Search bioRxiv⌕ Search

SEARCH · Search bioRxiv

Results for “Epidemiology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38Linked to original sources

NGMASTER: in silico Multi-Antigen Sequence Typing for Neisseria gonorrhoeae

Whole-genome sequencing (WGS) provides the highest resolution analysis for comparison of bacterial isolates in public health microbiology. However, although increasingly being used routinely for some pathogens such as Listeria monocytogenes and Salmonella enterica, the use of WGS is still limited for other organisms, such as Neisseria gonorrhoeae. Multi-antigen sequence typing (NG-MAST) is the most widely performed typing method for epidemiologic surveillance of gonorrhoea. Here, we present NGMASTER - a command-line software tool for performing in silico NG-MAST on assembled genome data. NGMASTER rapidly and accurately determined the NG-MAST of 630 assembled genomes, facilitating comparisons between WGS and previously published gonorrhoea epidemiological studies. The source code and user documentation are available at https://github.com/MDU-PHL/ngmaster.

Genomics↗

Autism genes are selectively targeted by environmental pollutants including pesticides, heavy metals, bisphenol A, phthalates and many others in food, cosmetics or household products.

The increasing incidence of autism suggests a major environmental influence. Epidemiology has implicated many candidates and genetics many susceptibility genes. Gene/environment interactions in autism were analysed using 206 autism genes (ASGs) to interrogate ~1 million chemical/gene interactions in the comparative toxicogenomics database. Bias towards ASGs was statistically determined for each chemical. Many suspect compounds identified in epidemiology, including tetrachlorodibenzodioxin, pesticides, particulate matter, benzo(a)pyrene, heavy metals, valproate, acetaminophen, SSRIs, cocaine, bisphenol A, phthalates, polyhalogenated biphenyls, flame retardants, diesel constituents, terbutaline and oxytocin, inter alia showed a significant degree of bias towards ASGs, as did relevant endogenous agents (retinoids, sex steroids, thyroxine, melatonin, folate, dopamine, serotonin). Numerous other endocrine disruptors selectively targeted ASGs including paraquat, atrazine and other pesticides not yet studied in autism and many compounds used in food, cosmetics or household products, including tretinoin, soy phytoestrogens, aspartame, titanium dioxide and sodium fluoride. Autism polymorphisms are known to influence sensitivity to some of these chemicals and these same genes play an important role in barrier function and control of respiratory cilia sweeping particulate matter from the airways. The close gene/environment relationships, for multiple suspect pollutants, suggest that the rising incidence of autism might be chemically driven by numerous environmental contaminants in a gene dependent manner. The protective dappled camouflage of the peppered moth was rendered invalid by industrial soot covering the trees, a situation reversed by clean air acts. The rising tide of neurodevelopmental and other childhood disorders linked to multiple pollutants may need a similar solution.

Neuroscience↗

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis of Saccharomyces cerevisiae.\n\nFundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem on a diffusion network. Utilizing all regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using a variant of time lagged transfer entropy, a method for quantifying information transfer between variables. Influence, for a particular number of sources, was measured using a diffusion model based on Markov chains with absorbing states. By maximizing over different numbers of sources, an influence ranking on genes was produced.\n\nThe influence ranking was compared to other metrics of network centrality. Although top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. This is quantified by computing node reachability between gene sets; on average, 59% of central genes can be reached when starting from the influential set, compared to 7% of influential genes when starting at another centrality measure.\n\nThe influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease.\n\nCode and example data can be found at: https://github.com/Gibbsdavidl/miergolf\n\nAuthor SummaryThe Influence Maximization Problem (IMP) is general and is applied in fields such as epidemiology, social network analysis, and as shown here, biological network analysis. The aim is to discover the set of regulatory genes with the greatest influence in the network dynamics. As gene regulation, fundamentally, is about the flow of information, the IMP was framed as an information theoretic problem. Dynamics were encoded as edge weights using time lagged transfer entropy, a quantity that defines information transfer across variables. The information flow was accomplished using a diffusion model based on Markov chains with absorbing states. Ant optimization was applied to solve the subset selection problem, recovering the most influential nodes.The influential nodes act as critical sources of information flow, potentially affecting the network state. Biological events that impact the influential nodes and thereby affecting normal information flow, could have a strong effect on the network, potentially leading to disease.

