Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa

doi:10.21203/rs.3.rs-7906379/v1

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Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa

https://doi.org/10.21203/rs.3.rs-7906379/v1

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Reconstructing the origins and transmission of the HIV epidemic in Ghana has, since the first diagnosis in 1986, yet to be reported. This study described the transmission clusters of HIV in the Ghanaian setting using a maximum-likelihood tree via the IQTree approach. Using 71 newly described full-length Ghanaian HIV-1 sequences, we performed molecular phylodynamic analysis to determine major drivers of HIV transmission in Ghana, a West African population where the HIV-1 CRF02_AG recombinant is prevalent. However, to reconstruct the origin of the most predominant subtype CRF02_AG, we combined 48 CRF02_AG sequences in our dataset with 140 full-length CRF02_AG sequences downloaded from the Los Alamos National Laboratory HIV database and utilized the ancestral trait reconstruction model in BEAST v.1.10.5 to reconstruct an MCC tree, summarized in TreeAnnotator and visualized with treeio package in R. Phylogeographic reconstruction to estimate the earliest introduction of HIV-1 showed that Cameroon and Nigeria were the sources of nine major introductions, with a time to most recent common ancestor (tMRCA) of 1964.2. Most intra-country transmission occurred from Greater Accra to other major regions. This is the first study to combine full-length HIV-1 genomic sequences with patient metadata to estimate the population dynamics and reconstruct the introduction of the predominant HIV-1 CRF02_AG in Ghana. This study illuminates our understanding of HIV transmission dynamics in Ghana and underscores the utility of combining demographic and molecular data in prospectively tracking HIV transmission to inform targeted public health interventions.

HIV

phylogenetic reconstruction

phylodynamics

molecular epidemiology

transmission dynamics

comparative genomics

Understanding the major dynamics of HIV transmission remains crucial. This study performed molecular phylodynamics – an advanced genetic analysis, to understand HIV molecular epidemiology and transmission dynamics of HIV in the Ghanaian setting within the West African sub-region. The analysis involved full genomic sequences of HIV from infected individuals in Ghana. The genetic relationships of the Ghanaian sequences were compared with global samples. The analysis showed that the most common type of HIV in Ghana, which is the CRF02_AG, likely came from Cameroon and Nigeria. The virus spread mostly from the Greater Accra region to other parts of the country. This study is the first to trace how HIV spread in Ghana since it was first identified in 1986. The findings highlight the importance of using both virus and host patient data to track HIV spread. Accumulation of such data can help health officials plan effective strategies to control the spread of the virus and protect vulnerable communities.

In Ghana, the first case of HIV was diagnosed in 1986, and soon after, the Ghana Health Service (GHS) introduced HIV sentinel surveillance across antenatal care (ANC) and sexually transmitted infections (STI) clinics [1, 2]. several programs have been rolled out since then. These include the ‘treat-all’ intervention initiated in September 2016 that makes available antiretroviral drugs to all persons diagnosed with HIV, irrespective of the CD4⁺ status [3]. For about two decades now, the HIV epidemic in Ghana has been predominantly due to the CRF02_AG subtype [4]. This finding has been reported mostly from cross-sectional studies by individual researchers using representative cohorts in selected areas. Despite advances in research and national preventive efforts, reports by the National AIDS/STI Control Programme indicate that has reported that, since the surveillance commenced, there have been about 334,095 cases with an estimated 17,774 new infections in 2023 [5]. The continued increase in the number of cases necessitates a multifaceted approach to tracking transmission in real time to help control the further spread of the virus. Knowledge of spread and transmission network sources would prove useful in our control endeavours.

Like most other pathogens, HIV transmission dynamics studies offer useful models that help to clarify some essential relations between epidemiological factors underlying an overall pattern of the HIV epidemic. For example, it is useful to estimate essential parameters such as the number of secondary infections produced by a primary infectious case; that is, effective reproductive number (Re), and describe the ancestral traits and demographic contributors of the HIV pandemic in a given population. They are also helpful in identifying the kinds of epidemiological data needed to make predictions about future trends. However, HIV molecular epidemiology studies in Ghana have been somewhat limited. Since the epidemic began, there has yet to be any data on HIV transmission dynamics in Ghana.

