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

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

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

Respiratory viral co-infections among SARS-CoV-2 cases confirmed by virome capture sequencing

Accumulating evidence supports the high prevalence of co-infections among Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) patients, and their potential to worsen the clinical outcome of COVID-19. However, there are few data on Southern Hemisphere populations, and most studies to date have investigated a narrow spectrum of viruses using targeted qRT-PCR. Here we assessed respiratory viral co-infections among SARS-CoV-2 patients in Australia, through respiratory virome characterization. Nasopharyngeal swabs of 92 SARS-CoV-2-positive cases were sequenced using pan-viral hybrid-capture and the Twist Respiratory Virus Panel. In total, 8% of cases were co-infected, with rhinovirus (6%) or influenzavirus (2%). Twist capture also achieved near-complete sequencing (> 90% coverage, > tenfold depth) of the SARS-CoV-2 genome in 95% of specimens with Ct < 30. Our results highlight the importance of assessing all pathogens in symptomatic patients, and the dual-functionality of Twist hybrid-capture, for SARS-CoV-2 whole-genome sequencing without amplicon generation and the simultaneous identification of viral co-infections with ease