Abstract

Background

SARS-CoV-2, which causes COVID-19, emerged at the end of 2019 and spread rapidly through populations. By March 2020, when the WHO declared the disease a pandemic, approximately 118,000 cases and over 4000 deaths had already been recorded across all continents [1]. At the peak of the COVID-19 pandemic, cases and mortality varied widely across the globe. Mortality became the measure by which the burden of COVID-19 was evaluated. Together, Europe and the Americas recorded more than 70% of deaths resulting from COVID-19 [2], and it was initially expected that Africa would be the worst hit by the pandemic. This assumption is because among other factors, health care systems are crucial in determining the general recorded outcomes of infected populations, and Africa is notable for low healthcare delivery [3]. However, the African continent recorded relatively low cases and mortality [2]. Several reasons have been attributed to these statistics, including underreporting, limited testing capacity, high tropical temperatures, increased use of herbal medicine, malaria coinfection with COVID-19 and the frequent use of anti-malarial drugs that inhibit SARS-CoV-2 replication [4, 5].

Host factors also contribute significantly to disease outcome resulting from SARS-CoV-2 infection [6], and identifying these factors is important in explaining individual and population variations in disease epidemiology. The interaction between the host and virus is complex and initiated at the site of entry. For SARS-CoV-2, the naso-oropharyngeal cavity is the major site for viral entry and infection initiation [7]. Here, the binding of the viral receptor to human angiotensin-converting enzyme 2 (ACE2) expressed in epithelial cells is crucial for establishing infection [8, 9]. The virus may lodge here for several weeks, replicating in epithelial cells and eliciting a cascade of immune responses that characterize the course of the disease. Low levels of ACE2 expression in the nasal cavity would therefore lead to decreased viral acquisition [10]. Another cellular activation known to be associated with SARS-CoV-2 infection is the expression of transmembrane serine protease-2 (TMPRSS2), which acts as a significant determinant of the entry pathway for the virus; overexpression inhibits viral infection [11]. These findings represent a few examples of our understanding of the intricacy of SARS-CoV-2 infection which could contribute to potential avenues for therapy.

Interactions between bacteria and viruses are common in viral infections. Bacteria may impact viral infectivity and stability, and these interactions could influence how the host responds to viral infections (reviewed in [12]). Oral bacteria could complicate respiratory infections through various suggested mechanisms including promoted adhesion of pathogens to mucosal surfaces and alteration of infection site tissues through periodontal-originating cytokines [13]. The human naso-oropharynx is an environment with a residing community of bacteria, some of which have been reported to be associated with various clinical statuses of COVID-19. Bacterial taxa linked to poor oral hygiene and some opportunistic microbes have been shown to proliferate in COVID-19 patients [14, 15]. Whether this dysbiosis results from the viral infection and facilitates the progress of the disease severity remains unclear. A decrease in gut and oral bacterial diversity has also been linked to elevated levels of proinflammatory cytokines which characterises SARS-CoV-2 [14] and other viral infections, such as hepatitis C virus (HCV) [16] and human immunodeficiency virus (HIV) [17]. Understanding the mechanism of the association of oral bacteria in COVID-19 infection could make useful contributions to our knowledge of COVID-19 pathogenesis and improve care through advanced therapeutics.

In this study, we focused on the naso-oropharyngeal microbiome of SARS-CoV-2-infected and uninfected individuals in an African population with the aim of identifying signature microbes whose known properties may potentially explain the reduced disease severity recorded. We also assessed potential changes in the naso-oropharyngeal microbiome following vaccination, as COVID-19 vaccines have been shown to cause dysbiosis of the gut microbiome [18]. Our study contributes to addressing recent concerns about the neglect of microbiome research in Africa [19, 20], particularly in the face of emerging infections.

Methods

Results

Sequence exploration and statistics

After accounting for and removing batch effects, 6,618,391 reads remained for the merged dataset (Table S1). The average number of reads was 45,644.07; minimum and maximum reads was 1514 and 111,200, respectively. The two datasets had positively skewed distributions but with different read frequencies, indicating that few samples had relatively large number of reads (Fig. ​(Fig.1A).1A). Each dataset was rarefied separately with their respective median read (pre-vaccine=43,932; post-vaccine=50,262) providing evidence that the sequencing depth and data processes applied resulted in adequate representations of the total bacterial content per sample and test group without sample size bias (Fig. ​(Fig.1B).1B). The median number of ASVs was higher in the pre-vaccine than in the post-vaccine population (Fig. ​(Fig.11B).

