Synopsis

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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 . . .

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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

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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

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Framework

• 7 types of integrating elements
• 17 different integration complexes (datasets)
• 200 variables (genomic features)
• 10,000 integration sites

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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
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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

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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

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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

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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

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Integration Complexes (Datasets)
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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

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The ROC Curve

• Used to analyze the effects of genomic features
  on integration
• Provide a measurement of a predictor variables
  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

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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

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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.

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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

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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

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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)

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ROC Curve Construction

1. Repeat this same procedure using data from the
   control sites
2. Rotate this histogram and upper tail areas graph
   90 clockwise
3. The ROC curve is constructed from the collection
   of true and false positive rates

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ROC Curve Construction

• For every possible cutpoint, plot the True
  Positive Rate on the y-axis and the False
  Positive Rate on the x-axis
• A cutpoint is defined as any value of a predictor

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A Compact Representationof 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

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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

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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

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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

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Effects of Nucleotide Sequence of the 20 Base
Pairs Surrounding the Point of Integration

• Each region is then scored for the expected
  number of integration events over the interval,
  and these interval scores are summed

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Effects of Nucleotide Sequence of the 20 Base
Pairs Surrounding the Point of Integration

1. The summed values are then tested for their
   ability to sort experimental integration sites
   from controls

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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

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Integration in Transcription Units and the Effect
of Gene Activity

• Analysis of DNA integration within TU's and exons

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• HIV (Red) positively correlated with TU's
• Others varied from slight, negative (green) to
  undistinguishable data (black)

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• 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

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How does G/C Content and Proximity to CpG Islands
Effect Integration?

• On average, G/C Content implies
• Gene rich
• Short introns
• High frequencies of ALu repeats
• Low frequencies of LINEs
• High Frequency of CpGs

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• 2 MLVs where integration was positive
• 3 HIVs that were negatively correlated, A/T
  preference
• Other datasets showed weaker and less consistent
  responses

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Whoa!? I Thought HIV Integrated in In Gene
Enriched Regions?
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Fig. 3 A
Fig. 4 A
A/T preference of HIV integrase-binding protein
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• 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

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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.

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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

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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

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• 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

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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

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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

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Fig. 5 D
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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

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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

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What genomic features influence integration of
new DNA?

• What weve learned about each integrating element

• 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

• 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- 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 near DNase 1 sites over long
  genomic intervals favored.

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What genomic features influence integration of
new DNA?

• What weve learned about each integrating element

• 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.
  Specific sequence known to have effect on
  integration.

• SFV- Cell specific integration influences.
  Integration near CpG islands and proximity to
  DNase 1 cut sites more evident in stem cells then
  fibroblasts.
• SB- Contradictory results in regards to proximity
  to CpG islands and gene density. Possibly because
  of cell type specific integration influences.
• AAV- Of all vectors, integration found least
  favorable into TUs. Contradictory to previous
  mouse liver studies.
• L1- Supports previous studies suggesting strong
  integration site nucleotide relationships.

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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.
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Future Studies
With this catalog of vector-feature interactions,
we can better understand novel insertion
influences as theyre 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
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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/
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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
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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
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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.
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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
• Huntingtons Disease
• Hereditary Hemochromatosis
• Ornithine Transcarboxylase Deficiency (OTCD)
• X-linked Severe Combined Immunodeficiency Disease
  (X-SCID) "bubble baby syndrome."

http//www.annasslant.com/doctor-shot.jpg
For more information about gene therapy
visit http//www.ornl.gov/sci/techresources/Human_
Genome/medicine/assist.shtml