Bioinformatics↗

Identifying genetic variants that affect viability in large cohorts

A number of open questions in human evolutionary genetics would become tractable if we were able to directly measure evolutionary fitness. As a step towards this goal, we developed a method to examine whether individual genetic variants, or sets of genetic variants, currently influence viability. The approach consists in testing whether the frequency of an allele varies across ages, accounting for variation in ancestry. We applied it to the Genetic Epidemiology Research on Aging (GERA) cohort and to the parents of participants in the UK Biobank. Across the genome, we find only a few common variants with large effects on age-specific mortality: tagging the APOE {varepsilon}4 allele and near CHRNA3. These results suggest that when large, even late onset effects are kept at low frequency by purifying selection. Testing viability effects of sets of genetic variants that jointly influence one of 42 traits, we detect a number of strong signals. In participants of the UK Biobank study of British ancestry, we find that variants that delay puberty timing are enriched in longer-lived parents (P~6x10-6 for fathers and P~2x10-3 for mothers), consistent with epidemiological studies. Similarly, in mothers, variants associated with later age at first birth are associated with a longer lifespan (P~1x10-3). Signals are also observed for variants influencing cholesterol levels, risk of coronary artery disease, body mass index, as well as risk of asthma. These signals exhibit consistent effects in the GERA cohort and among participants of the UK Biobank of non-British ancestry. Moreover, we see marked differences between males and females, most notably at the CHRNA3 locus, and variants associated with risk of coronary artery disease and cholesterol levels. Beyond our findings, the analysis serves as a proof of principle for how upcoming biomedical datasets can be used to learn about selection effects in contemporary humans.

evolutionary biology↗

Estimation of sub-epidemic dynamics by means of Sequential Monte Carlo Approximate Bayesian Computation: an application to the Swiss HIV Cohort Study

Our ability to accurately infer transmission patterns of infectious diseases is critical to monitor both their spread and the efficacy of public health policies. The use of phylogenetic methods for the reconstruction of viral ancestral relationships has garnered increasing interest, particularly in the characterization of HIV epidemics and sub-epidemics. In the case of this virus, the Swiss HIV Cohort Study (SHCS) contains a wide breadth of genomic data that have been widely used as a means of applying such methods. However, current approaches for quantifying the epidemiological dynamics of diseases are computationally intensive, and fail to scale well with this magnitude of data. To address this issue, we re-implement an Approximate Bayesian Computation (ABC) approach based on sequential Monte Carlo (SMC). By means of simulations, we demonstrate that our implementation is capable of inferring key epidemiological parameters of the Swiss HIV epidemic accurately, and that sampling intensity has no significant effect on the accuracy of our estimates. Applied to a subset of HIV sequences from the SHCS, we show that we can distinguish sub-epidemics that are circulating in culturally distinct Swiss regions. Given these findings, we propose that ABC-SMC samplers will allow us to evaluate the impact of new public health policies, such as the implementation of a needle exchange program in the case of HIV, based on genetic data sampled before and after the implementation of a new policy.

evolutionary biology↗

Biased phylodynamic inferences from analysing clusters of viral sequences

Phylogenetic methods are being increasingly used to help understand the transmission dynamics of measurably evolving viruses, including HIV. Clusters of highly similar sequences are often observed, which appear to follow a power law behaviour, with a small number of very large clusters. These clusters may help to identify subpopulations in an epidemic, and inform where intervention strategies should be implemented. However, clustering of samples does not necessarily imply the presence of a subpopulation with high transmission rates, as groups of closely related viruses can also occur due to non-epidemiological effects such as over-sampling. It is important to ensure that observed phylogenetic clustering reflects true heterogeneity in the transmitting population, and is not being driven by non-epidemiological effects.\n\nWe quantify the effect of using a falsely identified transmission cluster of sequences to estimate phylodynamic parameters including the effective population size and exponential growth rate. Our simulation studies show that taking the maximum size cluster to re-estimate parameters from trees simulated under a randomly mixing, constant population size coalescent process systematically underestimates the overall effective population size. In addition, the transmission cluster wrongly resembles an exponential or logistic growth model 95% of the time. We also illustrate the consequences of false clusters in exponentially growing coalescent and birth-death trees, where again, the growth rate is skewed upwards. This has clear implications for identifying clusters in large viral databases, where a false cluster could result in wasted intervention resources.

genetics↗

Pathogen mitigation in an Ecuadorian potato seed system: Insights from network analysis