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Fig. 1

Sequence distribution and rarefaction for sample sets analysed following removal of batch effects. Kernel density plot (A) show the reads frequency distribution for pre- and post-vaccine sample sets. Both curves show multimodal distribution and positively skewed distribution. Sub-sampling of resulting sequences produced rarefaction curves (B) that depict adequate saturation of the taxa richness from both sample sets

Within-group microbial diversity increased with vaccination

Alpha (⍺) diversity did not differ between infection status (positive vs negative) pre- or post-vaccine availability (Fig S1). When infected individuals from the two sample sets were compared, however, species richness and ⍺-diversity estimated by Shannon–Wiener index were higher among the population before the availability of the vaccine (P.adj<0.05) (Fig. ​(Fig.2A).2A). Uninfected individuals in both sample sets only differed in species richness, again being significantly higher pre-vaccine (P.adj=0.0001) (Fig. ​(Fig.2B).2B). Therefore, individual-to-individual microbial diversity was more varied in the study population prior to the introduction of vaccines than after people received COVID-19 vaccination.

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Fig. 2

Comparison of taxon richness, Shannon and Simpson alpha (⍺) diversity indices. SARS-CoV-2-infected (A) and uninfected (B) samples are compared separately between pre-vaccine and post-vaccine sample sets. Among the post-vaccine sample set, comparisons are made first between vaccination statuses (without considering infection status) (C) and while considering the virus infection status (D). Colored dots represent individual samples, and box plots show first and third quartiles of the distribution. The solid horizontal line in the box shows the median index value per group. Pairwise comparison was achieved with the Wilcoxon test with Benjamin-Holchberg (BH) P-adjusted (P.adj) correction

We further investigated the post-vaccine dataset to identify whether vaccination influenced within-group diversity. Overall, all vaccinated individuals (infected and infected) showed more variable richness than unvaccinated individuals (P.adj=0.006), but other ⍺-diversity indices were similar (Fig. ​(Fig.2C),2C), implying that the vaccinated group had many rare species. When infection statuses were considered (vaccination * infection), vaccinated individuals showed more varied richness whether they were positive (VI) or negative (VU) for the virus compared to their unvaccinated counterparts (VU vs UU [P.adj=0.02]; VI vs UI [P.adj=0.04) (Fig. ​(Fig.2D).2D). In addition, Shannon–Wiener index was higher in vaccinated infected (VI) compared to unvaccinated infected individuals (UI) (P.adj=0.024), suggesting a significant increase in taxa abundance and richness following vaccination. The impact of vaccination is further confirmed with increased within-group diversity among vaccinated uninfected (VU) compared to unvaccinated infected (UI) individuals (Shannon–Wiener and Simpson indices: P.adj<0.05) (Fig. ​(Fig.2D).2D). The significant Simpson index indicates a change in evenness between microbial diversity associated with being infected without vaccination, and receiving the attenuated virus through vaccines.

Phylogenetically distinct taxa explain higher diversity between groups

Principal coordinate analysis (PCoA) was used to visualize the ordination of dissimilarity between sample groups based on Bray‒Curtis (BC), which considers species composition and weighted UniFrac (wUF) distances for bacterial phylogenetic relatedness. Both approaches showed that infected and uninfected groups shared largely similar microbes with few taxa driving little yet significant differences between them. Variation based on bacteria abundance was between 4–7% (PERMANOVA: R²>0.03; P.adj=0.001) (Fig. ​(Fig.3A).3A). Diversity between infected and uninfected individuals in the post-vaccine population was lower than observed pre-vaccine with no difference between the infection groups (PERMANOVA: R²<0.015; P-adj.>0.05), unless vaccination statuses were considered (Fig. ​(Fig.33B).

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Fig. 3

Non-metric dimensional scaling (NMDS) showing sample ordination based on Bray–Curtis dissimilarity and weighted UniFrac phylogenetic distances. The microbial diversity is compared between SARS-CoV-2-positive and -negative individuals before vaccine (A) and post-vaccine (B) availability considering the vaccination status in the latter sample set. UU=unvaccinated uninfected; VU=vaccinated uninfected; UI=unvaccinated infected and VI=vaccinated infected. Samples are coloured dots and ellipses depict 95% confident intervals of the sample clustering per group. The inserted table are PERMANOVA results for vaccination*infection status groups, showing the F-statistic (effect size of variance between compared pair), R² (quantified variation) and P. adjusted value (significant results are in bold.)

