Download HIV favors integration in active transcription units (TUs)

Synopsis
• It has been estimated that at least 40% of
the total human genome sequence contains
the integrated fragments of genomic
parasites
• Retroviruses, Retrotransposons, DNA
transposons, and parvoviruses can
efficiently insert new sequence into the
human genome
• These integrating elements can be powerful
tools for discovering . . .
What genomic features affect
integration?
• Each element shows a different pattern of
favorable integration sites
• Favored specific nucleotide sequences can
be detected in the target DNA at the point of
integration for most of these elements
• Post-integration genomic DNA is harvested,
and the DNA flanking the integrated
element is cloned and sequenced
Intention
“Present a comprehensive statistical
comparison of the factors influencing
integration frequency by annotating
each base pair in the human genome
for its likelihood of hosting
integration events”
Framework
7 types of integrating elements
17 different integration complexes (datasets)
200+ variables (genomic features)
10,000+ integration sites
Previous research provided extensive
insertion site data
• HIV favors integration in active
transcription units (TUs)
• MLV favors integration near gene 5` ends
• ASLV integration is mostly random, but
TUs seem to be favored slightly
TUs are defined as regions of transcribed DNA
Previous research had provided
extensive insertion site data
• SFV integration is mostly random, but is favored
slightly near CpG islands
• SB favors integration in transcription units.
• AAV-based vectors show a modest preference for
regions neat transcription start sites
• Experiments concerning whether LINEs prefer to
integrate within TUs have been inconclusive
Some Variables (Genomic Features)
• Genes and Exons: Indicator variables for
whether the site falls into a gene or an exon
• Gene or Expression Density: The number of
genes or expressed genes per base pair in
the region surrounding the integration site
• Dnase I Site Density: The number or
density of DNAse I sites in regions
surrounding the integration
Some Variables (Genomic Features)
• GC Content: The GC percent in the 5kb
region containing the site
• CpG Islands: The site is in a CpG island
• CpG Island Density: The number or density
of CpG islands in the region surrounding
the site
• Transcription Start/Stop Features: The
relation of the site to transcription start/stop
position
Some Variables (Genomic Features)
• Positional Weight in Flanking Sequence:
The loglikelihood for integration versus
control site at each position in twenty bases
of flanking sequence (10 upstream and 10
downstream) and their sum
• Loglikelihood is defined as the log ratio of
the frequency of each of the four bases at
each position to the frequency in the
controls
Integration Complexes (Datasets)
Control Site Generation
Each dataset has one of two types of control:
• Matched (preferred): the integration sites
were created using a restriction enzyme.
The control site matches the distance from
the nearest restriction site in the direction of
transcription
• Random: The control site is merely a
random sequence from the genome
The ROC Curve
• Used to analyze the effects of genomic
features on integration
• Provide a measurement of a predictor
variable’s ability to discriminate between
two classes of events
• This measure can be interpreted as the
probability that a randomly drawn
integration site will have a value for its
genomic feature that exceeds that of a
control
The ROC Curve
The area under the
ROC curve is taken as
a measure of the
association between
genomic feature and
the likelihood of an
integration event
The ROC Curve
The area under the
curve is 1.0 when all
integration events
have higher values for
the feature than any
control event, and 0.0
for the opposite case.