Seed systems have an important role in the distribution of high quality seed and improved varieties. The structure of seed networks also helps to determine the epidemiological risk for seedborne disease. We present a new method for evaluating the epidemiological role of nodes in seed networks, and apply it to a regional potato farmer consortium (CONPAPA) in Ecuador. We surveyed farmers to estimate the structure of networks of farmer seed tuber and ware potato transactions, and farmer information sources about pest and disease management. Then we simulated pathogen spread through seed transaction networks to identify priority nodes for disease detection. The likelihood of pathogen establishment was weighted based on the quality and/or quantity of information sources about disease management. CONPAPA staff and facilities, a market, and certain farms are priorities for disease management interventions, such as training, monitoring and variety dissemination. Advice from agrochemical store staff was common but assessed as significantly less reliable. Farmer access to information (reported number and quality of sources) was similar for both genders. Women had a smaller amount of the market share for seed-tubers and ware potato, however. Understanding seed system networks provides input for scenario analyses to evaluate potential system improvements.

ecology↗

Evolution and manipulation of vector host choice

The transmission of many animal and plant diseases relies on the behavior of arthropod vectors. In particular, the choice to feed on either infected or uninfected hosts can dramatically affect the epidemiology of vector-borne diseases. I develop an epidemiological model to explore the impact of host choice behavior on the dynamics of these diseases and to examine selection acting on vector behavior, but also on pathogen manipulation of this behavior. This model identifies multiple evolutionary conflicts over the control of this behavior and generates testable predictions under different scenarios. In general, the vector should evolve the ability to avoid infected hosts. However, if the vector behavior is under the control of the pathogen, uninfected vectors should prefer infected hosts while infected vectors should seek uninfected hosts. But some mechanistic constraints on pathogen manipulation ability may alter these predictions. These theoretical results are discussed in the light of observed behavioral patterns obtained on a diverse range of vector-borne diseases. These patterns confirm that several pathogens have evolved conditional behavioral manipulation strategies of their vector species. Other pathogens, however, seem unable to evolve such complex conditional strategies. Contrasting the behavior of infected and uninfected vectors may thus help reveal mechanistic constraints acting on the evolution of the manipulation of vector behavior.

evolutionary biology↗

Co-evolution of virulence and immunosuppression through multiple infections

Many components of host-parasite interactions have been shown to affect the way virulence (i.e., parasite-induced harm to the host) evolves. However, coevolution of multiple parasite traits is often neglected. We explore how an immunosuppressive mechanism of parasites affects and co-evolves with virulence through multiple infections. Applying the adaptive dynamics framework to epidemiological models with coinfection, we show that immunosuppression is a double-edged-sword for the evolution of virulence. On one hand, it amplifies the adaptive benefit of virulence by increasing the abundance of coinfections through epidemiological feedbacks. On the other hand, immunosuppression hinders host recovery, prolonging the duration of infection and elevating the cost of killing the host. The balance between the cost and benefit of immunosuppression varies across different background mortality rates of hosts. In addition, we find that immunosuppression evolution is influenced considerably by the precise trade-off shape determining the effect of immunosuppression on host recovery and susceptibility to further infection. These results demonstrate that the evolution of virulence is shaped by immunosuppression while highlighting that the evolution of immune evasion mechanisms deserves further research attention.

evolutionary biology↗

Model-based analysis of experimental hut data elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes

BackgroundInsecticides used against Aedes aegypti and other disease vectors can elicit a multitude of dose-dependent effects on behavioral and bionomic traits. Estimating the potential epidemiological impact of a product requires thorough understanding of these effects and their interplay at different dosages. Volatile spatial repellent (SR) products come with an additional layer of complexity due to the potential for movement of affected mosquitoes or volatile particles of the product beyond the treated house. Here, we propose a statistical inference framework for estimating these nuanced effects of volatile SRs.\n\nMethodsWe fitted a continuous-time Markov chain model in a Bayesian framework to mark-release-recapture (MRR) data from an experimental hut study conducted in Iquitos, Peru. We estimated the effects of two dosages of transfluthrin on Ae. aegypti behaviors associated with human-vector contact: repellency, exiting, and knockdown in the treated space and in \"downstream\" adjacent huts. We validated the framework using simulated data.\n\nResultsThe odds of a female Ae. aegypti being repelled from a treated hut (HT) increased at both dosages (low dosage: odds = 1.64, 95% highest density interval (HDI) = 1.30-2.09; high dosage: odds = 1.35, HDI = 1.04-1.67). The relative risk of exiting from the treated hut was reduced (low: RR = 0.70, HDI = 0.62-1.09; high: RR = 0.70, HDI = 0.40-1.06), with this effect carrying over to untreated spaces as far as two huts away from the treated hut (H2) (low: RR = 0.79, HDI = 0.59-1.01; high: RR = 0.66, HDI = 0.50-0.87). Knockdown rates were increased in both treated and downstream huts, particularly under high dosage (HT: RR = 8.37, HDI = 2.11-17.35; H1: RR = 1.39, HDI = 0.52-2.69; H2: RR = 2.22, HDI = 0.96-3.86).\n\nConclusionsOur statistical inference framework is effective at elucidating multiple effects of volatile chemicals used in SR products, as well as their downstream effects. This framework provides a powerful tool for early selection of candidate SR product formulations worth advancing to costlier epidemiological trials, which are ultimately necessary for proof of concept of public health value and subsequent formal endorsement by health authorities.

