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Gianni Cesareni, Professor of Genetics at the University of Tor Vergata in Rome, Italy, is certainly one of these. ... FEBS Letters 582 (2008) 1291-1292 What does Gianni Cesareni do in his spare time, if he has any? He certainly does, despite the long working hours. ...doi:10.1016/j.febslet.2008.03.019 pmid:18358839 fatcat:od6nj3np6rfozmndrqtmbqeteu
F E B S L e t t e r s . o r g Gianni Cesareni Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy Fax: +39 062023500. ... E-mail address: firstname.lastname@example.org http://dx.doi.org/10.1016/j.febslet.2013.11.001 FEBS Letters 587 (2013) 3891 ...doi:10.1016/j.febslet.2013.11.001 fatcat:bxogwe3isvdfvnpu76mdw22svy
Gianni Cesareni Note from the FEBS Letters Editorial O¤ce As demonstrated by this issue dedicated to reviews on functional genomics, FEBS Letters is now interested in publishing three or four Special Issues ...doi:10.1016/s0014-5793(00)01770-1 pmid:10967321 fatcat:km7k5eea4neg7ivi3xtrxqsrka
more »... ant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein-protein interaction networks.
The study of the complex web of interactions that link biological molecules in a cell is the subject of interactomics -currently one of the fastest moving fields in molecular biology. The recent completion of highthroughput studies to investigate systematically all the possible interactions in a variety of model organisms has provided unique opportunities to compare interaction networks and ask questions about their conservation during evolution. It is expected that this approach will yield adoi:10.1016/j.tibtech.2007.08.002 pmid:17825444 fatcat:leu5mi64ifdmvjy5rfdn4xaz2q
more »... ientific return as rich as that obtained in the past decade from comparing genomes and proteomes from different organisms.
The behavior, morphology and response to stimuli in biological systems are dictated by the interactions between their components. These interactions, as we observe them now, are therefore shaped by genetic variations and selective pressure. Similar to what has been achieved by comparing genome structures and protein sequences, we hope to obtain valuable information about systemsÕ evolution by comparing the organization of interaction networks and by analyzing their variation and conservation.doi:10.1016/j.febslet.2005.01.064 pmid:15763559 fatcat:upvfuek5hfawth2xej7r6ccsvy
more »... ually, significantly we can learn whether and how to extend the network information obtained experimentally in well-characterized model systems to different organisms. We conclude from our analysis that, despite the recent completion of several high throughput experiments aimed at the description of complete interactomes, the available interaction information is not yet of sufficient coverage and quality to draw any biologically meaningful conclusion from the comparison of different interactomes. Thus, the transfer of network information obtained from simple organism to evolutionary distant species should be carried out and considered with caution. By using smaller higher-confidence datasets, a larger fraction of interactions is shown to be conserved; this suggests that with the development of more accurate experimental and informatic approaches, we will soon be in the position to study the network evolution.
The development of high-throughput high-content technologies and the increased ease in their application in clinical settings has raised the expectation of an important impact of these technologies on diagnosis and personalized therapy. Patient genomic and expression profiles yield lists of genes that are mutated or whose expression is modulated in specific disease conditions. The challenge remains of extracting from these lists functional information that may help to shed light on thedoi:10.3389/fgene.2021.694468 pmid:34178043 pmcid:PMC8226215 fatcat:d26ewljsz5f5hn23bp4megobvu
more »... s that are perturbed in the disease, thus setting a rational framework that may help clinical decisions. Network approaches are playing an increasing role in the organization and interpretation of patients' data. Biological networks are generated by connecting genes or gene products according to experimental evidence that demonstrates their interactions. Till recently most approaches have relied on networks based on physical interactions between proteins. Such networks miss an important piece of information as they lack details on the functional consequences of the interactions. Over the past few years, a number of resources have started collecting causal information of the type protein A activates/inactivates protein B, in a structured format. This information may be represented as signed directed graphs where physiological and pathological signaling can be conveniently inspected. In this review we will (i) present and compare these resources and discuss the different scope in comparison with pathway resources; (ii) compare resources that explicitly capture causality in terms of data content and proteome coverage (iii) review how causal-graphs can be used to extract disease-specific Boolean networks.
