15 research outputs found
Systems-based approaches enable identification of gene targets which improve the flavour profile of low-ethanol wine yeast strains
Metabolic engineering has been vital to the development of industrial microbes such as the yeast Saccharomyces cerevisiae. However, sequential rounds of modification are often needed to achieve particular industrial design targets. Systems biology approaches can aid in identifying genetic targets for modification through providing an integrated view of cellular physiology. Recently, research into the generation of commercial yeasts that can produce reduced-ethanol wines has resulted in metabolically-engineered strains of S. cerevisiae that are less efficient at producing ethanol from sugar. However, these modifications led to the concomitant production of off-flavour by-products. A combination of transcriptomics, proteomics and metabolomics was therefore used to investigate the physiological changes occurring in an engineered low-ethanol yeast strain during alcoholic fermentation. Integration of ‘omics data identified several metabolic reactions, including those related to the pyruvate node and redox homeostasis, as being significantly affected by the low-ethanol engineering methodology, and highlighted acetaldehyde and 2,4,5-trimethyl-1,3-dioxolane as the main off-flavour compounds. Gene remediation strategies were then successfully applied to decrease the formation of these by-products, while maintaining the ‘low-alcohol’ phenotype. The data generated from this comprehensive systems-based study will inform wine yeast strain development programmes, which, in turn, could potentially play an important role in assisting winemakers in their endeavour to produce low-alcohol wines with desirable flavour profiles
PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme
Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx
Visualizing Post-Translational Modifications in Protein Interaction Networks Using PTMOracle
Post-translational modifications (PTMs) of proteins act as key regulators of protein activity, including the regulation of protein-protein interactions (PPIs). However, exploring functional links between PTMs and PPIs can be difficult. PTMOracle is a Cytoscape app that facilitates the co-visualization and co-analysis of PTMs in the context of PPI networks. PTMOracle also allows extensive data to be integrated and co-analyzed, allowing the role of domains, motifs, and disordered regions to be considered. Here, we describe several PTMOracle protocols investigating complex PTM-associated relationships and their role in PPIs. This is assisted by OraclePainter for coloring proteins by the modifications present and visualizing these in the context of networks, by OracleTools for cross-matching PTMs with sequence feature for all nodes in the network, and by OracleResults for exploring specific proteins and visualizing their PTMs in the context of protein sequences. This unit aims to demonstrate how PTMOracle can be used to systematically explore network visualizations and generate testable hypotheses regarding the functional role of PTMs in PPIs, and how the results can be analyzed to better understand the regulatory role of PTMs in PPIs. © 2019 by John Wiley & Sons, Inc
Different Pathways Mediate Amphotericin-Lactoferrin Drug Synergy in Cryptococcus and Saccharomyces
Fungal infections are an increasing cause of morbidity and mortality. Current antifungal drugs are limited in spectrum, few new drugs are in development, and resistance is an increasing issue. Drug synergy can enhance available drugs and extend their lifetime, however, few synergistic combinations are in clinical use and mechanistic data on how combinations work is lacking. The multifunctional glycoprotein lactoferrin (LF) acts synergistically with amphotericin B (AMB) in a range of fungal species. Whole LF binds and sequesters iron, and LF can also be digested enzymatically to produce cationic peptides with distinct antimicrobial functions. To understand how LF synergizes AMB, we previously undertook a transcriptomic analysis in Saccharomyces and found a paradoxical down-regulation of iron and stress response, suggesting stress pathway interference was dysregulating an appropriate response, resulting in cell death. To extend this to a fungal pathogen, we here perform the same analysis in Cryptococcus neoformans. While both fungi responded to AMB in a similar way, the addition of LF produced remarkably contrasting results, with the Cryptococcus transcriptome enriched for processes relating to cellular stress, up-regulation of endoplasmic-reticulum-associated protein degradation (ERAD), stress granule disassembly and protein folding, endoplasmic reticulum-Golgi-vacuole trafficking and autophagy, suggesting an overall disruption of protein and lipid biosynthesis. These studies demonstrate that the mechanism of LF-mediated synergy is species-specific, possibly due to differences in the way LF peptides are generated, bind to and enter cells and act on intracellular targets, illustrating how very different cellular processes can underlie what appears to be a similar phenotypic response
Transcriptome and network analyses in Saccharomyces cerevisiae reveal that amphotericin B and lactoferrin synergy disrupt metal homeostasis and stress response.
Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Δ and zap1Δ mutants were hypersensitive to AMB and H₂O₂, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments
Cross-linking Mass Spectrometry Analysis of the Yeast Nucleus Reveals Extensive Protein-Protein Interactions Not Detected by Systematic Two-Hybrid or Affinity Purification-Mass Spectrometry
Saccharomyces cerevisiae has the most comprehensively characterized protein-protein interaction network, or interactome, of any eukaryote. This has predominantly been generated through multiple, systematic studies of protein-protein interactions by two-hybrid techniques and of affinity-purified protein complexes. A pressing question is to understand how large-scale cross-linking mass spectrometry (XL-MS) can confirm and extend this interactome. Here, intact yeast nuclei were subject to cross-linking with disuccinimidyl sulfoxide (DSSO) and analyzed using hybrid MS2-MS3 methods. XlinkX identified a total of 2,052 unique residue pair cross-links at 1% FDR. Intraprotein cross-links were found to provide extensive structural constraint data, with almost all intralinks that mapped to known structures and slightly fewer of those mapping to homology models being within 30 Ã…. Intralinks provided structural information for a further 366 proteins. A method for optimizing interprotein cross-link score cut-offs was developed, through use of extensive known yeast interactions. Its application led to a high confidence, yeast nuclear interactome. Strikingly, almost half of the interactions were not previously detected by two-hybrid or AP-MS techniques. Multiple lines of evidence existed for many such interactions, whether through literature or ortholog interaction data, through multiple unique interlinks between proteins, and/or through replicates. We conclude that XL-MS is a powerful means to measure interactions, that complements two-hybrid and affinity-purification techniques
Synergy and antagonism between iron chelators and antifungal drugs in Cryptococcus
Fungal infections remain very difficult to treat, and developing new antifungal drugs is difficult and expensive. Recent approaches therefore seek to augment existing antifungals with synergistic agents that can lower the therapeutic dose, increase efficacy and prevent resistance from developing. Iron limitation can inhibit microbial growth, and iron chelators have been employed to treat fungal infections. In this study, chequerboard testing was used to explore combinations of iron chelators with antifungal agents against pathogenic Cryptococcus spp. with the aim of determining how disruption to iron homeostasis affects antifungal susceptibility. The iron chelators ethylenediaminetetraacetic acid (EDTA), deferoxamine (DFO), deferiprone (DFP), deferasirox (DSX), ciclopirox olamine and lactoferrin (LF) were paired with the antifungal agents amphotericin B (AmB), fluconazole, itraconazole, voriconazole and caspofungin. All chelators except for DFO increased the efficacy of AmB, and significant synergy was seen between AmB and LF for all Cryptococcus strains. Addition of exogenous iron rescued cells from the antifungal effect of LF alone but could not prevent inhibition by AmB + LF, indicating that synergy was not due primarily to iron chelation but to other properties of LF that were potentiated in the presence of AmB. Significant synergy was not seen consistently for other antifungal–chelator combinations, and EDTA, DSX and DFP antagonised the activity of azole drugs in strains of Cryptococcus neoformans var. grubii. This study highlights the range of interactions that can be induced by chelators and indicates that most antifungal drugs are not enhanced by iron limitation in Cryptococcus
Children with islet autoimmunity and enterovirus infection demonstrate a distinct cytokine profile
Cytokines are upregulated in prediabetes, but their relationshipwith Enterovirus (EV) infection and development of islet autoimmunityis unknown. Cytokines (n = 65) were measured usingLuminex xMAP technology in a nested case-control study of 67children with a first-degree relative with type 1 diabetes: 27 withislet autoantibodies (Ab+) and 40 age-matched persistently autoantibodynegative (Ab2) control subjects. Of 74 samples, 37(50%) were EV-PCR+ in plasma and/or stool (EV+) and the remainderwere negative for EV and other viruses (EV2). Fifteencytokines, chemokines, and growth factors were elevated (P #0.01) in Ab+ versus Ab2 children (interleukin [IL]-1b, IL-5, IL-7,IL-12(p70), IL-16, IL-17, IL-20, IL-21, IL-28A, tumor necrosis factora,chemokine C-C motif ligand [CCL]13, CCL26, chemokine C-X-Cmotif ligand 5, granulocyte-macrophage colony-stimulating factor,and thrombopoietin); most have proinflammatory effects. InEV+ versus EV2 children, IL-10 was higher (P = 0.005), whileIL-21 was lower (P = 0.008). Cytokine levels did not differ betweenAb+EV+ and Ab+EV2 children. Heat maps demonstrated clusteringof some proinflammatory cytokines in Ab+ children, suggestingthey are coordinately regulated. In conclusion, children withislet autoimmunity demonstrate higher levels of multiple cytokines,consistent with an active inflammatory process in theprediabetic state, which is unrelated to coincident EV infection.Apart from differences in IL-10 and IL-21, EV infection wasnot associated with a specific cytokine profile. Diabetes 61:1500–1508, 201
Higher abundance of enterovirus A species in the gut of children with islet autoimmunity
Enteroviruses (EVs) are prime candidate environmental triggers of islet autoimmunity (IA), with potential as vaccine targets for type 1 diabetes prevention. However, the use of targeted virus detection methods and the selective focus on EVs by most studies increases the risk for substantial investigation bias and an overestimated association between EV and type 1 diabetes. Here we performed comprehensive virome-capture sequencing to examine all known vertebrate-infecting viruses without bias in 182 specimens (faeces and plasma) collected before or at seroconversion from 45 case children with IA and 48 matched controls. From >2.6 billion reads, 28 genera of viruses were detected and 62% of children (58/93) were positive for ≥1 vertebrate-infecting virus. We identified 129 viruses as differentially abundant between the gut of cases and controls, including 5 EV-A types significantly more abundant in the cases. Our findings further support EV’s hypothesised contribution to IA and corroborate the proposal that viral load may be an important parameter in disease pathogenesis. Furthermore, our data indicate a previously unrecognised association of IA with higher EV-A abundance in the gut of children and provide a catalog of viruses to be interrogated further to determine a causal link between virus infection and type 1 diabetes