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1. HIV-1 Infection in Cyprus, the Eastern Mediterranean European Frontier: A Densely Sampled Transmission Dynamics Analysis from 1986 to 2012

Author

Pineda-Peña, A.-C. (Andrea-Clemencia), Theys, K. (Kristof), Stylianou, D.C. (Dora C.), Demetriades, I. (I.), Puchhammer, E. (Elisabeth), Vandamme, A.M. (Anne Mieke), Aleksiev, I. (Ivailo), Lepej, S.Z. (Snjezana), Linka, M. (Marek), Fonager, J. (Jannik), Liitsola, K. (Kirsi), Kaiser, R. (Rolf), Hamouda, O. (Osamah), Paraskevis, D. (Dimitrios), Coughlan, S. (Suzie), Grossman, Z. (Zehava), Mor, O. (Orna), Zazzi, M. (Maurizio), Griskevicius, A. (Algirdas), Lipnickiene, V., Devaux, C. (Carole), Boucher, C. (Charles), Hofstra, M. (Marije), Wensing, A. (Amj), Bakken-Kran, A.-M. (Anne-Marte), Horban, A. (Andrzej), Camacho, R.J. (Ricardo Jorge), Paraschiv, C. (Corina), Otelea, D. (Dan), Stanojevic, M. (Maja), Stanekova, D. (Danica), Poljak, M. (Mario), Garcia, F. (Federico), Paredes, R. (Roger), Albert, J. (Jan), Abecasis, A.B. (Ana), Kostrikis, L.G. (Leondios), Pineda-Peña, A.-C. (Andrea-Clemencia), Theys, K. (Kristof), Stylianou, D.C. (Dora C.), Demetriades, I. (I.), Puchhammer, E. (Elisabeth), Vandamme, A.M. (Anne Mieke), Aleksiev, I. (Ivailo), Lepej, S.Z. (Snjezana), Linka, M. (Marek), Fonager, J. (Jannik), Liitsola, K. (Kirsi), Kaiser, R. (Rolf), Hamouda, O. (Osamah), Paraskevis, D. (Dimitrios), Coughlan, S. (Suzie), Grossman, Z. (Zehava), Mor, O. (Orna), Zazzi, M. (Maurizio), Griskevicius, A. (Algirdas), Lipnickiene, V., Devaux, C. (Carole), Boucher, C. (Charles), Hofstra, M. (Marije), Wensing, A. (Amj), Bakken-Kran, A.-M. (Anne-Marte), Horban, A. (Andrzej), Camacho, R.J. (Ricardo Jorge), Paraschiv, C. (Corina), Otelea, D. (Dan), Stanojevic, M. (Maja), Stanekova, D. (Danica), Poljak, M. (Mario), Garcia, F. (Federico), Paredes, R. (Roger), Albert, J. (Jan), Abecasis, A.B. (Ana), and Kostrikis, L.G. (Leondios)

Abstract

Since HIV-1 treatment is increasingly considered an effective preventionstrategy, it is important to study local HIV-1 epidemics to formulate tailored preventionpolicies. The prevalence of HIV-1 in Cyprus was historically low until 2005. To investigatethe shift in epidemiological trends, we studied the transmission dynamics of HIV-1 in Cyprususing a densely sampled Cypriot HIV-1 transmission cohort that included 85 percent ofHIV-1-infected individuals linked to clinical care between 1986 and 2012 based on detailedclinical, epidemiological, behavioral and HIV-1 genetic information. Subtyping andtransmission cluster reconstruction were performed using maximum likelihood and Bayesianmethods, and the transmission chain network was linked to the clinical, epidemiological andbehavioral data. The results reveal that for the main HIV-1 subtype A1 and B sub-epidemics,young and drug-naïve HIV-1-infected individuals in Cyprus are driving the dynamics of thelocal HIV-1 epidemic. The results of this study provide a better understanding of thedynamics of the HIV-1 infection in Cyprus, which may impact the development of preventionstrategies. Furthermore, this methodology for analyzing densely sampled transmissiondynamics is applicable to other geographic regions to implement effective HIV-1 preventionstrategies in local settings.

