Lecture 10- Systems biology of CVD Flashcards
systems engineering approach
what takes inputs and transfers them to outputs?
- reverse engineer rules and predict unknown input
complex systems theory approach
need networks to understand cooperative behaviour of system
commonalities of systems engineering and complex systems theory approach
- collection of experimental data
- understand rules that govern behaviour of system under different conditions
- collect data at different levels of resolution
reductionism
understand function of system by breaking it down into parts e.g. individual gene function
systems biology
understand function of system as a whole
normal biological systems
well regulated, robust to variation and damage e.g. maintain core temperature
disruption to normal biological systems
caused by mutations and environmental factors
- effects propagate across biological system
3 levels of resolution in biological network
- detailed- individual molecules
- interim- signalling pathways, metabolic networks
- simple interaction- one molecule interacts with another
3 fundamental changes in technology for systems biology
- technology- increase in measurement capabilities
- availability- commercialisation of techniques
- application- recognition of diseases at systems-level malfunction, application into biomedical research
human genome project and moore’s law
genome sequencing well beyond exponential
transcriptomes in medical research
to identify patterns of gene expression associated with a disease e.g. clinical use in cancer; Mamoprint
2 types of testing
research and clinical testing
research testing
finding unknown genes, learning how they work, developing tests for future clinical use, understand genetic conditions- results not available to patients
clinical testing
find out about inherited disorder, patients receive results, decisions about medical care e.g. reproduction
5 steps in systems biology approach
- define system to be examined
- identify components of system
- determine how components interact with each other
- model dynamics of system, mathematically, see how it changes over time, in response to disturbance
- validate computational model with specific experiments
3 systems biology in cardiovascular and heart disease
- systems biology in cardiac hypertrophy
- discovery of mechanisms of gene regulation
- population genetics and risk in CVD
cardiac hypertrophy
heart cells under stress don’t multiply, they just grow bigger- adaptive remodelling of tissue
what did systems biology in cardiac hypertrophy study?
study Ca mediated interaction between normal heartbeat system and maladaptive hypertrophic remodelling system
- insight into how cardiac hypertrophy can be turned off without adversely affecting cardiac function
key reason adult rat ventricular cardiomyocyte used
no cell cycle- dont divide, just grow bigger
excitation contraction coupling
beating of heart
- converts electrical signal to mechanical contraction using Ca as messenger
- expansion and contraction of Z discs
- process regulated by regular ca influx into cell
features of adult rat ventricular cardiomyocyte
cylindrical cells, large volume, repeating structural units (Z discs), 2 active nuclei, thin cytoplasm, 30% mitochondrial volume, no cell cycle
Ca handling proteins
RyRs, IP3Rs- tune properties of Ca transient/burst
-coregulate shape of overall Ca transient in cytosol and nucleus
stimulus- transcription coupling
Ca dependent alpha adrenergic pathway
various protein factors interact with calcium–>gives change in transcription factor NFAT
i.e. calcium activates the Ca-NFAT system
Ca-NFAT system
Calcium activates NFAT–>goes into nucleus–>causes change in transcriptional behaviour of cell
- relies on long term Ca signalling to activate transcirption associated with cardiac hypertrophy
2 signals that coexist in same cell
electrical signal- regular heart beat- regular influx of calcium
growth signal- longer sustained continued presence of calcium required (to maintain NFAT in nucleus)
3 hypotheses for how the 2 signals coexist in same cell
1- signals stack on top of each other
2- sustained calcium burst leads to prolonged NFAT signalling
3- restriction of calcium to certain parts of the cell
signals stack on top of each other
heart beat distinguishable from growth signal
- elevated baseline, oscillation- independent, mass transit of NFAT
sustained calcium burst leads to prolonged NFAT signalling
chronic micro-bursts, oscillation dependent, CaN signal integrator, NFAT accumulation
restriction of calcium to certain parts of the cell
specific spatial distributions of key proteins drive Ca events locally
line scan experiments negative and positive control
negative control- regular beating- consistent Ca transient
positive control- liberation of Ca within specific places temporarily raises local Ca but system quickly clears excess and returns to baseline Ca
summary of line scan experiments
- upstroke kinetics and basal Ca for both cytosol and nucleus were as expected
- decay kinetics (sharp decline before recovery), Ca exposure, peak amplitude- different to negative controls
what changed in line scan experiment?
decay kinetics, Ca exposure, peak amplitude
what didn’t change in line scan experiment?
upstroke kinetics and basal Ca
proposed mechanism of Ca control
- function of IP3R mediated Ca release in heart cells NOT to potentiate RyR opening but to DELAY closing through positive feedback loops
- ca-mediated transcriptional regulation occur during prolonged part of transient
excess IP3 generation
interferes with normal intracellular recovery- causes prolonged Ca exposure
why were hypothesis 1 and 3 not right?
1- no sustained uptake in baseline
3- no evidence for spatial segregation
why was hypothesis 2 right?
- decay kinetics were altered
- NFAT TF was slowly pulsing in nucleus rather than sustained expression
- separating hypertrophic signal form normal heart beat signal
how are transcription factors directed and signed?
directed- can only go from TF to gene not other way around
signed- activator or repressor
how do TFs and DNA interact
TF has a different motif/site in the DNA that it recognises
how do proteins function?
dont function alone, cascades of interactions–>transduce signal
- interactome is very big
NKX2-5 transcription factor
member of NK-2 Homeo-Domain (HD) class of TFs
NK-2 Homeo-Domain (HD)
highly conserved, binds directly to DNA
- also serves as interaction interface with other proteins
what does NKX2-5 recognise?
AAGTG
what is NKX2-5 important for?
critical for heart development in fish, mouse human
- loss of NKX2-5 in mouse–>arrested heart development, blocked progenitor growth, defective chamber, embryonic lethal (10days)
NKX2-5 mutations
common observed gene in congenital heart defects
- want to see whether it has role in other heart diseases
4 consequences of NKX2-5 mutations in DNA binding domain
- altered strength of binding of TF to DNA
- loss of regulation- binding sites in DNA no longer recognised by TF
- recognition of new binding sites by TF
- mutations affect partner proteins that the TF can interact with (since HD interface for protein-protein interactions)
NKX2-5 findings
- change in what was regulated between WT and mutant (what genes switched on/off by mutant)
- what proteins bound to TF and were disrupted
- identify different binding sites indicate new interaction partners for NKX2-5
- motifs point to specific binding partners
SUMMARY- identify sets of genes that were differentially regulated by mutant NKX2-5
what did population genetics and risk in CVD entail?
look at entire population, see which part of population is at more risk for CVD
CVD risk factors
substantial minority don’t have the traditional risk factors
genomic risk prediction
add genomic risk to standard risk criteria?
- less technical variation, constant over patient life, just blood sample
