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Epidemiologic and molecular characterization of human immunodeficiency virus

type 1 in Southern Brazil

Esmeralda A.J.M. Soares1, Rodrigo P. Santos2, José Augusto Pellegrini2, Eduardo

Sprinz2, Amilcar Tanuri1 and Marcelo A. Soares1*

aLaboratório de Virologia Molecular

Departamento de Genética Universidade Federal do Rio de Janeiro

CCS – Bloco A – Cidade Universitária – Ilha do Fundão 21944-970 Rio de Janeiro, RJ, Brazil

Telephone: 55-21-2562-6384 Fax: 55-21-2562-6397

E-mail: masoares@biologia.ufrj.br

bHospital de Clínicas de Porto Alegre Universidade Federal do Rio Grande do Sul

Rua Ramiro Barcellos, 2350 - Bairro Rio Branco 90035-003 Porto Alegre, RS, Brazil

* To whom correspondence should be addressed

Work supported by the AIDS/STD National Program, Brazilian Ministry of Health, the

State Science Foundation of Rio de Janeiro Grant E-26/151.970/00, the Brazilian Council

for Scientific and Technologic Development Grant 462394/00-0.

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ABSTRACT

Human immunodeficiency virus subtype C is the most prevalent subtype in the

world. Despite its recent expansion in Brazil, HIV-1C already prevails in the

southernmost state of Brazil, Rio Grande do Sul. This unique HIV epidemiology has

prompted us to characterize that population. Seventy-seven HIV-1-infected subjects

attending the largest HIV/AIDS clinic of the state had their viruses’ protease and reverse

transcriptase (RT) genes subtyped and genotyped. When subtype-specific infections were

plotted according to year of diagnosis, the prevalence of subtype C was shown to increase

over the last 18 years of the epidemic along with a concomitant decrease of subtype B.

Comparison of subtype C-infected treated and untreated subjects revealed amino acid

differences in protease and RT, in special the RT mutation D/G123S. The overall analysis

of drug resistance mutations in viruses from treated subjects has highlighted some

associations between subtypes and particular mutations, such as V82A/F/T/S in protease

and subtype F1, and M41L and L210W in RT and subtype B. The characterization of this

important population, which is one of a few in the developing world where a large

number of HIV-1C-infected subjects are under antiretroviral treatment, underscores its

potential usefulness in clinical, treatment and vaccine trials in Brazil.

Keywords: HIV-1, subtype C, AIDS epidemiology, drug resistance, Brazil

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INTRODUCTION

Human immunodeficiency virus type 1 (HIV-1) subtype C is currently the most

prevalent subtype in the world, accounting for over half of the infected individuals

worldwide (1). It now dominates several areas of the world, such as southern Africa,

Israel, India and China, including countries previously dominated by other subtypes (2,3).

It is also increasing in incidence in some developed nations (4-7).

The dynamic nature of subtype C epidemic in the world highlights its importance

for HIV vaccine development (8,9). Subtype-specific immune responses have been

reported (10,11), and differences in the elicitation of responses by protective vaccination

might be important towards adequate vaccine strategies in different regions. Moreover,

differences in the kinetic parameters of non-B HIV-1 protease enzymes due to

polymorphic signatures of these viruses (12,13) may impact the long-term treatment of

non-B HIV-1 infection worldwide (14).

Differently from well-established countries where HIV-1 subtype C prevails,

there are regions where this variant is responsible for local endemic infections. Brazil can

be included in this profile. The prevailing subtype in the country is B, and subtypes F1

and C have been previously described on an isolated basis (15-17). However, a recent

nationwide analysis conducted by our group has shown that a large proportion of HIV-1

infections in southern Brazil, including the states of Rio Grande do Sul (RS) and Parana,

is represented by subtype C (prevalences of 45% and 30%, respectively; 18). That

subtype has also increased in Rio de Janeiro and Sao Paulo when compared to previous

surveys (19,20), suggesting a northward increase of subtype C in the country. In a more

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detailed analysis of those variants, we have shown that the introduction of HIV-1 subtype

C in Brazil was likely to represent a single event (21).

