Multiplex assay for condition-dependent changes in protein-protein interactions

Compensatory mutations potentiate constructive neutral evolution by gene duplication

The functions of proteins generally depend on their assembly into complexes. During evolution, some complexes have transitioned from homomers encoded by a single gene to heteromers encoded by duplicate genes. This transition could occur without adaptive evolution through intermolecular compensatory mutations. Here, we experimentally duplicated and evolved a homodimeric enzyme to determine whether and how this could happen. We identified hundreds of deleterious mutations that inactivate individual homodimers but produce functional enzymes when coexpressed as duplicated proteins that heterodimerize. The structure of one such heteromer reveals how both losses of function are buffered through the introduction of asymmetry in the complex that allows them to subfunctionalize. Constructive neutral evolution can thus occur by gene duplication followed by only one deleterious mutation per duplicate.

Compensatory mutations potentiate constructive neutral evolution by gene duplication

Protein functions generally depend on their assembly into complexes. During evolution, some complexes have transitioned from homomers encoded by a single gene to heteromers encoded by duplicate genes. This transition could occur without adaptive evolution through intermolecular compensatory mutations. Here, we experimentally duplicate and evolve an homodimeric enzyme to examine if and how this could happen. We identify hundreds of deleterious mutations that inactivate individual homodimers but produce functional enzymes when co-expressed as duplicated proteins that heterodimerize. The structure of one such heteromer reveals how both losses of function are buffered through the introduction of asymmetry in the complex that allows them to subfunctionalize. Constructive neutral evolution can thus occur by gene duplication followed by only one deleterious mutation per duplicate.

Evaluation of Antifungal Selective Toxicity Using Candida glabrata ERG25 and Human SC4MOL Knock-In Strains

With only four classes of antifungal drugs available for the treatment of invasive systemic fungal infections, the number of resistant fungi is increasing, highlighting the urgent need for novel antifungal drugs. Ergosterol, an essential component of cell membranes, and its synthetic pathway have been targeted for antifungal drug development. Sterol-C4-methyl monooxygenase (Erg25p), which is a greater essential target than that of existing drugs, represents a promising drug target. However, the development of antifungal drugs must consider potential side effects, emphasizing the importance of evaluating their selective toxicity against fungi. In this study, we knocked in ERG25 of Candida glabrata and its human ortholog, SC4MOL, in ERG25-deleted Saccharomyces cerevisiae. Utilizing these strains, we evaluated 1181-0519, an Erg25p inhibitor, that exhibited selective toxicity against the C. glabrata ERG25 knock-in strain. Furthermore, 1181-0519 demonstrated broad-spectrum antifungal activity against pathogenic Candida species, including Candida auris. The approach of utilizing a gene that is functionally conserved between yeast and humans and subsequently screening for molecular target drugs enables the identification of selective inhibitors for both species.

Asymmetrical dose responses shape the evolutionary trade-off between antifungal resistance and nutrient use

Antimicrobial resistance is an emerging threat for public health. The success of resistance mutations depends on the trade-off between the benefits and costs they incur. This trade-off is largely unknown and uncharacterized for antifungals. Here, we systematically measure the effect of all amino acid substitutions in the yeast cytosine deaminase Fcy1, the target of the antifungal 5-fluorocytosine (5-FC, flucytosine). We identify over 900 missense mutations granting resistance to 5-FC, a large fraction of which appear to act through destabilization of the protein. The relationship between 5-FC resistance and growth sustained by cytosine deamination is characterized by a sharp trade-off, such that small gains in resistance universally lead to large losses in canonical enzyme function. We show that this steep relationship can be explained by differences in the dose-response functions of 5-FC and cytosine. Finally, we observe the same trade-off shape for the orthologue of FCY1 in Cryptoccocus neoformans, a human pathogen. Our results provide a powerful resource and platform for interpreting drug target variants in fungal pathogens as well as unprecedented insights into resistance-function trade-offs.

Contribution of Model Organisms to Investigating the Far-Reaching Consequences of PRPP Metabolism on Human Health and Well-Being

Phosphoribosyl pyrophosphate synthetase (PRS EC 2.7.6.1) is a rate-limiting enzyme that irreversibly catalyzes the formation of phosphoribosyl pyrophosphate (PRPP) from ribose-5-phosphate and adenosine triphosphate (ATP). This key metabolite is required for the synthesis of purine and pyrimidine nucleotides, the two aromatic amino acids histidine and tryptophan, the cofactors nicotinamide adenine dinucleotide (NAD+) and nicotinamide adenine dinucleotide phosphate (NADP+), all of which are essential for various life processes. Despite its ubiquity and essential nature across the plant and animal kingdoms, PRPP synthetase displays species-specific characteristics regarding the number of gene copies and architecture permitting interaction with other areas of cellular metabolism. The impact of mutated PRS genes in the model eukaryote Saccharomyces cerevisiae on cell signalling and metabolism may be relevant to the human neuropathies associated with PRPS mutations. Human PRPS1 and PRPS2 gene products are implicated in drug resistance associated with recurrent acute lymphoblastic leukaemia and progression of colorectal cancer and hepatocellular carcinoma. The investigation of PRPP metabolism in accepted model organisms, e.g., yeast and zebrafish, has the potential to reveal novel drug targets for treating at least some of the diseases, often characterized by overlapping symptoms, such as Arts syndrome and respiratory infections, and uncover the significance and relevance of human PRPS in disease diagnosis, management, and treatment.

