Virology and Immunology - HIV Glasgow · Immune recovery in acute and chronic HIV infection and the...

Virology and Immunology

P345 Immune recovery in acute and chronic HIV infection and the impact of thymic stromal lymphopoietin Gelpi, M*; Hartling, H; Thorsteinsson, K; Gerstoft, J; Ullum, H; Nielsen, S (Copenhagen, Denmark)

P347 Cardiovascular risk in HIV-positive subjects: analyses of T-cell phenotype and CD49d expression Zingaropoli, M*; D’Abramo, A; Iannetta, M; Oliva, A; d’Ettorre, G; Lichtner, M; Mastroianni, C; Ciardi, M; Vullo, V (Rome, Italy)

P348 Baseline myeloid and lymphoid activation markers can predict time to viral load reduction under 50 copies/mL and CD4 recovery, respectively, after highly-active antiretroviral therapy initiationIannetta, M*; Lichtner, M; Rossi, R; Savinelli, S; Vita, S; Mascia, C; Zuccalà, P; Marocco, R; Zingaropoli, M; Ciardi, M; d’Ettorre, G; Mastroianni, C; Vullo, V (Rome, Italy)

P349 Impact of oestrogen plasma levels in modulation of immune activation among HIV-infected women and men undergoing successful antiretroviral therapyMarocco, R*; Lichtner, M; Tieghi, T; Belvisi, V; Pozzetto, I; Mascia, C; Zuccalà, P; Rossi, R; Mengoni, F; Mastroianni, C (Latina, Italy)

P351 Geno2pheno [coreceptor-hiv2]: a new diagnostic tool for the genotypic determination of HIV-2 coreceptor usageDöring, M*; Borrego, P; Büch, J; Martins, A; Friedrich, G; Camacho, R; Eberle, J; Kaiser, R; Lengauer, T; Taveira, N; Pfeifer, N (Saarbrücken, Germany)

P352 High rates of multi-class drug resistance in HIV-1-infected individuals monitored with CD4 cell count in Ugandavon Braun, A*; Scherrer, A; Sekaggya, C; Kirangwa, J; Ssemwanga, D; Kaleebu, P; Günthard, H; Kambugu, A; Castelnuovo, B; Fehr, J (Kampala, Uganda)

P353 Prevalence and impact of transmitted drug resistance in recent HIV-1 infections, Germany 2013–2015Hauser, A*; Hofmann, A; Hanke, K; Bremer, V; Bartmeyer, B; Kücherer, C; Bannert, N (Berlin, Germany)

P354 Higher rates for transmission of NNRTI-resistant viruses for subtype A versus subtype B strains in Southern GreeceKostaki, E; Sypsa, V; Nikolopoulos, G; Gargalianos, P; Xylomenos, G; Lazanas, M; Chini, M; Skoutelis, A; Papastamopoulos, V; Antoniadou, A; Papadopoulos, A; Psichogiou, M; Daikos, G; Chrysos, G; Paparizos, V; Kourkounti, S; Sambatakou, H; Sipsas, N; Lada, M; Panagopoulos, P; Maltezos, E; Hatzakis, A; Paraskevis, D* (Athens, Greece)

P356 Low prevalence of pre-treatment HIV-1 drug resistance in Ugandan adultsvon Braun, A*; Sekaggya, C; Scherrer, A; Magambo, B; Ssemwanga, D; Kaleebu, P; Günthard, H; Kambugu, A; Fehr, J; Castelnuovo, B (Kampala, Uganda)

P357 Prevalence of resistance mutations to rilpivirine and etravirine in people starting antiretrovirals in ArgentinaBissio, E*; Barbás, M; Bouzas, M; Cudolá, A; Falistocco, C; Salomón, H (Buenos Aires, Argentina)

P358 Frequency of additional resistance relevant mutations in 2% and 1% population proportions in next-generation sequencing (NGS) in routine HIV-1 resistance diagnosticsEhret, R*; Moritz, A; Schuetze, M; Obermeier, M (Berlin, Germany)

P359 Impact of baseline NNRTI resistance in antiretroviral-naïve patients in a large urban clinicSteinberg, S*; Crouzat, F; Sandler, I; Varriano, B; Smith, G; Kovacs, C; Brunetta, J; Chang, B; Merkley, B; Tilley, D; Fletcher, D; Acsai, M; Knox, D; Sharma, M; Loutfy, M (Toronto, Canada)

P360 Enhanced surveillance to study HIV-1 drug resistance among naïve individuals in Southern Greece: the added value of molecular epidemiology to public healthParaskevis, D*; Kostaki, E; Magiorkinis, E; Gargalianos, P; Xylomenos, G; Lazanas, M; Chini, M; Skoutelis, A; Papastamopoulos, V; Antoniadou, A; Papadopoulos, A; Psichogiou, M; Daikos, G; Zavitsanou, A; Chrysos, G; Paparizos, V; Kourkounti, S; Oikonomopoulou, M; Sambatakou, H; Sipsas, N; Lada, M; Panagopoulos, P; Maltezos, E; Drimis, S; Hatzakis, A (Athens, Greece)

P361 Transmission patterns of HIV-1 subtype A resistant strains across Greece: evidence for country and regional level transmission networksParaskevis, D*; Skoura, L; Kostaki, E; Gargalianos, P; Xylomenos, G; Lazanas, M; Chini, M; Metallidis, S; Skoutelis, A; Papastamopoulos, V; Antoniadou, A; Papadopoulos, A; Psichogiou, M; Daikos, G; Pilalas, D; Zavitsanou, A; Chrysos, G; Paparizos, V; Kourkounti, S; Chatzidimitriou, D; Sambatakou, H; Sipsas, N; Lada, M; Panagopoulos, P; Maltezos, E; Drimis, S; Hatzakis, A (Athens, Greece)

P362 Occurrence and risk factors for primary integrase resistance-associated mutations in Austria in the years 2008–2013Zoufaly, A*; Kraft, C; Schmidbauer, C; Puchhammer, E (Vienna, Austria)

P363 Transmission of HIV-1 drug resistance in Tel Aviv, Israel, 2010–2015Turner, D*; Girshengorn, S; Braun, A; Tau, L; Leshno, A; Alon, D; Pupko, T; Zeldis, I; Matus, N; Gielman, S; Ahsanov, S; Schweitzer, I; Avidor, B (Tel Aviv, Israel)

P364 Development of T66I-mediated integrase inhibitor cross-resistance against elvitegravir under dolutegravir-containing first-line therapyWiesmann, F*; Däumer, M; Naeth, G; Knechten, H; Braun, P; Rump, J (Aachen, Germany)

P365 Patterns of emergent resistance-associated mutations after initiation of non-nucleoside reverse-transcriptase inhibitor-containing regimens in Taiwan: a multicenter cohort studyCheng, C*; Su, Y; Tsai, M; Yang, C; Liu, W; Cheng, S; Sun, H; Hung, C; Chang, S (Taoyuan, Taiwan)

P366 Association of therapeutic failure with low-level viremia in HIV-infected patients in the Arevir/RESINA cohort in GermanyLübke, N*; Pironti, A; Knops, E; Schülter, E; Jensen, B; Oette, M; Esser, S; Lengauer, T; Kaiser, R (Düsseldorf, Germany)

P367 Drug resistance mutations (DRM) among pregnant HIV-positive women in the Duesseldorf University Hospital, Germany, 2009–2016Haars, U*; Luebke, N; Jensen, B; Haeussinger, D (Essen, Germany)

P368 Prevalence of HIV type 1 drug resistance mutations in treatment-naïve patients participating in the GARDEL studyFigueroa, M*; Patterson, P; Cahn, P; Andrade-Villanueva, J; Arribas, J; Gatell, J; Lama, J; Norton, M; Sierra Madero, J; Sued, O; Rolon, M (Buenos Aires, Argentina)

P369 High prevalence of transmitted antiretroviral drug resistance in newly HIV-1 diagnosed Cuban patientsPerez Santos, L*; Machado, L; Kouri Cardella, V; Diaz, H; Aragones, C; Aleman, Y; Silva, E; Correa, C; Blanco de Armas, M; Perez, J; Dubed, M; Soto, Y; Ruiz, N; Limia, C; Nibot, C; Valdés, N; Ortega, M; Romay, D; Baños, Y; Rivero, B; Campos, J (Havana, Cuba)

P370 Viroseq protocol optimized for the detection of HIV-1 drug mutations in patients with low viral loadMonteiro, F*; Tavares, G; Ferreira, M; Amorim, A; Bastos, P; Rocha, C; Hortelão, D; Vaz, C; Koch, C; Araujo, F; Serrão, R; Sarmento, A (Porto, Portugal)

P371 The role of presepsin (sCD14-ST) as an indirect marker of microbial translocation and immune activationPaola, C*; Zuccaro, V; Cima, S; Sacchi, P; Bruno, R (Pavia, Italy)

P372 CRF19_cpx variant emergence in a cluster in naïve patients of southern Spain: clinical and phylogenetic characterizationGonzález-Domenech, C*; Viciana, I; Mayorga, M; Palacios, R; de la Torre, J; Jarilla, F; Castaño, M; del Arco, A; Márquez, M; Clavijo, E; Santos, J (Málaga, Spain)

P373 One-step real-time PCR for HIV-2 group A and B RNA plasma viral load in LightCycler 2.0Bastos, P; Monteiro, F*; Tavares, G; Amorim, A; Ferreira, M; Hortelão, D; Rocha, C; Vaz, C; Koch, C; Araujo, F; Serrão, R; Sarmento, A (Porto, Portugal)

P374 The association between high pre-HAART CD8 cell counts and poorer immunological outcome following antiretroviral therapyWong, C*; Wong, N; Lee, S (Hong Kong, Hong Kong)

Immune recovery in acute and chronic HIV infection and the impact of thymic stromal lymphopoietin

Marco Gelpi1, Hans J. Hartling1, Kristina Thorsteinsson2, Jan Gerstoft1, Henrik Ullum3, Susanne D. Nielsen1 Viro-Immunology Research Unit, Department of Infectious Diseases, University Hospital of Copenhagen Rigshospitalet,

Copenhagen, Denmark 1; Department of Infectious Disease, University Hospital of Copenhagen Hvidovre, Copenhagen, Denmark 2;

Department of Clinical Immunology, University Hospital of Copenhagen Rigshospitalet, Copenhagen, Denmark 3

Background Symptomatic primary HIV infection is associated with faster decline in CD4+ T cells count and progression to AIDS, and immediate initiation of combination antiretroviral therapy (cART) is recommended. However, little is known about immunological predictors of immune recovery. Thymic Stromal Lymphopoietin (TSLP) is a cytokine that promotes homeostatic polyclonal proliferation of CD4+ T cells and participates in regulating Th17/regulatory T-cell balance, immunological functions known to be affected during primary HIV infection. The aim of this study was to describe immune recovery in primary and chronic HIV infection and possible impact of TSLP.

Materials and Methods Prospective study including 100 HIV-infected individuals (primary HIV infection (N=14), early presenters (>350 CD4+ T cells/µL, N=42), late presenters without advanced disease (200-350 CD4+ T cells/µL, N=24) and late presenters with advanced disease (<200 CD4+ T cells/μL, N=20))(Table1). Plasma TSLP was determined using ELISA and CD4+ T cell subpopulations (recent thymic emigrants, naïve, and memory cells) were measured using flow cytometry at baseline and after 6, 12, and 24 months of cART.

Results Immune recovery was comparable in all groups, and no differences in immune homeostasis were found between primary HIV infection and early presenters. In primary HIV infection group, lower thymic output compared to late presenters without advanced disease was found. However, lower proportion of effector memory and higher proportion of late differentiated CD4+ T cell were found in primary HIV infection compared to late presenters. TSLP was elevated in primary HIV infection at baseline and after 24 months of cART (Table2). Interestingly, TSLP was negatively associated with proportion of recent thymic emigrants (correlation coefficient -0.60, P=0.030). However, TSLP was not associated with immune recovery in primary HIV infection. Finally, higher plasma TSLP was associated with lower CD4+ T cell recovery in the late presenters non advanced disease group (correlation coefficient -0.50, P = 0.034).

Mann-Whitney was used to compare PHI group with the chronic groups. Significant differences are marked: a: PHI vs. late presenters with advanced disease; b: PHI vs. late presenters without advanced disease; c: PHI vs. early presenters

Conclusions

Immune recovery was comparable in primary and chronic HIV infection whereas differences in absolute counts and proportions of CD4+ T cell subpopulations were found between primary HIV infection and late presenters supporting early initiation of cART. Higher plasma TSLP was found in primary HIV infection. Association between TSLP and a lower thymic output, but not with immune recovery was found in primary HIV infection. These findings indicate a possible role of TSLP in immune homeostasis in HIV infection but do not support TSLP to affect immune recovery in primary HIV infection.

Primary HIV (PHI) N=14

Chronic patients CD4 < 200 (LP-AD)

N=20

Chronic patients CD4 200-350 (LP-nonAD)

N=24

Chronic patients CD4 >350 (EP)

N=42 P*

Cells

/µL CD4

Baseline 550 (327)a,b 55.5 (110)a 290 (97)b 510 (172) < .001 After 24 months of cART 680 (240)a 269 (160)a 695 (290) 820 (317) < .001

% C

D4 Ce

lls

RTE Baseline 14 (11) 11 (16) 20 (15) 18 (14) .063 After 24 months of cART 18 (9)b 17 (10) 28 (11)b 17 (16) .009

Naive Baseline 43 (20)a 23 (30)a 40 (26) 44 (21) < .001 After 24 months of cART 36 (12)b 30 (16) 55 (16)b 37 (15) .002

EM Baseline 12 (7) 17 (14) 16 (12) 12 (6) .009 After 24 months of cART 9 (4)a 15 (6)a 6 (7) 7 (5) .043

CM Baseline 26 (6) 20 (22) 24 (10) 24 (10) .597 After 24 months of cART 24 (14) 30 (23) 24 (8) 32 (16) .784

LD Baseline 5 (4)b 3 (12) 1 (2)b 5 (8) .009 After 24 months of cART 9 (16)a,b 1 (1)a 3 (2)b 9 (13) .038

pg/m

L TSLP Baseline 2.8 (2.3)a,b,c 1.7 (0.8)a 2.1 (1.4)b 1.9 (0.7)c .023 After 24 months of cART 3.9 (2.8)a,c 1.3(1.4)a 2.4 (2.9) 2.1 (1.0)c .060

Comparing the four HIV groups by using Kruskal-Wallis test. If significant (<0.05) then Mann-Whitney was used tocompare PHI group with the other chronic groups. Only significant differences are marked: a: PHI vs LP-AD; b: PHI vs LP-nonAD; c: PHI vs EP

Contact: [email protected]

Primary HIV (PHI) N=14

Chronic patients CD4 < 200 (LP-AD)

N=20

Chronic patients CD4 200-350 (LP-nonAD)

N=24

Chronic patients CD4 >350 (EP)

N=42

HC N=18

Gender, males/females, (% males) 13/1 (92.9) 18/2 (90.0) 21/3 (87.5) 39/3 (92.9) 17/1 (94.4) Age, years, median (IQR) 47 (12) 42 (16) 38 (16) 44.5 (12) 42.5 (12) Time since diagnosis, days, median (IQR) 2 (3) 3 (9) 18 (269) 24 (983) NA

CD4+ nadir, cells/µL, median (IQR) 540 (335) 45 (113) 290 (95) 480 (170) NA CD4+ at baseline, cells/µL, median (IQR) 550 (327) 55 (110) 290 (97) 510 (172) 983 (540)

CD4/CD8 at baseline, median (IQR) 0.5 (0.3) 0.1 (0.1) 0.3 (0.1) 0.5 (0.3) 1.5 (0.9) Co-infection with chronic HBV/HCV, N 0/1 0/2 0/0 1/1 0/0

HIV-RNA at baseline, median (IQR) 151,775 (3,442,296) 196,589 (751,023) 65,990 (89,637) 49,422 (47,031) NA

AIDS defining events, N 0 1 0 0 NA Fiebig Stage I, N 1 NA NA NA NA Fiebig Stage II, N 1 NA NA NA NA Fiebig Stage III, N 1 NA NA NA NA Fiebig Stage IV, N 11 NA NA NA NA

Tabel 2.

Tabel 1. Clinical characteristics of the population

. CD4 count (A), immune recovery (B) and plasma TSLP (C) before cART and during 24 months of follow-up

P347

Cardiovascular risk in HIV positive subjects: analyses of T cell phenotype and CD49d expression

Zingaropoli, Maria Antonella; Iannetta, Marco; D'Abramo, Alessandra; Oliva, Alessandra; d'Ettorre, Gabriella; Lichtner, Miriam; Mastroianni, Claudio Maria; Ciardi, Maria Rosa; Vullo, Vincenzo

Department of Public Health and Infectious Diseases, Sapienza Rome Italy

It is well known that HIV positive subjects havea higher risk of non-AIDS-related comorbiditiesthan general population. Chronicimmuneactivation of T-cells plays an importantrole in HIV pathogenesis and relatedcomorbidities. In this context, the integrin-α4(CD49d), a transmembrane co-stimulatorymolecule, is involved in the lymphocyte homingfrom peripheral compartment to the gut (α4β7)and to the central nervous system (α4β1).Aim of the study was to evaluate CD49dexpression in T-lymphocyte subsets and therelationship with cardiovascular damage in HIVpositive individuals on effective combinedantiretroviral therapy (c-ART).

Thirty HIV positive subjects (6 females/24males) with a mean age (± standard deviation[SD]) of 52±10.1 years on effective c-ART and15 age and sex matched healthy donors (HD)were enrolled. T-lymphocyte immunophenotypeand CD49d expression, (measured as medianfluorescence intensity [MFI]), were assessed byflow cytometry (Figure1). Carotid-Intima MediaThickness (c-IMT) was measured withultrasonography. Normal and pathological c-IMT were defined as IMT<0.9 mm and >0.9mm, respectively.