To characterize the HIV-1-infected population from southern Brazil, which

accounts for over 20% of the country’s infections, we have surveilled the city of Porto

Alegre, capital of RS. This city has the highest reported rate of subtype C infections in

the country, and it is the city where subtype C sequences was initially described (17). It is

the main site in Brazil, and perhaps one of a few in the world, where a large number of

HIV-1 subtype C-infected individuals are treated at large. We describe here HIV-1

subtype prevalences and drug resistance mutation profiles from a major clinical site in

Porto Alegre. Hospital de Clínicas of Porto Alegre has the largest HIV/AIDS cohort and

provides care for HIV-infected people in that city as well as adjacent municipalities.

METHODS

Population and sample collection

Seventy-seven individuals regularly followed at the HIV/AIDS ambulatory care

unit in Hospital de Clinicas de Porto Alegre with confirmed HIV-1-positive serological

status were included in this study. Both treated and drug-naïve individuals for whom

extensive information, such as the date of diagnosis, HIV viral load and CD4 counts over

the period of months to years, and all histories of clinical manifestations and treatment

were included. Five of the patients had incomplete information and were discarded from

population-related analyses, but included in HIV-1 subtype prevalence. Subjects signed a

written consent for their inclusion in the study, and blood draw was part of their regular

follow up visits. The study was approved by the Brazilian IRB (project no. 526 –

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CONEP). Table 1 summarizes relevant baseline features and clinical data of the

individuals analyzed. All collections were done between July and September 2002, and

their plasma separated, aliquoted and stored at -80oC until processing.

RNA isolation, PCR and sequencing

Viral RNA was isolated as previously described (22). Following cDNA synthesis

using randon primers, nested PCR were conducted for the amplification of an HIV-1 pol

gene fragment spanning the entire protease (PR) coding region and the first 225 codons

of the reverse transcriptase (RT) region. In cases where no contiguous fragment was

obtained, separated PCR reactions were conducted for PR and RT. Primers and PCR

conditions used in the reactions have been previously described (22). PCR products were

purified in QIAGEN columns (QIAGEN, Inc., Valencia, CA) and sequenced in an ABI

3100 Genetic Analyzer (Applied Biosystems, Foster City, CA). Sequences were

assembled in PC/Windows computers using the software SeqMan (DNAStar, Inc.,

Madison, WI). All sequences obtained were subject to quality control procedures (23) to

ensure lack of sample mix-ups or contaminations. Sequences were reported to the

GenBank database under accession numbers AY275717 to AY275807.

Phylogenetic and Drug Resistance Mutation Analyses

Sequences were subjected to phylogenetic analysis for HIV-1 subtype

determination. Sequences in FASTA format (24) were aligned with an HIV-1 subtype

reference set from the Los Alamos database (http://hiv-web.lanl.gov) in ClustalW (25)

and manually edited for codon alignment in the Genetic Data Environment for Linux

(26). Phylogeny was performed using the neighbor-joining method with the F84 model of

substitution in PAUP* 4.0b2a (27). The HIV-1 group O sequence MVP5180 (28) was

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used as outgroup. Sequences from which a contiguous PR/RT genomic region was

available for subtyping, but with unclear classification had the PR and RT regions further

uncoupled and separately analyzed. Sequences with discordant subtype assignment in PR

and RT regions were considered mosaics.

Subtype C PR and RT sequences were grouped according to treatment status

(treated or untreated) and subjected to VESPA analysis for consensi generation. Consensi

were then aligned in ClustalW to evidence potential differences in both sequence groups.

Primary and secondary drug resistance mutations (DRM) described for PR and

RT were analyzed using the Stanford Sequence Database (29, http://hivdb.stanford.edu).

