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bioRxiv2018

A single test approach for accurate and sensitive detection and taxonomic characterization of Trypanosomes by comprehensive analysis of ITS1 amplicons

The World Health Organization has targeted stopping the transmission of Human African Trypanosomiasis by 2030. To achieve this, better tools are urgently required to identify and monitor Trypanosome infections in human, animals, and tsetse fly vectors. This study presents a single test approach for detection and identification of Trypanosomes and their comprehensive characterization at species and sub-group level. Our method uses newly designed ITS1 PCR primers (a widely used method for detection of African Trypanosomes, amplifying the ITS1 region of ribosomal RNA genes) coupled to Illumina sequencing of the amplicon. The protocol is based on the widely used Illuminas 16s bacterial metagenomic analysis procedure that makes use of multiplex PCR and dual indexing. We analyzed wild tsetse flies collected from Zambia and Zimbabwe. Our results show that the traditional method for Trypanosome species detection based on band size comparisons on a gel is unable to distinguish between T. vivax and T. godfreyi accurately. Additionally, this approach shows increased sensitivity of detection at species level. Through phylogenetic analysis, we identified Trypanosomes at species and sub-group level without the need for any additional tests. Our results show T. congolense Kilifi sub-group is more closely related to T. simiae than to other T. congolense sub-groups. This agrees with previous studies using satellite DNA and 18s RNA analysis. While current classification does not list any sub-groups for T. vivax and T. godfreyi, we observed distinct subgroups for these species. Interestingly, sequences matching T. congolense Tsavo (now classified as T. simiae Tsavo) clusters distinctly from the rest of the T. simiae Tsavo sequences suggesting that the Nannomonas group is more divergent than currently thought thus the need for a better classification criteria. This approach has the potential for refining classification of Trypanosomes and provide detailed molecular epidemiology information useful for surveillance and transmission control efforts.\n\nAuthor summaryDetection of Trypanosomes in the tsetse flies plays an important role in the control of African trypanosomiasis by providing information on circulating Trypanosome species in a given area. We have developed a method that combines multiplex PCR and next-generation sequencing for Trypanosome species detection. The method is based on the widely used bacterial metagenomic analysis protocol and uses a modular, two-step PCR process followed by sequencing of all amplicons in a single run, making sequencing of amplicons more efficient and cost-effective when dealing with large sample sizes. As part of this approach, we designed novel primers for amplifying the ITS1 region of the Trypanosome rRNA gene that is more sensitive than conventional primers. Identification of Trypanosome species is based on BLAST searches against the constantly updated NCBIs nt database, which facilitates the identification of Trypanosome subgroups. Our approach is more accurate than traditional gel-based analysis and shows how the latter is prone to misidentification. It is also sensitive and is able to discriminate between subgroups within Trypanosome species. Applied as an epidemiological tool, it has the potential to provide new, comprehensive and more accurate information on vector-pathogen-host interconnections which are key in the control and management of African trypanosomiasis.

epidemiology↗
bioRxiv2018

The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

Malaria control programs face difficult resource allocation decisions. Of particular concern for countries aiming for malaria elimination, the regular movement of individuals to and from endemic areas undermines local interventions by reintroducing infections and sustaining local transmission. Quantifying this movement of malaria parasites around a country has become a priority for national control programs, but remains methodologically challenging, particularly in areas with highly mobile populations. Here, we combined multiple data sources to measure the geographical spread of malaria parasites, including epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data. We collected parasite genetic barcodes and travel surveys from 2,090 patients residing in 176 unions in southeast Bangladesh. We developed a genetic mixing index to quantify the likelihood of samples being local or imported. We then inferred the direction and intensity of parasite flow between locations using an epidemiological model, and estimated the proportion of imported cases assuming mobility patterns parameterized using the travel survey and mobile phone calling data. Our results show that each data source provided related but different information about the patterns of geographic spread of parasites. We identify a consistent north/south separation of the Chittagong Hill Tracts region in Bangladesh, and found that in addition to imported infections from forested regions, frequent mixing also occurs in low transmission but highly populated areas in the southwest. Thus, unlike risk maps generated from incidence alone, our maps provide evidence that elimination programs must address ongoing movement of parasites around the lower transmission areas in the southwest.

