A fundamental mystery for dengue and other infectious pathogens is how observed patterns of cases relate to actual chains of individual transmission events. These pathways are intimately tied to how strains interact and compete across spatial scales. Phylogeographic methods have been used to characterize pathogen dispersal at global and regional scales, but have yielded few insights into the local spatio-temporal structure of endemic transmission. Using geolocated genotype (N=800) and serotype (N=17,291) data, we show that in Bangkok, Thailand, 60% of cases living <200m apart come from the same transmission chain, versus 3% of cases separated by 1–5km. At distances <200m from a case (enclosing an average of 1,300 people in Bangkok) the effective number of chains is 1.7. This increases by 7-fold for each 10-fold increase in enclosed population, whether due to density or increased area; though increases in density over 7,000 people per km2 do not lead to additional chains. Within Thailand these chains quickly mix, and by the next dengue season viral lineages are no longer highly spatially structured within the country. In contrast, viral flow to neighboring countries is limited. These findings are consistent with local, density dependent transmission; and implicate densely populated communities as key sources of viral diversity with home location the focal point of transmission. These findings have important implications for targeted vector control and active surveillance.
Background SARS-CoV-2 epidemiology implicates airborne transmission; aerosol infectiousness and impacts of masks and variants on aerosol shedding are not well understood. Methods We recruited COVID-19 cases to give blood, saliva, mid-turbinate and fomite (phone) swabs, and 30-minute breath samples while vocalizing into a Gesundheit-II, with and without masks at up to two visits two days apart. We quantified and sequenced viral RNA, cultured virus, and assayed sera for anti-spike and anti-receptor binding domain antibodies. Results We enrolled 49 seronegative cases (mean days post onset 3.8 ±2.1), May 2020 through April 2021. We detected SARS-CoV-2 RNA in 45% of fine (≤5 µm), 31% of coarse (>5 µm) aerosols, and 65% of fomite samples overall and in all samples from four alpha-variant cases. Masks reduced viral RNA by 48% (95% confidence interval [CI], 3 to 72%) in fine and by 77% (95% CI, 51 to 89%) in coarse aerosols; cloth and surgical masks were not significantly different. The alpha variant was associated with a 43-fold (95% CI, 6.6 to 280-fold) increase in fine aerosol viral RNA, compared with earlier viruses, that remained a significant 18-fold (95% CI, 3.4 to 92-fold) increase adjusting for viral RNA in saliva, swabs, and other potential confounders. Two fine aerosol samples, collected while participants wore masks, were culture-positive. Conclusion SARS-CoV-2 is evolving toward more efficient aerosol generation and loose-fitting masks provide significant but only modest source control. Therefore, until vaccination rates are very high, continued layered controls and tight-fitting masks and respirators will be necessary.
Human APOBEC3 proteins are cytidine deaminases that contribute broadly to innate immunity through the control of exogenous retrovirus replication and endogenous retroelement retrotransposition. As an intrinsic antiretroviral defense mechanism, APOBEC3 proteins induce extensive guanosine-to-adenosine (G-to-A) mutagenesis and inhibit synthesis of nascent human immunodeficiency virus-type 1 (HIV-1) cDNA. Human APOBEC3 proteins have additionally been proposed to induce infrequent, potentially non-lethal G-to-A mutations that make subtle contributions to sequence diversification of the viral genome and adaptation though acquisition of beneficial mutations. Using single-cycle HIV-1 infections in culture and highly parallel DNA sequencing, we defined trinucleotide contexts of the edited sites for APOBEC3D, APOBEC3F, APOBEC3G, and APOBEC3H. We then compared these APOBEC3 editing contexts with the patterns of G-to-A mutations in HIV-1 DNA in cells obtained sequentially from ten patients with primary HIV-1 infection. Viral substitutions were highest in the preferred trinucleotide contexts of the edited sites for the APOBEC3 deaminases. Consistent with the effects of immune selection, amino acid changes accumulated at the APOBEC3 editing contexts located within human leukocyte antigen (HLA)-appropriate epitopes that are known or predicted to enable peptide binding. Thus, APOBEC3 activity may induce mutations that influence the genetic diversity and adaptation of the HIV-1 population in natural infection.