Mutations were compiled according to the International AIDS Society USA consensus

(30).

Statistical Analyses

Differences in continuous characteristics of subtypes B and C-infected sub-

populations (HIV RNA viral load, CD4 T-cell counts, and mean ages and times of

infection) were evaluated by Kruskal-Willis ANOVA tests. Associations between HIV-1

subtypes and gender or treatment status, as well as the significance of differences in drug

resistance mutation frequencies among different HIV-1 subtypes, were evaluated by

Fisher’s exact tests. All statistical computations were performed in the package Analyze-

it® version 1.65 under Microsoft Excel for Windows XP.

RESULTS

Epidemiological Profile of the Population

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Major baseline characteristics of the 77 individuals analyzed from Hospital das

Clinicas can be seen in Table 1. Overall values, as well as B and C subtype-specific

infected groups are shown in the second and third columns, respectively. Fifty-six percent

of the individuals were female (male to female ratio of 0.8). Mean age was 39.1 years

(SD 10.4). Median CD4 cell count was 335.5 x 106 cells/l, whereas median HIV-RNA

level was 1,101 copies/ml. A large range of viral load was observed, from undetectable

(<80 copies/ml) to over 106 copies/ml. Most of the patients (60%) were at CDC clinical

stage A at the time of sampling, and a similar number was at CDC immunological stage

2. Eighty percent of the patients were drug-experienced. All treated patients had been

subjected to HAART, with exception of one patient under mono- and one patient under

dual therapy. Of the remaining 54 patients, 7 had only experienced NNRTI-containing

regimens in addition to NRTI, 23 had IP-containing regimens, and 24 had experienced

both classes of drugs. The average number of drugs taken by patient among the treated

subjects was 5.5, whereas the average duration of therapy was 41.8 months (range 1-

109). There were no significant associations between CD4 cell counts, viral load, mean

age or time of infection from diagnosis to collection date and infecting subtype (p-values

of 0.5716, 0.1118, 0.4684 and 0.5320, respectively). Furthermore, no association between

gender or treatment status and infecting subtype was found (p-values of 0.5168 and

0.2222, respectively). However, subtype B-infected subjects seemed to have longer drug

exposure than subtype C-infected counterparts (46.6 versus 34.8 months, respectively).

PCR products from all patients were obtained (PR and RT, PR alone or RT

alone). Fifty-eight patients (75%) had both PR and RT sequenced, whereas for 6 (8%)

patients only PR was sequenced, and for 13 (17%) patients only RT was obtained.

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Phylogenetic analysis of these fragments allowed HIV-1 subtype assignment for all

isolates (Table 1). A large number of subtype C viruses (40%) were observed, almost as

much as subtype B viruses (45%). Subtype F1 represented 12% of the samples. Two

mosaic viruses (with discordant phylogenies for PR and RT) was observed, accounting

for only 3% of the samples. These mosaic forms were not representative of the two most

prevalent subtypes in that area (B and C), but were rather similar to intersubtypic mosaics

previously characterized in Brazil, namely B/F and B/D (31,32).

HIV-1 Subtypes across the AIDS epidemic in Porto Alegre

Previous studies have suggested an increasing incidence of subtype C in the South

of Brazil (18,21), but we were able to analyze subtype prevalence over time in the city of

Porto Alegre. Samples were stratified by year of diagnosis and by infecting subtype (B,

C, F1 and mosaics), and then grouped in 5-year periods (1986-1990, 1991-1995, 1996-

2000, and 2001-2002). Figure 1 depicts that analysis. Our first subtype C virus appeared

as early as 1990, and to our knowledge this is the oldest subtype C infection described in

Brazil. Prior to that year, only subtype B-related viruses were detected. By 1991, the first

subtype F1 virus appeared in our dataset, again confirming the idea of a more recent

expansion of non-B subtypes in Brazil. A clear trend in the increase of subtype C

prevalence over the epidemic was observed: none prior to 1990, 39% from 1991 to 1995,

38% from 1996 to 2000, and 43% from 2001 and 2002. Direct comparisons between any

two consecutive time periods did not show statistical significance (data not shown).