ecology↗

Vibrio cholerae genomic diversity within and between patients

Cholera is a severe, waterborne diarrheal disease caused by toxin-producing strains of the bacterium Vibrio cholerae. Comparative genomics has revealed \"waves\" of cholera transmission and evolution, in which clones are successively replaced over decades and centuries. However, the extent of V. cholerae genetic diversity within an epidemic or even within an individual patient is poorly understood. Here, we characterized V. cholerae genomic diversity at a micro-epidemiological level within and between individual patients from Bangladesh and Haiti. To capture within-patient diversity, we isolated multiple (8 to 20) V. cholerae colonies from each of eight patients, sequenced their genomes and identified point mutations and gene gain/loss events. We found limited but detectable diversity at the level of point mutations within hosts (zero to three single nucleotide variants within each patient), and comparatively higher gene content variation within hosts (at least one gain/loss event per patient, and up to 103 events in one patient). Much of the gene content variation appeared to be due to gain and loss of phage and plasmids within the V. cholerae population, with occasional exchanges between V. cholerae and other members of the gut microbiota. We also show that certain intra-host variants have phenotypic consequences. For example, the acquisition of a Bacteroides plasmid and nonsynonymous mutations in a sensor histidine kinase gene both reduced biofilm formation, an important trait for environmental survival. Together, our results show that V. cholerae is measurably evolving within patients, with possible implications for disease outcomes and transmission dynamics.\n\nAuthor SummaryVibrio cholerae is the etiological agent of cholera, a severe diarrheal disease endemic to Bangladesh and responsible for global outbreaks, including one ongoing in Haiti. Certain bacterial pathogens can evolve and diversify within the human host, often altering virulence and antibiotic resistance. However, most examples of within-host evolution have come from chronic infections, in which the pathogen has sufficient time to mutate and diversify, and little attention has been paid to more acute infections such as the one caused by V. cholerae. The goal of this study was to measure the extent of within-host evolution of V. cholerae within individual infected patients. By sequencing multiple bacterial isolates_from each of eight patients from Bangladesh and Haiti, we found that cholera patients can harbor a diverse population of V. cholerae. As expected for an acute infection, this diversity is limited, ranging from zero to three point mutations (single nucleotide variants) per patient. However, gene gain/loss events are more prevalent than point mutations, occurring in every single patient, and sometimes involving the transfer of dozens of genes on plasmids. Even if rare, point mutations and gene gain/loss events may be maintained by natural selection, and can alter clinically-and environmentally-relevant phenotypes such as biofilm formation. Therefore, within-patient evolution has the potential to impact clinical and epidemiological outcomes. Together, our results demonstrate that within-patient evolution may be a general feature of both acute and chronic infections, and that gene gain/loss may be an important but under-appreciated feature of within-host evolution.

microbiology↗

Modulation of host learning in Aedes aegypti mosquitoes

How mosquitoes determine which individuals to bite has important epidemiological consequences. This choice is not random; most mosquitoes specialize in one or a few vertebrate host species, and some individuals in a host population are preferred over others. Here we show that aversive olfactory learning contributes to mosquito preference both between and within host species. Combined electrophysiological and behavioural recordings from tethered flying mosquitoes demonstrated that these odours evoke changes in both behaviour and antennal lobe (AL) neuronal responses. Using electrophysiological and behavioural approaches, and CRISPR gene editing, we demonstrate that dopamine plays a critical role in aversive olfactory learning and modulating odour-evoked responses in AL neurons. Collectively, these results provide the first experimental evidence that olfactory learning in mosquitoes can play an epidemiological role.

neuroscience↗

Evolution-informed forecasting of seasonal influenza A (H3N2)

