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      A New Description of Cellular Quiescence

      research-article
      1 , ¤ , , 1 , 2 , 1 , 3
      PLoS Biology
      Public Library of Science

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          Abstract

          Cellular quiescence, defined as reversible growth/proliferation arrest, is thought to represent a homogenous state induced by diverse anti-mitogenic signals. We used transcriptional profiling to characterize human diploid fibroblasts that exited the cell cycle after exposure to three independent signals—mitogen withdrawal, contact inhibition, and loss of adhesion. We show here that each signal caused regulation of a unique set of genes known to be important for cessation of growth and division. Therefore, contrary to expectation, cells enter different quiescent states that are determined by the initiating signal. However, underlying this diversity we discovered a set of genes whose specific expression in non-dividing cells was signal-independent, and therefore representative of quiescence per se, rather than the signal that induced it. This fibroblast “quiescence program” contained genes that enforced the non-dividing state, and ensured the reversibility of the cell cycle arrest. We further demonstrate that one mechanism by which the reversibility of quiescence is insured is the suppression of terminal differentiation. Expression of the quiescence program was not simply a downstream consequence of exit from the cell cycle, because key parts, including those involved in suppressing differentiation, were not recapitulated during the cell cycle arrest caused by direct inhibition of cyclin-dependent kinases. These studies form a basis for understanding the normal biology of cellular quiescence.

          Abstract

          Transcriptional profiling of fibroblasts induced to exit the cell cycle by distinct signals reveals distinctions and commonalities in the pathways to cellular quiescence.

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          Most cited references52

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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 Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

            The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.
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              Identification of genes periodically expressed in the human cell cycle and their expression in tumors.

              The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays. Transcripts of >850 genes showed periodic variation during the cell cycle. Hierarchical clustering of the expression patterns revealed coexpressed groups of previously well-characterized genes involved in essential cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion along with genes of uncharacterized function. Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle. However, some of the genes periodically expressed in the HeLa cell cycle do not have a consistent correlation with tumor proliferation. Cell cycle-regulated transcripts of genes involved in fundamental processes such as DNA replication and chromosome segregation seem to be more highly expressed in proliferative tumors simply because they contain more cycling cells. The data in this report provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery. The full dataset is available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                pbio
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                March 2006
                7 March 2006
                : 4
                : 3
                : e83
                Affiliations
                [1] 1 Department of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
                [2] 2 Molecular & Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
                [3] 3 Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
                Harvard Medical School United States of America
                Article
                10.1371/journal.pbio.0040083
                1393757
                16509772
                174eefd8-717b-4788-966a-7c837eabe6b4
                Copyright: © 2006 Coller et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 2 December 2004
                : 19 January 2006
                Categories
                Research Article
                Cell Biology
                In Vitro

                Life sciences
                Life sciences

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