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Malaria cases are frequently recorded in the Ethiopian highlands even at altitudes above 2000 m. The epidemiology of malaria in the Ethiopian highlands, and, in particular, the role of importation by human migration from the highly endemic lowlands is not well understood. We sequenced 187 Plasmodium falciparum samples from two sites in the Ethiopian highlands, Gondar (n = 159) and Ziway (n = 28), using a multiplexed droplet digital PCR (ddPCR)‐based amplicon sequencing method targeting 35 microhaplotypes and drug resistance loci. Here, we characterize the parasite population structure and genetic relatedness. We identify moderate parasite diversity (mean HE: 0.54) and low infection complexity (74.9% monoclonal). A significant percentage of infections share microhaplotypes, even across transmission seasons and sites, indicating persistent local transmission. We identify multiple clusters of clonal or near‐clonal infections, highlighting high genetic relatedness. Only 6.3% of individuals diagnosed with P. falciparum reported recent travel. Yet, in clonal or near‐clonal clusters, infections of travellers were frequently observed first in time, suggesting that parasites may have been imported and then transmitted locally. 31.1% of infections are pfhrp2‐deleted and 84.4% pfhrp3‐deleted, and 28.7% have pfhrp2/3 double deletions. Parasites with pfhrp2/3 deletions and wild‐type parasites are genetically distinct. Mutations associated with resistance to sulphadoxine–pyrimethamine or suggested to reduce sensitivity to lumefantrine are observed at near‐fixation. In conclusion, genomic data corroborate local transmission and the importance of intensified control in the Ethiopian highlands. [ABSTRACT FROM AUTHOR]

Zanzibar has made significant progress toward malaria elimination, but recent stagnation requires novel approaches. We developed a highly multiplexed droplet digital PCR (ddPCR)-based amplicon sequencing method targeting 35 microhaplotypes and drug-resistance loci, and successfully sequenced 290 samples from five districts covering both main islands. Here, we elucidate fine-scale Plasmodium falciparum population structure and infer relatedness and connectivity of infections using an identity-by-descent (IBD) approach. Despite high genetic diversity, we observe pronounced fine-scale spatial and temporal parasite genetic structure. Clusters of near-clonal infections on Pemba indicate persistent local transmission with limited parasite importation, presenting an opportunity for local elimination efforts. Furthermore, we observe an admixed parasite population on Unguja and detect a substantial fraction (2.9%) of significantly related infection pairs between Zanzibar and the mainland, suggesting recent importation. Our study provides a high-resolution view of parasite genetic structure across the Zanzibar archipelago and provides actionable insights for prioritizing malaria elimination efforts. Sequencing malaria parasites from low density infections in small amounts of dried blood is important for large-scale genomic surveillance. Here, the authors develop and validate a highly multiplexed droplet digital PCR-based amplicon deep sequencing assay and apply it to data from Zanzibar, Tanzania. [ABSTRACT FROM AUTHOR]

Background: Despite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response.Methods: We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies to assess sustainable vaccine manufacturing needs, vaccine stockpile requirements, and the potential impact of the outbreak response. This framework incorporates geographically variable zoonotic spillover rates, human-to-human transmission, and the implementation of reactive vaccination campaigns in response to disease outbreaks. As proof of concept, we applied the framework to four priority pathogens: Lassa virus, Nipah virus, MERS coronavirus, and Rift Valley virus.Results: Annual vaccine regimen requirements for a population-wide strategy ranged from > 670,000 (95% prediction interval 0-3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI 0-8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination.Conclusions: Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios. Uncertainties in our model estimates highlight several knowledge gaps that need to be addressed to target vulnerable populations more accurately. These include surveillance gaps that mask the true geographic distribution of each pathogen, details of key routes of spillover from animal reservoirs to humans, and the role of human-to-human transmission outside of healthcare settings. In addition, our estimates are based on the current epidemiology of each pathogen, but pathogen evolution could alter vaccine stockpile requirements. [ABSTRACT FROM AUTHOR]

