Contributions of Saccharomyces cerevisiae to understanding mammalian gene function and therapy

Improving the Detection of Protein Complexes by Predicting Novel Missing Interactome Links in the Protein-Protein Interaction Network

Identifying protein complexes within a protein-protein interaction (PPI) networks is a crucial task in computational biology that helps to facilitate a better understanding of the cellular mechanisms it is possible to observe in various organisms. Datasets of predicted PPIs have been determined using high-throughput experimental technology. However, the datasets typically contain many spurious interactions. It is essential that these interactions, observed in the given datasets, are validated before they are employed to predict protein complexes. This paper describes the identification of missing interactome links in the PPI network as a way of improving the detection of protein complexes. The missing links have been identified by extracting several topological features. These are subsequently employed in conjunction with a two-class boosted decision-tree classifier to develop a machine-learning model that is capable of distinguishing between existing and non-existing interactome links. The model was trained on a PPI network that consisted of 1,622 proteins and 9,074 interactions, then tested on another PPI network that consisted of 1,430 proteins and 6,531 interactions. All 6,531 interactions were identified with a precision of 0.994 and a recall of 1. The model was also able to detect 37 novel interactions that were then validated using a STRING database of known and predicted PPIs. The detection of the protein complexes using CIusterONE was improved by the inclusion of the 37 novel interactions.

Budding Yeast: An Ideal Backdrop for In vivo Lipid Biochemistry

Biological membranes are non-covalent assembly of lipids and proteins. Lipids play critical role in determining membrane physical properties and regulate the function of membrane associated proteins. Budding yeast Saccharomyces cerevisiae offers an exceptional advantage to understand the lipid-protein interactions since lipid metabolism and homeostasis are relatively simple and well characterized as compared to other eukaryotes. In addition, a vast array of genetic and cell biological tools are available to determine and understand the role of a particular lipid in various lipid metabolic disorders. Budding yeast has been instrumental in delineating mechanisms related to lipid metabolism, trafficking and their localization in different subcellular compartments at various cell cycle stages. Further, availability of tools and enormous potential for the development of useful reagents and novel technologies to localize a particular lipid in different subcellular compartments in yeast makes it a formidable system to carry out lipid biology. Taken together, yeast provides an outstanding backdrop to characterize lipid metabolic changes under various physiological conditions.

Generation and application of mammalian haploid embryonic stem cells

Haploid cells contain one set of chromosomes and are amenable for genetic analyses. In mammals, haploidy exists only in gametes. An intriguing question is whether haploid cells can be derived from gametes. Recently, by application of haploid cell enrichment using fluorescence-activated cell sorting, stable haploid embryonic stem cells (haESCs) have been successfully derived from oocyte-derived parthenogenetic and sperm-derived androgenetic embryos from several species. Whilst both parthenogenetic and androgenetic (AG)-haESCs enable whole-genome genetic screening at the cellular level, such as screening of drug resistance or disease-related genes, AG-haESCs, after intracytoplasmic injection into oocytes, can also be used to produce alive semi-cloned mice. Nevertheless, one major drawback associated with wild-type AG-haESCs is the very low birth rate of healthy semi-cloned mice. Of interest, after inhibiting the expression of two paternally imprinted genes (H19 and Gtl2) in AG-haESCs by removal of their differentially DNA methylated regions, double-knockout AG-haESCs can efficiently and stably support the generation of healthy semi-cloned pups. Importantly, double-knockout AG-haESCs are feasible for multiple genetic manipulations, followed by efficient generation of semi-cloned mice carrying multiple genetic traits; thus they could be used to validate candidate loci that have been identified in genome-wide association studies of multigenic diseases by generation of mouse models carrying multiple alterations. Of note, by combining a CRISPR-Cas9 library and double-knockout AG-haESCs, semi-cloned mice carrying different mutant genes can be efficiently generated in one step, enabling functional mutagenic screening in mice. HaESCs, therefore, provide a powerful tool for genetic analyses in mammals at both the cellular and organismal levels.

Contemporary, yeast-based approaches to understanding human genetic variation

Determining how genetic variation contributes to human health and disease is a critical challenge. As one of the most genetically tractable model organisms, yeast has played a central role in meeting this challenge. The advent of new technologies, including high-throughput DNA sequencing and synthesis, proteomics, and computational methods, has vastly increased the power of yeast-based approaches to determine the consequences of human genetic variation. Recent successes include systematic exploration of the effects of gene dosage, large-scale analysis of the effect of coding variation on gene function, and the use of humanized yeast to model disease. By virtue of its manipulability, small genome size, and genetic tractability, yeast is poised to help us understand human genetic variation.

Generation of haploid embryonic stem cells from Macaca fascicularis monkey parthenotes

Recent success in the derivation of haploid embryonic stem cells (haESCs) from mouse via parthenogenesis and androgenesis has enabled genetic screening in mammalian cells and generation of gene-modified animals. However, whether haESCs can be derived from primates remains unknown. Here, we report the derivation of haESCs from parthenogenetic blastocysts of Macaca fascicularis monkeys. These cells, termed as PG-haESCs, are pluripotent and can differentiate to cells of three embryonic germ layers in vitro or in vivo. Interestingly, the haploidy of one monkey PG-haESC line (MPH1) is more stable compared with that of the other one (MPH2), as shown by the existence of haploid cells for more than 140 days without fluorescence-activated cell sorting (FACS) enrichment of haploid cells. Importantly, transgenic monkey PG-haESC lines can be generated by lentivirus- and piggyBac transposon-mediated gene transfer. Moreover, genetic screening is feasible in monkey PG-haESCs. Our results demonstrate that PG-haESCs can be generated from monkeys, providing an ideal tool for genetic analyses in primates.

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method.

Functional expression of parasite drug targets and their human orthologs in yeast

Background

The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents.

Methodology/Principal Findings

Using pyrimethamine/dihydrofolate reductase (DHFR) as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi) to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p) expressing yeast (^ScDFR1), human (^HsDHFR), Schistosoma (^SmDHFR), and Trypanosoma (^TbDHFR and ^TcDHFR) DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium (^PfDHFR and ^PvDHFR) DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR (^Pfdhfr^51I,59R,108N) are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs) and N-myristoyl transferases (NMTs).

Conclusions/Significance

We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents.

Parasites kill millions of people every year and leave countless others with chronic debilitating disease. These diseases, which include malaria and sleeping sickness, mainly affect people in developing countries. For this reason, few drugs have been developed to treat them. To make matters worse, many parasites are developing resistance to the drugs that are available. Thus, there is an urgent need to develop new drugs, but this is hampered by the fact that most parasites are difficult or impossible to grow in the laboratory. To address this, we have engineered baker's yeast to be dependent on the function of enzymes from either parasites or humans. In all, our engineered yeast constructs encompass six parasites (causing malaria, schistosomiasis, leishmaniasis, sleeping sickness, and Chagas disease) and three different enzymes that are known or potential drug targets. Further, we have increased yeast's sensitivity to drugs by deleting the gene for its major drug efflux pump. Because yeast is robust and easy to grow in the laboratory, we can use a robot to screen for drugs that will kill yeast dependent on a parasite enzyme, but not touch yeast dependent on the equivalent human enzyme.