epidemiology↗
bioRxiv2018

Host contact dynamics shapes richness and dominance of pathogen strains

The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions. Genomic tools have made it increasingly easy to observe pathogenic strains diversity, but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors. Here, we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction. We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model. We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples. Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven, while strong community structure among hosts increased strain diversity. We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels. These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously. We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity. These results show how stochasticity and network structure affect the population ecology of pathogens and warns against interpreting observations as unambiguous evidence of epidemiological differences between strains.\n\nAuthor summaryPathogens are structured in multiple strains that interact and co-circulate on the same host population. This ecological diversity affects, in many cases, the spread dynamics and the efficacy of vaccination and antibiotic treatment. Thus understanding its biological and host-behavioral drivers is crucial for outbreak assessment and for explaining trends of new-strain emergence. We used stochastic modeling and network theory to quantify the role of host contact behavior on strain richness and dominance. We systematically compared multi-strain spread on different network models displaying properties observed in real-world contact patterns. We then analyzed the real-case example of Staphylococcus aureus spread in a hospital, leveraging on a combined dataset of carriage and close proximity interactions. We found that contact dynamics has a profound impact on a strain population. Contact heterogeneity, for instance, reduces strain diversity by reducing the number of circulating strains and leading few strains to dominate over the others. These results have important implications in disease ecology and in the epidemiological interpretation of biological data.

epidemiology↗
bioRxiv2014

Phylogenetic tree shapes resolve disease transmission patterns

Whole genome sequencing is becoming popular as a tool for understanding outbreaks of communicable diseases, with phylogenetic trees being used to identify individual transmission events or to characterize outbreak-level overall transmission dynamics. Existing methods to infer transmission dynamics from sequence data rely on well-characterised infectious periods, epidemiological and clinical meta-data which may not always be available, and typically require computationally intensive analysis focussing on the branch lengths in phylogenetic trees. We sought to determine whether the topological structures of phylogenetic trees contain signatures of the overall transmission patterns underyling an outbreak. Here we use simulated outbreaks to train and then test computational classifiers. We test the method on data from two real-world outbreaks. We find that different transmission patterns result in quantitatively different phylogenetic tree shapes. We describe five topological features that summarize a phylogenys structure and find that computational classifiers based on these are capable of predicting an outbreaks transmission dynamics. The method is robust to variations in the transmission parameters and network types, and recapitulates known epidemiology of previously characterized real-world outbreaks. We conclude that there are simple structural properties of phylogenetic trees which, when combined, can distinguish communicable disease outbreaks with a super-spreader, homogeneous transmission, and chains of transmission. This is possible using genome data alone, and can be done during an outbreak. We discuss the implications for management of outbreaks.

Evolutionary Biology↗
bioRxiv2014

Mapping a viral phylogeny onto outbreak trees to improve host transmission inference

BackgroundDeveloping methods to reconstruct transmission histories for viral outbreaks could provide critical information to support locating sources of disease transmission. Phylogenetic methods used to measure the degree of relatedness among sequenced viral samples have proven useful in identifying potential outbreak sources. The complex nature of infectious disease, however, makes it difficult to assign a rigorously defined quantitative confidence value assessing the likelihood of a true direct transmission event using genetic data alone.\n\nResultsA new method is presented to calculate a confidence value assessing the likelihood of a transmission event using both phylogenetic inference and limited knowledge of incubation and infectious duration times. The method is applied to simulations of a foot and mouth disease (FMD) outbreak to demonstrate how the combination of both phylogenetic and epidemiology data can be used to strengthen the assessment of the likelihood of direct transmission over methods using just phylogenetic data or infection timing data alone. The method is applied to a previous FMD outbreak to identify areas where over confidence in previously inferred direct transmission may exist.\n\nConclusionCombining knowledge from viral evolution and epidemiology within a single integrated transmission inference framework is an important approach to assess the potential likelihood of transmission events and makes clear how specific features of a virus spread through the course of an outbreak will directly determine the potential for confidence in inferred host transmission links.

