Transcriptomics

Transcriptome definition

Complete set of transcripts in a cell, or a population of cells, for a specific development stage or physiological condition

Transcriptome

• RNA content in the cell

• Includes IncRNA, miRNA and circular RNA

• Includes tRNA and rRNA but not often as they’re housekeeping genes

• Genome is the same in every cell in the organism

Transcriptome and Cancer

• In cancer, gene expression can be used to identify the evolution of cancer

• If a patient is asymptomatic, their transcriptome can be checked for biomarkers of a developing cancer to use as a diagnosis

Alternative splicing

• A sequence of exons doesn’t always correspond to the final protein made

• A few different variations can be formed if the gene comprises of more than one exon

• e.g. If exons are emitted from the final sequence or rearranged

• The final proteins of different exons can have slightly different functions too

Mechanisms: Spliceosome

• Structure made of RNA

• Recognise splicing sites

• Spliceosome directs which portion of the gene is being considered and spliced out

• Two main types - major and minor

• These snRNPs have different functions within spliceosome activity

• Transesterification

Major type

snRNPs U1, U2, U4, U55, U6

Minor types

snRNPs U11, U12, U4atac, U5, U6atac

snRNPs

Small nuclear ribonucleoproteins

Transesterification

• Introns are removed from an immature mRNA (pre-mRNA) by transesterification

• Mature mRNA molecule is made

• Guanine and adenine base are bonded by transesterification

• Hydroxyl (OH) group on the carbon atom of the adenine attacks the bond of the guanine nucleotide at the splice site

Spliceosome example

• Peromyscus mice

• Agouti gene gives whiter colouration

• It binds to melanocortin cell receptors, which is involved in regulating pheomelanin pigment synthesis (gives reddish pigments)

• In the belly, there is normal agouti genes

• On their back, the cells have a different transcription type that contains the 1D exon, which has a lot of start codons

• This reduces the efficiency of translation into a function agouti protein

Lamins example

• Class of genes encoding lamins are important for normal development

• Exon and intron mutations can alter splicing which causes premature stop codons and frameshifts

• Point mutations cause stop codons

• These can cause muscular dystrophy diseases as the truncated short protein doesn’t fully perform its function

Spliceosome therapies

• Certain therapies develop enhancers and silencers so splicing doesn’t happen int he same way

• To get different proteins or administer protein to patient and stop splicing happening so ‘bad’ protein doesn’t appear

Long non-coding RNAs (IncRNA)

• Transcribed by polymerase I

• Often spliced, sometimes capped and/or polyadenylated

• Appear and behave like mRNA

• Don’t code for proteins

IncRNA functions

• Act as a guide

• Interaction with a whole protein complex

• Transcription regulation

• MALAT-1 interacts with serin-rich proteins

• Stabilise mRNA

• Act as a decoy

IncRNA function: Act as a guide

• Inc-DC stabilises STAT3 protein through phosphorylation

• The protein is better suited to enter the nucleus and target specific genes

• Which indirectly affects gene expression

IncRNA function: Interaction with a whole protein complex

• RNA attaches to it

• Changes gene conformation, enhancers and markers

• Therefore, changes expression

IncRNA function: Transcription regulation

• IncRNACONCR activates DDX1

• This phosphorylates cohesion

• Cohesion is important for DNA, as it keeps strands together during cell cycle

IncRNA function: MALAT-1 interacts with serin-rich proteins

• Enhances splicing

• Facilitates it for the particular isoform of exon the cell requires

IncRNA function: Can stabilise mRNA

Leads to amyloid-peptides accumulation which can cause Alzheimer’s disease

IncRNA function: Act as a decoy

GAS5 IncRNA acts as a decoy for GR transcription factor, inhibiting apoptosis

miRNAs

• Short RNAs that regulate gene expression

• Original transcript is pri-miRNA

• Association between miRNA expression and cardio-vascular diseases finds cancer is quite frequent

• However, the exact role is not known – possible that miRNAs are biomarkers

• miRNAs can be used in gene therapy by targeting transcripts associated with disease

pri-miRNA

• 73 bases long

• pri-miRNA is cleaved twice to get rid of the loop structure and forms miRNA (20-25 bp)

• In this process, the passenger strand is unimportant and degraded

• The complex of proteins binds to active miRNA and form a RISC complex

• The RISC complex binds to mRNA to repress translation or degradation of that mRNA as it may no longer need to be degraded

Repress gene expression

Dependent on how well it matches mRNA - perfect match for miRNA/mRNA or partial match for miRNA/mRNA