Clustering based on phylogenetic distances always explained a higher variation between test groups than compositional dissimilarity based on counts (Fig. ​(Fig.3).3). However, there were also significant group dispersions (betadisp: Bray–Curtis [P.adj=0.005]; weighted UniFrac [P.adj=0.01]) in the pre-vaccine population suggesting that besides distinct taxa, β-diversity was also influenced by differences in taxa composition within groups in this sample set. Despite this, the microbial differences between SARS-CoV-2-positive and -negative groups were still larger than individual-to-individual differences within each group (⍺-diversity) (ANOSIM: R=0.17; P=0.001).

Bacillota and bacteroidota constitute majority of high abundant bacteria

The relative abundance of bacterial taxa was observed at the genus level and plotted to show those with>0.01 total relative abundance. Out of 200 genera identified in the merged dataset, 23 (pre-vaccine=21; post-vaccine=16) constituted those with relative abundance>0.01 and were considered ‘high abundant’ bacteria (Fig. ​(Fig.4).4). Majority (22 out of 23) of these genera belonged to 5 phyla that together made up>90% of the bacteria present (Fig S2). Nine genera belonged to Bacillota (formerly Firmicutes) and four were Bacteroidota (formerly Bacteroidetes). Twelve of these genera, including Prevotella, Lachnoanaerobaculum, Rothia, Actinomyces and Fusobacterium, were common among pre- and post-vaccine populations. Brevundimonas, Corynebacterium, Dolosigranulum and Finegoldia were ‘high abundant’ taxa only among pre-vaccination samples, while Methylobacterium-Methylorubrum and Leifsonia were prevalent among post-vaccine samples (Fig. ​(Fig.4).4). Distribution of bacteria was generally very patchy, with between 1–5 constituting majority (>90%) of the microbes in any given sample (Fig. ​(Fig.44).

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Fig. 4

Heatmaps of bacterial genera with average relative abundance>0.01 in disease groups pre-vaccine availability (A) and vaccination*infection status in the post-vaccine (B) population. Each coloured rectangular block represents the relative abundance of a genus in a sample

Many butyrate-producing bacteria are associated with infection and vaccination

Differentially abundant bacteria that are associated with test groups were also investigated at the genus level since species level classification with short reads of the 16SrRNA variable regions are often inconclusive [35]. The range of identification was not limited to ‘high abundant’ (total average relative abundance>0.01) but, included low-abundance taxa if they were at least 0.01 relative abundance in one of the tested groups. Although many bacteria could be identified as differentially significant between groups, linear discriminant analyses (LDA) were only plotted for bacteria that satisfied this criterion (Table S2) and had an LDA score≥3 instead of the default LDA score of 2 [34].

A total of 16 bacterial genera were differentially abundant between SARS-CoV-2-positive and SARS-CoV-2-negative individuals pre-vaccine availability (Fig. ​(Fig.5A).5A). These included 13 of the ‘high abundant’ taxa previously observed (Fig. ​(Fig.4A)4A) and 3 ‘low abundant’ bacteria. Two of these ‘low abundant’ or rare bacteria were differential discriminants of infected individuals (Fig. ​(Fig.5A).5A). About 64% of the differentially abundant bacteria in SARS-CoV-2 positive samples were members of the phylum Bacteroidota and Bacillota (particularly class Clostridia), which are known to be important butyrate producers [36]. Two members of class Clostridia; Finegoldia and Peptoniphilus, were also more abundant in negative individuals.

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Fig. 5

Linear discriminant analysis (LDA) plots for the identification of significant differential microbes between sample groups. The SARS-CoV-2-infected and uninfected persons are compared among pre-vaccine (A) and post-vaccine (B) populations. Individuals are also compared grouped according to their vaccination*infection status (C)

In the post-vaccine population, however, only Atopobium differed between infection status (Fig. ​(Fig.5B),5B), reiterating reduced diversity in this cohort. It was observed to be more abundant in positive individuals as was observed in the pre-vaccine population (Fig. ​(Fig.5A).5A). Accounting for vaccination status, 7 bacteria were found to be differentially prevalent among the test groups (Fig. ​(Fig.5C).5C). Interestingly, Finegoldia was again associated with unvaccinated uninfected (UU) individuals confirming its significant correlation with non-infection in the study population with regards to SARS-CoV-2. It is worth noting that Fusobacterium, another butyrate producer, was identified as a discriminating microbe, increasing in vaccinated uninfected (VU) persons pre-vaccine and, in positive persons before vaccines were available (Fig. ​(Fig.5A,5A, ​A,55C).