Bioinformatics↗
bioRxiv2015

Approaches to estimating inbreeding coefficients in clinical isolates of Plasmodium falciparum from genomic sequence data

The advent of whole-genome sequencing has generated increased interest in modeling the structure of strain mixture within clinicial infections of Plasmodium falciparum (Pf). The life cycle of the parasite implies that the mixture of multiple strains within an infected individual is related to the out-crossing rate across populations, making methods for measuring this process in situ central to understanding the genetic epidemiology of the disease. In this paper, we show how to estimate inbreeding coefficients using genomic data from Pf clinical samples, providing a simple metric for assessing within-sample mixture that connects to an extensive literature in population genetics and conservation ecology. Features of the P. falciparum genome mean that some standard methods for inbreeding coefficients and related F-statistics cannot be used directly. Here, we review an initial effort to estimate the inbreeding coefficient within clinical isolates of P. falciparum and provide several generalizations using both frequentist and Bayesian approaches. The Bayesian approach connects these estimates to the Balding-Nichols model, a mainstay within genetic epidemiology. We provide simulation results on the performance of the estimators and show their use on ~ 1500 samples from the PF3K data set. We also compare the results to output from a recent mixture model for within-sample strain mixture, showing that inbreeding coefficients provide a strong proxy for the results of these more complex models. We provide the methods described within an open-source R package pfmix.

Genomics↗
bioRxiv2015

On the identifiability of transmission dynamic models for infectious diseases

Understanding the transmission dynamics of infectious diseases is important for both biological research and public health applications. It has been widely demonstrated that statistical modeling provides a firm basis for inferring relevant epidemiological quantities from incidence and molecular data. However, the complexity of transmission dynamic models causes two challenges: Firstly, the likelihood function of the models is generally not computable and computationally intensive simulation-based inference methods need to be employed. Secondly, the model may not be fully identifiable from the available data. While the first difficulty can be tackled by computational and algorithmic advances, the second obstacle is more fundamental. Identifiability issues may lead to inferences which are more driven by the prior assumptions than the data themselves. We here consider a popular and relatively simple, yet analytically intractable model for the spread of tuberculosis based on classical IS6110 fingerprinting data. We report on the identifiability of the model, presenting also some methodological advances regarding the inference. Using likelihood approximations, it is shown that the reproductive value cannot be identified from the data available and that the posterior distributions obtained in previous work have likely been substantially dominated by the assumed prior distribution. Further, we show that the inferences are influenced by the assumed infectious population size which has generally been kept fixed in previous work. We demonstrate that the infectious population size can be inferred if the remaining epidemiological parameters are already known with sufficient precision.

Bioinformatics↗
bioRxiv2015

Incomplete protection against dengue virus type 2 re-infection in Peru

Background. Nearly half of the worlds population is at risk for dengue, yet no licensed vaccine or anti-viral drug is currently available. Dengue is caused by any of four dengue virus serotypes (DENV-1 through DENV-4), and infection by a DENV serotype is assumed to provide life-long protection against re-infection by that serotype. We investigated the validity of this fundamental assumption during a large dengue epidemic caused by DENV-2 in Iquitos, Peru, in 2010-2011, 15 years after the first outbreak of DENV-2 in the region. Methodology/Principal Findings. We estimated the age-dependent prevalence of serotype-specific DENV antibodies from longitudinal cohort studies conducted between 1993 and 2010. During the 2010-2011 epidemic, active dengue cases were identified through active community- and clinic-based febrile surveillance studies, and acute inapparent DENV infections were identified through contact tracing studies. Based on the age-specific prevalence of DENV-2 neutralizing antibodies, the age distribution of DENV-2 cases was markedly older than expected. Homologous protection was estimated at 35.1% (95% confidence interval: 0% -- 65.2%). At the individual level, pre-existing DENV-2 antibodies were associated with an incomplete reduction in the frequency of symptoms. Among dengue cases, 43% (26/66) exhibited elevated DENV-2 neutralizing antibody titers for years prior to infection, compared with 76% (13/17) of inapparent infections (age-adjusted odds ratio: 4.2; 95% confidence interval: 1.1 - 17.7). Conclusions/Significance. Our data indicate that protection from homologous DENV re-infection may be incomplete in some circumstances, which provides context for the limited vaccine efficacy against DENV-2 in recent trials. Further studies are warranted to confirm this phenomenon and to evaluate the potential role of incomplete homologous protection in DENV transmission dynamics. Author SummaryHomotypic immunity against DENV infection has been assumed to be complete and lifelong, and to our knowledge, instances of homologous DENV re-infection have not been rigorously documented. However, few long-term studies have been conducted in such a way that homologous re-infection could be observed, if it did in fact occur. Our study provides evidence that homologous re-infection may occur in certain circumstances. We draw from data collected during a 2010-2011 DENV-2 epidemic in northeastern Peru, 15 years after the initial DENV-2 outbreak in the region. This finding has significant implications for our understanding of dengue epidemiology and for dengue vaccine formulation, which may need to consider multiple genotypes of each serotype. Data from other long-term dengue epidemiology studies should be analyzed to determine of homologous re-infection is a more widespread phenomenon.