Recognition and Chaperoning by Pex19, Followed by Trafficking and Membrane Insertion of the Peroxisome Proliferation Protein, Pex11

Pex11, an abundant peroxisomal membrane protein (PMP), is required for division of peroxisomes and is robustly imported to peroxisomal membranes. We present a comprehensive analysis of how the Pichia pastoris Pex11 is recognized and chaperoned by Pex19, targeted to peroxisome membranes and inserted therein. We demonstrate that Pex11 contains one Pex19-binding site (Pex19-BS) that is required for Pex11 insertion into peroxisomal membranes by Pex19, but is non-essential for peroxisomal trafficking. We provide extensive mutational analyses regarding the recognition of Pex19-BS in Pex11 by Pex19. Pex11 also has a second, Pex19-independent membrane peroxisome-targeting signal (mPTS) that is preserved among Pex11-family proteins and anchors the human HsPex11γ to the outer leaflet of the peroxisomal membrane. Thus, unlike most PMPs, Pex11 can use two mechanisms of transport to peroxisomes, where only one of them depends on its direct interaction with Pex19, but the other does not. However, Pex19 is necessary for membrane insertion of Pex11. We show that Pex11 can self-interact, using both homo- and/or heterotypic interactions involving its N-terminal helical domains. We demonstrate that Pex19 acts as a chaperone by interacting with the Pex19-BS in Pex11, thereby protecting Pex11 from spontaneous oligomerization that would otherwise cause its aggregation and subsequent degradation.

OUP accepted manuscript

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

Yeast Nst1 is a novel component of P-bodies and is a specific suppressor of proteasome base assembly defects

Proteasome assembly utilizes multiple dedicated assembly chaperones and is regulated by signaling pathways that respond to diverse stress conditions. To discover new factors influencing proteasome base assembly, we screened a tiled high-copy yeast genomic library to identify dosage suppressors of a temperature-sensitive proteasome regulatory particle (RP) base mutant. The screen identified negative salt tolerance 1 (Nst1), a protein that when overexpressed specifically suppressed the temperature sensitivity and proteasome-assembly defects of multiple base mutants. Nst1 overexpression reduced cytosolic RP ATPase (Rpt) aggregates in nas6Δ rpn14Δ cells, which lack two RP assembly chaperones. Nst1 is highly polar and predicted to have numerous intrinsically disordered regions, characteristics commonly found in proteins that can segregate into membraneless condensates. In agreement with this, both endogenous and overexpressed Nst1 could form cytosolic puncta that colocalized with processing body (P-body) components. Consistent with the accumulation of translationally inactive mRNAs in P-bodies, Nst1 overexpression inhibited global protein translation in nas6Δ rpn14Δ cells. Translational inhibition is known to suppress aggregation and proteasome assembly defects in base mutants under heat stress. Our data indicate that Nst1 is a previously overlooked P-body component that, when expressed at elevated levels inhibits translation, prevents Rpt subunit aggregation and rescues proteasome assembly under stress conditions.

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.

Protein-fragment complementation assays for large-scale analysis of protein-protein interactions

Protein-protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

Phosphatidylinositol 3,5-bisphosphate regulates Ca2+ transport during yeast vacuolar fusion through the Ca2+ ATPase Pmc1

The transport of Ca²⁺ across membranes precedes the fusion and fission of various lipid bilayers. Yeast vacuoles under hyperosmotic stress become fragmented through fission events that requires the release of Ca²⁺ stores through the TRP channel Yvc1. This requires the phosphorylation of phosphatidylinositol-3-phosphate (PI3P) by the PI3P-5-kinase Fab1 to produce transient PI(3,5)P₂ pools. Ca²⁺ is also released during vacuole fusion upon trans-SNARE complex assembly, however, its role remains unclear. The effect of PI(3,5)P₂ on Ca²⁺ flux during fusion was independent of Yvc1. Here, we show that while low levels of PI(3,5)P₂ were required for Ca²⁺ uptake into the vacuole, increased concentrations abolished Ca²⁺ efflux. This was as shown by the addition of exogenous dioctanoyl PI(3,5)P₂ or increased endogenous production of by the hyperactive fab1^T2250A mutant. In contrast, the lack of PI(3,5)P₂ on vacuoles from the kinase dead fab1^EEE mutant showed delayed and decreased Ca²⁺ uptake. The effects of PI(3,5)P₂ were linked to the Ca²⁺ pump Pmc1, as its deletion rendered vacuoles resistant to the effects of excess PI(3,5)P₂ . Experiments with Verapamil inhibited Ca²⁺ uptake when added at the start of the assay, while adding it after Ca²⁺ had been taken up resulted in the rapid expulsion of Ca²⁺ . Vacuoles lacking both Pmc1 and the H⁺ /Ca²⁺ exchanger Vcx1 lacked the ability to take up Ca²⁺ and instead expelled it upon the addition of ATP. Together these data suggest that a balance of efflux and uptake compete during the fusion pathway and that the levels of PI(3,5)P₂ can modulate which path predominates.

A large accessory protein interactome is rewired across environments

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across nine growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a threefold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Psp2, a novel regulator of autophagy that promotes autophagy-related protein translation

Macroautophagy/autophagy defines an evolutionarily conserved catabolic process that targets cytoplasmic components for lysosomal degradation. The process of autophagy from initiation to closure is tightly executed and controlled by the concerted action of autophagy-related (Atg) proteins. Although substantial progress has been made in characterizing transcriptional and post-translational regulation of ATG/Atg genes/proteins, little is known about the translational control of autophagy. Here we report that Psp2, an RGG motif protein, positively regulates autophagy through promoting the translation of Atg1 and Atg13, two proteins that are crucial in the initiation of autophagy. During nitrogen starvation conditions, Psp2 interacts with the 5' UTR of ATG1 and ATG13 transcripts in an RGG motif-dependent manner and with eIF4E and eIF4G2, components of the translation initiation machinery, to regulate the translation of these transcripts. Deletion of the PSP2 gene leads to a decrease in the synthesis of Atg1 and Atg13, which correlates with reduced autophagy activity and cell survival. Furthermore, deactivation of the methyltransferase Hmt1 constitutes a molecular switch that regulates Psp2 arginine methylation status as well as its mRNA binding activity in response to starvation. These results reveal a novel mechanism by which Atg proteins become upregulated to fulfill the increased demands of autophagy activity as part of translational reprogramming during stress conditions, and help explain how ATG genes bypass the general block in protein translation that occurs during starvation.

