We explore the challenge of budgeting a priori for protected area expansion in the face of uncertainty, specifically considering the future expansion of protected areas in Queensland, Australia. The government has committed to adding ∼12 million ha to the reserve system, bringing the total area protected to 20 million ha by 2020. We used Marxan to estimate the costs of potential reserve designs with data on actual land value, market value, transaction costs, and land tenure. With scenarios, we explored three sources of budget variability: size of biodiversity objectives; subdivision of properties; and legal acquisition routes varying with tenure. Depending on the assumptions made, our budget estimates ranged from $214 million to $2.9 billion. Estimates were most sensitive to assumptions made about legal acquisition routes for leasehold land. Unexpected costs  responded non-linearly to inability to subdivide and percentage purchase of private land. A financially conservative approach - one that safeguards against large cost increases while allowing for potential financial windfalls - would involve less optimistic assumptions about acquisition and subdivision to allow Marxan to avoid expensive properties where possible while meeting conservation objectives. We demonstrate how a rigorous analysis can inform discussions about the expansion of systems of protected areas, including the identification of factors that influence budget variability.Many governments have recently gone on record promising large-scale expansions of protected areas to meet global commitments such as the Convention on Biological Diversity. As systems of protected areas are expanded to be more comprehensive, they are more likely to be implemented if planners have realistic budget estimates so that appropriate funding can be requested. Estimating financial budgets  International mandates such as the Convention on Biological Diversity (CBD) have become prominent in debates about extending protected areas Constraints on funding for conservation have motivated global financial analyses to estimate the costs of conservation commitments We identified ten factors likely to affect the cost of expanding protected areas . Some ofIn 2008, the Queensland government promised to add ∼12 million ha to the network of protected areas, 4 million ha of which would be acquired for national parks, bringing the total estate to 20 million ha by 2020 The primary objective of our study was to conduct a financial analysis of how much it will cost to expand the Queensland protected area estate, accounting for uncertainties around assumptions that make a range of potential costs more useful than a single figure. We therefore used a sensitivity analysis to examine two aspects of uncertainty in financial estimates: 1. the possible range of financial budgets required, depending on different assumptions made, termed “expected costs” and 2. the possible “unexpected costs” given departures from assumptions in the face of real-world constraints. We focused our study on three factors: the size of biodiversity objectives; willingness to subdivide properties; and legal acquisition routes dependent on tenure . We seleWe used sensitivity analysis because the promised rate of expansion of the protected area estate is unprecedented and not reflective of previous acquisitions. It was therefore not possible to derive accurate financial estimates from historic data. Historically, parks have not been located strategically, but rather in response to political imperatives, such as the Wet Tropics World Heritage Area, or based on ad hoc responses to availability of land and lengthy negotiations with landholders. Furthermore, future expansion of protected areas in Queensland will involve recent legal initiatives with uncertain applications. One of these initiatives is the Nature Refuge program which allows landholders to voluntarily place portions of their properties under conservation covenants. The covenants are attached to land titles in perpetuity, stipulate nature conservation as the primary use and constitute IUCN category VI protected areas. Nature refuges therefore contribute to the national reserve system and to meeting global commitments such as the CBD. However, NatureAssist, which involves competitive bidding by landholders to support management of Nature Refuges, provided its first round of funding only in 2007. It is unclear whether the last 5 years of funding will reflect future funding, or whether this will be sufficient incentive to engage many more landholders. A second recent legal initiative is the Delbessie Agreement, legislated in the last 5 years and untested in implementation. The Delbessie Agreement is a framework of legislation, policies and guidelines supporting the environmentally sustainable, productive use of rural leasehold land for For each of our 200 scenarios we used Marxan We identified the scenarios most likely to reflect the government's commitment to acquire 4 million additional ha of national park, which we also interpreted as 33% of the total expanded area required to meet objectives. Given an average of 23 million ha of additional protection needed, adding only 4 million ha to the national park estate would mean that covenants on private land would be relied upon heavily to meet conservation objectives. On the assumption that the government will commit to acquire 33% of the required land to meet conservation objectives, the area needing acquisition actually ranged from about 6 to 10 million ha. The total expected costs of scenarios involving an additional 4 million ha or 33% acquisition for national parks ranged from $250 million to $1.6 billion. The minimum cost estimate of $250 million was based on the 10%/1000 ha objectives and assumed property subdivision, 30% freehold acquisition, and 0% leasehold acquisition. However, given the uncertainties about implementation of the Delbessie Agreement and the potential need to purchase larger amounts of leasehold land, the maximum expected cost could rise to $2.3 billion .Total expected costs were 50–80% larger for scaled objectives which required ∼17% more area . For botPredictably, unexpected ability to subdivide properties reduced costs and unexpected inability to subdivide properties increased costs, but the responses were non-linear across scenarios . The larThe sensitivity of total costs to unexpected 10% increases in purchases of leasehold land increased with larger expected percentage purchases of freehold land and decreased with larger expected percentage purchases of leasehold land . HoweverWhile governments commonly identify lofty multi-year conservation goals, it is uncommon for them to estimate the required costs. When financial estimates are released, they might be serious underestimates, reflecting the pervasive problem of under-funding conservation. The Queensland government estimated the cost of its commitment of 20 million ha of protected areas by 2020 at $120 million a priori estimates and general rules of thumb difficult to derive.Total costs across our 200 scenarios varied by an order of magnitude from $214 million to $2.9 billion. All of the factors tested influenced this variation, with objectives and subdivision the most important. Cost was linearly influenced by percentage leasehold acquisition, which is to be expected because of the linear increments in percentage of properties acquired. However, when we considered unexpected costs, they responded non-linearly to unexpected inability to subdivide and percentage purchase of freehold. This is because freehold properties are small, much more expensive per ha than leasehold properties and have smaller proportions of native vegetation than leasehold properties. The wide range of cost estimates, the influence of underlying assumptions, interactions between assumptions, and non-linear responses make Many of the effects of key factors are difficult to anticipate without the kinds of analyses presented here. For example, scaled objectives raised costs by 50–80% despite the targeted area being about 2 million ha smaller. There were two reasons. First, rarer regional ecosystems had larger proportional objectives with the scaled method and these ecosystems were more expensive to protect per unit area. The Pearson correlation between log and log was −0.117 (p<0.001). The second reason was that larger scaled objectives reduced spatial flexibility for representing rarer regional ecosystems, providing few or no alternatives to more expensive properties. The costs of conservation can therefore be more sensitive to objectives for individual biodiversity features than to aggregate area goals such as percentages of a state.The amplitude of unexpected increases and reductions in costs could help to guide what assumptions are the most conservative. We define financially conservative assumptions here as those that safeguard against large cost increases while allowing for potential financial windfalls. We measured uncertainty by estimating how expected costs were changed when assumptions did not hold true. The largest deviations from expected costs occurred when very low acquisition assumptions (∼0–20%) proved inaccurate. When acquisition assumptions were low, property values were effectively smoothed to obscure spatial variability in purchase costs. This can result in selection of expensive properties and expose planners to large unexpected costs if landholders are less receptive to Nature Refuges than assumed. Selections of properties under larger acquisition assumptions recognized variability in costs and avoided expensive properties, where there were choices, so unexpected costs represented smaller increases when assumptions did not hold . TherefoUnexpected costs or savings resulting from inaccurate subdivision assumptions indicated that potential costs far outweighed potential savings. When subdivision unexpectedly occurred after selection of areas, costs were reduced by only ∼15%. In contrast, when subdivision unexpectedly did not occur, costs increased by ∼45%. A conservative approach – assuming no subdivision - therefore resulted in only a small loss of efficiency but avoided a large financial risk. Unexpected increases or reductions in costs related to subdivision also interacted with assumptions about percentages of freehold and leasehold land that would be acquired , so potea priori assumptions. The narrowed range of scenarios based on the acquisition promise of 4 million ha or 33% still resulted in costs varying by an order of magnitude due to unavoidable uncertainty around the responses of leaseholders to the Delbessie Agreement. Because the Queensland government is required under this Agreement to immediately purchase leasehold properties with conservation value, we explored the full range of leasehold percentage purchases, resulting in a wide cost range. Even long experience with acquisition of individual properties, sometimes involving protracted negotiations, does not necessarily equip an agency to accurately cost a massive expansion of protected areas across many hundreds of properties, involving many hundreds of landholders who have not previously dealt with agency officers, while implementing new, and largely untested, legislation.Expanding the Queensland protected area system could