Figure 1. Gating Strategy: T-cells immuneactivation and immunesenescence were evaluated by determining the percentage

of CD38 HLA-DR double positive events and the percentage of CD28- CD57+ events in the CD3+CD4+ and CD3+CD8+ gates,

respectively. (A) T-cell subpopulations is defined by CD45RO and CD57 markers (B).

N: naïve, CM: central memory, EM: effector memory, E: effectors, I: intermediate

In animal models a potential role of CD49d in macrophages activation has been demonstrated. In this study, the increase of CD49d expression in T-lymphocytes could be considered as a marker of immuneactivation during HIV infection. Furthermore, CD49d could represent a potential therapeutictarget for the immune system modulation in the context of HIV infection aiming to reduce non-AIDS related comorbidities, especially cardiovasculardiseases.

HIV positive subjects showed a lower count of CD4+ T-lymphocytes (p=0.04) and increased levelsof CD8+ T-lymphocytes, immuneactivation (CD4+ and CD8+ HLA-DR+CD38+, p<0.001 andp<0.001, respectively) and immunesenescence (CD4+ and CD8+ CD28-CD57+, p=0.02 andp<0.001, respectively) than HD. A decrease in CD4+ and CD8+ naïve [N] (p=0.02 and p=0.01) andan increase in CD8+ effector memory [EM] (p=0.007) percentages were observed in HIV positivesubjects compared to HD (Figure 2).

A

B

Contacts: [email protected]

Background

Materials and methods

Results

Conclusions

In HIV positive subjects CD49d expression was increased on CD4+ T-lymphocyte subsets (N:p=0.01, central memory [CM]: p<0.001, EM: p<0.001, effector [E]: p=0.05) and CD8+ T-lymphocytesubsets (N: p=0.0006, CM: p<0.001, EM: p<0.001, E: p=0.003 and intermediate [I]: p<0.001),compared to HD (Figure 3).

Among HIV positive patients, 15 (50%) had a normal c-IMTand 15 (50%) a pathological c-IMT. Moreover, HIV positivesubjects with pathological c-IMT showed higher levels of CD4CM CD49d expression (p=0.02) than HIV positive subjectswith normal c-IMT (Figure 6).

A positive correlation between CD49d expression in CD4+ T-cells and CD4+ HLA-DR+CD38+ (Spearman r=0.57, p=0.0012)was found in HIV positive subjects (Figure 4).In the HIV positive group c-IMT was higher (mean±SD:0.85±0.17 versus 0.28±0.24 mm, p<0.001) than HD. CD4+ T-cellCD49d expression and CD4+HLA-DR+CD38+ were positivelycorrelated with c-IMT (p=0.04, p=0.085, respectively) (Figure 5).

Figure 2. CD4 and CD8 naïve and CD8 effector memory percentage in HIV+ subjects compared to HD

Figure 3. CD49d expression on CD4+ and CD8+ T-lymphocyte subsets

Figure 4. Correlation between CD49d expression in CD4+ T-cells and CD4+ HLA-DR+CD38+

Figure 5. Evaluation of c-IMT in HIV positive patients compared HD

Figure 6. Correlation between c-IMT and CD4 CM CD49d expression

BackgroundDuringHIVinfec.onmyeloidandlymphoidac.va.onhasbeen reported1, together with eleva.on of monocyte/macrophage inflamma.on markers, such as soluble(s)CD14 and sCD1632-3. The advent of highly ac.vean.retroviral (ARV) therapies improved both lifeexpectancyandqualityoflifeofpersonslivingwithHIV4.However,thepersistenceofthevirusinthehostleadstoa state of chronic ac.va.on of the immune system, notcompletely reversed by ARV treatment5. We evaluatedboth myeloid and lymphoid ac.va.on markers andcorrelated them with CD4 recovery aQer 12 months ofan.retroviral (ARV) treatment and the .me (in days)neededtoachieveaviralloadbelow50copies/ml.

MatherialsandMethodsTreatment-naive HIV+ pa.ents were enrolled andfollowedupforoneyearaQertreatmentini.a.on.Bloodsampleswere collected before treatment ini.a.on (T0).Monocyte (Mo), dendri.c cell (DC) and T lymphocytephenotypes were assessed by flow-cytometry using alyse-no-washprotocol(ga.ngstrategy isshowninFigure1). sCD14 and sCD163 were measured in plasma withELISA. Seventeen age and sex matched healthy donors(HD) were enrolled. Sta.s.cal analysis was performedwiththesoQwareGraphPadPrismversion6.0.

Baselinemyeloidandlymphoidac6va6onmarkerscanpredict6metoviralloadreduc6onunder50copies/mlandCD4recovery,respec6vely,a?erhighlyac6ve

an6retroviraltherapyini6a6on

Ianne\aMarco,LichtnerMiriam,RossiRaffaella,SavinelliStefano,VitaSerena,MasciaClaudia,ZuccalaPaola,MaroccoRaffaella,ZingaropoliMariaAntonella,CiardiMariaRosa,d'E\orreGabriella,MastroianniClaudioMaria,VulloVincenzo

SapienzaUniversity,DepartmentofPublicHealthandInfec6ousDiseases,Rome,Italy

Figure1:Ga6ngstrategyA B

A) Ga#ng strategy for monocytes and dendri#c cells: a4erdoublets exclusion, lineage (Lin:CD56, CD19, CD3, CD235a)- andHLA-DR+ events were gated. According to CD14 and CD16expression monocyte were defined as classical (CD14++CD16-),intermediate (CD14++CD16+) and non-classical (CD14+CD16++).Slan-DCwere iden#fied in theCD14+CD16++gateanddefinedasCD11c+ and M-DC8(slan)+. Myeloid dendri#c cells (mDC) andplasmacytoid dendri#c cells (pDC) were iden#fied in the CD14-CD16- gate and defined as HLA-DR+CD11c+ and CD11c-DC123+,respec#vely.B) Ga#ng strategy for T lymphocyte immuneac#va#on: a4erdoubletsexclusion,CD4+CD45+andCD8+CD45+lymphocytesweregated.Immuneac#vatedCD4andCD8TlymphocyteweredefinedasHLA-DR+CD38+.

Bibliography:1.AppayV,KelleherAD.CurrOpinHIVAIDS.2016Mar;11(2):242-9.2.DutertreCA,AmraouiS,DeRosaAetal.,Blood.2012Sep13;120(11):2259-68.3.McKibbenRA,MargolickJB,GrinspoonSetal.,JInfectDis2015Apr15;211(8):1219-28.4.SamjiH,CesconA,HoggRS,etal.,PLoSOne2013;8:e81355.5.HearpsAC,MaisaA,ChengWJetal.,AIDS.2012Apr24;26(7):843-53.

ResultsWe recruited 34 naive pa.ents (8 women, 9 AIDSpresenters).15,10and6pa.entsstartedanARVtherapycontaining a protease, a non-nucleoside reverse-transcriptase and an integrase strand transfer inhibitor(PI, NNRTI, INSTI), respec.vely. Three pa.ents did notstart any treatment (1 elite controller and 2 long-termnonprogressors).NodifferencesinHIVviralloadandCD4cell counts were observed at T0, stra.fying pa.entsaccordingtoARVtherapy.

HIV+ HDNumber 34 17

Age:median[IQR]

37[28-44]

37[30-49]

Sex:M/F 26/8 13/4VL:medianlog/ml

[IQR]4,9log/ml[4,1-5,5] NA

#CD4:median[IQR]

434cells/µl[101-656] NA

CDCclassifica.onA1:13A2:5A3:2B1:1B2:3B3:1C3:9

NA

ARVtreatmentPI/NNRTI/INSTI 15/10/6 NA

Table1:Clinicalcharacteris6cofthestudypopula6on

VL:HIV-1viralload.HD:healthydonors.IQR:interquar#lerange.# CD4: CD4 absolute count. NA: not applicable. PI: proteaseinhibitor. NNRTI: non-nucleoside reverse-transcriptase inhibitor.INSTI:integrasestrandtransferinhibitor.

AtT0HIV+subjectsshowedlowerlevelsofpDC(3.976vs7.043cells/mlp<0,001)slanDC(11.644vs24.538cells/mlp=0,02)andhigher levelsofCD14++CD16+Mo (19.369vs7.157cells/mlp<0,001) compared toHD (figure2).HLA-DR was reduced on mDC of HIV+ subjects (22.556 vs37.358p<0,001)andincreasedonslanDC(13.680vs9979p=0,005) compared to HD. Levels of CD4+ and CD8+ T-lymphocyte immuneac.va.on were higher in HIV+subjectsthanHD(6,0vs1,8%p<0,001and9,4%vs1,1%p<0,001)(figure3).

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*

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cells

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Comparison of pDC, slanDC and intermediatemonocytes (CD14++CD16+)Mo,betweentreatmentnaiveHIV+subjectsandHealthyDonors(HD)

Figure2:pDC,slanDCandintermediateMonocytes

Figure3:Tlymphocyteimmuneac6va6on

HIV-1 HD0

10

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% o

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4

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% o

f CD

8

****

Comparison of immuneac#va#on levels of CD4 and CD8 Tlymphocyte,betweentreatmentnaiveHIV+subjectsandHealthyDonors(HD)

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Myeloid ac.va.on soluble markers sCD14 and sCD163were higher in HIV+ subjects compared to HD (2163 vs1363 ng/ml p<0,001 and 272,6 vs 149,1 ng/ml p=0.085)(figure 4). CD14++CD16+Mo and CD8 immune-ac.va.onwerenotcorrelatedwiththeclinicalstageofHIVsubjects,while pDC, mDC and slanDC cell counts were lower inAIDS than non-AIDS presenters. CD4 immuneac.va.onlevels were higher in AIDS than non-AIDS presenter(figure5).

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Figure4:Solubleinflamma6onmarkers

sCD14 and sCD163 levels in treatment naive HIV+ subjects andHealthyDonors(HD)

Figure5:ImmunologicalparametersinAIDSandnon-AIDSpresenters

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cells

/ m

l

****

C A or B0

50000

100000

150000

200000

250000

CD14+CD16++

cells

/ m

l

**

C A or B0

50000

100000

150000

slan-DC

cells

/ m

l

****

C A or B0

10

20

30

40406080

% o

f CD

4

CD4 immuneactivation

***

C A or B0

10

20

30

40


% o

f CD

8

CDC-C HIV+ pa#ents showed lower levels of mDC, pDC, slanDCand CD4 immuneac#va#on than HD. No differences in CD8immuneac#va#on levels and intermediate monocyte cell countswereobserved.

C A or B0

20000

40000

60000

80000

100000

mDC

cells

/ m

l

***

C A or B0

20000

40000

60000

80000

100000

CD14++CD16+

cells

/ m

l

C A or B0

5000

10000

15000

pDC

cells

/ m

l

****

C A or B0

50000

100000

150000

200000

250000

CD14+CD16++

cells

/ m

l

**

C A or B0

50000

100000

150000

slan-DC

cells

/ m

l

****

C A or B0

10

20

30

40406080

% o

f CD

4


***

C A or B0

10

20

30

40


% o

f CD

8

HIV-1viremianega.velycorrelatedwithpDCandslanDCcell counts and posi.vely correlated with CD14++CD16+Mo cell counts and CD4 immune-ac.va.on levels (table2).

Spearmanr ppDC -0,34 0,047

slanDC -0,52 0,002CD14++CD16+ +0,36 0,036

CD4+HLA-DR+CD38+ +0,50 0,002

Table2:Correla6onbetweenHIV-1viralloadandimmunologicalparametersatT0

The Kaplan-Meier analysis showed that higher baselineCD14++CD16+Mo countswerepredic.veof a lower rateof subjects with a viral load <50 copies/ml, within 150daysfromARVtherapyini.a.on(p=0.03)(Figure6A).AQer one year of ARV therapy, CD4 recovery posi.velycorrelated with basal levels of CD8 immune-ac.va.on(Figure6B),whilethechoiceoftrea.ngpa.entswithaPI,NNRTI or INSTI did not affect CD4 recovery. The threepa.ents who did not receive any ARV treatment wereexcludedfromtheanalysis.

Figure6:Predic6vevalueofCD14++CD16+MoandCD8immuneac6va6on

A)HigherCD14++CD16+Mocountswereassociatedtoalowerrateof subjects with a viral load under 50 copies/ml, a4er ARVtreatment ini#a#on. The cut-off of 16.000 cell/ml represents thehighestvalueobservedinthecontrolgroup.B) CD4 recovery a4er 12 months of ARV treatment posi#velycorrelatedwithbaselineCD8 immune-ac#va#on levels (Spearmanr:0,50andp:0,005).

0 50 100 1500

50

100

days

% p

atie

nts

with

VL>

50

CD14++CD16+ Mo <16.000

CD14++CD16+ Mo >16.000

-1000 0 500 10000

10

20

30

40

CD4 recovery

% o

f CD

8 H

LA D

R+

CD

38+

Mocountswereassociatedtoalowerrate

A B

ConcusionspDCandslanDCarereducedinHIV+individuals(especiallyinthose with a CDC-C clinical stage) before ARV treatmentini.a.on.mDC are reduced in AIDS compared to non-AIDSpresenters.InflammatoryCD14++CD16+monocytecountsareincreasedintreatmentnaïveHIV-1infectedpa.entsandareassociatedtoadelayinviralloaddecreaseunder50copies/ml. CD4 immuneac.va.on is associated with higher viralload at baseline,while higher CD8 immuneac.va.on levelsseemstopredictahigherCD4gain,aQer12monthsofARVtreatment. Monocyte subsets evalua.on and lymphocyteac.va.on permit to easily assess .me to virologicalundetectabilityandimmunologicalrecovery

Contacts:marco.ianne\[email protected],[email protected]

P348

Several sex differences have been described in the natural course of HIV-1 disease. Higher levels of TLR 7-medaited INF-aplha production together with greater levels of activated CD8-T cells were described in women compared with men for given HIV viral load. The role of sexual hormones in ART treated women is not completely understood and seem to be crucial to individualize possible eradication strategy in women that could b different that in men.

The aim of this study was to investigate the role of sexual hormones in determining innate immunity and immune activation in a cohort of HIV infected subjects undergoing effective antiretroviral treatment.

Impact of oestrogen plasma levels in modulation of immune activation among HIV-infected women and men undergoing

successful antiretroviral therapy Marocco R1 , Lichtner M1,2, Tieghi T1, Belvisi V 1,2 , Pozzetto I1, Mascia C2, Zuccalà P2, Rossi R2, Mengoni F2,

Mastroianni CM 1,2 1Infectious Diseases Department, Sapienza University, Polo Pontino, SM Goretti Hospital, Latina, Italy

2 Pubblic Health and Infectious Diseases, Sapienza University, Rome, Italy

SeveralSeveralSeveral sexsexsexsexsex differencesdifferencesdifferences

Health and Infectious Diseases, Sapienza University, Rome, Italy

differences


differences


Background:

Methods:

Conclusions:

Poster number: P 349

Results:

Study population

No significant differences in levels of circulating dendritic cell (mDC, pDC) between HIV+ women and men.

A positive correlation was found between mDC and serum oestradiol (p=0,03, r=0,30)

A trend of increased number of atypical inflammatory monocytes and MDC-8 in women.

A significant augmentation of DR+38+CD4+ T cells was found in men (p=0,02) and a negative correlation between DR+38+CD8+T and serum oestradiol levels in all HIV subjects and in women was observed (respectively p=0,002; r-0,67; p=0,006, r=-0,50).

Only in women a negative correlation between mDC and DR+38+CD8+ T cell was found(p=0,02; r=-0,43).

Regarding soluble markers of monocytes activation, we didn’t observe differences: women have a lower levels of cCD14 than men (pg/ml, median 2249 and 2685 pg/ml).

In HIV aviremic ART treated subjects, high levels of oestrogen seem to be associated to an expansion of mDC and lower activation of CD8 T cells, underlying the importance of consider hormonal status and not only gender and age in designing immunological and therapeutic studies.

