Adriana Maggi
DOCENTE DI BIOTECNOLOGIE FARMACOLOGICHE
CORSO DI LAUREA SPECIALISTICA IN BIOTECNOLOGIE DEL FARMACO
AA 2011/2012
Lezione 5
Bioinformatica nel processo di drug discovery
Bioinformatics in the
drug discovery process
Alessandro Villa
Center of Excellence on Neurodegenerative Diseases
Department of Pharmacological Sciences
University of Milan
Bioinformatics
Bioinformatics is about finding and interpreting
biological data using informatic tools, with the goal of
enabling and accelerating biological research
Bioinformatics spans a wide range of activities
- Data capture
- Automated recording of experimental results
- Data storage
- Visualization of raw data and analytical results
- Access to data using a multitude of databases
and query tools
Workflow
Experimental
Design
Sample collection
and analysis
Human based
Data collection,
filtering, and input
Data analysis
Output results
Computer aided
Critical analysis of the results
Definitely human based
Bioinformatics is web-centric
Most academic groups and many companies make
computing tools and data available on the web
Most of them are free
based on the contribution of worldwide researchers
Focus on
Bioinformatics strategies for
disease gene identification
Traditional Methods of Drug Discovery
natural
(plant-derived) treatment
for illness
↓
isolation of active
compound
(small, organic)
synthesis
of compound
↓
manipulation of
structure to get better
drug
(greater efficacy,
fewer side effects)
Modern Methods of Drug Discovery
What’s different?
• Drug discovery process begins
with a disease (rather than a treatment)
• Use disease model to pinpoint relevant
genetic/biological components (i.e. possible
drug targets)
Defining genetic disease
Genetic disorders are caused by abnormalities in the
genetic material
Abnormalities can range from a small mutation in a single gene to the
addition or subtraction of an entire chromosome or set of
chromosomes.
In general, four types of genetic disorders can be distinguished
Monogenetic
Monogenetic (also called Mendelian or single gene) disorders are caused by
a mutation in one particular pair of gene.
A mutated gene can result in a mutated protein, which can no longer carry
out its normal function.
Over 10,000 human diseases are known to be caused by defects in single
genes, affecting about 1% of the population as a whole.
Monogenetic disorders often have simple and predictable inheritance
patterns.
Monogenetic disorders
Thalassaemia
Sickle cell anemia
Haemophilia
Cystic Fibrosis
Tay sachs disease
Fragile X syndrome
Huntington's disease
Polygenic
Polygenic disorders are due to mutations in multiple genes in combination
with external factors, such as lifestyle and environment
Heritability presents the contritution of genetic factors in the formation of
multiple gene diseases. Higher heritability is generally interpreted as a larger
contribution of genes.
Examples of polygenic diseases include coronary heart disease, diabetes,
hypertension, and peptic ulcers.
At present, there are still many difficulties in prenatal diagnosis for multiplegene diseases, however, as technology develops, prenatal diagnosis for
common multiple-gene diseases will be available in the near future.
Polygenic disorders
Type 1 diabetes
Multiple sclerosis
Autism
Asthma
Celiac disease
Chromosomal
Abnormalities in the chromosomal number or structure, e.g. (partial)
deletion, extra copies, breakage, and (partial) rearrangements, can result in
disease.
Chromosomal
Down syndrome
Klinefelter's syndrome
Prader–Willi syndrome
Turner syndrome
Mitochondrial
Mitochondria, like the cell nucleus, contains DNA (mtDNA), which is the
biggest difference between mitochondria and other sub-units. mtDNA is only
inherited from the mother and exhibits higher mutation rate than that of
nuclear DNA as well as low repair capacity.
Mitochondrial diseases have threshold effects. That means mitochondrial
diseases could occur only if the abnormal mtDNA exceeds the threshold.
Although sometimes diseases would not happen in the female carriers, for
their underthreshold abnormal mtDNA or certain nuclear effects, mutant
mtDNA can also be passed from generation to generation.
Mitochondrial disorders
Kearns-Sayre syndrome
Chronic progressive external ophthalmoplegia
Mitochondrial encephalomyopathy with lactic acidosis
Leigh syndrome
Gene identification/finding of inherited disease
Every gene has a specific task
Identification of disease genes is similar to finding genes
responsible for normal functions
The mutation may be within a gene/protein or within a
regulatory part of the genome that, e.g., affects the amount of
protein being produced.