incur a wide range of financial costs, depending on biodiversity objectives and socio-political conditions encountered. While this variation might be reduced by estimating plausible bounds for key factors, a single exact estimate cannot be provided because there will always be uncertainty associated with To avoid undesirable surprises in Queensland, planners should not assume that large percentages of land will be protected through Nature Refuges at low cost to the government. Similarly, they should use conservative assumptions about subdivision. Assumptions of larger percentages purchased and smaller proportions of properties subdivided will cause the selection algorithm  to avoid expensive properties with extensive clearing of native vegetation. While increasing expected budgets, this would avoid large unexpected increases in costs. The selected properties could then be analyzed in detail with respect to remnant vegetation and attitudes of landholders to narrow the range of expected costs, targeting specific properties for subdivision and acquisition routes.Narrowing the range of potential costs of an expanded protected area system in Queensland requires a better understanding of landholders' interests in selling their properties in whole or part, negotiating leases, or participating in the Nature Refuge program, and how these interests vary geographically and by land use. Likely levels of participation in conservation programs as well as potential costs can be determined through local-scale experiments such as tendering processes or closed-bid auctions Particular care should also be given to setting interim versus long-term objectives, especially for the small regional ecosystems that can influence costs and for any regional ecosystems threatened by further reductions in extent. Achieving longer-term, scientifically defensible targets, which might have high financial costs, in the context of continuing, incremental depletion of native vegetation, requires an explicit strategy for scheduling conservation actions Although our analysis focused on Queensland, the same factors are likely to affect the cost of conservation in other parts of the world. Our findings are generic, in that the estimated costs of expanding protected area systems to meet policy goals or political commitments are likely to vary widely and to be highly sensitive to assumptions about influential factors.In our study, subdivision and conservation objectives were particularly important in influencing the costs of conservation for very different reasons. The ability to subdivide properties dramatically affected the amount of land required to meet the conservation objectives. This result is general and not context-specific and has been noted previously in relation to the total extent of selected areas Comparisons of the potential political pathways for acquiring and protecting land have been largely neglected in the literature. However, with the recent increase in conservation programs on private lands While conservation planners cannot be expected to have infallible foresight into the accuracies of their assumptions, they can be expected to be transparent about their uncertainties The study region included the whole state of Queensland, Australia, with a total area of 185 million ha of which approximately 8 million ha is protected . We implBased on conversations with DERM we developed two different methods for setting conservation objectives that reflected the department's thinking and minimum policy requirements for the Commonwealth Government to achieve 10% protection of all bioregions. We do not endorse these objectives as ecologically adequate, but present them here to illustrate their effect on costs of protected areas.At the core of Australia's biodiversity conservation strategy are the goals for protected areas to be comprehensive, adequate, and representative. Comprehensiveness refers to the need to sample all bioregions; adequacy relates to the persistence of biodiversity; and representativeness indicates how well ecosystems are sampled within bioregions The first method for defining objectives  was derived from minimal policy requirements p = 0.5:kt is the objective for regional ecosystem k, expressed as a proportion, p is the power, and x is the pre-clearing extent of regional ecosystem k. More extensive regional ecosystems  therefore had larger objectives, but the power function produced a diminishing rate of increase in objectives with increasing pre-clearing extent. We multiplied each objective by pre-clearing extent to express it in ha and, if necessary, trimmed it to total remnant area.The second method  used a power function to scale objectives based on extent of vegetation. This method reflects the fact that a very extensive ecosystem might not need 10% protection to ensure long-term viability, whereas a small, heavily cleared ecosystem could need a much larger percentage protected. If the pre-clearing extent was less than 1,000 ha, the objective was set to pre-clearing extent (i.e. 100%). If the pre-clearing extent was 1,000 ha or larger, we used the following equation with a power value of Objectives for the 10%/1,000 ha method totaled about 12.5 million ha compared to about 10.6 million ha for scaled objectives. However, the methods differed more importantly in their objectives for individual regional ecosystems. Compared to the 10%/1,000 ha objectives, scaled objectives gave larger values to regional ecosystems with smaller pre-clearing extents and smaller values to regional ecosystems with larger pre-clearing extents .a priori for large numbers of properties managed by people with no previous contact with DERM. Acquisition of whole properties is more expensive and will often involve purchase of land without native vegetation. Acquisition only of native vegetation is less expensive and more targeted. To bound our estimates of costs, we compared two subdivision scenarios, one without subdivision and one involving subdivision to protect only remnant native vegetation. In scenarios with subdivision, we assumed homogeneous cost across each property (below) and pro-rated the cost for remnant vegetation. In places, our subdivision assumption will underestimate costs, with protection of only unrealistically small patches of native vegetation. However, it reflects assumptions made in the literature Willingness of landholders to subdivide properties for sale is likely to be highly variable and difficult to estimate We explicitly considered the multiple acquisition routes for freehold (privately owned) land . FreeholWe also considered the multiple acquisition routes for leasehold land  . Under tx*costNR+y*costpurchase, where x and y are the percentages assumed for Nature Refuge and acquisition, respectively, and costNR and costpurchase are the costs of Nature Refuge and acquisition, respectively. For both freehold and leasehold land, we avoided a priori allocation of properties to individual acquisition routes to avoid idiosyncratic correlations between cost and particular examples of regional ecosystems.For each level of assumed acquisition of freehold and leasehold properties, we calculated the expected cost of properties as: Legal properties are the units with which managers implement conservation actions, but previous analyses of conservation costs have rarely used cadastral boundaries. To more accurately estimate the costs and extent of land needed to meet objectives, we used legal property boundaries to define planning units For each property, we used data on tenure, unimproved land value, and sales prices log. The local R2 for coastal properties was lower than for others because of the lack of predictors available to capture potential for coastal development (adjusted R2 = 0.688). For the remainder of properties, predictors were log, log, log and log (adjusted R2 = 0.904).For our geographically weighted regression, we first considered the entire state. However, because coastal properties in Queensland have different characteristics to those elsewhere , analyzing the entire state led to local multi-collinearities in coastal properties. We therefore applied geographically weighted analysis for coastal properties and the remainder of properties separately. For coastal properties, the only predictor without strong local correlations was log and log and the final predicted sales values are in log is of interest because this was the only predictor used across the entire state . The coefficient for log is of interest because land value is typically the only type of cost data used to estimate acquisition costs in other academic studies for Australia The spatially variable coefficients for TC) as an expected value based on the respective percentage acquisition assumptions, using the following equation:x and y were the percentages assumed for Nature Refuge and acquisition, respectively, costNR and costpurchase were the costs of Nature Refuge and acquisition, respectively, managementNR and managementNP were the one-year increase in management costs for Nature Refuge and national park, respectively, and transaction was the transaction cost , the total extent of selected areas, excluding existing reserves, and their total cost.We used a two-step process to assess the sensitivity of cost estimates to values of our variables. First, we calculated the total cost that would apply if all assumed values of key variables held true (“expected cost”). Expected costs would apply, for example, if areas were selected on the assumption that no subdivision of properties would be possible and, after negotiation with landholders, this proved to be the case. We recorded the expected cost for each run and regressed expected cost against our four variables: percentage acquisition route for freehold land , percentage acquisition route for leasehold land , type of objective , and subdivision . Second, we calculated the cost of each scenario with different amounts of deviation from scenario assumptions (“unexpected costs”). For example, areas might be selected and total costs estimated on the assumption that 50% of leasehold properties would be purchased. If the percentage requiring purchase was actually 60%, then 10% of properties expected to be inexpensive would prove to be otherwise and unexpected costs would apply. The increase in total cost would be greater than if 60% acquisition had been expected initially, which would have shifted selections more towards properties with lower acquisition costs. We calculated both the absolute and percentage differences between expected and unexpected costs.a priori assumptions about values of variables, no matter how well informed, will always be inaccurate to some extent. Our approach “commits” the agency to configurations of new reserves based on assumptions holding, even if the assumptions prove incorrect. In reality, the agency would revise configurations accordingly, although it might not be able to revise its financial estimates so easily. Nonetheless, our approach indicates how inaccurate cost estimates can be if values of key factors deviate from expected.This approach to estimating sensitivity to key variables is important because Information S1Supporting methods and tables for hedonic model for property sales value.(DOC)Click here for additional data file.