Until quite recently (2022), there were only 31 full-genome HIV sequences from Ghana available in the global sequence database, all of which were acquired in 2003 or earlier. In 2020–2022, we conducted the first-ever comprehensive HIV molecular epidemiology studies that employed both Sanger and next-generation sequencing techniques to produce 71 whole genome HIV sequences for the analysis of HIV subtypes, drug resistance and coreceptor usage [6]. The present study builds on the 2020–2022 study by utilizing full-length HIV-1 genomic sequences with patient metadata to estimate the Re and reconstruct HIV-1 transmission in Ghana. We do this by employing relevant phylogenetic tools and models that effectively estimate molecular epidemiology and transmission parameters across time and space [7, 8] and predict possible future outbreaks [9]. To the best of our knowledge and according to documented literature, this represents the first of its kind to be conducted in Ghana, West Africa.

Ethics statement

The protocol and ethics of this study were approved by the Scientific and Technical Committee (STC)/Institutional Review Board (IRB) of the Korle Bu Teaching Hospital, Ghana (KBTH-STC/IRB/00075/2020). Persons included in the study voluntarily gave written informed consent to participate, as well as permission for drawing blood. Consent was documented on a Consent Document Form, which was part of the protocol approved for the study. The procedures for participant assent/consent and blood sampling were done in accordance with the tenets of the Declaration of Helsinki.

Sample collection and sequence characteristics

Sequences analyzed in this study were obtained from blood samples collected from HIV-1 infected antiretroviral naïve Ghanaians accessing routine care at the Korle-Bu Teaching Hospital, Ghana between 2020 to 2022. This generated 71 Illumina MiSeq-acquired HIV whole genome consensus sequences from the previous study and deposited with the GenBank under accession numbers within OQ121842 – OQ121917. The samples were mostly (48, 68%) of the CRF02_AG subtype, the predominant subtype in the West African sub-region. The sociodemographic characteristics of the study subjects thus remain as previously described [6].

Maximum-likelihood tree inference and transmission cluster analyses

We selected 140 publicly available HIV-1 full-length genomic sequences from the Los Alamos National Laboratory HIV database (LANL) (https://www.hiv.lanl.gov) for which sampling location and year were available at the time of analysis. We aligned these sequences with the 71 Ghanaian full-length sequences using Maximum Alignment using Fast Fourier Transform (MAFFT) [10]. We selected the best nucleotide substitution model via ModelFinder [11] and a maximum likelihood phylogenetic tree was estimated directly from the chosen model, GTR + G4 in IQTree after 1000 iterations [12]. The ClusterPicker tool [13] was used to investigate putative transmission clusters in the dataset. Statistical support for clusters was defined by a non-parametric Shimodaira-Hasegawa (SH)-like test with node support of ≥ 99% and genetic distance of 0.05.

Estimation of the number of introductions of the CRF02_AG subtype in Ghana and phylogeographic reconstruction

The phylogeographic reconstruction analysis was performed to identify and quantify the introduction events of the 48 CRF02_AG subtype of the HIV lineage in Ghana. For this analysis, we used a time-scaled tree created with TreeTime v0.7.4 [14]. A time-scaled phylogenetic tree was constructed and evaluated for a temporal signal with TempEst [15] and outliers were removed. The Time to the Most Recent Common Ancestor (tMRCA) was estimated using simple least-squares regression in TreeTime v0.7.4 [14].

This time-scaled phylogeny was treated as a fixed empirical tree, considering two potential ancestral locations: "Ghana" and "other location." A discrete diffusion model was applied using the BEAST v1.10.4 software [16]. The Bayesian analysis via Markov chain Monte Carlo (MCMC) was run for 100 million steps. MCMC convergence and mixing properties were assessed using Tracer v1.72 [17], achieving effective sample sizes greater than 200 for all parameters. Visualization of the introductions of the CRF02_AG subtype was performed using a modification of the script described by Dellicour and colleagues utilizing the Seraphim package [18] to extract spatiotemporal information from the data and visualize the phylogeographic reconstructions.

Additionally, to count the number of introductions of the CRF02_AG subtype, we conducted a formal discrete phylogeography analysis to understand the dispersion of this subtype in Ghana. This analysis was performed using BEAST v1.10.5 [16] and the BEAGLE 3 library to improve computational performance [19]. The method employed a GTR + Γ parametrization, a relaxed clock model with rates drawn from an underlying lognormal distribution, and a chain length of 100 million steps, with log parameters recorded every 10,000 steps. The maximum clade credibility (MCC) tree was inferred using TreeAnnotator [20] and visualized using the treeio package in R [21].