Subtype C-Specific Mutational Analysis

To evaluate the impact of ARV treatment on PR and RT mutations in subtype C

viruses, consensi sequences from treated and untreated subjects were generated and

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compared. In the PR region, only codon 15 differed between consensi (an isoleucine

(60%) among untreated sequences and a valine (84%) among treated sequences). In RT,

4 codons differed: 123 (an Asp/Gly (50%) in untreated and a Ser (54%) in treated); 135

(a Thr (75%) in untreated and an Ile (58%) in treated); 173 (an Ala (50%) in untreated

and a Lys (50%) in treated); and 177 (a Glu (50%) in untreated and an Asp (54%) in

treated). Of those differences, only the presence of a Ser at position 123 of RT from

treated subjects was statistically significant (p=0.021).

Drug Resistance Mutation Profile of the Population

The availability of treated subtype C-infected individuals in our study prompted

us to assess the prevalence of primary drug resistance mutations (DRM) in those

sequences. Patients already initiated on ARV treatment were subjected to DRM analysis.

Primary DRM associated with protease inhibitors (PI) and all mutations associated with

RT inhibitors (RTI) present in the International AIDS Society consensus (30) were

compiled. Although PI-associated secondary DRM are also important and contribute to

drug resistance, the high incidence of subtype C samples in this population would

artificially bring those numbers to high values, as several of these mutations are natural

polymorphisms in that subtype (21). For that reason, they were not analyzed in this study.

Tables 2 and 3 summarize all primary PR and RT DRM identified in each subtype group.

Among 58 treated patients, 25 (43.1%) of their viral isolates showed DRM to at least one

of the three classes of inhibitors (PI, NRTI or NNRTI). Six isolates (10.3%) had DRM for

2 different ARV classes, whereas 5 (8.6%) had DRM for all three classes. Among 43

isolates for which the PR sequence was available, 11 (25.6%) had one or more primary

PI-associated DRM. Twenty-one (44.7%) of those for which RT sequence was available

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(n=47) had at least one NNRTI-associated mutation, whereas 9 (19.1%) had one or more

NNRTI-associated mutations.

The most prevalent PI-associated DRM was V82A/F/T/S (20.9%), followed by

M46I/L (11.6%) and L90M (9.3%). Mutations G48V, I50V and I84V were each

represented in one isolate. The mutation D30N was not found in any isolate analyzed in

this study, despite the use of the PI nelfinavir by 33.9% of the treated patients. Among

the NRTI mutations, the most prevalent was M184V (21.3%), followed by M41L and

T215Y/F (14.9%), V75I and K219Q/E (12.8%) and L210W (8.5%). Other mutations

appeared in lower frequencies, while mutations F77L and Y115F were not present in any

isolate. Finally, the main NNRTI-associated mutations found were K103N (14.9%),

L100I (8.5%) and V106A (4.2%). Mutations V108I and Y188C/H/L were not found

among our isolates. All observed mutations were compatible with their patient’s drug

treatment histories, with exception of three individuals (all subtype B-infected); one had

L74V but no use of ddI, ddC or ABC; one had T69D but no use of ddC; and one had

G190S but no use of NNRTI. When average numbers of DRM per genomic fragment

were calculated for each subtype, some differences were observed. In the RT, subtype B

(n=24) had a total of 54 DRM, and an average of 2.24 mutations per isolate, whereas

subtype C sequences had an average of 0.80 mutations per isolate (13 mutations in 17

isolates). Similarly, subtype B sequences had an average of 0.52 mutations per isolate in

the PR region, as compared to 0.07 for subtype C.

