Molecular Evolution and Medicine Symposium Temple University, Philadelphia, USA 16-17 September, 2017

1. Joaquín Dopazo
Clinical Bioinformatics Research Area,
Fundación Progreso y Salud,
Functional Genomics Node, (INB-ELIXIR-es),
Bioinformatics in Rare Diseases (BiER-CIBERER),
Sevilla, Spain.
A look at the human mutational
load from the systems biology
perspective
http://www.clinbioinfosspa.es/
http://www.babelomics.org
@xdopazo @ClinicalBioinfo
Molecular Evolution and Medicine Symposium
Temple University, Philadelphia, USA 16-17 September, 2017

2. A high level of deleterious variability
exists in the human genome
Variants predicted to severely affect the function of
human protein coding genes known as loss-of-function
(LOF) variants were thought:
To have a potential deleterious effect
To be associated to severe Mendelian disease
However, an unexpectedly large number of LOF
variants have been found in the genomes of apparently
healthy individuals: 281-515 missense substitutions per
individual, 40-85 of them in homozygous state and
predicted to be highly damaging.
A similar proportion was observed in miRNAs and
possibly occurs in any functional element in the genome
Problem: when a Loss-of-Function (LoF) variant is deleterious?
Highly relevant in diagnosis: more than 40% patients with Mendelian
diseases do not present a clear diagnostic variant, only VUS (variants
of unknown significance)

3. Deleterious variation in disease
Hypertension Diabetes
Dopazo et al., Mol Biol Evol, 2016
Deleterious variation of rare
diseases is population-specific
and is useful to detect new
disease genes
However, interpreting disease in terms of
single gene deleterious variation is naïve.
Most human genetic diseases (actually,
almost all traits) have a modular nature.
Causative genes for the same or
phenotypically similar diseases generally
reside in the same biological module, either
a protein complex, a sub-network of protein
interactions , or a pathway
Goh 2007 PNAS
Usher Marfan
CommonRare

4. Potentially deleterious mutations
(LoF) are conditionally deleterious
(learning from healthy individuals)
Abraham Wald’s original
WWII study. Where should
you put the armor?
Deleterious mutations in 1000g (up) and somatic CLL
deleterious mutations (down)
Garcia-Alonso 2014 Mol Syst Biol
• When LoF mutations
occur in the interactome
periphery you have
healthy people (1000g)
• When LoF mutations
occur in internal parts
you have disease (CLL)

5. LoF are conditionally deleterious
From left to right column: 1) Actual 1000g individuals, 2)
synthetic individuals with only LoFs observed in 1000g
randomly sampled, 3) same number of random LoF
Garcia-Alonso 2014 Mol Syst Biol
You may think that genes in the periphery
are less important but…
LoF observed in healthy
individuals are not
neutral with respect to
relevant network
properties.
Healthy individuals
seem to accommodate
LoF in a way that better
preserves network
properties

6. 𝑆 𝑛 = 𝜐 𝑛 ∙ 1 − 1 − 𝑠 𝑎
𝑠 𝑎 ∈𝐴
⋅ 1 − 𝑠𝑖
𝑠𝑖∈𝐼
From individual gene
expression profiles
To profiles of circuit
activity (and cell
functional activity)
Two types of activities
Linking genotype to phenotype via cell
function: models of signaling pathways
LoF mutations can be introduced in the model as KOs in the proper transcriptional context

7. Signaling models uncover the molecular
mechanisms behind cancer hallmarks
Hanahan, Weinberg, 2011
Hallmarks of cancer: the next
generation. Cell 144, 646
Negative regulation of release of cytochrome c
from mitochondria (inhibition of apoptosis)

8. Impact of LoF mutations occurring in
healthy people on human signaling
Signaling circuits that change across
the human population tree
There are many circuits affected by
mutations linked to functions such as:
immune system, melanogenesis,
metabolism of lipids, glycolysis, hormones
(thyroid, progesterone, etc.), several
cancer pathways, etc.
Asia
(EAS)
Africa
Europe
Asia
(SAS)
America

9. Tolerated LoF can combine without
increasing effect on signaling
Impactoncircuitactivity
Effect of mutations
on specific tissues
in KEGG pathways.
GTEx is used for
deriving an average
tissue value for
gene expression
and CADD>20 to
define a variant as
LoF. Recessive
model is assumed.
Like in PPI networks, pathways are robust structures that can undergo a
number of LoF mutations with lower impact than expected by chance

10. The use of new algorithms that enable the transformation of genomic
measurements into cell functionality measurements that account for
disease mechanisms and for drug mechanisms of action will ultimately
allow the real transition from today’s empirical medicine to precision
medicine and provide an increasingly personalized treatments
The real transition to precision medicine
Intuitive
Based on trial
and error
Identification of
probabilistic
patterns
Decisions and
actions based
on knowledge
Intuitive Medicine Empirical Medicine Precision Medicine
Today Tomorrow
Degree of personalization

11. Clinical Bioinformatics Research Area
Fundación Progreso y Salud, Sevilla, Spain, and…
...the INB-ELIXIR-ES, National Institute of Bioinformatics
and the BiER (CIBERER Network of Centers for Research in Rare Diseases)
@xdopazo @ClinicalBioinfoFollow us on twitter
https://www.slideshare.net/xdopazo/