1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
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Systematic screening of viral and human genetic variation identifies antiretroviral resistance and immune escape link

  1. Huyen Nguyen  Is a corresponding author
  2. Christian Wandell Thorball
  3. Jacques Fellay
  4. Jürg Böni
  5. Sabine Yerly
  6. Matthieu Perreau
  7. Hans H Hirsch
  8. Katharina Kusejko
  9. Maria Christine Thurnheer
  10. Manuel Battegay
  11. Matthias Cavassini
  12. Christian R Kahlert
  13. Enos Bernasconi
  14. Huldrych F Günthard
  15. Roger D Kouyos  Is a corresponding author
  16. The Swiss HIV Cohort Study
  1. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Switzerland
  2. Institute of Medical Virology, Swiss National Center for Retroviruses, University of Zurich, Switzerland
  3. School of Life Sciences, École Polytechnique, Switzerland
  4. Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Switzerland
  5. Laboratory of Virology, Geneva University Hospital, University of Geneva, Switzerland
  6. Division of Immunology and Allergy, University Hospital Lausanne, University of Lausanne, Switzerland
  7. Transplantation & Clinical Virology, Department of Biomedicine, University of Basel, Switzerland
  8. Infectious Diseases and Hospital Epidemiology, Department of Medicine, University Hospital Basel, Switzerland
  9. Clinical Virology, Laboratory Medicine, University Hospital Basel, Switzerland
  10. University Clinic of Infectious Diseases, University Hospital of Bern, University of Bern, Switzerland
  11. Department of Infectious Diseases, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Switzerland
  12. Division of Infectious Diseases and Hospital Epidemiology, Kantonsspital St. Gallen, Switzerland
  13. Division of Infectious Diseases, Regional Hospital, Switzerland
Research Article
Cite this article as: eLife 2021;10:e67388 doi: 10.7554/eLife.67388
3 figures, 4 tables and 4 additional files


Flowchart of methodology of obtaining the candidate DRM:HLA pairs with possible epitope relationship.

From the 3997 SHCS patients with both HLA-I data and drug resistance testing data, 5561 potential combinations of HLA-I type and DRMs were examinable, from which only 225 had sufficient power for testing. From these 225, three candidate pairs were found to have a significant HLA term in a logistic regression model predicting the resistance mutation in question. DRM, drug-resistant mutation; HLA, human leukocyte antigen.

Logistic regression models testing for interaction between the queried human leukocyte antigen (HLA) type and duration of infection in predicting the presence of drug-resistant mutation (DRM).

Of the three candidate DRM:HLA type pairs, one pair, RT-E138:HLA-B18, indicates a significant interaction term between the presence of the queried HLA type and the duration of HIV infection in a logistic regression model predicting the presence of a mutation at RT-E138 (A). (B) Details of all three candidates’ logistic regression models.

Hazard ratios and cumulative hazards of developing queried drug-resistant mutation over time in relation to the presence of human leukocyte antigen (HLA) type.

(A) Cox proportional hazard ratios for developing the queried drug-resistant mutation with the queried HLA-I type. (B, C) Cumulative hazard plots of the two pairs from (A) where the hazard ratios were significant, indicating cumulative hazards of developing the mutation among those initially wild type, with red lines indicating individuals with the queried HLA type and blue lines for those with another HLA type.


Table 1
General characteristics of SHCS patients and those with resistance mutation and human leukocyte antigen (HLA) data.

Overview of general characteristics of SHCS patients and the subsets with sequencing resistance testing data, HLA-I data, and both. IQR: interquartile range; MSM: men who have sex with men; HET: heterosexual; IDU: intravenous drug use.

All SHCS participantsSHCS patients with resistance testing dataSHCS patients with HLA-I dataSHCS patients with HLA-I and resistance testing data
Median age (IQR)56 (48–62)54 (47–60)55 (49–62)54 (47–60)
Male (%)15,064 (72.6%)9402 (71.2%)4836 (75.0%)3027 (75.7%)
Risk group:
8100 (39.1%)5226 (39.8%)2777 (43.1%)1784 (44.6%)
HET6841 (33.0%)4731 (36.1%)2173 (33.7%)1439 (36.0%)
IDU4840 (23.3%)2568 (19.6%)1255 (19.5%)620 (15.5%)
Other960 (4.6%)591 (4.5%)245 (3.8%)154 (3.9%)
White (%)14044 (67.7%)9993 (76.2%)5661 (87.8%)3487 (87.2%)
Table 2
Distribution of most common HLA-I A, B, and C alleles in study population.

Ten most common HLA-A, -B, and -C types in study population individuals with both HLA-I and DRM information. Frequency and percentage of individuals with each allele are indicated. DRM, drug-resistant mutation; HLA, human leukocyte antigen.

HLA-A typeFrequencyPercentage
HLA-B typeFrequencyPercentage
HLA-C typeFrequencyPercentage
Table 3
Distribution of most common drug-resistant mutations (DRMs) in study population.

Ten most common DRMs from the earliest available resistance testing of the study population, with the frequency and percentage of each among the study population indicated. Specific amino acid mutations represented in the population are shown.

GeneSpecific DRMFrequencyPercentage
Table 4
DRM:HLA pairs corroborated by each analytical approach.

Summary of HLA–drug-resistant mutation pairs in all three approaches. Methods that corroborate the HLA–mutation relationship are indicated by ‘yes.’ DRM, drug-resistant mutation; HLA, human leukocyte antigen.

DRM:HLA pairInteraction term in
cross-sectional logistic regression
survival analysis
Mechanistic plausibility

Additional files

Source data 1

Data files with select anonymized variables necessary for producing main figures.

Supplementary file 1

Overview of Benjamini–Hochberg adjustment of DRM:HLA candidate pairs.

Overview of the Benjamini–Hochberg procedure to correct for multiple testing in selecting HLA–mutation pairs. Pairs were ranked by the p-value of the HLA term in the adjusted logistic regression model predicting for the queried mutation. The numerical rank (I) is divided by the total number of pairs (m = 225) and multiplied by the false discovery rate of 0.2 (Q). With this adjustment, the lowest-ranked pairs where the p-value is lower than (I/m)Q, along with all pairs ranked above, are included after the adjustment (in bold in the table), yielding the three candidate pairs we investigated in-depth (in bold). Only the first 25 rows of the total 225 are shown.

Supplementary file 2

Table of NetMHCpan predictions of top binding peptides for each HLA–DRM candidate pair.

For each HLA–mutation pair, the binding peptides (defined as below a rank of 2% for weakly binding and below 0.5% for strongly binding) are listed ranked in decreasing predicted binding strength according to NetMHCpan. Peptides in bold denote the peptides without the mutation that bind more strongly than all other peptides for that position in the viral amino acid sequence. Peptides in bold and italics denote peptides without the mutation that bind more weakly than a mutated form.

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