Discussion

This study is the first to compare the microbial diversity of an African population in relation to infection with SARS-CoV-2 since the pandemic. This study was conducted to address the missing information on how the African microbiome correlated with significant variables that characterised the course of the disease. Two sample sets were obtained in a cross-sectional study design, allowing assessment of the microbial community of the study population at two significant milestones of the COVID-19 pandemic. ie. the peak of the pandemic and after vaccination against SARS-CoV-2 was introduced into the study country. Ghana first received the Oxford-AstraZeneca vaccine, which was primarily rolled-out during the vaccination campaign after which others, including Pfizer BioNTech, Johnson & Johnson and Moderna also became available by June 2022 when the second sample collection was done. While comparing the naso-oropharyngeal microbiome of infected and uninfected within each sample set, we also compared between the two sample sets to conceptualize microbiome changes in the population given the timelines presented. Based on infection status alone, we describe the study population as homogenous with dysbiosis and individual-individual microbial differences evident only when vaccination status is accounted for. While Atopobium was significantly associated with SARS-CoV-2 infection, several members of two major butyrate-producing taxa, Bacillota and Bacteroidota, dominated the naso-oropharyngeal microbiome in both infected and uninfected groups, whether vaccinated or not. Overall, our results are suggestive of a population with protective immunity that may be linked to high abundance of butyrate-producers.

Studies correlating naso-oropharyngeal or oral microbiome with COVI-19 have been conducted in developed countries on other continents including Asia, Europe and North America [14, 15, 37–39]. Their results generally showed significant differential diversity among infected persons and microbial dysbiosis between infected and uninfected groups. While some reported increased diversity [15], others showed a reduction in infected individuals [14, 37]. Contrary to these results, the current study population reports no differences in ⍺-diversity between infected and uninfected persons. These results are indicative of differential microbial responses to infection within and between geographically distant populations.

Human microbiomes differ across continents and, between significantly distant settings within the same geographical location (e.g. country) [40, 41]. It is becoming increasingly evident how the extent of this diversity has been largely underestimated [42, 43]. Among several confounding factors that explain these differences, the ones usually studied in association with microbiomes are ethnicity, age, diet and disease [42, 44–47]. This current study used samples received or collected at a COVID-19 testing centre in Accra, the capital city of Ghana which has the largest population of any region in the country. Information on ethnicity was not collected because it was not relevant for testing suspected cases of COVID-19, and because the samples were not purposely collected for microbiome analyses. However, all study participants were resident in Accra (data collected from questionnaire) and only 4 were foreign nationals. Accra is the most cosmopolitan city in Ghana, and the study participants evident from their names (data not shown) originated from various ethnic groups within the country. Their residency in Accra creates the avenue for several common urbanised lifestyles that can potentially promote homogeneity in their microbiomes.

During the pandemic, it became apparent that COVID-19 severity was linked to the age structure of the population, particularly that of confirmed cases [48–50]. The association of elderly population with COVID-19 severity has also been linked to differences in microbiome structure between old and young [15]. Africa is considered to have the youngest population in the world, having a median age of 19 [51]. More than 70% of Ghana’s population is 35 years and below [52]. The age structure of our study population had a median age of 35 (range 18–68) and 37 years as the median age of confirmed cases (Table S1), which is much younger than reported in oral microbiome studies conducted in Asia and Europe [14, 39]. Only one participant in the post-vaccination group was above 60. Although age data was missing for the pre-vaccination sample set, a previous study conducted in Ghana pre-vaccination demonstrated that 60% of confirmed cases are reported to be in the 20–39 age bracket [53].

Following COVID-19 vaccination, the SARS-CoV-2 spike protein is detectable in plasma and triggers inflammation in epithelial and mucosal sites like the gut [54, 55], and its association with immunity and the gut dysbiosis has been described [55, 56]. Vaccination against COVID-19 has been shown to decrease gut microbial diversity and correlate with stronger immune responses [56]. Similarly, we found our post-vaccination population to have reduced diversity in the naso-oropharynx compared to the study population prior, indicative of positive responses and increased immunity resulting from the vaccines. Given the physical connection and chemical communication between the oral-gut axis [57], we can imply that the dysbiosis observed at both sites are linked, but whether the same microbes are regulated at both sites will have to be confirmed with matched analyses of stool and naso-oropharyngeal swabs.