Background: Despite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response. Methods: We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies to assess sustainable vaccine manufacturing needs, vaccine stockpile requirements, and the potential impact of the outbreak response. This framework incorporates geographically variable zoonotic spillover rates, human-to-human transmission, and the implementation of reactive vaccination campaigns in response to disease outbreaks. As proof of concept, we applied the framework to four priority pathogens: Lassa virus, Nipah virus, MERS coronavirus, and Rift Valley virus. Results: Annual vaccine regimen requirements for a population-wide strategy ranged from > 670,000 (95% prediction interval 0–3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI 0–8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination. Conclusions: Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios. Uncertainties in our model estimates highlight several knowledge gaps that need to be addressed to target vulnerable populations more accurately. These include surveillance gaps that mask the true geographic distribution of each pathogen, details of key routes of spillover from animal reservoirs to humans, and the role of human-to-human transmission outside of healthcare settings. In addition, our estimates are based on the current epidemiology of each pathogen, but pathogen evolution could alter vaccine stockpile requirements. [ABSTRACT FROM AUTHOR]

Background: Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. Methods: The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated. Results: The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, Rc. Applying this approach to data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. Conclusions: These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution. [ABSTRACT FROM AUTHOR]

By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95%PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model's predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as expoBy March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95%PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model's predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.nential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States. [ABSTRACT FROM AUTHOR]

Clinical trials monitoring malaria drug resistance require genotyping of recurrent Plasmodium falciparum parasites to distinguish between treatment failure and new infection occurring during the trial follow up period. Because trial participants usually harbour multi-clonal P. falciparum infections, deep amplicon sequencing (AmpSeq) was employed to improve sensitivity and reliability of minority clone detection. Paired samples from 32 drug trial participants were Illumina deep-sequenced for five molecular markers. Reads were analysed by custom-made software HaplotypR and trial outcomes compared to results from the previous standard genotyping method based on length-polymorphic markers. Diversity of AmpSeq markers in pre-treatment samples was comparable or higher than length-polymorphic markers. AmpSeq was highly reproducible with consistent quantification of co-infecting parasite clones within a host. Outcomes of the three best-performing markers, cpmp, cpp and ama1-D3, agreed in 26/32 (81%) of patients. Discordance between the three markers performed per sample was much lower by AmpSeq (six patients) compared to length-polymorphic markers (eleven patients). Using AmpSeq for discrimination of recrudescence and new infection in antimalarial drug trials provides highly reproducible and robust characterization of clone dynamics during trial follow-up. AmpSeq overcomes limitations inherent to length-polymorphic markers. Regulatory clinical trials of antimalarial drugs will greatly benefit from this unbiased typing method. [ABSTRACT FROM AUTHOR]

Cigarette smoke comprises a large number of carcinogenic substances that can increase DNA mutation load in epithelial cells of the mouth, throat and lungs. While a strong C:A substitution preference is abundant in tobacco-related cancer genomes, detection of complex or less abundant somatic mutation signatures may be confounded by the heterogeneity of carcinogens present in smoke. Trinucleotide signatures are defined for a variety of somatic mutation processes, yet the extent to which this configuration optimally defines and discriminates between mutational processes is not clear. Here, we describe a method that determines whether trinucleotide patterns do a good job at encapsulating a mutation signature or whether they mask underlying heterogeneity that alternative pattern structures would better define. The approach works by mapping the dependency of trinucleotide signatures in relation to sequence context to establish a 'footprint' of context dependency. Applying this technique to smoke-associated cancers, we show that a robust tetranucleotide substitution is prevalent in 17% of lung squamous cell carcinoma genomes. The signature is dominated by the substitution CT(C:A)G and is strongly associated with gene expression level and intron-exon junctions. Intriguingly, its distribution across the genome is biased towards 5' splice junctions, suggesting a novel mechanism of mutation. [ABSTRACT FROM AUTHOR]

Background: Amplicon deep sequencing permits sensitive detection of minority clones and improves discriminatory power for genotyping multi-clone Plasmodium falciparum infections. New amplicon sequencing and data analysis protocols are needed for genotyping in epidemiological studies and drug efficacy trials of P. falciparum. Methods: Targeted sequencing of molecular marker csp and novel marker cpmp was conducted in duplicate on mixtures of parasite culture strains and 37 field samples. A protocol allowing to multiplex up to 384 samples in a single sequencing run was applied. Software "HaplotypR" was developed for data analysis. Results: Cpmp was highly diverse (He = 0.96) in contrast to csp (He = 0.57). Minority clones were robustly detected if their frequency was >1%. False haplotype calls owing to sequencing errors were observed below that threshold. Conclusions: To reliably detect haplotypes at very low frequencies, experiments are best performed in duplicate and should aim for coverage of >10'000 reads/amplicon. When compared to length polymorphic marker msp2, highly multiplexed amplicon sequencing displayed greater sensitivity in detecting minority clones. [ABSTRACT FROM AUTHOR]