Fisher’s exact test was applied to evaluate the significance of selected DRM

comparing any two subtypes and/or B versus non-B subtypes. Of note, mutation

V82A/F/T/S of PR was significantly more frequent in subtype F1 than in subtype B

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(p=0.0209). Similarly, mutation M41L of RT was significantly more frequent in subtype

B than in subtype C viruses (p=0.0308). This mutation was also more significantly found

in B subtype when compared to all non-B counterparts (p=0.011). We were not able to

find statistical significance in the difference between the occurrence of mutation L210W

in B and non-B subtypes, although a trend was observed towards a higher frequency in

subtype B (p=0.0554). No viruses isolated from drug-naïve individuals from our study had any primary

PI or RTI-associated mutations.

DISCUSSION

HIV/AIDS epidemic in Brazil is complex compared to other Latin American

countries regarding subtype distribution and prevalences. Early studies have shown a

predominance of subtype B, followed by subtype F1 and only sporadic instances of other

subtypes, such as C, D and A (15-17). More recently, however, we have described a

complex geographical pattern of HIV-1 subtype distribution in Brazil (18). We found that

subtype C is highly prevalent in the south of Brazil, ranging from 25% to 45% (18). In

the southeastern region, smaller frequencies of subtype C are found (5 to 10%), whereas

in the rest of the country it is virtually absent. In view of this complex distribution, and of

the importance of subtype C in the worldwide HIV/AIDS epidemic, we characterized the

HIV-infected population from the south of Brazil. We have chosen the largest AIDS

service in Porto Alegre, the capital of the state previously characterized with highest

proportion of subtype C in Brazil.

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We found a high frequency of subtype C-harboring individuals (40%; Table 1), in

agreement with Brindeiro et al. (18). The slightly higher prevalence of 45% reported by

Brindeiro et al. is probably due to the fact that their analysis has been done on recently-

diagnosed (and likely recently-infected) individuals in 2001-2, and our current analysis is

based on total prevalence over 18 years (from 1986 to 2002). These data add up to the

growing body of evidence suggesting that subtype C is increasing in prevalence since its

introduction in the south of Brazil. Previous data on AIDS patients survival rates in the

city of Rio Grande, another city in RS state, have shown a prevalence of 22% for subtype

C viruses (33). When we stratify our dataset by year of diagnosis, we see a clear trend

towards a decrease of subtype B prevalence over time, accompanied by an increase of

non-B subtypes, in particular of subtype C, as well as of intersubtypic mosaics (Figure 1).

Although our data did not reach statistical significance, we are currently analyzing more

isolates, and soon we will be able to more rigorously test this hypothesis. All these data

together strongly suggest a shift in the HIV-1 subtype epidemiology over time in the

AIDS epidemic in RS. Noteworthy, we have also reported in this study the oldest isolate

of HIV-1 subtype C in Brazil, from an individual diagnosed in 1990.

A very small amount of mosaics (2/77, 3%) was present in our dataset, when

compared to what Brindeiro et al. (18) have described for RS in 2001-2 (25%). Despite

the similar subtype prevalence, the only two mosaics observed in our study were a B/F1

and a B/D virus, also differently from Brindeiro et al., which described most of the

mosaics being B/C recombinants. Although we cannot rule out the possibility that our

“pure” viruses are in fact mosaics with discordant subtypes in other genomic regions

(e.g., gag or env), the pol regions analyzed were the same as the ones studies by

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Brindeiro et al. (18). This contrast argues in favor of a very recent start of intersubtypic

recombination events, with most of the B/C mosaic viruses being formed in the last few

years, as it has been previously suggested (21).