Eubenangee virus species of the genus Orbivirus, family Reoviridae. Full genome sequencing confirmed that EUBV and TILV  show high levels of aa sequence identity (>92%) in the conserved polymerase VP1(Pol), sub-core VP3(T2) and outer core VP7(T13) proteins, and are therefore appropriately classified within the same virus species. However, they show much lower amino acid (aa) identity levels in their larger outer-capsid protein VP2 (<53%), consistent with membership of two different serotypes - EUBV-1 and EUBV-2 (respectively). In contrast PATAV showed significantly lower levels of aa sequence identity with either EUBV or TILV (with <71% in VP1(Pol) and VP3(T2), and <57% aa identity in VP7(T13)) consistent with membership of a distinct virus species. A proposal has therefore been sent to the Reoviridae Study Group of ICTV to recognise ‘Pata virus’ as a new Orbivirus species, with the PATAV isolate as serotype 1 (PATAV-1). Amongst the other orbiviruses, PATAV shows closest relationships to Epizootic Haemorrhagic Disease virus (EHDV), with 80.7%, 72.4% and 66.9% aa identity in VP3(T2), VP1(Pol), and VP7(T13) respectively. Although Ngoupe virus was not available for these studies, like PATAV it was isolated in Central Africa, and therefore seems likely to also belong to the new species, possibly as a distinct ‘type’. The data presented will facilitate diagnostic assay design and the identification of additional isolates of these viruses.Eubenangee virus has previously been identified as the cause of Tammar sudden death syndrome (TSDS). Eubenangee virus (EUBV), Tilligery virus (TILV), Pata virus (PATAV) and Ngoupe virus (NGOV) are currently all classified within the  Eubenangee virus (EUBV) is one of 22 species  of the genus Orbivirus within the family Reoviridae. The orbiviruses are vector borne, non-enveloped, icosahedral viruses, with genomes composed of 10 segments of linear dsRNA. Bluetongue virus (BTV) is the type species of the genus, which also contains 14 currently unclassified strains, some of which may represent additional species Culicoides, mosquitoes and sand flies) and have a wide host-range that collectively includes domestic and wild ruminants, equines, marsupials, sloths, bats, birds and humans Eubenangee virus species/serogroup contains viruses that infect both marsupials and cattle, that have also been isolated from mosquitoes and CulicoidesCulicoides spp. and antibodies to the virus have been detected in cattle and marsupials Anopheles annulipes mosquitoes in Australia The Aedes palpalis mosquitoes in the Central African Republic Aedes tarsalis also in the Central African Republic Pata virus was isolated in 1968 from pool of The disease producing potential of EUBV was not widely explored until the virus was isolated in Australia between October 1998 and March 1999, from wallabies affected by the Tammar sudden death syndrome (TSDS) Culicoides in the region Another suspected outbreak of TSDS occurred in western Sydney in 2007 but no virus was isolated Orbivirus species have supported the development of faster and more reliable virus-species/serogroup/serotype specific diagnostic assays, by conventional and/or real-time RT-PCR The similar host range, distribution, co-circulation and relatively close serological relationships between EUBV, BTV and EHDV, has occasionally made diagnosis and identification of specific etiological agents difficult by serological methods Eubenangee virus. These data also support the reclassification of PATAV as a distinct new Orbivirus species. A proposal to this effect has been sent to the Reoviridae Study Group of the International Committee on Taxonomy of Viruses (ICTV).We report full genome sequence data for Eubenangee virus (EUBV), Pata virus (PATAV) and Tilligerry virus (TILV), which will facilitate development of relevant assays and identification of further isolates that belong to the species http://www.reoviridae.org/dsRNA_virus_proteins/ReoID/Eubenangee-isolates.htm#EUBV)) induced characteristic cytopathic effects (CPE) in BHK and BSR cell monolayers, between 48–72 hours post infection. Purified dsRNAs of these viruses were analysed by 1% agarose gel electrophoresis (AGE), along with representative BTV and EHDV strains  at IAH  have different sizes and positions from those of EUBV and TILV, leading to the production of proteins with different sizes. The coding assignments of EUBV, TILV and PATAV genome segments are similar to those of BTV and the other and EUBV .5′-GU……UAC-3′) while the first and last 2 nucleotides of each genome segment are also inverted complements, characteristics of the genus Orbivirus (http://www.reoviridae.org/dsRNA_virus_proteins/CPV-RNA-Termin.htm). These results confirm an early report of the conserved termini of Tilligerry virus genome segments (5′-GU–A………….AC-UAC-3′) as identified by enzymatic digestion and electrophoresis The genome segments of all three viruses have shorter 5′ non coding regions (NCR)  than 3′ NCRs  with conserved terminal hexanucleotides that are comparable to, but in some cases not identical to those of BTV or EHDV . Two nucATGG), except for the first ORFs of Seg-9 (CTGATGA) and Seg-10 (GC/ATATGT), both of which have a ‘weak’ context, but also encode additional smaller proteins from downstream initiation sites (generating NS4 and NS3a respectively). The ORF's of PATAV genome segments have the initiation sequence RNNATG, followed by ‘G’ in Seg-1,5,6,7,8 (strong Kozak context); ‘A’ in Seg-2, 3 (adequate Kozak context); ‘C’ in Seg-4, and -10, and ‘T’ in Seg-9 (weak context).Most of the ORF's of the EUBV and TILV genome segments have a strong Kozak sequence ; while in the second group, VP3(T2) is encoded by Seg-2 of two orbivirus sub-groups that are transmitted either by ticks (Great island virus [GIV]), or by mosquitoes . St Croix River virus [SCRV] branches separately, representing a more distantly related orbivirus that may be a tick-virus rather than a tick-borne-virus Orbivirus species in the ‘Culicoides borne group’, although they cluster with other species that infect marsupials   was identified as the virion's sub-core-shell ‘T2’ protein, by comparisons with BTV proteins using BlastX. Nucleotide (nt) and amino acid (aa) identities in VP3(T2) of EUBV, TILV and PATAV, as compared to other orbiviruses, are given in o virus) .Culicoides borne group, clustering more closely with EHDV and BTV. Although EUBV, TILV and PATAV were initially isolated from mosquitoes, EUBV has also been isolated from CulicoidesCulicoides borne orbiviruses, suggesting that these insects may act as their biological vectors.In contrast VP3(T2) of PATAV branches independently from EUBV and TILV within the Orbivirus species were compared and used to construct an unrooted NJ phylogenetic tree (Culicoides borne orbiviruses. EUBV and TILV cluster very closely together (consistent with membership of a single virus species), while PATAV branches separately  gene encoded by Seg-1 of all orbiviruses is highly conserved within the genus. Phylogenetic comparisons of this genome segment and protein have been used to classify new virus isolates at the species and genus level tic tree   of BTV is a strongly immunodominant antigen and is the primary determinant of virus-serogroup Culicoides borne orbiviruses; 20.63% to 26.01% aa identity with mosquito borne orbiviruses; 21.5% to 24.5% aa identity with the tick borne orbiviruses; and only 20.41%–22.65% aa identity with the most distantly related SCRV (The VP7(T13) protein of EUBV, TILV and PATAV shares 37.93% to 67.53% aa sequence identity with the Orbivirus species, using the p-distance and pairwise deletion parameters in MEGA5 ; <65% identity in the polymerase VP1(Pol) and <57.5% aa identity in VP7(T13) with TILV or EUBV  and 4, cMost of the genome segments in EUBV