The big picture: Genes to proteins Flashcards
List all the control points for gene expression
Transcription, RNA processing, RNA export and localisation, Translation, PTM, localisation
What are the 4 omic approaches, what do they each study and what do they study overall
Transcriptomics - RNA
Proteomics - protein
metabolomics - metabolites
genomics - genomes
You can an overview of the cell’s molecular composition
What is the actual, detailed definition of proteonomics
study of protein levels and PTMs in a cell or organism
How do you use proteomics experimentally
analyse large amounts of protein at the same time using mass spectrometry, or gel/.liquid chromatography
Analyse the change in protein composition and quantity under different conditions, using a normal control
Explain some specific applications of protonomics in medicine
Cardiovascular disease - look for changes in protein composition of plasma proteosomes in patients
-literally hust do a stain and see what’s different compared to normal - could be looking for specific expression or different motility of proteins
detect parasitic proteins in humans - use a 2d gel method
The proper defintiion of transcriptomics and what you do
The study of RNA expression in a cell or organism under specific conditions
You identify and quantify 1000s of RNAs at the same time by high through out sequencing or hybridising to DNA microarrays containing 1000’s of genes or exons
NB in microarray could have cDNA in place of RNA
Explain the microarray output
red = level has increased green = level has decreased
What does transcripomics allow
cluster analysis of data from multiple microarray experiments so you can identify COREGULATED GENES
Identify genes implicated in disease
what doe genes with similar patterns of expression probably have
similar regulatory inputs
What does cluster analysis allow you to do
pick out key genes in a screening manner, rather than choose 1 gene early in the process and just look at that. Far less limiting
What are features of E2H2
oncogene overexpressed in various cancer types. High E2H2 correlates with poor prognosis - pancreatic cancer patients with less have less chance of relapse 20 months after surgery
Discovered because ‘red’ expression in cancer microarrays
molecularly what is E2H2 and what does it do
-an oncogenic histone methyl transferase (-ase so enzymatic activity)
- trimethylates lysine 27 on histone 3 (associated with having the effect of gene silencing)
-also recruits inhibitory histone deacetylases (HDACs - remove acetyl from histone)
Overall reduces gene expression
What genes does E2H2 repress?
tumour surpressors, inhibitors of cell migration. Good for cancer, bad for us
What makes E2H2 a good drug target?
It’s enzymatic activity
How is E2H2 exploited to treat lymphomas in mice
In lymphomas, E2H2 is frequently activated by mutations in it’s catalytic domain
Drug that inhibits E2H2 methyl transferase activity can eradicate lymphomas in mice
Why are epigenetic changes good drug targets
Unlike mutations they are reversible
What drugs are clinically already in use tackling epigentic changes to DNA (type rather than name)
HDAC inhibitors - in many cancers, tumour surpressors are silenced through chromatin mediated change. HDAC inhibs can sometimes reactivate silenced genes
E.g. SAHA.
What does using proteomics and transcriptomics together allow you yo do
compatre mRNA and protein levels
What were the findings when mRNA and protein levels in mouse fibroblasts were compared
median abundance of mRNA = 17 molecules per cell - discovered by high throughput sequencing
median abundance of protein = 5000 per cell - mass spec
900x protein from mRNA
Found that rare proteins were encoded by rare mRNAs, and the opposite for abundant ones
Clearly correlation between mRNA and protein levels but lots of variation
What were findings when stability of protein and mRNA in mouse fibroblasts was compared and how was it done
Done via - pulse labelling
on average proteins were 5x more stable than mRNAs, based on the median half life - 9hrs (mRNA) vs 46hrs
No correlation between half lives - unlike when just comparing levels
Why are protein half lives very varied
because they have a variety of different function
Give examples of types of proteins and their half lives
signalling proteins and transcription factors (tend to be unstable) - e.g.P53 1/2 life = 5-30mins unless stablised by stress signals
Histones (very stable) - e.g. in mouse liver cells 4months
housekeeping proteins, for metab and translation (tend to be stable) - e.g. mature ribosomes in rat liver 5 days
highly structured proteins (more stable than unstructured) - e.g. eye lens crystallin lasts a life time
What were the findings when comparing mRNA and protein synthesis in mouse fibroblast, and how was this investigated
- used data on abundance and 1/2 lives to calculate rates of synthesis - ultimately a translation/protein comparison
- median mRNA synth = 2 molecules per hour (only counting mRNA that’s actually expressed)
- median protein synthesis = 40 protein molecules per mRNA per hour
- abundant proteins are 100x more efficently translated than rare (therefore actually a wide range of protein abundance)
What determines RNA and protein levels
balance between synthesis and decay of each - ie. 4 processes
What process has the biggest impact protein levels in mouse fibroblasts, and does this make sene
rates of translation ie protein synthesis
instinctive because of it’s high energy cost - more costly than mRNA synth so make the most of it
Does it make sense for generally the rate of synthesis to be more impactful than the rate of degradation when contributing to protein or mRNA levels
Yes because organisms are economical and don;t make things just so they can be destroyed, just don’t make them in 1st place
In the mouse fibroblast investigation, what wasn;t taken into account when anayling mRNA and protein levels
1000s of genes that aren’t expressed, may be actively silenced and not transcribed
How can gene expression analysis be used as a guide to therapeutics
can help define a disease by identifying gene expression pattern, to define the molecular abberration that defines that disease, regardless of misleading symptoms. This can show which treatment would work best on diff patients with same disease
What is personal medicine? And is it realistic?
define disease by idenitfying gene expression and therefore molecular nature, to establish which treatments would work best, if treatment is required, prognosis
Sequence of complete transcriptome is relatively accessible so yes
What were the 4 molecularly defined subgroups of breast cancer, identified with cancer cell expression in microarrays
luminal A, luminal B, ERBB2, basal
Of the breast cancer subtypes, what were their relative prognosese
ERBB2 worst prospects - need drastic treatments
Luminal A - good prognosis - may not require any therapies (big issue is is it worth treating patients)
In breast cancer, what’s unusual about the seriousness of prognosis and expression levels
The worst prognosis (ERBB2) deviates less from the normal that luminal A (a less severe form of the disease)
Explain the breast cancer therapies based on ERBB2 and examples
Therapies target the ERBB2 tyrosine kinase (HER2)
E.g. monoclona anitbody hereceptin
-the ab recognises the HER2 and triggers an immune response against the tumour
Describe a therapy for basal breast cancer patients
BRAC1 mutation falls in this category - so use DNA damage repair therapies
What can be used as multiple biomarkers
the individual gene expression patter ie the molecular happenings rather than individual molecules
What is commonly used as a biomarker
mRNA, but can be proteins or miRNA
What can mRNA biomarkers achieve?
assess prognosis identify molecular subtype predict response to specific therapies monitor disease progression determine response to therapy
What is the best way to identify a biomarker, give example
non-ivasively e.g. by urine test rather than tissure biopsy
