El jueves y viernes 18 y 19 de enero del 2018 se organizó en la Fundación Ramón Areces un Simposio Internacional: Patología del Sueño: de la Neurobiología a las manifestaciones sistémicas. En colaboración con la Sociedad Española de Sueño.

1. International Symposium: Sleep disorders: from Neurobiology to Systemic Consequences
Life & Earth Sciences | Madrid, January 18-19, 2018
Systems genetics of sleep homeostasis
paul.franken@unil.ch
Center for Integrative Genomics

2. Sleep is complex and is studied at many levels
• NREM & REM sleep
• Brain activity (EEG) & metabolism
• Brain anatomy & neurophysiology
• Functional & evolutionary considerations
• Dreams
• Sleep disorders
Van Gogh 1890SLEEP
• Sleep-Wake history & Circadian time
• Environmental influences
• Stress / Drugs / Disease / Aging
• Socio-economic demands (shiftwork)
• Genetics
• Quality of life
• Cognitive performance &
memory processes
• Accidents
• Increased disease risk

3. Three processes regulate sleep
«Short-list of 78»
Mongrain et al. SLEEP 2010

4. d
EEG delta power quantifies
the amplitude and prevalence of EEG slow waves (1-4 Hz)

5. Franken, Chollet, Tafti J Neurosci 2001
EEG delta power: reflection of an hourglass measuring
the duration of prior wakefulness
Sleep as a fundamental property of neuronal
assemblies
Krueger et al. Nat Rev Neurosci 2008
Sleep function and synaptic homeostasis
Tononi & Cirelli Sleep Med Rev 2006
Timing of human sleep: recovery process
gated by a circadian pacemaker
Daan, Beersma, Borbély Am J Physiol 1984

6. Forward Genetics (“from-phenotype-to-gene”)
 Mutagenesis screens
 Quantitative-Trait-Loci (QTL) approach
 Genome-wide association (GWA) studies
Reverse Genetics (“from-gene-to-phenotype”)
 Candidate genes in transgenics (knock out)
 Association and candidate gene studies in humans
Molecular genetics (“from-phenotype-to-mRNA”)
 Transcriptome analyses
 (Proteomics……..)
Genetic approaches to identify the molecular pathways
shaping the response to sleep loss

7. Genetic Reference Populations (GRPs) 
BXD Recombinant Inbred (RI) strains of mice
BXD-220

8. PhenotypeQuantitative Trait Locus (QTL) analysis

9. PhenotypeGenotype
Chromosome13
Quantitative Trait Locus (QTL) analysis

10. Quantitative Trait Locus (QTL) analysisPhenotypeGenotype
Chromosome13
Franken, Chollet, Tafti. J Neurosci 2001
Maret, Dorsaz,… Franken, Tafti. PNAS 2007
Homer1a

11. Genome-wide association of multiple
complex traits in outbred mice by
ultra-low-coverage sequencing.
Nicod et al. Nat Genet 2016
Ppargc1a Unc13c
Sleep fragmentation
Genome-wide association (GWA) studies in CFW outbred mice

12. Civelek & Lusis Nature Reviews Genetics 2014
‘Systems genetics’ approaches to understand complex traits
Intermediate phenotypes
Environmental
perturbation
Genotype
Phenotype

13. Central and Peripheral Consequences of Sleep Loss:
A Systems Genetics Approach in Mice
Use a mouse Genetic Reference Population (GRP) to map the genes and molecular
pathways involved in regulating sleep by combining multi-level information:
from genotype  brain & liver transcriptomes 
plasma metabolome  sleep-wake phenome
with sleep deprivation as an ‘environmental’ challenge.
The BXD/RwwJ panel is a set of ~161 advanced recombinant inbred
(ARI) lines in which two fully sequenced genomes
(i.e., C57Bl/6J and DBA/2J) segregate
Peirce et al., BMC Genet 2004
Rob W. Williams @ UT Memphis

14. 1) Sleep-wake phenome (96h recording; n=261; 37 BXD lines, B6, D2, F1s)
325 phenotypes: sleep-wake state, EEG activity, locomotor activity
2) Transcriptome & metabolome (@ZT6; n=286)
RNA-seq 15.0K gene transcripts in cerebral cortex
12.5K in liver
Targeted metabolomics (124 from 4 compound classes) in blood plasma
3) Genotype maps using 11k SNPs from RNA-seq and
The experiment