Published
2018
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2. HIV-1 Infection in Cyprus, the Eastern Mediterranean European Frontier: A Densely Sampled Transmission Dynamics Analysis from 1986 to 2012

Author

Pineda-Peña, AC, Theys, K, Stylianou, D C, Demetriades, I, Puchhammer, E, Vandamme, AM, Aleksiev, I, Lepej, SZ, Linka, M, Fonager, J, Liitsola, K, Kaiser, R, Hamouda, O, Paraskevis, D, Coughlan, S, Grossman, Z, Mor, O, Zazzi, M, Griskevicius, A, Lipnickiene, V, Devaux, C, Boucher, Charles, Hofstra, M, Wensing, A, Bakken-Kran, AM, Horban, A, Camacho, R, Paraschiv, S, Otelea, D, Stanojevic, M, Stanekova, D, Poljak, M, Garcia, F, Paredes, R, Albert, J, Abecasis, AB, Kostrikis, LG, Pineda-Peña, AC, Theys, K, Stylianou, D C, Demetriades, I, Puchhammer, E, Vandamme, AM, Aleksiev, I, Lepej, SZ, Linka, M, Fonager, J, Liitsola, K, Kaiser, R, Hamouda, O, Paraskevis, D, Coughlan, S, Grossman, Z, Mor, O, Zazzi, M, Griskevicius, A, Lipnickiene, V, Devaux, C, Boucher, Charles, Hofstra, M, Wensing, A, Bakken-Kran, AM, Horban, A, Camacho, R, Paraschiv, S, Otelea, D, Stanojevic, M, Stanekova, D, Poljak, M, Garcia, F, Paredes, R, Albert, J, Abecasis, AB, and Kostrikis, LG

Published
2018

3. RegaDB: Community-driven data management and analysis for infectious diseases

Author

Libin, P, Beheydt, G, Deforche, K, Imbrechts, S, Ferreira, F, Van Laethem, K, Theys, K, Carvalho, A, Cavaco-Silva, J, Lapadula, G, Torti, C, Assel, M, Wesner, S, Snoeck, J, Ruelle, J, De Bel, A, Lacor, P, De Munter, P, Van Wijngaerden, E, Zazzi, M, Kaiser, R, Ayouba, A, Peeters, M, De Oliveira, T, Alcantara, L, Grossman, Z, Sloot, P, Otelea, D, Paraschiv, S, Boucher, C, Camacho, R, Vandamme, A, Libin P., Beheydt G., Deforche K., Imbrechts S., Ferreira F., Van Laethem K., Theys K., Carvalho A. P., Cavaco-Silva J., Lapadula G., Torti C., Assel M., Wesner S., Snoeck J., Ruelle J., De Bel A., Lacor P., De Munter P., Van Wijngaerden E., Zazzi M., Kaiser R., Ayouba A., Peeters M., De Oliveira T., Alcantara L. C. J., Grossman Z., Sloot P., Otelea D., Paraschiv S., Boucher C., Camacho R. J., Vandamme A. -M., Libin, P, Beheydt, G, Deforche, K, Imbrechts, S, Ferreira, F, Van Laethem, K, Theys, K, Carvalho, A, Cavaco-Silva, J, Lapadula, G, Torti, C, Assel, M, Wesner, S, Snoeck, J, Ruelle, J, De Bel, A, Lacor, P, De Munter, P, Van Wijngaerden, E, Zazzi, M, Kaiser, R, Ayouba, A, Peeters, M, De Oliveira, T, Alcantara, L, Grossman, Z, Sloot, P, Otelea, D, Paraschiv, S, Boucher, C, Camacho, R, Vandamme, A, Libin P., Beheydt G., Deforche K., Imbrechts S., Ferreira F., Van Laethem K., Theys K., Carvalho A. P., Cavaco-Silva J., Lapadula G., Torti C., Assel M., Wesner S., Snoeck J., Ruelle J., De Bel A., Lacor P., De Munter P., Van Wijngaerden E., Zazzi M., Kaiser R., Ayouba A., Peeters M., De Oliveira T., Alcantara L. C. J., Grossman Z., Sloot P., Otelea D., Paraschiv S., Boucher C., Camacho R. J., and Vandamme A. -M.

Abstract

RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. © 2013 The Author. Published by Oxford University Press.

