mechanisms act simultaneously and in a coordinated manner to direct pathfinding and that they are mediated by mechanistically and evolutionarily conserved ligand-receptor systems.
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              Creating the gene ontology resource: design and implementation.

              (2001)
              The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information.
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                Author and article information

                Journal
                BMC Neurosci
                BMC Neuroscience
                BioMed Central
                1471-2202
                2007
                23 November 2007
                : 8
                : 100
                Affiliations
                [1 ]Department of Molecular and Cell Biology, University of California, Berkeley, California, USA
                [2 ]Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
                [3 ]Functional Genomics Laboratory, University of California, Berkeley, California, USA
                [4 ]Division of Biostatistics, University of California, Berkeley, California, USA
                [5 ]Department of Statistics, University of California, Berkeley, California, USA
                Article
                1471-2202-8-100
                10.1186/1471-2202-8-100
                2245955
                18036227
                ef17da9c-28b6-4b1e-bd01-a7eb81fbcb12
                Copyright © 2007 Szpara et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 June 2007
                : 23 November 2007
                Categories
                Research Article

                Neurosciences
                Neurosciences

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