and TILV are monocistronic encoding single proteins from single open reading frames (ORF). However, Seg-10 of both viruses has two in-frame initiation sites at, or near the start of the same ORF leading to translation of NS3 and NS3a . A similATGA and GAGATGA). In contrast the 2nd ORF of PATAV is much further downstream on Seg-9, at 265–504 bp (80 aa), and has a weak Kozak context (AAGATGC), similar to those observed for NS4 of BTV and some other orbiviruses Seg-9 of EUBV, TILV and PATAV also has an additional down-stream ORF (in reading-frame +2) . The 2ndHydrophobicity profiles of NS4 proteins from BTV, EHDV, GIV, EUBV, TILV and PATAV show broadly similar patterns of conserved domains indicating that they are generally hydrophilic ; conserved terminal nucleotide sequences; serological cross-reactions; comparison of homologous genome segments or proteins by sequence analysis or cross-hybridisation; host and vector range and the nature of clinical signs induced Orbivirus species were initially recognised as distinct ‘serogroups’. However, reference antisera for these existing species are not widely available, making serological identification of new virus isolates more difficult. Data concerning the potential for reassortment of new orbivirus isolates with members of existing species would also require access to reference strains and would be very labour intensive. In contrast, nt and deduced aa sequence data generated for each species can be easily and reliably disseminated and can be used to analyse and identify new or existing virus isolates Parameters recognised by the International Committee on Taxonomy of Viruses (ICTV) for the ‘polythetic definition’ of individual Orbivirus species/serogroup usually have similar dsRNA migration profiles during in 1% agarose gel electrophoresis (AGE) Viruses within a single Eubenangee virus does not appear to be recorded. It does not cross-react strongly in serological assays with other EUBV isolates and it is not closely related to EUBV by hybridrization The rationale for the current inclusion of Pata virus (PATAV) in the species Low level cross-reactions have previously been shown between EUBV, and BTVs or EHDVs in cross-hybridization and serological assays (including CF and agar gel immunoprecipitation tests) Orbivirus species remains a barrier for molecular identification, diagnostic assay development and taxonomic classification of novel isolates.Recent advancements in molecular biology and sequencing technologies Eubenangee virus  as part of a wider programme generating representative sequences for all of the different Orbivirus species This study was therefore designed to generate full-genome sequences for available members of the species Culex annulirostris and Culicoides marksi, EUBV multiplied to high titres in the recognised orbivirus vector Culicoides variipennis after both intra-thoracic inoculation and oral ingestion, but only replicated in C. nubeculosus  after intra-thoracic inoculation Culicoides borne orbiviruses BTV, EHDV and AHSV, suggesting that it could have a role in core particle attachment to Culicoides cells Culicoides spp. as biological vectors.Although EUBV and TILV have been isolated from both Culicoides borne orbiviruses have intermediate G+C content from 39.89% in Chuzan virus (CHUV) to 45.89% in equine encephalosis virus (EEV) Culicoides borne orbiviruses, again suggesting Culicoides as a biological vector.The G+C content of the mosquito borne orbiviruses is between 36.72% in Peruvian horse sickness virus (PHSV) and 41.55% in Yunnan orbivirus (YUOV), in the tick borne orbiviruses it is between 57.29% in Great Island virus (GIV) and 51.93% in St Croix River virus (SCRV), while the Orbivirus species Orbivirus species, with highest similarities to EHDV and BTV. PATAV shows lower levels of identity with the Eubenangee viruses (EUBV and TILV) supporting earlier hybridization and reassortment studies The orbivirus sub-core-shell ‘T2’, polymerase ‘Pol’ and outer-core ‘T13’ proteins are highly conserved with >83%, >73% and >73% aa identity respectively within a single in vivo studies will confirm the functional role of these proteins in the infected cell.The orbivirus NS4 protein is highly variable in size, ranging from 77 aa in BTV to 190 aa in GIV Culicoides (which have VP3(T2)) and those transmitted by ticks or mosquitoes, (in which the homologous protein is VP2(T2)). This difference appears to be primarily due to the presence of a significantly larger outer-capsid ‘VP2’ protein in the Culicoides borne orbiviruses. Analyses of the orbivirus T2 .Data presented here confirm that EUBV and TILV are both members of the Isolates of Eubenangee viruses which had been stored at −80°C, were obtained from the Orbivirus Reference Collection (ORC) at the Institute for Animal Health (IAH), including: EUBV (ORC Strain Number: AUS1963/01); TILV (AUS1978/03); and PATAV (CAF1968/01). These samples were taken from naturally infected animals in the field, by qualified veterinarians, as part of normal diagnostic testing procedures in the respective countries and did not therefore require Ethics Committee approval. TILV was propagated in BHK-21 cells , while EUBV and PATAV were grown in BSR cells (a clone of BHK) g for 10 min at 4°C. Single stranded RNA (ssRNA) was removed by 2 M LiCl precipitation at 4°C overnight, followed by centrifugation at 10,000× g for 5 min. An equal volume of isopropanol, containing 750 mM ammonium acetate, was added to the supernatant then mixed and the viral dsRNA allowed to precipitate for a minimum of 2 hours at −20°C. The dsRNA was pelleted by centrifugation at 10,000× g for 10 min. The pellet was washed with 70% ethanol, air dried and suspended in nuclease free water (NFW). The RNA was either used immediately or stored at −20°C.Intact genomic dsRNA was extracted from EUBV, TILV or PATAV infected cell cultures, using a guanidinium isothiocyanate extraction procedure, as described by Attoui et al Viral genome segments of EUBV, TILV and PATAV were reverse-transcribed into cDNA using the full-length amplification of cDNAs (FLAC) technique described by Maan et al Amplified cDNAs were purified and cloned. TILV genes were cloned into the Strataclone blunt-end PCR cloning vector ‘pSC-B-amp/kan’ supplied in the StrataClone Blunt PCR cloning kit (Stratagene). EUBV and PATAV amplicons were cloned into the ‘pCR®-Blunt’ vector supplied with the Zero Blunt® PCR Cloning Kit (Invitrogen). Recombinant plasmid-vectors containing TILV inserts were transformed into Solopack® competent cells (Agilent Technologies), while EUBV and PATAV plasmids were transformed into One Shot® TOP10 competent cells, supplied with the respective cloning kits. Clones containing relevant inserts were identified by touch PCR using M13 universal primers. Plasmids were extracted from the clones identified, using the QIAprep Spin MiniPrep Kit (Qiagen). The plasmids and PCR products were sequenced using an automated ABI 3730 DNA sequencer (Applied Biosystems).http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastHome). Multiple alignments of consensus sequences were performed using Clustal X (Version 2.0) http://www.ebi.ac.uk/Tools/msa/clustalo/) and MAFFT  to ensure proper alignment. Pairwise distance (aa and nt) calculations and phylogenetic trees constructions were done using MEGA 5 software http://pfam.sanger.ac.uk/search). The presence of ‘coiled-coils’ was analysed using the program ‘COILS’ (available at http://www.ch.embnet.org/software/COILS_form.html). Nuclear localisation signals were predicted using cNLS Mapper (available at http://nls-mapper.iab.keio.ac.jp/cgi-bin/NLS_Mapper_form.cgi).‘Raw’ ABI sequence data, was assembled into ‘contigs’ using the SeqManII sequence analysis package (DNAstar version 5). The ORFs of EUBV and TILV genome segments were identified using EditSeq and NCBI ORF finder, and translated to aa sequences for further analysis. Putative functions were assigned to individual proteins by comparisons to sequences in GenBank and identification of homologous BTV proteins, using BlastX , BTV NS4 (blue line), EUBV NS4 (green line), TILV NS4 (Magenta line), PATAV NS4 (purple line) and EHDV (black line).(DOCX)Click here for additional data file.Table S1Nucleotide accession numbers for sequences used in phylogenetic analysis.(DOCX)Click here for additional data file.Table S2Percent aa identity of EUBV, TILV and PATAV Polymerase (Pol) protein with other orbiviruses.(DOCX)Click here for additional data file.Table S3Identity levels in the outer-core protein VP7(T13) of EUBV, TILV and PATAV compared to other Orbiviruses.(DOCX)Click here for additional data file.Table S4Nuclear localization signals (NLS) in NS4 of Eubenangee, Tilligerry and Pata viruses.(DOCX)Click here for additional data file.


