Mapping Mutations Patterns in the HIV DNA

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Mapping Mutations Patterns in the HIV DNA

• 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 Introduction.
• What so important about the HIV DNA mutations?
• Early stages Exploring RNA mutations
• From RNA to Amino Acid Mutations
• Recent studies and current work.
• Results and future research.

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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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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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Data Processing

• DNA Sequence Extraction
• DNA Sequence Alignment
• Identifying and Filtering mutations
• Creating consensus sequence and mutation matrix.
• Find correlations between treatment and mutation
  patterns.

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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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From Sequences to Mutation Matrix
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Data Overlook
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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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DNA processing 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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Mutation Table
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Results 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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Moving from DNA to Amino Acid mutations

• Because DNA is translated to AA that forms the
  protein, protein functional studies only focus on
  the AA aspects of the DNA.
• Because 3 DNA nucleotides conforms to 1 AA we
  reduce our dimensionality 3 times (though each
  dimension contains 22 AA).
• Several sequences conforms to the same AA
  reduce variability and noise.
• HIV mutation research focus mainly on AA,
  therefore provides more comparison data.

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Moving from DNA to Amino Acid mutations
Second nucleotide

• Translating DNA sequence to AA sequence is
  straight forward

ATAAAGAKAGGGGGACAGCTAAAAGAGGC ATAARAATAGGGGGACAGCT
AAAAGAGGC ATAAGAGTAGGGGGACAGCTAAAAGAGGC GTAAAAATAG
RAGGACAGCTAAAAGAAGC GTAAAAATAGGAGGACAGCTAAAAGAAGC
GTAAAAATAGGAGGACAGCTMAAAGAAGC GTAAGAATAGGAGGACAGCT
AAAAGAAGC GTAAGAATAGGAGGACAGCTAAAAGAAGC
KWKPKIIGGIGGFVKVRQYDEVVVEICGK KWKPKMIG?IGGFIKVRQYD
QILIEICGK KWKPKMIGGIG?FI?VRQYEEILIEICGK ?WKPKMIGGI
GGFIKVRQYDQV?IEIC?K KWKPKMIGGIGGF?KVRQYDQIPIEICGK
RWKPKMIGGIGGFIKVRQY?QI?IEICGK KWKPKMIGGIGGFIKVRQYD
QILIEICGK KWKPKMIGGIGGF?KVRQYDQILIEICGK KWKPKIIGGI
GGLIKV?QYDNISIEICGK
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Moving from DNA to Amino Acid mutations

• A consensus AA sequence is then calculated, noisy
  data is filtered, and ambiguous AA are converted
  to the consensus values.
• Though a Mutation can receive 20 mutated values,
  through filtering and comparison to literature, a
  max of 4 mutation per AA index is set.
• Mutation frequency matrix is then calculated-
  where every mutation, even in the same index
  add a frequency column to the mutation matrix.

Mutations
0A000 V000L 0P00L VA000 000G0 0P000
010000 100001 001001 110000 000010 001000
Samples
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Why searching for patterns?

• 1 Dimensional AA sequence folds into a 3D protein
  structure.
• The protein active sites located along its folds,
  usually contains more than one AA.
• Protein mutated behavior occurs along its active
  sites
• The AA 3D proximity is different than their
  sequence proximity.

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Active sites patternfrom - http//www.rcsb.org/pd
b/index.html
AA Sequence
Protease Protein 3D Structure
ADDTVLEEINLPEKWTPKMIGGIGGFVKVRQYDQIPIEICGKKVIGAVL
VGPTPANVIGRNL
ADDTMLEEINLPEKWTPKMIGGIGGFVKVRQMDQIPICICGKKVIGAVL
VGPTPANVIGRNL
Sequence mutation pattern
Active site changing
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Problem definition

• Find a correlation between specific pattern all
  over the samples and a specific treatment

Mutations
NFV Treated
Chi Calculation
01110100 10000100 00100101 11100000 00000000 00100
110 00010000 00100100
1 0 1 0 0 0 0 1
Samples
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Recent Biological studies

• A thorough research and data gathering is done in
  Stanford university The HIV drug resistance
  database
• Each sample can contribute only one vote to a
  patterns count, though many sub-patterns can be
  located in one sample.