Published
2013

4. Characterization of amino acids Arg, Ser and Thr at position 70 within HIV-1 reverse transcriptase.

Author

Megens, Sarah, De Wit, Stéphane, Bernatchez, J, Dekeersmaeker, Nathalie, Vinken, L, Covens, Kris, Theys, K, Camacho, Ricardo Jorge, Vandamme, Anne-Mieke, Gottesfeld, Joel M, Van Laethem, Kristel, Megens, Sarah, De Wit, Stéphane, Bernatchez, J, Dekeersmaeker, Nathalie, Vinken, L, Covens, Kris, Theys, K, Camacho, Ricardo Jorge, Vandamme, Anne-Mieke, Gottesfeld, Joel M, and Van Laethem, Kristel

Abstract

The amino acid position 70 in HIV-1 reverse transcriptase (RT) plays an important role in nucleoside RT inhibitor (NRTI) resistance. K70R is part of the thymidine analog mutations, but also other amino acid changes have been associated with NRTI resistance, such as K70E and K70G. In this study, we investigated the in vivo selection of the HIV-1 RT mutations K70S and K70T and their in vitro effect on drug resistance and replication capacity., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2014

5. HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

Author

Sangeda, R.Z. (Raphael), Theys, K. (Kristof), Beheydt, G. (Gertjan), Rhee, S.Y. (Soo Yoon), Deforche, K. (Koen), Vercauteren, J. (Jurgen), Libin, P. (Pieter), Imbrechts, S. (Stijn), Grossman, Z. (Zehava), Camacho, R.J. (Ricardo Jorge), Laethem, K. (Kristel) van, Pironti, A. (Alejandro), Zazzi, M. (Maurizio), Sönnerborg, A. (Anders), Incardona, F. (Francesca), Luca, A. (Andrea) de, Torti, C. (Carlo), Ruiz, L. (Lidia), Vijver, D.A.M.C. (David) van de, Shafer, R.W. (Robert), Bruzzone, B. (Bianca), Wijngaerden, E. (Eric) van, Vandamme, A.M. (Anne Mieke), Sangeda, R.Z. (Raphael), Theys, K. (Kristof), Beheydt, G. (Gertjan), Rhee, S.Y. (Soo Yoon), Deforche, K. (Koen), Vercauteren, J. (Jurgen), Libin, P. (Pieter), Imbrechts, S. (Stijn), Grossman, Z. (Zehava), Camacho, R.J. (Ricardo Jorge), Laethem, K. (Kristel) van, Pironti, A. (Alejandro), Zazzi, M. (Maurizio), Sönnerborg, A. (Anders), Incardona, F. (Francesca), Luca, A. (Andrea) de, Torti, C. (Carlo), Ruiz, L. (Lidia), Vijver, D.A.M.C. (David) van de, Shafer, R.W. (Robert), Bruzzone, B. (Bianca), Wijngaerden, E. (Eric) van, and Vandamme, A.M. (Anne Mieke)

Abstract

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response un

Published
2013
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6. RegaDB: Community-driven data management and analysis for infectious diseases

Author

Libin, P. (Pieter), Beheydt, G. (Gertjan), Deforche, K. (Koen), Imbrechts, S. (Stijn), Ferreira, F. (Fossie), Laethem, K. (Kristel) van, Theys, K. (Kristof), Carvalho, A.P. (Ana Patricia), Cavaco-Silva, J. (Joana), Lapadula, G. (Giuseppe), Torti, C. (Carlo), Assel, M. (Matthias), Wesner, S. (Stefan), Snoeck, M.M.J. (M. M J), Ruelle, J. (Jean), Bel, A.V. (Annelies) de, Lacor, P. (Patrick), Munter, P. (Paul) de, Wijngaerden, E. (Eric) van, Zazzi, M. (Maurizio), Kaiser, R. (Rolf), Ayouba, A. (Ahidjo), Peeters, M.C. (Marian), Oliveira, T. (Tulio) de, Alcantara, L.C.J. (Luiz), Grossman, Z. (Zehava), Sloot, P.M.A. (Peter), Otelea, D. (Dan), Paraschiv, C. (Corina), Boucher, C.A.B. (Charles), Camacho, R.J. (Ricardo Jorge), Vandamme, A.M. (Anne Mieke), Libin, P. (Pieter), Beheydt, G. (Gertjan), Deforche, K. (Koen), Imbrechts, S. (Stijn), Ferreira, F. (Fossie), Laethem, K. (Kristel) van, Theys, K. (Kristof), Carvalho, A.P. (Ana Patricia), Cavaco-Silva, J. (Joana), Lapadula, G. (Giuseppe), Torti, C. (Carlo), Assel, M. (Matthias), Wesner, S. (Stefan), Snoeck, M.M.J. (M. M J), Ruelle, J. (Jean), Bel, A.V. (Annelies) de, Lacor, P. (Patrick), Munter, P. (Paul) de, Wijngaerden, E. (Eric) van, Zazzi, M. (Maurizio), Kaiser, R. (Rolf), Ayouba, A. (Ahidjo), Peeters, M.C. (Marian), Oliveira, T. (Tulio) de, Alcantara, L.C.J. (Luiz), Grossman, Z. (Zehava), Sloot, P.M.A. (Peter), Otelea, D. (Dan), Paraschiv, C. (Corina), Boucher, C.A.B. (Charles), Camacho, R.J. (Ricardo Jorge), and Vandamme, A.M. (Anne Mieke)

