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La medicina del domani: invecchiare con successo Workshop di medicina anabiotica & medicina anti-ageing: from bench to bedside. Elisa Cevenini. Systems Biology and Longevity. CIG, C entro I nterdipartimentale “Luigi G alvani” Università di Bologna, Italy. Riviera di Taormina

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La medicina del domani:

invecchiare con successo

Workshop di medicina anabiotica & medicina anti-ageing:

from bench to bedside

Elisa Cevenini

Systems Biology and Longevity

CIG, Centro Interdipartimentale “Luigi Galvani”

Università di Bologna, Italy

Riviera di Taormina

December 6-7, 2007


Jeanne Calment, who died in 1997, lived to be 122 years and 164 days old


How to study human longevity ?


How to study human longevity ?

  • Centenarians and/or their offspring

    2. Very old sibpairs

    3. MZ and DZ Twins

    5. Longitudinal studies on

    cohorts of different age


the model of centenarians

selection

remodelling

+

Centenarians represent

an extreme phenotype: 1:7-10,000

WORKING HYPOTHESIS:

centenarians are likelynot simply the more robustbutthose people who adapted and remodelled better and quicker


Immunological and Genetic Studies in Centenarians

  • The result of an extensive collaborations among several groups:

  • S. Salvioli, M. Capri, S. Valensin, E. Mariani,AND MANY OTHERS

  • (Univ. of Bologna and Modena)

  • - F. Olivieri, F. Marchegiani, M. Cardelli, E. Mocchegiani (INRCA Ancona)

  • - P. Sansoni, R. Vescovini, F. Fagnoni (University of Parma)

  • - G. De Benedictis, G. Passarino, G. Rose (Univ. of Calabria)

  • D. Monti, A. Cossarizza (Univ. of Florence and Modena)

  • G. Paolisso, M. Barbieri (Univ. of Naples)

  • L. Deiana, G. Baggio, G. Pes, C. Carru (Univ. Sassari)

  • C. Caruso, D. Lio (Univ. of Palermo)

  • R. Paganelli (University of Chieti)

  • M. Rea (University of Belfast)

  • B. Jeune, Q. Tan, K. Christensen (Univ. of Odense)

  • J. Vaupel, A. Yashin (MPI for Demography, Rostock)

  • G. Attardi (CALTECH Pasadena)

  • G. Pawelec (University of Tubingen)

  • J. Powell, D. Mazzatti (UNILEVER, UK)


Most centenarians

are remarkably healthy


Centenarian phenotype

well preserved:

  • cell proliferation (fibroblasts)

  • telomere length (fibroblasts)

  • hemopoiesis (CD34+ hemopoietic stem cells)

  • proteasome activity

  • anthropometric parameters

  • glucose metabolism


CLINICAL CHARACTERISTICS OF THE STUDY GROUPS (n=466)

CONTROLS

CENTENARIANS

n = 298

n = 168

148/150

51/117

*p<0.01 vs control group

Paolisso et al., Exp. Gerontol., 37, 149-156, 2001


INSULINE RESISTANCE IN THE WHOLE POPULATION (n=466)

p for trend < 0.01

20-40 40-50 50-60 60-70 70-80 80-90 90-100 >100

Paolisso et al., Exp. Gerontol., 37:149-156, 2001


IGF-1 plasma levels are low in centenarians

Bonafè et al., J Clin Endocrinol Metabol 2003


Centenarians, Genetics and Human Longevity

  • Parents of centenarians lived longer than people of the same cohort

  • Siblings of centenarians have a “risk” to reach 100 several times higher than that of people of the same cohort

  • Offspring of centenarians have a lower mortality and are protected from CVD and cancer

  • Astrong familiar component of longevity


Centenarians, Genetics and Human Longevity

The survival advantage of offspring of long-living sibs is not shared by their spouses

despite the fact that they shared the same environment for most of their life

Thus the strong familiar component of longevity is likely a genetic component

and long living sibs should be highly enriched in longevity genes


  • 1697 FHS offspring members were grouped as following:

  • Neither parent survived 85 years (n=705)

  • One parent survived 85 years (n=804)

  • Both parents survived 85 years (n=188)

  • Score for cardiovascular risk factors was most favorable in individuals

  • with both parents surviving to 85 or older and was progressively worse

  • in those with one or no long-lived parent


GEHA

GEnetics of Healthy Aging

Integrated Project of EU 6thFP

25 Partners

Recruitment and Genome Scanning

(nuclear and mitochondrial genomes)

of 2650 90+ sibpairs and 2650 young controls

collected in 11 countries and China

May 1st 2004- April 30th 2009

(www.geha.unibo.it)

Coordinator:Prof. Claudio Franceschi

Project Manager:Dr. Alessandra Malavolta

Scientific Manager:Dr. Silvana Valensin


The genetics of human longevity

very old sibpairs

should derive from

families where longevity is running

and their DNA is likely

“enriched” in longevity genes

2100 complete trios are recruited so far (October 31, 2007)