Approximately 70 scientists met, from May 11-13 2001, in Villa Mondragone in the hills south of Rome to discuss strategies to describe the complete protein interaction network inside a cell. Very few of the participants still needed to be convinced that this is an essential step if we want to try to interpret the functional information contained in genomic databases. This was well accepted before the workshop started. The discussion revolved around how this can be achieved most effectively anddoi:10.1002/cfg.98 pmid:18629241 pmcid:PMC2448402 fatcat:xmnev5idqzcfflvxpp2smdsbwq
more »... hich methods we should focus on if we want to get reliable biological information. Most of the high throughput methods that are currently used in large genomic protein interaction projects were represented by at least one of the 17 invited speakers. For the sake of simplicity the workshop presentations were divided into four sessions encompassing genetic methods, protein and peptide arrays, mass spectrometry and bioinformatic methods. Pierre Legrain, whose presentation is reported in more detail on page 301, focussed on the critical comparison of the different approaches that have been utilized in recent large scale 2-hybrid interaction screenings. Particularly surprising, and perhaps disappointing, is the finding that two large projects that aimed at deciphering the complete protein interaction map in S. cerevisiae show only a 15% overlap and recapitulate no more than 13% of the published interactions detected by the community of yeast biologists. Pierre Legrain suggested that an approach based on the expression of protein fragments, instead of full-length proteins, might contribute to decreasing the number of false negatives, as demonstrated in the Helicobacter pylori protein interaction project. Andreas Pluckthun and Brian Kay described two alternative methods, ribosome display and phage-display, that have a genomic potential. Ribosome display, although still in a development phase, holds great promises since it offers the potential to screen a number of partners that is by three to four logs larger than conventional display methods. Panning of peptide repertoires of random sequence displayed on filamentous phage capsids, on the other hand, not only permits one to infer the identity of natural protein partners but also allows precise mapping of the interaction sites (reviewed on page 304). Furthermore, this approach provides leads to develop molecules that, by binding at high affinity to either partner, disrupt the formation of a protein complex in a cell. Genetic methods are selective, although a large-scale screen in an array format has been described. By this approach each single interaction is tested independently and problems due to selective growth disadvantage of specific clones may be overcome. The array approach can be better implemented when proteins or peptides are orderly spotted or synthesized on solid supports, for instance a cellulose membrane, or a glass slide, as in DNA array. The technology of protein chips is far from being as accessible to the non-specialised laboratory as DNA chip technology. The problems to be overcome range from the difficulties experienced in
The concerted activities of kinases and phosphatases modulate the phosphorylation levels of proteins, lipids and carbohydrates in eukaryotic cells. Despite considerable effort, we are still missing a holistic picture representing, at a proteome level, the functional relationships between kinases, phosphatases and their substrates. Here we focus on phosphatases and we review and integrate the available information that helps to place the members of the protein phosphatase superfamilies into thedoi:10.1016/j.febslet.2012.05.008 pmid:22626554 pmcid:PMC3437441 fatcat:tkbbjw37brfbpgfh4e5fbtd3si
more »... uman protein interaction network. In addition we show how protein interaction domains and motifs, either covalently linked to the phosphatase domain or in regulatory/adaptor subunits, play a prominent role in substrate selection.
