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Genetic regulation of human brain aging
Herve Rhinn and Asa AbeliovichDepartments of Pathology, Neurology, and Cell BiologyTaub Institute for Alzheimer’s disease and the Aging BrainColumbia University
Disclosure: Co-Founder, Consultant
Healthy aging, longevity, and age-associated disease
Longevity and healthspan are distinct
Life expectancy (LE) and healthy life years (HLYs)
at 50 years of age for all EU countries for women.
HLYs=healthy life years. LE=life expectancy.
Carol Jagger et al., Lancet 2009
Michaelangelo, Cumaean Sybil
Alzheimer's Association 2014 Facts and Figures Report
Do mechanisms implicated in aging by studies in rare human genetic disorders (eg, Progerias), or other organisms, play a key role in healthy aging?
But do these affect brain healthspan?
Burtner and Kennedy, Nature Reviews MCB 2010 Pitt and Kaeberlein, PLoS Biology 2014
Benayoun and Brunet, Nature Reviews MCB 2015
Lopez-Otın et al., Cell 2013
Mechanisms regulating human healthspan
Molecules (proteins, RNA transcripts, other) cells, tissues, are altered in an age-dependent manner…. >>But what changes are causal versus secondary to the aging process?
Meta-analysis of 4 datasets n=716 individuals total >25 years old gene expression array of human frontal cortex from autopsy material without known CNS disease
Braincloud [Colantuoni, C. et al. PLoS genetics (2007).]TGEN [Myers, A. J. et al. [Nature genetics (2007); Webster, J. A. et al. American journal of human genetics (2009).]BrainEQTL [Gibbs, J. R. et al. PLoS genetics (2010).]HBTRC [Zhang, B. et al. Cell 153, (2013).]
3329 genes significantly correlated in expression with chronological age (false discovery rate [FDR]<5% by linear regression, after correction for gender and batch effects)
❖ Datasets used:
-5
0
5
20 40 60 80Age (years)
-5
0
5
20 40 60 80Age (years)
PN
OC
leve
ls (
a.u
.)
GFA
P le
vels
(a.
u.)
Causality: what drives human brain aging phenotypes?
Most changes are secondary to the aging process
Molecules (proteins, RNA transcripts, other) cells, tissues, are altered in an age-dependent manner…. >>But what changes are causal versus secondary to the aging process?
Meta-analysis of 4 datasets n=716 individuals total >25 years old gene expression array of human frontal cortex from autopsy material without known CNS disease
Braincloud [Colantuoni, C. et al. PLoS genetics (2007).]TGEN [Myers, A. J. et al. [Nature genetics (2007); Webster, J. A. et al. American journal of human genetics (2009).]BrainEQTL [Gibbs, J. R. et al. PLoS genetics (2010).]HBTRC [Zhang, B. et al. Cell 153, (2013).]
3329 genes significantly correlated in expression with chronological age (false discovery rate [FDR]<5% by linear regression, after correction for gender and batch effects)
❖ Datasets used:
Causality: what drives human brain aging phenotypes?
-20%
-10%
0%
10%
20%
30%
40%
Gen
e-se
ts e
xpre
ssio
n l
evel
(%ch
ange
/ d
ecad
e)
*** **
*
***
*
*** **
* ***
❖ Effect of age on cell types gene-sets:
Genesets based on data fromA survey of human brain transcriptome diversity at the single cell level.Proc Natl Acad Sci U S A. 2015 Jun 9;112(23):7285-90
-- Aging phenotypes appear remarkably diverse across the human population, at any given age
>> Genetic? Environmental? Random?
Aging appears inherently diverse
Chronological age (years)
Gen
eric
agi
ng
trai
t
Individual 1Individual 2Individual 3
Δ
Aging
Chronological age (years)
Δ
Red: Individual appearing older than actual chronological age
Blue: Individual appearing younger than actual chronological age
Δ
Gen
eric
agi
ng
trai
t
-- Aging phenotypes appear remarkably diverse across the human population, at any given age
>> Genetic? Environmental? Random?
-- Diversity across aging phenotypes, tissues, brain regions within an individual
Dukart et al., Plos Computational Biology 2013
Aging appears inherently diverse
Quantifying human brain aging: a transcriptomic approach
-5
0
5
20 40 60 80
Age (years)
-5
0
5
20 40 60 80Age (years)
PN
OC
leve
ls (
a.u
.)
GFA
P le
vels
(a.
u.)
Braincloud [Colantuoni, C. et al. PLoSgenetics (2007).]TGEN [Myers, A. J. et al. [Nature genetics (2007); Webster, J. A. et al.American journal of human genetics(2009).]BrainEQTL [Gibbs, J. R. et al. PLoSgenetics (2010).]HBTRC [Zhang, B. et al. Cell 153, (2013).]
