Genotypic Drug resistance from proviral DNA and circulating RNA among Subtype C HIV1 infected patien

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Genotypic Drug resistance from proviral DNA and
circulating RNA among Subtype C HIV-1 infected
patients
Lauren Banks, Elizabeth White and David
Katzenstein Stanford University
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Objective

• To determine the susceptibility and potential
  efficacy of ART combinations in drug experienced
  patients.
• Approved methods for Genotyping use plasma viral
  RNA (vRNA) pol gene.
• However, RNA can be difficult to work with
• Viral RNA is less stable than proviral DNA
• Requires RT step before PCR and sequencing

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• Research Question
• Does the drug resistance information obtained
  from proviral PBMC DNA differ from that obtained
  from circulating plasma vRNA?

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The Cohort

• 25 patients from The Center in Harare, Zimbabwe
• Samples collected in 2001, 2003, and 2004
• 6 samples have 2 or more time points
• 32 samples in total
• 22 of 25 patients were failing drug therapy(1000
  copies RNA/ml)
• Most patients were on Combination ART after
  previous treatments.

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Patient Characteristics
Range
Drug Regimens Past and Current
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Methods

• RNA isolated from plasma, reverse transcribed
  and protease and half of RT were amplified by two
  rounds of PCR
• DNA isolated from PBMCs. Protease and half of RT
  were amplified by two rounds of PCR with same
  primers
• Assembled sequences analyzed by Stanford
  Genotypic Resistance Interpretation Algorithm
  HIVSeq at the Stanford HIV Database website
  (hivdb.stanford.edu)
• Phylogenetic analysis performed and genetic
  distances between RNA and DNA sequences obtained
  by DNAdist and Neighbor (BioEdit)

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Resistance Analysis

• Resistance profiles by drug class
• Amino acid mutations were used to calculate a
  Genotypic Resistance Score
• Each vRNA and proviral DNA sequence within each
  drug class and ARV were categorized as
• susceptible
• potential low resistance
• low resistance
• intermediate resistance
• high resistance.

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Resistance Information Analysis Contd

• Numerical coding system
• Susceptible 0
• Potential low resistance 0.5
• Low resistance 1
• Intermediate resistance 2
• High resistance 3
• Collapsed coding system
• Susceptible - numerical score
• Resistant - numerical score 2

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Protease mutations
8 samples with mutations 3/8 identical
mutations in RNA and DNA 5/8 different mutations
More Protease Inhibitor mutations in RNA compared
to DNA
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Only 3 of the 5 samples have different
susceptibilities to PI drugs
Different scores for 5 drugs but difference in
R/S for only 1 drug Lopinavir resistance in RNA
only
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NRTI Mutations
22/32 samples had mutations
9 (41) had same mutations
13 (59) had different mutations
NRTI Both RNA and DNA had unique mutations 6
samples Mutations affected resistance
interpretation
Mutations found only in RNA or DNA
RNA
DNA
T69insert A62AV, K65R, K219KQ L74LV F116Y, M184V
TC008 TC041 TC050 TC118 TC204 TC216
L74LV, V75AV, Y115FY, V118I, M184MV L74V,
M184V K65R M184V
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6 samples (27) had discordant R/S scores
RNA Resistant DNA Susceptible
RNA Susceptible DNA Resistant
TC041
R
R
R
R
R
S
S
S
S
S
S
Because of different mutations in RNA and DNA,
different susceptibilities for tenofovir and
abacavir
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NNRTI Mutations
16 Samples with mutations
8 with same mutations
8 with different mutations
Mutations found only in RNA or DNA
RNA
DNA
TC052 TC059 TC060 TC109 TC111 TC118 TC201 TC215
V108IV V106MV P236LP K238EGK
Y188H P225HP Y181C V179D V108IV V108I
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8 Samples with different NNRTI RNA/DNA mutations
6/8 had identical collapsed scores R or S
2/8 samples had discordant susceptibility to
Etravirine
RNA
DNA
TC052 TC059 TC060 TC109 TC111 TC118 TC201 TC215
V108IV V106MV P236LP K238EGK
Y188H P225HP Y181C V179D V108IV V108I
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Differences in RNA and DNA Comparison of
mutations to R/S score
Samples w/ mutations 8 22 16
Different mutations 63 59 50
Different R/S 38 27 13
PI NRTI NNRTI
Different R/S Difference in R/S to at least one
drug
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Summary of Results

• More PI mutations in RNA than DNA
• In RT, variation in NRTI and NNRTI mutations were
  found in both RNA and DNA
• For NNRTI mutations, most differences between RNA
  and DNA did not affect resistance profile.
• Of 36 mutations found only in RNA or DNA, 17
  (47) were mixtures

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Conclusions

• In multidrug experienced patients, genotypic
  resistance scores from proviral DNA and viral RNA
  may provide different information about drug
  resistance.
• Differences between DNA and RNA drug resistance
  scores were most prominent for NRTI drugs,
  reflecting a past history of exposure and
  selection of drug resistance to drugs in this
  class.
• Conversely, protease inhibitor mutations were
  less likely to be identified in PBMC DNA and more
  common in viral RNA consistent with concommitant
  treatment.

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Conclusions

• Similar drug resistance profiles from viral RNA
  and PBMC DNA suggest that PBMCs may be useful as
  a drug resistance surveillance tool for public
  health resistance monitoring.
• Proviral DNA sequences may be generated cheaply
  and efficiently to determine the prevalence of
  NRTI and NNRTI drug resistance in a heavily
  treated population

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Acknowledgements

• Katzenstein Lab, Stanford University
• Elizabeth White
• David Katzenstein
• University of Zimbabwe
• Lynn Zijenah
• Patrick Mateta
• Gerard Kadzirange
• The patients at The Centre, Harare, Zimbabwe