Comparable to other reports of gut and oral microbiome association with COVID-19, we found several opportunistic periodontitis species correlating with infection. Among these Prevotella [14, 38], Atopobium [14], Actinomyces [58], Porphyromonas [15] Lachnospiraceae, and Leptotrichia [39] significantly increased in infected persons. Although these bacteria were not identified to the species level in the current study, their general lactic acid-producing characteristics and the formation of biofilms [59] support their links with the naso-oropharynx in COVID-19 infections. Particularly, Atopobium was found significantly associated with SARS-CoV-2 positivity in both pre- and post-vaccine groups, and even in vaccinated-infected persons. Based on these results, Atopobium appears to be a striking microbial marker for detecting SARS-CoV-2 infection in this population, but further studies are required to confirm their predictive efficiency. Prevotella is known to be the most abundant genus of the oral microbiome [60] and a notably abundant characteristic microbe in the gut composition of African populations [40, 43] due to high plant and fibre diets [43, 61]. They are also known to be linked to epithelial cytokines and neutrophil recruitment in acute respiratory and chronic diseases [62–64], which would explain their increased abundance in infected persons, primarily in the pre-vaccine population which was presumably novel to the virus. Prevotella, Alloprevotella and Leptotrichia, also have liposaccharide (LPS)-producing properties which are recognised for their involvement in immunoinhibitory pathways [65, 66] but can also be an important host immune stimulant depending on the associated microbial species (reviewed in [67]).

About 61% of all differentially abundant bacteria observed belonged to phyla Bacillota (particularly, class Clostridia) and Bacteroidota, two major butyrate-producing taxa [68–71]. While we find some of these butyrate-producing taxa in uninfected persons, the richness of these microbes was further increased in the presence of the viral infection. Although vaccination reduced species diversity, the bacteria that were differentially abundant in those vaccinated comprised of two Bacillota; Solobacterium and Lachnoanaerobaculum. Butyrate is a short-chain fatty acid that is significant in pathophysiological processes in humans related to inflammatory diseases. They are involved in reducing mucosal inflammation, influencing the fortification of epithelial barriers and promoting the relief of oxidative stress [72]. Butyrate-producing microbes form an essential part of the human gut and oral cavity, are acquired in infancy [73] and are maintained in the adult gut through an increase in fibrous diets [74]. In SARS-CoV-2 infection, butyrate has been shown to be effective in regulating the expression of ACE2, proinflammatory cytokines and other genes linked to the progression and disease outcomes of COVID-19 [75] and are predicted to be low in severe COVID-19 [76]. A recent study has directly linked butyrate to reduced cell apoptosis and upregulation of immunity against SARS-CoV-2 infection in experimental mice [77]. Higher abundance of these bacteria in the current study population therefore could be linked to low disease severity and high prevalence of asymptomatic infections [78, 79].

Conclusion

Our study is limited by the small number of participants, particularly in each group. There is also demographic information about the participants we did not have access to such as their disease progression, number of times they have had the infection, and the time lapse between when they were vaccinated and when they were enrolled into the study. These important confounding factors could have helped explain our results better. However, no similar study has collected all of these in their microbiome-related research, reiterating the importance of standardizing methods for microbiome studies. Despite these the data presented here has shown results that can be used as the basis for further investigating the significance of high abundance of butyrate-producers to disease outcomes, particularly in African populations. The use of other high-throughput techniques such shotgun metagenomics can provide comprehensive functional information of the bacteria associated with infection and vaccination.

Supplementary Information

Acknowledgements

Authors’ contributions

JA, MO, and IOD conceptualized the study. JA and MO designed the experiments. MO, MSA, GYKB, RYI, EO-A, and DA-A collected and analysed postvaccinated samples for viral detection and extracted DNA. JA and FA-T analysed the data and drafted the manuscript. All the authors have read and approved the final manuscript.

Funding

This study was partly supported by funds from TIBA Out of Africa Fellowship (funded by the National Institute of Health) to JA, National Research Foundation COVID-19 Africa Rapid Grant Fund (COV19200608529373) to JA and the NMIMR Office for Research Support Fund (EC/P25421/03) to JA. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information file. The demultiplexed sequence reads generated in this study are available in the NCBI SRA database under BioProject PRJNA995054.

Declarations

Footnotes

References