Recessive mutations in POLR1C cause a leukodystrophy by impairing biogenesis of RNA polymerase III

Contains fulltext : 153818.pdf (publisher's version ) (Open Access)A small proportion of 4H (Hypomyelination, Hypodontia and Hypogonadotropic Hypogonadism) or RNA polymerase III (POLR3)-related leukodystrophy cases are negative for mutations in the previously identified causative genes POLR3A and POLR3B. Here we report eight of these cases carrying recessive mutations in POLR1C, a gene encoding a shared POLR1 and POLR3 subunit, also mutated in some Treacher Collins syndrome (TCS) cases. Using shotgun proteomics and ChIP sequencing, we demonstrate that leukodystrophy-causative mutations, but not TCS mutations, in POLR1C impair assembly and nuclear import of POLR3, but not POLR1, leading to decreased binding to POLR3 target genes. This study is the first to show that distinct mutations in a gene coding for a shared subunit of two RNA polymerases lead to selective modification of the enzymes' availability leading to two different clinical conditions and to shed some light on the pathophysiological mechanism of one of the most common hypomyelinating leukodystrophies, POLR3-related leukodystrophy

ROCS: a reproducibility index and confidence score for interaction proteomics

Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones. RESULTS: To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Proteins account for measures of protein identification as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures. We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions or in combination with other AP-MS scoring methods to significantly improve inferences. CONCLUSIONS: Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or especially in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called "ROCS", freely available from the CRAN repository http://cran.r-project.org/

Multifractal Scaling in Sinai Diffusion

We consider the mean first passage time of random walks to go from one end of a segment of a Sinai lattice to the other. We show that the distribution of the mean first passage time over Sinai disorder obeys a finite-size-multiscaling form

SAINT-MS1: Protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

10.1021/pr201185rJournal of Proteome Research1142619-2624JPRO

Computational framework for analysis of prey–prey associations in interaction proteomics identifies novel human protein–protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.<br/