Reconstruction of the Transmission History of RNA Virus Outbreaks Using Full Genome Sequences: Foot-and-Mouth Disease Virus in Bulgaria in 2011

<div><p>Improvements to sequencing protocols and the development of computational phylogenetics have opened up opportunities to study the rapid evolution of RNA viruses in real time. In practical terms, these results can be combined with field data in order to reconstruct spatiotemporal scenarios that describe the origin and transmission pathways of viruses during an epidemic. In the case of notifiable diseases, such as foot-and-mouth disease (FMD), these analyses provide important insights into the epidemiology of field outbreaks that can support disease control programmes. This study reconstructs the origin and transmission history of the FMD outbreaks which occurred during 2011 in Burgas Province, Bulgaria, a country that had been previously FMD-free-without-vaccination since 1996. Nineteen full genome sequences (FGS) of FMD virus (FMDV) were generated and analysed, including eight representative viruses from all of the virus-positive outbreaks of the disease in the country and 11 closely-related contemporary viruses from countries in the region where FMD is endemic (Turkey and Israel). All Bulgarian sequences shared a single putative common ancestor which was closely related to the index case identified in wild boar. The closest relative from outside of Bulgaria was a FMDV collected during 2010 in Bursa (Anatolia, Turkey). Within Bulgaria, two discrete genetic clusters were detected that corresponded to two episodes of outbreaks that occurred during January and March-April 2011. The number of nucleotide substitutions that were present between, and within, these separate clusters provided evidence that undetected FMDV infection had occurred. These conclusions are supported by laboratory data that subsequently identified three additional FMDV-infected livestock premises by serosurveillance, as well as a number of antibody positive wild boar on both sides of the border with Turkish Thrace. This study highlights how FGS analysis can be used as an effective on-the-spot tool to support and help direct epidemiological investigations of field outbreaks.</p> </div

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

The initial laboratory diagnosis of equine influenza in Australia in 2007

Until August 2007, Australia had not recorded an outbreak of equine influenza; indeed, significant quarantine precautions exist to safeguard against such an event. This article outlines the lead up to virus confirmation and the procedures to first test, then contain it

Genomic interplay in bacterial communities: Implications for growth promoting practices in animal husbandry

The discovery of antibiotics heralded the start of a "Golden Age" in the history of medicine. Over the years, the use of antibiotics extended beyond medical practice into animal husbandry, aquaculture and agriculture. Now, however, we face the worldwide threat of diseases caused by pathogenic bacteria that are resistant to all existing major classes of antibiotic, reflecting the possibility of an end to the antibiotic era. The seriousness of the threat is underscored by the severely limited production of new classes of antibiotics. Evolution of bacteria resistant to multiple antibiotics results from the inherent genetic capability that bacteria have to adapt rapidly to changing environmental conditions. Consequently, under antibiotic selection pressures, bacteria have acquired resistance to all classes of antibiotics, sometimes very shortly after their introduction. Arguably, the evolution and rapid dissemination of multiple drug resistant genes en-masse across microbial pathogens is one of the most serious threats to human health. In this context, effective surveillance strategies to track the development of resistance to multiple antibiotics are vital to managing global infection control. These surveillance strategies are necessary for not only human health but also for animal health, aquaculture and plant production. Shortfalls in the present surveillance strategies need to be identified. Raising awareness of the genetic events that promote co-selection of resistance to multiple antimicrobials is an important prerequisite to the design and implementation of molecular surveillance strategies. In this review we will discuss how lateral gene transfer (LGT), driven by the use of low-dose antibiotics in animal husbandry, has likely played a significant role in the evolution of multiple drug resistance (MDR) in Gram-negative bacteria and has complicated molecular surveillance strategies adopted for predicting imminent resistance threats

Nucleotide and amino acid substitutions occurring along the genome of the FMDV sequences.

<p><b>A</b>) Data for sequences from Bulgaria (8 genomes): graphs represent the distribution of total nucleotide (nt) (black line) and non-synonymous (red) substitutions across the different genomic regions of FMDV (shown below). The pie chart and the bar chart show percentage of nt substitutions for each region, and nt variability within the region, respectively. <b>B</b>) Similar analysis to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049650#pone-0049650-g002" target="_blank">figure 2A</a>) undertaken for the 11 FMDVs genomes from Turkey and Israel.</p

Estimated time in which FMDV might have been introduced causing the different Bulgarian outbreaks.