A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks

Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.

Protein Topology Prediction Algorithms Systematically Investigated in the Yeast Saccharomyces cerevisiae

Membrane proteins perform a variety of functions, all crucially dependent on their orientation in the membrane. However, neither the exact number of transmembrane domains (TMDs) nor the topology of most proteins have been experimentally determined. Due to this, most scientists rely primarily on prediction algorithms to determine topology and TMD assignments. Since these can give contradictory results, single-algorithm-based predictions are unreliable. To map the extent of potential misanalysis, the predictions of nine algorithms on the yeast proteome are compared and it is found that they have little agreement when predicting TMD number and termini orientation. To view all predictions in parallel, a webpage called TopologYeast: http://www.weizmann.ac.il/molgen/TopologYeast was created. Each algorithm is compared with experimental data and a poor agreement is found. The analysis suggests that more systematic data on protein topology are required to increase the training sets for prediction algorithms and to have accurate knowledge of membrane protein topology.

Applications of Protein Fragment Complementation Assays for Analyzing Biomolecular Interactions and Biochemical Networks in Living Cells

Protein-protein interactions (PPIs) are indispensable for the dynamic assembly of multiprotein complexes that are central players of nearly all of the intracellular biological processes, such as signaling pathways, metabolic pathways, formation of intracellular organelles, establishment of cytoplasmic skeletons, etc. Numerous approaches have been invented to study PPIs both in vivo and in vitro, including the protein-fragment complementation assay (PCA), which is a widely applied technology to study PPIs and biomolecular interactions. PCA is a technology based on the expression of the bait and prey proteins in fusion with two complementary reporter protein fragments, respectively, that will reassemble when in close proximity. The reporter protein can be the enzymes or fluorescent proteins. Recovery of the enzymatic activity or fluorescent signal can be the indicator of PPI between the bait and prey proteins. Significant effort has been invested in developing many derivatives of PCA, along with various applications, in order to address specific questions. Therefore, a prompt review of these applications is important. In this review, we will categorize these applications according to the scenarios that the PCAs were applied and expect to provide a reference guideline for the future selection of PCA methods in solving a specific problem.

The Co-chaperone Cns1 and the Recruiter Protein Hgh1 Link Hsp90 to Translation Elongation via Chaperoning Elongation Factor 2

The Hsp90 chaperone machinery in eukaryotes comprises a number of distinct accessory factors. Cns1 is one of the few essential co-chaperones in yeast, but its structure and function remained unknown. Here, we report the X-ray structure of the Cns1 fold and NMR studies on the partly disordered, essential segment of the protein. We demonstrate that Cns1 is important for maintaining translation elongation, specifically chaperoning the elongation factor eEF2. In this context, Cns1 interacts with the novel co-factor Hgh1 and forms a quaternary complex together with eEF2 and Hsp90. The in vivo folding and solubility of eEF2 depend on the presence of these proteins. Chaperoning of eEF2 by Cns1 is essential for yeast viability and requires a defined subset of the Hsp90 machinery as well as the identified eEF2 recruiting factor Hgh1.

Global analysis of protein homomerization in Saccharomyces cerevisiae

In vivo analyses of the occurrence, subcellular localization, and dynamics of protein–protein interactions (PPIs) are important issues in functional proteomic studies. The bimolecular fluorescence complementation (BiFC) assay has many advantages in that it provides a reliable way to detect PPIs in living cells with minimal perturbation of the structure and function of the target proteins. Previously, to facilitate the application of the BiFC assay to genome-wide analysis of PPIs, we generated a collection of yeast strains expressing full-length proteins tagged with the N-terminal fragment of Venus (VN), a yellow fluorescent protein variant, from their own native promoters. In the present study, we constructed a VC (the C-terminal fragment of Venus) fusion library consisting of 5671 MATα strains expressing C-terminally VC-tagged proteins (representing ∼91% of the yeast proteome). For genome-wide analysis of protein homomer formation, we mated each strain in the VC fusion library with its cognate strain in the VN fusion library and performed the BiFC assay. From this analysis, we identified 186 homomer candidates. We further investigated the functional relevance of the homomerization of Pln1, a yeast perilipin. Our data set provides a useful resource for understanding the physiological roles of protein homomerization. Furthermore, the VC fusion library together with the VN fusion library will provide a valuable platform to systematically analyze PPIs in the natural cellular context.

A biosensor-based approach reveals links between efflux pump expression and cell cycle regulation in pleiotropic drug resistance of yeast

Multidrug resistance is highly conserved in mammalian, fungal, and bacterial cells, is characterized by resistance to several unrelated xenobiotics, and poses significant challenges to managing infections and many cancers. Eukaryotes use a highly conserved set of drug efflux transporters that confer pleiotropic drug resistance (PDR). To interrogate the regulation of this critical process, here we developed a small molecule-responsive biosensor that couples transcriptional induction of PDR genes to growth rate in the yeast Saccharomyces cerevisiae Using diverse PDR inducers and the homozygous diploid deletion collection, we applied this biosensor system to genome-wide screens for potential PDR regulators. In addition to recapitulating the activity of previously known factors, these screens identified a series of genes involved in a variety of cellular processes with significant but previously uncharacterized roles in the modulation of yeast PDR. Genes identified as down-regulators of the PDR included those encoding the MAD family of proteins involved in the mitotic spindle assembly checkpoint (SAC) complex. Of note, we demonstrated that genetic disruptions of the mitotic spindle assembly checkpoint elevate expression of PDR-mediating efflux pumps in response to exposure to a variety of compounds that themselves have no known influence on the cell cycle. These results not only establish our biosensor system as a viable tool for investigating PDR in a high-throughput fashion, but also uncover critical control mechanisms governing the PDR response and a previously uncharacterized link between PDR and cell cycle regulation in yeast.