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Recent research Bayesian networks

• K Deforche et al. has studied the dependencies
  relationships of treatment type combined with AA
  mutations using Bayesian networks.

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Recent research Bayesian networks

• Though it seems a promising way of finding
  relationships between a mutation and the
  treatment Bayesian Network looks for connection
  between one variable and another, where in our
  case we may want to look at the relationship
  between a group of variables and another.
• Interpreting the Bayesian Network is also a hard
  task, and it may only give us directions or clues
  toward regions where we must research again the
  data in order to prove statistical significance
  between the variables.

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Data Challenges

• Because each samples can contain interesting
  and non-interesting mutations, or mutations
  from different patterns we must treat every
  mutation pattern in the sample as candidate.
• We then sum the number of appearances of each
  pattern candidate in order to calculate the CHI
  statistics.

Samples total patterns 00100100110 1 00100
000011 1 10100101110 2 00010010000 2 0010010011
0 3 00110100100 4 00000100100 4 00110100110 5
10100100000 5
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Data Challenges

• The complexity of naïve traversing through all
  the patterns is O((N2)(2K)) , where K is
  largest number of mutations in a single sample.
  And N is the number of samples.
• In our data K can reach 30 and N is 1000, so
  naïve search is not feasible.
• Since p(a,b/T) is hard to predict from p(a/T) and
  p(b/T), gradient decent methods of traversing
  through the mutation pattern space (where in
  every step we add a mutation to the pattern), may
  be fruitless.
• There is also no apparent trait of the statistic
  function we want to maximize, that may ease our
  search.

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Branch and BoundLittle et. al (1963)

• An algorithmic technique to find the optimal
  solution by keeping the best solution found so
  far. If a partial solution cannot improve on the
  best, it is abandoned
• When we can determine that a given node in the
  solution space does not lead to the optimal
  solution-either because the given solution and
  all its successors are infeasible or because we
  have already found a solution that is guaranteed
  to be better than any successor of the given
  solution. In such cases, the given node and its
  successors need not be considered.
• In effect, we can prune  the solution tree,
  thereby reducing the number of solutions to be
  considered.

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Branch and Bound
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Branch and Bound

• Save pattern results in order to save calculation
• Lower Bounds
• If (ab) lt 3
• If p(A) gt 0.5 and p(B) gt 0.5 no need to check
  (AB) empirically studied, probably has
  biological reasoning.
• Upper Bound
• Statistically significance

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Branch and Bound results

• Discover all single major mutations that appears
  in data.
• Discover three major pattern groups two of them
  known, one is new need to find if there is any
  biological meaning.

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Branch and Bound Results
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Branch and Bound Results

• Pros
• Exhaustive good patterns cannot escape
• Simple to understand and implement
• Cons
• Probability lower bound isnt well defined
• Can take too long in DNA pattern calculations

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BiclusteringCheng and. Church , 2000

• A clustering process of simultaneously mining
  column and row (say row for observation/gene and
  column for dimension/sample).
• A bi-cluster is a subset of rows that exhibit
  similar behavior across a subset of columns, and
  vice versa.
• Each node can relate to several bi-clusters at a
  time.
• Originally developed for mining gene expression
  data.

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Biclustering SAMBAA. Tanay, R. Sharan, and R.
Shamir, 2002

• Statistical-Algorithmic Method for Bicluster
  Analysis
• Create a bi-partite graph from the data, where
  the left side is the genes and the right is the
  conditions.
• Connect edges between the vertices on the two
  sides according to their similarity
  expression level, and weight it accordingly.

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Biclustering SAMBAA. Tanay, R. Sharan, and R.
Shamir, 2002

• Tanay et al. has shown how to assign weights to
  the vertex pairs so that a maximum weight
  bicluster corresponds to a maximum likelihood
  bicluster.
• Therefore we can reduce the problem to finding
  heaviest sub graph in a bi-partite graph a
  known combinatorial problem.

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Adapting SAMBA

• For each treatment
• Samples on one side, mutations on the other.
• Add edge, if the sample contains the mutation
• Modify weighting scheme so it can relate to the
  CHI square statistic

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The End!

• Thank you for listening.
• Any Questions?