Abstract

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB pro

Published
2013
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7. HIV-1 subtype distribution and its demographic determinants in newly diagnosed patients in Europe suggest highly compartmentalized epidemics

Author

Abecasis, A.B. (Ana), Wensing, A.M.J. (Annemarie), Paraskevis, D. (Dimitrios), Vercauteren, J. (Jurgen), Theys, K. (Kristof), Vijver, D.A.M.C. (David) van de, Albert, J. (Jan), Åsjö, B. (Birgitta), Balotta, C. (Claudia), Beshkov, D. (Danail), Camacho, R.J. (Ricardo Jorge), Clotet, B., Gascun, C. (Cillian) de, Griskevicius, A. (Algirdas), Grossman, Z. (Zehava), Hamouda, O. (Osamah), Horban, A. (Andrzej), Kolupajeva, T. (Tatjana), Korn, K., Kostrikis, L.G. (Leondios), Kücherer, C. (Claudia), Liitsola, K. (Kirsi), Linka, M. (Marek), Nielsen, C. (Claus), Otelea, D. (Dan), Paredes, R. (Roger), Poljak, M. (Mario), Puchhammer-Stöckl, E. (Elisabeth), Schmit, J.-C. (Jean-Claude), Sonnerborg, A. (Anders), Stanekova, D. (Danica), Stanojevic, M. (Maja), Struck, D. (Daniel), Boucher, C.A.B. (Charles), Vandamme, A.M. (Anne Mieke), Abecasis, A.B. (Ana), Wensing, A.M.J. (Annemarie), Paraskevis, D. (Dimitrios), Vercauteren, J. (Jurgen), Theys, K. (Kristof), Vijver, D.A.M.C. (David) van de, Albert, J. (Jan), Åsjö, B. (Birgitta), Balotta, C. (Claudia), Beshkov, D. (Danail), Camacho, R.J. (Ricardo Jorge), Clotet, B., Gascun, C. (Cillian) de, Griskevicius, A. (Algirdas), Grossman, Z. (Zehava), Hamouda, O. (Osamah), Horban, A. (Andrzej), Kolupajeva, T. (Tatjana), Korn, K., Kostrikis, L.G. (Leondios), Kücherer, C. (Claudia), Liitsola, K. (Kirsi), Linka, M. (Marek), Nielsen, C. (Claus), Otelea, D. (Dan), Paredes, R. (Roger), Poljak, M. (Mario), Puchhammer-Stöckl, E. (Elisabeth), Schmit, J.-C. (Jean-Claude), Sonnerborg, A. (Anders), Stanekova, D. (Danica), Stanojevic, M. (Maja), Struck, D. (Daniel), Boucher, C.A.B. (Charles), and Vandamme, A.M. (Anne Mieke)

Abstract

Background: Understanding HIV-1 subtype distribution and epidemiology can assist preventive measures and clinical decisions. Sequence variation may affect antiviral drug resistance development, disease progression, evolutionary rates and transmission routes.Results: We investigated the subtype distribution of HIV-1 in Europe and Israel in a representative sample of patients diagnosed between 2002 and 2005 and related it to the demographic data available. 2793 PRO-RT sequences were subtyped either with the REGA Subtyping tool or by a manual

Published
2013
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8. HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

Author

Sangeda, RZ, Theys, K, Beheydt, G, Rhee, SY, Deforche, K, Vercauteren, J, Libin, P, Imbrechts, S, Grossman, Z, Camacho, RJ, Van Laethem, K, Pironti, A, Zazzi, M, Sonnerborg, A, Incardona, F, Luca, A, Torti, C, Ruiz, L, van de Vijver, David, Shafer, RW, Bruzzone, B, van Wijngaerden, E, Vandamme, AM, Sangeda, RZ, Theys, K, Beheydt, G, Rhee, SY, Deforche, K, Vercauteren, J, Libin, P, Imbrechts, S, Grossman, Z, Camacho, RJ, Van Laethem, K, Pironti, A, Zazzi, M, Sonnerborg, A, Incardona, F, Luca, A, Torti, C, Ruiz, L, van de Vijver, David, Shafer, RW, Bruzzone, B, van Wijngaerden, E, and Vandamme, AM