GEHA LOGISTIC AND SAMPLE SIZE

  • 2650 sib pairs 90+ years old (for a total of 5300 subjects)

  • 2650 Younger Controls from the same geographic areas

7950 samples

from 11 European countries

All samples are centralized

at KTL inHelsinki

  • extraction

  • quality control

  • repository

DNA


GEHA DESIGN

younger CONTROLS(mean age: 62 years)

90+ SIBS

genetically unrelated ethnically-matched

SIB1 90+SIB1’ 90+

SIB2 90+

SIB2’ 90+

SIBn 90+

SIBn’ 90+

2

CASE-CONTROL ASSOCIATION

STUDIES – LD mapping

genome-wide (hypothesis-free)

or

region-focused (hypothesis- or data-driven)

1

LINKAGE analysis (ASP Analysis)

genome-wide (hypothesis-free)

non parametric

(no assumption on the

model of heritability of the trait)


1. ASSOCIATION STUDIES

Association analysis is expected to be more powerful for the detection ofcommonalleles that confermodestdisease risks

Today it is possible to perform genome-wide association studies using high throughput technologies and several hundred thousands SNPs (300K Illumina 500 K Affimetrix)


2. LINKAGE ANALYSIS

looks for co-inheritance of chromosomal regions with the trait in families

It is more powerful than association analysis for identifyingrare high-riskdisease alleles


  • The advantage of LINKAGE STUDIES is that

  • they are not influenced by population admixture

  • The advantage of ASSOCIATION (CASE-CONTROL)

  • STUDIES is that they require less genotyping

  • to obtain equivalent power


GEHA approach to increase the power of the genetic analysis


Mitochondrial DNA analyses

Inherited variability

Haplogroups (J)

and sub haplogroups

Epigenetic variability

Heteroplasmy at position C150T


  • HETEROGEITY OF HUMAN POPULATIONS

  • human longevity is a unique source of information

  • Humans areoutbredpopulations

  • The environment isrich of antigenic stimuli(bacteria, viruses, parasites)

  • Development(both in utero and after birth) is a long process

  • Lifespanis quite long

  • Cultural/anthropological determinantshave a strong impact on lifespan


  • HETEROGEITY OF HUMAN POPULATIONS

  • human longevity is a unique source of information

  • Genderdifferences are marked

  • Genomeis different from that of model systems

  • Information on diseasesis quite large and detailed

  • Demographic dataare extensive

  • Several cell types are mitotically competentand stem cells repair occurs in several tissues


Franceschi et al., 2007


Owing to the heterogeneity of the centenarian phenotype

genetic studies must be replicated

in different populations


Is human longevity population-specific?

-Human populations are genetically heterogeneous

- Genes potentially involved in longevity interact with the environment, dominated in humans by evolutionary history,socialand cultural factors

Associations between genetic variability and longevity may be difficult to replicate in different populations.


Example of positive replications:

- ApoE gene

- PON1 gene

- mtDNA haplogroup J

- mtDNA 150T mutation (polymorphism)


Example of negative replications:

- CEPT gene

  • MTP gene

    - mtDNA haplogroup J

    - mtDNA 150T mutation (polymorphism)


  • The Impact of Geography and Demography

  • 2. Increased Homozygosity

  • 3. The impact of mtDNA

  • 4. The Complex Timing of Alleles involved in Longevity

  • 5. Remodelling versus Antagonistic Pleiotropy

The unusual genetics of longevity


WORKING HYPOTHESIS

The phenotype of aged subjects

is the result of the capability of the body

to respond and to adapt

to non repaired damages

and their signalling capability

REMODELLING

accumulation of damages (mutations)

+

adaptative response


STRESSORS

MAINTENANCE SYSTEMS

(Defence and Repair)

ADAPTATION

Successful

REMODELLING

Unsuccessful

REMODELLING

LONGEVITY

DISEASE and DEATH


STRESSORS

MAINTENANCE SYSTEMS

(Defence and Repair)

Change in body microenvironment

Changes in gene

expression

Changes in protein

abundance, composition

interaction

Progressive change of internal milieu


  • Human aging and longevity are at the intersection of two Darwinian processes:

  • - the first occurssomatically at the level of single soma (individual remodelling)

  • the second occurred at the level of

  • H. sapiens as a species (evolutionary and ecological constraints)

  • - these two Darwinian processes are not coordinated


The unusual immunology and

genetics of ageing and longevity

AdaptationRemodelling

Antagonist

Pleiotropy

The levelling off of morbidity/mortality

at very advanced age


Age-related remodelling

gene expression profiling in peripheral blood T cells

in collaboration with D. Mazzatti and J Powell, UNILEVER, UK

(EU T-CIA project coordinated by G. Pawelec)


Data set

  • 25 arrays 19K: 5 age groups

    • A: 20 to 30 years old

    • B: 30 to 40 years old

    • C: 40 to 60 years old

    • D: 60 to 80 years old

    • E: 80 to >90 years old


Pathways significance analysis

the 1600 probes were embedded in KEGG pathway database, and a significance analysis was performed onto each KEGG pathway based on the ratio of significant probes (hypergeometric function test).


