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Gene to function - Eukaryotes > The big picture: Genes to proteins > Flashcards

The big picture: Genes to proteins Flashcards

1
Q

List all the control points for gene expression

A

Transcription, RNA processing, RNA export and localisation, Translation, PTM, localisation

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2
Q

What are the 4 omic approaches, what do they each study and what do they study overall

A

Transcriptomics - RNA
Proteomics - protein
metabolomics - metabolites
genomics - genomes

You can an overview of the cell’s molecular composition

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3
Q

What is the actual, detailed definition of proteonomics

A

study of protein levels and PTMs in a cell or organism

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4
Q

How do you use proteomics experimentally

A

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

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5
Q

Explain some specific applications of protonomics in medicine

A

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

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6
Q

The proper defintiion of transcriptomics and what you do

A

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

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7
Q

Explain the microarray output

A 
red = level has increased
green = level has decreased
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8
Q

What does transcripomics allow

A

cluster analysis of data from multiple microarray experiments so you can identify COREGULATED GENES
Identify genes implicated in disease

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9
Q

what doe genes with similar patterns of expression probably have

A

similar regulatory inputs

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10
Q

What does cluster analysis allow you to do

A

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

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11
Q

What are features of E2H2

A

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

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12
Q

molecularly what is E2H2 and what does it do

A

-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

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13
Q

What genes does E2H2 repress?

A

tumour surpressors, inhibitors of cell migration. Good for cancer, bad for us

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14
Q

What makes E2H2 a good drug target?

A

It’s enzymatic activity

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15
Q

How is E2H2 exploited to treat lymphomas in mice

A

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

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16
Q

Why are epigenetic changes good drug targets

A

Unlike mutations they are reversible

17
Q

What drugs are clinically already in use tackling epigentic changes to DNA (type rather than name)

A

HDAC inhibitors - in many cancers, tumour surpressors are silenced through chromatin mediated change. HDAC inhibs can sometimes reactivate silenced genes
E.g. SAHA.

18
Q

What does using proteomics and transcriptomics together allow you yo do

A

compatre mRNA and protein levels

19
Q

What were the findings when mRNA and protein levels in mouse fibroblasts were compared

A

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

20
Q

What were findings when stability of protein and mRNA in mouse fibroblasts was compared and how was it done

A

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

21
Q

Why are protein half lives very varied

A

because they have a variety of different function

22
Q

Give examples of types of proteins and their half lives

A

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

23
Q

What were the findings when comparing mRNA and protein synthesis in mouse fibroblast, and how was this investigated

A
  • 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)
24
Q

What determines RNA and protein levels

A

balance between synthesis and decay of each - ie. 4 processes

25
Q

What process has the biggest impact protein levels in mouse fibroblasts, and does this make sene

A

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

26
Q

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

A

Yes because organisms are economical and don;t make things just so they can be destroyed, just don’t make them in 1st place

27
Q

In the mouse fibroblast investigation, what wasn;t taken into account when anayling mRNA and protein levels

A

1000s of genes that aren’t expressed, may be actively silenced and not transcribed

28
Q

How can gene expression analysis be used as a guide to therapeutics

A

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

29
Q

What is personal medicine? And is it realistic?

A

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

30
Q

What were the 4 molecularly defined subgroups of breast cancer, identified with cancer cell expression in microarrays

A

luminal A, luminal B, ERBB2, basal

31
Q

Of the breast cancer subtypes, what were their relative prognosese

A

ERBB2 worst prospects - need drastic treatments

Luminal A - good prognosis - may not require any therapies (big issue is is it worth treating patients)

32
Q

In breast cancer, what’s unusual about the seriousness of prognosis and expression levels

A

The worst prognosis (ERBB2) deviates less from the normal that luminal A (a less severe form of the disease)

33
Q

Explain the breast cancer therapies based on ERBB2 and examples

A

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

34
Q

Describe a therapy for basal breast cancer patients

A

BRAC1 mutation falls in this category - so use DNA damage repair therapies

35
Q

What can be used as multiple biomarkers

A

the individual gene expression patter ie the molecular happenings rather than individual molecules

36
Q

What is commonly used as a biomarker

A

mRNA, but can be proteins or miRNA

37
Q

What can mRNA biomarkers achieve?

A 
assess prognosis
identify molecular subtype
predict response to specific therapies
monitor disease progression
determine response to therapy
38
Q

What is the best way to identify a biomarker, give example

A

non-ivasively e.g. by urine test rather than tissure biopsy

Gene to function - Eukaryotes (13 decks)

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