Conserved mRNA-granule component Scd6 targets Dhh1 to repress translation initiation and activates Dcp2-mediated mRNA decay in vivo

Scd6 protein family members are evolutionarily conserved components of translationally silent mRNA granules. Yeast Scd6 interacts with Dcp2 and Dhh1, respectively a subunit and a regulator of the mRNA decapping enzyme, and also associates with translation initiation factor eIF4G to inhibit translation in cell extracts. However, the role of Scd6 in mRNA turnover and translational repression in vivo is unclear. We demonstrate that tethering Scd6 to a GFP reporter mRNA reduces mRNA abundance via Dcp2 and suppresses reporter mRNA translation via Dhh1. Thus, in a dcp2Δ mutant, tethered Scd6 reduces GFP protein expression with little effect on mRNA abundance, whereas tethered Scd6 has no impact on GFP protein or mRNA expression in a dcp2Δ dhh1Δ double mutant. The conserved LSm domain of Scd6 is required for translational repression and mRNA turnover by tethered Scd6. Both functions are enhanced in a ccr4Δ mutant, suggesting that the deadenylase function of Ccr4-Not complex interferes with a more efficient repression pathway enlisted by Scd6. Ribosome profiling and RNA-Seq analysis of scd6Δ and dhh1Δ mutants suggests that Scd6 cooperates with Dhh1 in translational repression and turnover of particular native mRNAs, with both processes dependent on Dcp2. Our results suggest that Scd6 can (i) recruit Dhh1 to confer translational repression and (ii) activate mRNA decapping by Dcp2 with attendant degradation of specific mRNAs in vivo, in a manner dependent on the Scd6 LSm domain and modulated by Ccr4.

Changes of Cell Biochemical States Are Revealed in Protein Homomeric Complex Dynamics

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins, or other small molecules based on "homomer dynamics" protein-fragment complementation assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating the confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health span-affecting drugs, the immunosuppressant rapamycin and the type 2 diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency and includes a change in protein-bound iron levels. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatment of diabetes, cancers, and other chronic diseases of aging.

Biomarkers for Early Stages of Johne's Disease Infection and Immunization in Goats

Background: Mycobacterium avium subsp. paratuberculosis (M. paratuberculosis) is the causative agent of Johne's disease, a chronic enteric infection of ruminants. Infection occurs within the first few months of life but remains subclinical for an average of 2-5 years. Current diagnostics to detect early subclinical infections lack diagnostic sensitivity, which hinders disease control resulting in significant economic losses to the dairy industry worldwide. The pathophysiology of early infection with M. paratuberculosis is still not well understood and represents a key hurdle toward the development of better diagnostics. Methods: The present study employed a large-scale RNA-Sequencing technology to better understand early stages of M. paratuberculosis infection and immunization. Specifically, gene expression profiles of peripheral blood mononuclear cells (PBMCs) from infected or vaccinated goats were compared to controls. Results: When compared to the naïve control goats, we identified a large number of transcripts (N = 226, 1018, 1714) that were differentially expressed in the M. paratuberculosis-infected goats, goats vaccinated with live attenuated or inactivated vaccines. There were also 1133 differentially expressed (DE) transcripts between vaccinated goats and infected ones. Bioinformatics evaluation of the DE genes indicated the regulation of a large number of genes with immunity and inflammatory functions including IL-18BP, IFN-γ, IL-17A, NOS2, LIPG, and IL-22. Interestingly, a large number of goat genes (N = 667) were regulated whether live or inactivated vaccine were used. Some of the regulated genes (e.g., IL-17A, IFN-γ) continued its unique transcriptional profile up to 12 months post-challenge. Conclusion: Overall, transcriptome analysis of infected and/or immunized goats identified potential targets for developing early diagnostics for Johne's disease and a potential approach to differentiate infected from vaccinated animals. A similar approach could be used to analyze later stages of Johne's disease or other chronic infections.

Quantification of Hsp90 availability reveals differential coupling to the heat shock response

The heat shock response (HSR) is a protective gene expression program that is activated by conditions that cause proteotoxic stress. While it has been suggested that the availability of free chaperones regulates the HSR, chaperone availability and the HSR have never been precisely quantified in tandem under stress conditions. Thus, how the availability of chaperones changes in stress conditions and the extent to which these changes drive the HSR are unknown. In this study, we quantified Hsp90 chaperone availability and the HSR under multiple stressors. We show that Hsp90-dependent and -independent pathways both regulate the HSR, and the contribution of each pathway varies greatly depending on the stressor. Moreover, stressors that regulate the HSR independently of Hsp90 availability do so through the Hsp70 chaperone. Thus, the HSR responds to diverse defects in protein quality by monitoring the state of multiple chaperone systems independently.

The genetic landscape of a physical interaction

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes – interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions.

Proteins, the molecular workhorses of the cell, are made of small units called amino acids attached together like the links of a chain. Each protein is composed of a unique combination of amino acids, which is determined by a specific sequence of DNA called a gene. A change in a gene – a mutation – can create a variation in the protein it codes for, for instance by swapping a type of amino acid for another. Different mutations in the same gene can alter a protein in different ways. Some of these changes are harmless, but other can hinder how the protein performs its role. For example, a small change in the structure of a protein could affect how it will bind to other molecules.

