Eukaryotic genomes are complex 3D structures comprised of modified and unmodified DNA, - PowerPoint Presentation

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Eukaryotic genomes are complex 3D structures comprised of modified and unmodified DNA, RNA and many types of interacting proteins

Most DNA is wrapped around a “histone core”, to form nucleosomes

The classical histone protein complexes bind very tightly to DNA and prevent association with other proteins

Modifications of the classical histones, or their replacement with unusual histone types under certain conditions, can “loosen” the interaction with DNA, allowing access to transcription factors, RNA polymerase, and other proteins

Regulatory elements can be far away: most enhancers interact with promoters and each other through long-range chromatin loops

Regulatory elements are essentially “docking sites” for specific types of DNA-binding proteins

Transcription factors, TATA-binding factors, and others

These proteins serve to attract co-factors, which then mediate protein: protein interactions across chromatin loops

Very long range interactions are common in vertebrates, less so in invertebrate species with lower

coding:nocoding ratiosChIP with an antibody that binds to “E” DNA will bring down “P” DNA as wellProteins are crosslinked very efficiently to each other, as well as to DNA, by formaldehyde treatmentWhen crosslinking is reverse the complex falls apart, and Both DNA fragments are released independentlyOnly one sequence binds to the TF!Common issue in analysis of ChIP

Shear chromatin

(Sonication or restriction enzyme)

TF

How to find the regulatory needles in the haystack?

Vertebrate genomes are mostly non-coding~2% coding; ~5% noncoding and evolutionarily conserved (at the DNA sequence alignment level)Conservation has been used to identify important functional elements, but not all functional elements are conserved at the level that DNA sequence alignments can detect

Furthermore the important question is: which elements are

accessible

in a particular cell type at a particular time and in a particular state?

Focusing on accessible chromatinEven well conserved motifs cannot be accessed in closed regions of chromatin

accessible

Not accessible

e.g. H3K9Me3, H3K27Me3

e.g. H3K27Ac

All four histones in the tetramer have “tails” that can be modified in various ways, but the most consequential modifications, with respect to transcriptional activity, appear to involve methylation or acetylation of

Lysines (K) in histone H3

How to find open chromatin? Chromatin ImmunoPrecipitation (ChIP

)

Antibody to a DNA binding protein is used to “fish out” DNA bound to the protein in a living cell

DNA and protein are

crosslinked

in the cell using brief treatment with low concentration of high quality formaldehydeCrosslinked chromatin is sheared, usually by sonication, to yield short fragments of DNA+protein complexesAntibody to a TF or other binding protein used to fish out fragments containing that DNA binding proteinDNA is then “released” and can be analyzed by various methods:Original method is PCR: query for enrichment of specific (known or suspected) DNA binding regions in ChIP-enriched DNACreates a pool of sequences highly enriched in binding sites for a particular proteinRequires availability of excellent antibodies that can detect the protein in its in vivo context

A basic ChIP-like approach can be used to map nucleic acid:protein interactions of virtually any type

Histone modifications:Secondary interactions (no direct linkage to DNA)Histone modifying proteins, such as SWI/SNF, histone deacetylases, histone methylases

Cofactors that bind to TFs at particular sites, and that

stablize

chromatin loopsProteins that link chromatin to nuclear matrix

RNA polymerase and elongation factors, to find promoters and active sites of transcriptionProteins involved in DNA recombination, repair, and replicationAll of these methods require highly specific and efficient antibodies (which are rare!)

ChIP

computational issues

First step is to map reads: BOWTIE,

Novalign

, BWA or otherChIP seq reads surround but may not contain the DNA binding siteSequence is generated from the ends of randomly sheared fragments, which overlap at the protein binding siteGives rise to two adjacent sets of read peaks separated by ~ 2X fragment length

D

efines a “shift” distance between read peaks at which you will find the true ChIP peak

summit

Programs like MACS and HOMER automatically subtracts your control (genomic input) from sample reads to define a final set of peaks

Binding site

ChIP fragments

Seq reads

ChIP Analytical challenges

Genomic neighborhoodsShear efficiency is not really “random”Some genomic regions are fragile and sensitiveSome regions are protected from shear or degradationOther artifactsCentromeres: repeat sequences that are not all represented in the genome sequence build

Polymorphic regions, and e.g. regions that are amplified in cell line DNA

Repeats: most programs cannot manage sequence reads that are not mapped uniquely

Peak width

Transcription factors are typically sharp peaks; chromatin marks are more diffuseThe best tools permit the user to modify these parametersMACS ( Xiaole Liu Lab; Zhang et al, 2008; Feng et al. Nature Methods 2102) is a user-friendly and widely used toolHOMER, a highly versatile tool with many different annotation features and high sensitivity (Chris Benner, http://homer.salk.edu/homer/ngs/)

Analyzing ChIP data

User-friendly toolsMACS: ‘Model based” peak detection, is sensitive to peak enrichment and backgroundZhang et al, Genome Biology 2008, Feng et al. 2012, Nat

Procols

PMID: 22936215 (Xiaole Liu lab);

MACS1 is best for sharp peaks (TFs); will break diffuse peaks into smaller regionsMACS2 is designed to allow broad- or sharp-peak detectionHOMER (http://homer.salk.edu/homer)Can be easily tweaked for more sensitive peak detectionComes packaged wiith a rich set of peak annotation toolsTools for DNAse-seq, High-C, differential ChIP analysis and many moreBoth tools permit generation of “wiggle files” or similar that can be viewed in the UCSC browserLooking at your data is a very important step! Peak finders can miss peaks that you can easily see by eye!

