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P Elfferich,
A Z Juniarto,
H J Dubbink, M E van Royen,
M Molier,
J Hoogerbrugge,
A B Houtsmuller,
J Trapman,
A Santosa,
F H de Jong,
S L S Drop,
S M H Faradz,
H Bruggenwirth,
A O Brinkmann
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ABSTRACT: Mutations in the androgen receptor (AR) gene, rendering the AR protein partially or completely inactive, cause androgen insensitivity syndrome, which is a form of a 46,XY disorder of sex development (DSD). We present 3 novel AR variants found in a cohort of Indonesian DSD patients: p.I603N, p.P671S, and p.Q738R. The aim of this study was to determine the possible pathogenic nature of these newly found unclassified variants. To investigate the effect of these variants on AR function, we studied their impact on transcription activation, AR ligand-binding domain interaction with an FxxLF motif containing peptide, AR subcellular localization, and AR nuclear dynamics and DNA-binding. AR-I603N had completely lost its transcriptional activity due to disturbed DNA-binding capacity and did not show the 114-kDa hyperphosphorylated AR protein band normally detectable after hormone binding. The patient with AR-I603N displays a partial androgen insensitivity syndrome phenotype, which is explained by somatic mosaicism. A strongly reduced transcriptional activity was observed for AR-Q738R, together with diminished interaction with an FxxLF motif containing peptide. AR-P671S also showed reduced transactivation ability, but no change in DNA- or FxxLF-binding capacity and interferes with transcriptional activity for as yet unclear reasons.
Sexual Development 10/2009; 3(5):237-44. · 2.27 Impact Factor
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ABSTRACT: To study protein-protein interactions by fluorescence energy transfer (FRET), the proteins of interest are tagged with either a donor or an acceptor fluorophore. For efficient FRET, fluorophores need to have a reasonable overlap of donor emission and acceptor excitation spectra. However, given the relatively small Stokes shift of conventional fluorescent proteins, donor and acceptor pairs with high FRET efficiencies have emission spectra that are difficult to separate. GFP and YFP are widely used in fluorescence microscopy studies. The spectral qualities of GFP and YFP make them one of the most efficient FRET donor-acceptor couples available. However, the emission peaks of GFP (510 nm) and YFP (527 nm) are spectrally too close for separation by conventional fluorescence microscopy. Difficulties in simultaneous detection of GFP and YFP with a fluorescence microscope are eliminated when spectral imaging and subsequent linear unmixing are applied. This allows FRET microscopy using these tags to study protein-protein interactions. We adapted the linear unmixing procedure from commercially available software (Zeiss) for use with acceptor photobleaching FRET using GFP and YFP as FRET pair. FRET efficiencies up to 52% for a GFP-YFP fusion protein were measured. To investigate the applicability of the procedure, we used two constituents of the nucleotide excision repair system, which removes UV-induced single-strand DNA damage. ERCC1 and XPF form a heterodimeric 5' endonuclease in nucleotide excision repair. FRET between ERCC1-GFP and XPF-YFP occurs with an efficiency of 30%.
Journal of Microscopy 07/2008; 231(Pt 1):97-104. · 1.63 Impact Factor
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ABSTRACT: Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.
Medical image analysis 04/2008; 12(6):764-77. · 3.09 Impact Factor
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ABSTRACT: Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.
Medical Image Analysis.