The rate at which viruses replicate and disseminate in competent arthropod vectors is limited by the temperature of their environment, and this can be an important determinant of geographical and seasonal limits to their transmission by arthropods in temperate regions.Culicoides vectors. Our analyses show that there can be differences in vector competence for different orbiviruses in the same vector species and for the same orbivirus in different vector species. Both the rate of virus replication (approximately 0.017-0.021 per degree-day) and the minimum temperature required for replication (11-13°C), however, were generally consistent for different orbiviruses and across different Culicoides vector species. The estimates obtained in the present study suggest that previous publications have underestimated the replication rate and threshold temperature because the statistical methods they used included an implicit assumption that all negative vectors were infected.Here, we present a novel statistical methodology for estimating the relationship between temperature and the extrinsic incubation period (EIP) and apply it to both published and novel data on virus replication for three internationally important orbiviruses (African horse sickness virus (AHSV), bluetongue virus (BTV) and epizootic haemorrhagic disease virus (EHDV)) in their Robust estimates of the temperature dependence of arbovirus replication are essential for building accurate models of transmission and for informing policy decisions about seasonal relaxations to movement restrictions. The methodology developed in this study provides the required robustness and is superior to methods used previously. Importantly, the methods are generic and can readily be applied to other arbovirus-vector systems, as long as the assumptions described in the text are valid. BTV causes a non-contagious, infectious disease called bluetongue (BT) in ruminants, particularly improved breeds of sheep. This disease has come to particular attention over the past decade following an unprecedented series of economically damaging outbreaks in Europe Odocoileus virginianus) and, less frequently, a bluetongue-like illness in cattle Orbiviruses are vector-borne pathogens transmitted between vertebrate hosts by haematophagous arthropods. Three orbivirus species, all of which are transmitted by Culicoides incubated at controlled temperatures under laboratory conditions Culicoides population may be incapable of developing a fully disseminated infection Several previous studies have examined the replication of orbiviruses in Culicoides sonorensis (a confirmed vector species) were experimentally infected with a strain of BTV subjected to relatively few tissue passages. The methods are also applied to previously published data on orbivirus replication Culicoides-orbivirus systems  was used. This strain was selected for study as it represented the most northerly outbreak in Europe prior to the current BTV-8 epizootic and had been previously demonstrated to be capable of infecting UK populations of Culicoides6.5 tissue culture infectious dose 50 (TCID50)/mL when combined with blood in equal volume. Use of the chick embryo was conducted under Home Office project licence PPL 70/5793.Groups of 2-3 day old Culicoides were allowed to feed for 30 min and then anaesthetised briefly with CO2 to remove and discard non-feeding females and males. Blood-fed females were then placed in netted, waxed pill-boxes  and stored in incubators at temperatures of 15, 20, 25 and 30°C. The incubating C. sonorensis females were provided with 5% sucrose via a cotton wool pad applied to the netting and changed daily. At 15, 20, 25 and 30°C a sample of 25 females was removed immediately after the blood meal and then every day until too few were left for further sampling (10–23 days depending on temperature). In a subsequent experiment carried in a methodologically identical fashion but using 12°C incubation, a sample of 25 females was removed immediately after the blood meal and then every five days for 40 days.Adult midges were blood-fed using an artificial feeding apparatus g for 5 min. One hundred µL of the resulting supernatant was then used to prepare a 1∶10 diluted sample using MEM in an additional Eppendorf tube for each sample. Virus titrations were carried out in tissue culture grade, 96-well microtitre plates containing a monolayer of BHK-21 cells and 100 µL of MEM supplemented with 2% tryptose phosphate broth  and antibiotics. Then, 100 µL of each sample and of 1∶10, 1∶100 and 1∶1000 dilutions of each sample were inoculated onto plates in four replicates, together with a positive control of the original virus used (at 10−4 to 10−7 dilution), and a negative control of diluent. Plates were sealed and incubated in an incubator at 37°C with microscopic observation for cytopathic effects at 3 and 5 days. Where bacterial contamination occurred, samples were passed through a 0.2 µL disposable filter (Sartorius UK) using a 5 mL syringe. Titres of positive samples were calculated using the method of Spearman and Kärber Culicoides sonorensis females containing ≥2.5 log10 TCID50 of BTV were considered to have replicated the virus to transmissible levels Following incubation, individual midges were placed in autoclaved 1.5 mL Eppendorf tubes containing 100 µL of chilled Glasgow minimal essential medium (MEM) with 0.6% antibiotics . They were then homogenized using sterilized, motor-driven, polypropylene pestles. Nine hundred µL of MEM was then added to each sample, and the tubes were centrifuged at 12,000 C. sonorensisC. bolitinos and C. imicola in southern Africa Culicoides possessing ≥2.5 log10 TCID50 of BTV/individual and the number and titre of infected midges during the previous time period.Further data were extracted from two papers: one on the replication of four orbiviruses  in colony C. sonorensis AK strain Data from two other papers were not analysed. The first presented an investigation of the temperature dependence of the EIP of BTV-11 in colony k is the scale parameter . The temperature dependence of the EIP is reflected in the reciprocal of the mean (i.e. ν) which is given by,Tmin is the threshold temperature for virus replication and α reflects the rate of replication above the threshold (with 1/α giving the number of degree-days above the threshold temperature required for a vector to complete the EIP).The EIP was assumed to follow a gamma distribution with probability density function given by,t days post infection when infected at age a and reared at temperature T is in each of these states can be written as,f is the probability density function for the gamma-distributed EIP (see equation (1)) and µI and µS are the age- and temperature-dependent mortality rates for infected (I) and uninfected (S) vectors, respectively. The probability that a midge tested t days after infection when reared at temperature T has completed the EIP is given by,aN is the number of Culicoides of age a at the time of infection and the Culicoides are the same age at infection; and (ii) there is no differential mortality between infected and uninfected vectors  =  µS), this probability, (4), simplifies to,In the laboratory experiments each midge tested can be in one of three states: (1) infected and has completed the EIP; (2) infected, but has yet to complete the EIP; or (3) uninfected. The probabilities that a midge tested We note that the same expression would also be obtained if the vector mortality rates are independent of age .tTI is the number of midges with a fully disseminated infection and tTN is the number of midges sampled on day t after being reared at temperature T. Non-informative priors were used for the parameters: Uniform for ϕ; diffuse Normal for α and Tmin; and diffuse exponential for k. The priors were assumed to be independent.Model parameters were estimated in a Bayesian framework, which has two components: a likelihood function and a joint prior distribution for the parameters. The number of midges with a fully disseminated infection is drawn from a binomial distribution with the number of midges sampled and the probability that a sampled midge has completed the EIP (given by equation (5)), as parameters. Hence, up to a constant of proportionality, the likelihood for the data is given by,L is the likelihood, (6), and π is the joint prior distribution for the parameters. Samples from the joint posterior distribution were generated using Markov chain-Monte Carlo (MCMC) methods, more specifically, a random-walk Metropolis algorithm 2 tests The joint posterior density for the parameters is proportional to,C. sonorensis by the strain of BTV used was three days at 30°C, four days at 25°C, five days at 20°C, and 20 days at 15°C (The time to first transmissible infection of  at 15°C . No tranC. imicola) to 0.91 (EHDV-1 in C. sonorensis) (Tmin) was between 11.4°C and 13.3°C for the BTV and AHSV isolates, but was markedly higher (19.5°C) for EHDV-1 (k) were often very high (P<0.05) different from one for all orbiviruses in the study.The probability of infection (ϕ) ranged from 0.04 (BTV-1 in orensis) . The virorensis) ; Table 2r EHDV-1 ; Table 2P>0.05) for all the BTV isolates  tested in C. sonorensis, but were significantly (P<0.01) different for the other orbiviruses/vector species tested. The virus replication rate (α) for EHDV-1 in C. sonorensis was significantly (P<0.001) higher than for the other orbiviruses/vector species . Finally, the threshold temperature (Tmin) was significantly (P<0.001) higher for EHDV-1 in C. sonorensis compared with the other orbiviruses/vector species tested and for BTV-9 in C. sonorensis and BTV-1 in C. bolitinos compared with BTV-16 in C. sonorensis (P<0.01).Formal comparison of the parameter estimates for the different orbiviruses/vectors (by sampling from the joint posterior distributions) showed that those for the probability of infection (ϕ) were similar  derived in the present study (Tmin (Tmin obtained in k estimated independently by maximum likelihood methods using equation (6)) shows a poor fit of the model to data for all four viruses (χ2 goodness-of-fit tests: P<0.05). In particular, there is a tendency to overestimate the number of positive midges at lower temperatures and underestimate the number of positive midges at higher temperatures when using the earlier parameters (Comparison of the estimates for the virus replication rate (α) and threshold temperature –(3) and the parameter estimates in Culicoides spp. infected with orbiviruses, though there is evidence for increased mortality in mosquito vectors infected with a number of arboviruses  will be unaffected if there is differential mortality, but the probability of infection (ϕ) will be underestimated. Regarding the second assumption  there is no differential mortality between infected and uninfected vectors; and (ii) there is no age-dependent mortality . Differential mortality has not been studied for licoides , age-deplicoides , where tk) to be an integer, it is straight-forward to implement the EIP as series of exponential distributions k) differs significantly from one and, hence, the commonly-made assumption of an exponential distribution for the EIP By constraining the scale parameter  and expected (posterior median (red diamonds) and 95% credible interval (error bars)) number of positive C. sonorensis infected with different orbiviruses: African horse sickness virus (AHSV); bluetongue virus (BTV); and epizootic haemorrhagic disease virus (EHDV) (the number indicates serotype). The data were extracted from Tmin obtained by k estimated independently by maximum likelihood methods.Observed and expected number of (TIF)Click here for additional data file.Figure S2Culicoides bolitinos or Culicoides imicola with a fully disseminated infection when reared at different temperatures. Each figure shows the observed (blue circles) and expected  number of positive C. bolitinos or C. imicola infected with bluetongue virus serotype 1. The data were extracted from Observed and expected number of (TIF)Click here for additional data file.Table S1Culicoides sonorensis.Previous estimates for the temperature dependence of the extrinsic incubation period of different orbiviruses in (DOCX)Click here for additional data file.


In 2010, vaccinia virus caused an outbreak of bovine vaccinia that affected dairy cattle and rural workers in Pará State, Brazil. Genetic analyses identified the virus as distinct from BeAn58058 vaccinia virus (identified in 1960s) and from smallpox vaccine virus strains. These findings suggest spread of autochthonous group 1 vaccinia virus in this region. During outbreaks, lesions developed on teats and udders of dairy cattle and caused a decrease in milk production  can be divided into 2 groups: group 1 and group 2 , Pará State, in the Amazon region of Brazil  of this outbreak was not identified. At the study site, 44 lactating dairy cows became sick and had painful vesicular lesions on teats, udders, and inner thighs that rapidly progressed to ulcerative lesions and scabs , panel D,We collected 4 scabs and 44 serum samples from the 44 sick animals and 3 serum samples from the 3 dairy workers. Serum samples were tested by using 50% plaque reduction neutralization tests as described .www.ncbi.nlm.nih.gov/pmc/articles/PMC308517), and alignments were manually checked with MEGA version 4.0 software .To investigate the relationship between PARV and BAV, virus gene A56R (hemagglutinin) was amplified and sequenced , including VACV-TO CA (GU322359) (identity 99.9%), an amplicon obtained from blood of an Amazon monkey in Tocantins State, Brazil, in 2002 . PARV anPhylogenetic trees of the A56R , panel A,Our results confirm circulation of a new group 1 VACV-BR isolate in Pará State in the Amazon region of Brazil in the same location where BAV (group 2 VACV-BR) was isolated (,Several isolates belong to group 1, which is most commonly isolated from sick cattle or cow milkers; some isolates were detected in peridomestic rodents and wild monkeys (Although group 2 VACV-BR isolates, including BAV, have not been detected in the Amazon region of Brazil in recent years, we believe that these viruses may be silently circulating or associated with bovine vaccinia outbreaks. As in other regions in Brazil, VACV outbreaks are underreported in the Amazon region because of its large size and the natural complexity involved in surveillance of zoonotic diseases. Our results reinforce the need for studies on VACV diversity and its transmission chain, which would be useful for the Amazon region in Brazil.