WOMEN MEN p

Age 50 y (24-76) 48 y(23-70) 0,33

CD4+ Nadir 215 cell/mmc (4-640) 173 cell/mmc (8-472) 0,45

CD4+ 660 cell/mmc ( 178-1425) 709 cell/mmc (243-1550) 0,69

HIV-RNA Zenith cp/ml 60779 cp/ml 96000 cp/ml 0,06

HIV-RNA

<20 cp/ml <20 cp/ml NS

Infection Years 16 y ( 3-27) 18 y ( 1-28 0,54

Therapy: INSTI+ PI PI NNRTI

9 15 17

6 14 13

Whole blood samples evaluating mDC, pDC , SlanDC and typical, atypical and intermediate monocytes with a cytofluorimetric method based on 7 fluorochromes

HLA-DR/CD38 CD4 and CD8 T lymphocytes were also evaluated.

sCD14 and sCD163 level (pg/ml) were measured by ELISA. Sex hormones (oestradiol, progesterone, testosterone)

were using CLIA kit. Non parametric Mann-Whitney test and Spearman

coefficient correlation were used.

p=0,03 r=0,30

mDC cell/ml 0,0 2,0e+4 4,0e+4 6,0e+4 8,0e+4 1,0e+5 1,2e+5

Oes

trad

iol p

g/m

l

0,1

1

10

100

1000

HIV+ subject

p=0,26

mDC-8 women

mDC-8 Men

p=0,26

%CD4+ donne

%CD4 uomini

0

5

10

15

%linfociti TCD4+ HLA-DR+

p=0,02

% C

D4+

(38+

HLA

-DR

+ )

%CD4+ Women

%CD4+ Men

%CD4+ T HLA-DR/CD38

p=0.006 r=-0.50

%CD8+DR+

0 10 20 30 40 50 60 70

Oes

trad

iol p

g/m

l

0,1

1

10

100

1000

%CD8+DR+: HIV+ subjects p= 0,0002 r=-0,67

%CD8+DR+

0 10 20 30 40 50

Oes

trad

iol

pg/m

l

0,1

1

10

100

1000

Women

p=0.007 r=-0.40

%CD8 DR+

0 10 20 30 40 50 60 70

mD

C c

ell/m

l

1e+2

1e+3

1e+4

1e+5

1e+6

%CD8DR+: HIV+ subjects

p= 0.02 r=-0,43

%CD8+ DR+ 0 10 20 30 40 50

mD

C c

ell/m

l

1e+2

1e+3

1e+4

1e+5

Women

oestra

diol W

oestra

diol M

proges

terone W

proges

terone M

testo

stero

ne W

testo

stero

ne M0

50

100

150

horm

ones

leve

l

women Men

0

2000

4000

6000

sCD14

p=0,09

pg/m

l

%CD8+ donne

%CD8+ uomini-2

0

2

4

6

%CD8+ HLA-DR+

p=0,90

%C

D8+(

38+H

LA-D

R+)

%CD8+ T HLA-DR/CD38

%CD8+ Men

%CD8+ Women

p=0,02

geno2pheno[coreceptor-hiv2]a new diagnostic tool for the genotypic determination of HIV-2 coreceptor usage

M. Doring1, P. Borrego2, J. Buch1, A. Martins2, G. Friedrich1, R. J. Camacho3, J. Eberle4, R. Kaiser5, T. Lengauer1, N. Taveira2, 6, N. Pfeifer1

1 Department for Computational Biology & Applied Algorithmics, Max Planck Institute for Informatics, Saarbrucken, Germany.2 Research Institute for Medicines (imed.ULisboa), Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.

3 Department of Microbiology & Immunology, Rega Institute for Medical Research, KU Leuven, Belgium.4 Department of Virology, Max von Pettenkofer-Institut, Ludwig-Maximilians-University, Munich, Germany.

5 Institute for Virology, University of Cologne, Cologne, Germany.6 Instituto Superior de Ciencias da Saude Egas Moniz (ISCSEM), Monte de Caparica, Portugal.

P351

Relevance of HIV-2 coreceptor usage

Figure 1: HIV coreceptors (www.viivhcdxresource.com)

The selection of HIV-2 variants using the CXCR4coreceptor (X4-capable) should be prevented be-cause X4-capable variants are harder to neutralizethan viruses using only CCR5 (R5)[1].

Before prescribing CCR5-coreceptor antagonists topatients infected with HIV-2, clinicans should ruleout the existence of X4-capable variants.

Goal: differentiate R5 and X4-capable HIV-2 vari-ants based on the amino acid sequence of the V3loop.

Materials and methods

Support vector machines (SVMs) were trained on a dataset of 73 R5 and 52 X4-capable samples to classify binary-encoded V3 amino acid sequences as either R5 or X4-capable. Classifier performance was evaluated using 10-fold nested cross-validation (CV). The predicted probabil-ities indicating whether a sequence originates from an X4-capable variant were transformed to false positive rates(FPRs).We developed a visual representation of position-specificclassifier weights to indicate amino acids associated withR5 and X4-capable variants (see Fig. 2). We evaluatedestablished discriminatory sequence features from arules-based approach by Visseaux et al. [2] and novelfeatures detected by the SVM using Fisher’s exact test withmultiple testing correction (Benjamini and Hochberg).

Results

A linear SVM (AUC=0.95) outperformed other mod-els and was used in all subsequent analyses.

For a set of 126 V3 sequences, the 10-fold nested CVsensitivity was 76.9% and the specificity was 97.3%.

All samples from a set of nine, newly phenotyped V3sequences were classified correctly by the SVM.

We validated existing markers for X4-capability [2]and identified new, significant features (p ≤ 0.05):variants 27K, 15G, and 8S.

Visualization of model weights

Figure 2: SVM weights for the V3 loop of a ROD10 isolate.

Highlights of the tool

Accuracy: high sensitivity and specificity

Interpretability: visualization of sequence-specific weights and output of FPRs

Availability: an online web service is avail-able at coreceptor-hiv2.geno2pheno.org

Opportunities: enables large-scale epidemi-ological studies on HIV-2 coreceptor usage

References[1] J. M. Marcelino et al. Resistance to antibody neutralization in HIV-2 infection occurs in late stage disease and is

associated with X4 tropism. AIDS, 26(18):2275–2284, 2012.[2] B. Visseaux et al. Molecular determinants of HIV-2 R5–X4 tropism in the V3 loop: development of a new genotypic

tool. Journal of Infectious Diseases, 205(1):111–120, 2012.

Contact: [email protected]

Background

Until a recent change in guidelines, HIV-infected patients on antiretroviral therapy (ART) in Uganda were monitored using CD4 cell counts

only. So far, little is known about prevalence of drug resistance among HIV-infected patients with virological failure (VF) after

immunological treatment monitoring in Uganda.

Amrei von Braun (1,2), Alexandra Scherrer (2,3), Christine Sekaggya (1), Joseph Kirangwa (4), Deogratius Ssemwanga (4), PontianoKaleebu (4), Huldrych Günthard (2,3), Andrew Kambugu (1), Barbara Castelnuovo (1), Jan Fehr (2)

1. Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda2. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland3. Institute of Medical Virology, University of Zurich, Zurich, Switzerland4. MRC/UVRI, Uganda Research Unit on AIDS, Entebbe Uganda

Methods

From June 4th – September 30th, 2015, HIV-RNA was measured in HIV-infected adults (≥18 years) on ART for at least 6 months

presenting to the Infectious Diseases Institute in Kampala. In case of VF (>1000 copies/mL), HIV genotyping was requested.

Sequencing of partial polymerase gene was conducted using an in-house protocol. All sequences were submitted to the Stanford

University HIV Drug Resistance database and the surveillance drug resistance mutations were identified using the 2009 WHO mutations

list. HIV-1 subtypes were determined using REGA version 3.0.

Results

HIV-RNA measurements were done in 2511 patients, who had been on ART for a median time of 4.7 years (interquartile range (IQR)

2.5-8.7). A total of 199 patients (7.9%) had VF with a median viral load of 4.4 log10 copies/mL (IQR:3.9-4.9). The majority of patients

with VF (140, 70.4%) were on first-line ART, 138 (69.3%) were female, and the median age was 37 years (IQR:30-43). HIV genotyping

tests were available in 163 (81.9%). HIV-1 subtypes A (46%) and D (34%) were most common. Relevant drug resistance mutations

were observed in 135 (82.8%) (Figure), of which 103 (63.2%) had resistance to two drug classes, and 11 (6.8%) had resistance to all

three drug classes available in Uganda.

P352High rates of multi-class drug resistance in HIV-1 infected individuals

monitored with CD4 cell count in Uganda

Conclusions

With 92% of all patients virologically suppressed, the

overall prevalence of VF was low, and is in-line with

the third of the 90-90-90 UNAIDS targets. However,

the majority of failing patients had developed

resistance to more than one drug class, suggesting

that failing regimens – not identified as such by CD4

monitoring - had been in place for a prolonged period

of time. This is a call for action to get access to close

virological monitoring for patients on ART, as well as

access to new antiretroviral drugs such as integrase

inhibitors.

Acknowledgements: We would like to acknowledge all patients and their families.

Funding: Swiss HIV Cohort Study, Gilead Sciences

Corresponding author: [email protected]

Figure: Type and frequency of most prevalent resistance-associated mutations observed. Figure legend: NRTI = Nucleoside/Nucleotide Reverse Transcriptase Inhibitors; TAM = Thymidine analogue mutation; NNRTI = Non- Nucleoside/Nucleotide Reverse Transcriptase Inhibitors; PI = Protease Inhibitors;

INTRODUCTIONTransmitted drug resistance (TDR) in new HIV‐infections has

significant clinical consequences for the treatment success.

Therefore, monitoring of TDR in currently circulating HIV‐strains is

an important public health issue of the Robert Koch‐Institute.

Prevalence and impact of transmitted drug resistance in recent HIV‐1 infections, Germany 2013‐2015

Andrea Hauser1, Alexandra Hofmann2, Kirsten Hanke1, Viviane Bremer2, Barbara Bartmeyer2, Claudia Kuecherer1, Norbert Bannert1

1Division of HIV and Other Retroviruses, Robert Koch Institute, Berlin, Germany²Division of HIV/AIDS, STI and Blood‐borne Infections, Robert Koch Institute, Berlin, Germany

PATIENTS & METHODS

Conclusion

estimate the prevalence of TDR to Protease and Reverse

Transcriptase Inhibitors (PIs; RTIs) in new HIV‐infections among

newly diagnosed cases

To assess the impact on antiretroviral treatment according to

the currently recommended first‐line regimens (European AIDS

Clinical Society (EACS) HIV Guidelines Version 8.0) Figure 1.

Sample collection

Diagnostic laboratories provided dried serum

spot (DSS) of ~60% of all newly diagnosed

HIV‐infections in Germany reported to the RKI

(2013 ‐2015).

Andrea HauserHIV and other Retroviruses

Robert Koch‐InstituteNordufer 20

D‐13353 Berlin, [email protected]

P 353

Funding: The study was funded by the German Ministry of Health

OBJECTIVES

TDR prevalence in recent HIV‐1 infections among newly diagnosed cases in Germany (2013‐2015) remained high (>10%) and is comparable to other European countries.

TDR was mainly caused by the first‐generation NNRTI‐selected K103NS, by long‐term persisting TAMs and the PI‐selected M46IL and V82FL. While the K103NS is associated

with failure of current efavirenz‐containing first‐line regimens, the impact of TAMs and frequent PI‐mutations on the success of current first‐line therapies is predicted to be

low and decreases the proportion of TDR mutations relevant for initial regimens from 10.8% to 5.4%. However, to allow an optimal therapeutic sequencing, genotypic

resistance testing remains important prior to treatment initiation and when switching to distinct second line regimen due to persistent mutations/T215 revertants .

Methodology

HIV‐1 genotyping was performed from “recent infections”

(<155 days: BED‐CEIA (Sedia); exclusive cases with CD4<100

cells/µl, CDC C) to identify resistance‐associated mutations

according to the WHO surveillance drug resistance mutations

(Figure 2).

Study population 2013‐2015 (n = 1,460) %

Gender: male 88.1

female 11.4

no data 0.5

Transmission group: unknown 26.0

Men who have sex with men (MSM) 59.3

Persons with heterosexual transmission 11.0

Persons with intravenous drug use 3.6

Median age (IQR): 34 (27‐44)

Table 1: Characteristics of the study population Between 2013‐2015 3,114/9,799 DSS (33%)

originated from a recent infection. Of these, 1,460

(46%) were successfully sequenced and analyzed.

The proportion of total TDR was 10.8%, comprising

3.8% with mono resistance to nucleotide reverse

transcriptase inhibitors (NRTI), 2.8% to non‐NRTIs,

2.9% to protease inhibitors and 1.2% with

dual/multi‐class resistances (N= 56, 41, 43, 17, respectively)

(Figure 3).

80% (82/102) of all NRTI‐selected mutations were thymidine analogue mutations (TAMs) and T215 revertants:

M41L, K219NQR, D67EGN, T215Y, K70R, L210W and T215CDEIS, conferring low/intermediate resistance to

zidovudine (AZT) and stavudine (D4T). 60% (38/64) of NNRTI‐resistance was caused by K103NS conferring resistance

to efavirenz (EFV) and nevaripine (NVP). The most frequent PI‐mutations M46IL and V82FL are associated with

low/intermediate resistance to tipranavir (TPV), nelfinavir (NFV) and fosamprenavir (FPV) (Figure 4+5).

RESULTS

Figure 3: Proportion of HIV‐1 variants with and withouttransmitted drug resistance. (2013‐2015; N=1,460)

Considering only primary resistance mutations which

impact drugs currently recommended in first‐line

regimens (EACS V8.0), the prevalence of TDR

mutations was 5.4% (0.8% NRTI; 3.1% NNRTI; 0.6% PI;

0.9% multi drug resistance; N= 12, 45, 9, 13, respectively)

(Figure 6).

Figure 6: Proportion of HIV‐1 variants with and withouttransmitted resistance mutations which impact drugs fromfirst‐line regimens recommended in EACS Guidelines V 8.0.

Figure 2:Workflow for sample preparation and analysis.

HIV-1 drug resistance analysis(WHO surveillance drug resistance mutation list)Linked to Information of HIV notification surveillance Database

Figure 1: EACS Guidelines V8.03TC Lamivudin; ABC Abacavir; FTC Emtricitabin; TDF Tenofovir;EFV Efavirenz; RPV Rilpivirin; ATV Atazanavir; DRV Darunavir; LPVLopinavir; DTG Dolutegravir; EVG Elvitegravir, RAL Raltegravir

Figure 4: Number of transmitted drug resistance mutationsaccording to drug classes in the study population (N=1,460)

Light color: low/intermediate resistance; dark color: high resistance

Figure 5: Predicted susceptibility to antiretroviral drugs withrespect to levels of resistance in the study population.

Higher rates for transmission of NNRTI resistant viruses for subtype A versus subtype B strains in Southern Greece

Discussion

Results (Cont.)

Materials and Methods

We analyzed all sequences with E138A from 179and 68 HIV-1 treatment naïve individuals sampledin Southern Greece during 01/01/2003 -31/06/2015 infected with subtype A and B,respectively. Similarly we analyzed 56 and 18sequences with K103N from subtypes A and B.Sequences were available in the PT/RT

Phylodynamic analyses were performed using aBayesian approach as implemented inBEASTv1.8, by using the HKY as nucleotidesubstitution model with gamma (Γ) heterogeneitymodel, an uncorrelated lognormal relaxed clockmodel with TipDates and the birth-death basicreproductive number models (BDM). Non-informative priors were used for the MCMC runs.The Markov chain Monte Carlo (MCMC) analysiswas run for each dataset for 30x10^6 generations,sampled every 3.000 steps with the first 10% ofsamples discarded as burn-in

Statistical analysis for simple comparisons of therelevant distributions across different levels ofcategorical variables was based on Pearson’s chi-square tests as implemented in STATA 12

This is one of the few studies highlightingdifferences in transmission dynamics ofresistant strains belonging to differentsubtypes

Specifically, our study suggests that E138Aand K103N resistant mutations aretransmitted at higher rates in subtype A thanin subtype B strains

Figure Bayesian skyline plots estimated by BEASTv1.8 using birth-death models (BDM) presenting the number of lineages(transmissions) over time for the NNRTI-resistance mutations (E138A, K103N) from different subtypes (A and B)

Introduction

We have previously found that the most prevalentNNRTI resistant mutations among drug naïveindividuals in Southern Greece were E138A andK103N

Our aim was to estimate the transmissiondynamics of E138A and K103N resistant strainsand to investigate for potential differencesbetween subtypes A and B

E. Kostaki1, V. Sypsa1, E. Magiorkinis1, G. Nikolopoulos2 P. Gargalianos3, G. Xylomenos3, M. Lazanas4, M. Chini4, A. Skoutelis5,V. Papastamopoulos5, A. Antoniadou6, A. Papadopoulos6, M. Psichogiou7, G.L. Daikos7, A. Zavitsanou1, G. Chrysos8, V. Paparizos9,S. Kourkounti9, H. Sambatakou10, N.V. Sipsas11, M. Lada12, P. Panagopoulos13, E. Maltezos13, S. Drimis8, A. Hatzakis1, D. Paraskevis*1

P354

The distributions of transmission risk groupswere similar for subtypes A and B for bothE138A and K103N (Table 1)

Specifically:

1. Men who have sex with men (MSM)represented 69% (N=124) and 63% (N=43) ofinfections with E138A in subtypes A and B,respectively (Table 1)

2. Similarly, MSM comprised 68% (N=38) and61% (N=11) of individuals with K103N insubtypes A and B, respectively (Table 1)

*Contact Information: [email protected] .gr

MSM: Men who have Sex with Men MSW: Men who have Sex with Women PWID: People Who Inject Drugs

Aim

1Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, 2Medical School,University of Cyprus, Nicosia, 31st Department of Internal Medicine, G. Genimatas GH, Athens, 43rd Internal Medicine Department-Infectious Diseases, Red Cross Hospital, Athens, 55th Department of Medicine and Infectious Diseases, Evaggelismos GH, Athens, 64thDepartment of Medicine, Attikon GH, Medical School, National and Kapodistrian University of Athens, Athens, Laikon GH, MedicalSchool, National and Kapodistrian University of Athens, Athens (1st Department of Medicine7 and Pathophysiology11), 8Department ofInternal Medicine, Tzaneio GH, Piraeus, 9HIV/AIDS Unit, A. Syngros Hospital of Dermatology and Venereology, Athens, 10HIV Unit, 2ndDepartment of Internal Medicine, Hippokration GH, Medical School, National and Kapodistrian University of Athens, Athens, 122ndDepartment of Internal Medicine, Sismanogleion GH, Athens, 13Department of Internal Medicine, University GH, Democritus University ofThrace, Alexandroupolis

Subtype

A B

NNRTI-resistance mutation E138A K103N E138A K103N

Transmission risk group

MSM 124 (69) 38 (68) 43 (63) 11 (61)

MSW 18 (10) 3 (5) 11 (16) 1 (6)

PWID 9 (5) 1 (2) 4 (6) 2 (11)

Other/Unknown 28 (16) 14 (25) 10 (15) 4 (22)

Total 179 (100) 56 (100) 68 (100) 18 (100)

Table 1. Distribution of transmission risk groups for the NNRTI-resistance mutations from different subtypes

Table 2. Characteristics for the NNRTI-resistance mutations from different subtypes

Subtype NNRTI-resistance mutation tMRCA (median estimate) 95% HPD Intervals

AE138A 1992.0 1987.6-1995.6K103N 1999.0 1994.7-2002.5

BE138A 1982.6 1973.7-1990.6K103N 1991.8 1979.1-2000.8

tMRCA: time of the Most Recent Common Ancestor 95% HPD Intervals: 95% Higher Posterior Density Intervals

Acknowledgements: The study was in part supported by the Hellenic

Society for the study of AIDS and STDs

Molecular clock analyses revealed that:

Results

4. The slope of the number of lineages(transmissions) over time estimated atthe exponential phase of the BDMskylines for E138A sequences ofsubtype A (median: 10.13, 95%CI: 9.30-10.90) was 10 times that of subtype B(median: 1.04, 95%CI: 0.96-1.11)(Figure)

5. For K103N, the slope for subtype Atransmissions was approximately 2.5times (median: 6.16, 95%CI: 5.80-6.52)that for subtype B (median: 2.50, 95%CI:2.45-2.55) (Figure)

1. The time of the most recent commonancestor (tMRCA) for E138A was estimatedin 1992.0 [95%HPD: 1987.6-1995.6] and1982.6 [95%HPD: 1973.7-1990.6] forsubtypes A and B, respectively (Table 2)

2. For K103N, the tMRCA was estimated in1999.0 [95%HPD: 1994.7-2002.5] and1991.8 [95%HPD: 1979.1-2000.8] forsubtypes A and B, respectively (Table 2)

3. The transmission dynamics for subtypes Aand B for both E138A and K103N differedgreatly (Figure)

Given that the distributions oftransmissions risk groups were similarbetween the two clades (subtypes A andB), observed differences in transmissiondynamics could be due to highertransmissibility of subtype A or higher riskbehavior of the individuals infected withthis subtype

Background

Previous studies on pre-treatment drug resistance from sub-Saharan Africa have shown the highest prevalence in Uganda, particularly in

Kampala, with a prevalence of 12.3%. Antiretroviral therapy (ART) has been publicly available in Uganda since 2000, with initial use -

although limited - of mono/dual thymidine analogues. This study aims to describe type and frequency of pre-treatment resistance in HIV-

infected Ugandan adults seeking care at one of the largest public-sector providers in Kampala, Uganda.

