The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Phylogenetic Variants of <i>Rickettsia africae</i>, and Incidental Identification of "<i>Candidatus</i> Rickettsia Moyalensis" in Kenya

<div><p>Background</p><p><i>Rickettsia africae</i>, the etiological agent of African tick bite fever, is widely distributed in sub-Saharan Africa. Contrary to reports of its homogeneity, a localized study in Asembo, Kenya recently reported high genetic diversity. The present study aims to elucidate the extent of this heterogeneity by examining archived <i>Rickettsia africae</i> DNA samples collected from different eco-regions of Kenya.</p><p>Methods</p><p>To evaluate their phylogenetic relationships, archived genomic DNA obtained from 57 ticks <i>a priori</i> identified to contain <i>R</i>. <i>africae</i> by comparison to <i>ompA</i>, <i>ompB</i> and <i>gltA</i> genes was used to amplify five rickettsial genes i.e. <i>gltA</i>, <i>ompA</i>, <i>ompB</i>, 17kDa and <i>sca4</i>. The resulting amplicons were sequenced. Translated amino acid alignments were used to guide the nucleotide alignments. Single gene and concatenated alignments were used to infer phylogenetic relationships.</p><p>Results</p><p>Out of the 57 DNA samples, three were determined to be <i>R</i>. <i>aeschlimanii</i> and not <i>R</i>. <i>africae</i>. One sample turned out to be a novel rickettsiae and an interim name of “<i>Candidatus</i> Rickettsia moyalensis” is proposed. The bonafide <i>R</i>. <i>africae</i> formed two distinct clades. Clade I contained 9% of the samples and branched with the validated <i>R</i>. <i>africae str ESF-5</i>, while clade II (two samples) formed a distinct sub-lineage.</p><p>Conclusions</p><p>This data supports the use of multiple genes for phylogenetic inferences. It is determined that, despite its recent emergence, the <i>R</i>. <i>africae</i> lineage is diverse. This data also provides evidence of a novel Rickettsia species, <i>Candidatus</i> Rickettsia moyalensis.</p></div

Plasmodium falciparum population structure inferred by msp1 amplicon sequencing of parasites collected from febrile patients in Kenya

Abstract Background Multiplicity of infection (MOI) is an important measure of Plasmodium falciparum diversity, usually derived from the highly polymorphic genes, such as msp1, msp2 and glurp as well as microsatellites. Conventional methods of deriving MOI lack fine resolution needed to discriminate minor clones. This study used amplicon sequencing (AmpliSeq) of P. falciparum msp1 ( Pfmsp1) to measure spatial and temporal genetic diversity of P. falciparum. Methods 264 P. falciparum positive blood samples collected from areas of differing malaria endemicities between 2010 and 2019 were used. Pfmsp1 gene was amplified and amplicon libraries sequenced on Illumina MiSeq. Sequences were aligned against a reference sequence (NC_004330.2) and clustered to detect fragment length polymorphism and amino acid variations. Results Children  5–14 (= 25.3 ± 5 SD), and those > 15 (= 25.1 ± 6 SD). Of the alleles detected, 553 (54.5%) were K1, 250 (24.7%) MAD20 and 211 (20.8%) RO33 that grouped into 19 K1 allelic families (108–270 bp), 14 MAD20 (108–216 bp) and one RO33 (153 bp). AmpliSeq revealed nucleotide polymorphisms in alleles that had similar sizes, thus increasing the K1 to 104, 58 for MAD20 and 14 for RO33. By AmpliSeq, the mean MOI was 4.8 (± 0.78, 95% CI) for the malaria endemic Lake Victoria region, 4.4 (± 1.03, 95% CI) for the epidemic prone Kisii Highland and 3.4 (± 0.62, 95% CI) for the seasonal malaria Semi-Arid region. MOI decreased with age: 4.5 (± 0.76, 95% CI) for children  15. Females’ MOI (4.2 ± 0.66, 95% CI) was not different from males 4.0 (± 0.61, 95% CI). In all regions, the number of alleles were high in the 2014–2015 period, more so in the Lake Victoria and the seasonal transmission arid regions. Conclusion These findings highlight the added advantages of AmpliSeq in haplotype discrimination and the associated improvement in unravelling complexity of P. falciparum population structure

Phylogeny of Rickettsia sequences from this study and those collected previously in Kenya [23, 31].

