MarVis: a tool for clustering and visualization of metabolic biomarkers

<p>Abstract</p> <p>Background</p> <p>A central goal of experimental studies in systems biology is to identify meaningful markers that are hidden within a diffuse background of data originating from large-scale analytical intensity measurements as obtained from metabolomic experiments. Intensity-based clustering is an unsupervised approach to the identification of metabolic markers based on the grouping of similar intensity profiles. A major problem of this basic approach is that in general there is no prior information about an adequate number of biologically relevant clusters.</p> <p>Results</p> <p>We present the tool MarVis (Marker Visualization) for data mining on intensity-based profiles using one-dimensional self-organizing maps (1D-SOMs). MarVis can import and export customizable CSV (Comma Separated Values) files and provides aggregation and normalization routines for preprocessing of intensity profiles that contain repeated measurements for a number of different experimental conditions. Robust clustering is then achieved by training of an 1D-SOM model, which introduces a similarity-based ordering of the intensity profiles. The ordering allows a convenient visualization of the intensity variations within the data and facilitates an interactive aggregation of clusters into larger blocks. The intensity-based visualization is combined with the presentation of additional data attributes, which can further support the analysis of experimental data.</p> <p>Conclusion</p> <p>MarVis is a user-friendly and interactive tool for exploration of complex pattern variation in a large set of experimental intensity profiles. The application of 1D-SOMs gives a convenient overview on relevant profiles and groups of profiles. The specialized visualization effectively supports researchers in analyzing a large number of putative clusters, even though the true number of biologically meaningful groups is unknown. Although MarVis has been developed for the analysis of metabolomic data, the tool may be applied to gene expression data as well.</p

Machine Learning Reveals a Non-Canonical Mode of Peptide Binding to MHC class II Molecules

MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or less flat in the MHC groove, with a fixed distance of nine amino acids between the first and last residue in contact with the MHCII. While confirming that the great majority of peptides bind to the MHC using this canonical mode, we report evidence for an alternative, less common mode of interaction. A fraction of observed ligands were shown to have an unconventional spacing of the anchor residues that directly interact with the MHC, which could only be accommodated to the canonical MHC motif either by imposing a more stretched out peptide backbone (an 8mer core) or by the peptide bulging out of the MHC groove (a 10mer core). We estimated that on average 2% of peptides bind with a core deletion, and 0·45% with a core insertion, but the frequency of such non‐canonical cores was as high as 10% for certain MHCII molecules. A mutational analysis and experimental validation of a number of these anomalous ligands demonstrated that they could only fit to their MHC binding motif with a non‐canonical binding core of length different from nine. This previously undescribed mode of peptide binding to MHCII molecules gives a more complete picture of peptide presentation by MHCII and allows us to model more accurately this event.Fil: Andreatta, Massimo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Jurtz, Vanessa I.. Technical University of Denmark; DinamarcaFil: Kaever, Thomas. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina. Technical University of Denmark; Dinamarc

The Length Distribution of Class I-Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele-Specific Binding Preference

HLA class I-binding predictions are widely used to identify candidate peptide targets of human CD8+ T cell responses. Many such approaches focus exclusively on a limited range of peptide lengths, typically 9 aa and sometimes 9-10 aa, despite multiple examples of dominant epitopes of other lengths. In this study, we examined whether epitope predictions can be improved by incorporating the natural length distribution of HLA class I ligands. We found that, although different HLA alleles have diverse length-binding preferences, the length profiles of ligands that are naturally presented by these alleles are much more homogeneous. We hypothesized that this is due to a defined length profile of peptides available for HLA binding in the endoplasmic reticulum. Based on this, we created a model of HLA allele-specific ligand length profiles and demonstrate how this model, in combination with HLA-binding predictions, greatly improves comprehensive identification of CD8+ T cell epitopes.Fil: Trolle, Thomas. Technical University of Denmark; DinamarcaFil: McMurtrey, Curtis. Oklahoma State University; Estados UnidosFil: Sidney, John. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Bardet, Wilfried. Oklahoma State University; Estados UnidosFil: Osborn, Sean C.. Oklahoma State University; Estados UnidosFil: Kaever, Thomas. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Sette, Alessandro. La Jolla Institute for Allergy and Immunology; Estados UnidosFil: Hildebrand, Willliam H.. Oklahoma State University; Estados UnidosFil: Nielsen, Morten. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas ; Argentina. Universidad Nacional de San Martín; ArgentinaFil: Peters, Bjoern. La Jolla Institute for Allergy and Immunology; Estados Unido

<i>Toxoplasma gondii</i> peptide ligands open the gate of the HLA class I binding groove