Amrei von Braun (1,2), Christine Sekaggya (1), Alexandra Scherrer (2,3), Brian Magambo (4), Deogratius Ssemwanga (4), PontianoKaleebu (4), Huldrych Günthard (2,3), Andrew Kambugu (1), Jan Fehr (2), Barbara Castelnuovo (1)

1. Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda2. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland3. Institute of Medical Virology, University of Zurich, Zurich, Switzerland4. MRC/UVRI Uganda Research Unit on AIDS, Entebbe Uganda

Methods

From June 4th – September 30th, 2015, ART-naïve adults (≥18 years) presenting to the Infectious Diseases Institute (IDI) in Kampala and

willing to participate in this study, were asked to give a plasma sample for pre-treatment HIV genotyping. Sequencing of partial

polymerase gene was conducted using an in-house protocol. All sequences were submitted to the Stanford University HIV Drug

Resistance database and the surveillance drug resistance mutations were identified using the 2009 WHO mutations list.

Results

Pre-treatment drug resistance testing was available from 152 ART-naïve HIV-infected adults, of which 96 (63.2%) were female with a

median age of 33 years (interquartile range (IQR) 26-41), and a median CD4 cell count of 511 cells/uL (IQR 284-713). Mutations

associated with HIV drug resistance were found in 9/152 (5.9%) patients. Five patients (5/152, 3.3%) harbored NRTI mutations, and

8/152 (5.3%) had NNRTI mutations. Five (3.3%) patients had one class mutations, and 4 (2.6%) showed double class resistance.

Protease inhibitor mutations were not observed (for specific mutations see table).

3518131Low prevalence of pre-treatment HIV-1 drug resistance in Ugandan adults

Drug class / mutations Total N = 152, (%)

Any NRTI mutation

K65R

M184V

Other (M41L, T215I)

5 (3.3)

1 (0.7)

2 (1.3)

2 (1.3)

Any NNRTI mutation

K101E

Y181C

K103N

Other (M230L, G190A/S, Y188L)

8 (5.3)

3 (2.0)

2 (1.3)

2 (1.3)

4 (2.6)

Table: Observed transmitted drug resistance mutations

Conclusions

Contrary to previous reports, we found a low

prevalence of pre-treatment drug resistance among

Ugandan adults in Kampala. We hypothesize that the

use of mono/dual thymidine analogues in the past

contributed to a higher circulation of TAMs, as

observed in developed settings. The subsequent swift

scale-up of triple ART in the region may have reduced

pre-treatment resistance over time.

Acknowledgements: We would like to acknowledge all patients and their families.

Funding: Swiss HIV Cohort Study, Gilead Sciences

Corresponding author: [email protected]

Prevalence of HIV type 1 drug resistance mutations in treatment-naïve patients participating in the GARDEL Study

Maria Inés Figueroa, Patricia Patterson, Pedro Cahn, Jaime Andrade-Villanueva, José R Arribas, José M Gatell, Javier R Lama, Michael Norton, Juan Sierra Madero, Omar Sued, Maria José Rolón, on behalf of the GARDEL Study Group*

BACKGROUND

Combination antiretroviral therapy has greatly reducedthe rate of morbidity and mortality among HIV-1 infectedpatients. However, high mutation and recombinationrates of HIV-1 lead to the emergence of various subtypesand drug-resistance viruses, rendering first line ARV-therapy ineffective in many patients.The aim of this sub study is to describe the prevalence ofHIV-1 subtypes and the patterns of drug resistancemutations among ARV-naïve HIV-1-infected patients fromsix different countries participating in the GARDEL Study

MATERIALS AND METHODS

543 naïve patients from 6 countries (Argentina, Chile,Spain, Mexico, Peru and US) were screened betweenDec-2010 to May 2012, and 534 HIV-sequences wereanalyzed following the IAS-USA 2014 Drug ResistanceMutations Panel. Genotypic assays performed atscreening visit were: PhenoSense HIV assay(Monogram Biosciences, San Francisco, CA, USA),ViroSeq HIV-1 (ViroSeq HIV-1 Genotyping System v2.0;Celera, Alameda, CA), TRUGENE® HIV-1 GenotypingAssay (Siemens Healthcare Diagnostics), according toavailability at each site.

RESULTS

Of the 534 patients screened, 74% were Hispanic/Latino.Median time of infection at SCR was: 10.5 months. CDCstage A: 82%. Of 450 viral subtypes available, the mostfrequent was subtype B in all three regions (Fig 1) A totalof 113 samples (21.2%) had major resistant mutations; 22samples (4.1%) had major protease mutations (M46I wasthe most common mutation: 1.5%), 85 samples (15.9 %)had NNRTIs mutations (K103N/S was the most commonmutation: 4.9%), and 17 samples had mutations to NRTIs(3.2%) ,M41L (1.3%) was the most common mutation toPIs, only 2 patients had more than one mayor mutation(2/22)(Fig 2). The more frequent minormutationswere:M36I/L/V(216/534),L63P (120/534),L10I/F/V/R (115/534) and K20R/M/I:59/534. The globalresistance analysis by regions showed 21% for LA, 22.8%for US/Mexico and 14.7% for Spain, being NNRTIresistance by regions 16.4%; 15.4% and 11.8%respectively. PI resistance was 3.1% for LA and Mexico/USand NRTI resistance was 3.1% for LA, 3.4% for US/Mexicoand 2.9% for Spain. No Q151M, 69ss or K65R wereidentified.(Fig3)

CONCLUSIONS

In our study we found a primary resistance rate of 21.2%, similar in LA and US/Mexico but lower in Spain. Levels of NNRTIresistance are similar in the three analyzed regions, as previously reported in naïve populations, and reinforces the need ofperforming genotypic testing in ARV naïve patients, especially in LA were the first line therapy is still based on NNRTI drugs

Author correspondence: María Inés Figueroa [email protected]

72%

92%

91%

LA

US/MEX

SPAIN

HIV-1 subtypes

B other

LA US/MEX Spain

Global resistance analysis by regions 21% 22% 14%

Pis* 3,1 3,1 none

NNRTs 16,4 15,4 11,8

NRTIs 3,1 3,4 2,9

* major protease mutations

3,2%

15.9%

4.1%

INTR NNRTI IPFig 2: Global resistance by drug class

(Fig 3)

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Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are particularly prone to treatment failure as high-level drug resistance has been associated with a single point mutations within the binding site of reverse transcriptase. Thus, transmitted NNRTI mutations, may contribute to an increased risk of virologic failure for patients prescribed their first ART regimen.

Our study investigated the NNRTI resistance profiles of antiretroviral–naïve patients in a large urban clinic setting (Maple Leaf Medical Clinic, Toronto, ON) and assessed their response to their initial antiretroviral therapy (ART).

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The three objectives of this study are:

1. To assess the frequency of NNRTI, NRTI and PI mutations2. To report if the frequency of baseline NNRTI mutations affects time to virologic suppression3. To report if the frequency of baseline NNRTI mutation affects time to virologic rebound in those who have achieved virologic suppression

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This was a retrospec�ve clinical chart review of ART-naive pa�ents with available baseline genotypes whom were prescribed their first ARV regimen.

Inclusion criteria:

1. HIV-posi�ve 2. Aged 16 years or older at baseline3. Has a baseline genotype between January 1, 1997 and July 16, 2015 prior to star�ng ART

Sta�s�cal Analysis:

For demographic and clinical data, categorical variables were summarized using frequencies and propor�ons and compared using the Chi-square (Fisher) test. Con�nuous variables were summarized using medians and interquar�le range and compared using the Wilcoxon rank sum test. Baseline NNRTI, NRTI, and PI resistance muta�ons were reported (Table 2) using frequencies and propor�ons.

• Cox regression was used to determine correlates of virologic suppression [defined as viral load (VL) < 40 (or <50 depending on era) by 6 months] with presence of baseline NNRTI resistance as the primary correlate.

• Of those with virologic suppression, we conducted Cox regression to determine correlates of virologic rebound (defined as VL ≥ 200 copies/mL).

• Censoring occurred for those who did not have any follow-up VL results and at last VL or visit date for those without evidence of viral suppression.

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Baseline demographic are shown in Table 1. Of the 1338 patients with a baseline genotype, we further looked at the 1218 that subsequently initiated ARV’s.

14 College Street, Toronto, Ontario, Canada, M5G 1K2www.mlmedical.com

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SAMANTHA STEINBERG1,2, FRED CROUZAT1, INA SANDLER1, BRENDA VARRIANO1,3, GRAHAM SMITH1, COLIN KOVACS1,4, JASON BRUNETTA1, BENNY CHANG1, BARRY MERKLEY1, DAVID TILLEY1, DAVID FLETCHER1, MEGAN ACSAI1, DAVID KNOX1, MALIKA SHARMA1, MONA LOUTFY1,4,5

1Maple Leaf Medical Clinic; 2University of Guelph; 3Institute of Medical Science, University of Toronto; 4Department of Medicine, University of Toronto; 5Women’s College Research Institute, Women’s College Hospital

International Congress on Drug Therapy in HIV Infection

23 - 26 October 2016 Glasgow, UK

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When treated, patients without baseline NNRTI mutations (n = 1135) were prescribed NNRTI-containing regimens in 43.9% of cases, PI-containing regimens in 34.7% of cases and INI-containing regimens in 14.3% of cases. Treated patients with baseline NNRTI resistance (n = 83) were prescribed PI-containing regimens in 51.8% of cases and INI-containing regimens in 28.9% of cases. Baseline mutation frequencies by class are shown in Table 2.

Virologic suppression was observed in 1024 out of 1218 (84.07%) individuals whom were prescribed ARV’s. 83.13% of patients with baseline NNRTI mutation acheived viral suppression while 84.14% without NNRTI mutations achieved suppression. (Table 3).

• In univariate and mul�variate Cox regression, the presence of baseline NNRTI resistance did not impact virologic suppression (HR = 0.98; 95%CI = 0.76-1.24).

• For virologic rebound, the presence of baseline NNRTI resistance also did not impact its occurrence (HR = 1.11; 95%CI = 0.68-1.81).

• In mul�variable analysis, a�er adjus�ng for age, gender, baseline VL and CD4 count, dura�on of HIV and baseline PI muta�ons, the presence of NNRTI muta�ons also did not impact virologic rebound (aHR = 1.09; 95%CI = 0.66-1.78) (Table 4).

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• Baseline NNRTI mutations were present in 6.7% of our antiretroviral-naive patients.

• Despite having baseline NNRTI mutations, the majority of patients (83.13%) reached virologic suppression and did not experience increased risk virologic rebound.

• Few new mutations were developed in those who started ART.

• Patients with NNRTI mutations are being treated effectively with increased use of other ARV classes.

Enhanced surveillance to study HIV-1 drug resistance among naive individuals in Greece: the added value of molecular epidemiology to public health

Discussion

Results


We analyzed sequences from 3,428 HIV-1 treatmentnaïve individuals available in the PT/RT. Sequenceswere sampled in Southern Greece during 01/01/2003 -31/06/2015Phylogenetic analysis was performed on subtype A(N=235) and B (N=86) sequences with resistance toNNRTIs (K103N and E138A) (Table 1 and 2), alongwith sequences isolated from seropositives withoutresistance from Greece sampled during 1998 - 2013(subtype A: N=904; subtype B: N=1,615) and arandomly selected global dataset (subtype A: N=5,907;subtype B: N=3,984). Phylogenetic trees were inferredby maximum likelihood (ML) method as implemented inRAxML v8.0.20

Table 1. Distribution of HIV-1 subtypes for NNRTI-resistance mutations

Figure 1. Unrooted ML phylogenetic trees estimated by RAxML using sequences from Greece and a global reference dataset, for HIV-1subtypes: A. A and B. B. Sequences from Greece are marked in light blue in contrast with those from other geographic countriesmarked in dark green. Sequences with NNRTI-resistance mutations (K103N, E138A) are marked in different colors

Our study suggests that the most prevalent mutations associated with resistance to NNRTIs weretransmitted through local networks in Greece

Notably, phylodynamic analysis allows estimating that resistance in the last few years has beenactively propagated with an increasing incidence

Those belonging to the active TDR networks are the priority population for prevention (TasP) Our study highlights the added value of the latest advances in molecular epidemiology to public

health since these allow us to estimate critical epidemiological parameters and therefore thepriority population to intervene

NNRTI-resistance mutation (N, %)

E138A K103N

Subtype Total

A 179 (68) 56 (70) 235 (69)

B 68 (26) 18 (23) 86 (25)

Other 16 (6) 6 (7) 22 (6)

Total 263 (100) 80 (100) 343(100)

Figure 2. Bayesian skyline plots estimated by BEAST2 using birth-death models (BDM) presenting the effective reproductive number(Re) over time for the five transmission networks

Introduction

HIV-1 transmitted drug resistance (TDR) to NNRTIshas been shown to compromise first-line response totreatment. The prevalence of resistance to NNRTIs waspreviously estimated to be 16.9% among drug naïveindividuals in Greece

Our aim was to investigate the dispersal patterns ofHIV-1 resistant strains and to estimate the effectivereproductive number (Re) and transmission dynamicsfor locally transmitted resistance

D. Paraskevis*1, E. Kostaki1, E. Magiorkinis1, P. Gargalianos2, G. Xylomenos2, M. Lazanas3, M. Chini3, A. Skoutelis4, V. Papastamopoulos4,A. Antoniadou5, A. Papadopoulos5, M. Psichogiou6, G.L. Daikos6, A. Zavitsanou1, G. Chrysos7, V. Paparizos8, S. Kourkounti8, H. Sambatakou9,N.V. Sipsas10, M. Lada11, P. Panagopoulos12, E. Maltezos12, S. Drimis7, A. Hatzakis1

Molecular clock analyses revealed that: Τhe time of the Most Recent Common

Ancestor (tMRCA) was in 2007 (95% HPD:2004 - 2009) for the K103N cluster versus1995 (95% HPD: 1991 - 1999), 1996 (95%HPD: 1989 - 2000), 1997 (95% HPD: 1991 -2001) and 2004 (95% HPD: 2000 - 2007) forE138A LNTs (Table 3)

For the K103N sub-outbreak the Re washigher than 1 between 2008 and the first halfof 2013 (maximum value of median Re = 2.8)(Table 3, Figure 2). On the other hand, for allE138A LTNs the Re was higher between 2011and 2015, except the most recent one wherethe Re was approximately equal to 1 (Figure2)

P360

Phylogenetic analyses revealed that: For subtype A the majority of individuals

infected with resistant strains (209 out of235, 88.9%) belonged to monophyleticclusters (local transmission networks, LTNs)(Figure 1 A). Specifically, 48 out of 56(85.7%) of sequences with K103N, and 148out of 179 (82.7%) with E138A belonged toone and four LNTs, respectively (Figure 1A). These findings suggest that the viruseswith the most prevalent resistancemutations spread locally

For subtype B either non-clusteredsequences or small LTNs (range: 2-6sequences), were identified (Figure 1 B)

*Contact Information: [email protected] .gr

MSM: Men who have Sex with Men MSW: Men who have Sex with Women PWID: People Who Inject Drugs