It is possible for people to have identical mutations in the same genes, but experience different consequences. For instance, two persons could carry the same disease-inducing mutation, but one has a severe version of the condition and the other only mild symptoms. One reason is that changes in other genes cancel out or enhance the effect of a mutation. This phenomenon is known as a genetic interaction and it remains poorly understood, especially at the molecular level.

Here, Diss and Lehner developed a method, called deepPCA, to study the consequences of mutations in proteins in the laboratory. The experiments focused on two human genes which code for two proteins that normally attach to each other. Two mutations were artificially created, either one in each gene, or two in one of them. Diss and Lehner then examined how strongly the two mutated proteins could still attach to each other. By repeating this process with over 120,000 different pairs of mutations, it became possible to study how one mutation can have different effects depending on the presence of other mutations in the same protein or in the binding partner.

Overall, Diss and Lehner found that genetic interactions are the result of two mechanisms. In the first one, the two mutations together cause specific structural changes that modify how proteins bind to each other. In the second one, the changes solely depend on the magnitude of the initial, thermodynamic effects of individual mutations, but not on their specific physical and chemical properties. To predict the consequences of this second type of genetic interactions, knowing the identity or the exact effects of the two mutations is not necessary.

Understanding and predicting genetic interactions is important to develop personalized medicine, where treatments are tailored based on the genetic make up of an individual. This knowledge will also help to study how genes have evolved together.

Multi-Objective Optimization Algorithm to Discover Condition-Specific Modules in Multiple Networks

The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accuracy. To address this issue, a multi-objective genetic algorithm for condition-specific modules in multiple networks (MOGA-CSM) is developed to discover the condition-specific modules. By using the artificial networks, we demonstrate that the MOGA-CSM outperforms state-of-the-art methods in terms of accuracy. Furthermore, MOGA-CSM discovers stage-specific modules in breast cancer networks based on The Cancer Genome Atlas (TCGA) data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm provide an effective way to analyze multiple networks.

Identifying condition-specific modules by clustering multiple networks

Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the Specific Modules in Multiple Networks (SMMN), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.

A Yeast Global Genetic Screen Reveals that Metformin Induces an Iron Deficiency-Like State.. [DOI: 10.1101/190389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins or other small molecules based on “homomer dynamics” Protein-fragment Complementation Assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health-span-affecting drugs, immunosuppressant rapamycin and type II diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatments of diabetes, cancers and other chronic diseases of aging.

Quantitative analysis of protein interaction network dynamics in yeast

Many cellular functions are mediated by protein–protein interaction networks, which are environment dependent. However, systematic measurement of interactions in diverse environments is required to better understand the relative importance of different mechanisms underlying network dynamics. To investigate environment‐dependent protein complex dynamics, we used a DNA‐barcode‐based multiplexed protein interaction assay in Saccharomyces cerevisiae to measure in vivo abundance of 1,379 binary protein complexes under 14 environments. Many binary complexes (55%) were environment dependent, especially those involving transmembrane transporters. We observed many concerted changes around highly connected proteins, and overall network dynamics suggested that “concerted” protein‐centered changes are prevalent. Under a diauxic shift in carbon source from glucose to ethanol, a mass‐action‐based model using relative mRNA levels explained an estimated 47% of the observed variance in binary complex abundance and predicted the direction of concerted binary complex changes with 88% accuracy. Thus, we provide a resource of yeast protein interaction measurements across diverse environments and illustrate the value of this resource in revealing mechanisms of network dynamics.

A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions

Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

A method for high-throughput production of sequence-verified DNA libraries and strain collections

The low costs of array‐synthesized oligonucleotide libraries are empowering rapid advances in quantitative and synthetic biology. However, high synthesis error rates, uneven representation, and lack of access to individual oligonucleotides limit the true potential of these libraries. We have developed a cost‐effective method called Recombinase Directed Indexing (REDI), which involves integration of a complex library into yeast, site‐specific recombination to index library DNA, and next‐generation sequencing to identify desired clones. We used REDI to generate a library of ~3,300 DNA probes that exhibited > 96% purity and remarkable uniformity (> 95% of probes within twofold of the median abundance). Additionally, we created a collection of ~9,000 individually accessible CRISPR interference yeast strains for > 99% of genes required for either fermentative or respiratory growth, demonstrating the utility of REDI for rapid and cost‐effective creation of strain collections from oligonucleotide pools. Our approach is adaptable to any complex DNA library, and fundamentally changes how these libraries can be parsed, maintained, propagated, and characterized.

Phosphoribosyl Diphosphate (PRPP): Biosynthesis, Enzymology, Utilization, and Metabolic Significance

Phosphoribosyl diphosphate (PRPP) is an important intermediate in cellular metabolism. PRPP is synthesized by PRPP synthase, as follows: ribose 5-phosphate + ATP → PRPP + AMP. PRPP is ubiquitously found in living organisms and is used in substitution reactions with the formation of glycosidic bonds. PRPP is utilized in the biosynthesis of purine and pyrimidine nucleotides, the amino acids histidine and tryptophan, the cofactors NAD and tetrahydromethanopterin, arabinosyl monophosphodecaprenol, and certain aminoglycoside antibiotics. The participation of PRPP in each of these metabolic pathways is reviewed. Central to the metabolism of PRPP is PRPP synthase, which has been studied from all kingdoms of life by classical mechanistic procedures. The results of these analyses are unified with recent progress in molecular enzymology and the elucidation of the three-dimensional structures of PRPP synthases from eubacteria, archaea, and humans. The structures and mechanisms of catalysis of the five diphosphoryltransferases are compared, as are those of selected enzymes of diphosphoryl transfer, phosphoryl transfer, and nucleotidyl transfer reactions. PRPP is used as a substrate by a large number phosphoribosyltransferases. The protein structures and reaction mechanisms of these phosphoribosyltransferases vary and demonstrate the versatility of PRPP as an intermediate in cellular physiology. PRPP synthases appear to have originated from a phosphoribosyltransferase during evolution, as demonstrated by phylogenetic analysis. PRPP, furthermore, is an effector molecule of purine and pyrimidine nucleotide biosynthesis, either by binding to PurR or PyrR regulatory proteins or as an allosteric activator of carbamoylphosphate synthetase. Genetic analyses have disclosed a number of mutants altered in the PRPP synthase-specifying genes in humans as well as bacterial species.

