SAFER, an Analysis Method of Quantitative Proteomic Data, Reveals New Interactors of the C. elegans Autophagic Protein LGG-1.

Affinity purifications followed by mass spectrometric analysis are used to identify protein-protein interactions. Because quantitative proteomic data are noisy, it is necessary to develop statistical methods to eliminate false-positives and identify true partners. We present here a novel approach for filtering false interactors, named "SAFER" for mass Spectrometry data Analysis by Filtering of Experimental Replicates, which is based on the reproducibility of the replicates and the fold-change of the protein intensities between bait and control. To identify regulators or targets of autophagy, we characterized the interactors of LGG1, a ubiquitin-like protein involved in autophagosome formation in C. elegans. LGG-1 partners were purified by affinity, analyzed by nanoLC-MS/MS mass spectrometry, and quantified by a label-free proteomic approach based on the mass spectrometric signal intensity of peptide precursor ions. Because the selection of confident interactions depends on the method used for statistical analysis, we compared SAFER with several statistical tests and different scoring algorithms on this set of data. We show that SAFER recovers high-confidence interactors that have been ignored by the other methods and identified new candidates involved in the autophagy process. We further validated our method on a public data set and conclude that SAFER notably improves the identification of protein interactors.

APPINetwork: an R package for building and computational analysis of protein-protein interaction networks.

Background: Protein-protein interactions (PPIs) are essential to almost every process in a cell. Analysis of PPI networks gives insights into the functional relationships among proteins and may reveal important hub proteins and sub-networks corresponding to functional modules. Several good tools have been developed for PPI network analysis but they have certain limitations. Most tools are suited for studying PPI in only a small number of model species, and do not allow second-order networks to be built, or offer relevant functions for their analysis. To overcome these limitations, we have developed APPINetwork (Analysis of Protein-protein Interaction Networks). The aim was to produce a generic and user-friendly package for building and analyzing a PPI network involving proteins of interest from any species as long they are stored in a database. Methods: APPINetwork is an open-source R package. It can be downloaded and installed on the collaborative development platform GitLab (https://forgemia.inra.fr/GNet/appinetwork). A graphical user interface facilitates its use. Graphical windows, buttons, and scroll bars allow the user to select or enter an organism name, choose data files and network parameters or methods dedicated to network analysis. All functions are implemented in R, except for the script identifying all proteins involved in the same biological process (developed in C) and the scripts formatting the BioGRID data file and generating the IDs correspondence file (implemented in Python 3). PPI information comes from private resources or different public databases (such as IntAct, BioGRID, and iRefIndex). The package can be deployed on Linux and macOS operating systems (OS). Deployment on Windows is possible but it requires the prior installation of Rtools and Python 3. Results: APPINetwork allows the user to build a PPI network from selected public databases and add their own PPI data. In this network, the proteins have unique identifiers resulting from the standardization of the different identifiers specific to each database. In addition to the construction of the first-order network, APPINetwork offers the possibility of building a second-order network centered on the proteins of interest (proteins known for their role in the biological process studied or subunits of a complex protein) and provides the number and type of experiments that have highlighted each PPI, as well as references to articles containing experimental evidence. Conclusion: More than a tool for PPI network building, APPINetwork enables the analysis of the resultant network, by searching either for the community of proteins involved in the same biological process or for the assembly intermediates of a protein complex. Results of these analyses are provided in easily exportable files. Examples files and a user manual describing each step of the process come with the package.

Characterisation and correction of signal fluctuations in successive acquisitions of microarray images.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Genomewide and biochemical analyses of DNA-binding activity of Cdc6/Orc1 and Mcm proteins in Pyrococcus sp.

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.

PPIDD: an extraction and visualisation method of biological protein-protein interfaces.

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.

MAnGO: an interactive R-based tool for two-colour microarray analysis.

UNLABELLED: 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/.

Development of an in silico method for the identification of subcomplexes involved in the biogenesis of multiprotein complexes in Saccharomyces cerevisiae.

BACKGROUND: Large sets of protein-protein interaction data coming either from biological experiments or predictive methods are available and can be combined to construct networks from which information about various cell processes can be extracted. We have developed an in silico approach based on these information to model the biogenesis of multiprotein complexes in the yeast Saccharomyces cerevisiae. RESULTS: Firstly, we have built three protein interaction networks by collecting the protein-protein interactions, which involved the subunits of three complexes, from different databases. The protein-protein interactions come from different kinds of biological experiments or are predicted. We have chosen the elongator and the mediator head complexes that are soluble and exhibit an architecture with subcomplexes that could be functional modules, and the mitochondrial bc 1 complex, which is an integral membrane complex and for which a late assembly subcomplex has been described. Secondly, by applying a clustering strategy to these networks, we were able to identify subcomplexes involved in the biogenesis of the complexes as well as the proteins interacting with each subcomplex. Thirdly, in order to validate our in silico results for the cytochrome bc1 complex we have analysed the physical interactions existing between three subunits by performing immunoprecipitation experiments in several genetic context. CONCLUSIONS: For the two soluble complexes (the elongator and mediator head), our model shows a strong clustering of subunits that belong to a known subcomplex or module. For the membrane bc 1 complex, our approach has suggested new interactions between subunits in the early steps of the assembly pathway that were experimentally confirmed. Scripts can be downloaded from the site: http://bim.igmors.u-psud.fr/isips .

An in silico approach combined with in vivo experiments enables the identification of a new protein whose overexpression can compensate for specific respiratory defects in Saccharomyces cerevisiae.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.