Biology subjects

Dudas, G.

Publications and source records attributed to Dudas, G..

26 records · Page 2Linked to original sources

bioRxiv2020

Single mosquito metatranscriptomics recovers mosquito species, blood meal sources, and microbial cargo, including viral dark matter

Mosquitoes are major infectious disease-carrying vectors. Assessment of current and future risks associated with the mosquito population requires knowledge of the full repertoire of pathogens they carry, including novel viruses, as well as their blood meal sources. Unbiased metatranscriptomic sequencing of individual mosquitoes offers a straightforward, rapid and quantitative means to acquire this information. Here, we profile 148 diverse wild-caught mosquitoes collected in California and detect sequences from eukaryotes, prokaryotes, 24 known and 46 novel viral species. Importantly, sequencing individuals greatly enhanced the value of the biological information obtained. It allowed us to a) speciate host mosquito, b) compute the prevalence of each microbe and recognize a high frequency of viral co-infections, c) associate animal pathogens with specific blood meal sources, and d) apply simple co-occurrence methods to recover previously undetected components of highly prevalent segmented viruses. In the context of emerging diseases, where knowledge about vectors, pathogens, and reservoirs is lacking, the approaches described here can provide actionable information for public health surveillance and intervention decisions.

microbiology↗

bioRxiv2019

Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses

Reassortment is an important source of genetic diversity in segmented viruses and is the main source of novel pathogenic influenza viruses. Despite this, studying the reassortment process has been constrained by the lack of a coherent, model-based inference framework. We here introduce a novel coalescent based model that allows us to explicitly model the joint coalescent and reassortment process. In order to perform inference under this model, we present an efficient Markov chain Monte Carlo algorithm to sample rooted networks and the embedding of phylogenetic trees within networks. Together, these provide the means to jointly infer coalescent and reassortment rates with the reassortment network and the embedding of segments in that network from full genome sequence data. Studying reassortment patterns of different human influenza datasets, we find large differences in reassortment rates across different human influenza viruses. Additionally, we find that reassortment events predominantly occur on selectively fitter parts of reassortment networks showing that on a population level, reassortment positively contributes to the fitness of human influenza viruses.

evolutionary biology↗

bioRxiv2019

The ability of single genes vs full genomes to resolve time and space in outbreak analysis

Inexpensive pathogen genome sequencing has had a transformative effect on the field of phylodynamics, where ever increasing volumes of data have promised real-time insight into outbreaks of infectious disease. As well as the sheer volume of pathogen isolates being sequenced, the sequencing of whole pathogen genomes, rather than select loci, has allowed phylogenetic analyses to be carried out at finer time scales, often approaching serial intervals for infections caused by rapidly evolving RNA viruses. Despite its utility, whole genome sequencing of pathogens has not been adopted universally and targeted sequencing of loci is common in some pathogen-specific fields. In this study we aim to highlight the utility of sequencing whole genomes of pathogens by re-analysing a well-characterised collection of Ebola virus sequences in the form of complete viral genomes (~19kb long) or the rapidly evolving glycoprotein (GP, ~2kb long) gene. We quantify changes in phylogenetic, temporal, and spatial inference resolution as a result of this reduction in data and compare these to theoretical expectations. We propose a simple intuitive metric for quantifying temporal resolution, i.e. the time scale over which sequence data might be informative of various processes as a quick back-of-the-envelope calculation of statistical power available to molecular clock analyses.

epidemiology↗

bioRxiv2018

Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

Population dynamics can be inferred from genetic sequence data using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume the parameters describing the dynamics to be constant through time in structured populations. Inference methods allowing for structured populations and parameters to vary through time involve many parameters which have to be inferred. Each of these parameters might be however only weakly informed by data. Here we introduce an approach that uses so-called predictors, such as geographic distance between locations, within a generalized linear model to inform the population dynamic parameters, namely the time-varying migration rates and effective population sizes under the marginal approximation of the structured coalescent. By using simulations, we show that we are able to reliably infer the parameters from phylogenetic trees. We then apply this framework to a previously described Ebola virus dataset. We infer incidence to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This allows us to show not only on simulated data, but also on real data, that we are able to identify reasonable predictors. Overall, we provide a novel method that allows to identify predictors for migration rates and effective population sizes and to use these predictors to quantify migration rates and effective population sizes. Its implementation as part of the BEAST2 software package MASCOT allows to jointly infer population dynamics within structured populations, the phylogenetic tree, and evolutionary parameters.