Yeast Gup1(2) Proteins Are Homologues of the Hedgehog Morphogens Acyltransferases HHAT(L): Facts and Implications

In multiple tissues, the Hedgehog secreted morphogen activates in the receiving cells a pathway involved in cell fate, proliferation and differentiation in the receiving cells. This pathway is particularly important during embryogenesis. The protein HHAT (Hedgehog O-acyltransferase) modifies Hh morphogens prior to their secretion, while HHATL (Hh O-acyltransferase-like) negatively regulates the pathway. HHAT and HHATL are homologous to Saccharomyces cerevisiae Gup2 and Gup1, respectively. In yeast, Gup1 is associated with a high number and diversity of biological functions, namely polarity establishment, secretory/endocytic pathway functionality, vacuole morphology and wall and membrane composition, structure and maintenance. Phenotypes underlying death, morphogenesis and differentiation are also included. Paracrine signalling, like the one promoted by the Hh pathway, has not been shown to occur in microbial communities, despite the fact that large aggregates of cells like biofilms or colonies behave as proto-tissues. Instead, these have been suggested to sense the population density through the secretion of quorum-sensing chemicals. This review focuses on Gup1/HHATL and Gup2/HHAT proteins. We review the functions and physiology associated with these proteins in yeasts and higher eukaryotes. We suggest standardisation of the presently chaotic Gup-related nomenclature, which includes KIAA117, c3orf3, RASP, Skinny, Sightless and Central Missing, in order to avoid the disclosure of otherwise unnoticed information.

Systematic Mapping of Chemical-Genetic Interactions in Saccharomyces cerevisiae

Chemical-genetic interactions (CGIs) describe a phenomenon where the effects of a chemical compound (i.e., a small molecule) on cell growth are dependent on a particular gene. CGIs can reveal important functional information about genes and can also be powerful indicators of a compound's mechanism of action. Mapping CGIs can lead to the discovery of new chemical probes, which, in contrast to genetic perturbations, operate at the level of the gene product (or pathway) and can be fast-acting, tunable, and reversible. The simple culture conditions required for yeast and its rapid growth, as well as the availability of a complete set of barcoded gene deletion strains, facilitate systematic mapping of CGIs in this organism. This process involves two basic steps: first, screening chemical libraries to identify bioactive compounds affecting growth and, second, measuring the effects of these compounds on genome-wide collections of mutant strains. Here, we introduce protocols for both steps that have great potential for the discovery and development of new small-molecule tools and medicines.

Epirubicin, Identified Using a Novel Luciferase Reporter Assay for Foxp3 Inhibitors, Inhibits Regulatory T Cell Activity

Forkhead box protein p3 (Foxp3) is crucial to the development and suppressor function of regulatory T cells (Tregs) that have a significant role in tumor-associated immune suppression. Development of small molecule inhibitors of Foxp3 function is therefore considered a promising strategy to enhance anti-tumor immunity. In this study, we developed a novel cell-based assay system in which the NF-κB luciferase reporter signal is suppressed by the co-expressed Foxp3 protein. Using this system, we screened our chemical library consisting of approximately 2,100 compounds and discovered that a cancer chemotherapeutic drug epirubicin restored the Foxp3-inhibited NF-κB activity in a concentration-dependent manner without influencing cell viability. Using immunoprecipitation assay in a Treg-like cell line Karpas-299, we found that epirubicin inhibited the interaction between Foxp3 and p65. In addition, epirubicin inhibited the suppressor function of murine Tregs and thereby improved effector T cell stimulation in vitro. Administration of low dose epirubicin into tumor-bearing mice modulated the function of immune cells at the tumor site and promoted their IFN-γ production without direct cytotoxicity. In summary, we identified the novel action of epirubicin as a Foxp3 inhibitor using a newly established luciferase-based cellular screen. Our work also demonstrated our screen system is useful in accelerating discovery of Foxp3 inhibitors.

Technology development for natural product biosynthesis in Saccharomyces cerevisiae

The explosion of genomic sequence data and the significant advancements in synthetic biology have led to the development of new technologies for natural products discovery and production. Using powerful genetic tools, the yeast Saccharomyces cerevisiae has been engineered as a production host for natural product pathways from bacterial, fungal, and plant species. With an expanding library of characterized genetic parts, biosynthetic pathways can be refactored for optimized expression in yeast. New engineering strategies have enabled the increased production of valuable secondary metabolites by tuning metabolic pathways. Improvements in high-throughput screening methods have facilitated the rapid identification of variants with improved biosynthetic capabilities. In this review, we focus on the molecular tools and engineering strategies that have recently empowered heterologous natural product biosynthesis.

Quantitative CRISPR interference screens in yeast identify chemical-genetic interactions and new rules for guide RNA design

BACKGROUND

Genome-scale CRISPR interference (CRISPRi) has been used in human cell lines; however, the features of effective guide RNAs (gRNAs) in different organisms have not been well characterized. Here, we define rules that determine gRNA effectiveness for transcriptional repression in Saccharomyces cerevisiae.

