RESEARCH TRAINING EXERCISE (Laboratory Rotation)- TECHNICAL COURSES I

Coordinators

Nikos Fyllas, Ph.D., Assistant Professor, Department of Biology, National and Kapodistrian University of Athens

Stamatis Pagakis, Senior Research Scientist, Basic Research Center, Foundation for Biomedical Research of the Academy of Athens

Teaching hours

This is a 1^st semester course of about 8 weeks that corresponds to 10 ECTs and 39 total hours of lectures and approximately 320 total hours of laboratory presence/training.

The course is subdivided into the following parts:

A)      Molecular Biology-Omics

B)      Elements of Bioinformatics (big data bases)

A)             Technical courses II: Molecular Biology-Omics

Description

These series of lectures will provide

A)             an overview of Next Generation Sequencing (NGS) and the most relevant biomedical NGS applications: Targeted sequencing, Whole-exome sequencing, Whole-genome sequencing, RNA-seq (mRNA-seq, smallRNA-seq, etc., approaches to study RNA structure and RNA-protein interactions), approaches to interrogate the composition and structure of chromatin (ChIP-seq, ATAC-seq, chromosome conformation capture techniques, etc.).

B)             an introduction to proteomic methodologies and their application to the study of human diseases.

C)             an introduction of the significance but also the challenges of applying metabolomic analysis in neurophysiology research. The students will be presented with the major changes in the way problems in life sciences are now approached in the context of the systems biology and the omic analyses revolution, focusing on brain research and the field of systems neurophysiology. The muti-step metabolomic analysis will be described and its contribution to the reconstruction of an accurate metabolic physiology map for the brain will be discussed. Experimental and computational protocol standardization challenges that need to be addressed for its vast deployment in neurophysiology research and practice will be described. An example of brain metabolomic analysis in a mouse model will be presented.

D)             Examples of genetic and biochemical approaches to develop agents interfering with protein aggregation. Many neurodegenerative diseases are associated with protein misfolding and protein self-assembly, which lead to the formation of protein oligomers and/or higher-order aggregates with neurotoxic properties. Thus, understanding these pathogenic processes is of fundamental importance for neurobiology. Furthermore, chemical and biological agents interfering with protein aggregation are much sought-after factors in the quest for effective drugs against these conditions.

Course Overview

The course will cover basic principles of NGS technologies, description of the omic analysis revolution and the consequent fundamental changes in the way problems in life sciences are now approached, mass spectrometry and applications involving differential proteomics, identification of post-translational modifications and analysis of protein complexes as well as of metabolomics. Description of the multi-step experimental and computational analysis process that needs to be carefully designed and standardized for its accurate and vast application in neurophysiology research.

Furthermore, biochemical, biophysical and biological assays, which can be utilized for high-throughput screenings of chemical and biological libraries so as to discover modulators of protein aggregation will be described. Furthermore, the design, development and outcomes of recently developed biotechnological platforms for producing chemical libraries with greatly expanded diversities and for identifying chemical rescuers of pathogenic protein misfolding and aggregation in an ultrahigh-throughput fashion will also be covered. 

Skills & Learning Outcomes

The objective will be to familiarize students with

1. the principles underlying NGS and the main biomedical applications in which NGS is employed
2. the experimental design of proteomic experiments and current challenges in their application to the study of human diseases
3. the holistic perspective of biological system analysis gained from systems and network biology research and shown the complementary role of the various omic analyses in deciphering the complexity of brain function
4. metabolomic analysis and it’s various experimental and computational components
5. network reconstruction and how this could contribute to the elucidation of brain metabolic physiology and the reconstruction of the entire brain connectome
6. the experimental and computational challenges that need to be considered for accurate application of metabolomics in brain research
7. state-of-the-art high-throughput approaches for monitoring neurodegeneration-associated protein aggregation and for identifying chemical and biological inhibitors of these processes
8. recently developed biotechnological approaches for the discovery of chemical rescuers of protein misfolding and aggregation with potentially therapeutic effects against major neurodegenerative diseases