For the most part metazoan genomes are highly methylated and harbor only small regions with low or absent methylation. In contrast, partially methylated domains (PMDs), recently discovered in a variety of cell lines and tissues, do not fit this paradigm as they show partial methylation for large portions (20%–40%) of the genome. While in PMDs methylation levels are reduced on average, we found that at single CpG resolution, they show extensive variability along the genome outside of CpG islands and DNase I hypersensitive sites (DHS). Methylation levels range from 0% to 100% in a roughly uniform fashion with only little similarity between neighboring CpGs. A comparison of various PMD-containing methylomes showed that these seemingly disordered states of methylation are strongly conserved across cell types for virtually every PMD. Comparative sequence analysis suggests that DNA sequence is a major determinant of these methylation states. This is further substantiated by a purely sequence based model which can predict 31% (R2) of the variation in methylation. The model revealed CpG density as the main driving feature promoting methylation, opposite to what has been shown for CpG islands, followed by various dinucleotides immediately flanking the CpG and a minor contribution from sequence preferences reflecting nucleosome positioning. Taken together we provide a reinterpretation for the nucleotide-specific methylation levels observed in PMDs, demonstrate their conservation across tissues and suggest that they are mainly determined by specific DNA sequence features. [ABSTRACT FROM AUTHOR]

Summary: QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization. [ABSTRACT FROM AUTHOR]

During the past 100 years, Rift Valley fever virus (RVFV), a mosquito-borne virus, has caused potentially lethal disease in livestock, and has been associated with significant economic losses and trade bans. Spillover to humans occurs and can be fatal. Here, we combined data on RVF disease in humans (22 countries) and animals (37 countries) from 1931 to 2020 with seroprevalence studies from 1950 to 2020 (n = 228) from publicly available databases and publications to draw a more complete picture of the past and current RVFV epidemiology. RVFV has spread from its original locus in Kenya throughout Africa and into the Arabian Peninsula. Throughout the study period seroprevalence increased in both humans and animals, suggesting potentially increased RVFV exposure. In 24 countries, animals or humans tested positive for RVFV antibodies even though outbreaks had never been reported there, suggesting RVFV transmission may well go unnoticed. Among ruminants, sheep were the most likely to be exposed during RVF outbreaks, but not during periods of cryptic spread. We discuss critical data gaps and highlight the need for detailed study descriptions, and long-term studies using a one health approach to further convert the patchwork of data to the tale of RFV epidemiology. [ABSTRACT FROM AUTHOR]

Longitudinal tracking of individual Plasmodium falciparum strains in multi-clonal infections is essential for investigating infection dynamics of malaria. The traditional genotyping techniques did not permit tracking changes in individual clone density during persistent natural infections. Amplicon deep sequencing (Amp-Seq) offers a tool to address this knowledge gap. The sensitivity of Amp-Seq for relative quantification of clones was investigated using three molecular markers, ama1-D2, ama1-D3, and cpmp. Amp-Seq and length-polymorphism based genotyping were compared for their performance in following minority clones in longitudinal samples from Papua New Guinea. Amp-Seq markers were superior to length-polymorphic marker msp2 in detecting minority clones (sensitivity Amp-Seq: 95%, msp2: 85%). Multiplicity of infection (MOI) by Amp-Seq was 2.32 versus 1.73 for msp2. The higher sensitivity had no effect on estimates of force of infection because missed minority clones were detected in preceding or succeeding bleeds. Individual clone densities were tracked longitudinally by Amp-Seq despite MOI > 1, thus providing an additional parameter for investigating malaria infection dynamics. Amp-Seq based genotyping of longitudinal samples improves detection of minority clones and estimates of MOI. Amp-Seq permits tracking of clone density over time to study clone competition or the dynamics of specific, i.e. resistance-associated genotypes. [ABSTRACT FROM AUTHOR]