The universal free access to ARV drugs in Brazil has created a particular situation

in the south of the country where large numbers of subtype C-harboring individuals are

under treatment. We were able to compared subtype C sequences from treated and

untreated subjects. Of significance, we found that 54% of our subtype C sequences from

treated subjects had a serine residue at codon 123 of RT, whereas that residue was not

found in drug-naïve sequences of that subtype. A more careful analysis of ARV histories

from viruses with that substitution revealed that all had taken ZDV and/or 3TC. An

analysis of 30 subtype C-infected patients undergoing NRTI-based therapy from Stanford

database has also shown that 43% of them, but only 20% of drug-naïve isolates, harbored

that mutation. Those differences were also statistically significant (p=0.006). The

analysis of over 1,300 NRTI-treated and 665 untreated subtype B sequences from the

Stanford database revealed that both groups had very low and also similar (1-2%)

prevalences of 123S. It is possible that this mutation is only selected in the context of a

subtype C background under treatment, but a more extensive analysis with in vitro

phenotyping will be required to elucidate this issue.

We also assessed the patterns of known drug resistance mutations to currently

available ARV drugs among treated patients. Some significant subtype-specific

differences could be observed. Mutation V82A/F/T/S of PR was more frequent in

subtype F1-infected subjects than to subtype B counterparts (p=0.0209). This result

seems to corroborate previous observations by Caride et al. (34) in non-B-infected

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individuals failing antiretroviral therapy. Their difference did not reach statistical

significance, probably due to the low number of isolates analyzed. In our study, however,

we were able to highlight that difference with statistical confidence for the first time.

Mutations in codon V82 do not seem to be significantly different between subtypes B and

C, corroborating previous observations (35), and may indeed be a specific feature of

subtype F1 and other subtypes not yet analyzed. However, it is important to note that our

number of subtype F1 sequences was still low and the analysis of larger numbers should

be done to draw definitive conclusions. In RT, mutation M41L was significantly more

frequent in subtype B than in subtype C (p=0.0308), and this difference was even more

significant when comparing subtypes B and non-B (p=0.011). Finally, mutation L210W

seemed to be more associated with non-B than with B subtype, although only a trend was

observed (p=0.0554). The importance of RT-associated drug resistance mutations in

different HIV-1 subtypes is less well known and understood than those of PR mutations

and only a few studies have been conducted to look at this issue (35-37). Of note, the

study by Grossman et al. has also noticed a statistically significant association between

mutations M41L and L210W with subtype B when compared to subtype C (35). It was

hypothesized that this difference could be due to differences in times of previous

exposure to NRTI mono- and/or dual therapy between both subtypes. Indeed, the average

times under therapy for each group in our study differed (46.7 months for subtype B and

34.8 months for subtype C), and this could explain higher rates of NRTI-associated

mutations in subtype B. The current inclusion of more isolates to this study and the

matching of NRTI exposure times among both groups will be needed to clarify this issue.

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The idea that the efficacy of selected ARV drugs might be influenced by HIV-1

genetic polymorphisms and by specific baseline sequence patterns of different HIV-1

subtypes is of increasing concern (34-37). The similar prevalences of subtypes B and C in

our casuistic makes southern Brazil a perfect setting for clinical, treatment and vaccine

trials, where a control group of subtype B-infected individuals with similar socio-

demographical and ethnical characteristics is readily available.

ACKNOWLEDGEMENTS

We are in profound debt to Caroline Rech, Giancarlo Marafon and Mauricio Kunz

from Hospital de Clinicas de Porto Alegre for helping with subjects’ follow-up, study

enrollment and sample collection, and to Andre F.A. Santos and Thatiana M. Souza from

Universidade Federal do Rio de Janeiro for helping with processing and sequencing of

the samples.

