ISSN: 2153-0777
Journal of Bioengineering and Bioelectronics
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Bioinformatics, Transcriptomics, Proteomics and Dental Follicle Precursor Cells
| Martin Gosau* and Christian Morsczeck | ||
| Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Germany | ||
| Corresponding Author : | Martin Gosau Department of Oral and MaxillofacialSurgery University Hospital Regensburg Franz-Josef Strauss Allee 11 93053 Regensburg, Germany Tel: +49(0)941 944 6345 E-mail: martin.gosau@ukr.de |
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| Received April 16, 2012; Accepted April 19, 2012; Published April 22, 2012 | ||
| Citation: Gosau M, Morsczeck C (2012) Bioinformatics, Transcriptomics, Proteomics and Dental Follicle Precursor Cells. J Biochip Tissue chip 2:e111. doi:10.4172/2153-0777.1000e111 | ||
| Copyright: © 2012 Gosau M, 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. | ||
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| Over the past ten years, knowledge on complex biological systems has been significantly improved by novel molecular biology methods, such as transcriptomics or proteomics. These sophisticated molecular techniques in combination with bioinformatics are used for top-down approaches to generate new hypotheses, for example on cellular signaling in stem cells that are often verified by subsequent laboratory experiments. One example for such an approach is the biology oforal stem cells, in particular, the molecular cell biology of dental follicle precursor cells (DFPCs). | |
| The dental follicle is an embryonic tissue of the tooth germ, which is derived from neural crest cells; the dental follicle becomes the periodontal apparatus after tooth eruption. The periodontal apparatus is the supporting tissue of the tooth and contains three different tissues: the periodontal ligament (PDL), the alveolar bone, and the mineralized bone-like cementum covering the tooth root surface. The PDL, which is responsible for tooth attachment, is a specialized connective tissue, henceits fibers are embedded in the cementum and in the alveolar bone. The development of the tooth attachment apparatus is an excellent system for studying oral development, and DFPCs are the optimal cell type for such studies [1]. DFPCs are fibroblast-like, colony-forming, and plastic adherent cells expressing putative stem cell markers, such as CD105, CD44, Notch-1, Nestin, and Stro-1. The differentiation capacity of DFPCs in to tissues of the periodontal apparatus has been demonstrated under in vitro and in vivo conditions [1]. Gene expression profiles of typical osteogenic differentiation markers were obtained from differentiated DFPCs for the analysis of molecular processes. Similar to bone marrow-derived mesenchymal stem cells (hMSCs), the expression of the bone morphogenetic protein (BMP2) was increased [2]. Moreover, the induction of the transcription factor DLX3 in DFPCs, which is regulated by a BMP2 feedback control, supports osteogenic differentiation [3]. These data suggest that a BMP2/ DLX3-dependent mechanism is involved in the differentiation of DFPCs. However, in another study, the significance of BMP-signaling was evaluated for inducing differentiation with dexamethasone [4]. Surprisingly, the differentiation of DFPCs with dexamethasone was independent of the activation of BMP-signaling. Genome wide gene expression profiles of DFPCs showed that dexamethasone, but not BMP2, highly induces the transcription factor ZBTB16 [4]. Although the induction of ZBTB16 is known to be BMP-independent, further studies have to disclose ZBTB16-dependent mechanisms during DFPC differentiation. For more information about molecular mechanisms, proteomes were also analyzed before and after cell differentiation [5]. Here, the transcription factor TP53, which is associated with a number of biological processes, was identified as a putative key factor for the differentiation of DFPCs by bioinformatics. Interestingly, a subsequent study could show that TP53 is at least involved in the process of DFPC differentiation [6]. However, further experiments are necessary to show a possible relation between the expression of ZBTB16 and the function of TP53 in differentiated DFPCs. | |
| The neural differentiation of the ectomesenchymal DFPCs is another example for a top-down approach. DFPCs differentiate into neural- like cellsin a two-step neural cell differentiation approach [7]. In this study, DFPCs formed neurosphere-like cell clusters after cultivation under serum-free cell culture conditions. These pre-differentiated DFPCs differentially expressed early neural cell markers, such asnestin or β-III tubulin. During a second cultivation step, single cells migrated out from these spheres and differentiated into neuron-like cells, which expressed late neural cell markers, such as MAP-2 and neurofilament [7]. Although this approach was promising, the mechanisms of neural differentiation are not well understood, so that a comparison of DFPCs with neuroectodermal progenitor cells may be helpful. Interestingly, retinal Müller cells dedifferentiate into neural stem cell-like cells, generating neurosphere-like cell clusters under similar serum-free cell culture conditions [8]. To reveal molecular processes of neural stem celllike cells, proteomes of neurosphere-like cell clusters were analyzed and compared with neural pre-differentiated DFPCs and dedifferentiated retinal Müller-cells [9]. More than 600 proteins were similarly expressed in both cell types. Interestingly, many of these proteins are expressed in the brain. Bioinformatics discovered an overrepresentation of proteins associated with pathways for neural diseases, such as Huntington or Alzheimer disease [9]. However, although these pathways could be essential, nothing is known about their possible role in neurospheres. Further experiments have to prove how these pathways are involved in the maintenance or formation of neurosphere-like cell clusters. Such subsequent experiments will again combine gene expression profiles with bioinformatics. | |
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