A B

Aim

1Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, 21st Department of Internal Medicine, G.Genimatas GH, Athens, 33rd Internal Medicine Department-Infectious Diseases, Red Cross Hospital, Athens, 45th Department of Medicine and InfectiousDiseases, Evaggelismos GH, Athens, 54th Department of Medicine, Attikon GH, Medical School, National and Kapodistrian University of Athens, Athens, LaikonGH, Medical School, National and Kapodistrian University of Athens, Athens (1st Department of Medicine6 and Pathophysiology10), 7Department of InternalMedicine, Tzaneio GH, Piraeus, 8HIV/AIDS Unit, A. Syngros Hospital of Dermatology and Venereology, Athens, 9HIV Unit, 2nd Department of Internal Medicine,Hippokration GH, Medical School, National and Kapodistrian University of Athens, Athens, 112nd Department of Internal Medicine, Sismanogleion GH, Athens,12Department of Internal Medicine, University GH, Democritus University of Thrace, Alexandroupolis

Subtype

A B

NNRTI-resistance mutation E138A K103N E138A K103N

Sampling period 2003-2015 2004-2015 2003-2015 2004-2014

Transmission risk group

MSM 124 (69) 38 (68) 43 (63) 11 (61)

MSW 18 (10) 3 (5) 11 (16) 1 (6)

PWID 9 (5) 1 (2) 4 (6) 2 (11)

Other/Unknown 28 (16) 14 (25) 10 (15) 4 (22)

Total 179 (100) 56 (100) 68 (100) 18 (100)

Table 2. Distribution of transmission risk groups and sampling periods for theNNRTI-resistance mutations from different subtypes

Table 3. Characteristics for the five transmission networks

Transmission network

tMRCA (median; 95% HPD)

Re (maximum value of median)

K103N 2007 (2004-2009) 2.8

E138A_1 1995 (1991-1999) 2.1

E138A_2 1996 (1989-2000) 1.8

E138A_3 1997 (1991-2001) 2.0

E138A_4 2004 (2000-2007) 2.5tMRCA: time of the Most Recent Common Ancestor Re: Effective reproductive number

Phylodynamic analyses were performed using birth-death models (BDM) allowing estimation of importantepidemiological parameters such as the effectivereproductive number (Re), as implemented in BEAST2.The Re is defined as the number of expectedsecondary infections per infected individual

Funding: This study has been supported by Gilead Sciences

Transmission patterns of HIV-1 subtype A resistant strains across Greece: Evidence for country and regional level transmission networks

Discussion


Results

We analyzed sample of subtype A1sequences (N=1,104) available in the polgene (PT/RT)

Sequences were sampled in Northern andSouthern Greece during 1999 and middle-2015. We included sequences only fromGreece since we have shown previouslythat subtype A1 sequences have beenmostly found within a single monophyleticcluster

Figure. Unrooted ML phylogenetic tree estimated by RAxML using HIV-1 sequences from Greece.Sequences without NNRTI resistance mutations from Northern and Southern Greece are marked inlight purple in contrast with those with NNRTI resistance mutations (E138A, K103N, Y181C) marked indifferent colors. Sequences from Southern Greece with NNRTI resistance mutations are shown in red(E138A) and yellow (K103N). Sequences from Northern Greece with NNRTI resistance mutations areshown in blue (E138A), green (K103N) and light blue (Y181C)

A high prevalence of NNRTI resistance mutations was previouslyreported for the subtype A1 strains circulating in Greece andespecially in Northern Greece

The majority of these resistant viruses were transmitted withincommon transmission networks

Introduction

Our aim was to investigate the dispersal patterns ofHIV-1 resistant strains across Greece

D. Paraskevis1, L. Skoura2, E. Kostaki1, E. Magiorkinis1, P. Gargalianos3, G. Xylomenos3, M. Lazanas4, M. Chini4, S. Metallidis2, A. Skoutelis5, V. Papastamopoulos5, A. Antoniadou6, A. Papadopoulos6, M. Psichogiou7, G.L. Daikos7, D. Pilalas8, Α. Zavitsanou1, G. Chrysos9, V. Paparizos10, S.

Kourkounti10, D. Chatzidimitriou11, H. Sambatakou12, N.V. Sipsas13, M. Lada14, P. Panagopoulos15, E. Maltezos15, S. Drimis9, A. Hatzakis1

P361

1Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, 2Department of Microbiology, AHEPA UniversityHospital, Aristotle University of Thessaloniki, Thessaloniki, 31st Department of Internal Medicine, G. Genimatas GH, Athens, 43rd Internal Medicine Department-InfectiousDiseases, Red Cross Hospital, Athens, 55th Department of Medicine and Infectious Diseases, Evaggelismos GH, Athens, 64th Department of Medicine, Attikon GH, MedicalSchool, National and Kapodistrian University of Athens, Athens, Laikon GH, Medical School, National and Kapodistrian University of Athens, Athens (1st Department ofMedicine7 and Pathophysiology13), 8Medical School, Aristotle University of Thessaloniki, Thessaloniki, 9Department of Internal Medicine, Tzaneio GH, Piraeus, 10HIV/AIDSUnit, A. Syngros Hospital of Dermatology and Venereology, Athens, 11Department of Microbiology, Μedical School, Aristotle University of Thessaloniki, Thessaloniki, 12HIVUnit, 2nd Department of Internal Medicine, Hippokration GH, Medical School, National and Kapodistrian University of Athens, Athens, 142nd Department of InternalMedicine, Sismanogleion GH, Athens, 15Department of Internal Medicine, University GH, Democritus University of Thrace, Alexandroupolis

Acknowledgments:The study was in part supported by the Hellenic Society for the study of

AIDS and STDs

The prevalence of mutations conferring resistanceto NNRTIs was previously reported to be higherthan 15% among drug naïve individuals both inNorthern and Southern Greece. The most prevalentresistance mutations were E138A, K103N andY181C associated mostly with subtype A1

Phylogenetic topology (tree) wasestimated from the underlying nucleotidesequences using approximate maximumlikelihood (ML) method with bootstrappingas implemented in RAxML v8.0.20

Specifically, analysis was performed underthe Generalized Time Reversible(GTR+cat) model of nucleotidesubstitution model including a Γ distributedrate of heterogeneity among sites

Phylogenetic analysis revealed that:

E138A and K103N resistant strains have spread through largemonophyletic clusters spanning both Northern and Southern Greece,suggesting that all transmissions within these clusters occurredregionally (Figure)

Conversely to E138A and K103N, Y181C formed a subnetwork(monophyletic cluster) limited in Northern Greece with only a singlespill over to Southern Greece (Figure)

For K103N strains we found a large (N=49) and a small cluster (N=5)including sequences from both areas (Figure)

Sequences from Northern Greece formed two specific subnetworks,suggesting local dispersal (Figure)

Sequences with E138A from Northern Greece formed two specificsubnetworks within the E138A monophyletic clades found for Greece.The latter consisted of four major clades of 53, 41, 29 and 25sequences from both regions (Figure)

Overall, E138A and K103N spread through common networks acrossthe country with evidence of local transmissions in Northern Greece(Figure)

On the other hand, Y181C has spread only in Northern Greece withvery limited dispersal to Southern Greece (Figure)

Significant clustering of sequences from Northern Greece as well asthe existence of a regional cluster suggest high transmissionnetworking of the population in this area; a finding that might explainthe higher prevalence of transmitted drug resistance (TDR) inNorthern Greece

Our study highlights the priority population to prevent TDR in thefuture

Contact Information: [email protected]

A. Zoufaly 1, Kraft C1, Schmidbauer C1, Puchhammer-Stöckl E2

Occurrence and Risk Factors for Primary Integrase Resistance-associated Mutations in Austria in the years 2008-2013

1 Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; 2Department of Virology, Medical University Vienna, Austria

Introduction:In Europe, country specific treatment guidelines often do not advocate testing for Integrase inhibitor resistance associated mutations (IRAM) before initiation of first line ART given the extremely low prevalence of mutations found in older surveillance studies. However, increased use of integrase inhibitors (INSTI) might have led to the emergence of treatment limiting mutations in more recent years. We aimed to determine the prevalence of IRAM in Austria in the 5 years following introduction of INSTI and to analyze trends and factors associated with their detection.

References1) Stekler JD et al, Antivir Ther. 2015;20(1):77-802) Saladini F et al, Clin Microbiol Infect. 2012;18(10)3) Gutierrez C et al, HIV Clin Trials. 2013;14(1):10-64) DAIG, Deutsch-Österreichische Leitlinien. 2015

Methods:Samples of ART naïve patients in Austria between 2008 and 2013 were analyzed for the existence of IRAM using bulk sequencing with published primers and drug resistance penalty scores (Stanford HIVdb algorithm) were calculated to estimate response to antiretroviral drugs Demographic and virological data including age, sex, viral subtype, drug resistance associated mutations to PI and RTI were extracted from a database. Comparative statistics and logistic regression models were used to analyse risk factors for the occurrence of IRAM.

Results:A total of 303 samples were analyzed. Patient characteristics are shown in Table 1. Overall prevalence of IRAM was 2.3%. 6% had a DPS >=10 for Raltegravir or Elvitegravir, respectively, indicating at least potential low level resistance. 1% had a GSS >=10 for Dolutegravir (Table 2). One major mutation was observed (F121Y) in a patient sample from 2012 leading to 5-10 fold reduced susceptibility to Raltegravir and Elvitegravir Two patients carried the major accessory mutations E138K and G140A, respectively, which both lie on the Q148 pathway (Table 3). No temporal trend was observed (p=0.16). Risk factors associated with occurrence with IRAM are shown in Table 4.

Table 3: Patients with Integrase resistance associated mutations

Table 2: Drug penalty scores (Stanford HIVdb algorithm) indicating susceptibility to Integraseinhibitors

Table 4: Risk factors for Integrase resistance associated mutations

Table 1: Patient Characteristics

Conclusions: Major primary IRAM are rarely found despite increasing use of INSTI in Austria There is potential for reduced susceptibility to these drugs in selected patients No clear risk factors for occurrence of IRAM can be identified Routine resistance testing seems prudent to avoid the consequences including accumulation of further mutations and therapeutic failure

year Raltegravir (n) Elvitegravir (n) Dolutegravir(n)

0-9 10-14 15-29 30-59 0-9 10-14 15-29 30-59 0-9 10-142008 47 0 2 0 47 1 1 0 49 02009 47 0 2 2 47 0 2 2 50 12010 50 0 1 0 50 0 1 0 51 02011 48 0 2 0 48 0 2 0 50 02012 46 1 5 1 46 3 3 1 53 02013 48 0 1 0 48 0 1 0 48 1

0-9 susceptible 10-14 potential low level 15-29 low level resistance 30-59 intermediate level resistance

VariableMean age (years, SD) 38 (12)Male sex (n, %) 235 (77.6%)Year of sample (n, %)

2008 49 (16.2%)2009 51 (16.8%)2010 51 (16.8%) 2011 50 (16.5%)2012 53 (17.5%)2013 49 (16.2%)

Viral load categories (HIV RNA copies/ml,n,%)

<1x104 7 (6.7%)1x104<5x104 23 (21.9%)5x104<1x105 20(19.1%)1x105<1x106 37(35.2%)

>1x106 18(17.1%)Viral subtype (n, %)

A 22 (7.3%)B 188 (62.1%)C 35 (11.6%)

other 58 (19.1%)Major/primary drug resistance present (n, %)

NRTI 4 (1.3%)NNRTI 20 (6.6%)

PI 5 (1.7%)INSTI 1 (0.3%)

Patient Sex Age Subtype Year of detection

IRAM RTI resistance mutation

PI

1 male 45 B 2008 T97Aa none none2 male 30 B 2009 G140Aa none A71Vc

3 female 31 CRF01_AE 2012 T97Aa none none4 female 22 D 2012 L74Ma none none5 male 30 B 2012 F121Yb none none6 male 48 C 2012 T97Aa none none7 male 39 B 2013 E138Ka none none

Risk factor OR 95% CI PMale sex 0.80 0.36 27395 0.58Age (per year) 0.98 0.96 1.01 0.26PI/NRTI or NNRTI mutation 0.44 0.10 1.96 0.28Calendar year 1.04 0.86 1.27 0.44Subtype B virus 0.54 0.06 4.60 0.57

Acknowledgement and financial disclosureThe performance of this study was supported by GILEAD

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Irena Zeldis, PupkoPupkoPupkoPupko Irena ZeldisIrena ZeldisMatusMatusNatasha

Israel Israel , Israel , , AvivAviv-Aviv-Aviv-Aviv-TelTelAviv University, Aviv University, Aviv University, -Aviv University, Aviv University, -Aviv University, -Aviv University, -TelTelTelFaculty of Medicine , Faculty of Medicine , Faculty of Medicine , SacklerSacklerSackleraffiliated to the affiliated to the affiliated to the Medical Center, Medical Center, Medical Center, SouraskySouraskySouraskyAviv Aviv Aviv -Aviv Aviv -Aviv -Aviv -AIDS Center TelAIDS Center TelKoblerKoblerCrusaid Crusaid 1 Israel Israel Israel , AvivAvivAviv, Aviv, Israel.AvivAvivIsrael.Israel.Israel.,

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TelTelfor Viruses and Molecular Biology, of Cell Research and Immunologyof Cell Research and Immunology

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Laboratory Laboratory 3

Table 1. Demographic Characteristics

MSM - Men who have sex with men. IVU - Intravenous drug users. EMEA - Eastern Mediterranean European and Middle East countries, except Israel and North Africa.

Table 1b. Percentage of HIV subtypes among different exposure risk category (ERC)

MSM - Men who have sex with men. IVU - Intravenous drug users. Hetero - Heterosexuals.

Table 2. Transmission of HIV drug resistance-associated mutations*

These trees support clustered transmission of TDR throughout this period among MSM in subtype A and B. One cluster in subtype A without TDR represents an outbreak. Lack of clusters among IVU Harboring Subtypes A and C could represent infections acquired Before immigration to Israel from former Soviet Union countries and Ethiopia,respectively.

Figure A : 188 Sequences. Outgroup represented by 2 subtype B viruses. Figure B : 370 Sequences. Outgroup represented by 2 subtype C viruses.

Figure C : 52 Sequences. Outgroup represented by 2 subtype viruses.

Subtypes A B C other

ERC n(%)MSM 27 7.0 320 83.3 10 2.6 27 7.0IVU 83 86.5 11 11.5 0 0.0 2 2.1Hetero 71 41.0 37 21.4 42 24.3 23 13.3Unknown 5 55.6 3 33.3 1 11.1 0.0Child 3 42.9 1 14.3 0.0 3 42.9

TDRbyDrugClasses

Year n NRTI NNRTI PI MDR TDR %2010 119 4 6 7 1 18 15.12011 100 1 3 6 3 13 13.02012 112 3 3 1 1 8 7.12013 133 4 3 1 8 6.02014 101 5 7 2 14 13.92015 104 5 7 3 15 14.4Total 669 18 30 22 6 76 11.4

*Excluding the integrase inhibitor regionTDR, transmission drug resistance mutation; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor

MSM Heterosexuals IVUYear n TDR % n TDR % n TDR %2010 68 13 19.1 34 5 14.7 15 0 0.02011 63 8 12.7 20 2 11.8 16 3 18.82012 66 6 9.1 23 1 4.3 22 1 4.52013 77 7 9.1 30 1 3.2 22 0 0.02014 63 11 17.5 26 2 7.7 9 1 11.12015 47 8 17.0 40 5 14.3 12 0 0.0

Table 3. Rate of transmission of drug resistance mutations by exposure risk category (ERC)*

MSM - Men who have sex with men. IVU - Intravenous drug users.

Age mean36.8 (sd 10.1)

Gender n (%)Male 545 81.5Female 124 18.5

ERCMSM 384 57.4IVU 96 14.3Heterosexuals 173 25.9Unknown 9 1.3Child 7 1.0

Countryorigin

Israel 363 54.3Ex-SovietUnion 216 32.3SubSaharanAfrica 42 6.3WesternEurope 12 1.8South&centralAmerica 17 2.5NorthAmerica 4 0.6EastAsia 3 0.4EMEA 2 0.3NorthAfrica 4 0.6Australia 2 0.3Unknown 4 0.6

Background: As of 2010, testing for HIV drug resistance is performed routinely to all new HIV patients followed-up in Tel-Aviv. Thus, the objective of this study was to evaluate transmission drug resistance mutations (TDR) among HIV-1 treatment-naïve patients in Tel Aviv from 2010 to 2015.

Methods: The first blood samples obtained between 2010 and 2015 from each treatment-naïve patients joining Tel Aviv HIV clinic after the diagnosis of HIV were sequenced for protease and reverse transcriptase (RT) regions. TDR in these two regions were defined according to the criteria proposed by Bennett et al. [2009. PLoS One. 4:e4724].Subtyping of the isolates was based on the Stanford HIV Drug Resistance Database. Phylogenetic reconstruction was inferred using polsequences. Multiple sequence alignments were computed using MAFFT v. 7. The phylogenetic tree was then inferred using maximum likelihood as implemented in PHYLIP. Likelihood computations were based on the HKY model, taking among site rate variation into account (i.e., the gamma parameter). Confidence in tree estimation was based on 100 bootstrap replications. Ethical approval for the study was granted by the institutional ethics committee.

Results: Table 1 shows the characteristic of the patients. MSM was the major exposure risk category (ERC) group followed in Tel-Aviv, 76 % among them were born in Israel, and 83 % harbor subtype B viruses. Other groups include intravenous drug users (IVU); 78 % of them were born in the former Soviet Union countries and 86% harbor subtypes A viruses. The heterosexuals group is very heterogeneous and includes patients born in Israel, Ethiopian immigrants, immigrants from the former Soviet Union, and worker immigrants mainly from Africa. Rate of TDR is described in tables 2 and 3. The resistance rate decreased from 15.1% in 2010 to 6% in 2013 ( P < 0.05). In 2014 and 2015 we noted an increase to 13.9% and 14.4% respectively. In 2010-2011 protease inhibitors (PIs) was the major resistance mutation , while in 2014-2015 NNRTI resistance mutation was dominant.

