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!