Role of genomic regions encoding non-structural proteins of human immunodeficiency virus type 1 in determining its genetic variant

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Introduction

HIV infection remains a global public health issue. According to World Health Organization estimates, the global number of people living with HIV at the end of 2023 was 39.9 million, with 1.3 million of them newly diagnosed and approximately 630,000 deaths attributable to HIV-related causes¹.

One of the key elements in the system of measures aimed at controlling HIV infection is molecular genetic surveillance, the main purpose of which is to dynamically observe circulating variants of the virus and assess their genetic variability² [1–5]. In Russia, the following genetic variants of HIV-1 have been mainly identified: A6, B, circulating recombinant forms (CRFs): CRF02, CRF03, CRF63, CRF133 and CRF157 [6–8].

Information on circulating variants of HIV-1 is necessary to verify the diagnostic effectiveness of new molecular diagnostic tests, as well as to develop strategies for specific prevention and treatment of HIV infection³. The data obtained also make it possible to solve a number of problems in the field of practical epidemiology — to predict the epidemic situation, to intervene in a timely manner in the course of the epidemic process, and to conduct local epidemiological studies (in conjunction with phylogenetic analysis) [9–11]. Finally, the identification and study of new genetic variants of HIV-1 is of fundamental importance, expanding our understanding of the mechanisms underlying the variability of the virus [12].

At the same time, the commonality of methodology allows the analysis of HIV-1 drug resistance and the assessment of the prevalence of drug-resistant variants of the virus to be considered part of molecular genetic surveillance, which makes it possible to evaluate the effectiveness of the therapy used. In clinical and laboratory practice, nucleotide sequences of the pol gene region are obtained for HIV resistance analysis, as it encodes viral enzymes — protease (PR), reverse transcriptase (RT), and integrase (INT) — which are targets of antiretroviral drugs [13]. Thus, the pol gene is the most studied region of the HIV-1 genome, while other regions of the viral genome are rarely analyzed.

One of the current demand objectives is to study the non-structural proteins of HIV-1. Thus, in previous studies (in vitro, culture, and animal models), functionally significant domains of HIV-1 non-structural proteins and natural polymorphism mutations have been identified that can lead to an increase/decrease in their activity and affect the course of the disease and the effectiveness of antiretroviral therapy. Functional differences in non-structural proteins in viruses of different genetic variants have been identified [14–16]. At the same time, it would be useful to assess the possibility of determining the genetic variant of HIV-1 based on the regions of the genome analyzed in specific studies (most often fundamental ones), as well as when whole genome sequencing is not possible.

The aim of this study was to evaluate the role of genomic regions encoding non-structural proteins of HIV-1 in determining its genetic variant, particularly in viruses circulating in Russia.

Materials and methods

In the first stage of the study, an analysis was conducted of the genomic maps of reference strains of HIV-1 CRFs, available at https://www.hiv.lanl.gov/components/sequence/HIV/crfdb/crfs.comp (accessed on 20.01.2025), in order to identify the regions where the genetic variant of HIV-1 changes most frequently (i.e., where recombination breakpoints (RBs) are located). For this purpose, the presented genomic maps of the virus were divided:

• by genomic region (n = 12: gag, PR, RT, RNase, INT, vif, vpr, tatrev1exon, vpu, gp120, gp41 and nef), encoding the corresponding HIV-1 proteins);
• by 500 base pairs (bp) per window.

The total number of RBs was recorded in each of the regions and windows presented. CRF09, CRF30, and CRF117 were not included in the study because their genome maps were either unavailable or have not yet been accurately identified. CRFs with multiple regions of uncertain genetic variant in their genomes (CRF04, CRF05, CRF10, CRF11, CRF18, CRF25, CRF26, CRF37, CRF45, CRF49, CRF91, CRF92, CRF93) were also excluded from analysis, as this could affect the results of the analysis by artificially inflating the number of RBs. Thus, 139 genome maps from 139 CRFs were included in the analysis.

The average number of RBs in a given region and window was then calculated by dividing the sum of all RBs in these regions by the total number of CRFs studied. For 8 of the 139 CRFs, complete information on HIV-1 genetic variants in the region of the genome encoding the nef gene was not available. These sequence data were not excluded from the analysis, but calculations were performed considering variants as either a complete absence of RBs or the presence of 1 to 3 RBs in this region in different variations. The maximum possible number of RBs in this case was taken as 3, based on the total length of the region (621 bp), compared to the number of RBs identified in regions of the genome encoding other HIV-1 non-structural proteins.