 Elements of Bioinformatics (big data bases)

Description

This is an intensive two and a half week course focused on computational analysis and robust interpretation of molecular data streams, generated by a broad spectrum of high-throughput experimental technologies, termed as -omics.  Overall these technologies revolutionize the landscape of modern biological research, enabling adoption of holistic approaches in the study and modification of biological mechanisms, yet their efficient integration in the discovery cycle entails great challenges, due to their immense complexity. The derivation of the instrumental molecular networks, behind disease emergence and progression, requests the intelligent utilization of powerful, computational strategies, in order to single out of the millions of biological measurements, those pivotal for the disease interrogated. Moreover, it is crucial to prioritize the important cellular events, in order to be able to propose a rational, combinatorial therapeutic approach, targeting these events, with novel combinations of compounds. In this direction, the various pillars of computational analysis that aid efficient and robust integration, analysis and interpretation of high-dimensional, omic data, potentially from multiple layers of dissection (cross-omics) will be examined, as well as the respective experimental technologies they support. 

Course Overview

Ultimate goal of this intensive course is that students are gaining familiarization with this broad pool of experimental technologies, under the umbrella of -omics, together with the various sorts of bio-informatic analytical algorithms and workflows, deployed at different stages and for different data-types. In addition, emphasis will be given in the meaningful integration and robust functional interpretation, in terms of the active emergent molecular modules that shape the phenotypic landscape of the biological problem interrogated, as well as the reliable association of molecular with phenotypic markers. The course will review the application of these concepts in the field of epidemiological stratification, pharmacogenomics analysis and personalized medicine. 

Topics to be discussed will cover

•             Next generation sequencing technologies, providing an introduction coupled by an overview of the main Next Generation sequencing methodologies (Gen-Seq, Exome seq, RNA- Seq, ChIP-Seq, etc), from the point of view of the computational analysis (de-novo / Reference Genome-based Assembly, Filtering, Signal Estimation, Differential Expression, Statistical Selection, Variant Calling) 

•             Microarray technologies and their processing (background correction, signal estimation, normalization, filtering and statistical selection)

•             Bioinformatic methodologies for multi-layered structural and functional characterization  of nucleotide and aminoacid sequences (homology based screening, ORF and gene function prediction, taxonomic and phylogenetic analysis, protein domain analysis, machine learning for functional characterization of proteins, regulatory motif analysis, hot-spot prediction, metagenomic screening)

•             Translational, integrative bioinfomatic analysis of omic datasets, which highlight the critical biological processes implicated in the biological problem interrogated (integrative methodologies, enrichment statistics, ontologies and controlled vocabularies, resampling based correction, gene set analysis, pathway prioritization, pharmacogenomic  knowledgebases)

•             Target prioritization and diagnostic stratification discussing the methodologies for the inference of small-sized, highly informative signatures for diagnostic classification, pharmacogenomic analysis, combinatorial treatment (semantic networks, interaction networks, network inference and analysis, derivation of molecular signatures, classification and clustering methodologies, machine learning techiques)

Skills & Learning Outcomes

Upon successful completion of this course, students will be able to define, describe and discern critical functional features of:

1.            the main Next Generation Sequencing Methodologies and their fundamental computational steps.

2.            the current state-of-the-art microarray technologies, popular platform configurations for various omic experimental designs, and the requisite algorithmic processing steps.

3.            the main structural bioinformatic algorithmic tools that are available for the analysis and functional characterization of nucleotide and aminoacid sequences.

4.            various molecular enrichment, gene set and pathway analysis tools / platforms.

5.            Network based methodologies for target prioritization, connectivity with drug-related databases, geometric estimation of complexity (Principal Component Analysis) supervised (machine learning, Linear discriminant analysis, PLS) and unsupervised (clustering) classification of phenotypic categories

Titles of lectures and names of the lecturers