Project description:Antimicrobial resistance is a major public health concern worldwide. Albeit to a lesser extent than bacteria, fungi are also becoming increasingly resistant to antifungal drugs. Moreover, due to the small number of antifungal classes, therapy options are limited, complicating the clinical management of mycoses. In this view, antimicrobial peptides (AMPs) are a potential alternative to conventional drugs. Among these, Proline-rich antimicrobial peptides (PrAMPs), almost exclusively of animal origins, are of particular interest due to their peculiar mode of action. In this study, a search for new arginine- and proline-rich peptides from plants has been carried out with a bioinformatic approach by sequence alignment and antimicrobial prediction tools. Two peptide candidates were tested against planktonic cells and biofilms of Candida albicans and Candida glabrata strains, including resistant isolates. These peptides showed similar potent activity, with half-maximal effective concentration values in the micromolar range. In addition, some structural and functional features, revealing peculiar mechanistic behaviors, were investigated.
Project description:Quantitative proteomics has evolved considerably over the last decade with the advent of higher order multiplexing (HOM) techniques. With the development of methods such as-multitagging, cPILOT, hyperplexing, BONPlex, and MITNCAT, the HOM technique is rapidly taking the center stage in multiplexed quantitative proteomics. These studies combined MS1 and MS2 labels in a single experiment enabling higher sample throughput. While HOM is highly promising, the computational analysis is still a big challenge, as the available tools cannot harness its power completely. We have developed a new quantitative pipeline, HyperQuant to aid in accurately quantitating complex HOM data. The pipeline uses identification results from either MaxQuant or any other search engine and quantitation results from QuantWizIQ. The Mapper and Combiner modules of HyperQuant allow facile integration of the labeled data, along with peptide spectrum match (PSM) intensity/ratio integration for proteins, respectively, for each PSM label combination. This also includes appropriate combination of replicates/fractions before summarizing the protein intensity/ratio, leading to robust quantitation. To the best of our knowledge, this is the first tool for the quantitation of HOM data with flexibility for any combination of MS1 and MS2 labels. We demonstrate its utility in analyzing two 18-plex data sets from the hyperplexing and the BONplex studies. The tool is open source and freely available for noncommercial use. HyperQuant is a highly valuable tool that will help in advancing the field of multiplexed quantitative proteomics.
Project description:A major scientific challenge at the present time for cancer research is the determination of the underlying biological basis for cancer development. It is further complicated by the heterogeneity of cancer's origin. Understanding the molecular basis of cancer requires studying the dynamic and spatial interactions among proteins in cells, signaling events among cancer cells, and interactions between the cancer cells and the tumor microenvironment. Recently, it has been proposed that large-scale protein expression analysis of cancer cell proteomes promises to be valuable for investigating mechanisms of cancer transformation. Advances in mass spectrometry technologies and bioinformatics tools provide a tremendous opportunity to qualitatively and quantitatively interrogate dynamic protein-protein interactions and differential regulation of cellular signaling pathways associated with tumor development. In this review, progress in shotgun proteomics technologies for examining the molecular basis of cancer development will be presented and discussed.
Project description:Mass spectrometry-based proteomics experiments typically start with the digestion of proteins using trypsin, chosen because of its high specificity, availability, and ease of use. It has become apparent that the sole use of trypsin may impose certain limits on our ability to grasp the full proteome, missing out particular sites of post-translational modifications, protein segments, or even subsets of proteins. To tackle this problem, alternative proteases have been introduced and shown to lead to an increase in the detectable (phospho)proteome. Here, we argue that there may be further room for improvement and explore the protease EndoPro. For optimal peptide identification rates, we explored multiple peptide fragmentation techniques (HCD, ETD, and EThcD) and employed Byonic as search algorithm. We obtain peptide IDs for about 40% of the MS2 spectra (66% for trypsin). EndoPro cleaves with high specificity at the C-terminal site of Pro and Ala residues and displays activity in a broad pH range, where we focused on its performance at pH = 2 and 5.5. The proteome coverage of EndoPro at these two pH values is rather distinct, and also complementary to the coverage obtained with trypsin. As about 40% of mammalian protein phosphorylations are proline-directed, we also explored the performance of EndoPro in phosphoproteomics. EndoPro extends the coverable phosphoproteome substantially, whereby both the, at pH = 2 and 5.5, acquired phosphoproteomes are complementary to each other and to the phosphoproteome obtained using trypsin. Hence, EndoPro is a powerful tool to exploit in (phospho)proteomics applications.
Project description:The intrinsic conformational preferences of a new nonproteinogenic amino acid have been explored by computational methods. This tailored molecule, named ((?)Pro)Arg, is conceived as a replacement for arginine in bioactive peptides when the stabilization of folded turn-like conformations is required. The new residue features a proline skeleton that bears the guanidilated side chain of arginine at the C(?) position of the five-membered pyrrolidine ring, in either a cis or a trans orientation with respect to the carboxylic acid. The conformational profiles of the N-acetyl-N'-methylamide derivatives of the cis and trans isomers of ((?)Pro)Arg have been examined in the gas phase and in solution by B3LYP/6-31+G(d,p) calculations and molecular dynamics simulations. The main conformational features of both isomers represent a balance between geometric restrictions imposed by the five-membered pyrrolidine ring and the ability of the guanidilated side chain to interact with the backbone through hydrogen bonds. Thus, both cis- and trans-((?)Pro)Arg exhibit a preference for the ?(L) conformation as a consequence of the interactions established between the guanidinium moiety and the main-chain amide groups.