Phylogenetic analysis of subtypes A, B and C was performed on 610 sequences (Fig. 1). In subtype A viruses we found a cluster among IVU at 2012 during an outbreak, without resistance associated mutation. However, a cluster with viruses harboring resistance mutation at position 103 was found in five MSM and one IVU female. The analysis subtype B viruses support clustered TDR among MSM. Among subtype C viruses there were no specific clusters.

Discussion: TDRs among patients followed in Tel-Aviv were represented by clusters in MSM. These clusters contain resistance associated mutations to drugs less prescribed in recent years. Although the region of the integrase gene is not routinely sequenced in treatment-naïve patients followed-up in our center, low rate of InI TDR is reported in other studies.

Phylogenetic tree analysis of 610 HIV-1viruses from subtypes A, B and C. Sequences of patients with TDR are represented by colored branches.

Development of T66I-mediated integrase inhibitor cross-resistance against

elvitegravir under dolutegravir containing firstline therapy

Background

ConclusionsConclusions

ResultsResults

Wiesmann F.1, Braun P.1, Naeth G.1, Rump JA2 and Heribert Knechten1

1 PZB, Aachen, HIV&Hepatitis Research Group, Aachen, Germany2 Medical Center for Internal Medicine and Rheumatology, Freiburg, Germany

Corresponding adress: PZB Aachen / Blondelstr. 9 / 52062 Aachen / Germany / Phone: +49-241-470970 / E-mail: [email protected]

*Lepik KJ et al. 2016 – CROI 2016, poster 492LB

MethodsMethods

Although being extreme rarely observed, INI-resistant HIV variants may also occur under DTG firstline treatment. The T66I alone does not necessarily limit the susceptibility to DTG itself but could be a first step of resistance development against DTG.It is reported that T66I confer high-level resistance against EVG and may also putatively lower the resistance barrier against RAL.

Poster-No.: P364

As second generation integrase inhibitor (INI), dolutegravir(DTG) has shown a superior barrier to resistance as compared to profiles of raltegravir (RAL) or elvitegravir(EVG). Current findings suggest that resistance mutations against INIs (Fig.1) extreme rarely occur under DTG-containing first line antiretroviral therapy (ART)*. This case report unveils a possible development of a T66I-mediated cross-resistance against EVG under a DTG firstline regimen (Tab.1).

A firstline treatment with lamivudin/abacavir, lopinavir and dolutegravir was initiated by a 44 years old man with a diagnosis of HIV in 11/2015 (CDC status: B2, CD4 nadir: 219/µl, HIV-1 RNA: 350,000 copies/mL). Ultra-deep sequencing was performed by using population sequencing and ultra-deep sequencing (UDS, Illumina MiSeq) at baseline and at time of therapy failure. Resistance interpretation was estimated by using the HIV-Grade 12/2015, Stanford HIV-db 7.0.1, Rega 9.1.0 and the ANRS 25_09/2015 database. Viral load was quantified with Abbott Realtime.

Before start of therapy, no resistance-associated variants could be detected neither by population or by UDS in HIV protease, reverse transcriptase and integrase (Table 2). After start of DTG-firstline therapy, HIV viral load dropped from 302,815 copies/ml to 2,400 copies/ml within four weeks of follow up and was undetectable at week 8. CD4 cell counts increased from 219/µl to 479/µl (13.4%). However, 20 weeks after initiation of ART, HIV viral load increased to 105 copies/ml and maintained low viremic four weeks later at 112 copies/ml most likely due to inadequate adherence although plasma drug levels turned out to be above critical limits.More importantly, the development of the INI-resistance mutation T66I was then verified by UDS showing a minority population of 36.1%. The variant T66I is a non-polymorphic mutation and reduces EVG susceptibility by ~15-fold while susceptibility to RAL or DTG is reported to be unaffected. There was no evidence for protease or reverse transcriptase resistance mutations at this time. 28 weeks after start of therapy the viral load decreased to undetectable levels without any changes.

Results (continued)

0

5

10

15

20

25

30

35

40

45

50

T66I/A L74M E92Q T97A G140S Y143C/R Q148H N155H E157Q G163R/K

%

2009-2010 2011-2012 2013 2014 2015

0 0 0 1 4 1 1 0 1 3 1 0 2 2 2 3 2 5 5 3 2 5 1 3 2 5 5 3 3 0 4 5 2 3 2 3 121111 8 1 0 0 2 1 1 1 3 4 2N=

Year of analysis T66 variant RT/P resistance?

ART Subtype

2014 T66A Yes LPV/r, RAL B

2015 T66A No Naive B

T66K Yes TDF/FTC/COB/EVG

B

T66I Yes TDF/FTC, DRV/r

CRF02_AG

T66I Yes TDF/FTC/COB/EVG

B

2015/16 T66I No 3TC/ABC, LPV/r, DTG

B

Date 11/2015 12/2015 01/2016 04/2016 05/2016 06/2016

ART 3TC/ABC, LPV/r, DTG

3TC/ABC, LPV/r, DTG

3TC/ABC, LPV/r, DTG

3TC/ABC, LPV/r, DTG

3TC/ABC, LPV/r, DTG

3TC/ABC, LPV/r, DTG

DTG TDM(opt.>500ng/ml)

--- 1599 ng/ml (18 h)

--- --- 2619 ng/ml (10 h)

---

Viral load 303,815 2,430 0 105 112 0

CD4 (abs.) 219 313 479 427 485 587

Resistance(Pop.-Seq)

No RAMs --- --- --- T66I/T ---

Resistance(UDS)

No RAMs --- --- --- T66I (36,1%)

---

Fig.1: Prevalence of integrase-mutations in patients with confirmed integrase-inhibitor resistance2009 – 2015 in our centre (Patients with INI-RAMs: 2009-2010; n=15 / 2011-2012; n=25 / 2013; n=11 / 2014; n=23 / 2015; n=14)

Tab.1: Prevalence of resistance-associated substitutions at position T66 in HIV-1 integrase

Tab.2: Case report: Development of the T66I variant under DTG-containing firstline-treatment.

Patternsofemergentresistance-associatedmutationsafterinitiationofnon-nucleosidereverse-transcriptaseinhibitor-containingregimensinTaiwan:amulticentercohortstudyChien-YuCheng1 ,Yi-Ching Su2,Wen-ChunLiu2,Shu-Hsing Cheng1,Hsin-YunSun2,Chien-Ching Hung2,Sui-YuanChang3

1DepartmentofInternalMedicine,Taoyuan GeneralHospital,MinistryofHealthandWelfare,Taoyuan,Taiwan2DepartmentofInternalMedicine,NationalTaiwanUniversityHospital,Taipei,Taiwan3DepartmentofLaboratoryMedicine,NationalTaiwanUniversityHospital,Taipei,Taiwan

Background

Non-nucleoside reverse-transcriptase inhibitor (NNRTI)-containing

antiretroviral therapy (ART) remains the recommended first-line regimens

for adults infected with HIV in many resource-limited countries. Increasing

trends of resistance-associated mutations (RAMs) to nNRTIs have caused

concerns about the effectiveness of the regimens in national programs in

these regions [1-3] . In this multicenter study, we aimed to investigate the

incidence of emergent RAMs of HIV-1 to antiretrovirals (ARVs) in HIV-

positive adults who developed virological failure to first-line nNRTI-

containing ART in Taiwan.

Correspondence:Chien-YuChengE-mail:[email protected]

Methods

Between June 2012 and March 2016, ARV-naïve HIV-positive adults who

initiated 2 NRTIs plus NNRTI at participating hospitals were included for

analysis. Plasma HIV RNA load (PVL) was determined at baseline, and

week 4-6 and subsequently every 12 to 16 weeks after ART initiation.

Virological failure was defined as a decrease of PVL <1.0 log10 copies/ml in

4 to 6 weeks of ART initiation; or PVL ≥200 copies/ml at 6 months of ART

initiation; or confirmed HIV RNA ≥ 200 copies/ml after viral suppression

(PVL<50 copies/ml). Population sequencing was used to detect RAMs.

Detection of RAMs at baseline was performed retrospectively. RAMs were

interpretedusing the IAS-USA 2015 mutations list.

ResultsDuring the 3.5-year study period, 1642 patients initiated nNRTI-containing

regimens, and 454 (27.4%) had to switch first-line ART because of adverse effects

or intolerance (n=323, 19.7%), retrospective detection of RAMs at baseline (41,

2.5%), and virological failure (83, 5.1%). Virological failure to 2 NRTIs plus

nevirapine, efavirenz, and rilpivirine with baseline PVL < 5 long10 was 4.9%

(12/245), 1.9% (11/573), and 0.7% (2/277); virological falure to 2 NRTIs plus

nevirapine and efavirenz with baseline PVL > 5 log10 was 16.4% (29/177) and

7.8% (29/373) respectively (Figure 1). In 68 patients (3.8%) emergent RAMs were

identified: 42 patients (62.7%) with NRTI RAMs; 28 (41.2%), 1 (1.5%) and 48

patients (71.6%) with nNRTI, protease inhibitors (PI), and any ARV RAMs,

respectively, and 21 (31.3%) with resistance to 2 or more classes of ARV. The

common emergent RAMs to NRTIs were K65R (25%), M184I (10.3%), and M184V

(36.8%), and RAMs to nNRTIs included V90I (5.9%), K101E (5.9%), K103N

(19.1%), V108I (7.4%), Y181C (11.8%), and G190A (5.9%) (Figure 2).

Figure2.Patternsofemergentresistance-associatedmutationsafterinitiation

ofthreedifferentnon-nucleosidereversetranscriptaseinhibitor-containing

regimens.

Conclusions

While a substantial proportion of the patients discontinued first-line NNRTI-

containing regimens due to adverse effects, virological response to nNRTI-

containing regimens remained good in patients who were able to tolerate the

regimens in Taiwan. Most common RAMs in those with virological failure were

related to exposure to tenofovir disoproxil fumarate, lamivudine, nevirapine, and

efavirenz.

AbstractNo.P365

References1. RheeSY,etal.PLoS Med2015;12:e1001810.2. LaiCC,etal.JAntimicrob Chemother. 2012;67(5):1254-60.3. TheTenoRes StudyGroup.LancetInfectDis2016;16(5):565-75.

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

NVP EFV RPV

RAMofNNRTI(%)

V90I A98G L100I K101EK101P K103N K103S V108IE138G V179D+K103R V179DEV V179EV179T Y181C Y188L G190AG190Q G190S H221HY P225HM230L

16.4%

4.9%

7.8%

1.9%

0.7%0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

>5log10 <5log10 >5log10 <5log10 <5log10

NRTIs+NVP NRTIs+EFV NRTIs+RPV

P=0.003

P=0.024

P=0.004

P=0.513

Figure1.Prevalenceofemergentresistance-associatedmutations

amongthreedifferentnon-nucleoside reversetranscriptaseinhibitor-

containingregimens.

SUMMARY&CONCLUSION§ lowprevalenceofLLVinpatientsonsuppressiveART(4.8%)§ VFsubsequenttoLLVobservedin19%ofthecases§ StrongestpredictorforVFsubsequenttoLLVwasatreatment

regimencontainingdrugsapprovedbefore2005§ EpisodesofLLVinpatientstreatedwithdrugswithhighpotency

andahighbarriertoresistancearenotpredictivetoVF

BACKGROUND§ LLVhasbeenpreviouslyassociatedtovirological failure(VF)[1,2]§ therapeuticsuccess:reductionoftheHIV-1viralload(VL)below50copies/ml(German-AustrianguidelinesforthetreatmentofHIVinfection)§ Lowlevelviremia(LLV):repeatedVLmeasurementsbetween50and200copies/mlafterinitialtherapeuticsuccess

MATERIAL&METHODS• AREVIR/RESINAdatabase:clinicalandvirological dataoftherapy-naïve

(TN)andtherapy-experienced(TE)HIV-1-infectedpatientsinNorthRhine-Westphalia,Germany

• Queryofthedatabase:• 2,485firstlineand3657further-linetherapies• patientswhoattainedconfirmedtherapeuticsuccessunderART

andexperiencedconfirmedLLVthereafter• therapiesinwhichtheVLwasmeasuredatleastonceevery24

weeks• VF:confirmedviralloadgreaterthan200copies/mlfollowing

therapeuticsuccess• p-valueswerecalculatedwithFishers’exactandWilcoxonranksum

test.

LITERATURE1. Laprise,C.,etal.,Virologic failurefollowingpersistentlow-levelviremiainacohortofHIV-positivepatients: resultsfrom12yearsofobservation. Clin InfectDis,2013.57(10):p.1489-96.2. Navarro,J.,etal.,Impactoflow-levelviraemia onvirological failureinHIV-1-infectedpatientswithstableantiretroviral treatment. Antivir Ther,2016.21(4):p.345-52.

NadineLübke1,AlejandroPironti2,ElenaKnops3,BjörnJensen4,MarkOette5,StefanEsser6,ThomasLengauer2,JörgTimm1 andRolfKaiser3fortheResina StudyGroup

AssociationoftherapeuticfailuretolowlevelviremiainHIVAssociationoftherapeuticfailuretolowlevelviremiainHIV-AssociationoftherapeuticfailuretolowlevelviremiainHIV-1infectedpatients

AlejandroPironti2 ElenaKnops3 BjörnJensen4 MarkOette5 StefanEsser

AssociationoftherapeuticfailuretolowlevelviremiainHIVAssociationoftherapeuticfailuretolowlevelviremiainHIVAssociationoftherapeuticfailuretolowlevelviremiainHIV 1infectedpatients1infectedpatientsintheAREVIR/RESINAcohortinGermany

OBJECTIVES§ independentanalysisoftheassociationofLLVandotherfactorswith

VF

RESULTSI§ LLVoccurredin294/6142documentedtherapies(4.8%)(Figure1)

§ First-line:47/2485(1.9%)§ Further-line:247/3657(6.8%)

§ MeantimetoLLV:27months(σ=20.7)

§ no significant differences between first- or further line treatment(p=0.46)

§ VFoccurredin56/294(19%)casessubsequenttoLLV(Figure1)

§ Medianviralloadatfailure:472copies/ml(range203-116590copies/ml)

§ MeanLLVepisode:77.4weeks(σ=68.0)

§ VFrateincreasedinTEpatients(19.4%)versusTNpatients(10.6%)(Figure1)

Figure1:Frequencyoflowlevelviremiaandsubsequentvirological failureintheAREVIR/RESINAcohort.

Figure 2: Risk of virological failure after low level viremia according to the drug class.Virological failure rates present the percentages of the drug related low level viremia prevalence.

Contact:[email protected]

1InstituteofVirology,UniversityofDüsseldorf,Germany;2TheComputationalBiologyandAppliedAlgorithmics Department,MaxPlanckInstituteforInformatics,Saarbrücken,Germany;3InstituteofVirology,UniversityofCologne,Germany;4 DepartmentofGastroenterology,HepatologyandInfectiology,UniversityofDüsseldorf,Germany;5ClinicforGeneralMedicine,

GastroenterologyandInfectiousDiseases,Augustinerinnen Hospital,Cologne,Germany;6DepartmentofDermatology,UniversityofDuisburgEssen,Essen,Germany

Figure 2 Risk of virological failure after low level viremia according the drug class

Figure 3: Risk of virological failure after low level viremia according to the drug.* Drug approval ≤ 2004; Virological failure rates present the percentages of the drug related low levelviremia prevalence.

RESULTSII§ Mostriskoflowlevelviremia(Figure2):

§ PI-basedtherapies:165/294(56.1%)§ NNRTI-basedtherapies:76/294(25.9%)

§ ComparableVFratesofNRTI-,NNRTI- andPI-basedtherapies(Ø=20%,range17.1-22.2%)(Figure2)

§ VFwasneverrelatedtoentryinhibitorsorintegraseinhibitors§ NoriskofVFsubsequenttoLLVwithdrugsapproved≥2005(p<0.001)

(Figures3and4)

Figure4:RiskofLLVandVFaccordingtodrugapproval

Figure 3: Risk of virological failure after low level viremia according to the drug

U.E.H. Haars1, N. Lübke2, B.E.O. Jensen1, D. Häussinger1

U.E.H. HaarsU.E.H. Haars1, N. LübkeU.E.H. Haars , N. Lübke2 B.E.O. Jensen, N. Lübke B.E.O. JensenB.E.O. JensenB.E.O. Jensen1, D. HäussingerB.E.O. Jensen1B.E.O. Jensen , D. Häussinger, D. Häussinger

Drug Resistance Mutations (DRM) among Pregnant HIV-Positive Women in the Duesseldorf University Hospital, Germany, 2009-2016

Background: Combination antiretroviral Therapy (cART) has resulted in significant reduction of mother-to-child-transmission (MTCT) from 40% to 1-2% in the last two decades. Choosing an individualized cART is one key factor for successful suppression of viral load until delivery. Thus, drug resistance testing during pregnancy before cART initiation or in case of increasing viral load is recommended. The Prevalence of DRM in pregnant women in Germany hasn't been characterised yet.