Abstract

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naive patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naive and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment respon

Published
2013

9. HIV-1 subtype distribution and its demographic determinants in newly diagnosed patients in Europe suggest highly compartmentalized epidemics

Author

Abecasis, AB, Wensing, AMJ, Paraskevis, D, Vercauteren, J, Theys, K, van de Vijver, David, Albert, J, Asjo, B, Balotta, C, Beshkov, D, Camacho, RJ, Clotet, B, De Gascun, C, Griskevicius, A, Grossman, Z, Hamouda, O, Horban, A, Kolupajeva, T, Korn, K, Kostrikis, LG, Kucherer, C, Liitsola, K, Linka, M, Nielsen, C, Otelea, D, Paredes, R, Poljak, M, Puchhammer-Stockl, E, Schmit, JC, Sonnerborg, A, Stanekova, D, Stanojevic, M, Struck, D, Boucher, Charles, Vandamme, AM, Abecasis, AB, Wensing, AMJ, Paraskevis, D, Vercauteren, J, Theys, K, van de Vijver, David, Albert, J, Asjo, B, Balotta, C, Beshkov, D, Camacho, RJ, Clotet, B, De Gascun, C, Griskevicius, A, Grossman, Z, Hamouda, O, Horban, A, Kolupajeva, T, Korn, K, Kostrikis, LG, Kucherer, C, Liitsola, K, Linka, M, Nielsen, C, Otelea, D, Paredes, R, Poljak, M, Puchhammer-Stockl, E, Schmit, JC, Sonnerborg, A, Stanekova, D, Stanojevic, M, Struck, D, Boucher, Charles, and Vandamme, AM

Abstract

Background: Understanding HIV-1 subtype distribution and epidemiology can assist preventive measures and clinical decisions. Sequence variation may affect antiviral drug resistance development, disease progression, evolutionary rates and transmission routes. Results: We investigated the subtype distribution of HIV-1 in Europe and Israel in a representative sample of patients diagnosed between 2002 and 2005 and related it to the demographic data available. 2793 PRO-RT sequences were subtyped either with the REGA Subtyping tool or by a manual procedure that included phylogenetic tree and recombination analysis. The most prevalent subtypes/CRFs in our dataset were subtype B (66.1%), followed by sub-subtype A1 (6.9%), subtype C (6.8%) and CRF02_AG (4.7% Conclusions: The association of subtype with demographic parameters suggests highly compartmentalized epidemics, determined by social and behavioural characteristics of the patients.

Published
2013

10. HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

Author

Sangeda, Rz, Theys, K, Beheydt, G, Rhee, S, Deforche, K, Vercauteren, J, Libin, P, Imbrechts, S, Grossman, Z, Camacho, Rj, Van Laethem, K, Pironti, A, Zazzi, M, Sönnerborg, A, Incardona, F, De Luca, Andrea, Torti, C, Ruiz, L, Van De Vijver, Damc, Shafer, Rw, Bruzzone, B, Van Wijngaerden, E, Vandamme, A., De Luca, Andrea (ORCID:0000-0002-8311-6935), Sangeda, Rz, Theys, K, Beheydt, G, Rhee, S, Deforche, K, Vercauteren, J, Libin, P, Imbrechts, S, Grossman, Z, Camacho, Rj, Van Laethem, K, Pironti, A, Zazzi, M, Sönnerborg, A, Incardona, F, De Luca, Andrea, Torti, C, Ruiz, L, Van De Vijver, Damc, Shafer, Rw, Bruzzone, B, Van Wijngaerden, E, Vandamme, A., and De Luca, Andrea (ORCID:0000-0002-8311-6935)

Abstract

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response

Published
2013

11. HIV-1 Subtype Is an Independent Predictor of Reverse Transcriptase Mutation K65R in HIV-1 Patients Treated with Combination Antiretroviral Therapy Including Tenofovir