Project description:Iron is an essential element for life, as it is critical for oxygen transport, cellular respiration, DNA synthesis, and metabolism. Disruptions in iron metabolism have been associated with several complex diseases like diabetes, cancer, infection susceptibility, neurodegeneration, and others; however, the molecular mechanisms linking iron metabolism with these diseases are not fully understood. A commonly used model to study iron deficiency (ID) is yeast, Saccharomyces cerevisiae. Here, we used quantitative (phospho)proteomics to explore the early (4 and 6 h) and late (12 h) response to ID. We showed that metabolic pathways like the Krebs cycle, amino acid, and ergosterol biosynthesis were affected by ID. In addition, during the late response, several proteins related to the ubiquitin-proteasome system and autophagy were upregulated. We also explored the proteomic changes during a recovery period after 12 h of ID. Several proteins recovered their steady-state levels, but some others, such as cytochromes, did not recover during the time tested. Additionally, we showed that autophagy is active during ID, and some of the degraded proteins during ID can be rescued using KO strains for several key autophagy genes. Our results highlight the complex proteome changes occurring during ID and recovery. This study constitutes a valuable data set for researchers interested in iron biology, offering a temporal proteomic data set for ID, as well as a compendium the proteomic changes associated with episodes of iron recovery.
Project description:We report that a computational systems approach can identify cell type specification genes that act synergistically, and demonstrate its application for reprogramming of fibroblasts to prostate tissue. We have employed three such master regulators (FOXA1, NKX3.1, and androgen receptor, AR) in a primed conversion strategy to generate prostate tissue from mouse fibroblasts, involving expression of pluripotency factors, tissue recombination with embryonic urogenital mesenchyme, and renal grafting.
Project description:The proper handling of samples to be analyzed by mass spectrometry (MS) can guarantee excellent results and a greater depth of analysis when working in quantitative proteomics. This is critical when trying to assess non-traditional sources such as ear wax, saliva, vitreous humor, aqueous humor, tears, nipple aspirate fluid, breast milk/colostrum, cervical-vaginal fluid, nasal secretions, bronco-alveolar lavage fluid, and stools. We intend to provide the investigator with relevant aspects of quantitative proteomics and to recognize the most recent clinical research work conducted with atypical samples and analyzed by quantitative proteomics. Having as reference the most recent and different approaches used with non-traditional sources allows us to compare new strategies in the development of novel experimental models. On the other hand, these references help us to contribute significantly to the understanding of the proportions of proteins in different proteomes of clinical interest and may lead to potential advances in the emerging field of precision medicine.
Project description:Despite the plasma proteome being able to provide a unique insight into the health and disease status of individuals, holding singular promise as a source of protein biomarkers that could be pivotal in the context of personalized medicine, only around 100 proteins covering a few human conditions have been approved as biomarkers by the US Food and Drug Administration (FDA) so far. Mass spectrometry (MS) currently has enormous potential for high-throughput analysis in clinical research; however, plasma proteomics remains challenging mainly due to the wide dynamic range of plasma protein abundances and the time-consuming procedures required. We applied a new MS-based multiplexed proteomics workflow to quantitate proteins, encompassing 67 FDA-approved biomarkers, in >1300 human plasma samples from a clinical cohort. Our results indicate that this workflow is suitable for large-scale clinical studies, showing good accuracy and reproducibility (coefficient of variation (CV) < 20 for 90% of the proteins). Furthermore, we identified plasma signature proteins (stable in time on an individual basis), stable proteins (exhibiting low biological variability and high temporal stability), and highly variable proteins (with low temporal stability) that can be used for personalized health monitoring and medicine.
Project description:BackgroundKeloid is a pathological skin scar formation with complex and unclear molecular pathology mechanism. Novel biomarkers and associated mechanisms are needed to improve current therapies.ObjectivesTo identify novel biomarkers and underlying pathological mechanisms of keloids.MethodsSix pairs of keloid scar tissues and corresponding normal skin tissues were quantitatively analyzed by a high-resolution label-free mass spectrometry-based proteomics approach. Differential protein expression data was further analyzed by a comprehensive bioinformatics approach to identify novel biomarkers and mechanistic pathways for keloid formation. Candidate biomarkers were validated experimentally.ResultsIn total, 1359 proteins were identified by proteomic analysis. Of these, 206 proteins exhibited a significant difference in expression between keloid scar and normal skin tissues. RCN3 and CALU were significantly upregulated in keloids. RCN1 and PDGFRL were uniquely expressed in keloids. Pathway analysis suggested that the XBP1-mediated unfolded protein response (UPR) pathway was involved in keloid formation. Moreover, a PDGFRL centric gene coexpression network was constructed to illustrate its function in skin.Conclusions and clinical relevanceOur study proposed four novel biomarkers and highlighted the role of XBP1-mediated UPR pathway in the pathology of keloids. It provided novel biological insights that contribute to develop novel therapeutic strategies for keloids.