Inter-pandemic or seasonal influenza exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus antigenic evolution. We propose here a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino-acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States over 10 years, we demonstrate the feasibility of prediction ahead of season and an accurate real-time forecast for the 2016/2017 influenza season.\n\nSUMMARYSkillful forecasting of seasonal (H3N2) influenza incidence ahead of the season is shown to be possible by means of a transmission model that explicitly tracks evolutionary change in the virus, integrating information from both epidemiological surveillance and readily available genetic sequences.

systems biology↗

Rift Valley fever in northern Senegal : a modelling approach to analyse the processes underlying virus circulation recurrence

Rift Valley fever (RVF) is endemic in northern Senegal, a Sahelian area characterized by a temporary pond network that drive both RVF mosquito population dynamics and nomadic herd movements. To investigate the mechanisms that explain RVF recurrent circulation, we modelled a realistic epidemiological system at the pond level integrating vector population dynamics, resident and nomadic ruminant herd population dynamics, and nomadic herd movements recorded in Younoufere area [1]. To calibrate the model, serological surveys were performed in 2015-2016 on both resident and nomadic herds in the same area. Mosquito population dynamics were obtained from a published model trained in the same region [2]. Model comparison techniques were used to compare five different scenarios of virus introduction by nomadic herds associated or not with vertical transmission in Aedes vexans. Our serological results confirmed a long lasting RVF endemicity in resident herds (IgG seroprevalence rate of 15.3%, n=222), and provided the first estimation of RVF IgG seroprevalence in nomadic herds in West Africa (12.4%, n=660). Multivariate analysis of serological data suggested an amplification of the transmission cycle during the rainy season with a peak of circulation at the end of that season. The best scenario of virus introduction combined yearly introductions of RVFV from 2008 to 2015 (the study period) by nomadic herds, with a proportion of viraemic individuals predicted to be larger in animals arriving during the 2nd half of the rainy season (3.4%). This result is coherent with the IgM prevalence rate (4%) found in nomadic herds sampled during the 2nd half of the rainy season. Although the existence of a vertical transmission mechanism in Aedes cannot be ruled out, our model demonstrates that nomadic movements are sufficient to account for this endemic circulation in northern Senegal. Author summaryRift Valley fever (RVF) is one of the most important vector borne disease in Africa, seriously affecting the health of domestic ruminants and humans and leading to severe economic consequences. This disease is endemic in northern Senegal, a Sahelian area characterized by a temporary pond network that drive both RVF mosquito population dynamics and nomadic herd movements. Two non-exclusive mechanisms may support this endemicity: recurrent introductions of the virus by nomadic animals, and vertical transmission of the virus (i.e. from infected female mosquito to eggs) in local Aedes populations. The authors followed up during 1 year resident and nomadic herds. They used the data thus obtained to model a realistic epidemiological system at the pond level integrating vector population dynamics, resident and nomadic ruminant herd population dynamics. They found that the best scenario explaining RVF remanence combined yearly introductions of RVFV by nomadic herds, with a proportion of viraemic predicted to be larger in animals arriving during the 2nd half of the rainy season, which is consistent with an amplification of virus circulation in the area during the rainy season. Although the existence of a vertical transmission mechanism in Aedes cannot be ruled out, their results demonstrates that nomadic movements are sufficient to account for this endemic circulation in northern Senegal.

ecology↗

Endemic and epidemic human alphavirus infections in Eastern Panama, An Analysis of Population-Based Cross-Sectional Surveys

