Mutational signature dynamics indicate SARS-CoV-2's evolutionary capacity is driven by host antiviral molecules.

The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to more efficient human infection since the emergence of this new human coronavirus in late 2019. Here, we use machine learning models to identify shared signatures, i.e., common underlying mutational processes and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and virus properties were associated with the waves of regional SARS-CoV-2 reported infection numbers and this impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity and the effectiveness of government stringency. To assess underlying evolutionary change, we used non-negative matrix factorisation and observed three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 1, 2 and 3 were biased to C→T, T→C/A→G and G→T point mutations. We hypothesise assignments of these mutational signatures to the host antiviral molecules APOBEC, ADAR and ROS respectively. We observe a shift amidst the pandemic in relative mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. This could represent changes in how the virus and the host immune response interact and indicates how SARS-CoV-2 may continue to generate variation in the future. Linkage of the detected mutational signatures to the VOC-defining amino acids substitutions indicates the majority of SARS-CoV-2's evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors.

Rhinovirus dynamics across different social structures.

Rhinoviruses (RV), common human respiratory viruses, exhibit significant antigenic diversity, yet their dynamics across distinct social structures remain poorly understood. Our study delves into RV dynamics within Kenya by analysing VP4/2 sequences across four different social structures: households, a public primary school, outpatient clinics in the Kilifi Health and Demographics Surveillance System (HDSS), and countrywide hospital admissions and outpatients. The study revealed the greatest diversity of RV infections at the countrywide level (114 types), followed by the Kilifi HDSS (78 types), the school (47 types), and households (40 types), cumulatively representing >90% of all known RV types. Notably, RV diversity correlated directly with the size of the population under observation, and several RV type variants occasionally fuelled RV infection waves. Our findings highlight the critical role of social structures in shaping RV dynamics, information that can be leveraged to enhance public health strategies. Future research should incorporate whole-genome analysis to understand fine-scale evolution across various social structures.

Whole genome sequencing of two human rhinovirus A types (A101 and A15) detected in Kenya, 2016-2018.

Background: Virus genome sequencing is increasingly utilized in epidemiological surveillance. Genomic data allows comprehensive evaluation of underlying viral diversity and epidemiology to inform control. For human rhinovirus (HRV), genomic amplification and sequencing is challenging due to numerous types, high genetic diversity and inadequate reference sequences. Methods: We developed a tiled amplicon type-specific protocol for genome amplification and sequencing on the Illumina MiSeq platform of two HRV types, A15 and A101. We then assessed added value in analyzing whole genomes relative to the VP4/2 region only in the investigation of HRV molecular epidemiology within the community in Kilifi, coastal Kenya. Results: We processed 73 nasopharyngeal swabs collected between 2016-2018, and 48 yielded at least 70% HRV genome coverage. These included all A101 samples (n=10) and 38 (60.3%) A15 samples.  Phylogenetic analysis revealed that the Kilifi A101 sequences interspersed with global A101 genomes available in GenBank collected between 1999-2016. On the other hand, our A15 sequences formed a monophyletic group separate from the global genomes collected in 2008 and 2019. An improved phylogenetic resolution was observed with the genome phylogenies compared to the VP4/2 phylogenies. Conclusions: We present a type-specific full genome sequencing approach for obtaining HRV genomic data and characterizing infections.

Trends and Intensity of Rhinovirus Invasions in Kilifi, Coastal Kenya, Over a 12-Year Period, 2007-2018.

BACKGROUND: Rhinoviruses (RVs) are ubiquitous pathogens and the principal etiological agents of common cold. Despite the high frequency of RV infections, data describing their long-term epidemiological patterns in a defined population remain limited. METHODS: Here, we analyzed 1070 VP4/VP2 genomic region sequences sampled at Kilifi County Hospital on the Kenya coast. The samples were collected between 2007 and 2018 from hospitalized pediatric patients (<60 months of age) with acute respiratory illness. RESULTS: Of 7231 children enrolled, RV was detected in 1497 (20.7%) and VP4/VP2 sequences were recovered from 1070 samples (71.5%). A total of 144 different RV types were identified (67 Rhinovirus A, 18 Rhinovirus B, and 59 Rhinovirus C) and at any month, several types co-circulated with alternating predominance. Within types, multiple genetically divergent variants were observed. Ongoing RV infections through time appeared to be a combination of (1) persistent types (observed up to 7 consecutive months), (2) reintroduced genetically distinct variants, and (3) new invasions (average of 8 new types annually). CONCLUSIONS: Sustained RV presence in the Kilifi community is mainly due to frequent invasion by new types and variants rather than continuous transmission of locally established types/variants.

Molecular Epidemiology of Human Rhinovirus From 1-Year Surveillance Within a School Setting in Rural Coastal Kenya.

BACKGROUND: Human rhinovirus (HRV) is the most common cause of the common cold but may also lead to more severe respiratory illness in vulnerable populations. The epidemiology and genetic diversity of HRV within a school setting have not been previously described. The objective of this study was to characterize HRV molecular epidemiology in a primary school in a rural location of Kenya. METHODS: Between May 2017 and April 2018, over 3 school terms, we collected 1859 nasopharyngeal swabs (NPS) from pupils and teachers with symptoms of acute respiratory infection in a public primary school in Kilifi County, coastal Kenya. The samples were tested for HRV using real-time reverse transcription polymerase chain reaction. HRV-positive samples were sequenced in the VP4/VP2 coding region for species and genotype classification. RESULTS: A total of 307 NPS (16.4%) from 164 individuals were HRV positive, and 253 (82.4%) were successfully sequenced. The proportion of HRV in the lower primary classes was higher (19.8%) than upper primary classes (12.2%; P < .001). HRV-A was the most common species (134/253; 53.0%), followed by HRV-C (73/253; 28.9%) and HRV-B (46/253; 18.2%). Phylogenetic analysis identified 47 HRV genotypes. The most common genotypes were A2 and B70. Numerous (up to 22 in 1 school term) genotypes circulated simultaneously, there was no individual re-infection with the same genotype, and no genotype was detected in all 3 school terms. CONCLUSIONS: HRV was frequently detected among school-going children with mild acute respiratory illness symptoms, particularly in the younger age groups (<5-year-olds). Multiple HRV introductions were observed that were characterized by considerable genotype diversity.

Genome Sequences of Human Coronavirus OC43 and NL63, Associated with Respiratory Infections in Kilifi, Kenya.

Coding-complete genomes of two human coronavirus OC43 strains and one NL63 strain were obtained by metagenomic sequencing of clinical samples collected in 2017 and 2018 in Kilifi, Kenya. Maximum likelihood phylogenies showed that the OC43 strains were genetically dissimilar and that the NL63 strain was closely related to NL63 genotype B viruses.