The power of protein interaction networks for associating genes with diseases

Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92060/1/pmic7070.pd

Evolution of mechanical properties of lava dome rocks across the Soufrière Hills eruption, and application in discrete element models

&amp;lt;p&amp;gt;Lava dome collapses pose a hazard to surrounding populations, but equally represent important processes for deciphering the eruptive history of a volcano. Models examining lava dome instability rely on accurate physical and mechanical properties of volcanic rocks. Here we focus on determining the physical and mechanical properties of a suite of temporally-constrained rocks from different phases of the 1995&amp;amp;#8211;2010 eruption at Soufri&amp;amp;#232;re Hills volcano in Montserrat. We determine the uniaxial compressive strength, tensile strength, density, porosity, permeability, and Young&amp;amp;#8217;s modulus using laboratory measurements, complemented by Schmidt hammer testing in the field.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;By viewing a snapshot of each phase, we find the highest tensile and compressive strengths in the samples attributed to Phase 4, corresponding to a lower permeability and an increasing proportion of isolated porosity. Samples from Phase 5 show lower compressive and tensile strengths, corresponding to the highest permeability and porosity of the tested materials. Overall, this demonstrates a reliance of mechanical properties primarily on porosity, however, a shift toward increasing prevalence of pore connectivity in weaker samples identified by microtextural analysis demonstrates that here pore connectivity also contributes to the strength and Young&amp;amp;#8217;s Modulus, as well as controlling permeability. The range in UCS strengths are supported using Schmidt hammer field testing. We determine a narrow range in mineralogy across the sample suite, but identify a correlation between increasing crystallinity and increasing strength. We correlate these changes to residency-time in the growing lava dome during the eruption, where stronger rocks have undergone more crystallization. In addition, subsequent recrystallization of silica polymorphs from the glass phase may further strengthen the material.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;We incorporate the variation in physical and mechanical rock properties shown within the Soufri&amp;amp;#232;re Hills eruptive into structural stability models of the remaining over-steepened dome on Montserrat, considering also the possible effect of upscaling on the edifice-scale rock properties, and the resultant dome stability.&amp;lt;/p&amp;gt;</jats:p

Identifying true protein complex constituents in interaction proteomics: the example of the DMXL2 protein complex

A typical high-sensitivity antibody affinity purification-mass spectrometry experiment easily identifies hundreds of protein interactors. However, most of these are non-valid resulting from multiple causes other than interaction with the bait protein. To discriminate true interactors from off-target recognition, we propose to differentially include an (peptide) antigen during the antibody incubation in the immuno-precipitation experiment. This contrasts the specific antibody-bait protein interactions, versus all other off-target protein interactions. To exemplify the power of the approach, we studied the DMXL2 interactome. From the initial six immuno-precipitations, we identified about 600 proteins. When filtering for interactors present in all anti-DMXL2 antibody immuno-precipitation experiments, absent in the bead controls, and competed off by the peptide antigen, this hit list is reduced to ten proteins, including known and novel interactors of DMXL2. Together, our approach enables the use of a wide range of available antibodies in large-scale protein interaction proteomics, while gaining specificity of the interactions. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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

Computational detection of protein complexes in AP-MS experiments

10.1002/pmic.201100508Proteomics12101663-1668PROT

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/