<p>This illustration was built according to the date of sample collection, the virus and serological results of the collected samples and the age of the lesions of the animals with clinical signs. The date of sample collection is coloured in yellow in case of the virus-positive outbreaks, and in dark green in case of the seropositive-only outbreaks. The age of the lesions of the animals with clinical signs (if any), according to the National Veterinary surgeons involved in the outbreaks, is coloured in orange. The incubation time, estimated to be 14 days, is coloured in blue. In the case of the seropositive-only outbreaks, two different times were considered to explain the presence of antibody-positive/virus-negative samples, depending on whether clinical signs where unobserved or mild (hypothesis 1, H1) or whether the lesions had healed (H2). In case of H1, a minimum of 5 days post-infection was estimated (shaded with vertical stripes), whereas a minimum of 21 days post-infection for H2 (pale green). All estimated times were based on previous studies <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049650#pone.0049650-Alexandersen1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049650#pone.0049650-Cottam3" target="_blank">[9]</a>. Only genetic data can prove a link between waves one and two.</p

Summary of full genome sequenced viruses generated in this study.

a<p>WRLFMD = World Laboratory Reference for Foot-and-Mouth Disease, The Pirbright Institute, United Kingdom; SAP = Foot-and-Mouth Disease Institute, Turkey; DTU = National Veterinary Institute, Denmark.</p>b<p>E = Epithelium; CC = Cell culture.</p>c<p>Sample nomenclature assigned by DTU.</p>d<p>Sample nomenclature assigned by SAP.</p

Spatiotemporal dynamics of the FMDV epidemic in Bulgarian 2011.

<p>The simplest option was considered for the selection of the gamma-random-relaxed-walk (RRW) continuous diffusion model. The isolate from Israel was excluded from this analysis,. Satellite imagery: GoogleEarth. Date accessed: 25 May 2012. Co-ordinates: 40°57′25.89″N, 28°27′28.38″E (A); 42°04′27.07″N, 27°39′47.60″E (B) 42°09′01.30″N, 27°09′42.18″E (C). <b>A</b>. FMDV spread from the North-West of Turkey throughout Bursa (Anatolia, Turkey) to Brugas (Bulgaria). The uncertainity on the location the virus is represented by transparent polygons (80% HPD). Turkish Thrace might have been infected before November 2010, which is plausible with the serological results in wild boar. <b>B</b>. The transmission infection pathways between the wild boar, outbreaks 1, 2, 3 and the second wave of outbreaks is not clarified. It might be explained by un-sampled notified sites/outbreaks or by a reservoir in wildlife (i.e. wild boar), both hypotheses are compatible with a genetically and spatiotemporally close FMDV replicating within a host. <b>C</b>. The genetic spatiotemporal reconstruction of the second wave of outbreaks linked them to each other, in agreement with epidemiological data, i.e. owners from animals in outbreak 6 had animals in the location of outbreak 4.</p

Summary of the FMDV outbreaks which occurred in Burgas, Bulgaria, 2011.

<p>W = Wild boar; C = Cattle; S = Sheep; G = Goat; P = Pig; B = Buffalo; U = Unobserved (FMDV-seropositive-only holding);</p>a<p>1a Seropositive free range pigs (lesions) and cattle; 1b Village with seropositive sheep, goats and pigs; 1c Virus positive Hereford cattle.</p>b<p>Partial sampling.</p>c<p>Clinical signs seen in sheep at culling.</p

Statistical parsimony trees as implemented by TCS using the full genomes of 19 sequenced FMDVs.

<p><b>A</b>. Edited TCS tree in which putative virus ancestors (○), except those corresponding to nodes, were removed. The length of the branches is directly proportional to the number of nucleotide (nt) changes. The vertical axis represents a time scale which denotes the date when the viruses were collected. <b>B</b>. Detailed TCS tree showing the viruses corresponding to the Bulgarian outbreaks and their closest ancestor within the Middle East. Open circles and lines correspond to putative genetic intermediates separated by single nt changes. Putative common (red circle) and secondary ancestors for each wave are shaded (blue circle, first; green circle, second). Lines in bold correspond to non-synonymous changes. The square shows the number of nt versus non-synonymous changes. The specific amino-acid changes are indicated, as well as the viral proteins involved. Non-conservative amino-acid substitutions (according to GONNET matrix, as implemented in BioEdit software) are highlighted in bold.</p