RESULTS

We create an inducible single plasmid CRISPRi system for gene repression in yeast, and use it to analyze fitness effects of gRNAs under 18 small molecule treatments. Our approach correctly identifies previously described chemical-genetic interactions, as well as a new mechanism of suppressing fluconazole toxicity by repression of the ERG25 gene. Assessment of multiple target loci across treatments using gRNA libraries allows us to determine generalizable features associated with gRNA efficacy. Guides that target regions with low nucleosome occupancy and high chromatin accessibility are clearly more effective. We also find that the best region to target gRNAs is between the transcription start site (TSS) and 200 bp upstream of the TSS. Finally, unlike nuclease-proficient Cas9 in human cells, the specificity of truncated gRNAs (18 nt of complementarity to the target) is not clearly superior to full-length gRNAs (20 nt of complementarity), as truncated gRNAs are generally less potent against both mismatched and perfectly matched targets.

CONCLUSIONS

Our results establish a powerful functional and chemical genomics screening method and provide guidelines for designing effective gRNAs, which consider chromatin state and position relative to the target gene TSS. These findings will enable effective library design and genome-wide programmable gene repression in many genetic backgrounds.

Split protein biosensor assays in molecular pharmacological studies

Cellular signalling is commonly mediated through dynamic protein-protein interactions (PPIs). When pivotal PPIs are deregulated, cellular signalling can be altered; it is therefore attractive to monitor regulated PPIs to understand their role in health and disease. Genetically encoded biosensors that rely on protein fragment complementation have made it feasible to monitor PPIs in living cells precisely and robustly. In particular, split protein biosensors using fluorescent proteins or luciferases are frequently applied. Further, split TEV and split ubiquitin biosensor platforms flexibly allow using readouts of choice, including transcriptional barcode reporters that are amenable to multiplexed high-throughput formats and next-generation sequencing. Combining these technologies will enable assessing drug target activities and cellular response profiles in parallel, thereby opening up new avenues in drug discovery.

Multi-scale perturbations of protein interactomes reveal their mechanisms of regulation, robustness and insights into genotype-phenotype maps

Cellular architectures and signaling machineries are organized through protein-protein interactions (PPIs). High-throughput methods to study PPIs in yeast have opened a new perspective on the organization of the cell by allowing the study of whole protein interactomes. Recent investigations have moved from the description of this organization to the analysis of its dynamics by experimenting how protein interaction networks (PINs) are rewired in response to perturbations. Here we review studies that have used the budding yeast as an experimental system to explore these altered networks. Given the large space of possible PPIs and the diversity of potential genetic and environmental perturbations, high-throughput methods are an essential requirement to survey PIN perturbations on a large scale. Network perturbations are typically conceptualized as the removal of entire proteins (nodes), the modification of single PPIs (edges) or changes in growth conditions. These studies have revealed mechanisms of PPI regulation, PIN architectural organization, robustness and sensitivity to perturbations. Despite these major advances, there are still inherent limits to current technologies that lead to a trade-off between the number of perturbations and the number of PPIs that can be considered simultaneously. Nevertheless, as we exemplify here, targeted approaches combined with the existing resources remain extremely powerful to explore the inner organization of cells and their responses to perturbations.

Genome-wide protein-protein interaction screening by protein-fragment complementation assay (PCA) in living cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein's function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

A Comprehensive Membrane Interactome Mapping of Sho1p Reveals Fps1p as a Novel Key Player in the Regulation of the HOG Pathway in S. cerevisiae

Sho1p, an integral membrane protein, plays a vital role in the high-osmolarity glycerol (HOG) mitogen-activated protein kinase pathway in the yeast Saccharomyces cerevisiae. Activated under conditions of high osmotic stress, it interacts with other HOG pathway proteins to mediate cell signaling events, ensuring that yeast cells can adapt and remain viable. In an attempt to further understand how the function of Sho1p is regulated through its protein-protein interactions (PPIs), we identified 49 unique Sho1p PPIs through the use of membrane yeast two-hybrid (MYTH), an assay specifically suited to identify PPIs of full-length integral membrane proteins in their native membrane environment. Secondary validation by literature search, or two complementary PPI assays, confirmed 80% of these interactions, resulting in a high-quality Sho1p interactome. This set of putative PPIs included both previously characterized interactors, along with a large subset of interactors that have not been previously identified as binding to Sho1p. The SH3 domain of Sho1p was found to be important for binding to many of these interactors. One particular novel interactor of interest is the glycerol transporter Fps1p, which was shown to require the SH3 domain of Sho1p for binding via its N-terminal soluble regulatory domain. Furthermore, we found that Fps1p is involved in the positive regulation of Sho1p function and plays a role in the phosphorylation of the downstream kinase Hog1p. This study represents the largest membrane interactome analysis of Sho1p to date and complements past studies on the HOG pathway by increasing our understanding of Sho1p regulation.

A functional screen for copper homeostasis genes identifies a pharmacologically tractable cellular system

BACKGROUND

Copper is essential for the survival of aerobic organisms. If copper is not properly regulated in the body however, it can be extremely cytotoxic and genetic mutations that compromise copper homeostasis result in severe clinical phenotypes. Understanding how cells maintain optimal copper levels is therefore highly relevant to human health.

RESULTS

We found that addition of copper (Cu) to culture medium leads to increased respiratory growth of yeast, a phenotype which we then systematically and quantitatively measured in 5050 homozygous diploid deletion strains. Cu's positive effect on respiratory growth was quantitatively reduced in deletion strains representing 73 different genes, the function of which identify increased iron uptake as a cause of the increase in growth rate. Conversely, these effects were enhanced in strains representing 93 genes. Many of these strains exhibited respiratory defects that were specifically rescued by supplementing the growth medium with Cu. Among the genes identified are known and direct regulators of copper homeostasis, genes required to maintain low vacuolar pH, and genes where evidence supporting a functional link with Cu has been heretofore lacking. Roughly half of the genes are conserved in man, and several of these are associated with Mendelian disorders, including the Cu-imbalance syndromes Menkes and Wilson's disease. We additionally demonstrate that pharmacological agents, including the approved drug disulfiram, can rescue Cu-deficiencies of both environmental and genetic origin.

