Removing bias against membrane proteins in interaction networks

<p>Abstract</p> <p>Background</p> <p>Cellular interaction networks can be used to analyze the effects on cell signaling and other functional consequences of perturbations to cellular physiology. Thus, several methods have been used to reconstitute interaction networks from multiple published datasets. However, the structure and performance of these networks depends on both the quality and the unbiased nature of the original data. Due to the inherent bias against membrane proteins in protein-protein interaction (PPI) data, interaction networks can be compromised particularly if they are to be used in conjunction with drug screening efforts, since most drug-targets are membrane proteins.</p> <p>Results</p> <p>To overcome the experimental bias against PPIs involving membrane-associated proteins we used a probabilistic approach based on a hypergeometric distribution followed by logistic regression to simultaneously optimize the weights of different sources of interaction data. The resulting less biased genome-scale network constructed for the budding yeast <it>Saccharomyces cerevisiae </it>revealed that the starvation pathway is a distinct subnetwork of autophagy and retrieved a more integrated network of unfolded protein response genes. We also observed that the centrality-lethality rule depends on the content of membrane proteins in networks.</p> <p>Conclusions</p> <p>We show here that the bias against membrane proteins can and should be corrected in order to have a better representation of the interactions and topological properties of protein interaction networks.</p

Genome-wide analysis of Homo sapiens, Arabidopsis thaliana, and Saccharomyces cerevisiae reveals novel attributes of tail-anchored membrane proteins

Abstract Background Tail-anchored membrane proteins (TAMPs) differ from other integral membrane proteins, because they contain a single transmembrane domain at the extreme carboxyl-terminus and are therefore obliged to target to membranes post-translationally. Although 3–5% of all transmembrane proteins are predicted to be TAMPs only a small number are well characterized. Results To identify novel putative TAMPs across different species, we used TAMPfinder software to identify 859, 657 and 119 putative TAMPs in human (Homo sapiens), plant (Arabidopsis thaliana), and yeast (Saccharomyces cerevisiae), respectively. Bioinformatics analyses of these putative TAMP sequences suggest that the list is highly enriched for authentic TAMPs. To experimentally validate the software predictions several human and plant proteins identified by TAMPfinder that were previously uncharacterized were expressed in cells and visualized at subcellular membranes by fluorescence microscopy and further analyzed by carbonate extraction or by bimolecular fluorescence complementation. With the exception of the pro-apoptotic protein harakiri, which is, peripherally bound to the membrane this subset of novel proteins behave like genuine TAMPs. Comprehensive bioinformatics analysis of the generated TAMP datasets revealed previously unappreciated common and species-specific features such as the unusual size distribution of and the propensity of TAMP proteins to be part of larger complexes. Additionally, novel features of the amino acid sequences that anchor TAMPs to membranes were also revealed. Conclusions The findings in this study more than double the number of predicted annotated TAMPs and provide new insights into the common and species-specific features of TAMPs. Furthermore, the list of TAMPs and annotations provide a resource for further investigation

Rupturing of Biological Spores As a Source of Secondary Particles in Amazonia.

Airborne biological particles, such as fungal spores and pollen, are ubiquitous in the Earth's atmosphere and may influence the atmospheric environment and climate, impacting air quality, cloud formation, and the Earth's radiation budget. The atmospheric transformations of airborne biological spores at elevated relative humidity remain poorly understood and their climatic role is uncertain. Using an environmental scanning electron microscope (ESEM), we observed rupturing of Amazonian fungal spores and subsequent release of submicrometer size fragments after exposure to high humidity. We find that fungal fragments contain elements of inorganic salts (e.g., Na and Cl). They are hygroscopic in nature with a growth factor up to 2.3 at 96% relative humidity, thus they may potentially influence cloud formation. Due to their hygroscopic growth, light scattering cross sections of the fragments are enhanced by up to a factor of 10. Furthermore, rupturing of fungal spores at high humidity may explain the bursting events of new particle formation in Amazonia

A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities

Improved efforts are necessary to define the functional product of cancer mutations currently being revealed through large-scale sequencing efforts. Using genome-scale pooled shRNA screening technology, we mapped negative genetic interactions across a set of isogenic cancer cell lines and confirmed hundreds of these interactions in orthogonal co-culture competition assays to generate a high-confidence genetic interaction network of differentially essential or differential essentiality (DiE) genes. The network uncovered examples of conserved genetic interactions, densely connected functional modules derived from comparative genomics with model systems data, functions for uncharacterized genes in the human genome and targetable vulnerabilities. Finally, we demonstrate a general applicability of DiE gene signatures in determining genetic dependencies of other non-isogenic cancer cell lines. For example, the PTEN(−/−) DiE genes reveal a signature that can preferentially classify PTEN-dependent genotypes across a series of non-isogenic cell lines derived from the breast, pancreas and ovarian cancers. Our reference network suggests that many cancer vulnerabilities remain to be discovered through systematic derivation of a network of differentially essential genes in an isogenic cancer cell model