Infectious disease modeling has played a prominent role in recent outbreaks, yet integrating these analyses into public health decision-making has been challenging. We recommend establishing ‘outbreak science’ as an inter-disciplinary field to improve applied epidemic modeling.
Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, see Authors & Referees and the Editorial Policy Checklist.
Here we report the findings from the first two years of an arbovirus surveillance study conducted in Machala, Ecuador, a dengue endemic region (2014-2015). Patients with suspected dengue virus (DENV) infections (index cases, n=324) were referred from five Ministry of Health clinical sites. A subset of DENV positive index cases (n = 44) were selected, and individuals from the index household and four neighboring homes within 200-meters were recruited (n = 400). Individuals who entered the study, other than index cases, are referred to as associates. In 2014, 70.9% of index cases and 35.6% of associates had acute or recent DENV infections. In 2015, 28.3% of index cases and 12.8% of associates had acute or recent DENV infections. For every DENV infection captured by passive surveillance, we detected an additional three acute or recent DENV infections in associates. Of associates with acute DENV infections, 68% reported dengue-like symptoms, with the highest prevalence of symptomatic acute infections in children under 10 years of age. The first chikungunya virus (CHIKV) infections were detected on epidemiological week 12 in 2015. 43.1% of index cases and 3.5% of associates had acute CHIKV infections. No Zika virus infections were detected. Phylogenetic analyses of isolates of DENV from 2014 revealed genetic relatedness and shared ancestry of DENV1, DENV2 and DENV4 genomes from Ecuador with those from Venezuela and Colombia, indicating presence of viral flow between Ecuador and surrounding countries. Enhanced surveillance studies, such as this, provide high-resolution data on symptomatic and inapparent infections across the population.
Next generation sequencing (NGS) combined with bioinformatics has successfully been used in a vast array of analyses for infectious disease research of public health relevance. For instance, NGS and bioinformatics approaches have been used to identify outbreak origins, track transmissions, investigate epidemic dynamics, determine etiological agents of a disease, and discover novel human pathogens. However, implementation of high-quality NGS and bioinformatics in research and public health laboratories can be challenging. These challenges mainly include the choice of the sequencing platform and the sequencing approach, the choice of bioinformatics methodologies, access to the appropriate computation and information technology infrastructure, and recruiting and retaining personnel with the specialized skills and experience in this field. In this review, we summarize the most common NGS and bioinformatics workflows in the context of infectious disease genomic surveillance and pathogen discovery, and highlight the main challenges and considerations for setting up an NGS and bioinformatics-focused infectious disease research public health laboratory. We describe the most commonly used sequencing platforms and review their strengths and weaknesses. We review sequencing approaches that have been used for various pathogens and study questions, as well as the most common difficulties associated with these approaches that should be considered when implementing in a public health or research setting. In addition, we provide a review of some common bioinformatics tools and procedures used for pathogen discovery and genome assembly, along with the most common challenges and solutions. Finally, we summarize the bioinformatics of advanced viral, bacterial, and parasite pathogen characterization, including types of study questions that can be answered when utilizing NGS and bioinformatics.
Variations in disease enhancement Secondary Dengue virus (DENV) infections can be dangerous if levels of antibodies from prior infection are inadequate to clear the virus. This RNA flavivirus exploits the presence of lower levels of heterotypic antibodies to infect immunoglobulin Fcγ receptor–bearing cells. Many RNA viruses also exhibit antigenic variation, which classically allows evasion of immune responses. Katzelnick et al . investigated whether antigenic variation in DENV has a biological function in a virus that courts immune responses to enhance replication (see the Perspective by Rohani and Drake). Using antigenic cartography on a panel of more than 400 DENV1-4 subtype samples isolated in Bangkok, Thailand, the authors found that antigenic variation in virus populations oscillated between similarity and dissimilarity across subtypes over time, with outbreaks correlating with periods of antigenic dissimilarity within serotypes. This pattern may be at least in part a result of the conflicting evolutionary pressures of immune evasion and immune enhancement. —CA
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.