The mutation changes the protein, which alters the way the
task is usually performed
DISEASE
Gene identification/finding of inherited disease
Timeline
1983 – Invention of Polymerase Chain Reaction (PCR) technique by Kary Mullis
1989 – the National Center for Human Genome Research is created
1990 – the Human Genome Project (HGP) starts to map and sequence human DNA
1996 – the DNA sequence of the first eukaryotic genome (S. Cerevisiae) is completed
2002 – the mouse genome sequence is completed
2003 – the human genome sequence is completed
Now – the genome sequences are still frequently updated with new and rearranged
sequences, and some parts are still missing.
Gene identification/finding of inherited disease
We have a huge amount of genetic data in place.
And now?
Find a candidate gene!
Candidate gene definition
A candidate gene is a gene that is suspected
to be involved in a genetic disease
It is located in a chromosome region suspected of
being involved in the expression of a trait such as a
disease, whose protein product suggests that it could
be the gene in question.
Disease genes identification in complex disorders
Complex disorders are multifactorial and many such
diseases, like heart and vascular disease are quite
common.
Five steps are applicable to research of
a complex disease:
I. Establish that the disease is indeed (partially ) caused
by genetic factors
To prove that the candidate is in fact a gene, demonstration of a
genetic mutation is needed.
Mutation analysis can be done by direct sequencing. Changes in
the splicing process of the gene may be missed when screening
protein-coding DNA sequences only, but are detectable at the
RNA level using RT-PCR. With RT-PCR and related methods it is
possible to evaluate whether the spatio-temporal gene
expression pattern is compatible with the phenotype of
interest.
Final proof may require the examination of the effect of
induced mutation in model organisms.
Mutation analysis
II. Perform segregation analysis on individual pedigree to
determine the type of inheritance.
Inheritance can vary from Mendelian to polygenic,
depending on penetrance and environment.
The mode of inheritance determines the linkage analysis
methods applicable (next step).
Segregation analysis
E.g. Genetic Association Interaction Analysis Software (GAIA)
http://www.bbu.cf.ac.uk/html/research/biostats.htm
III. Perform linkage analysis to map susceptibility loci
Genetic linkage analysis is a statistical method that is used to
associate functionality of genes to their location on
chromosomes. The main idea is that markers which are found in
vicinity on the chromosome have a tendency to stick together
when passed on to offsprings. Thus, if some disease is often
passed to offsprings along with specific markers, then it can be
concluded that the gene(s) which are responsible for the disease
are located close on the chromosome to these markers.
Parametric, useful for Mendelian traits;
Non parametric, useful for complex diseases.
Pedigree Analysis Software
E.g. MERLIN
http://www.sph.umich.edu/csg/abecasis/Merlin/index.html
IV. Fine mapping of the susceptibility gene by
population-association studies
After using linkage to get an idea where disease genes may be
located, use association to try to better locate the gene.
Association allows to test candidate genes, or very small
genetic regions, to see if they are associated with the
phenotype in study. These tests can result in the location of a
risk gene.
Association studies require the use of DNA from many
individuals. However, association studies do not use families.
Rather, they look at DNA from affected individuals compared
to DNA of controls (non-affected individuals who do not have
to be relatives.)
V. Elucidation of the DNA sequences/genes
Confirm their molecular and biochemical action and involvement.
Relatively easy in case of Mendelian disorders, because the
disease is due to a single change.
In complex disorders the susceptibility is often modelled as a
quantitative trait locus (QTL)
In silico positional cloning
Once the critical region for a genetic disease has been
determined by linkage analysis, population-association, etc., the
human genome sequence can be used to identify positional
candidate disease genes.
Genome browsers, biological databases, and other bioinformatics
tools all contribute to the gene finding strategy.
Bioinformatics approach to disease gene identification
The release of genomic sequences, full-lenght cDNA sequences,
expressed sequence tags (ESTs), and large-scale expression microarray data of human and model organisms (e.g. Mus Musculus)
offer invaluable resources for studying genetic diseases.
This huge amount of data is stored in numerous different
databases, thus making the use of high performance computing an
essential tool for decoding the information contained in these
databases.
DNA Data Bank of Japan
http://www.ddbj.nig.ac.jp/index-e.html
European Molecular Biology Laboratory database
http://www.ensembl.org/
GenBank
http://www.ncbi.nlm.nih.gov/genbank/
First Step
To search for all genes between two genetic markers on the
chromosome under study
Essential is a proper description of the location of genes and
other annotations like regulatory elements
Databases and computational tools have been developed to
identify all genes on the human genome sequence. None is
perfect and genes may be missed, or false genes may be
annotated  manual evaluation is necessary or..
Multiple sequence analyses on different databases
should be performed
USCS Genome Browser
http://genome.ucsc.edu/
Second Step
Functional cloning and candidate gene selection
We identified all the genes between the genetic markers
In theory, every gene within the disease critical region can cause the disease.