CONCLUSIONS

A functional screen in yeast has expanded the list of genes required for Cu-dependent fitness, revealing a complex cellular system with implications for human health. Respiratory fitness defects arising from perturbations in this system can be corrected with pharmacological agents that increase intracellular copper concentrations.

The prion-like domain in the exomer-dependent cargo Pin2 serves as a trans-Golgi retention motif

Prion and prion-like domains (PLDs) are found in many proteins throughout the animal kingdom. We found that the PLD in the S. cerevisiae exomer-dependent cargo protein Pin2 is involved in the regulation of protein transport and localization. The domain serves as a Pin2 retention signal in the trans-Golgi network (TGN). Pin2 is localized in a polarized fashion at the plasma membrane of the bud early in the cell cycle and the bud neck at cytokinesis. This polarized localization is dependent on both exo- and endocytosis. Upon environmental stress, Pin2 is rapidly endocytosed, and the PLD aggregates and causes sequestration of Pin2. The aggregation of Pin2 is reversible upon stress removal and Pin2 is rapidly re-exported to the plasma membrane. Altogether, these data uncover a role for PLDs as protein localization elements.

A systems wide mass spectrometric based linear motif screen to identify dominant in-vivo interacting proteins for the ubiquitin ligase MDM2

Linear motifs mediate protein-protein interactions (PPI) that allow expansion of a target protein interactome at a systems level. This study uses a proteomics approach and linear motif sub-stratifications to expand on PPIs of MDM2. MDM2 is a multi-functional protein with over one hundred known binding partners not stratified by hierarchy or function. A new linear motif based on a MDM2 interaction consensus is used to select novel MDM2 interactors based on Nutlin-3 responsiveness in a cell-based proteomics screen. MDM2 binds a subset of peptide motifs corresponding to real proteins with a range of allosteric responses to MDM2 ligands. We validate cyclophilin B as a novel protein with a consensus MDM2 binding motif that is stabilised by Nutlin-3 in vivo, thus identifying one of the few known interactors of MDM2 that is stabilised by Nutlin-3. These data invoke two modes of peptide binding at the MDM2 N-terminus that rely on a consensus core motif to control the equilibrium between MDM2 binding proteins. This approach stratifies MDM2 interacting proteins based on the linear motif feature and provides a new biomarker assay to define clinically relevant Nutlin-3 responsive MDM2 interactors.

Modulation of the yeast protein interactome in response to DNA damage

Cells deploy diverse mechanisms to physiologically adapt to potentially detrimental perturbations. These mechanisms include changes in the organization of protein-protein interaction networks (PINs). Most PINs characterized to date are portrayed in a single environmental condition and are thus likely to miss important connections among biological processes. In this report, we show that the yeast DHFR-PCA on high-density arrays allows to detects modulations of protein-protein interactions (PPIs) in different conditions by testing more than 1000 PPIs in standard and in a drug-inducing DNA damage conditions. We identify 156 PPIs that show significant modulation in response to DNA damage. We provide evidence that modulated PPIs involve essential genes (NOP7, EXO84 and LAS17) playing critical roles in response to DNA damage. Additionally, we show that a significant proportion of PPI changes are likely explained by changes in protein localization and, to a lesser extent, protein abundance. The protein interaction modules affected by changing PPIs support the role of mRNA stability and translation, protein degradation and ubiquitylation and the regulation of the actin cytoskeleton in response to DNA damage. Overall, we provide a valuable tool and dataset for the study of the rewiring of PINs in response to environmental perturbations.

BIOLOGICAL SIGNIFICANCE

We show that the DHFR-PCA is a high-throughput method that allows the detection of changes in PPIs associated with different environmental conditions using DNA damage response as a testbed. We provide a valuable resource for the study of DNA damage in eukaryotic cells. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Integrative avenues for exploring the dynamics and evolution of protein interaction networks

Over the past decade, the study of protein interaction networks (PINs) has shed light on the organizing principles of living cells. However, PINs have been mostly mapped in one single condition. We outline three of the most promising avenues of investigation in this field, namely the study of first, how PINs are rewired by mutations and environmental perturbations; secondly, how inter-species interactions affect PIN achitectures; thirdly, what mechanisms and forces drive PIN evolution. These investigations will unravel the dynamics and condition dependence of PINs and will thus lead to a better functional annotation of network architecture. One major challenge to reach these goals is the integration of PINs with other cellular regulatory networks in the context of complex cellular phenotypes.

Forward chemical genetics in yeast for discovery of chemical probes targeting metabolism

The many virtues that made the yeast Saccharomyces cerevisiae a dominant model organism for genetics and molecular biology, are now establishing its role in chemical genetics. Its experimental tractability (i.e., rapid doubling time, simple culture conditions) and the availability of powerful tools for drug-target identification, make yeast an ideal organism for high-throughput phenotypic screening. It may be especially applicable for the discovery of chemical probes targeting highly conserved cellular processes, such as metabolism and bioenergetics, because these probes would likely inhibit the same processes in higher eukaryotes (including man). Importantly, changes in normal cellular metabolism are associated with a variety of diseased states (including neurological disorders and cancer), and exploiting these changes for therapeutic purposes has accordingly gained considerable attention. Here, we review progress and challenges associated with forward chemical genetic screening in yeast. We also discuss evidence supporting these screens as a useful strategy for discovery of new chemical probes and new druggable targets related to cellular metabolism.