HLA class I presentation of pathogen-derived peptide ligands is essential for CD8+ T-cell recognition of Toxoplasma gondii infected cells. Currently, little data exist pertaining to peptides that are presented after T. gondii infection. Herein we purify HLA-A*02:01 complexes from T. gondii infected cells and characterize the peptide ligands using LCMS. We identify 195 T. gondii encoded ligands originating from both secreted and cytoplasmic proteins. Surprisingly, T. gondii ligands are significantly longer than uninfected host ligands, and these longer pathogen-derived peptides maintain a canonical N-terminal binding core yet exhibit a C-terminal extension of 1-30 amino acids. Structural analysis demonstrates that binding of extended peptides opens the HLA class I F' pocket, allowing the C-terminal extension to protrude through one end of the binding groove. In summary, we demonstrate that unrealized structural flexibility makes MHC class I receptive to parasite-derived ligands that exhibit unique C-terminal peptide extensions

c-di-AMP Is a New Second Messenger in Staphylococcus aureus with a Role in Controlling Cell Size and Envelope Stress.

Published versio

Establishment, Validation, and Initial Application of a Sensitive LC-MS/MS Assay for Quantification of the Naturally Occurring Isomers Itaconate, Mesaconate, and Citraconate.

Itaconate is derived from the tricarboxylic acid (TCA) cycle intermediate cis-aconitate and links innate immunity and metabolism. Its synthesis is altered in inflammation-related disorders and it therefore has potential as clinical biomarker. Mesaconate and citraconate are naturally occurring isomers of itaconate that have been linked to metabolic disorders, but their functional relationships with itaconate are unknown. We aimed to establish a sensitive high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) assay for the quantification of itaconate, mesaconate, citraconate, the pro-drug 4-octyl-itaconate, and selected TCA intermediates. The assay was validated for itaconate, mesaconate, and citraconate for intra- and interday precision and accuracy, extended stability, recovery, freeze/thaw cycles, and carry-over. The lower limit of quantification was 0.098 µM for itaconate and mesaconate and 0.049 µM for citraconate in 50 µL samples. In spike-in experiments, itaconate remained stable in human plasma and whole blood for 24 and 8 h, respectively, whereas spiked-in citraconate and mesaconate concentrations changed during incubation. The type of anticoagulant in blood collection tubes affected measured levels of selected TCA intermediates. Human plasma may contain citraconate (0.4-0.6 µM, depending on the donor), but not itaconate or mesaconate, and lipopolysaccharide stimulation of whole blood induced only itaconate. Concentrations of the three isomers differed greatly among mouse organs: Itaconate and citraconate were most abundant in lymph nodes, mesaconate in kidneys, and only citraconate occurred in brain. This assay should prove useful to quantify itaconate isomers in biomarker and pharmacokinetic studies, while providing internal controls for their effects on metabolism by allowing quantification of TCA intermediates

Identification of Cerebrospinal Fluid Metabolites as Biomarkers for Enterovirus Meningitis

Enteroviruses are among the most common causes of viral meningitis. Enteroviral meningitis continues to represent diagnostic challenges, as cerebrospinal fluid (CSF) cell numbers (a well validated diagnostic screening tool) may be normal in up to 15% of patients. We aimed to identify potential CSF biomarkers for enteroviral meningitis, particularly for cases with normal CSF cell count. Using targeted liquid chromatography-mass spectrometry, we determined metabolite profiles from patients with enteroviral meningitis (n = 10), and subdivided them into those with elevated (n = 5) and normal (n = 5) CSF leukocyte counts. Non-inflamed CSF samples from patients with Bell&rsquo;s palsy and normal pressure hydrocephalus (n = 19) were used as controls. Analysis of 91 metabolites revealed considerable metabolic reprogramming in the meningitis samples. It identified phosphatidylcholine PC.ae.C36.3, asparagine, and glycine as an accurate (AUC, 0.92) combined classifier for enterovirus meningitis overall, and kynurenine as a perfect biomarker for enteroviral meningitis with an increased CSF cell count (AUC, 1.0). Remarkably, PC.ae.C36.3 alone emerged as a single accurate (AUC, 0.87) biomarker for enteroviral meningitis with normal cell count, and a combined classifier comprising PC.ae.C36.3, PC.ae.C36.5, and PC.ae.C38.5 achieved nearly perfect classification (AUC, 0.99). Taken together, this analysis reveals the potential of CSF metabolites as additional diagnostic tools for enteroviral meningitis, and likely other Central nervous system (CNS) infections

St. John's Daily Star, 1920-03-15

The St. John's Daily Star was published daily except Sunday between 17 April 1915 - 23 July 1921