Immunology↗
bioRxiv2015

Local population structure and patterns of Western Hemisphere dispersal for Coccidioides spp., the fungal cause of Valley Fever

Coccidioidomycosis (or Valley Fever) is a fungal disease with high morbidity and mortality that affects tens of thousands of people each year. This infection is caused by two sibling species, Coccidioides immitis and C. posadasii, which are endemic to specific arid locales throughout the Western Hemisphere, particularly the desert southwest of the United States. Recent epidemiological and population genetic data suggest that the geographic range of coccidioidomycosis is expanding as new endemic clusters have been identified in the state of Washington, well outside of the established endemic range. The genetic mechanisms and epidemiological consequences of this expansion are unknown and require better understanding of the population structure and evolutionary history of these pathogens. Here we perform multiple phylogenetic inference and population genomics analyses of 68 new and 18 previously published genomes. The results provide evidence of substantial population structure in C. posadasii and demonstrate presence of distinct geographic clades in central and southern Arizona as well as dispersed populations in Texas, Mexico, South America and Central America. Although a smaller number of C. immitis strains were included in the analyses, some evidence of phylogeographic structure was also detected in this species, which has been historically limited to California and Baja Mexico. Bayesian analyses indicated that C. posadasii is the more ancient of the two species and that Arizona contains the most diverse subpopulations. We propose a southern Arizona-northern Mexico origin for C. posadasii and describe a pathway for dispersal and distribution out of this region.

Genomics↗
bioRxiv2015

Integrated analysis of oral tongue squamous cell carcinoma identifies key variants and pathways linked to risk habits, HPV, clinical parameters and tumor recurrence

Oral tongue squamous cell carcinomas (OTSCC) are a homogenous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (<50yrs) is on a rise; many of who lack the typical associated risk factors of alcohol and/or tobacco exposure. We present data on SNVs, indels, regions with LOH, and CNVs from fifty-paired oral tongue primary tumors and link the significant somatic variants with clinical parameters, epidemiological factors including HPV infection and tumor recurrence. Apart from the frequent somatic variants harbored in TP53, CASP8, RASA1, NOTCH and CDKN2A genes, significant amplifications and/or deletions were detected in chromosomes 6-9, and 11 in the tumors. Variants in CASP8 and CDKN2A were mutually exclusive. CDKN2A, P1K3CA, RASA1 and DMD variants were exclusively linked to smoking, chewing, HPV infection and tumor stage. We also performed whole-genome gene expression study that identified matrix metalloproteases to be highly expressed in tumors and linked pathways involving arachidonic acid and NF-{kappa}-B to habits and distant metastasis, respectively. Functional knockdown studies in cell lines demonstrated the role of CASP8 in HPV-negative OTSCC cell line. Finally, we identified a 38-gene minimal signature that predicts tumor recurrence using an ensemble machine learning method. Taken together, this study links molecular signatures to various clinical and epidemiological factors in a homogeneous tumor population with a relatively high HPV prevalence.

Genomics↗
bioRxiv2015

Shared genomic variants: identification of transmission routes using pathogen deep sequence data

Sequencing pathogen samples during a communicable disease outbreak is becoming an increasingly common procedure in epidemiological investigations. Identifying who infected whom sheds considerable light on transmission patterns, high-risk settings and subpopulations, and infection control effectiveness. Genomic data shed new light on transmission dynamics, and can be used to identify clusters of individuals likely to be linked by direct transmission. However, identification of individual routes of infection via single genome samples typically remains uncertain. Here, we investigate the potential of deep sequence data to provide greater resolution on transmission routes, via the identification of shared genomic variants. We assess several easily implemented methods to identify transmission routes using both shared variants and genetic distance, demonstrating that shared variants can provide considerable additional information in most scenarios. While shared variant approaches identify relatively few links in the presence of a small transmission bottleneck, these links are highly confident. Furthermore, we proposed hybrid approach additionally incorporating phylogenetic distance to provide greater resolution. We apply our methods to data collected during the 2014 Ebola outbreak, identifying several likely routes of transmission. Our study highlights the power of pathogen deep sequence data as a component of outbreak investigation and epidemiological analyses.

