Study of Parasitic Infections, Cancer, and other Diseases with Mass-Spectrometry and Quantitative Proteome-Disease Relationships

Lazaro   G.   Perez-Montoto; Francisco      Prado-Prado; Florencio   M.   Ubeira; Humberto      Gonzalez-Diaz

Abstract

We can understand Mass-Spectra Quantitative Proteome-Disease Relationships (MS-QPDRs) as models useful to detect Disease Biomarkers or to predict Drug Toxicity effects based on Mass-Spectra outcomes from samples of human body tissues, parasites, or other organisms. MS-QPDR development and practical use is an emerging area combining Proteomics and Bioinformatics; which involves computational, molecular, and legal sciences. We detect, at least two tendencies on QPDR development. The first tendency (type 1) uses Statistical, Artificial Intelligence, Machine Learning and/or Non-Linear Signal processing to fish for single MS biomarker signals directly within MS data. A recent alternative (type 2) uses Graph Theory to construct Complex Network representations of MS data. Next, we can calculate graph parameters called Mass-Spectra Topological Indices (MS-TIs) useful to describe the graph. The last step is similar to the first tendency but it uses MS-TIs as inputs (instead of MS signals) to seek the MS-QPDR model. There are many examples of QPDR models based on scheme 1. However, there has been little effort to seek QPDR models with scheme 2. On the other hand, MS-QPDR models can be obtained from different body fluids; the case of Human Blood Proteome (BP) is one of the most interesting. The outcomes obtained by Mass Spectrometry (MS) analysis of Serum Protein Profile (SPP) of Blood Proteome (BP) are very useful for the early detection of diseases and drug induced toxicities. In the present work we review, discuss, and outline some perspectives on the use of QPDR models based on the two types of schemes. We also refer to the recent implementation of the internet portal called BioAims for QPDR analysis (http://miaja.tic.udc.es/Bio-AIMS/ ) for free use by the research community.

Keywords: Parasite proteomics, Leishmania, Plasmodium falciparum, malaria, blood proteome, cancer, toxico-proteomics, complex networks, graph theory, mass spectrometry