evolutionary biology↗

bioRxiv2017

Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100046). The recent Ebola virus (EBOV) outbreak in West Africa witnessed considerable efforts to obtain viral genomic data as the epidemic was unfolding. If such data can be deployed in real-time, molecular epidemiological investigations could play a role in complementing contact tracing undertaken by public health agencies. Analysing the EBOV genomes accumulated to date can also deliver insights into epidemic dynamics. Such analyses have been shown that metapopulation dynamics were critical for EBOV dispersal between rural and urban areas during the epidemic, but the implications for specific intervention scenarios remain unclear. Here, we address this issue using a collection of phylodynamic approaches. We show that long-distance dispersal events (between administrative areas >250 km apart) were not crucial for epidemic expansion and that preventing viral lineage movement to any given administrative area would, in most cases, have had little impact. However, urban areas - specifically those encompassing the three capital cities and their suburbs - were critical in attracting and further disseminating the virus: preventing viral lineage movement to all three simultaneously would have contained epidemic size by two-thirds. Using continuous phylogeographic reconstructions we estimate a distance kernel for EBOV spread and reveal considerable heterogeneity in dispersal velocity through time. We also show that announcements of border closures were followed by a significant but transient effect on international virus dispersal. By quantifying the hypothetical impact of different intervention strategies as well as the impact of barriers on dispersal frequency, our study illustrates how phylodynamic analyses can help to address specific epidemiological and outbreak control questions.

epidemiology↗

bioRxiv2017

MERS-CoV spillover at the camel-human interface

Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic virus from camels causing significant mortality and morbidity in humans in the Arabian Peninsula. The epidemiology of the virus remains poorly understood, and while case-based and seroepidemiological studies have been employed extensively throughout the epidemic, viral sequence data have not been utilised to their full potential. Here we use existing MERS-CoV sequence data to explore its phylodynamics in two of its known major hosts, humans and camels. We employ structured coalescent models to show that long-term MERS-CoV evolution occurs exclusively in camels, whereas humans act as a transient, and ultimately terminal host. By analysing the distribution of human outbreak cluster sizes and zoonotic introduction times we show that human outbreaks in the Arabian peninsula are driven by seasonally varying zoonotic transfer of viruses from camels. Without heretofore unseen evolution of host tropism, MERS-CoV is unlikely to become endemic in humans.

epidemiology↗

bioRxiv2017

Genetic characterization of the Zika virus epidemic in the US Virgin Islands

Here we release draft genome sequences of Zika virus (ZIKV) that were sequenced from PCR-positive diagnostic specimens collected by the United States Virgin Islands Department of Health (USVI DoH) as part of their ongoing response to the ZIKV outbreak. We will use these sequences to conduct a genomic epidemiological study of ZIKV transmission in the USVI. We are releasing these genomes in the hope that they are useful for those individuals involved in the public health response to ZIKV and to other groups working to understand Zika virus transmission and evolution.

epidemiology↗

bioRxiv2017

Multiple introductions of Zika virus into the United States revealed through genomic epidemiology

Zika virus (ZIKV) is causing an unprecedented epidemic linked to severe congenital syndromes1,2. In July 2016, mosquito-borne ZIKV transmission was first reported in the continental United States and since then, hundreds of locally-acquired infections have been reported in Florida3. To gain insights into the timing, source, and likely route(s) of introduction of ZIKV into the continental United States, we tracked the virus from its first detection in Miami, Florida by direct sequencing of ZIKV genomes from infected patients and Aedes aegypti mosquitoes. We show that at least four distinct ZIKV introductions contributed to the outbreak in Florida and that local transmission likely started in the spring of 2016 - several months before its initial detection. By analyzing surveillance and genetic data, we discovered that ZIKV moved among transmission zones in Miami. Our analyses show that most introductions are phylogenetically linked to the Caribbean, a finding corroborated by the high incidence rates and traffic volumes from the region into the Miami area. By comparing mosquito abundance and travel flows, we describe the areas of southern Florida that are especially vulnerable to ZIKV introductions. Our study provides a deeper understanding of how ZIKV initiates and sustains transmission in new regions.

epidemiology↗