The course consists of two parts: a theoretical part which aims to address the main methods and problems related to the study of proteins, and a practical part of a computer laboratory which aims to provide the basis for the analysis of proteomic Big-Data.
From biochemistry to proteomics The course aims to provide the basis for understanding the main techniques applied for the study of proteins, including: one-dimensional polyacrylamide gel electrophoresis in native or denatured and denatured reduced and alkylated conditions, zymograms, different types of two-dimensional electrophoresis (i.e. titration curves, blue native electrophoresis, and high-resolution two-dimensional gel electrophoresis), western blots, different methods of proteins detection gels (coomassie, colloidal, silver nitrate and fluorescence staining), main chromatography techniques including methods that use magnetic nano beads, particularly suitable for robotic automation systems, mass spectrometry high resolution, direct and indirect ELISA, membrane and microscope slide protein arrays. Protein samples preparation The course aims to provide an overview of the main biochemical-physical methods to address and solve most of the problems in the preparation of protein samples such as most common biological fluids (serum, plasma, liquor and urine), cells, extracellular vesicles and tissues for the methods indicated above. Bioinformatics analysis Finally, the course aims to provide the basis for the analysis of big data obtained from protein microarray and/or mass spectrometry experiments. In particular, attention will be paid to: a) how to normalize data, b) the use of the most recent classification, prediction, and correlation algorithms between experimental features and biochemical-clinical data of samples such as weighted gene co-expression network analysis, machine learning and supervised /unsupervised cluster analysis, c) which statistical tests to use, d) how to choose the variables of interest, e) and the gene ontology enrichment analysis for the identification of biological processes and metabolic pathways perturbed in a case-control experiment.
Acquire the knowledge necessary for the study of proteins by applying the main technologies currently available in the bio-medical field. Understand the Big-Data analysis process and use it for the identification of diagnostic and prognostic biomarkers and biological processes associated with health/Disease.
Basic notions of Biochemistry and Statistics.
Theoretical lectures (20 hours of instructional activities) will cover the topics outlined in the course syllabus, with an interactive approach and ample opportunities for in-class practical exercises. In case of emergencies, activities may be conducted online following University guidelines. Students with documented Specific Learning Disorders (SLD) or special educational needs are required to contact the course instructor(s) and the designated SLD representative within the Department before the start of classes to agree on appropriate teaching methods and ensure the achievement of learning objectives and outcomes.
One-dimensional polyacrylamide gel electrophoresis
One-dimensional electrophoresis under native or denaturing or denaturing and reducing conditions
Zymograms
Two-dimensional polyacrylamide gel electrophoresis
Titration curve
Blue Native polyacrylamide gel electrophoresis (BN-PAGE)
High-resolution two-dimensional electrophoresis (2D-PAGE)
Western Blot
The main techniques for visualizing proteins on polyacrylamide gels
Coomassie blue
Colloidal coomassie
Silver
Fluorescence
The main techniques of protein separation with chromatographic systems
Gel filtration
Affinity
Ion exchange
Reverse phase
Use of functionalized magnetic beads
High-resolution mass spectrometry
Results validation
Dot Blot
direct and indirect ELISAs
The main methods for preparing protein samples.
Blood
Urine
Cerebrospinal fluid
Extracellular vesicles
Cells
Tissue
Bioinformatics analysis of proteomics data
Data normalization
Main statistical tests and algorithms applicable for data quality control and discrimination between two or more groups of samples.
Generate a priority list of variables that maximize the discrimination between two or more samples.
Overview of machine learning for discrimination and prediction of samples
Overview of protein co-expression network analysis (WGCNA)
Enrichment analysis of gene ontology annotation terms to identify biological processes and biochemical pathways involved in a proteomic experiment.
How to visualize the results obtained from the statistical analysis.
Lecture notes on AulaWeb.
Ricevimento: On appointment , Prof. Maurizio Bruschi, IRCCS Istituto Giannina Gaslini. Laboratorio di Nefrologia Molecolare (Padiglione 12, fondi) Via Gerolamo Gaslini, 5 – 16147 Genova (GE) E-mail: maurizio.bruschi@unige.it
MAURIZIO BRUSCHI (President)
ELENA ZOCCHI
Consult the detailed timetable on AulaWeb.
The timetable for this course is available here: EasyAcademy
The exam will consist of a practical part of the analysis of proteomic data and a written test (multiple choice quiz) on the topics covered during the course carried out in person or on a digital platform.
The final grade will be averaged (weighted average) with the two parts of the exam.
The exam is passed with at least 18/30.
The exam will verify the achievement of the training objectives; in particular, the student's knowledge of the main techniques applied for the study of proteins and the ability to analyze the results obtained will be ascertained.
