Integrated Omics for the global mapping of biomolecules in LDL

Circulating low density lipoproteins (LDL) are thought to play a crucial role in the onset and development of atherosclerosis, though the detailed molecular mechanisms responsible for their biological effects remain controversial. The complexity of biomolecules (lipids, glycans and protein) and structural features (isoforms and chemical modifications) found in LDL particles hampers the complete understanding of the mechanism underlying its atherogenicity. For this reason the screening of LDL for features discriminative of a particular pathology in search of biomarkers is of high importance. Three major biomolecule classes (lipids, protein and glycans) in LDL particles were screened using mass spectrometry coupled to liquid chromatography. Dual-polarity screening resulted in good lipidome coverage, identifying over 300 lipid species from 12 lipid sub-classes. Multivariate analysis was used to investigate potential discriminators in the individual lipid sub-classes for different study groups (age, gender, pathology). Additionally, the high protein sequence coverage of ApoB-100 routinely achieved (≥70%) assisted in the search for protein modifications correlating to aging and pathology. The large size and complexity of the datasets required the use of chemometric methods (Partial Least Square-Discriminant Analysis, PLS-DA) for their analysis and for the identification of ions that discriminate between study groups. The peptide profile from enzymatically digested ApoB-100 can be correlated with the high structural complexity of lipids associated with ApoB-100 using exploratory data analysis. In addition, using targeted scanning modes, glycosylation sites within neutral and acidic sugar residues in ApoB-100 are also being explored. Together or individually, knowledge of the profiles and modifications of the major biomolecules in LDL particles will contribute towards an in-depth understanding, will help to map the structural features that contribute to the atherogenicity of LDL, and may allow identification of reliable, pathology-specific biomarkers. This research was supported by a Marie Curie Intra-European Fellowship within the 7th European Community Framework Program (IEF 255076). Work of A. Rudnitskaya was supported by Portuguese Science and Technology Foundation, through the European Social Fund (ESF) and "Programa Operacional Potencial Humano - POPH".

Identifiying human MHC supertypes using bioinformatic methods

Classification of MHC molecules into supertypes in terms of peptide-binding specificities is an important issue, with direct implications for the development of epitope-based vaccines with wide population coverage. In view of extremely high MHC polymorphism (948 class I and 633 class II HLA alleles) the experimental solution of this task is presently impossible. In this study, we describe a bioinformatics strategy for classifying MHC molecules into supertypes using information drawn solely from three-dimensional protein structure. Two chemometric techniques–hierarchical clustering and principal component analysis–were used independently on a set of 783 HLA class I molecules to identify supertypes based on structural similarities and molecular interaction fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77. action fields calculated for the peptide binding site. Eight supertypes were defined: A2, A3, A24, B7, B27, B44, C1, and C4. The two techniques gave 77% consensus, i.e., 605 HLA class I alleles were classified in the same supertype by both methods. The proposed strategy allowed “supertype fingerprints” to be identified. Thus, the A2 supertype fingerprint is Tyr9/Phe9, Arg97, and His114 or Tyr116; the A3-Tyr9/Phe9/Ser9, Ile97/Met97 and Glu114 or Asp116; the A24-Ser9 and Met97; the B7-Asn63 and Leu81; the B27-Glu63 and Leu81; for B44-Ala81; the C1-Ser77; and the C4-Asn77.