Genomics↗
bioRxiv2016

Isolation of infective Zika virus from urine and saliva of patients in Brazil

BACKGROUNDZika virus (ZIKV) is an emergent threat provoking a worldwide explosive outbreak. Since January 2015, 41 countries reported autochthonous cases. In Brazil, an increase in Guillain-Barre syndrome and microcephaly cases was linked to ZIKV infections. A recent report describing low experimental transmission efficiency of its main putative vector, Ae. aegypti, in conjunction with apparent sexual transmission notifications prompted the investigation of other potential sources of viral dissemination. Urine and saliva have been previously established as useful tools in ZIKV diagnosis. However, no evidence regarding the infectivity of ZIKV particles present in saliva and urine has been obtained yet.\n\nMETHODOLOGY/PRINCIPAL FINDINGSNine urine and five saliva samples from nine patients from Rio de Janeiro presenting rash and other typical Zika acute phase symptoms were inoculated in Vero cell culture and submitted to specific ZIKV RNA detection and quantification through, respectively, NAT-Zika, RT-PCR and RT-qPCR. Two ZIKV isolates were achieved, one from urine and one from saliva specimens. ZIKV nucleic acid was identified by all methods in four patients. Whenever both urine and saliva samples were available from the same patient, urine viral loads were higher, corroborating the general sense that it is a better source for ZIKV molecular diagnostic. In spite of this, from the two isolated strains, each from one patient, only one derived from urine, suggesting that other factors, like the acidic nature of this fluid, might interfere with virion infectivity. The complete genome of both ZIKV isolates was obtained. Phylogenetic analysis revealed similarity with strains previously isolated during the South America outbreak.\n\nCONCLUSIONS/SIGNIFICANCEThe detection of infectious ZIKV particles in urine and saliva of patients during the acute phase may represent a critical factor in the spread of virus. The epidemiological relevance of this finding, regarding the contribution of alternative non vectorial ZIKV transmission routes, needs further investigation.\n\nAUTHOR SUMMARYThe American continent has recently been the scene of a devastating epidemic of Zika virus and its severe manifestations, such as microcephaly in newborns and Guillain-Barre Syndrome. Zika virus, first detected in 1947 in Africa, only from 2007 started provoking outbreaks. Zika, dengue and chikungunya viruses are primarily transmitted by Aedes mosquitoes. Dengue is endemic in Brazil for almost 30 years, and the country is largely infested by its main vector, Aedes aegypti. Chikungunya virus entered the country in late 2014 and Zika presence was confirmed eight months later. Nevertheless, Zika notifications multiplied and spread across the country with unprecedented speed, raising the possibility of other transmission routes. This hypothesis was strengthened by some recent reports of Zika sexual transmission in Ae. aegypti-free areas and by the description of a low transmission efficiency to Zika virus in local Ae. aegypti. We found Zika active particles in both urine and saliva of acute phase patients, and a finding that was promptly announced by Fiocruz via Press Conference on February 5, 2016. In this work, we bring up the potential alternative person-to-person infection routes beyond the vectorial transmission, that might have epidemiological relevance.

Microbiology↗
bioRxiv2016

InfoDengue: a nowcasting system for the surveillance of dengue fever transmission

This study describes the development of an integrated dengue alert system (InfoDengue), operating initially in the city of Rio de Janeiro, Brazil. It is a project developed as a partnership between academia and the municipal health secretariat. At the beginning of each epidemiological week, the system captures climate time series, dengue case reporting and activity on a social network. After data pre-processing, including a probabilistic correction of case notification delay, and calculation of dengue's effective reproductive number, indicators of dengue transmission are coded into four dengue situation levels, for each of the city's ten health districts. A risk map is generated to inform the public about the week's level of attention and the evolution of the disease incidence and suggest actions. A report is also sent automatically to the municipality's situation room, containing a detailed presentation of the data and alert levels by health district. The preliminary analysis of InfoDengue in Rio de Janeiro, using historical series from 2011 to 2014 and prospective data from January to December 2015, indicates good degree of confidence and accuracy. The successful experience in the city of Rio de Janeiro is a motivating argument for the expansion of InfoDengue to other cities. After a year in production, InfoDengue has become a unique source of carefully curated data for epidemiological studies, combining epidemological and environmental variables in unprecedented spatial and temporal resolutions.\n\nEthical committee approval: 26910214.7.0000.5240

Bioengineering↗
bioRxiv2016

Using Y chromosomal haplogroups in genetic association studies and suggested implications