Traditional methods fail with broad, flat peaks

Most tools designed for TF proteins: discreet, sharp peaksCertain chromatin proteins, and modified histones in certain regions, bind continuously to large regions of chromatin and do not yield “peaks”MACS in default mode will carve the “mesa” into many peaks, or not detect it at allNew settings in MACS 2 can be set to overcome this problemHOMER has a wide variety of settings ideal for data of different types

ChIP analysis workflow

FASTQC -> BOWTIE -> Peak finder (MACS or HOMER)This same workflow and tools can be used for a variety of methods e.g. Methyl DIP, ATAC-seq, DNase seqDownstream analysis:Mapping peaks to nearby genes (and perhaps, DEGs)Identifying enriched motifs

For your factor

For co-binding factors

Overlapping with other genome features e.g. open chromatin, known binding sites, etc.

An ecumenical approach to open chromatin: ATAC-seq

Uses Tn5 transposase and a Transposon modified to contain Illumina primers at each endTransposon “jumps” preferentially (and randomly) into accessible chromatinBecause of the design the transposon breaks DNA where it jumps in, tagging the site with the primer

Two insertions close together yield fragments of the size amenable for

Illumina

sequencingPCR amplification between primers is all you need to make a library

Since it skips library-making steps (ligation etc), can be done with small amounts of input chromatin – e.g. 50,000 vs 1,000,000 cellsBuenrostro et al., 2013, 2015

TN5

transposase

insertion

(e.g.

Illumina

library

oligos

)

tagmentation

Continued reaction

PCR

Ready to sequence

ATAC seq: transposons preferentially “jump” into open chromatin

From

Schmidl et al., Nature Methods 2015Other Transposon-based methods

:

ChIP-

tagmentationChiP-mentation

Also based on transposon-Based library constructionSo reduce requirementsFor input chromatin!Analysis is identical to ChIP,Only the experimental methods(and input chromatin) are different

Issues related to Tagmentation Protocols

Ratio of DNA: transposaseHas to be adjusted for each cell type and chromatin prepNeed even fragmentation to avoid bias, and small enough fragments, in general, for illumina

Need to avoid making fragments too small

Bias observed in DNA: controls are complicated

Solution in “ChiPmentation”

Tagmentation while DNA is still protected by the antibody and cross-linked chromatin, still on the beadProtects from over-tagmentation, this allowing a full digestion without fear of losing the DNAAllows the protocol to work over a 25X range of DNA: transposon and lessens worries about time

Current summary

ATAC-seq and H3K27Ac ChIP win the daySimple technology, can be completed with relatively low input and low sequencing readsExcellent kits are available for beginners, and many sequencing centers will do the work for a fee

Methods work for all species and cell types

Robust computational tools are readily available

Issues related to Tagmentation Protocols

Ratio of DNA: transposaseHas to be adjusted for each cell type and chromatin prepNeed even fragmentation to avoid bias, and small enough fragments, in general, for illumina

Need to avoid making fragments too small

Bias observed in DNA: controls are complicated

Solution in “ChiPmentation”

Tagmentation while DNA is still protected by the antibody and cross-linked chromatin, still on the beadProtects from over-tagmentation, this allowing a full digestion without fear of losing the DNAAllows the protocol to work over a 25X range of DNA: transposon and lessens worries about time

How to tie back to 3D structure?Probing 3-dimensional chromatin structure with conformation capture

from Wit and de

Laat

, 2012

TFs do not act alone:Probing 3-dimensional chromatin structure with conformation capture

from Wit and de

Laat

, 2012

Requires analysis methods that are different from ChIP

Provides the essential “big picture” view, since it is otherwise impossible to predict long-range enhancer-enhancer or enhancer-promoter interactionsSequenced fragments contain a bit of DNA from two distant regionsData need to be trimmed and mapped to allow non-contiguous sequencesLong-distant contacts are numerous, and each contact point is relatively rare: peaks are small are require deep sequencing

For most of these methods, restriction enzymes are used to shear, not sonication, and your endpoints may be spread over a restriction fragment

Analytical methods create a restriction map of your viewpoint region in 4C, and bin reads to those fragments

Hi C kits are now readily available and quite reliable, giving a whole-genome view of interactions

Lots of interactions and lots of noise! Computational issues are trickyAll 3D methods require deep sequencing and paired-end reads

Summary and Overview

Many user-friendly methods and analytical tools are available to identify active elements in large genomesThe issue is finding out “who is talking to whom?”Enhancers can be shared by multiple genes

Alternative promoters for the same gene can have very different regulatory partners

Position relative to the TSS is not a reliable indicator in large vertebrate genomes

3D methods are necessary to tie enhancers and promoters together

Fortunately, 3D genomic interaction tools are becoming easier and more cost-effective so are accessible to virtually any lab!