15. Heritability
α-aminoadipic acid (αAAA)REM sleep theta frequency

16. 61 significant phenotypic Quantitative Trait Loci (phQTLs)
21 significant metabolomic or mQTLs
First mapping results: phenome & metabolome

17. time-of-day
phQTLs in baseline

18. phQTLs in baseline
Nav2 = Neuron navigator 2
time-of-day

19. 7214 expression or cis-eQTLs
First mapping results: transcriptome

20. Effects of sleep deprivation: phenome
7 ΔphQTLs

21. Effects of sleep deprivation: transcriptome
78% 60%
“Short-list of 78” Mongrain et al. SLEEP 2010
195 Dcis-eQTLs

22. Effects of sleep deprivation: metabolome
51%
C18:1-L-Carnitine

23. Example: Systems genetics of NREM sleep recovery

24. Systems genetics of NREM sleep recovery

25. Systems genetics of NREM sleep recovery
significant
suggestive
C38:2

26. 2012

27. Networks:
Genotype [rs13477919]  Gene expression  Metabolite  Phenotype

28. Acot11: Tissue- and genotype-specific effects of sleep deprivation

29. Hypotheses:
Sleep restriction  Acot11  free fatty acids  type-2 diabetes risk
Sleep loss is associated with insulin resistance and increased risk for type 2 diabetes.
Increased circulating free fatty acids (NEFA) can lead to insulin resistance & metabolic
disease.
Sleep restriction resulted in:
• Increased NEFA during the nocturnal and early-morning hours.
• Decreased insulin sensitivity
• Insulin sensitivity  correlates with NEFA
Increased free fatty acids may contribute to insulin resistance and the elevated
diabetes risk associated with sleep loss.

30. Results
 Large effect of genetic back-ground on all levels
including the influence of sleep deprivation
 “Many-to-many-to-many” instead of “1-to-1”
 Strength for hypotheses building
 Development of new analyses tools
e.g. gene prioritization, system genetics visualization, EEG annotation
 Extracting ‘genotype’-independent biomarkers for sleep loss
Always more……
 analyses:
 Bayesian networks direction of the flow of information
 Epistatic effects
 levels:
 Epigenomics
 proteomics? mbiome?
Conclusion
31003A_173182
Genetic dissection of the epigenomic
consequences of sleep loss


31. https://bxd.vital-it.ch

32. Ioannis Xenarios
Nicolas Guex
Maxime Jan
Mark Ibberson
Frédéric Burdet
Jérôme Dauvillier
Robin Liechti
Marco Pagni
Shanaz Diessler
Yann Emmenegger
Charlotte Hor
Collaborators & Funding
Debra Skene
Benita Middleton
Patrick Gouait
Mathieu Piguet
Josselin Soyer
My group
CIG Animal caretakers
Lausanne Genomics Technologies Facility (GTF)
Keith Harshman
Manuel Bueno
Floriane Consales Barras
IECB
doctoral program
StarOmics

34. AbsoluteIDQ p180 Kit (Biocrates Life Sciences)
quantifies up to 184 metabolites in 5 4
compound classes
Total of 124 metabolites used for further
analyses
Metabolite Class Biological Relevance #
acyl-Carnitines Energy metabolism, fatty acid
transport, mitochondrial fatty acid b-
oxidation, ketosis, oxidative stress,
mitochondrial membrane damage
8/40
Amino Acids Amino acid metabolism, urea cycle,
activity of gluconeogenesis and
glycolysis, insulin sensitivity/resistance,
neurotransmitter metabolism,
oxidative stress
20/21
Biogenic Amines Neurological disorders, cell
proliferation, cell cycle progression,
DNA stability, oxidative stress
7/21
Hexoses Carbohydrate metabolism 0/1
Phosphatidylcholines
(PCs)
Dyslipidemia, membrane composition
and damage, fatty acid profile, activity
of desaturases
67/73
Lyso-
Phosphatidylcholines
(lysoPCs)
Degradation of phospholipids
(phospholipase activity), membrane
damage, signalling cascades, fatty acid
profile
8/14
Sphingomyelins
(SMs)
Signalling cascades, membrane
damage (e.g., neurodegeneration)
14/14
Targeted metabolomics
Glycerophospholipids
X

35. Effects of
sleep deprivation