<p><i>ompA</i> nucleotide sequences of study isolates and other <i>R</i>. <i>africae</i> reported from previous studies [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004788#pntd.0004788.ref023" target="_blank">23</a>,<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004788#pntd.0004788.ref031" target="_blank">31</a>] were analysed by Maximum Likelihood method using MEGA v7 based on the Hasegawa-Kishino-Yano (HKY) model of substitution. The tree has a log likelihood ratio of -1049 and involved all codon positions. Members of clade I, II and III are shown beside the bolded red, blue and green lines respectively. Sequences from Parola et al 2001 [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004788#pntd.0004788.ref023" target="_blank">23</a>] are shown as black triangles and those from Macaluso et al 2003 [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004788#pntd.0004788.ref031" target="_blank">31</a>] by black circles. Numbers at the nodes are bootstrap proportions with 1000 replicates. Only bootstrap values >50% are shown. The scale bar indicates the number of substitutions per nucleotide position. Clearly, five of our sequences (044, 045 and 164 from Wajir, 176 Moyale and 195 Machakos) are distinct from those described previously.</p

Bayesian probability tree of study samples with validated <i>Rickettsia</i> species.

<p>The tree is based on partitioned concatenated datasets of <i>gltA</i>, <i>ompA</i>, <i>ompB</i>, 17kDa and <i>sca4</i> partial nucleotide sequences. Amino acid alignments were used to guide the nucleotide alignments. The tree is estimated using a GTR+G substitution model as implemented in MrBayes v3.2. The tree is a consensus of 15,002 trees (post burn-in) pooled from two independent Markov Chain run in parallel. Thin lines indicate posterior probability values of < 1. Lineage diversity within the <i>R</i>. <i>africae</i> study samples is highlighted in red and blue to indicate clades i and ii respectively. Samples previously misclassified as <i>R</i>. <i>africae</i> are now classified as <i>R</i>. <i>aeschlimanii</i> (black diamond). Study sample 176_Moyale branches distinctly from other rickettsiae and is considered a novel rickettsia species and a provisional name "<i>Candidatus</i> rickettsia moyalensis" (black circle) is proposed. NB: Although 293_Migori (open circle) branched as a lone taxon, it clustered with <i>R</i>. <i>aeschlimanii</i> by Maximum Likelihood method. Non-spotted fever group lineages are highlighted orange for transition group and grey for typhus group. The status of <i>R</i>. <i>helvetica</i> (shown in black cross), originally in spotted fever group is now uncertain [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004788#pntd.0004788.ref020" target="_blank">20</a>].</p

Primers used for PCR and sequencing.

<p>Primers used for PCR and sequencing.</p

Phylogeny of Rickettsia study samples isolated from diverse eco-regions of Kenya.

<p>Maximum Likelihood trees were obtained from (A) <i>gltA</i>, (B) <i>ompA</i>, (C) <i>ompB</i>, (D) 17kDa and (E) <i>sca4</i> partial nucleotide sequences. Members of clade I, II and III are shown beside the bolded red, blue and green lines respectively. Numbers at the nodes are bootstrap proportions with 1000 replicates. Only bootstrap values >50% are shown. The scale bar indicates the number of substitutions per nucleotide position.</p

High Seroprevalence of Antibodies against Spotted Fever and Scrub Typhus Bacteria in Patients with Febrile Illness, Kenya

Serum samples from patients in Kenya with febrile illnesses were screened for antibodies against bacteria that cause spotted fever, typhus, and scrub typhus. Seroprevalence was 10% for spotted fever group, <1% for typhus group, and 5% for scrub typhus group. Results should help clinicians expand their list of differential diagnoses for undifferentiated fevers