To the Editor: Cryptococcosis is a systemic mycosis most commonly caused by 2 species of encapsulated yeast: Cryptococcus neoformans and C. gattii. C. gattii is a globally emerging pathogen. In the United States, an outbreak of C. gattii infection caused by molecular type VGII has been ongoing since 2004, primarily in the Pacific Northwest . An HIV antibody test result was negative, and CD4 count was 800 cells/mL . A lumbar puncture showed normal opening pressure, 27 leukocytes/mL (reference 0–5 cells/mL) (89% lymphocytes [reference 40%–80%]), protein 464 mg/dL (reference 15–45 mg/dL), and glucose 21 mg/dL (reference 40–70 mg/dL). The cerebrospinal fluid (CSF) cryptococcal antigen was 1:4,096 . Magnetic resonance imaging of the brain indicated mild enhancement of the lining of the lateral ventricles and mild dilatation. C. gattii was isolated from the femur wound  and CSF. Phenotypic testing was performed at ARUP Laboratories . In addition, the isolate was identified by multilocus sequence typing analysis as C. gattii type VGIIb by the Centers for Disease Control and Prevention   .C. gattii infection with musculoskeletal, central nervous system, and pulmonary involvement. Repeat lumbar puncture revealed a normal opening pressure. CSF studies were not performed on this specimen. The patient gradually improved and was discharged on oral voriconazole (to be continued for 1 year) after 4 weeks of hospitalization. By July 2011, the patient was walking with crutches and had no symptoms other than persistent swelling and pain of his left leg. As of July 2012, he had fully recovered except for some residual pain and weakness in his left leg.The patient was treated with liposomal amphotericin B and 5-flucytosine for 4 weeks for disseminated C. gattii is known to be endemic to Australia, Papua New Guinea, South and Southeast Asia, and some parts of Mexico and southern California  outside the Pacific Northwest or other regions to which it is known to be endemic. Although the source of this patient’s infection remains unknown, his previous horse-related injury is intriguing as a possible source (The case reported here involved C. gattii outbreak strains (VGIIa/b/c) is characterized primarily by pulmonary complaints and pneumonia, with or without meningitis (C. gattii outside the Pacific Northwest will help shed more light on the spectrum of clinical manifestations. In the United States, C. gattii is likely to be seen increasingly outside the Pacific Northwest and other regions to which it is endemic.Clinically, infection caused by


De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis.We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses.  There are 219 virus species known to infect humans; calculations based on the rate of virus discovery indicate that there may be 265 human virus species yet to be discovered Fecal material is a useful place to seek mammalian viruses for a number of reasons. The high titers and stable virions of fecal viruses results in increased sequence recovery. Fecal viruses are often found in high titers further improving detection. In addition to the enteric viruses that actually replicate in the gut, the fecal compartment can contain respiratory viruses (e.g. coronavirus) and hepatitis viruses improving the range of detection. The utility of searching for novel viruses in this compartment is well documented and deep sequencing of fecal derived nucleic acids is a rich source of new viruses from bats These studies demonstrate that there are many useful ways to discover viruses in the fecal material using deep sequencing. All of the studies so far have used manual or batch BLAST searching to identify the resulting sequences. Although this is quite effective, it is time consuming work, both implementing the BLAST searches themselves or parsing the BLAST output to extract useful information. Furthermore, as sequencing technologies have improved (e.g. moving from 454 to Illumina platforms) the total number of sequence reads to be processed has increased 10 to 100 fold, further increasing the processing work. Furthermore, a common challenge facing all virus detection in fecal material is the high content of bacteria and dietary nucleic acid that can interfere with the detection of mammalian viruses. This bystander nucleic acid both consumes precious sequencing resources and can dominate the resulting sequence data. Methods that improve or simplify virus detection amidst large amounts of peripheral nucleic acid are needed. The work described here provides a computational solution to simplify virus detection amidst the increasing amount of sequence data now available.The VIDISCA method was developed as a sensitive process for recovering viral nucleic acid from many types of samples and for efficient amplification and identification of viral sequences based on restriction digestion, ligation of common adapters and amplification with adapter specific primers. The method has been used successfully in a number of virus detection applications Fecal samples were obtained from a sample bank of HIV-1 infected adult patients with diarrhea, aged above 18 who visited the out-patients clinic at the Academic Medical Center in the years 1994–1995 Illumina MiSeg library prep was performed by standard Illumina methods. Briefly, each sample was sheared and size fractionated to 400–500 bp in length, ligated to Illumina adapters with a unique barcode per sample, then PCR amplified and multiplexed with 10 samples per run and sequencing was performed with an Illumina MiSeq instrument to generate ca. 1–3 million 150 nt paired-end reads per sample. Residual adapter containing reads were removed and reads were trimmed from the 3′ to a median phred score of 30 and minimum length of 50 nt using QUASR For each sample a random set of 100,000 reads was subjected to nucleotide BLAST analysis using a local instance of BLAST+ against the nr nucleotide database. Only hits with an expectation values < 0.001 were collected. A comparative taxonomic classification of the BLAST output was performed with MEGAN version 4.70.4 For each sample a random set of 100,000 reads was selected and processed through an iterative blast algorithm with the following features: the first read was subjected to a BLASTN search limiting search entries to 2000–500,000 bp in length. The first BLASTN match with an e values below 0.001 was identified, the GenBank entry for that BLAST hit was retrieved and all reads in the readset that map to this sequence were identified. The identity of the BLAST hit was used to classify these mapped reads as viral or non-viral. If the read returned no significant BLASTN hit, it was placed in the mystery bin. At the end of each round of BLAST/GenBank retrieval/mapping, all mapped or mystery reads were removed from the readset to generate a remaining-reads set which was passed into the next round. In this manner, abundant reads are classified and removed in the initial cycle of the processing. The SLIM algorithm is written in Python and is available upon request.De novo genome assembly was performed using SPAdes de novo assembled genome and reporting the number of reads with Phred quality score of 30 or above for each position. Mean coverage values for all full genomes is shown in Full virus genomes were prepared from the raw dataset in the following manner. Initially SLIM was used to identify the closest full virus genomes for each sample. All reads in each sample were then mapped to the closest full genome reference using BWA 6 reads/sample. Raw sequencing data was trimmed to remove low quality reads, analyzed for content using SLIM , and another sample had ca. 20% reads mapping to Sphingobacteria bacterium, etc. Because the samples contents were so varied, a strategy to rapidly remove high frequency, sample-specific, non-viral reads was developed.The species content of each sample was obtained by performing a nucleotide BLAST analysis of 100,000 random reads from each sample and parsing the NCBI taxonomic data in the BLAST output using the Lowest Common Ancestor algorithm implemented in MEGAN One approach to discovering viruses in deep sequence data is to first remove the abundant reads that dominate the data. These abundant, non-interesting reads are often host or commensal organism ribosomal or repetitive sequences. Manual subtraction of sequences mapping to individual species