1 Heinrich-Heine-University, Department of Gastroenterology, Hepatology and Infectious Diseases, Duesseldorf , Germany 2 Heinrich-Heine-University, Institute for Virology , Duesseldorf , Germany

Table 1: VL=HI-Viral Load, HIV-1 Subtypes, Resistance Testing by Sanger Sequencing and Next generation Sequencing (NGS), PR= Protease Mutations, RT= Reverse Transkriptase mutations, ART History

References: 1. Noguera-Julian M, Cozzi-Lepri A., di Giallonardo F et al. ; CROI 2014; Poster 600 2. Oette M. et al.; Intervirology 2012;55(2):154-9

Figure 1: Origin of n=85 HIV-positive pregnant women

Materials and Methods: From 01/2009 to 03/2016 HIV-Drug-Resistance was observed in HIV-positive pregnant Women in our special consultation for pregnancies in the Department of Gastroenterology, Hepatology and Infectious Diseases in the Duesseldorf University Outpatient Clinic. The Genotypic Resistance Testing was done concerning German-Austrian pregnancy guidelines either during their first visit when they were treatment naïve or being already on cART with detectable HI-Viral Load. Resistance Testing was performed by using Sanger Sequencing and Next generation Sequencing (NGS) by means of Illumina MiSeq-technology. Resistance Interpretation was performed by the HIV-Grade HIV-1-Tool (www.hiv-grade.de)

Results: Data of 85 HIV-positive pregnant women and 103 live births were analysed. The majority ( 88%) of these women were migrants, 75% (64/85) from Subsahara Africa, 12% (10/85) South-East-Europe, 12% (10/85) from Germany and 1% (1/85) from Asia. In 34% (29/85) they had their HIV diagnosis in their first pregnancy, in 66% (56/85) the diagnosis was upraised independently from their first pregnancy. In 64/85 cases (75%) resistance testing was requested, with 61/64 ( 95%) successful analyses. The majority of the patients were infected with non-B-Subtypes (54/61, 88%), mainly 02_AG (23/61, 38%), followed by C (8/61, 13%) and A (7/61, 11%) (Figure 2) . In 14/61 (23%) resistance tests DRM were found (Table 1), in 9/14 due to ART-history. Patients No. 1 and 4 received PMTCT in Africa, Patient 5 was perinatally infected. 5/14 patients (No. 2,7,8,12 and 13) were Therapy-naïve with presumably transmitted DRM (tDRM) or in Patient No. 8 DRM due to immunological mechanisms like APOBEC3G/F ( M184I, M230I) [1]. 5/14 patients contained a 2-class-resistance against NRTI/NNRTI. Most common mutations were: M184VI (5/14), T215Y/F/N (4/14), Y181C (3/14) and K103N (3/14). NGS-analysis showed additional mutations in 2/14 patients in comparison to Sanger: in Patient No. 1 (T215FY) and in Patient No. 8 (M230I) to Sanger DRM. In 2/14 Therapy-naïve patients tDRM could be shown only in NGS-sequencing : the revertant T215N in patient No. 12 and the K65R in patient No. 2. No case of MTCT has been observed.

References:

Conclusions: In 23% (14/61) of all HIV-positive pregnant women in our study DRM have been observed, in 8% tDRM (5/61). The prevalence of tDRM in pregnant women in our population is lower than in general German population of HIV-positive individuals [2]. Using resistance testing by NGS resulted in the identification of additional relevant DRM compared to Sanger. Considering the importance of viral load suppression in Pregnancy and the limited amount of time to achieve this goal, the choice of cART should be optimal and take these mutations into account. Especially women from Subshara Africa harbour the risk of tDRM because of the cART regimen in High prevalence countries. The number of drug Resisance testing in developing countries is increasing. Genotypic Resistance Testing should be therefore considered for all pregnant women to optimize the success of cART and hence prevent mother to child transmission.

02_AG 28%

06_CPX 2%

A 9%

B 9%

C 10%

D 1%

F 3%

G 6%

J 1%

K 1%

n.d. 27%

neg. 3% n=85

Ivory Coast 1%

Gambia 1%

Morocco 1%

Namibia 1% Tansania

1%

Thailand 1%

Eritrea 1%

Angola 3% Guinea

3%

Mosambik 3%

Kenia 6%

Kongo 6%

Togo 12%

Ghana 13%

Germany 14%

South-East Europe 14%

Nigeria 16%

Figure 2: Resistance Testing in n=85 HIV-positive pregnant women, Subtype distribution, n.d.= not done, neg.= no resistance result obtained

Prevalence of HIV type 1 drug resistance mutations in treatment-naïve patients participating in the GARDEL Study

Maria Inés Figueroa, Patricia Patterson, Pedro Cahn, Jaime Andrade-Villanueva, José R Arribas, José M Gatell, Javier R Lama, Michael Norton, Juan Sierra Madero, Omar Sued, Maria José Rolón, on behalf of the GARDEL Study Group*

BACKGROUND

Combination antiretroviral therapy has greatly reducedthe rate of morbidity and mortality among HIV-1 infectedpatients. However, high mutation and recombinationrates of HIV-1 lead to the emergence of various subtypesand drug-resistance viruses, rendering first line ARV-therapy ineffective in many patients.The aim of this sub study is to describe the prevalence ofHIV-1 subtypes and the patterns of drug resistancemutations among ARV-naïve HIV-1-infected patients fromsix different countries participating in the GARDEL Study

MATERIALS AND METHODS

543 naïve patients from 6 countries (Argentina, Chile,Spain, Mexico, Peru and US) were screened betweenDec-2010 to May 2012, and 534 HIV-sequences wereanalyzed following the IAS-USA 2014 Drug ResistanceMutations Panel. Genotypic assays performed atscreening visit were: PhenoSense HIV assay(Monogram Biosciences, San Francisco, CA, USA),ViroSeq HIV-1 (ViroSeq HIV-1 Genotyping System v2.0;Celera, Alameda, CA), TRUGENE® HIV-1 GenotypingAssay (Siemens Healthcare Diagnostics), according toavailability at each site.

RESULTS

Of the 534 patients screened, 74% were Hispanic/Latino.Median time of infection at SCR was: 10.5 months. CDCstage A: 82%. Of 450 viral subtypes available, the mostfrequent was subtype B in all three regions (Fig 1) A totalof 113 samples (21.2%) had major resistant mutations; 22samples (4.1%) had major protease mutations (M46I wasthe most common mutation: 1.5%), 85 samples (15.9 %)had NNRTIs mutations (K103N/S was the most commonmutation: 4.9%), and 17 samples had mutations to NRTIs(3.2%) ,M41L (1.3%) was the most common mutation toPIs, only 2 patients had more than one mayor mutation(2/22)(Fig 2). The more frequent minormutationswere:M36I/L/V(216/534),L63P (120/534),L10I/F/V/R (115/534) and K20R/M/I:59/534. The globalresistance analysis by regions showed 21% for LA, 22.8%for US/Mexico and 14.7% for Spain, being NNRTIresistance by regions 16.4%; 15.4% and 11.8%respectively. PI resistance was 3.1% for LA and Mexico/USand NRTI resistance was 3.1% for LA, 3.4% for US/Mexicoand 2.9% for Spain. No Q151M, 69ss or K65R wereidentified.(Fig3)

CONCLUSIONS

In our study we found a primary resistance rate of 21.2%, similar in LA and US/Mexico but lower in Spain. Levels of NNRTIresistance are similar in the three analyzed regions, as previously reported in naïve populations, and reinforces the need ofperforming genotypic testing in ARV naïve patients, especially in LA were the first line therapy is still based on NNRTI drugs

Author correspondence: María Inés Figueroa [email protected]

72%

92%

91%

LA

US/MEX

SPAIN

HIV-1 subtypes

B other

LA US/MEX Spain

Global resistance analysis by regions 21% 22% 14%

Pis* 3,1 3,1 none

NNRTs 16,4 15,4 11,8

NRTIs 3,1 3,4 2,9

* major protease mutations

3,2%

15.9%

4.1%

INTR NNRTI IPFig 2: Global resistance by drug class

(Fig 3)

The highly effective antiretroviral therapy has changed the natural history of HIV /aids, delaying thedisease progression and improving the quality of life of the infected individuals. In treated HIV-1population in Cuba, several factors might have contributed to high drug resistance levels such asprescription of suboptimal regimens containing non-boosted PI, prolonged exposure to failing therapiesdue to limited access to laboratory monitoring and limited options for antiviral drug substitutions ifrequired. This might also result in the subsequent spread of drug resistant strains. The performedstudies in untreated population have shown high levels of HIV resistance to the antiretroviral therapyranging from 12% to 21%. The aim of this study is determine the levels of primary HIV drug resistance innewly diagnosed patients Cubans on a representative sample of the country.

1–99 aa OF PR AND 1–335 aa OF RT

THE PREVALENCE OF GENOTYPIC DRUGRESISTANCE WAS ANALYZED USING THECALIBRATED POPULATION RESISTANCE (CPR)TOOL VERSION 6.0 AND BASED ON THESURVEILLANCE DRUG RESISTANCE MUTATION(SDRM) LIST 2009 (BENNETT ET AL., 2009).

HIV-1 SUBTYPING: REGA SUBTYPING TOOL VERSION 3

SAMPLE PROCESSING1 ML OF PLASMA WASULTRA-CENTRIFUGED AT20,000 X G FOR 1 H

RNAEXTRACTION

RT-PCR ANDNESTED PCR

PCRPURIFICATIONPRODUCT

SEQUENCE REACTIONTHE SEQUENCES PRODUCTS:CEQ 8800 GENETIC ANALYSISSYSTEM

THE ELECTROPHEROGRAMS WEREDISPLAYED,AND ASSEMBLED. SEQUENCESWERE MANUALLY EDITED USINGSEQUENCHER VERSION 4.10.1 AND HIV-1SUBTYPE B STRAIN HXB2 AS A REFERENCE.

CHARACTERISTICS OF SAMPLES WITH EVIDENCE OF TRANSMITTED DRUG RESISTANCE

This study confirms the high levels of resistance in untreated population, it demonstrates the commitment of first-line therapies used in the country and could put at risk future therapies tokeep or increase these figures. It highlights the need for studies to elucidate the factors that are influencing detected high levels of resistance in newly diagnosed population in order to takeaction or to correct the behavior or factors involved in the phenomenon. It also shows the need for resistance testing in patients who are starting the therapy.

STUDY DESIGN CROSS-SECTIONAL STUDY. APRIL 2013-APRIL 2014

263 INDIVIDUALS NEWLY DIAGNOSED WITH HIV-1 INFECTION. REPRESENTATIVE FOR

THE ALL COUNTRY

patients Cubans on a representative

Experiments were successful for 189samples from 263 patients The mean ageat sampling was 33.5 years (17-74), the80.9% of the patients were men and themajor transmission route was the MSM(80.3%).The 27.5% of patients had chronicinfection and 72.4% recent infection. Thehighest number of analyzed samples wasfrom Havana with 38.6%, followed by theeastern region of Cuba (29.6%), theMidwest (16.9%) and finally the westernregion of the country (14.8%). The medianvalue viral load at the time of sampling was58 000 RNA copies/mL (16 700-127 000)and median CD4 count value was 371cells/mm3 (270-573).

In the 17.4% (33/189) of the studiedviruses, transmitted resistance mutationswere detected. Simple non-nucleosidemutants contributed the highest amount(45.5%), followed by double classresistance against NRTI and NNRTI(27.3%) and single mutants to the PRI (12.1%).

The most common mutations associated with resistance toNRTI were M184V (24.2%) followed by thymidine analoguemutations (TAMs) such as L215Y (12.1%), K219N/Q (9%),D67N (6%), M41L (6%). For NNRTI, K103N (45.4%), Y181C(30.3%) and G190A (9.1%) were the most frequentmutations. The most prevalent PI was D30N (6%).

Conversely to that reported so far in the Cuban epidemic, wheresubtype B was the genetic form predominating, the BG recombinantsresulted the most frequent subtype detected (28%), followed bysubtype B (24%), CRF19_cpx (20%), the unique recombinant forms(URF) (11%) and CRF18_cpx (10%), although other subtypes were alsopresent.

From the 33 patients with TDR, 22 (66.6%) were HSH, 26 (78.8%) were diagnosed with a recent HIV-1 infection, 13 (39.4%) are fromHavana and 9 (27.2%) were infected with CRF19_cpx.

Type of infection Year Region ofresidence

HIV-1 subtype Sex Route oftransmission

TDR mutations

NRTI NNRTI PIChronic 26 HO B F HT K103NRecent 20 PR CRF24_BG F HT G190A N83DRecent 51 LH CRF19 M MSM M41L, M184V,T 215CY K103N

Chronic 30 LH CRF20_BG F HT K103NRecent 31 LH Recombinant M HT K103NRecent 23 IJ Recombinant M HT L23IChronic 56 CM CRF18 F HT K103NRecent 28 GT B M MSM D67N, M184V K103NRecent 39 PR CRF23_BG M MSM D67N, M184V, K219N Y181C

Recent 58 LH CRF19_cpx M MSM K103NChronic 45 LH CRF18_cpx M MSM K103NRecent 23 LH Recombinant M MSM K101ERecent 23 LH CRF19_cpx M MSM K219N Y181CRecent 25 LH B M MSM L74V, M184V K103NRecent 24 CM CRF24_BG M MSM L210W, T215Y K103N, Y181CRecent 20 LH CRF19_cpx M MSM I47VRecent 31 AR CRF20_BG F HT M184V Y181CRecent 22 CM CRF18_cpx M MSM K101E, G190ARecent 37 CA G M MSM Y181CRecent 19 CA CRF19_cpx M MSM M41L, L74V, M184V, T215SY K103N D30N

Recent 57 PR CRF19_cpx M MSM L74V, M184V, T215Y K103N D30N, N88D

Chronic 17 SC CRF20_BG F HT M184V K101E, K103N, G190A

Recent 29 CM B M MSM K103N Recent 37 LH CRF18_cpx M MSM L90MRecent 19 HO B F HT Y181CRecent 44 HO Recombinant M MSM K219Q Y181CRecent 22 LH CRF19_cpx M HT Y181CRecent 49 CM CRF20_BG M MSM G190ARecent 39 LT G F HT K103NChronic 42 HO CRF19_cpx M MSM F77LRecent 40 LH B M MSM K219QChronic 59 LH CRF19_cpx M MSM Y181CRecent 23 AR Recombinant M MSM F53L

Characteristics of the new diagnostic HIV-1 infected study population, April 2013-April 2014

The detection of a mutation transmitted resistance to ARVswas associated with VL over 100 000 copies/mL (p = 0.025;OR = 2.464 (1.148 - 5.288)).The DRMs, simple mutant, or triple, were not associated withany other of the variables (type of infection, sex, sexualbehavior, subtype, region or CD4 count) .

ANY MUTATIONS NRTI NNRTI PI NRTI+NNRTI NRTI+NNRTI+

PIRI 78.8 3 30.3 12.1 24.2 9.1CI 21.2 3 15.1 0 3 0

0

20

40

60

80

100

120 %

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6.1

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Any mutations NRTI NNRTIPI NRTI+NNRTI NRTI+NNRTI+PI

%

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9.1

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6

0

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M184V T215Y K219N/Q K103N G190A Y181C D30N

% NNRTI

IP

NRTI

Subtype B24% Subtype C

2%

Subtype G4%

Subtype H1%

CRF18_cpx10%

CRF19_cpx20%

CRF20-23-24_BG 28%

URF 11%

Background Material and Methods

Results

Conclusion

Viroseq protocol optimized for the detection of HIV-1 drug mutations in patients with low viral load.

Genotypic resistance testing is paramount for themonitorization of the emergence of antiretroviral drugresistant virus. The Viroseq HIV-1 genotyping system v2.0is an IVD assay for sequencing of HIV-1 from plasma butonly feasible if the viral load is at least 1000 cp/mL.However, some patients have a persistent low HIV-1viraemia inferior to 1000 cp/mL, being resistance testingand antiretroviral therapy hampered by this. So, for theirclinical management, resistance testing solutions must bemade available1. With this regard we developed an inhouse assay, adapting the Viroseq v2.0 with a nested-PCRprotocol.

Blood samples from 36 patients on HAART with a viral loadbetween 20 cp/mL and 1000 cp/mL (range 36-934 cp/mL;mean = 357 cp/mL) were collected in K3EDTA and theplasma separated 6 h after sampling and stored at -80°C.HIV-1 was concentrated by centrifugation of 1 mL of plasmaat 24,000g for 1 h at 4ºC. After removal of the supernatant, 1mL of plasma was added and the sample thoroughlyhomogenized. RNA extraction was performed in theQIASymphonySP equipment from QIAGEN (Hilden, Germany)using the QIAsymphony Virus/Pathogen Mini Kit and an inhouse protocol, rendering a final volume of 30 μL. TheViroseq protocol was performed according to themanufacturer instructions, followed by a nested -PCRprotocol based on the previously described by N. Mackie etal.2 The 50 μL PCR mix contained 0,5 μM of each primer, 1xIncomplete NH4

+ Reaction Buffer (DFS-Taq DNA Polymerase– Bioron Life Science), 0,2 mM of deoxyribonucleotide, 2,5Units of DFS-Taq DNA Polymerase and 5 μL from theproducts of the first PCR. The PCR was performed on aPerkin Elmer PE9700 thermocycler and consisted on aninitial denaturation for 5 min at 95°C, followed by 40 cycles of95°C for 30s; 55°C for 30s, 72ºC for 120s and a extension at72º C for 7 min. PCR products were sequenced on the3130xl DNA Analyzer (Applied Biosystems) and analyzed inViroseq v2.8.

Viral RNA isolation

Sequence analysisSequencingPCRRT-PCR

Nested PCR

YESAMPLIFICATION?

Sequencing and drug resistance testing was successful in70% (9/13) of the samples with a viral load 36-200 cp/mL;in 93% (13/14) of the samples comprising 200-500 cp/mLand in 100% (9/9) of the samples with 500-1000 cp/mL.

Viral Load Successful Sequencing36-200 cp/mL 70%

200-500 cp/mL 93%

500-1000 cp/mL 100%

Figure 1: Detection of amplification product after PCR. No detectable amplification for Sample 8 and low amplification for Sample 18.

Figure 2: Amplification products of samples 8 and 18 after execution of the nested PCR protocol.

Genotypic resistance testing is essential for themonitorization of the emergence of antiretroviral drugresistant virus being necessary the development of assaysfor patients with low viral loads.

