Enhancers
6 points
- segments of DNA containing multiple TF binding elements (usually non-coding DNA)
- sufficient to enhance transcription from a gene containing a core promoter
- size is quite variable (typically 200 to 2000 bp)
- position-independent (can be found several 10s or 100s of kb upstream or
downstream of a core promoter, an even within coding sequences or introns - orientation-independent
- enhancer sequences are usually recognized by TFs that promote transcription (called activators), and act to recruit chromatin modifying enzymes
- Regulatory modules
3 points
- probably more accurate to think about these regulatory elements as ‘modules’
- modules can contain a variety of nucleotide sequence elements that can activate or repress transcription
- the modules receive complex combinatorial inputs and results in a functionally
integrated response
**Modular organization of regulatory DNA: (eve) locus
**
- * In situ hybridization with labeled antisense RNA probe reveals and this is Important for
- experiment for Modular organization of regulatory DNA – eve locus
-*region reconized by 4 different transcritpion factors *
- In situ hybridization with labeled antisense RNA probe reveals
expression in 7 transverse stripes along anterior-posterior axis
* Important for forming body plan of fruit fly
- In situ hybridization with labeled antisense RNA probe reveals
- Created transcriptional reporters and introduced them into the fly
* Detect reporter mRNA using ISH probe that hybridizes with LacZ mRNA
* Promoter truncations identify a sequence that drives specific expression of Stripe 2
* This region is recognized by 4 different
transcription factors
* Bicoid (5 sites – B1-5)
* Hunchback (H)
* Kruppel (3 sites – K)
* Giant (3 sites – G1-3)
- Created transcriptional reporters and introduced them into the fly
cis determinants of gene regulation
*cis components are genetically linked to the gene whose expression they affect
*E.g., enhancers, silencers, binding sites, promoters (DNA regulatory
elements)
*Mutations in these will usually segregate (close to promoter) with the gene being monitored for transcription (they are found nearby on the same chromosome)
trans determinants of gene regulation
- trans components are not genetically linked (produced in genetically different locus)
*E.g., transcription factors, mediator subunits
*Mutations in these trans-acting genes will usually assort independently of
the gene being monitored for transcription (i.e they are encoded by different
genes) but they nevertheless have the potential to regulate transcription of
the gene being monitored
Consequences of modular gene regulatory elements
cis-regulatory code
- A particular combination of transcription factor binding sites create
a ‘code’for gene expression at a particular time and place - Extended to all of the genes in a genome, if we can understand how
this code operates, we should be able to predict how, when, and where
a gene is expressed! But we’re not there yet!
RNA-seq – Alignment of short reads
1. Alignment software actively being developed to
2. Important problem is
3. These alignment programs usually depend on
Take short reads and map to gene level sequence + coding sequence
1. * Alignment software actively being developed to map reads to gene models
2. Important problem is gaps created by splice junctions
3. These alignment programs usually depend on existing gene models as guides
Comparing two (or more) transcriptomes
- normaliziation
- controls
- reference comparison example
- Making any measurement for 1000s of different genes is technically challenging. Even the most advanced technologies are not perfect.
- Normalization procedures are required in every experiment in order to enable accurate comparisons between experiments and between samples
- Including negative controls (DNA/RNA that is NOT expected to change or be detected) and positive controls (DNA/RNA that IS expected to change or be detected)
- Usually transcriptome measurements for each gene under each condition are
compared to some type of reference. Examples would be: - Cells treated with a drug compared to cells not treated with the same drug.
- Cells/organisms with a mutation will be compared to wild type (normal)
cells/organisms. - Advanced computational and statistical procedures are often needed to account for ‘noisy’ measurements and technical variation in the experiments.
Technical variation vs Biological variation
Technical variation is a result of factors related to the experimental procedure. (Experimenter influence)
* cDNA synthesis, fragmentation method, variability in library preparation
* Sequencing Depth / number of reads that map after alignment
- Biological variation is a result of factors related to the biological samples (technical variation)
- Differential gene expression / steady state RNA abundance
- Alternative isoform usage
- Differential splicing
- Differential cleavage and polyadenylation
- Differential promoter usage / transcription start sites
* Biological variation is what we are generally more interested in, but we
have to make sure to account for technical variation
Cluster analysis
1. * After comparing two or more transcriptomes
2. what genes may have related functions
3. how can genes be grouped
4. comlumns represent
5. Genes (rows) are ordered closer together based on
6. This re-ordering allows
Cluster analysis : visualization of genes with similar expression profiles
1. After comparing two or more transcriptomes, complex differences in gene expression patterns can be distinguished
2. Genes that display similar expression ‘profiles (how they behave across a spectrum of condition)’ under the different temporal and/or environmental conditions examined may have related functions
3. Genes can be grouped by a method of hierarchical clustering where the expression intensity is assigned a value that indicates the degree of relatedness between expression levels
4. Columns represent growth conditions tested
5. Genes (rows) are ordered closer together based on expression pattern similarity (also called ‘expression profiles’)
6. This re-ordering allows potential functional relationships to be observed among genes that are ordered next to each other
Chromatin Immunoprecipitation (ChIP) coupled with high-throughput sequencing (ChIP-seq)
- begining of CHIP protocol is the same
- however, insread of using pCR after purfying DNA, add adapters that are compatilble with illumina sequencing
- sequencing sample gives genome wide snapshot of all fragments that were enriched by IP instead of testing one location at a time with pCR
- Short sequences (reads) are then mapped back to the genome and counted
- Number of times each base is counted by a read can be represented as a ‘peak’
- this can be used to predict tissue specific enhancers of transcription