The EMBO Journal vol.5 no.12 pp.3391—3399, 1986 Functional analysis of the yeast plasmid partition locus STB James A.H.Murray and Gianni Cesareni European Molecular Biology Laboratory, Postfach 10.2209 ... Dente,L., Sollazzo,M., Baldari,C., Cesareni,G. and Cortese,R. (1985) In Glover,D.M. (ed.), DNA Cloning. A Practical Approach. IRL Press, Oxford, Vol. 1, pp. 101—108. ...doi:10.1002/j.1460-2075.1986.tb04655.x fatcat:nuxtkc6qobhgdnofaitjm6qkae
Protein interaction databases represent unique tools to store, in a computer readable form, the protein interaction information disseminated in the scientific literature. Well organized and easily accessible databases permit the easy retrieval and analysis of large interaction data sets. Here we present MINT, a database (http ://cbm.bio.uniroma2.it/mint/ index.html) designed to store data on functional interactions between proteins. Beyond cataloguing binary complexes, MINT was conceived todoi:10.1016/s0014-5793(01)03293-8 pmid:11911893 fatcat:hvc6ejihvzfa5i4674gkehjb6a
more »... e other types of functional interactions, including enzymatic modifications of one of the partners. Release 1.0 of MINT focuses on experimentally verified protein^protein interactions. Both direct and indirect relationships are considered. Furthermore, MINT aims at being exhaustive in the description of the interaction and, whenever available, information about kinetic and binding constants and about the domains participating in the interaction is included in the entry. MINT consists of entries extracted from the scientific literature by expert curators assisted by'MINT Assistant', a software that targets abstracts containing interaction information and presents them to the curator in a user-friendly format. The interaction data can be easily extracted and viewed graphically through MINT Viewer'. Presently MINT contains 4568 interactions, 782 of which are indirect or genetic interactions. ß 2002 Published by Elsevier Science B.V. on behalf of the Federation of European Biochemical Societies.
Cesareni, unpublished). This procedure is based on a two step selection for a double recombination event between partially homologous sequences in a plasmid and in a lambda phage. ...doi:10.1093/nar/11.6.1645 pmid:6300771 pmcid:PMC325826 fatcat:xvsitfe7grcgjkzdqilw76yf4q
The gene for a previously unidentified small nuclear RNA has been cloned from Saccharomyces cerevisiae and its nucleotide sequence has been determined. The RNA, snR30, was mapped to a unique coding sequence 605 nucleotides long. SnR30 appears to be one of the most abundant snRNAs of S. cerevisiae in that it can be resolved by ethidium bromide staining on one-dimensional denaturing gels of total yeast RNA. Like other snRNAs, snR30 is enriched in nuclei preparations and possesses a trimethyldoi:10.1093/nar/16.12.5291 pmid:2898766 pmcid:PMC336768 fatcat:oktgjz2ocvcz3jzesb47sbrp64
more »... sine cap structure at its 5' end. After substituting one allele of the wild type gene in a diploid strain for a deleted gene, after sporulation, haploid strains carrying the deletion were unable to grow, indicating that snR30 is required for an essential, but as yet, unknown function. The nucleotide sequence close to the initiation site of the SNR30 gene is similar to that of other yeast SNR genes whose transcripts are associated with pre-rRNA, suggesting that snR30 is related to this group of snRNAs. At least 24 small nuclear RNAs (snRNAs) have been identified in S. cerevisiae on the basis of the 2,2,7-trimethyl guanosine (m3G) cap structure found at the 5' end of nearly all snRNAs (1). Among these, the yeast snRNAs involved in precursor messenger RNA (pre-mRNA) splicing have been identified: Ul, snR19 (2, 3); U2, LSR1/snR20 (4); U4, snR14; U6, snR6 (5), and U5, snR7 (6). (For reviews on splicing, see 7, 8.) All of these possess Sm-binding sites, conserved nucleotide sequences required for the binding of the core proteins of snRNPs recognized by Sm antibodies (9) and all of these yeast snRNAs, except U6, have been observed to be immunoprecipitated in the form of ribonucleoprotein (RNP) in the presence of Sm antisera (10, 11, 12, 3) . Other yeast snRNAs appear to be hydrogen-bonded to various precursor ribosomal RNA species (pre-rRNA) and are less readily released from isolated preparations of yeast nuclei. These include snR17 (U3), snR3, snR4, snR5, snR8, snR9 and snR10 (13). All the spliceosomeassociated snRNAs tested so far have been shown to be essential for yeast viability. SnR17 (U3) is also essential (12), but no phenotypic change is observed when the genes of several of the other pre-rRNA associated snRNAs are deleted. The deletion mutant of snrlO however exhibits slow growth and cold-sensitivity (14) along with deficiency in certain cleavage steps in the pre-rRNA processing pathway (13).
Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT.doi:10.1093/nar/gku830 pmid:25217587 pmcid:PMC4384001 fatcat:vimbvvqkxjdkpl2ksi3w5c6btm
more »... We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus-virus and virushost interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. Virus-Mentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.
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