The ROC Curve
Values very near 1.0
occur when higher
values of the feature
predict integration, and
values very near 0.0
occur when lower
values of the feature
predict integration
The ROC Curve
When the area is 0.50,
it is equally likely that
either has a higher
value
Values near 0.50 are
consistent with having
no predictive value
ROC Curve Construction
1) Values for the integration
sites are tallied to create
the histogram and the
upper tail areas of the
histogram, which shows
the fraction of integration
sites (vertical axis) that
have values for the feature
that exceed a given value
(horizontal axis)
ROC Curve Construction
2) Repeat this same
procedure using data
from the control sites
3) Rotate this histogram
and upper tail areas
graph 90˚ clockwise
4) The ROC curve is
constructed from the
collection of true and
false positive rates
ROC Curve Construction
5) For every possible
cutpoint, plot the True
Positive Rate on the yaxis and the False
Positive Rate on the xaxis
A cutpoint is defined as
any value of a predictor
A Compact Representation
of these Associations
• The absolute difference
between the area and 0.50
is plotted
• Values around 0.0 indicate
no useful predictive
information in the feature
• Values near 0.50 indicate
that the feature is nearly
perfect in separating
integration sites from the
controls
Color-coded ‘‘Heat Maps’’
• Color-coded heat maps are matrices displaying
associations for each type of genomic feature
using rows of the matrix for features and columns
for data sets
Color-coded ‘‘Heat Maps’’
• Bright green represents ROC curve areas near 0.0
• Black represents ROC curve areas of 0.50
• Bright red represents ROC curve areas near 1.0
Effects of Nucleotide Sequence of
the 20 Base Pairs Surrounding
the Point of Integration
1) To determine how important different
features are in directing integration
towards a region, each base in the interval
is treated as the edge of an integration site
Effects of Nucleotide Sequence of
the 20 Base Pairs Surrounding
the Point of Integration
2) Each region is then scored for the
expected number of integration events
over the interval, and these interval scores
are summed
Effects of Nucleotide Sequence of
the 20 Base Pairs Surrounding
the Point of Integration
3) The summed values are then tested for
their ability to sort experimental
integration sites from controls
Effects of Nucleotide Sequence of
the 20 Base Pairs Surrounding
the Point of Integration
Interval Size
Integrating Elements
Results are presented as areas under the ROC
curve for this variable
Integration in Transcription Units
and the Effect of Gene Activity
• Analysis of DNA
integration within
TU's and exons
• HIV: (Red) positively correlated with TU's
• Others varied from slight, negative (green) to
undistinguishable data (black)
• This figure summarizes the effects
of gene density in differently sized
genomic intervals 100kb-4 Mb
– Utilized Affimetrix arrays to do
transcriptional profiling
– Each expression scores for all genes
in a interval divided by interval
width
• All datasets resulted in weakly
positive for insertion in at least one
integral. And…
– "There was no clear pattern of
interval size, type of gene call. or
expression level.“
• Suggests that Gene density features
were most significant
• -Strong effects seen in HIV and
MLV datasets
• Weakest response from non-dividing
cells or macrophage
How does G/C Content and Proximity to
CpG Islands Effect Integration?
On average, G/C Content implies …
1.
2.
3.
4.
5.
Gene rich
Short introns
High frequencies of ALu repeats
Low frequencies of LINEs
High Frequency of CpGs
• 2 MLVs where integration was positive
• 3 HIVs that were negatively correlated, A/T preference
• Other datasets showed weaker and less consistent responses
Whoa!? I Thought HIV Integrated
in In Gene Enriched Regions?
Fig. 3 A
Fig. 4 A
A/T preference of
HIV integrasebinding protein
• GpC Island density
– Increasing length 1K-32 M
• Correlates to gene density
• Within short regions, proximity to CpG
islands correlate to proximity to
regulatory regions
• Long intervals span many genes
DNase I Cleavage Sites
•
DNase I cleaves the sites in chromatin where the
binding of transcription factors occurs along with the
presence of CpG islands, and gene control regions.
Integration Near Transcription
Factor Binding Motifs
• Summarizes how integration is affected by
its proximity to transcription factor binding
sites
• TRANSFAC PWM- scores how well the integration
site or control matches a PWM and this score
generates an ROC describing the effects of that
PWM
• Lack of strength when analyzed with other
factors
Proximity to Transcription Start and
Stop Features
• To compare the integration frequency between start and
stop codons for experimental and matched random controls
expressed as ROC areas. Fig 4C
• Boundary.dx: Distance from 5' or 3' end
• Start.dx: distance to the nearest gene start
sites
• closer to the start (green)
• Signed.dx: High probability at the start sites
(red)
• General.width- length of introns
Improved Models Incorporating Score.20
Together with Other Genomic Features
• Score.20 was the most effective method
for differentiating between site selection
of the different vehicles
• Addition of other variables to accentuate
our results.
– Non-redundant
– Lack of correlation
Increase in ROC Area by the
Addition of a Genomic Feature
• Histogram: Found little correlation of score.20 with other
features
• Predictors of Integration targeting can be constructed based
on score.20 and another feature
• The fitting process leads to values that rank higher than
random match controls
Fig. 5 D
A Single Model!
• Regression models would be too complex
• Want to analyze various features
• Bayes Model Averaging (BMA)
– Reinforces that score. 20 and other features are
independent
• Models with high posterior probability were
collected and used to evaluate the importance of
various features
• Random sites are scored for the logarithmic odds
of integration with BMA models
Hierarchical clustering
• Major grouping of
retrovirus HIV
• Amongst our 17 datasets,
with each branch different
element types were
resolved
• Verifies that integration site
selection is dominated by
element encoded
recombination enzymes
What genomic features influence
integration of new DNA?
What we’ve learned about each integrating element:
•
HIV favors integration in active transcription units (TUs)
•HIV- Found to be weakly attracted to integration sites near DNase 1 cleavage domains over
long intervals. Probably because of the correlation of HIV insertion sites and DNase 1 cut sites
with gene dense regions. Also revealed a strong integration attraction to A/T rich sequences,
contradictory to previous presumptions correlating insertion with C/G dense areas.