Perfect match for miRNA/mRNA

• Deadenylation - followed by decapping and degradation

• Proteolysis - degradation of nascent peptide

Partial match for miRNA/mRNA

• Initiation block - repressed cap recognition or 60S joining

• Elongation block - slowed elongation or ribosome drop-off

Circular RNAs

• No longer functional RNA

• Formed by back-splicing

• Act as decoys more efficiently than incRNA

• miRNA can degrade RNA when it shouldn’t be so it needs to quickly be soaked up so it doesn’t degrade useful RNA

Circular RNAs decoy example

The regulator never reaches the nucleus (and therefore, the target) as it becomes bound to circular RNA

Epitranscriptomics

• Emerging field that combines epigenetics and transcriptomics because RNA can be modified the same way DNA can be modified by markers, readers and writers

• Most common modification is methylation of 6 adenosine that happens after transcription, and causes a writer

• Methylation can cause alternative splicing, which can be involved in structural changes (e.g. formation of loop where there wasn’t one before)

m6A methylation

• Associated with myD88 alternative splicing

• MyD88 interacts with Toll-like receptors

• MyD88S inhibits inflammatory response through NF-KB response pathway

• A decrease of m6A methylation to MyD88 will cause enhanced inhibition of signalling pathway

m6A methylation in celiac disease

• Excessive inflammation

• It was found that individuals with the allele associated with celiac disease have more m6A methylation

cDNA microarrays

• First high-throughput method for transcriptomics

• Tag cDNA with fluorescent tags and it will emit light

• Brightness and duration - approximate how much in sample

High-throughput sequencing: Illumina

• Dominant type of next-generation sequencing and most accurate sequencing

• Limit of sequencing length so new ones have been made e.g. PacBio, oxford nanopore

• Transcriptome is easy and cost-effective as genes there are less genes and they are closer/easier to find

• After DNA purification in Illumina, they need to be fragmented because Illumina has a limit to how many bases it can read

• Paired-end sequencing gives more accuracy by sequencing the forward and reverse strands at the sample amplified spot

• Parallel sequencing

Assembling a transcriptome

• For humans, there will be a reference genome

• If there is no reference genome, it will be assembled de novo

• The shorter reads will be assembled and overlayed into a consensus sequence

• Once genes have been mapped to reference, gene expression can be compared between different treatments

• However, RNA is not stable and needs to undergo library preparation

• They’re mapped onto exons

• Need at least 3 samples to draw conclusions about transcriptome

Library preparation

• cDNA is shattered into fragments using an ultrasound

• The ends are sequenced and onto map reads

RNAseq output for a single gene

• When comparing tissue types, must consider the amount of genes that may be expressed as it can change between tissue types

• Two types of graphs can be made - volcano plot and heat map

• In the graph, Xenopus VegT is expressed in the ovary, but not the skin, as it is mainly expressed in the egg

Volcano plot

• The x-axis of LogFC shows how much more of the gene is expressed than a base level expression

• The y-axis shows how significant it is

• Blue is under expressed

• Red is overexpressed

• The highest gene on the graph may be picked as a candidate gene

Heat map

• Used to compare two different conditions – wild type and individuals with the condition

• Intensity of colour symbolises how much they’re expressed

• There are several columns to show the different times the experiment has been replicated

ENCODE project

• DNA elements encyclopaedia for human genome and mouse genome

• To make a very comprehensive catalogue of transcripts

• Map sequence reads to the genome sequence to identify the genes, exons and transcriptional start sites

ENCODE project problem

Cell specific expression

ENCODE project solution

Repeat for 15 different cell types

ENCODE project techniques

• CAGE

• RNA-PET

CAGE

Selects RNAs for the methylated 5’ cap which defines the start of transcription

RNA-PET

Selects 5’ cap and 3’ polyA together which gives full length RNA

Deriving transcriptomes for different cell types

• Must prep as the transcriptome is only as specific as the cell preparation

• Single cell transcriptomics - brain, different brain regions and cells

• Individual dissociated cells can be sorted by microfluidics

• Can examine cell differentiation dynamics

• RNA-seq can identify genes that have just started to be expressed, is in stable expression and are turning off

Single cell transcriptomic: Brain

Extract RNA and sequence, then you get the average transcriptome of the brain

Single cell transcriptomic: Different brain regiond

• Isolate specific brain regions

• Extract RNA

• Sequence

• Get average temporal lobe transcriptome

Single cell transcriptomic: Cells

• Isolate single cells from a specific region

• Extract RNA

• Sequence

• Get the precise temporal lobe cell transcriptome

Individual dissociated cells can be sorted by microfluidics

1. Dissociate cells

2. Place into individual aqueous droplets containing sequencing reagents in an oil medium

3. Add a bead containing sequencing primers with a barcode that is unique to that droplet

4. Anneal and make cDNA

5. High-throughput sequence all the droplets together

6. Sort the sequences to each cell by barcode

7. Devise mathematic processes to analyse the data