Sro7 and Sro77, the yeast homologues of the Drosophila lethal giant larvae (Lgl), regulate cell proliferation via the Rho1-Tor1 pathway

The putative transient receptor potential channel protein encoded by the orf19.4805 gene is involved in cation sensitivity, antifungal tolerance, and filamentation in Candida albicans

Transient receptor potential (TRP) channels, an ancient family of cation channels, are highly conserved in eukaryotes and play various physiological functions, ranging from sensation of ion homeostasis to reception of pain and vision. Calcium-permeable TRP channels have been identified from the plant Arabidopsis thaliana (AtCsc1) and the budding yeast Saccharomyces cerevisiae (ScCsc1). In this study, we characterized the functions of the Csc1 homolog, orf19.4805, in Candida albicans. Orf19.4805 is a protein of 866 amino acids and 11 transmembrane domains, which shares 49% identity (69% similarity) in amino acid sequence with ScRsn1. Here, we demonstrate that deletion of the orf19.4805 gene causes C. albicans cells to be sensitive to SDS (sodium dodecyl sulfate) and antifungal drugs, and tolerance to zinc, manganese, and cadmium ions. Candida albicans cells lacking orf19.4805 show a defect in filamentation in vitro. Therefore, orf19.4805 is involved in the regulation of cation homeostasis and filamentation in C. albicans.

Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities

Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.