Fátima Monteiro1, Gilberto Tavares1, Marina Ferreira1, Ana Amorim1, Pedro Bastos1, Carolina Rocha1, Dina Hortelão1, Claudia Vaz1, Rosário Serrão2, António Sarmento2, Fernando Araújo1, M. Carmo Koch1

1 Molecular Biology Center, Blood Bank and Transfusion Department, Hospital S. João, Porto, Portugal2 Infectious Diseases Department, Hospital S. João, Porto, Portugal

1. Ryscavage P , Kelly S , Li Z , Harrigan PR, Taiwo B. Significance and clinical management of persistent low-level viremia and very-low-level viremia in HIV-1-infected patients. Antimicrob Agents Chemother. 2014 Jul;58(7):3585-98.2. Mackie NE, Phillips AN, Kaye S, Booth C, Geretti AM. Antiretroviral drug resistance in HIV-1-infected patients with low-level viremia. J Infect Dis. 2010 May 1;201(9):1303-7.

HIV Drug Therapy Glasgow 201623-26 October 2016

Background Material and Methods

ResultsResults

Conclusion

Viroseq protocol optimized for the detection of HIV-1 drug mutations in patients with low viral load.

Genotypic resistance testing is paramount for themonitorization of the emergence of antiretroviral drugresistant virus. The Viroseq HIV-HIV-HIV 1 genotyping system v2.0is an IVD assay for sequencing of HIV-HIV-HIV 1 from plasma butonly feasible if the viral load is at least 1000 cp/mL.However, some patients have a persistent low HIV-HIV-HIV 1viraemia inferior to 1000 cp/mL, being resistance testingand antiretroviral therapy hampered by this. So, for theirclinical management, resistance testing solutions must bemade available1. With this regard we developed an inhouse assay, adapting the Viroseq v2.0 with a nested-PCRprotocol.

Blood samples from 36 patients on HAART with a viral loadbetween 20 cp/mL and 1000 cp/mL (range 36-934 cp/mL;mean = 357 cp/mL) were collected in K3EDTA and theplasma separated 6 h after sampling and stored at -80°C.HIV-HIV-HIV 1 was concentrated by centrifugation of 1 mL of plasmaat 24,000g for 1 h at 4ºC. After removal of the supernatant, 1mL of plasma was added and the sample thoroughlyhomogenized. RNA extraction was performed in theQIASymphonySP equipment from QIAGEN (Hilden, Germany)using the QIAsymphony Virus/Pathogen Mini Kit and an inhouse protocol, rendering a final volume of 30 μL. TheViroseq protocol was performed according to themanufacturer instructions, followed by a nested -PCRprotocol based on the previously described by N. Mackie etalal.2 TheThe 5050 μLμL PCRPCR mixmix containedcontained 00,55 μMμM ofof eacheach primer, 1xIncomplete NH4

+ Reaction Buffer (DFS-Taq DNADNA Polymerase– Bioron Life Science), 0,2 mM of deoxyribonucleotide,deoxyribonucleotide, 2,5Units of DFS-Taq DNA Polymerase and 5 μL from theproducts of the first PCR. The PCR was performedperformed on aPerkin Elmer PE9700 thermocycler and consistedconsisted on aninitial denaturation for 5 min at 95°C, followed byby 40 cycles of95°C for 30s; 55°C for 30s, 72ºC for 120s and aa extension at72º C for 7 min. PCR products were sequencedsequenced on the3130xl DNA Analyzer (Applied Biosystems) andand analyzed inViroseq v2.8.

Viral RNA isolation

Sequence analysisSequencingSequencingPCRRT-RT-RT PCR

Nested PCR

YESAMPLIFICATION?

Sequencing and drug resistance testing was successful in70% (9/13) of the samples with a viral load 36-200 cp/mL;in 93% (13/14) of the samples comprising 200-500 cp/mLand in 100% (9/9) of the samples with 500-1000 cp/mL.

Viral Load Successful Sequencing36-200 cp/mL 70%

200-500 cp/mL 93%

500-1000 cp/mL 100%

Figure 1: Detection of amplification product after PCR. No detectable amplification for Sample 8 and low amplification for Sample 18.

Figure 2: Amplification products of samples 8 and 18 after execution of the nested PCR protocol.

Genotypic resistance testing is essentialessential for themonitorization of the emergence of antiretroviralantiretroviral drugresistant virus being necessary the developmentdevelopment of assaysfor patients with low viral loads.

Fátima Monteiro1, Gilberto Tavares1, Marina Ferreira1, Ana Amorim1, Pedro Bastos1, Carolina Rocha1, Dina Hortelão1, Claudia Vaz1, Rosário Serrão2, António Sarmento2, Fernando Araújo1, M. Carmo Koch1

1 Molecular Biology Center, Blood Bank and Transfusion Department, Hospital S. João, Porto, Portugal2 Infectious Diseases Department, Hospital S. João, Porto, Portugal

1. Ryscavage P , Kelly S , Li Z , Harrigan PR, Taiwo B. Significance and clinical management of persistent low-level viremia and very-low-level viremia in HIV-level viremia in HIV-level viremia in HIV 1-infected patients. Antimicrob Agents Chemother. 2014 Jul;58(7):3585-98.2. Mackie NE, Phillips AN, Kaye S, Booth C, Geretti AM. Antiretroviral drug resistance in HIV-2. Mackie NE, Phillips AN, Kaye S, Booth C, Geretti AM. Antiretroviral drug resistance in HIV-2. Mackie NE, Phillips AN, Kaye S, Booth C, Geretti AM. Antiretroviral drug resistance in HIV 1-infected patients with low-level viremia. J Infect Dis. 2010 May 1;201(9):1303-7.

HIV Drug Therapy Glasgow 2016HIV Drug Therapy Glasgow 201623-26 October 2016

P. Columpsi (1), V. Zuccaro (1), P. Sacchi (1), S. Cima (1), S. Toppino (1), S. Paolucci (2), G. Comolli (2), F. Baldanti (2), M. Mariconti (1), R. Bruno (1)

1) Dipartimento di Malattie Infettive, Fondazione IRCCS Policlinico San Matteo, Pavia .2) Unità di Virologia Molecolare, S.C. di Microbiologia e Virologia, Fondazione IRCCS Policlinico San Matteo, Pavia

• Presepsin is a newly discovered soluble fragment of CD14 studied as a sepsis biomarker.

• The mechanism of its secretion is involved in the TLR4 activation cascade and it is related to

mCD14 and sCD14, which are monocyte activation markers, indirectly representing the

presence of bacterial translocation. Therefore Presepsin could be employed as an immune

activation marker, and it could allow for the estimation of bacterial translocation rates(1).

• The aim of this study was to assess the correlations between Presepsin serum concentration

and bacterial translocation, immune activation and fibrosis markers in subjects with HIV and

HCV mono-infections and in HIV/HCV co-infection, compared to healthy controls.

• This is a cross-sectional study included 80 subjects followed up at the

Department of infectious Diseases of Policlinico San Matteo, Pavia

University.

• The study population included patients with HIV mono-infection (n = 20),

HCV mono-infection (n = 20), HIV/HCV co-infection (n = 20), and

healthy controls (n =20). Peripheral blood was analyzed to determine

the levels of Presepsin, Forkhead box 3 (Foxp3+) T cells, TGF- β1,

CD14 (soluble and surface isoforms), IL-17 and bacterial translocation

products.

• These measurements were correlated to the severity of liver fibrosis,

measured with the FIB-4 score and transient elastography.

• This is a cross sectional study included 80 subjects followed up at the

• Presepsin concentration was significantly higher in the HIV patients (HIV monoinfected and HCV / HIV co-infected). The same group showed increased

levels of sCD14 and mCD14, expression of immune activation.

• Statistical analysis show a significant correlation between presepsin and both forms of CD14 only in HIV / HCV group, where the percentage of

bacterial translocation and chronic inflammation is high, as shown by the significant increase in bacterial DNA levels, sCD14, mCD14 and IL-17.

Presepsin is associated to FIB4 values in the HCV group.

Presepsin is a biomarker of chronic immune activation, as demonstrated by its correlations with sCD14, mCD14 and CD4+CD25+Foxp3+ lymphocytes,

particularly in HIV infection. Its concentration is correlated to liver fibrosis markers, such as FIB4, particularly in HCV mono-infected patients.

Considering presepsin and a direct correlation between the levels of fibrosis and an inverse correlation with Treg cells in this group, the low levels of

Treg cells may be involved in increasing the state fibrosis in chronic HCV patients.

Reference1. Yaegashi, Y., Shirakawa, K., et al.Evaluation of a newly identified soluble CD14 subtype as a marker for sepsis. Journal of Infection and Chemotherapy, 11(5), 234–238. doi:10.1007/s10156-005-0400-4 (2005).

The role of Presepsin (sCD14-ST) as an indirect marker of microbial translocation and immune activation

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CRF19_cpx variant emergence in a cluster in naïve patients of

southern Spain. Clinical and phylogenetic characterization González-‐Domenech, CM1; Viciana, I1,2; Mayorga, M3; Palacios, R1,2; de la Torre, J4; Jarilla, F5; Castaño, M3; del Arco, A5; Márquez,

M1,2; Clavijo, E2; *Santos, J1,2 1InsOtuto de InvesOgación Biomédica de Málaga (IBIMA), Spain; 2Hospital Virgen de la Victoria, UGC InfecOous Diseases and Microbiology, Malaga, Spain;

3Hospital Carlos Haya, InfecOous Diseases, Malaga, Spain; 4Hospital Costa del Sol, InfecOous Diseases, Marbella, Spain; 5Hospital de Antequera, Internal Medicine, Malaga, Spain.

*email: [email protected]

P-‐372

Background HIV CRF19_cpx has been described as a highly pathogenic recombinant from Cuba [1]. Furthermore, these infecAons are typically associated to higher viral load (VL) at diagnosis and rapid progression to AIDS [2]. Here, we describe the emergence of this CRF19_cpx variant in southern Spain, clustering in men having sex with men (MSM).

Material and Methods •  The study was undertaken at the Virgen de la Victoria Hospital, a reference center for the analysis of HIV-‐1 genotypic drug resistance in Malaga (Spain). •  Genotypic test was performed in 2298 naive paAents from four hospitals in 2011-‐2016.

•  The subtype for each FASTA sequence provided was assigned through REGA v3.0. Sequences consigned as a CRF19_cpx variant (Fig. 1) were confirmed by phylogeneAc analysis with other 195 reference sequences retrieved from LANL.

•  Protease and reverse transcriptase (RT) genes were aligned by ClustalX and the phylogeneAc reconstrucAon inferred by maximum likelihood method (RAxML).

•  The reliability of the clades was supported on bootstrapping, with 1,000 replicaOons.

•  For analysis of RT and protease resistance mutaAons Standford algorithm v7.1.1 was used.

•  AddiAonally, we collected epidemiological, clinical and inmunovirological data.

Results

Conclusions

1. CRF19_cpx variant has emerged affecAng MSM naïve paOents from southern Spain. 2. All cases but one are related to a local cluster. 3. Half of paOents showed the G190A resistance mutaOon. 4. Unlike previous studies, the variant from Malaga seems less pathogenic, with few cases of AIDS and excellent response to ART.

References [1] Casado G, Thomson MM, Sierra M, et al. IdenAficaAon of a novel HIV-‐1 circulaAng ADG intersubtype recombinant form (CRF19_cpx) in Cuba. JAIDS.2005; 40: 532-‐537. [2] Kouri V, Khouri R, Aleman Y, et al.CRF19_cpx is an EvoluAonary fit HIV-‐1 Variant Strongly Associated With Rapid Progression to AIDS in Cuba.EBioMedicine.2015; 2:244-‐54.

Fig.1. GeneAc organizaAon of CRF19_cpx variant reference strain.

CharacterisOcs n (%) Median age (years) 35.0 (26.3-‐41.5)

HIV transmission

MSM 48 (98.0) HTX 1 (2.0)

Studies background

No studies/primary school 6 (12.2)

Undergraduate 21 (42.9) University 13 (26.5)

Origin Spain 46 (94.0) ArgenOna 2 (4.0) France 1 (2.0)

Seroconversion Ome (months)* 24.8 (10.8-‐21.0) Lymphocyte CD4 nadir (cel/μL) 361 (254-‐416)

Baseline VL (log copies/mL)** 4.9 (4.5-‐5.4) Baseline lymphocyte CD4 (cel/μL) 388 (259-‐470) AIDS cases 3 (6.1) Death 1 (2.04) AnOretroviral therapy*** 46 (93.9)

The quanAtaAve variables are expressed as median (IQR) and the qualitaAve variables as n (%). MSM: Men sex with men; HTX: heterosexual transmission.

Table 1. CharacterisOcs of the 49 cases

*21 cases **Baseline VL was lower in paAents with G190A mutaAon (4.6 vs 5.1, p=0.02). **All the paAents treated with first-‐line ART regimens responded.

49 cases Prevalence: 2.1%

Fig 2. A). PhylogeneAc tree with CRF19_cpx reference sequences, and subtree containing the clustering of our 49 paAents obtained by ML. Only support values ≥70% are shown; B). Closest phylogeneAc relaAons with sequences from Genbank database.

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Cases of CRF 19_cpx variants over Ome

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Fig.3.

Cuba

Israel

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Bulgaria

A)

B)

HIVDrugTherapyGlasgow2016

One-StepReal-TimePCRforHIV-2groupAandBRNAplasmaviralloadinLightCycler2.0

PedroBastos1,FátimaMonteiro1,GilbertoTavares1,MarinaFerreira1,AnaAmorim1,CarolinaRocha1,DinaHortelão1,ClaudiaVaz1,RosárioSerrão2,AntónioSarmento2,FernandoAraújo1,M.CarmoKoch1

1 MolecularBiologyCenter,BloodBankandTransfusionDepartment,HospitalS.João,Porto,Portugal.2 InfectiousDiseasesDepartment,HospitalS.João,Porto,Portugal

Background

Although with a lower prevalence than HIV-1, HIV-2 isresponsible for localized epidemies, being Portugal the nonAfrican country with the greatest expression of the infection.Clinical management of the infection is hampered by the lack ofvalidated commercial RNA viral load assays, thus their in housedevelopment using the available equipment is mandatory.

Material and Methods

SamplesHIV-2 was confirmed by Innolia™ (Innogenetics, Gent, Belgium).Blood samples were collected in K3EDTA and the plasmaseparated 6 h after sampling and stored at -80°C. The BIOQ HIV-2 RNA group A quantification panel (Biocentric, Bandol, France)was used as an external standard.

HIV-2 RNA isolationRNA extraction was performed from 1000 μL of plasma in theQIASymphonySP (QIAGEN, Hilden, Germany) usingthe QIAsymphony Virus/Pathogen Mini Kit and a genericprotocol, rendering a final volume of 60 μL. RNA from thesamples and standards was isolated under the same conditions.

HIV-2 RNA quantification (RT-qPCR)The protocol was based on the previously described byAvettand-Fenoel et al.1 Primers and probes are described ontable 1. The one step RT-qPCR was performed on theLightCycler 2.0 (Roche Diagnostics, Mannheim, Germany) withthe Lightcycler RNA Virus Master kit from Roche (Roche LifeSciences, Mannheim, Germany) was used. The 20 μL reactionmixture contained 0,5 μM of each primer, 0,25 μM of eachprobe, 0,4 μl of Enzyme Blend and 7,5 μL of the isolated RNA.RT-qPCR cycling conditions consisted on 10 min at 60°C and 60sat 95°C, followed by 50 cycles of 95°C for 5s; 60°C for 50s and72ºC for 10 min.

Results

The standard curve generated by the LightCycler software(version 4.05) presented an efficiency of 2.079 (103%), an errorof 0.0657 and a r2 of 1.0 (Fig. 1). Besides the detection of Bsubtypes, this RT-qPCR provides a linear range between 5.03 x106 cp/mL and 5.03 x 102 cp/mL, adequate to the low HIV-2 viralloads in plasma.

To evaluate repeatability and reproducibility, clinical samplestested with the previous method and serial dilutions (105-102

cp/mL) of the NIBSC HIV-2 NIH-Z strain (AdvancedBiotechnology Incorporated, Columbia, Maryland) were testedin replicates (Fig. 2) in the same and in independent runs withdifferent operators, with a Ct CV lower than 0.28 and SD lowerthan 0.8 (data not shown).

Conclusion

This assay allows us to evaluate HIV-2 A and B subtypes viralload in plasma with satisfactory sensibility and reproducibility,supporting the clinical management of the infection.

References1. Avettand-Fenoel V, Damond F, Gueudin M, Matheron S, Mélard A, Collin G,Descamps D, Chaix ML, Rouzioux C, Plantier JC. New sensitive one-step real-timeduplex PCR method for group A and B HIV-2 RNA load. J Clin Microbiol. 2014Aug;52(8):3017-22.Table1.PrimersandProbessequences.

LTRregion Sequence(5’-3’)PrimerLTRF 5'-TCTTTAAGCAAGCAAGCGTGG-3PrimerLTRR 5'-AGCAGGTAGAGCCTGGGTGTT-3ProbeLTRP 5'FAM-CTTGGCCGGYRCTGGGCAGA-BHQ1-3GAGregion Sequence(5’-3’)PrimergagF3 F35'-GCGCGAGAAACTCCGTCTTG-3PrimergagR1 R15'-TTCGCTGCCCACACAATATGTT-3ProbeS65GAG2 5'FAM-TAGGTTACGGCCCGGCGGAAAGA-BHQ1-3

Figure2.RepeatabilityevaluationwithNIBSCHIV-2NIH-Zstrain.

Figure1.StandardcurvegeneratedintheLightCyclersoftware.

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