The Mann–Whitney test with Bonferroni correction was used to compare RB frequencies. Differences were considered significant at p < 0.05. In this case, comparisons (in the case of breakdown by HIV-1 genomic regions) were made within the regions encoding the structural proteins of the virus (gag, PR, RT, RNase, INT, gp120, gp41), and regions encoding its non-structural proteins (vif, vpr, tatrev1exon, vpu, nef). Thus, in both groups, regions were identified where a change in the HIV-1 genetic variant is most likely to occur compared to others. Then, a comparative analysis of HIV-1 genetic variants was performed in these regions.

In the second stage of the study, an analysis of HIV-1 nucleotide sequences obtained from HIV-infected patients (without prior therapy) was performed at the Moscow Regional Center for AIDS Prevention and Control. The regions of the genome that were selected in the first stage of this study (with the highest frequency of RB occurrence) were analyzed in order to compare the identified genetic variants (determining the percentage of their similarity and difference).

The study was conducted with the voluntary informed written consent of the patients. The study protocol was approved by the Biomedical Ethics Committee of the N.F. Gamaleya National Research Center for Epidemiology and Microbiology (protocol No. 16 dated February 8, 2019). The study design is presented in Fig. 1.

 

Fig. 1. Study design

 

Amplification was performed using a two-round nested polymerase chain reaction (PCR). Nucleotide sequences were determined using the Sanger dideoxy method. The characteristics of the primers used are presented in Table 1.

 

Table 1. Primer characteristics

Region	PCR round	Nucleotide sequence (5’→3’)	Coordinates
pol	I	AAATTTAGGAGTCTTTCCCCATATTACTATGC	3685–3716
		GAAAAAGGGCTGTTGGAAATGTGGAA	2016–2041
	II (and sequencing reaction)	TGCCTCTGTTAATTGTTTTACATCATTAGTGTG	3630–3662
		GCTAATTTTTTAGGGAAGATCTGGCCTT	2080–2107
nef	I	GTAGCTGGGTGGACAGATAGGGTTAT	8688–8713
		GCACTCAAGGCAAGCTTTATTGAGGC	9607–9632
	II (and sequencing reaction)	ACATACCTAGGAGAATCAGACAGGGC	8749–8774
		GCAGCATCTGAGGGTTAGC	9492–9510
vif	I	GCAGGTAAGAGAGCAAGCTGAACA	4718–4741
		GTCTCCGCTTCTTCCTGCCATAGGA	5966–5990
	II (and sequencing reaction)	GCTACTCTGGAAAGGTGAAGG	4949–4969
		TACAAGGAGTCTTGGGCTGAC	5877–5897

 

Genetic variants of HIV-1 and its genetic diversity were determined using specialized online programs: COMET (https://comet.lih.lu/) and REGA HIV Subtyping Tool (https://www.genomedetective.com/app/typingtool/hiv), phylogenetic analysis, and a program for identifying recombinant forms of the virus — Recombination Identification Program (v. 3.0) (https://www.hiv.lanl.gov/content/sequence/RIP/RIP.html). All nucleotide sequences obtained during the study were deposited in the international GenBank database (sequence numbers are available in Appendix 1 on the journal website).

Statistical analysis was performed using the Python programming language.

Results

The average number of RBs per genome region was: gag — 1.04 (95% CI 0.82–1.25); PR — 0.2 (95% CI 0.13–0.28); RT — 1.42 (95% CI 1.19–1.64); RNase — 0.23 (95% CI 0.15–0.31); INT — 0.73 (95% CI 0.57–0.89); vif — 0.37 (95% CI 0.26–0.49); vpr — 0.14 (95% CI 0.08–0.21); tatrev1 — 0.19 (95% CI 0.12–0.26); vpu — 0.22 (95% CI 0.14–0.29); gp120 — 0.49 (95% CI 0.36–0.64); gp41 — 0.81 (95% CI 0.65–0.96); nef — 0.5 (95% CI 0.36–0.63).

The results of comparing the frequencies of RB occurrence in the regions of the genome encoding HIV-1 structural proteins (gag, PR, RT, RNase, INT, gp120, gp41) are presented in Table 2, and those encoding non-structural proteins (vif, vpr, tatrev1exon, vpu, nef) of HIV-1 are presented in Table 3.

