[Show abstract][Hide abstract] ABSTRACT: The mitochondrial inner membrane contains five large complexes that are essential for oxidative phosphorylation. Although the structure and the catalytic mechanisms of the respiratory complexes have been progressively established, their biogenesis is far from being fully understood. Very few complex III assembly factors have been identified so far. It is probable that more factors are needed for the assembly of a functional complex, but that the genetic approaches used to date have not been able to identify them. We have developed a systems biology approach to identify new factors controlling complex III biogenesis.
We collected all the physical protein-protein interactions (PPI) involving the core subunits, the supernumerary subunits and the assembly factors of complex III and used Cytoscape 2.6.3 and its plugins to construct a network. It was then divided into overlapping and highly interconnected sub-graphs with clusterONE. One sub-graph contained the core and the supernumerary subunits of complex III, it also contained some subunits of complex IV and proteins participating in the assembly of complex IV. This sub-graph was then split with another algorithm into two sub-graphs. The subtraction of these two sub-graphs from the previous sub-graph allowed us to identify a protein of unknown function Usb1p/Ylr132p that interacts with the complex III subunits Qcr2p and Cor1p. We then used genetic and cell biology approaches to investigate the function of Usb1p. Preliminary results indicated that Usb1p is an essential protein with a dual localization in the nucleus and in the mitochondria, and that the over-expression of this protein can compensate for defects in the biogenesis of the respiratory complexes.
Our systems biology approach has highlighted the multiple associations between subunits and assembly factors of complexes III and IV during their biogenesis. In addition, this approach has allowed the identification of a new factor, Usb1p, involved in the biogenesis of respiratory complexes, which could not have been found using classical genetic screens looking for respiratory deficient mutants. Thus, this systems biology approach appears to be a fruitful new way to study the biogenesis of mitochondrial multi-subunit complexes.
BMC Systems Biology 10/2011; 5(1):173. DOI:10.1186/1752-0509-5-173 · 2.44 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: There are many sources of variation in dual labelled microarray experiments, including data acquisition and image processing. The final interpretation of experiments strongly relies on the accuracy of the measurement of the signal intensity. For low intensity spots in particular, accurately estimating gene expression variations remains a challenge as signal measurement is, in this case, highly subject to fluctuations.
To evaluate the fluctuations in the fluorescence intensities of spots, we used series of successive scans, at the same settings, of whole genome arrays. We measured the decrease in fluorescence and we evaluated the influence of different parameters (PMT gain, resolution and chemistry of the slide) on the signal variability, at the level of the array as a whole and by intensity interval. Moreover, we assessed the effect of averaging scans on the fluctuations. We found that the extent of photo-bleaching was low and we established that 1) the fluorescence fluctuation is linked to the resolution e.g. it depends on the number of pixels in the spot 2) the fluorescence fluctuation increases as the scanner voltage increases and, moreover, is higher for the red as opposed to the green fluorescence which can introduce bias in the analysis 3) the signal variability is linked to the intensity level, it is higher for low intensities 4) the heterogeneity of the spots and the variability of the signal and the intensity ratios decrease when two or three scans are averaged.
Protocols consisting of two scans, one at low and one at high PMT gains, or multiple scans (ten scans) can introduce bias or be difficult to implement. We found that averaging two, or at most three, acquisitions of microarrays scanned at moderate photomultiplier settings (PMT gain) is sufficient to significantly improve the accuracy (quality) of the data and particularly those for spots having low intensities and we propose this as a general approach. For averaging and precise image alignment at sub-pixel levels we have made a program freely available on our web-site http://bioinfome.cgm.cnrs-gif.fr to facilitate implementation of this approach.
[Show abstract][Hide abstract] ABSTRACT: Today, the information for generating reliable protein-protein complex datasets is not directly accessible from PDB structures. Moreover, in X-ray protein structures, different types of contacts can be observed between proteins: contacts in homodimers or inside heterocomplexes considered to be specific, and contacts induced by crystallogenesis processes, considered to be non-specific. However, none of the databases giving access to protein-protein complexes allows the crystallographic interfaces to be distinguished from the biological interfaces. For this reason we developed PPIDD (Protein-Protein Interface Description Database), an innovative tool, which allows the extraction and visualisation of biological protein-protein interfaces from an annotated subset of crystallographic structures of proteins. This tool is focused on the description of protein-protein interfaces corresponding to well-identified classes of protein assemblies. It permits the representation of any of these protein-protein assemblies (duplex) and their interfaces as well as the export of the corresponding molecular structures under a flexible format, which is an extension of the PDBML. Moreover, PPIDD facilitates the construction of subsets of interfaces presenting user-specified common characteristics, to enhance the understanding of the determinants of specific protein-protein interactions.
[Show abstract][Hide abstract] ABSTRACT: MAnGO (Microarray Analysis at the Gif/Orsay platform) is an interactive R-based tool for the analysis of two-colour microarray experiments. It is a compilation of various methods, which allows the user (1) to control data quality by detecting biases with a large number of visual representations, (2) to pre-process data (filtering and normalization) and (3) to carry out differential analyses. MAnGO is not only a 'turn-key' tool, oriented towards biologists but also a flexible and adaptable R script oriented towards bioinformaticians. Availability: http://bioinfome.cgm.cnrs-gif.fr/.
[Show abstract][Hide abstract] ABSTRACT: The origin of DNA replication (oriC) of the hyperthermophilic archaeon Pyrococcus abyssi contains multiple ORB and mini-ORB repeats that show sequence similarities to other archaeal ORB (origin recognition box). We report here that the binding of Cdc6/Orc1 to a 5 kb region containing oriC in vivo was highly specific both in exponential and stationary phases, by means of chromatin immunoprecipitation coupled with hybridization on a whole genome microarray (ChIP-chip). The oriC region is practically the sole binding site for the Cdc6/Orc1, thereby distinguishing oriC in the 1.8 M bp genome. We found that the 5 kb region contains a previously unnoticed cluster of ORB and mini-ORB repeats in the gene encoding the small subunit (dp1) for DNA polymerase II (PolD). ChIP and the gel retardation analyses further revealed that Cdc6/Orc1 specifically binds both of the ORB clusters in oriC and dp1. The organization of the ORB clusters in the dp1 and oriC is conserved during evolution in the order Thermococcales, suggesting a role in the initiation of DNA replication. Our ChIP-chip analysis also revealed that Mcm alters the binding specificity to the oriC region according to the growth phase, consistent with its role as a licensing factor.
Nucleic Acids Research 02/2007; 35(10):3214-22. DOI:10.1093/nar/gkm212 · 9.11 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: MAnGO (Microarray Analysis at the Gif/Orsay platform) is an interactive R-based tool for the analysis of two-colour microarray experiments. It is a compilation of various methods, which allows the user (1) to control data quality by detecting biases with a large number of visual representations, (2) to pre-process data (filtering and normalization) and (3) to carry out differential analyses. MAnGO is not only a 'turn-key' tool, oriented towards biologists but also a flexible and adaptable R script oriented towards bioinformaticians. Availability: http://bioinfome.cgm.cnrs-gif.fr/ Contact: email@example.com