Estimation of the temporal dynamics of the effective reproductive number Re, and geographic dispersal of HIV in Ghana

We performed phylodynamic analyses using the Bayesian Skygrid coalescent tree prior implemented in BEAST v1.10.5 [16]. This approach allows us to estimate the Re over time, which gives us insights into the dynamics of HIV transmission within the population. The Bayesian Skygrid model, which accounts for fluctuations in population size and transmission rates, is particularly useful for understanding how the epidemic evolves and for predicting future trends. For the phylogeographic analysis, SPREAD (Spatial Phylogenetic Reconstruction of Evolutionary Dynamics) software was used to track the spatial spread of HIV subtypes in different regions. The results were visualized using Google Earth to provide a clear, geographical representation of the movement and spread of the disease.

Study sequence characteristics

The distribution of HIV subtypes (A, B, CRF02_AG, CRF06_cpx, G) over time in samples from Ghana is shown in Fig. 1A. It is noteworthy that the number of these subtypes is generally decreasing, except subtype B, which shows a rising peak in 2021. Figure 1B shows the distribution of marital status in the study population over the years. The data shows a relatively stable distribution, with the categories 'Married' and 'Single' consistently being the most common. Figure 1C shows that the distribution of educational level remains largely stable, the most common categories being 'Primary education' and 'Secondary education'. Figure 1D shows the distribution of risk factors for the disease studied. The 'heterosexual' category consistently has the highest number, while 'homosexual" and 'needle prick' have significantly lower values. This distribution indicates that heterosexual transmission is the predominant mode of transmission in this population group.

The distribution of HIV subtypes within the Ghanaian population varies across different tribes, as depicted in Fig. 2A. The Akan tribe has the highest number of HIV patients and exhibits the greatest diversity of subtypes. CRF02_AG is the most prevalent subtype in this tribe, followed by CRF02_cpx, subtype A, and subtype G, with subtype B being the least common. In the Northern tribe, CRF02_AG, CRF02_cpx, and subtype A are present. CRF02_AG and CRF02_cpx subtypes are present in other tribes as well. Figure 2B illustrates the distribution of subtypes according to sex, with men exhibiting the most diversity in subtypes. Figure 3C presents the distribution of subtypes across regions. The figure shows that all described subtypes are present in the Greater Accra region.

Maximum-likelihood and transmission cluster analyses

The best tree was selected after 700 iterations using the Generalized Time Reversible (GTR) model and gamma distribution across sites with four categories (G4) using IQTree. In the presence of global data, two putative transmission clusters of two sequences each were found for the Ghanaian data at ≥ 99% bootstrap and 0.05 genetic distance when ClusterPicker was used. The inclusion of the global data was to help determine any spurious clustering between Ghanaian and global data. However, no spurious clustering was found (Fig. 3).

Phylogeographic reconstruction and introductions of subtype CRF02_AG to Ghana

Times for the most recent common ancestor (tMRCA) were calculated using TreeTime. The results indicated an approximate date of 1964.2 (Fig. 4A). The root-to-tip regression analysis revealed a strong, positive correlation between the genetic distance of HIV-1 sequences and their sampling dates, confirming the presence of a molecular clock. The estimated substitution rate was 1.78e-03 substitutions per site per year.

Focusing on the HIV subtype CRF02_AG, which is more prevalent among the studied population, we determined the number of introductions of this subtype into Ghana. The analysis identified a minimum of 9 introduction events for this recombinant subtype (95% HPD interval = [7–10]), based on the phylogenetic analysis of 48 of the samples studied.

Figure 4B shows the phylogenetic tree, in which the number of introductions is marked with red triangles, and branches in green correspond to samples from Ghana. Eight of the nine introduction events were independent, while one was responsible for the dissemination within the transmission cluster.

The most probable origin of the CRF02_AG subtype in Ghana is Cameroon (CM) (Fig. 4C), as the most recent common ancestor of all viruses of this subtype traces back to this location, from which the subtype began its dissemination. The largest clade of samples from Ghana also shares a common ancestor with samples from CM. However, not all Ghanaian samples have the same ancestral origin, indicating that introductions came from different locations. Another significant importation to Ghana likely originated from Nigeria (NG). CM and NG are the primary countries from which the CRF02_AG subtype began spreading to other regions.