 

Table 2. Comparison of frequencies of RB occurrence in genomic regions encoding HIV-1 structural proteins

Region	gag	PR	RT	RNase	INT	gp120	gp41
gag	1	< 0.001	0.064	< 0.001	1	0.001	1
PR		1	< 0.001	1	< 0.001	0.134	< 0.001
RT			1	< 0.001	< 0.001	< 0.001	0.002
RNase				1	< 0.001	0.399	< 0.001
INT					1	0.584	1
gp120						1	0.029
gp41							1

 

Table 3. Comparison of frequencies of RB occurrence in genomic regions encoding HIV-1 non-structural proteins

Region	vif	vpr	tatrev1	vpu	nef
vif	1	0.034	0.478	0.889	1
vpr		1	1	1	< 0.001
tatrev1			1	1	0.007
vpu				1	0.018
nef					1

Note. The calculation is presented with the option of complete absence of RBs in 8 CRFs with no complete information available on genetic variants in the nef gene. Thus, in any case, the frequency of RB occurrence in nef is higher than in other regions.

 

Among the regions of the HIV-1 genome encoding its structural proteins, RB was most frequently found in pol (RT) (coordinates relative to HIV-1 HXB2: 2550–3870), less frequently in PR and RNase; among the regions encoding HIV-1 non-structural proteins, they were found in nef (coordinates: 8797–9417), less frequently in vpr.

Since the regions of the HIV-1 genome vary considerably in length, a comparison of the frequencies of RB occurrence in 500 bp per window was also performed for clarification; the results are presented in Fig. 2.

 

Fig. 2. Frequency of occurrence (%) of RBs along the HIV-1 genome (500 bp per window).

 

Significantly (p < 0.05, χ² criterion adjusted for Benjamin–Hochberg multiplicity) fewer RBs were found in the 500–1000 and 6501–8000 bp genomic regions, i.e., at the beginning of the gag gene and within the env gene. RBs were most frequently found in the 2501–3000 bp region of the genome (PR-RT), as well as on the flanks of the env gene (6001–6500 and 8001–9000).

The following genomic regions were selected for further analysis:

• pol, encoding HIV-1 enzymes — protease and reverse transcriptase, and most frequently analyzed in clinical practice (coordinates relative to HIV-1 HXB2: 2253–3870),
• nef, encoding the non-structural protein Nef (coordinates: 8797–9417),
• vif, encoding the non-structural protein Vif (coordinates: 5041–5619) (additional).

A comparative analysis of HIV-1 genetic variants in these regions was performed for the CRFs analyzed. Since analysis of the pol gene region is used in clinical practice, the HIV-1 genetic variant in this region was considered the reference, and the influence of each of the other two regions (nef, vif) on the change in genetic variant was assessed relative to it. At the same time, for 8 CRFs with incomplete information about genetic variants in the nef gene, calculations were performed considering both the absence and presence of influence in different variations.

It was found that in most cases (on average 70%) the HIV-1 genetic variant can be determined by the pol gene. For 3 (2%) CRFs, reliable determination of the genetic variant required analysis of all 3 regions of the genome (pol, nef and vif).

The result of genotyping the nef gene region more often influenced the final result of determining the HIV-1 genetic variant, but no significant difference in influence was found compared to vif. At the same time, in 8% of cases, nef and vif had an equal influence.

At the final stage of the study, a random sample of HIV-1 nucleotide sequences obtained from HIV-infected patients at the Moscow Regional Center for AIDS Prevention and Control was analyzed. The total volume of the study was 153 samples. The results of the phylogenetic analysis are presented in Fig. 3.

 

Fig. 3. Phylogenetic analysis of HIV-1 pol genomic region.

Here and in the Figs 4, 5: pink indicates reliable clusters formed by HIV-1 subtype A6, blue indicates subtype B, orange indicates CRF02_AG, purple indicates CRF03_A6B, and light green indicates CRF63_02A6.

Reference nucleotide sequences are marked in red, while the nucleotide sequences under study are marked in black. Potential unique recombinants are marked with red asterisks.

 

Fig. 4. Phylogenetic analysis of HIV-1 nef genomic region.

 

Fig. 5. Phylogenetic analysis of HIV-1 vif genomic region.

 

Based on the results of phylogenetic analysis of the pol gene region encoding viral enzymes—protease and 2/3 reverse transcriptase (coordinates relative to HIV-1 HXB2: 2253-3553), it was established that 92.16% belong to HIV-1 sub-subtype A6 (one of which was assessed as a potential unique recombinant URF_A6/B), 2.61% to HIV-1 subtype B (one of which was assessed as a potential URF_B/G), 1.96% each to CRF02_AG and CRF63_02A6, and 0.65% to CRF03_A6B.