Author

Theys, K, Vercauteren, J, Snoeck, J, Zazzi, M, Camacho, Rj, Torti, C, Schülter, E, Clotet, B, Sönnerborg, A, De Luca, Andrea, Grossman, Z, Struck, D, Vandamme, A, Abecasis, Ab, De Luca, Andrea (ORCID:0000-0002-8311-6935), Theys, K, Vercauteren, J, Snoeck, J, Zazzi, M, Camacho, Rj, Torti, C, Schülter, E, Clotet, B, Sönnerborg, A, De Luca, Andrea, Grossman, Z, Struck, D, Vandamme, A, Abecasis, Ab, and De Luca, Andrea (ORCID:0000-0002-8311-6935)

Abstract

Subtype-dependent selection of HIV-1 reverse transcriptase resistance mutation K65R was previously observed in cell culture and small clinical investigations. We compared K65R prevalence across subtypes A, B, C, F, G, and CRF02_AG separately in a cohort of 3,076 patients on combination therapy including tenofovir. K65R selection was significantly higher in HIV-1 subtype C. This could not be explained by clinical and demographic factors in multivariate analysis, suggesting subtype sequence-specific K65R pathways.

Published
2013

12. Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

Author

Theys, K. (Kristof), Deforche, K. (Koen), Vercauteren, J. (Jurgen), Libin, P. (Pieter), Vijver, D.A.M.C. (David) van de, Albert, J. (Jan), Åsjö, B. (Birgitta), Balotta, C. (Claudia), Bruckova, M. (Marie), Camacho, R.J. (Ricardo Jorge), Clotet, B. (Bonaventura), Coughlan, S. (Suzie), Grossman, Z. (Zehava), Hamouda, O. (Osamah), Horban, A. (Andrzej), Korn, K. (Klaus), Kostrikis, L.G. (Leondios), Kücherer, C. (Claudia), Nielsen, C. (Claus), Paraskevis, D. (Dimitrios), Poljak, M. (Mario), Puchhammer-Stöckl, E. (Elisabeth), Riva, C. (Chiara), Ruiz, L. (Lidia), Liitsola, K. (Kirsi), Schmit, J.-C. (Jean-Claude), Schuurman, R. (Rob), Sonnerborg, A. (Anders), Stanekova, D. (Danica), Stanojevic, M. (Maja), Struck, D. (Daniel), Laethem, K. (Kristel) van, Wensing, A.M.J. (Annemarie), Boucher, C.A.B. (Charles), Vandamme, A.M. (Anne Mieke), Theys, K. (Kristof), Deforche, K. (Koen), Vercauteren, J. (Jurgen), Libin, P. (Pieter), Vijver, D.A.M.C. (David) van de, Albert, J. (Jan), Åsjö, B. (Birgitta), Balotta, C. (Claudia), Bruckova, M. (Marie), Camacho, R.J. (Ricardo Jorge), Clotet, B. (Bonaventura), Coughlan, S. (Suzie), Grossman, Z. (Zehava), Hamouda, O. (Osamah), Horban, A. (Andrzej), Korn, K. (Klaus), Kostrikis, L.G. (Leondios), Kücherer, C. (Claudia), Nielsen, C. (Claus), Paraskevis, D. (Dimitrios), Poljak, M. (Mario), Puchhammer-Stöckl, E. (Elisabeth), Riva, C. (Chiara), Ruiz, L. (Lidia), Liitsola, K. (Kirsi), Schmit, J.-C. (Jean-Claude), Schuurman, R. (Rob), Sonnerborg, A. (Anders), Stanekova, D. (Danica), Stanojevic, M. (Maja), Struck, D. (Daniel), Laethem, K. (Kristel) van, Wensing, A.M.J. (Annemarie), Boucher, C.A.B. (Charles), and Vandamme, A.M. (Anne Mieke)

Abstract

Background: The effect of drug resistance transmission on disease progression in the newly infected patient is not well understood. Major drug resistance mutations severely impair viral fitness in a drug free environment, and therefore are expected to revert quickly. Compensatory mutations, often already polymorphic in wild-type viruses, do not tend to revert after transmission. While compensatory mutations increase

Published
2012
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13. Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

