OVERVIEW

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.

AIMS AND CONTENT

LEARNING OUTCOMES

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.

AIMS AND LEARNING OUTCOMES

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.

PREREQUISITES

Basic notions of Biochemistry and Statistics.

TEACHING METHODS

Face-to-face or online lessons and computer laboratory practices, available on AulaWeb, according to the anti-COVID provisions issued by the University.

Any Student with documented Specific Learning Disorders (SLD), or with any special needs, shall reach out to the Lecturer(s) and to the dedicated SLD Representative in the Department before class begins, in order to liase and arrange the specific teaching methods and ensure proper achievement of the learning aims and outcomes.

SYLLABUS/CONTENT

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.

RECOMMENDED READING/BIBLIOGRAPHY

Lecture notes on AulaWeb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Consult the detailed timetable on AulaWeb.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

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.

ASSESSMENT METHODS

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.