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Morzel M.,University of Burgundy | Palicki O.,University of Burgundy | Chabanet C.,University of Burgundy | Lucchi G.,CLIPP Clinical Innovation Proteomic Platform | And 3 more authors.
Archives of Oral Biology | Year: 2011

Objective: The objective of this study was to describe the changes in salivary protein profiles in infants between the ages of 3 and 6 months, and to evaluate the impact of teeth eruption and introduction of solid foods on such profiles. Design: 73 infants were followed longitudinally at 3 and 6 months of age. Their whole saliva proteins were separated by SDS-PAGE electrophoresis and semi-quantified by image analysis. Amylase activity was also measured on a sub-sample of the population (n = 42 infants). Bands which abundance was significantly different between the two ages according to paired comparisons were identified by mass spectrometry techniques. Results: Out of 21 bands, 13 were significantly different between 3 and 6 months of age. Two short variants of amylase increased in abundance with age, as did amylase activity. Other changes possibly translated developmental physiological events, for example maturation of the adaptive immune system. The balance between S-type cystatins and cystatins A and B was modified, in favour of S-type cystatins at 6 months of age. Teeth eruption resulted in an increase in albumin abundance, whilst introduction of solid foods was associated with higher levels of β-2 microglobulin and S-type cystatins. Conclusions: Salivary profiles were modified substantially between the ages of 3 and 6 months. Both teeth eruption and diet had an impact on abundance changes for some proteins, revealing dynamic interactions between saliva proteome, oral physiology and diet. © 2011 Elsevier Ltd. Source

Largeot A.,University of Burgundy | Paggetti J.,University of Burgundy | Paggetti J.,CRP Sante | Broseus J.,University of Burgundy | And 12 more authors.
Biochimica et Biophysica Acta - Molecular Cell Research | Year: 2013

MOZ and MLL encoding a histone acetyltransferase and a histone methyltransferase, respectively, are targets for recurrent chromosomal translocations found in acute myeloblastic or lymphoblastic leukemia. We have previously shown that MOZ and MLL cooperate to activate HOXA9 gene expression in hematopoietic stem/progenitors cells. To dissect the mechanism of action of this complex, we decided to identify new proteins interacting with MOZ. We found that the scaffold protein Symplekin that supports the assembly of polyadenylation machinery was identified by mass spectrometry. Symplekin interacts and co-localizes with both MOZ and MLL in immature hematopoietic cells. Its inhibition leads to a decrease of the HOXA9 protein level but not of Hoxa9 mRNA and to an over-recruitment of MOZ and MLL onto the HOXA9 promoter. Altogether, our results highlight the role of Symplekin in transcription repression involving a regulatory network between MOZ, MLL and Symplekin. © 2013 Elsevier B.V. Source

Mostacci E.,CLIPP Clinical Innovation Proteomic Platform | Mostacci E.,French National Center for Scientific Research | Mostacci E.,University of Burgundy | Truntzer C.,CLIPP Clinical Innovation Proteomic Platform | And 5 more authors.
Proteomics | Year: 2010

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI-TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre-processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to obtain better denoising of the data so as to extract the meaningful proteomic biological information from the raw spectra and reach meaningful clinical conclusions. The proposed methods were evaluated and compared with the classical soft thresholding denoising method using both real and simulated data sets. It was shown that taking into account common structures of the signals by adding a dimension reduction step on approximation coefficients through PCA provided more effective denoising when combined with soft thresholding on detail coefficients. © 2010 Wiley-VCH Verlag GmbH & Co. KGaA. Source

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