Juan A. G. Ranea

The Functional Interaction of 14-3-3 Proteins with the ERK1/2 Scaffold KSR1 Occurs in an Isoform-specific Manner

Identifying 14-3-3 isoform-specific substrates and functions may be of broad relevance to cell signaling research because of the key role played by this family of proteins in many vital processes. A multitude of ligands have been identified, but the extent to which they are isoform-specific is a matter of debate. Herein we demonstrate, both in vitro and in vivo, a specific, functionally relevant interaction of human 14-3-3γ with the molecular scaffold KSR1, which is mediated by the C-terminal stretch of 14-3-3γ. Specific binding to 14-3-3γ protected KSR1 from epidermal growth factor-induced dephosphorylation and impaired its ability to activate ERK2 and facilitate Ras signaling in Xenopus oocytes. Furthermore, RNA interference-mediated inhibition of 14-3-3γ resulted in the accumulation of KSR1 in the plasma membrane, all in accordance with 14-3-3γ being the cytosolic anchor that keeps KSR1 inactive. We also provide evidence that KSR1-bound 14-3-3γ heterodimerized preferentially with selected isoforms and that KSR1 bound monomeric 14-3-3γ. In sum, we have demonstrated ligand discrimination among 14-3-3 isoforms and shed light on molecular mechanisms of 14-3-3 functional specificity and KSR1 regulation.

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

A gene function prediction method suitable for the design of targeted RNAi libraries is described and used to predict chromosome condensation genes. Systematic experimental validation of candidate genes in a focused RNAi screen by automated microscopy and quantitative image analysis reveals many new chromosome condensation factors.

Evolutionary expansion of the Ras switch regulatory module in eukaryotes

Ras proteins control many aspects of eukaryotic cell homeostasis by switching between active (GTP-bound) and inactive (GDP-bound) conformations, a reaction catalyzed by GTPase exchange factors (GEF) and GTPase activating proteins (GAP) regulators, respectively. Here, we show that the complexity, measured as number of genes, of the canonical Ras switch genetic system (including Ras, RasGEF, RasGAP and RapGAP families) from 24 eukaryotic organisms is correlated with their genome size and is inversely correlated to their evolutionary distances from humans. Moreover, different gene subfamilies within the Ras switch have contributed unevenly to the module’s expansion and speciation processes during eukaryote evolution. The Ras system remarkably reduced its genetic expansion after the split of the Euteleostomi clade and presently looks practically crystallized in mammals. Supporting evidence points to gene duplication as the predominant mechanism generating functional diversity in the Ras system, stressing the leading role of gene duplication in the Ras family expansion. Domain fusion and alternative splicing are significant sources of functional diversity in the GAP and GEF families but their contribution is limited in the Ras family. An evolutionary model of the Ras system expansion is proposed suggesting an inherent ‘decision making’ topology with the GEF input signal integrated by a homologous molecular mechanism and bifurcation in GAP signaling propagation.

Insights into Polypharmacology from Drug-Domain Associations

MOTIVATION Polypharmacology (the ability of a single drug to affect multiple targets) is a key feature that may explain part of the decreasing success of conventional drug discovery strategies driven by the quest for drugs to act selectively on a single target. Most drug targets are proteins that are composed of domains (their structural and functional building blocks). RESULTS In this work, we model drug-domain networks to explore the role of protein domains as drug targets and to explain drug polypharmacology in terms of the interactions between drugs and protein domains. We find that drugs are organized around a privileged set of druggable domains. CONCLUSIONS Protein domains are a good proxy for drug targets, and drug polypharmacology emerges as a consequence of the multi-domain composition of proteins. CONTACT amoyag@uma.es SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Comparative evolutionary analysis of protein complexes in E. coli and yeast

BackgroundProteins do not act in isolation; they frequently act together in protein complexes to carry out concerted cellular functions. The evolution of complexes is poorly understood, especially in organisms other than yeast, where little experimental data has been available.ResultsWe generated accurate, high coverage datasets of protein complexes for E. coli and yeast in order to study differences in the evolution of complexes between these two species. We show that substantial differences exist in how complexes have evolved between these organisms. A previously proposed model of complex evolution identified complexes with cores of interacting homologues. We support findings of the relative importance of this mode of evolution in yeast, but find that it is much less common in E. coli. Additionally it is shown that those homologues which do cluster in complexes are involved in eukaryote-specific functions. Furthermore we identify correlated pairs of non-homologous domains which occur in multiple protein complexes. These were identified in both yeast and E. coli and we present evidence that these too may represent complex cores in yeast but not those of E. coli.ConclusionsOur results suggest that there are differences in the way protein complexes have evolved in E. coli and yeast. Whereas some yeast complexes have evolved by recruiting paralogues, this is not apparent in E. coli. Furthermore, such complexes are involved in eukaryotic-specific functions. This implies that the increase in gene family sizes seen in eukaryotes in part reflects multiple family members being used within complexes. However, in general, in both E. coli and yeast, homologous domains are used in different complexes.

Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, “the human diseases networks” (HDN) and “the orphan disease networks” (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the “Human Phenotype Ontology”. The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module. Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

Uncovering the Molecular Machinery of the Human Spindle-An Integration of Wet and Dry Systems Biology

The mitotic spindle is an essential molecular machine involved in cell division, whose composition has been studied extensively by detailed cellular biology, high-throughput proteomics, and RNA interference experiments. However, because of its dynamic organization and complex regulation it is difficult to obtain a complete description of its molecular composition. We have implemented an integrated computational approach to characterize novel human spindle components and have analysed in detail the individual candidates predicted to be spindle proteins, as well as the network of predicted relations connecting known and putative spindle proteins. The subsequent experimental validation of a number of predicted novel proteins confirmed not only their association with the spindle apparatus but also their role in mitosis. We found that 75% of our tested proteins are localizing to the spindle apparatus compared to a success rate of 35% when expert knowledge alone was used. We compare our results to the previously published MitoCheck study and see that our approach does validate some findings by this consortium. Further, we predict so-called “hidden spindle hub”, proteins whose network of interactions is still poorly characterised by experimental means and which are thought to influence the functionality of the mitotic spindle on a large scale. Our analyses suggest that we are still far from knowing the complete repertoire of functionally important components of the human spindle network. Combining integrated bio-computational approaches and single gene experimental follow-ups could be key to exploring the still hidden regions of the human spindle system.

The study of severe cutaneous drug hypersensitivity reactions from a systems biology perspective.

Purpose of reviewStevens–Johnson syndrome and toxic epidermal necrolysis are severe hypersensitivity reactions, the majority of which are drug induced. The underlying mechanisms are not fully understood. Here, we review recent findings concerning both mechanistic and genetic factors related to these diseases and propose future approaches to unravel their complexity. Recent findingsGenome-wide association study studies have identified several variants in the human leukocyte antigen region associated with these reactions. These are highly dependent on the population studied and the triggering drug. The T-cell receptor repertoire of the patient is also key. Fas–Fas ligand interactions, perforin and granulysin have also been identified as important players. Furthermore, a high-throughput gene expression study has identified a number of genes that increase in expression in patients during the acute phase of these reactions. SummaryWe review recent high-throughput studies on these diseases and suggest ways in which the data can be combined and reanalyzed using integrative systems biology techniques. We also suggest future lines of research using recent technology that could shed further light on their underlying mechanisms.

Systems biology approaches to enhance our understanding of drug hypersensitivity reactions

Hypersensitivity drug reactions (HDRs) encompass a wide spectrum of unpredictable clinical entities. They represent an important health problem, affecting people of all ages, and lead to a large strain on the public health system. Here, we summarize experiments that use high‐throughput genomics technologies to investigate HDRs. We also introduce the field of systems biology as a relatively recent discipline concerned with the integration and analysis of high‐throughput data sets such as DNA microarrays and next‐generation sequencing data. We describe previous studies that have applied systems biology techniques to related fields such as allergy and asthma. Finally, we present a number of potential applications of systems biology to the study of HDRs, in order to make the reader aware of the types of analyses that can be performed and the insights that can be gained through their application.

How can functional annotations be derived from profiles of phenotypic annotations

BackgroundLoss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requires careful interpretation of phenotypic descriptions and assessment of phenotypic similarity. Understanding how functions and phenotypes are linked will be crucial for the development of methods for the automatic conversion of gene loss-of-function phenotypes to gene functional annotations.ResultsWe explored the relation between cellular phenotypes from RNAi-based screens in human cells and gene annotations of cellular functions as provided by the Gene Ontology (GO). Comparing different similarity measures, we found that information content-based measures of phenotypic similarity were the best at capturing gene functional similarity. However, phenotypic similarities did not map to the Gene Ontology organization of gene function but to functions defined as groups of GO terms with shared gene annotations.ConclusionsOur observations have implications for the use and interpretation of phenotypic similarities as a proxy for gene functions both in RNAi screen data analysis and curation and in the prediction of disease genes.