BackgroundMadariaga virus (MADV), has recently been associated with severe human disease in Panama, where the closely related Venezuelan equine encephalitis virus (VEEV) also circulates. In June, 2017, a fatal MADV infection was confirmed in a community of Darien province. MethodsWe conducted a cross-sectional outbreak investigation with human and mosquito collections in July 2017, where sera were tested for alphavirus antibodies and viral RNA. Additionally, by applying a catalytic, force-of-infection statistical model to two serosurveys from Darien province in 2012 and 2017, we investigated whether endemic or epidemic alphavirus transmission occurred historically. ResultsIn 2017, MADV and VEEV IgM seroprevalence was 1.6% and 4.4%, respectively; IgG antibody prevalences were MADV: 13.2%; VEEV: 16.8%; Una virus (UNAV): 16.0%; and Mayaro virus (MAYV): 1.1%. Active viral circulation was not detected. Evidence of MADV and UNAV infection was found near households -- raising questions about its vectors and enzootic transmission cycles. Insomnia was associated with MADV and VEEV infection, depression symptoms were associated with MADV, and dizziness with VEEV and UNAV. Force-of-infection analyses suggest endemic alphavirus transmission historically, with recent increased human exposure to MADV and VEEV in some regions. ConclusionsThe lack of additional neurological cases suggest that severe MADV and VEEV infections occur only rarely. Our results indicate that, over the past five decades, alphavirus infections have occurred at low levels in eastern Panama, but that MADV and VEEV infections have recently increased -- potentially during the past decade. Endemic infections and outbreaks of MADV and VEEV appear to differ spatially. Author summaryPrior to 2010, it was believed that the Madariaga virus (MADV) was primarily associated with equine disease. However, an outbreak reported in Panama, in an endemic area where Venezuelan equine encephalitis virus (VEEV) also circulates, suggested a change in its epidemiological profile. We aimed to reconstruct the epidemiological dynamics of MADV and VEEV, as well as additional alphaviruses known to circulate in the region in order to understand MADV emergence. For this, cross-sectional serosurveys were used to demonstrate that the Alphaviruses MADV, VEEV and Una virus have repeatedly infected humans in eastern Panama over the past five decades. Whilst their historical transmission has been low, we confirm that the transmission has recently increased for both MADV and VEEV.

microbiology↗

Emerging viruses in British Columbia salmon discovered via a viral immune response biomarker panel and metatranscriptomic sequencing

The emergence of infectious agents poses a continual economic and environmental challenge to aquaculture production, yet the diversity, abundance and epidemiology of aquatic viruses are poorly characterised. In this study, we applied salmon host transcriptional biomarkers to identify and select fish in a viral disease state but only those that we also showed to be negative for established viruses. This was followed by metatranscriptomic sequencing to determine the viromes of dead and dying farmed Atlantic (Salmo salar) and Chinook (Oncorhynchus tshawytscha) salmon in British Columbia. We found that the application of the biomarker panel increased the probability of discovering viruses in aquaculture populations. We discovered viruses that have not previously been characterized in British Columbian Atlantic salmon farms. To determine the epidemiology of the newly discovered or emerging viruses we conducted high-throughput RT-PCR to reveal their prevalence in British Columbia (BC), and detected some of the viruses we first discovered in farmed Atlantic salmon in Chinook and sockeye salmon, suggesting a broad host range. Finally, we applied in-situ hybridisation to confirm infection and explore the tissue tropism of each virus.

genomics↗

Gut Microbiome Profiling of a Rural and Urban South African Cohort Reveals Biomarkers of a Population in Lifestyle Transition

BackgroundComparisons of traditional hunter-gatherers and pre-agricultural communities in Africa with urban and suburban Western North American and European cohorts have clearly shown that diet, lifestyle and environment are associated with gut microbiome composition. Yet, little is known about the gut microbiome composition of most African adults. South Africa comprises a richly diverse ethnolinguistic population that is experiencing an ongoing epidemiological transition and concurrent spike in the prevalence of obesity, largely attributed to a shift towards more Westernized diets and increasingly inactive lifestyle practices. To better characterize the microbiome of African adults living in more mainstream lifestyle settings and to investigate associations between the microbiome and obesity, we conducted a pilot study in two South African cohorts that are representative of urban and rural populations. The study was designed collaboratively with community leaders. As the rate of overweight and obesity is particularly high in women, we collected single time-point stool samples from 170 HIV-negative women (51 at Soweto; 119 at Bushbuckridge), performed 16S rRNA gene sequencing on these samples and compared the data to concurrently collected anthropometric data. ResultsWe found the overall gut microbiome of our cohorts to be reflective of their ongoing epidemiological transition. Specifically, our results show a relatively higher than expected abundance of Western gut-associated taxa such as Barnesiella and the presence of Bifidobacteria and Bacteroides together with the more traditionally non-Western gut-associated Prevotella, Treponema and Succinivibrio. Interestingly, we observed a relatively higher abundance of the Melainabacteria, Vampirovibrio, a predatory bacterium, in the rural cohort. We also found Prevotella, despite its generally high prevalence relative to all taxa present in the cohort, to be associated with obesity. ConclusionsAltogether, this work identifies putative microbial features associated with host health in a historically understudied community. Furthermore, we note the crucial role of community engagement to the success of a study in an African setting, the importance of more population-specific studies to inform targeted interventions as well as present a basic foundation for future research in this regard.

microbiology↗

Treating symptomatic infections and the co-evolution of virulence and drug resistance

AO_SCPLOWBSTRACTC_SCPLOWAntimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution.

evolutionary biology↗