Meta-analysis of 4 datasets n=716 individuals total >25 years old gene expression array of human frontal cortex from autopsy material without known CNS disease
3329 genes significantly correlated in expression with chronological age (false discovery rate [FDR]<5% by linear regression, after correction for gender and batch effects)
We first define apparent/biological age based on population transcriptome analysis
Identify set of all age-dependent
genes
Transcriptome-wideGene expression profiling
across a population
Identify set of all age-dependent
genes
Transcriptome-wideGene expression profiling
across a population
Quantify Δ-Aging for each individual
= (apparent biological age) - ( true chronological age )
TMEM106B gene variants associated with Delta-Aging
Chronological age (years)
Δ
Red: Individual appearing older than actual chronological age
Blue: Individual appearing younger than actual chronological age
Δ
D-A
gin
g
From gene expression to Delta-aging
Bio
logi
cal a
ge
Chronological age
Δ < 0
Δ > 01. Theory
From gene expression to Delta-aging
Bio
logi
cal a
ge
Chronological age
Δ < 0
Δ > 01. Theory
Gle
vel
Chronological ageChrAgeI ExpAgeI,G
σG,I
GI
ΔI,G
ΔI,G= ExpAgeI,G - ChrAgeI = σG,I/ aG
(G level ) = aG x ChrAge + bG
GI= aG. ChrAgeI + bG + σG,I
GI= aG. ExpAgeI,G + bG
2. Application to a single gene
. . .
for a given individual by integrating all the genes affected by aging:
ΔI= 1𝑁σ𝐺=1𝑁 σG,I
a𝐺
Combination across all the genes associated with ageG
ene
1
Gen
e2
Gen
e3
Gen
e N
Delta Age for individual IResidual for individual I of a linear fit of G levels in function of age across individuals
Linear regression coefficient of a linear fit of G levels in function of age across individuals
From gene expression to Delta-aging
Bio
logi
cal a
ge
Chronological age
Δ < 0
Δ > 01. Theory
Gle
vel
Chronological ageChrAgeI ExpAgeI,G
σG,I
GI
ΔI,G
ΔI,G= ExpAgeI,G - ChrAgeI = σG,I/ aG
(G level ) = aG x ChrAge + bG
GI= aG. ChrAgeI + bG + σG,I
GI= aG. ExpAgeI,G + bG
2. Application to a single gene
3. Extension to multiple genes
Delta-Age has the dimension of a time and is age-independent Other co-factors (gender, experimental batches…) can be corrected for using a multiple regression
Identify set of all age-dependent
genes
Transcriptome-wideGene expression profiling
across a population
Quantify Δ-Aging for each individual
= (apparent biological age) - ( true chronological age )
Genome-wide scan for SNP genetic
modifiers of Δ-Aging across populations
-lo
g 10
(Pva
l)
Reco
mb
inatio
n rate (cM
/Mb
)
Position on chr7 (Mb)Chromosome-l
og 1
0 (P
val)
TMEM106B
GRN
Chromosomes
TMEM106B gene variants associated with Delta-Aging
TMEM106B gene variants associated with Delta-Aging
Δ-A
gin
g (y
ears
)
-12
-9
-6
-3
0
3
6
AA GA GGChronological age
Ap
par
ent
bio
logi
cal a
ge
TMEM106B Risk allele:: Carriers: Non-carriers
65yo
TMEM106B modifies FTD with or without Progranulin mutations
Common variants at 7p21 are associated with frontotemporal lobar degeneration with TDP-43 inclusions.Van Deerlin et al., V. Lee, Nature Genetics 2010
TMEM106B
TMEM106B phenotypes
Stagi et al., MCN 2014
Cruchaga et al., Arch Neurol. 2011
Yu et al., Neurology. 2015 Nelson et al., Acta Neuropathol. 2014Rutherford et al., Neurology. 2012
❖ TMEM106B is associated with age at onset in Progranulin mutation carriers
❖ TMEM106B is associated with TDP-43 pathology and hippocampal sclerosis
❖ TMEM106B regulates lysosomes at a cellular level
††
MM
SE s
core
24
25
26
27
28
29
30
RR PR PP RR PR PP
<65yo >65yo
TMEM106B gene variants associated with cognition in older cohorts
TMEM106B risk variants induce a pro-inflammatory polarization of innate immune inflammatory cells
• Among the innate immune-associated genes, TMEM106B genotype affects age-associated myeloid cells polarization: cells appear more inflammatory
0%
2%
4%
6%
8%
RR PR PP
†††
M1
gen
e-se
t le
vels
(% c
han
ge/d
ecad
e)
0%
5%
10%
RR PR PP
n.s.
M2
gen
e-se
t le
vels
(% c
han
ge/d
ecad
e)
Human myeloid cells (macrophage, microglia, dendritic cells)
Inflammatory Polarization mRNA analysis
M1-like gene set expression profile
M2-like gene set expression profile
M2-like
M1-like
❖ Working hypothesis:
rs1990622 rs1990622
TMEM106B protective allele
• At a cell level, inflammatory innate immune cell-related genes go up, neuron genes go down
Conclusions
• TMEM106B and GRN modulate healthy aging in frontal cortex
• Different genetic determinants for longevity and healthy aging
• Neuroinflammation appears as a candidate mechanism for healthy aging
Δ
Aging
Protective
Risk
0
20
40
60
80
100
0 20 40 60 80 100Chronological age (years)
Ap
par
ent
Bio
logi
cal a
ge (
year
s)
Younger reference set
Tested after exercise
Δ
Older reference set
• Genetic determinant of aging rates in other organs
• Environmental determinant of aging
• Biomarker for anti-aging interventions >>>
• Application to phenotypes other than aging
Longitudinal analysis of Δ-aging in serial muscle tissue biopsies from elderly individuals before and after a 6mo-long vigorous exercise routine program
GSE8479
Perspectives
Philip De Jaeger and the Immvar consortium
Datasets depositors:TGEN , Amanda MyersNABEC, Andrew SingletonROSMAP, David Bennett UKBEC, John Hardy, Mina RytenHBTRC, Eric Schadt
Datasets hosts:GEO (NIH)Synapse (Sage Bionetworks)dbGAP (NIH)NIAGADS (NIA)AMP AD (NIA, FNIH)
Many Thanks!