Y chromosomal (Y-DNA) haplogroups are more widely used in population genetics than in genetic epidemiology, although associations between Y-DNA haplogroups and several traits (including cardio-metabolic traits) have been reported. In apparently homogeneous populations, there is still Y-DNA haplogroup variation which will result from population history. Therefore, hidden stratification and/or differential phenotypic effects by Y-DNA haplogroups could exist. To test this, we hypothesised that stratifying individuals according to their Y-DNA haplogroups before testing associations between autosomal SNPs and phenotypes will yield difference in association. For proof of concept, we derived Y-DNA haplogroups from 6,537 males from two epidemiological cohorts, ALSPAC (N=5,080, 816 Y-DNA SNPs) and 1958 Birth Cohort (N=1,457, 1,849 Y-DNA SNPs). For illustration, we studied well-known associations between 32 SNPs and body mass index (BMI), including associations involving FTO SNPs. Overall, no association was replicated in both cohorts when Y-DNA haplogroups were considered and this suggests that, for BMI at least, there is little evidence of differences in phenotype or gene association by Y-DNA structure. Further studies using other traits, Phenome-wide association studies (PheWAS), haplogroups and/or autosomal SNPs are required to test the generalisability of this approach.

Genetics↗
bioRxiv2016

Evidence for reassortment of highly divergent novel rotaviruses from bats in Cameroon, without evidence for human interspecies transmissions

Bats are an important reservoir for pathogenic human respiratory and hemorrhagic viruses but only little is known about bat viruses causing gastroenteritis in humans, including rotavirus A strains (RVA). Only three RVA strains have been reported in bats in Kenya (straw-colored fruit bat) and in China (lesser horseshoe and a stoliczkas trident bat), being highly divergent from each other. To further elucidate the potential of bat RVAs to cause gastroenteritis in humans we started by investigating the genetic diversity of RVAs in fecal samples from 87 straw-colored fruit bats living in close contact with humans in Cameroon using metagenomics. Five samples contained significant numbers of RVA Illumina reads, sufficient to obtain their (near) complete genomes. A single RVA strain showed a close phylogenetic relationship with the Kenyan bat RVA strain in six gene segments, including VP7 (G25), whereas the other gene segments represented novel genotypes as ratified by the RCWG. The 4 other RVA strains were highly divergent from known strains (but very similar among each other) possessing all novel genotypes. Only the VP7 and VP4 genes showed a significant variability representing multiple novel G and P genotypes, indicating the frequent occurrence of reassortment events.\n\nComparing these bat RVA strains with currently used human RVA screening primers indicated that several of the novel VP7 and VP4 segments would not be detected in routine epidemiological screening studies. Therefore, novel VP6 based screening primers matching both human and bat RVAs were developed and used to screen samples from 25 infants with gastroenteritis living in close proximity with the studied bat population. Although RVA infections were identified in 36% of the infants, Sanger sequencing did not indicate evidence of interspecies transmissions.\n\nThis study identified multiple novel bat RVA strains, but further epidemiological studies in humans will have to assess if these viruses have the potential to cause gastroenteritis in humans.

Microbiology↗
bioRxiv2016

Monomorphic genotypes within a generalist lineage of Campylobacter jejuni show signs of global dispersion

The decreased costs of genome sequencing have increased capability to apply whole-genome sequence on epidemiological surveillance of zoonotic Campylobacter jejuni. However, knowledge about how genetically similar epidemiologically linked isolates can be is vital for correct application of this methodology. To address this issue in C. jejuni we investigated the spatial and temporal signals in the genomes of a major clonal complex and generalist lineage, ST-45 CC, by exploiting the population structure and genealogy and applying genome-wide association analysis of 340 isolates from across Europe collected over a wide time-range. The occurrence and strength of the geographical signal varied between sublineages and followed the clonal frame when present, while no evidence of a temporal signal was found. Certain sublineages of ST-45 CC formed discrete and genetically isolated clades to which geography and time had left only negligible traces in the genomes. We hypothesize that these ST-45 CC clades form globally expanded monomorphic clones possibly spread across Europe by migratory birds. In addition, we observed an incongruence between the genealogy of the strains and MLST typing, thereby challenging the existing clonal complex definition and use of a common MLST-based nomenclature for the ST-45 CC of C. jejuni.

Microbiology↗
bioRxiv2016

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↗
bioRxiv2016

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↗