Mapping Mutations in HIV RNA

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Mapping Mutations in HIV RNA

• By Nimrod Bar-Yaakov nimrod-b_at_orbotech.com
• With co-operation of Dr. Zehava Grossman of the
  Israels Multi-Center AIDS Study Group, National
  HIV reference Laboratory in Tel-Hashomer.

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Todays Topics

• HIV What is it and how it operates.
• What so important about the HIV DNA mutations?
• Extracting the RNA sequence for analyze.
• Naïve view of the HIV RNA sequences
• Locating the RNA mutations
• Analysis of the RNA mutation interactions

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Virus Overview

• Viruses may be defined as acellular organisms
  whose genomes consist of nucleic acid, and which
  obligately replicate inside host cells using host
  metabolic machinery and ribosomes to form a pool
  of components which assemble into particles
  called VIRIONS, which serve to protect the genome
  and to transfer it to other cells.

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Virus Animation
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Virus Overview

• The concept of a virus as an organism challenges
  the way we define life
• viruses do not respire,
• nor do they display irritability
• they do not move
• and nor do they grow,
• however, they do most certainly reproduce, and
  may adapt to new hosts.

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What is an HIV

• human immunodeficiency virus, A type of
  retrovirus that is responsible for the fatal
  illness Acquired Immunodeficiency Syndrome (AIDS)
• Retrovirus A virus that's carry their genetic
  material in the form of RNA rather than DNA and
  have the enzyme reverse transcriptase that can
  transcribe it into DNA.
• In most animals and plants, DNA is usually made
  into RNA, hence "retro" is used to indicate the
  opposite direction

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How does the HIV infects the body cells?

• HIV begins its infection of a susceptible host
  cell by binding to the CD4 receptor on the host
  cell
• The genetic material of the virus, which is RNA,
  is released and undergoes reverse transcription
  into DNA, which enters the host cell nucleus
  where it can be integrated into the genetic
  material of the cell.
• Activation of the host cells results in the
  transcription of viral DNA into messenger RNA
  (mRNA), which is then translated into viral
  proteins.
• The viral RNA and viral proteins assemble at the
  cell membrane into a new virus.
• The virus then buds forth from the cell and is
  released to infect another cell.

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Treatment related to the active RNA sites

• The HIV DNA generates proteins that are essential
  to the virus life-cycle.
• Medical treatment interfere or block the
  operation of these proteins.
• Reverse Transcriptase medicines
• Inhibits the transcription of the HIV RNA into
  the cells DNA
• The HIV protease protein, is required to process
  other HIV proteins into their functional forms.
• Protease inhibitors medicines, act by blocking
  this critical maturation step.

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RNA mutations

• Environmental/Biological processes may cause
  mutations in the HIV RNA.
• The mutated HIV RNA merge into the infected
  cells DNA.
• The generated Amino-Acids sequence is then
  altered.
• A different Protein is generated by the cell.
• The altered protein may resist the medical
  treatment!

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Mutation families

• The HIV RNA has a high mutation rate (a 1000
  times more than a regular cell).
• Fast evolutionary processes causes the best
  mutated viruses to increase their population in
  the infected body.
• Well focus on 3 main mutation families
• Resistance mutations
• Clade mutations
• Other noise/random

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The importance of identifying the resistance
mutations

• Selecting the best medicine treatments
• Understanding the way different medicines
  interacts with the HIV
• Understanding the functional interpretation of
  the RNA sequence

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Extracting the RNA Sequence

• The RNA sequences are transcript into DNA
  sequences.
• The DNA sequences then multiplied several times
• A DNA sequencer read the aligned DNA sequences.
• The decision how to interpret a specific DNA
  segment is based over image processing algorithms
  (define the segment boundaries and find the best
  match for the segment pattern) and isnt
  deterministic!

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Sequence Alignment (from Ron Shamirs Course)
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(No Transcript)
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Sequence Alignment

• Before alignment
• AtaaagakagggggacagctaaaagaggctctcTTAGACACAGGAGCAGA
  TGATACA
• ACTCTTTGGCAGCGaCCCCGTTGTCACaATAAAAATagGGGGACAGCTAA
  gGGagGc
• TAAAAGAGGCTCTCTTAGCACACAGGMGCAGAYGAYACAGTMCTTASCAA
  GAAATAA
• ACTCTTTGGCAGCGACCCCTTGTcACAATAAAAGTAGAGGGACAGCTAAG
  GGAKGCT
• ACTCTTTGGCAGCGaCCCCTTGTCACAATAAAAATAGGGGACAGCTAAGG
  GAGGCTC
• ACTCTTTGGcAGCGACCCCTtGTCACAATAAAAGtAGGGGGaCAGCTAAA
  gGAGGCT
• aCTnTTnGRCAGCGaCCCCTTgTCYCARtAAAAATAGGGGGGCAGRTAAR
  GGAGGCt
• After Alignment
• ------------------------------ATAAAGAKAGGGGG-ACAG-
  CTAAAAGAGG
• ------------C-GACCCC--TTGTCACAATAARAATAGGGGG-ACAG-
  CTAAAAGAGG
• ACTCTTTGGCAAC-GACCCC--TTGTCACAATAAGAGTAGGGGG-ACAG-
  CTAAAAGAGG
• -CTCTTTGGCAAC-GA-CCCC-TTGTCACAGTAAAAATAGRAGG-ACAG-
  CTAAAAGAAG
• ACTCTTTGGCAAC-GA-CCCC-TTGTCACAGTAAAAATAGGAGG-ACAG-
  CTAAAAGAAG
• ACTCTTTGGCAAC-GA-CCCC-TTGTCACAGTAAAAATAGGAGG-ACAG-
  CTMAAAGAAG
• ACTCTTTGGCAAC-GA-CCCC-TTGTCACAGTAAGAATAGGAGG-ACAG-
  CTAAAAGAAG
• Degapping

