Presentation made by Abeliovich and Rhinn on the 20th of April, 2017, at the live webinar hosted by Alzforum: http://www.alzforum.org/webinars/webinar-cortex-aging-too-fast-blame-tmem106b-and-progranulin

1. Genetic regulation of human brain aging
Herve Rhinn and Asa Abeliovich
Departments of Pathology, Neurology, and Cell Biology
Taub Institute for Alzheimer’s disease and the Aging Brain
Columbia University
Disclosure:
Co-Founder, Consultant

2. 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

3. 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

4. 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 80
Age (years)
PNOClevels(a.u.)
GFAPlevels(a.u.)
Causality: what drives human brain aging
phenotypes?
Most changes are secondary to the aging process

5. 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%
Gene-setsexpressionlevel
(%change/decade)
***
***
***
*
***
***
***
❖ Effect of age on cell types gene-sets:
Genesets based on data from
A 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

6. -- Aging phenotypes appear remarkably diverse
across the human population, at any given age
>> Genetic? Environmental? Random?
Aging appears inherently diverse
Chronological age (years)
Genericagingtrait
Individual 1
Individual 2
Individual 3
Δ
Aging
Chronological age (years)
Δ
Red: Individual appearing older
than actual chronological age
Blue: Individual appearing younger
than actual chronological age
Δ
Genericagingtrait

7. -- 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

8. Quantifying human brain aging: a transcriptomic approach
-5
0
5
20 40 60 80
Age (years)
-5
0
5
20 40 60 80
Age (years)
PNOClevels(a.u.)
GFAPlevels(a.u.)
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).]
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-wide
Gene expression profiling
across a population

9. Identify set of all
age-dependent
genes
Transcriptome-wide
Gene 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-Aging

10. From gene expression to Delta-aging
Biologicalage
Chronological age
Δ <
0
Δ >
01. Theory

11. From gene expression to Delta-aging
Biologicalage
Chronological age
Δ <
0
Δ >
01. Theory
Glevel
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

12. . . .
for a given individual by integrating all the genes affected by aging:
ΔI=
1
𝑁
σ 𝐺=1
𝑁 σG,I
a 𝐺
Combination across all the genes associated with ageGene1
Gene2
Gene3
GeneN
Delta Age for individual I
Residual 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
Biologicalage
Chronological age
Δ <
0
Δ >
01. Theory
Glevel
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

13. Identify set of all
age-dependent
genes
Transcriptome-wide
Gene 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
-log10(Pval)
Recombinationrate(cM/Mb)
Position on chr7 (Mb)
Chromosome
-log10(Pval)
TMEM106B
GRN
Chromosomes
TMEM106B gene variants associated with Delta-Aging

14. TMEM106B gene variants associated with Delta-Aging
Δ-Aging(years)
-12
-9
-6
-3
0
3
6
AA GA GG
Chronological age
Apparentbiologicalage
TMEM106B Risk allele:
: Carriers
: Non-carriers
65yo

15. 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

16. TMEM106B phenotypes
Stagi et al., MCN 2014
Cruchaga et al., Arch Neurol. 2011
Yu et al., Neurology. 2015
Nelson et al., Acta Neuropathol. 2014
Rutherford 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

17. ††
MMSEscore
24
25
26
27
28
29
30
RR PR PP RR PR PP
<65yo >65yo
TMEM106B gene variants associated with cognition
in older cohorts

18. 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
†††
M1gene-setlevels
(%change/decade)
0%
5%
10%
RR PR PP
n.s.
M2gene-setlevels
(%change/decade)
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

19. 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

20. 0
20
40
60
80
100
0 20 40 60 80 100
Chronological age (years)
ApparentBiologicalage(years)
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

21. Philip De Jaeger and the Immvar consortium
Datasets depositors:
TGEN , Amanda Myers
NABEC, Andrew Singleton
ROSMAP, David Bennett
UKBEC, John Hardy, Mina Ryten
HBTRC, Eric Schadt
Datasets hosts:
GEO (NIH)
Synapse (Sage Bionetworks)
dbGAP (NIH)
NIAGADS (NIA)
AMP AD (NIA, FNIH)
Many Thanks!