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.

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 .

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.

Yeast as a system for modeling mitochondrial disease mechanisms and discovering therapies.

Mitochondrial diseases are severe and largely untreatable. Owing to the many essential processes carried out by mitochondria and the complex cellular systems that support these processes, these diseases are diverse, pleiotropic, and challenging to study. Much of our current understanding of mitochondrial function and dysfunction comes from studies in the baker's yeast Saccharomyces cerevisiae. Because of its good fermenting capacity, S. cerevisiae can survive mutations that inactivate oxidative phosphorylation, has the ability to tolerate the complete loss of mitochondrial DNA (a property referred to as 'petite-positivity'), and is amenable to mitochondrial and nuclear genome manipulation. These attributes make it an excellent model system for studying and resolving the molecular basis of numerous mitochondrial diseases. Here, we review the invaluable insights this model organism has yielded about diseases caused by mitochondrial dysfunction, which ranges from primary defects in oxidative phosphorylation to metabolic disorders, as well as dysfunctions in maintaining the genome or in the dynamics of mitochondria. Owing to the high level of functional conservation between yeast and human mitochondrial genes, several yeast species have been instrumental in revealing the molecular mechanisms of pathogenic human mitochondrial gene mutations. Importantly, such insights have pointed to potential therapeutic targets, as have genetic and chemical screens using yeast.

Human GABARAP can restore autophagosome biogenesis in a C. elegans lgg-1 mutant.

We recently described in C. elegans embryos, the acquisition of specialized functions for orthologs of yeast Atg8 (e.g., mammalian MAP1LC3/LC3) in allophagy, a selective and developmentally regulated autophagic process. During the formation of double-membrane autophagosomes, the ubiquitin-like Atg8/LC3 proteins are recruited to the membrane through a lipidation process. While at least 6 orthologs and paralogs are present in mammals, C. elegans only possesses 2 orthologs, LGG-1 and LGG-2, corresponding to the GABARAP-GABARAPL2/GATE-16 and the MAP1LC3 families, respectively. During allophagy, LGG-1 acts upstream of LGG-2 and is essential for autophagosome biogenesis, whereas LGG-2 facilitates their maturation. We demonstrated that LGG-2 directly interacts with the HOPS complex subunit VPS-39, and mediates the tethering between autophagosomes and lysosomes, which also requires RAB-7. In the present addendum, we compared the localization of autophagosomes, endosomes, amphisomes, and lysosomes in vps-39, rab-7, and lgg-2 depleted embryos. Our results suggest that lysosomes interact with autophagosomes or endosomes through a similar mechanism. We also performed a functional complementation of an lgg-1 null mutant with human GABARAP, its closer homolog, and showed that it localizes to autophagosomes and can rescue LGG-1 functions in the early embryo.

Does the study of genetic interactions help predict the function of mitochondrial proteins in Saccharomyces cerevisiae?

Mitochondria are complex organelles of eukaryotic cells that contain their own genome, encoding key subunits of the respiratory complexes. The successive steps of mitochondrial gene expression are intimately linked, and are under the control of a large number of nuclear genes encoding factors that are imported into mitochondria. Investigating the relationships between these genes and their interaction networks, and whether they reveal direct or indirect partners, can shed light on their role in mitochondrial biogenesis, as well as identify new actors in this process. These studies, mainly developed in yeasts, are significant because mammalian equivalents of such yeast genes are candidate genes in mitochondrial pathologies. In practice, studies of physical, chemical and genetic interactions can be undertaken. The search for genetic interactions, either aggravating or alleviating the phenotype of the starting mutants, has proved to be particularly powerful in yeast since even subtle changes in respiratory phenotypes can be screened in a very efficient way. In addition, several high throughput genetic approaches have recently been developed. In this review we analyze the genetic network of three genes involved in different steps of mitochondrial gene expression, from the transcription and translation of mitochondrial RNAs to the insertion of newly synthesized proteins into the inner mitochondrial membrane, and we examine their relevance to our understanding of mitochondrial biogenesis. We find that these genetic interactions are seldom redundant with physical interactions, and thus bring a considerable amount of original and significant information as well as open new areas of research.

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.