When the critical region is large, or the gene density is high,
positional candidates are many.
Strategies:
•There may be already a suspicion on the biochemical/pathogenic background of the
disease
•If a genetic disorder affects e.g. the liver, select only genes expressed in liver
•For known genes, the knowledge in literature can be used to select the candidate genes
•Genes located within the critical disease region that have a functional similarity to genes
involved in related diseases are good candidates
The Gene Ontology project is a major bioinformatics initiative with the aim
of standardizing the representation of gene and gene product attributes
across species and databases. The project provides a controlled vocabulary
of terms for describing gene product characteristics and gene product
annotation data from GO Consortium members, as well as tools to access
and process this data.
Database for Annotation, Visualization and Integrated Discovery
http://david.abcc.ncifcrf.gov/home.jsp
Systematic and integrative analysis of large gene lists using DAVID Bioinformatics
Resources. (2009) Nat Protoc. 4(1):44 -57.
Further, knowledge of model organisms makes
comparative candidate selection possible
This situation applies when a gene is known, which
causes a similar phenotype in other species.
Transfer of knowledge by phenotype is strightforward
in Mus Musculus, being evolutionarily close to humans
This grid, called Oxford grid, shows the relationship between human and mouse
chromosomes. Chromosome location of either of the species often predicts the
chromosome location in the other species.
When none of the known genes has mutations, it is possible to
try to find new genes in the critical region.
Comparative genome analysis of related species
present us with a wealth of opportunities for studying
evolution and gene/protein function.
Homology-based function-prediction transfers information from
known genes/proteins to unknow sequences and remains the
primary method to determine the function of a new gene
Example of homology-based methods are
Basic Local Alignment Search Tool (BLAST)
Evolutionary annotation database (EVOLA)
BLAST
http://blast.ncbi.nlm.nih.gov/
EVOLA
http://www.h-invitational.jp/evola/search.html
EVOLA
http://www.h-invitational.jp/evola/search.html
Biological Networks
Over the last years, the wealth of information derived from
high-throughput interaction screening methods have been
used to map different biological interactions.
These maps provide a vision of the molecular networks in
biological systems.
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Biological Networks
Gene regulatory and signal transduction networks describe how genes
can be activated or repressed and therefore which proteins are
produced in a cell at a particular time.
Protein-protein interaction networks represent the interaction
between proteins such as the building of protein complexes and the
activation of one protein by another protein.
Metabolic networks show how metabolites are transformed, for
example to produce energy or synthesize specific substances.
Gene regulatory, protein-protein interaction and metabolic networks
interact with each other and build a complex network of interactions.
Biological Networks
The study of biological network is essential to understand the
role of candidate genes in genetic diseases
Finally they are very useful to identify the
genotypes that are associated with phenotypes,
a major goal in genetic research
Ingenuity Pathway Analysis Software
http://www.ingenuity.com/index.html
Confirming a candidate gene
Selected genes have to be tested individually to see if there is
evidence that mutations in them do cause the disease in question.
Mutation screening. Identifying mutations in several unrelated affected individuals
strongly suggests that the correct candidate gene has been chosen, but formal proof
requires additional evidence.
Restoration of normal phenotype in vitro. If a cell line that displays the mutant
phenotype can be cultured from the cells of a patient, transfection of a cloned normal
allele into the cultured disease cells may result in restoration of the normal phenotype
by complementing the genetic deficiency.
Production of a mouse model of the disease. Once a putative disease gene is
identified, a transgenic mouse model can be constructed. If the human phenotype is
known to result from loss of function, gene targeting can be used to generate a
germline knockout mutation in the mouse ortholog. The mutant mice are expected to
show some resemblance to humans with the disease.
Suggested readings
1. A. L. Barabási, N. Gulbahce, J. Loscalzo, Network medicine: a network-based
approach to human disease. Nature Reviews Genetics 12, 56 (2011).
2. J. K. DiStefano, Disease Gene Identification: Methods and Protocols. (Humana Press,
2011).
3. S. D. Mooney, V. G. Krishnan, U. S. Evani, Bioinformatic tools for identifying disease
gene and SNP candidates. Methods Mol. Biol 628, 307 (2010).
4. A. Schlicker, T. Lengauer, M. Albrecht, Improving disease gene prioritization using the
semantic similarity of Gene Ontology terms. Bioinformatics 26, i561 (2010).
5. N. Tiffin et al., Integration of text-and data-mining using ontologies successfully
selects disease gene candidates. Nucleic acids research 33, 1544 (2005).
6. Y. Zhang et al., Systematic analysis, comparison, and integration of disease based
human genetic association data and mouse genetic phenotypic information. BMC
medical genomics 3, 1 (2010).