•
MLV favors integration near gene 5` ends
•MLV- Integration associations with CpG islands and DNase 1 hypersensitive sites found to be
amplified when a larger scale of interest is used. The influence of the local nucleotide sequence
also increased with a larger interval. Strong correlation for integration near areas of gene
expression.
•
ASLV integration is mostly random, but TUs seem to be favored slightly
•ASLV- Integration near DNase 1 sites over long genomic intervals favored.
What genomic features influence
integration of new DNA?
What we’ve learned about each integrating element:
•
SFV integration is mostly random, but is favored slightly near CpG islands
•SFV- Cell specific integration influences. Integration near CpG islands and proximity to
DNase 1 cut sites more evident in stem cells then fibroblasts.
•
SB favors integration in transcription units.
•SB- Contradictory results in regards to proximity to CpG islands and gene density. Possibly
because of cell type specific integration influences.
•
AAV-based vectors show a modest preference for regions neat transcription start sites
•AAV- Of all vectors, integration found least favorable into TU’s. Contradictory to previous
mouse liver studies.
•
Experiments concerning whether LINEs prefer to integrate within TUs have been
inconclusive. Specific sequence known to have effect on integration.
•L1- Supports previous studies suggesting strong integration site nucleotide relationships.
What genomic features influence
integration of new DNA?
When asking this question, the scale of interest is very
important because it can influence the results.
For example; You use a vector that you think integrates near the sequence:
GATTACA,
When you focus on a 20 bp segment, it can be very easy to predict where the
vector will integrate.
Conversely, if that same vector is integrated into a 1kbp segment, or 20kb, or 3
billion base pair segment, the integration site is going to be harder to predict.
Especially if there are other, less understood influences acting in concert. As
seen in our case.
Other factors were seen to increase their influence with increased area, as seen
in MLV and ASLV.
Future Studies
With this catalog of vector-feature interactions, we can better
understand novel insertion influences as they’re identified. They
can be studied and compared in cooperation with the current
comprehensive predictive models incorporating all currently
known genomic features. In doing so, we will gain better
insertion prediction abilities with each new independent variable
genomic feature discovered.
One such new feature could be the
relative locations of nucleosomes, or
other epigenetic factors, like
methylation or acetylation of the
DNA strand.
http://en.wikipedia.org/wiki/Nucleosome
Future Studies
This paper mentioned many potential future studies surrounding
each individual potential insertion vector, for example, SB cell
specific integration and AAV likeliness of TU insertion.
Many other areas of research could collaborate upon the findings
presented in this article. Stronger mathematical modeling systems
could be of great value.
http://www.bioscience.heacademy.ac.uk/network/sigs/numeracy/
Future Studies
Also using a different approach utilizing the advances in
proteomics to isolate and identify some of the functional proteins
used by these potential insertion vectors could expand our
understanding of the mechanisms used.
A bioinformatics data base could then be used to see if there any
DNA binding proteins, chromatin related proteins, DNase
proteins, DNA ligase proteins, etc were found.
http://www.dartmouth.edu/~toxmetal/TXQAas.shtml
Future Studies
A second novel use of the vector-feature interaction library is as a
reference in respect to the feature in question.
If you were working with CpG islands, you could look up what
kind of insertion vectors have a probability of inserting near your
CpG island of interest.
http://www.pb.ethz.ch/research/chromatin_technics/TDI.jpg/image
Big Future Studies
The purpose of this research was to better understand the
factors influencing various vector insertions.
This is useful for the hope of creating a reliable, predictable,
vehicle for integrating DNA elements into humans. This
innovation could turn gene therapy into a plausible reality.
We need to be able to insert desired segments with pin point
accuracy as illustrated at the beginning of this paper. A
previous study ‘successfully’ treated human X-SCID while
also indirectly causing leukemia in three of the patients,
Unlike mice, it has to work the first try, every try.
Gene Therapy
Typically gene therapy is most successful when used to treat a single
gene, or monogenic genetic disorder
•Cystic Fibrosis
•Sickle Cell Anemia
•Marfan Syndrome
•Huntington’s Disease
•Hereditary Hemochromatosis
•Ornithine Transcarboxylase Deficiency (OTCD)
http://www.annasslant.com/doctor-shot.jpg
•X-linked Severe Combined Immunodeficiency Disease (X-SCID)
"bubble baby syndrome."
For more information about gene therapy visit
http://www.ornl.gov/sci/techresources/Human_Genome/medicine/assist.shtml