Estimation of the temporal dynamics of the effective Re, and geographic dispersal of HIV in Ghana

To explore the changes in population dynamics we analyzed the distribution of the general trend in population growth from 1986 to 2022. The analysis showed a significant increase in population size until around 2010, after which growth either plateaued or declined slightly. The vertical dotted lines in the graph represent the estimated time of disease emergence, and the epidemiological threshold, marked by a Re of 1, indicates the point at which the disease can sustain itself in the population (Fig. 5A).

To understand the domestic spread of HIV subtypes in Ghana, SPREAD analysis was performed and the results were visualized with Google Earth programs (Fig. 5B). The results as displayed in the map point to the origin of the epidemic in the Greater Accra Region and its subsequent spread to other parts of Ghana. The black lines on the map illustrate the direction and possible transmission routes of the virus.

With regards to controlling the HIV epidemic in Ghana, the available record shows that by the end of 2020, 63% of HIV-infected individuals knew their infection status, 95% of diagnosed individuals were on antiretroviral therapy (ART), and 73% of those on ART achieved viral suppression [23, 24]. These indicators somewhat place Ghana far from realizing the United Nations Joint Programme on HIV/AIDS (UNAIDS) ambitious targets set in December 2020 for ending AIDS – the 95-95-95 targets, which aim for 95% of people living with HIV to know their status, 95% of diagnosed individuals to be on ART, and 95% of those on ART to achieve viral suppression by 2025 [25]. Achieving these targets certainly requires intensified efforts and multifaceted approaches to tackling the epidemic. Understanding of the major drivers of HIV transmission remains crucial. This underscores the importance of molecular epidemiology studies [26].

The stable distribution of risk factors, with 'heterosexual' being the most common, aligns with previous findings suggesting that heterosexual transmission remains a significant concern. The relatively lower prevalence of 'homosexual' and 'needle prick' transmission underscores the need for targeted prevention and education efforts, especially within high-risk groups that are less frequently represented in the data. The diversity of HIV subtypes observed among different tribes, particularly the predominance of CRF02_AG in the Akan tribe, suggests varying regional transmission patterns. This diversity could be reflective of historical migration patterns, cultural practices, or different levels of healthcare access.

Additionally, using the Skygrid model in BEAST v1.10.4, we estimated past population dynamics of the Ghanaian dataset from 1986 to 2022 and the Re after 2005 to 2022. Generally, the population size was relatively higher before 2000 and after 2010. The reproductive number was also less than the epidemiological threshold (< 1) until 2015 when it began to rise above the threshold (> 1). This shift in the growth pattern can be attributed to various factors like improvements in healthcare, economic development or changes in birth and death rates. Improved access to healthcare could reduce mortality rates, while economic progress could influence birth rates and migration patterns. Furthermore, the introduction of mass education and other prevention strategies such as condom use was communicated and well promoted among the public and in the mainstream media after the year 2000 [2]. This coincided with the introduction of ART.

Notwithstanding the reduced population size seen between the years 2000–2010, the Re value increased substantially until it dropped again after 2020, perhaps due to other interventions like pre-exposure prophylaxis use and potent ARTs that enhance reduced and prolonged viral load suppression. Moreover, our estimates of the Re are indicative of efforts to increase contact tracing and subsequent genotyping of cases. Thus, cases which are not analyzed here are likely to consist of undiagnosed infections.

Phylodynamic reconstruction to estimate the earliest introduction of the CRF02_AG using our dataset showed that Nigeria was the source of two major introductions. It is worth noting that the predominant HIV-1 subtypes in Nigeria have mostly been subtypes A, G and CRF02_AG as reported by many studies [27–29]. Of note, in Ghana, the transmission of the CRF02_AG subtype occurred from the country’s capital, Accra in the Greater Accra region into other major regions of the country. Factors such as transportation networks, population density and social interactions are likely to have influenced the observed spread of the disease.

Even though there could be blind spots with no sequence data due to financial constraints to routinely sequence HIV-1 in clinical settings and the general population, the socio-demographic characteristics, public health service delivery and population mobility patterns may lend credence to the observations made in this study. The Nigeria-Ghana relationship goes as far back as the 1980s – specifically during the 1983 famine when many Ghanaians left for Nigeria for survival, and their subsequent expulsion back to Ghana in the mid to late 1980s, a period described as “Ghana must go” [30, 31]. This period coincided with the period in which HIV was first detected in Ghana [1]. Moreover, the Greater Accra region represents the economic hub of the country and where the majority of infection and testing are likely to take place; hence the larger number of sequences obtained from this region.