Biclustering of 1600 significant genes


Top 50 genes


K-means clustering of the significant gene set: 8 clusters


K-means clustering of the significant gene set: 12 clusters


The 16 genes among 1600 significant that are found to be among the 156 genes

that discriminate the different T cell populations(naïve, effectors and memory).


AGE-GROUPS gene profiling

1-way ANOVA selection

RED - upregulated; GREEN - downregulated


Twins data set:28 MZ couples(males and females)eight age classes

20-30

30-40

40-50

50-60

60-70

70-80

80-90

>90

54K probes


TWINSET profiling

twin correlation in time, whole genome (54k probes)

-0.004092 (-0.006922, -0.001263)

Epigentetic differences in old MZ twins?


High Dimensionality of genetic

and genomic data

Systems Biology of Ageing

Systems Immunology


Systems Biology

Aim: the study of the complex interactions of biological systems

“Both structure of the system and components play an indispensable role forming symbiotic state of the system as a whole” (Kitano)

Integrate approch: different tecniques, different methodologies, different types of data and different experties (biology, medicine, mathematics, informatics, physics)

Powerful tools are necessary:Mathematical Models, Bioinformatics, Network Analysis


A systems biology perspective

the genes associated with aging and

longevity might be functionally important

“highly connected hubs”

in biochemical pathways

and metabolic networks

(e.g. key genes for inflammatory responses)


A systems biology perspective

Aging might be related to

a marked

remodelling of functional networks

and/or inactivation

of highly connected genes


Biological networks are scale-free

characterized by few nodes which have many links (hubs)

Network topology: different system structure, different behaviour

Barabasi & Oltvai, Network Biology, understanding the cell’s functional organization. Nat. Rev. Genet. 2004


Afew hubs with many interactions;

Many nodes with few interactions


Yeast protein-protein interaction network

Only a few highly connected nodes (“hubs”) hold the network togheter

Barabasi & Oltway, Nat. Rev. Genet., 2004


Oltvai & Barabasi, Life’s complexity pyramid, Science2002


The immune system as a complex system

A network of cells communicating through chemical signaling (cytokines, chemokines, among others)


Bioinformatics 2005


Immune system integrated intercellular signalling network

TGF-b, RANK Ligand, MF derived Chemokine

Other 7 mediators

eB,D=10

eB,B=17

ACTH

CXCR3

Endorphins

Other 14 mediators

Dendritic cell

eD,D=11

CD100/Sema4D

CD-27 Ligand

IL-11

Other 8 mediators

B lymphocyte

eD,B=17

TNF-a, TGF-b, Substance P

Other 14 mediators

eB,M=3

IL-10

MIP-1a, b

TNF-a

IL-7

IL-10

TNF-a

eD,G=3

eD,M=5

IL-10

IL-15

IL-16

MIP-1a, b

TNF-a

eB,G=3

IL-6

IL-10

TNF-a

GM-CSF

MIP-1a, b

TGF -b

IL-12

IL-16

eM,D=5

TGF-b

IL-8/CXCL-8

CD30L

eG,B=3

TGF-b

eG,D=1

IL-12

IL-13

IL-15

Other 6 mediators

eM,B=9

eM,M=6

Eotaxin/CCL11

IL-15

MIP-1a, b

Other 3 mediators

Granulocyte

Mast cell

eM,G=1

TNF-a

Tieri et al., Bioinformatics, 2005


E(G)= the immune system represented as a valued directed graph G

N= cell types (vertices of the graph)

M = soluble mediators

K = arcs (links)

A directed arc fromvertex jis defined by the existence of at least

one mediatorsecreted by cell i and affecting cell j

eij= efficiency of pathway communication between vertices (matrix)


ra = relevance of each mediator a (a = 1,…M)


NETWORK RELEVANCE r

OF THE MEDIATORS OF THE IMMUNE CELL NETWORK.

The 14 mediators in the highest ranks,

playing a crucial role for the network efficient communication,

are pro- or anti-inflammatory mediators


Mathematical modeling of the Immune System

Results indicate that mediators involved in the inflammatory process have the more central role in the immune network, mirroring the fact that many of the major age-related diseases have an inflammatory pathogenesis.


BOW TIE

the architectural basis of biological complex systems

Core

Limited immune-

neuroendocrine

elements

Limited number

of elements

highly

evolutionary

conserved

A variety of

fine tuned

responses

Many

Stimuli

Integrative activity

OPTIMIZATION ROBUSTNESS EVOLVABILITY


Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size […] The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture.

JS Mattick and MJ Gaggen Mol Biol Evol 18:1611-1630, 2001


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