Phylogenetic analysis of the nef gene region also revealed a predominance of HIV-1 sub-subtype A6 (94.12%), followed by CRF02_AG (2.61%), CRF63_02A6 — 1.31%, subtype B and CRF03_A6B — 0.65% each genetic variant, one sample (1311001125) was classified as URF_A6/B (0.65%) based on the results of primary nucleotide sequence analysis and phylogenetic analysis.

Based on the results of phylogenetic analysis of the vif gene region, the following ratio of HIV-1 genetic variants was identified: A6 — 93.46%, B — 1.96%, CRF02_AG — 1.96%, CRF63_02A6 — 1.31%, CRF03_A6B — 0.65%; 1 sample (1311001125) was classified as URF_A6/B (0.65%) based on the results of the initial analysis of nucleotide sequences and phylogenetic analysis.

Then, sequences were selected in which HIV-1 genetic variants differed from each other in at least two regions of the genome. The results of comparing the identified genetic variants in the selected regions of the genome (pol, nef, vif) are presented in Table 4.

 

Table 4. Comparison of identifiable genetic variants (different from A6 in all 3 studied genomic regions) of HIV-1 in the pol, nef and vif regions in the studied samples

Unique sample number	Genomic region			Final version	Similarity	Which gene has an effect (except PR-RT)
	PR-RT	nef	vif
1311000005	B	A6	B	URF_A6B	PR-RT, vif (100%)	nef
1311000329	CRF02_AG	CRF02_AG	CRF02_AG	CRF02_AG	PR-RT, nef, vif (100%)	–
1311000386	CRF02_AG	CRF02_AG	CRF02_AG	CRF02_AG	PR-RT, nef, vif (100%)	–
1311000432	CRF03_A6B	A6 (CRF03_A6B)	CRF03_A6B	CRF03_A6B	PR-RT, nef, vif (100%)	–
1311000491	B	B	B	B	PR-RT, nef, vif (100%)	–
1311000520	CRF63_02A6	A6	A6	URF_63A6	nef, vif (100%)	nef и vif similar effect
1311000659	CRF02_AG	CRF02_AG	CRF02_AG	CRF02_AG	PR-RT, nef, vif (100%)	–
1311000722	CRF63_02A6	CRF63_02A6	CRF63_02A6	CRF63_02A6	PR-RT, nef, vif (100%)	–
1311001098	B (potential recombinant B/G)	A6	A6	URF_A6B	nef, vif (100%)	nef и vif similar effect
1311001099	CRF63_02A6	CRF63_02A6	CRF63_02A6	CRF63_02A6	PR-RT, nef, vif (100%)	–
1311001105	B	CRF02_AG	B	URF_02B	PR-RT, vif (100%)	nef
1311001125	B	URF_A6/B	URF_A6/B	URF_A6/B	nef, vif (100%)	nef и vif similar effect
1311001027	A6 (potential recombinant A6/B)	A6	A6	URF_A6/B	PR-RT, nef, vif (100%)	–

 

For the HIV-1 sequences studied, obtained from patients, it was also found that in most cases (62%) the genetic variant was better typed by the pol gene. In 23% of cases, the genetic variant in the nef and vif regions was the same and influenced the final variant. In 15% of cases, the HIV-1 genetic variant in the nef region was decisive. Consequently, the result of genotyping the nef gene region more often influenced (in 38% of cases in total) the final result of determining the HIV-1 genetic variant.

Given the greater length of the pol fragment compared to others, as well as its significance in clinical practice, it would be incorrect to ignore the genotyping results for this region. Thus, genotyping for the nef and vif genes is best performed in addition to genotyping for the pol gene.

Discussion

A characteristic feature of HIV-1, as with all RNA-containing viruses, is its very high rate of evolution, which in turn determines the wide genetic diversity of the virus both within a single infected organism and across the entire population of HIV-infected individuals. Recombinant HIV-1 genomes contribute significantly to this diversity [17–20]. The rearrangement of the viral genome through recombination can affect both the biological properties of the virus and the effectiveness of the therapy used [21–27]. It is assumed that the presence of new properties beneficial to the virus, caused by the mosaic structure of its genome, contributed to their spread and, as a result, to the formation of CRFs.