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Reduction from Bio problem to CS Problem

• Generation of a consensus RNA sequence.
• For each sequence, generate a matching binary
  sequence, each 1 represents a mismatch between
  the consensus and the original sequence, and 0
  represents a match.
• Now we have a binary feature vector for each
  sample.
• We can now calculate the correlations between the
  mutations to the treatment and between the
  mutations to themselves.

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From Sequences to Mutation Matrix
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So where are the problems?

• Curse of dimensionality
• Noisy data
• Sequenced data are of stochastic nature
• Small number of samples
• Clades and sub-clades
• Vague definitions of independent variables
  values.
• Silent mutations
• Talk Bio language!

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Data Overlook
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Frequencies of Mutations occurrences
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Filtering the Data

• Mutations that occur less than 5 times in a
  specific RNA index cannot considered significant
  (well see it later in the Chi square slides)
• Well filter all the mutations that occur less
  than 3 times and replace them with the consensus
  value.
• Thus filtering much of the noise.

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Naïve clustering of Data
Clustering of 671 RNA samples using Centroid
linkage
Total Cases
A C B
Clade Distribution
Treated Non-Tr
Treatment Distribution
120 9 12 59 8
29 215 65 147 7
Cluster Size
671
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Feature Extraction

• Better to have misdetection than a false alarm.
• Filter the noisy data
• Work within the clades
• Locate the mutations (features) that are highly
  correlate with treatment.
• Now we have only few dozens of features to work
  on.

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Finding mutations and treatment correlation

• We want to find for each RNA index i whether
  P(Mut_in_i) is significantly different from
  P(Mut_in_ i/ Treatment).
• Well use the CHI square distribution test for
  each index to find that.

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Chi Square Overview

• We will use the Chi-Square test to check the
  probability that our observed results had came
  from the same statistical population as the
  expected (chance) results.
• A probability of less than 0.05 means that the
  results are significant, I.e the populations are
  significally different .

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Chi Square Calculations

• Calculating the chi-square statistic
• The probability Q that a X2 value calculated for
  an experiment with d degrees of freedom (where
  dk-1) is due to chance is

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Example Mutation V82A
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Mutation Table
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Calculating the mutations Correlations Matrix

• Because the treatment is a major artifact in all
  the treatment mutations, well have to find the
  correlations within the treated samples
• P(mut_A/Treat.) P(mut_A/mut_b,Treat.)
• Our Chi-Square table will be (all in treated
  cases)

Mut B Non -Mut B Total
Mut A
Non Mut A
Total
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Example correlation results
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Example Mutation D30N

• D30N is an important resistance mutation. But it
  appears at frequency of 0.0258 in the C clade
  compare with 0.0945 in the B clade, Whats the
  explanation for this?
• Correlation analysis reveals that in clade B,
  D30N is highly correlated with other resistance
  Mutations. In clade C its not.
• One assumption can be that the Clade B structure
  can influence the connections between resistance
  mutations.

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Using CART to find mutations interactions

• A regression tree is a sequence of questions that
  can be answered as yes or no, plus a set of
  fitted response values. Each question asks
  whether a predictor satisfies a given condition.
• In our research we will ask whether a mutation i
  (1 value at i index), predicts the existence of
  mutation j (1 value at j index).
• This way we can identify relationships between
  the mutations.

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CART results D95M
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Using clustering to find mutations patterns

• Well cluster the mutation sample vectors in
  order to locate mutation patterns.
• Our distance function will be the sum of
  differences between two samples.
• Well use the ward method to cluster nodes.

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Ward Clustering

• Centroid linkage uses the distance between the
  centroids of the two groups
• Where and Xs defined
  similarly.
• Ward linkage uses the incremental sum of squares
  that is, the increase in the total
• within-group sum of squares as a result of
  joining groups r and s. It is given by
• Where drs is the distance between cluster r and
  cluster s defined in the Centroid linkage. The
• within-group sum of squares of a cluster is
  defined as the sum of the squares of the distance
  between all objects in the cluster and the
  centroid of the cluster.

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Cluster results
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Using clustering to find mutations patterns

• When we filter the mutation only to significant
  ones, we can see mutations pattern as a result of
  clustering -

Samples
Mutations
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Whats next?

• Biological interpretation of the findings
• Locating Amino-Acid and protein functional
  changes. May lead to better understand of
  resistance behavior.
• Identifying new resistance mutations and specific
  treatment/resistance correlations.
• Focus on specific treatments, apply additional
  research in order to investigate the efficiency
  of such treatment.

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The End!

• Thank you for listening