In this study, newly acquired HIV-1 full-length sequences were subjected to phylodynamic analysis to unravel the reproductive numbers and transmission pattern of the HIV-1 epidemic in Ghana. Although this study used data that represented only a small percentage of the country’s reported HIV-1 cases, it is the first study ever, since HIV was first diagnosed in 1986 [1], to use full-length HIV-1 sequences in a statistically rigorous Bayesian phylogenetic approach to better understand and reconstruct the HIV epidemic in Ghana. Overall, the findings of this study provide insights into the population dynamics and epidemiology of HIV disease in Ghana and bring to the fore growth trends and the geographical distribution pattern of HIV outbreaks.

This study described the transmission dynamics of HIV using full-genome sequences recently obtained in Ghana. Though our dataset was relatively modest in size, the sequences analyzed were nevertheless representative of the epidemiology of HIV in Ghana. To the best of our knowledge, this study represents the first to perform a Bayesian phylogenetic analysis of full-length HIV-1 sequences to estimate and reconstruct the HIV epidemic in Ghana. Although public health efforts have likely decreased the rate of transmission over the last 5 years, declines are not uniform across key populations. Invariably, this study found there is an increasing emergence of other less prevalent HIV-1 subtypes and thus demonstrates the importance of combining demographic data with molecular data for analysis to prospectively inform real-time targeted public health interventions.

AIDS

Acquired Immunodeficiency Syndrome

ART

Antiretroviral Therapy

BEAST

Bayesian Evolutionary Analysis Sampling Trees

CD4+

Cluster of Differentiation 4

ESS

Effective Sampling Size

GHS

Ghana Health Service

GTR

Generalized Time Reversible

HIV

Human Immunodeficiency Virus

HPD

Highest Posterior Density

LANL

Los Alamos National Laboratory

MAFFT

Multiple Alignment using Fast Fourier Transform

MCC

Maximum Clade Credibility

MCMC

Markov Chain Monte Carlo

Protease

Effective Reproductive Number

Reverse Transcriptase

SPREAD

Spatial Phylogenetic Reconstruction of Evolutionary Dynamics

STI

Sexually Transmitted Infection

UNAIDS

Joint United Nations Programme on HIV/AIDS

Acknowledgements

We gratefully acknowledge people living with HIV and all the individuals whose samples were used for this study.

Authors’ contributions

Conceptualization: Nicholas I. Nii-Trebi, Billal M. Obeng Billal M. Obeng, Hugo G. Castelán-Sánchez

Formal Analysis: Billal M. Obeng, Hugo G. Castelán-Sánchez

Methodology: Billal M. Obeng, Hugo G. Castelán-Sánchez, Nicholas I. Nii-Trebi

Data curation: Akua K. Yalley, Makafui Seshie

Resources: Nicholas I. Nii-Trebi, Kwamena W. C. Sagoe

Supervision: Nicholas I. Nii-Trebi

Writing – original draft: Billal M. Obeng, Nicholas I. Nii-Trebi, Akua K. Yalley

Writing – review and editing: Nicholas I. Nii-Trebi, Billal M. Obeng, Hugo G. Castelán-Sánchez

All authors reviewed and approved the final version of the manuscript.

Funding

The study reported here received no external funding. Funding declaration is not applicable.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

This study did not generate a new dataset; hence data sharing does not apply to this article. Sequence datasets analyzed in this study are available in public databases. These have been duly referenced in the text of the article.

ORCID

Billal Musah Obeng https://orcid.org/0000-0002-1158-7922

Hugo G. Castelán-Sánchez https://orcid.org/0000-0002-4763-0267

Nicholas Israel Nii-Trebi https://orcid.org/0000-0001-5012-9564

Akua Koaso Yalley https://orcid.org/0000-0002-7429-1707

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No competing interests reported.

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You are reading this latest preprint version

Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa

Status:

Version 1

Abstract

Figures

Manuscript’s importance

INTRODUCTION

MATERIALS AND METHODS

Ethics statement

Sample collection and sequence characteristics

Maximum-likelihood tree inference and transmission cluster analyses

Estimation of the number of introductions of the CRF02_AG subtype in Ghana and phylogeographic reconstruction

RESULTS

Study sequence characteristics

Maximum-likelihood and transmission cluster analyses

Phylogeographic reconstruction and introductions of subtype CRF02_AG to Ghana

DISCUSSION

CONCLUSION

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1