Recombination occurs during the reverse transcription of the genomic RNA of a heterozygous virus as a result of the viral enzyme reverse transcriptase jumping from one RNA template to another. The regions of nucleotide sequences where template switching occurs are called RBs [18]. To date, a number of studies have been conducted on the distribution of RBs in the HIV-1 genome — the identification of hot and cold recombination spots [17–20]. At the same time, every year there is an increase in the genetic diversity of HIV-1 and its recombinant forms [28]. Thus, between 2020 and 2025, more than 50 new CRFs were discovered.

This study analyzed the genomes of 139 circulating recombinant forms of HIV-1 identified up to and including 2025. The data obtained are generally consistent with the results of previous studies — RBs were most frequently found in the pol (RT) region of the genome and flanking the env gene [14–17]. Presumably, natural selection may influence this distribution of RBs: positive selection promotes the transfer of env from one matrix to another in the form of an integral cassette [17, 18, 29, 30]. At the same time, the lowest frequency of RB occurrence was observed within the env region, which may indicate a tendency for this region to recombine as a whole. It is worth noting that previously, the same pattern of RB distribution was observed in both the CRFs and URFs groups [20].

Currently, HIV-1 genetic variant determination is most often performed as part of HIV-1 drug resistance monitoring studies, in which case the virus variant is determined based on the HIV-1 pol gene region encoding protease and 2/3 of reverse transcriptase (coordinates relative to HIV-1 HXB2: 2253-3553). A number of studies have focused on other regions of the HIV-1 genome, such as its non-structural proteins. The regions of the genome encoding HIV-1 non-structural proteins are also located on the flanks of the env gene. The results of the study showed that the frequency of RB occurrence in the regions of the genome encoding HIV-1 non-structural proteins is higher in nef. A comparative analysis of genetic variants by genomic region showed that among the genes encoding non-structural proteins of the virus, nef is the most important (along with pol).

Conclusion

The regions of the HIV-1 genome with a high frequency of RB occurrence have been identified. The pol (RT) region is among those encoding structural proteins of the virus, and the nef region is among those encoding non-structural proteins of the virus. The data obtained may be useful in fundamental research, as well as for determining the genetic variant of HIV-1 when whole genome sequencing is not possible.

 

¹ WHO. HIV and AIDS. 13.07.2023.

URL: https://www.who.int/news-room/fact-sheets/detail/hiv-aids

² Epidemiological Surveillance of HIV Infection: Guidelines. Moscow; 2016. 75 p. (In Russ.)

³ Feldblyum I.V., Isaeva N.V., Koryukina I.P., Khafizov K.M. New Technologies in Organizing Epidemiological Surveillance of HIV Infection in the Context of a Drug-Dependent Type of Epidemic Process: Guidelines. Perm; 2002. Part 2. 38 p. (In Russ.)

About the authors

Larisa A. Protasova

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: larisa.protasova.03@mail.ru
ORCID iD: 0009-0001-0430-1578

laboratory researcher, Laboratory of leukemia viruses

Russian Federation, Moscow

Anastasiia A. Antonova

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Author for correspondence.
Email: anastaseika95@mail.ru
ORCID iD: 0000-0002-9180-9846

Cand. Sci. (Biol.), senior researcher, Laboratory of leukemia viruses

Russian Federation, Moscow

Daria A. Ogarkova

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: daria@ogarkova-dvorkina.ru
ORCID iD: 0000-0002-1152-4120

junior researcher, Laboratory of mechanisms of population variability of pathogenic microorganisms

Russian Federation, Moscow

Kristina V. Kim

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: kimsya99@gmail.com
ORCID iD: 0000-0002-4150-2280

junior researcher, Laboratory of leukemia viruses

Russian Federation, Moscow

Yana M. Munchak

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: yana_munchak@mail.ru
ORCID iD: 0000-0002-4792-8928

junior researcher, Laboratory of leukemia viruses

Russian Federation, Moscow

Alexei G. Prilipov

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: a_prilipov@mail.ru
ORCID iD: 0000-0001-8755-1419

D. Sci. (Biol.), leading researcher, Head, Laboratory of molecular genetics

Russian Federation, Moscow

Anna I. Kuznetsova

National Research Center for Epidemiology and Microbiology named after the Honorary Academician N.F. Gamaleya

Email: a-myznikova@list.ru
ORCID iD: 0000-0001-5299-3081

Cand. Sci. (Biol.), leading researcher, Head, Laboratory of leukemia viruses

Russian Federation, Moscow

References

Supplementary files