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21

Table 1. Baseline features of HIV-1-infected individuals included in this study TOTAL SUBTYPE B SUBTYPE C Mean Age (yr) (SD) 39.1 (10.4) 38.4 (10.9) 40.0 (10.2)

Sex (%)

Male 32 (44%) 17 (49%) 10 (37%)

Female 40 (56%) 18 (51%) 17 (63%)

Mean time of HIV infection (yr) (SD) 6.4 (3.6) 6.9 (3.8) 6.0 (3.4)

CDC Clinical Stage (%)

A 43 (60%) 16 (45%) 19 (70%)

B 12 (17%) 9 (26%) 1 (4%)

C 17 (23%) 10 (29%) 7 (26%)

CDC Immunological Stage (%)

1 15 (21%) 6 (17%) 7 (26%)

2 41 (57%) 19 (54%) 16 (59%)

3 16 (22%) 10 (29%) 4 (15%)

Mean CD4 cell count x 106/l (SD) 351.2 (192.9) 315 (183) 392.6 (206)

Median CD4 cell count x 106/l (IQR) 335.5 (210-454) 319 (173-401) 346 (250-515)

Treated 318 (166-406) 397 (282-500)

Untreated 313 (274-348) 336 (257-540)

Mean HIV RNA x 103/l (SD)

46,658 (163,787)

56,821 (210,869)

37,112 (117,516)

Median HIV RNA log10 x 103/l (IQR)

1,102 (0-14,515)

298 (0-12,074)

2,061 (0-12,781)

Treated 264 (0-10,376) 183 (0-3,213) 680 (806-16,796)

Untreated 7,200 (2,343-24,264)

12,416 (8,812-45,926)

3,902 (2,202-6,694)

Undetectable# viral load (%) 19 (26.4%) 10 (28.6%) 6 (22.2%)

Treatment Status (%)

Treated 58 (80%) 29 (83%) 21 (78%)

Untreated 14 (20%) 6 (17%) 6 (22%)

HIV-1 subtype (%)

B 35 (45%) N/A N/A

C 31 (40%) N/A N/A

F1 9 (12%) N/A N/A

Mosaics 2 (3%) N/A N/A

Yr, year; SD, standard deviation; IQR, inter-quartile range; N/A, not applied

# Defined as less than 80 copies of HIV RNA per milliliter of plasma

22

Table 2. Prevalence of primary protease inhibitor-associated drug resistance mutations

among drug-experienced subjects analyzed in this study

MUTATION Sub B (n=21)

Sub C (n=15)

Sub F1 (n=5)

Mosaics (n=2)

D30N 0 0 0 0

M46I/L 3 0 1 1

G48V 0 0 1 0

I50V 0 0 1 0

V82A/F/T/S 3 1 4 1

I84V 1 0 0 0

L90M 4 0 0 0

23

Table 3. Prevalence of reverse transcriptase inhibitor-associated drug resistance

mutations among drug-experienced subjects analyzed in this study

MUTATION Sub B (n=24)

Sub C (n=17)

Sub F1 (n=4)

Mosaics (n=2)

NRTI M41L 7 0 0 0 E44D 1 0 0 0 A62V 0 1 0 0 K65R 1 0 0 0 D67N 1 1 1 0 T69D 1 1 0 0 69ins 0 0 0 0 K70R 2 0 1 0 L74V 2 0 0 0 V75I 5 1 0 0 F77L 0 0 0 0

Y115F 0 0 0 0 F116Y 0 1 0 0 V118I 1 1 0 0

Q151M 0 1 0 0 M184V 8 1 0 1 L210W 4 0 0 0

T215Y/F 5 1 1 0 K219Q/E 4 1 1 0 NNRTI L100I 3 1 0 0 K103N 5 2 0 0 V106A 2 0 0 0 V108I 0 0 0 0

Y181C/I 1 0 0 0 Y188C/L/H 0 0 0 0

G190S/A 1 0 0 0 NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor

24

FIGURE LEGENDS

Figure 1. HIV-1 subtype prevalence according to year of diagnosis of infected individuals

followed at Hospital de Clínicas de Porto Alegre, Brazil

25

Subtype Prevalence by Year of Diagnosis

0102030405060708090

1986-1990 1991-1995 1996-2000 2001-2002

time (years)

% o

f sub

type

s

sub Bsub Csub F1mosaics

4

14

5

1

2

3

1 11

17

2

9

12