Variation in gene expression profiles of human monocytic U937 cells exposed to various fluxes of nitric oxide.

We examined early and late alterations in gene expression patterns and phosphorylation levels of key regulators of selected signaling pathways in U937 cells exposed to various (*)NO fluxes. cDNA microarray analysis and real-time quantitative PCR identified 45 NO-sensitive genes (>or=2-fold change), among which KLF2, KLF6, TSC22D3, DDIT4, MKP-5 (up-regulated), KIF23, histone H4, ARL6IP2, CLNS1A, SLC7A6, CDKN3, SRP19, and BCL11A (down-regulated) have not been reported before. For two selected genes, KLF2 and DDIT4, the sensitivity to (.)NO was also proven at the protein level. Among the examined genes, only KLF2 had a higher sensitivity to slow release of NO (DETA-NO) than to high-dose, short-duration exposure (DPTA-NO), reaching an about 50-fold increase in mRNA level. Our study revealed that fast and slow NO donors activate similar signaling pathways and induce phosphorylation of MAP kinases and downstream transcription factors ATF2 and c-Jun. Inhibitory analysis of major signaling pathways showed that activity of p38 MAPK and tyrosine kinases is indispensable for gene induction in cells exposed to DPTA-NO, whereas G-protein Rho suppression caused superinduction of KLF2 in (*)NO-stimulated cells. Finally, we showed that both (*)NO donors caused a marked decrease in phosphorylation of p70S6K, an mTOR substrate and regulator of mRNA translation, and protein kinase Akt, an upstream positive regulator of mTOR.

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.

Expression ratio evaluation in two-colour microarray experiments is significantly improved by correcting image misalignment.

MOTIVATION: Two-colour microarrays are widely used to perform transcriptome analysis. In most cases, it appears that the 'red' and 'green' images resulting from the scan of a microarray slide are slightly shifted one with respect to the other. To increase the robustness of the measurement of the fluorescent emission intensities, multiple acquisitions with the same or different PMT gains can be used. In these cases, a systematic correction of image shift is required. RESULTS: To accurately detect this shift, we first developed an approach using cross-correlation. Second, we evaluated the most appropriate interpolation method to be used to derive the registered image. Then, we quantified the effects of image shifts on spot quality, using two different quality estimators. Finally, we measured the benefits associated with a systematic image registration. In this study, we demonstrate that registering the two images prior to data extraction provides a more reliable estimate of the two colours' ratio and thus increases the accuracy of measurements of variations in gene expression. AVAILABILITY: http://bioinfome.cgm.cnrs-gif.fr/.

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.

Analysis of differentially expressed genes in nitric oxide-exposed human monocytic cells.

In this study we examined the gene expression pattern of *NO-dependent genes in U937 and Mono Mac 6 monocytes exposed to the synthetic NO-donor DPTA-NO using microarray technology. cDNA microarray data were validated by Northern blot analysis and quantitative real-time PCR. This approach allowed the identification of 17 *NO-sensitive genes that showed at least a twofold difference in expression, in both U937 cells and Mono Mac 6 cells exposed to 500 microM DPTA-NO for 4 h. NO-stimulated genes belong to various functional groups, including transcription factors, signaling molecules, and cytokines. Among the selected genes, 11 (ATF-4, c-maf, SGK-1, PBEF, ATPase 8, NADH dehydrogenase 4, STK6, TRAF4-associated factor 1, molybdopterin synthase, CKS1, and CIDE-B) have not been previously reported to be sensitive to *NO. Because several *NO-stimulated genes are transcription factors, we analyzed the mRNA expression profile in U937 cells exposed to DPTA-NO for 14 h. We found that long-term *NO treatment influenced transcription rates of a rather limited set of genes, including CIDE-B, BNIP3, p21/Cip1, molybdopterin synthase, and TRAF4-associated factor 1. To accelerate formation of nitrosating species, U937 cells were exposed to DPTA-NO along with suboptimal concentrations of 2-phenyl-4,4,5,5-tetramethylimidazole-1-oxyl 3-oxide (PTIO). PTIO-mediated increase in nitrosating species remarkably enhanced *NO-dependent induction of IL-8, p21/Cip1, and MKP-1 and built a specific gene expression profile.