Author

Theys, K, Deforche, K, Vercauteren, J, Libin, P, van de Vijver, David, Albert, J (Jan), Asjo, B, Balotta, C, Bruckova, M, Camacho, RJ, Clotet, B, Coughlan, S, Grossman, Z, Hamouda, O, Horban, A, Korn, K, Kostrikis, LG, Kucherer, C, Nielsen, C, Paraskevis, D, Poljak, M, Puchhammer-Stockl, E, Riva, C, Ruiz, L, Liitsola, K, Schmit, JC, Schuurman, R (Rob), Sonnerborg, A, Stanekova, D, Stanojevic, M, Struck, D, Van Laethem, K, Wensing, AMJ, Boucher, Charles, Vandamme, AM, Theys, K, Deforche, K, Vercauteren, J, Libin, P, van de Vijver, David, Albert, J (Jan), Asjo, B, Balotta, C, Bruckova, M, Camacho, RJ, Clotet, B, Coughlan, S, Grossman, Z, Hamouda, O, Horban, A, Korn, K, Kostrikis, LG, Kucherer, C, Nielsen, C, Paraskevis, D, Poljak, M, Puchhammer-Stockl, E, Riva, C, Ruiz, L, Liitsola, K, Schmit, JC, Schuurman, R (Rob), Sonnerborg, A, Stanekova, D, Stanojevic, M, Struck, D, Van Laethem, K, Wensing, AMJ, Boucher, Charles, and Vandamme, AM

Abstract

Background: The effect of drug resistance transmission on disease progression in the newly infected patient is not well understood. Major drug resistance mutations severely impair viral fitness in a drug free environment, and therefore are expected to revert quickly. Compensatory mutations, often already polymorphic in wild-type viruses, do not tend to revert after transmission. While compensatory mutations increase fitness during treatment, their presence may also modulate viral fitness and virulence in absence of therapy and major resistance mutations. We previously designed a modeling technique that quantifies genotypic footprints of in vivo treatment selective pressure, including both drug resistance mutations and polymorphic compensatory mutations, through the quantitative description of a fitness landscape from virus genetic sequences. Results: Genotypic correlates of viral load and CD4 cell count were evaluated in subtype B sequences from recently diagnosed treatment-naive patients enrolled in the SPREAD programme. The association of surveillance drug resistance mutations, reported compensatory mutations and fitness estimated from drug selective pressure fitness landscapes with baseline viral load and CD4 cell count was evaluated using regression techniques. Protease genotypic variability estimated to increase fitness during Conclusions: These results suggest that polymorphic compensatory mutations in protease, reported to be selected during treatment, may improve the replicative capacity of HIV-1 even in absence of drug selective pressure or major resistance mutations. The presence of this polymorphic variation may either reflect a history of drug selective pressure, i.e. transmission from a treated patient, or merely be a result of diversity in wild-type virus. Our findings suggest that transmitted drug resistance

Published
2012

14. Decreasing population selection rates of resistance mutation K65R over time in HIV-1 patients receiving combination therapy including tenofovir

Author

Theys, K., Snoeck, J., Vercauteren, J., Abecasis, A. B., Vandamme, A.-M, Camacho, R. J., Theys, K., Snoeck, J., Vercauteren, J., Abecasis, A. B., Vandamme, A.-M, and Camacho, R. J.

Abstract

Objectives The use of tenofovir is highly associated with the emergence of mutation K65R, which confers broad resistance to nucleoside/nucleotide analogue reverse transcriptase inhibitors (NRTIs), especially when tenofovir is combined with other NRTIs also selecting for K65R. Although recent HIV-1 treatment guidelines discouraging these combinations resulted in reduced K65R selection with tenofovir, updated information on the impact of currently recommended regimens on the population selection rate of K65R is presently lacking. Methods In this study, we evaluated changes over time in the selection rate of resistance mutation K65R in a large population of 2736 HIV-1-infected patients failing combination antiretroviral treatment between 2002 and 2010. Results The K65R resistance mutation was detected in 144 patients, a prevalence of 5.3%. A large majority of observed K65R cases were explained by the use of tenofovir, reflecting its wide use in clinical practice. However, changing patterns over time in NRTIs accompanying tenofovir resulted in a persistent decreasing probability of K65R selection by tenofovir-based therapy. The currently recommended NRTI combination tenofovir/emtricitabine was associated with a low probability of K65R emergence. For any given dual NRTI combination including tenofovir, higher selection rates of K65R were consistently observed with a non-nucleoside reverse transcriptase inhibitor than with a protease inhibitor as the third agent. Discussion Our finding of a stable time trend of K65R despite elevated use of tenofovir illustrates increased potency of current HIV-1 therapy including tenofovir

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