Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease (Xavier de la Cruz) Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease
*Watch the video at the end of the presentation
Seminar led by Dr. Xavier de la Cruz, ICREA Research Professor. Head of the Translational Bioinformatics in Neuroscience group of VHIR, at VHIR (22nd November 2012).
Content: The need to identify the pathological character of mutations may arise in different contexts in biomedical research. However, the methods available to address this problem essentially depend on the number of cases under analysis. When we work with only a few mutations we can use an artisan-like approach, where all information available on protein sequence, structure and function is manually retrieved and studied. However, when we need to characterize many variants, as can be the case in exome projects, faster methods are required to assess their pathogenicity. In my talk I will illustrate the principles underlying these two approaches with examples from the study of Fabry disease mutations, resulting from our collaborative work at the VHIR.
Semelhante a Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease (Xavier de la Cruz) Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease
Semelhante a Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease (Xavier de la Cruz) Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease (20)
Registros de enfermedades raras: Sistemas de información básicos para el fome...
Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease (Xavier de la Cruz) Identification of pathological mutations from the single-gene case to exome projects: lessons from the Fabry disease
1. Identification of pathological mutations
from the single-gene case to exome
projects: lessons from the Fabry disease
Xavier de la Cruz
3. From base pairs to bedside
(Green & Guyer, Nature, 2011)
Understanding Understanding Improving
genome disease biology healthcare
structure Understanding Advancing effectiveness
genome biology medical science
1990-2003
Genome
Project
2004-2010
2011-2020
Beyond 2020
4.
5. So, is there a problem?
2017
$517000
$285000
INTERPRETATION COST
<$100
6. From base pairs to …
Sample
Exome sequencing
Variant identification
and
quality control
INTERPRETATION
7. The interpretation problem
“…enormous amounts of raw data, but
still very little understanding of what it
means”
Exome sequencing context:
◦ Identify disease causative variants
◦ Prioritize of variants
◦ Speed: can we do this for 100‟s-1000‟s
variants in “reasonable” time?
◦ Reliability: can we provide good error models
for counseling/diagnosis/prognosis?
12. Type III Hereditary Hemochromatosis – TFR2
• TFR2, a dimeric type II transmembrane membrane protein expressed mostly in
the liver and CD71+ early erythroids.
• At least 50 families and 69 patients have been described with mutations in
TFR2 gene.
13.
14. Fabry disease
Systemic disorder characterized by:
progressive renal failure,
cardiovascular or cerebrovascular
disease, etc.
Caused by mutations in lysosomal
enzyme -galactosidase A
23. Protein
stability Functional interactions
Protein damage
…KKRHCSGWL…
Unspecific cellular
Y interactions
24. Conceptual context: impact of mutations
on protein structure/function
Empirical rules from site-directed
mutagenesis (‟80s, „90s):
◦ break disulphide bridges, burial of
charged residues, hydrogen bond loss,
disturb protein-protein interface, etc
◦ protein structure destabilization is
associated to function loss
Evolutionary conservation is linked to
biological function
25. Mutation properties
Sequence-based: V, , Blosum62
elements, etc
Structure-based: relate to mutation
location: accessibility, contact number
and type, etc
Evolutionary-based properties:
◦ wild-type (wt) conservation degree
◦ mutant rarity
◦ sequence variability at the mutation locus
(entropy)
27. MSA: thetechnicalside
Forverydivergentproteinsgood MSA
are veryhard to obtain
Protocol to buildalignments:
◦ RecoverfamilymemberswithPsiBlast (E-
value:0.001; seq.id.>40%) UniRef100
◦ Align with MUSCLE
Conservation may be misleading:
◦ proteinfunctionishighlyrelevantfor living
beings. E.g. histones. OK !
◦ databasebias. E.g.
onlyhominidaesequences are available.
PROBLEM ?!
44. Futuredirections:
pathogenicityprediction/analysis
Extend to more genes:
◦ Enoughmutation data
◦ Notenoughmutation data
Can
wepredictotherdiseasephenotypes?
◦ First tests suggest a similar
approachcouldworkforseverity
45. Summary
Thereisroomforimprovement in
mutationannotationtools
We are developping a new, gene-
basedtoolthatimprovespresentmethod
s
Ourmethodwillworkforlarge-
scalescoringprojects (exome) andfor
single-mutationanalyses