Loss of p19Arf in a Rag1(-/-) B-cell precursor population initiates acute B-lymphoblastic leukemia

In human B-acute lymphoblastic leukemia (B-ALL), RAG1-induced genomic alterations are important for disease progression. However, given that biallelic loss of the RAG1 locus is observed in a subset of cases, RAG1's role in the development of B-ALL remains unclear. We chose a p19Arf(-/-)Rag1(-/-) mouse model to confirm the previously published results concerning the contribution of CDKN2A (p19ARF /INK4a) and RAG1 copy number alterations in precursor B cells to the initiation and/or progression to B-acute lymphoblastic leukemia (B-ALL). In this murine model, we identified a new, Rag1-independent leukemia-initiating mechanism originating from a Sca1(+)CD19(+) precursor cell population and showed that Notch1 expression accelerates the cells' self-renewal capacity in vitro. In human RAG1-deficient BM, a similar CD34(+)CD19(+) population expressed p19ARF. These findings suggest that combined loss of p19Arf and Rag1 results in B-cell precursor leukemia in mice and may contribute to the progression of precursor B-ALL in humans.     

A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI

Within-subject analysis in fMRI essentially addresses two problems, i.e., the detection of activated brain regions in response to an experimental task and the estimation of the underlying dynamics, also known as the characterisation of Hemodynamic response function (HRF). So far, both issues have been treated sequentially while it is known that the HRF model has a dramatic impact on the localisation of activations and that the HRF shape may vary from one region to another. In this paper, we conciliate both issues in a region-based joint detection-estimation framework that we develop in the Bayesian formalism. Instead of considering function basis to account for spatial variability, spatially adaptive General Linear Models are built upon region-based non-parametric estimation of brain dynamics. Regions are first identified as functionally homogeneous parcels in the mask of the grey matter using a specific procedure [Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., Poline, J.-B., August 2006. Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Hum. Brain Mapp. 27 (8), 678-693.]. Then, in each parcel, prior information is embedded to constrain this estimation. Detection is achieved by modelling activating, deactivating and non-activating voxels through mixture models within each parcel. From the posterior distribution, we infer upon the model parameters using Markov Chain Monte Carlo (MCMC) techniques. Bayesian model comparison allows us to emphasize on artificial datasets first that inhomogeneous gamma-Gaussian mixture models outperform Gaussian mixtures in terms of sensitivity/specificity trade-off and second that it is worthwhile modelling serial correlation through an AR(1) noise process at low signal-to-noise (SNR) ratio. Our approach is then validated on an fMRI experiment that studies habituation to auditory sentence repetition. This phenomenon is clearly recovered as well as the hierarchical temporal organisation of the superior temporal sulcus, which is directly derived from the parcel-based HRF estimates.     

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems

In Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983–993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model, called bilinear dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli of the experimental paradigm. The BDS model parameters are estimated using an expectation–maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983–993] the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full Variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use on real fMRI data in one slice of the brain.     

Morquio A disease: clinical and molecular study of Tunisian patients

Type IVA mucopolysaccharidosis or Morquio A disease is a lysosomal storage disease, autosomal recessive, caused by deficiency of the N-acetylgalactosamine-6-sulfate sulfatase or GALNS. The severe phenotype is characterized by a severe skeletal dysplasia without any mental retardation. The aim of this study was to propose a strategy of molecular and prenatal diagnosis of this pathology. A molecular study was carried out on 7 patients MPS IVA issued from 5 unrelated families recruited from different Tunisian regions. All the patients were offspring of consanguineous marriages. The clinical and biologic study confirmed the diagnosis of MPS IVA within the 7 studied patients. Three GALNS mutations were identified by molecular analysis: IVS1+1G>A, G66R and A85T. The unions between Tunisian relatives are important and increase the Morquio A incidence in Tunisia. The identification of GALNS mutations in the Tunisian population permits better understanding of the Morquio A phenotype, a reliable genetic counselling and a molecular prenatal diagnosis to Tunisian at-risk relatives.     

Diagnostic strategy of metachromatic leukodystrophy in Tunisia

We recruited a 44-year-old woman who had a dementia with behavioral and personality troubles. A biochemical analysis which includes a qualitative study of urinary sulfatides by thin layer chromatography followed by the determination of the enzymatic activity of arylsulfatase A (ARSA) was performed. The Molecular analysis concerned the research of the most frequent mutations (459 +1 G> A, p.P426L, p.I179S). The profile that has revealed the presence of 3-O-sulfogalactosylceramide fraction was in favor of metachromatic leukodystrophy. The activity of arylsulfatase A was collapsed in the index case which confirmed the phenotype of the adult form of the diagnosed MLD. The molecular study showed the presence of the mutation p.I179S in the homozygous state in the index case.     

Gene therapy of metabolic diseases

Summary This review surveys the potential application of gene therapy to metabolic diseases. Proof of principle has been achieved in the treatment of two inherited immunodeficiency conditions. A significant safety issue has also been observed. Several strategies are being experimentally tested for a number of metabolic diseases (more than 20) and may provide a rationale for the future development of clinical trials. Presented at the 42nd Annual Meeting of the SSIEM, Paris, 6–9 September 2005 Competing interests: None declared