is possible, but becomes time-consuming when there are multiple species to identify and remove. An iterative search-classify-remove process, SLIM, was developed that addresses this difficulty , it is clear that it is not necessary to perform a BLAST search on each of the 100,000 reads. If a virus is present at sufficient levels for full genome assembly, it should be detected in the first 50 cycles of the SLIM process.de novo assembled genomic contig from this sample .The three adenovirus peaks are a consequence of the stringency used for the mapping step. Each peak is the consequence of the actual read returning a different adenovirus genome after BLASTn searching. For the mapping and removal step in the process, the stringency is set to high level to avoid removing (and possibly misclassifying) weakly homologous reads. We considered it more effective to leave the weaker homology reads in the remaining read set and allow BLAST to identify a closer homology hit at a later step. Thus reads in cycle 2 returned hits to Adenovirus 51 (GenBank JN226765) and Adenovirus 20 (GenBank JN226749) and mapping identified and removed 5315 reads from the set. The cycle 12 peak was due to reads mapping to Human adenovirus 43 (GenBank KC529648) and the mapping identified and removed 295 reads. The cycle 17 peak was due to a read mapping to Human adenovirus 30 (GenBank KF268335). All of the adenovirus BLASTn hits from this sample fall within the same Adenovirus species D as does the This iterative classification process was applied to 100,000 random reads from each of the 20 samples to identify the viral sequence content . The totde novo assembly with SPAdes For several mammalian viruses, a substantial number of short reads were identified. Assembly of complete genomes from these data was performed using In total, 12 full genomes of mammalian viruses could be generated. Viruses belonged to the RNA and DNA viruses .Human adenovirus reads could be detected in 16 of the 20 samples  with sufPicornaviridae family first described in 2008 de novo assembly of the sample 4 reads generated a full length genome (Cosavirus_Amsterdam_1994) with the expected conserved features of a cosavirus (TTTTCCTTTT). Cosaviruses typically encode a single large open reading frame that is translated into a polyprotein and processed into 11 proteins. The Cosavirus_Amsterdam_1994 genome shows the expected large open reading frame of a cosavirus  and a complete genome assembled from sample 17  was detected in 14 of the 20 samples  consisteA large number of reads mapping to plant viruses and to bacteriophages were identified. The bacteriophage and plant viral content of the mammalian gut virome has been described previously A virus associated with non-A, non-B hepatitis (NANB-1) was identified in patients in the early 1980s Sequences mapping to an Enterococcus phage were identified in several samples, sufficient reads were present in sample 9 to assembly a genome-sized contig.All samples contained at least one plant virus and most contained sequences from several plant viruses. Complete genome-sized contigs related to Tobacco mild green mosaic virus (TMGMV), Paprika mild mottle virus, Cucumber green mottle mosaic virus, Tomato mosaic virus (TMV) and a partial genomes of Grapevine rupestris vein (GRV) feathering virus were assembled from the data. These plant viruses are not known to be associated with human disease and their presence may simply reflect recent dietary consumption.a priori sequence information on the target viruses. For many of the viruses detected, the depth of sequencing provided by the method allows assembly of full viral genomes. In order to process the increased quantity of sequence data generated by this method, we have developed a virus identification algorithm, SLIM which employs iterative cycles of BLAST identification, clustering and read removal. The combined ViSeq and SLIM method facilitated the identification and assembly of 12 complete mammalian virus genomes (including a previously unknown cosavirus genotype) from the 20 fecal samples without prior sequence data on these viruses.The novel process reported here, ViSeq, provides a quantitative picture of the viral content of human fecal samples. The method shows detection sensitivity approaching real-time PCR assay levels, however without The goal of the virus detection is to provide sufficient evidence that a virus is present in a sample and we believe that full genome assembly is a useful standard. One can calculate that a 5000 nt virus genome at 10 fold coverage requires 50,000 nt of sequence or 333, 149 nt reads. 333 reads in a dataset of 1 million reads corresponds to 33 reads in 100,000 (0.033%) and would be a good practical cutoff for reliable virus detection.et al. The SLIM algorithm provides a useful tool for virus detection work. The algorithm does not require large amounts of computer memory and can be run locally on a laptop as long as internet access is available for the BLAST and GenBank calls. One obvious step for future improvement lies in the handling of the BLAST unidentified reads. The current virus identification methods, including those described in this work, rely heavily on the data in GenBank. However, a large fraction of the sequence data generated here was not classified by nucleotide BLAST searches. Malboeuf Recently it has been hypothesized that the enteropathy associated with AIDS may be a consequence of elevated replication of enteric viruses promoted by the immune system decline late in AIDS


Flavivirus (family Flaviviridae), Alphavirus (Togaviridae), Orthobunyavirus (Bunyaviridae), and Phlebovirus (Bunyaviridae).Arthropod-borne viruses are important emerging pathogens world-wide. Viruses transmitted by mosquitoes, such as dengue, yellow fever, and Japanese encephalitis viruses, infect hundreds of millions of people and animals each year. Global surveillance of these viruses in mosquito vectors using molecular based assays is critical for prevention and control of the associated diseases. Here, we report an oligonucleotide DNA microarray design, termed ArboChip5.1, for multi-gene detection and identification of mosquito-borne RNA viruses from the genera Culex flavivirus, and a Quang Binh-like virus from mosquitoes collected in Thailand in 2011 and 2012.The assay utilizes targeted PCR amplification of three genes from each virus genus for electrochemical detection on a portable, field-tested microarray platform. Fifty-two viruses propagated in cell-culture were used to evaluate the specificity of the PCR primer sets and the ArboChip5.1 microarray capture probes. The microarray detected all of the tested viruses and differentiated between many closely related viruses such as members of the dengue, Japanese encephalitis, and Semliki Forest virus clades. Laboratory infected mosquitoes were used to simulate field samples and to determine the limits of detection. Additionally, we identified dengue virus type 3, Japanese encephalitis virus, Tembusu virus, We demonstrated that the described assay can be utilized in a comprehensive field surveillance program by the broad-range amplification and specific identification of arboviruses from infected mosquitoes. Furthermore, the microarray platform can be deployed in the field and viral RNA extraction to data analysis can occur in as little as 12 h. The information derived from the ArboChip5.1 microarray can help to establish public health priorities, detect disease outbreaks, and evaluate control programs. Approximately half of the world's population is at risk of viral, mosquito-borne illness such as dengue, yellow fever, Japanese encephalitis, Rift Valley fever, and chikungunya. In the past, these viruses have been regarded as pathogens of the tropics; however, they are emerging as global causes of illness. Very few effective drugs and vaccines have been developed for mosquito-borne viral infections and even less are available to people in resource-limited countries. An important aspect of disease prevention is mosquito surveillance to determine geographical range and seasonal prevalence of the associated viruses. However, there are hundreds of viruses transmitted by mosquitoes that are pathogenic to humans and animals. Using a portable microarray, we developed an assay with the ability to detect most of the known medically important viruses transmitted by mosquitoes. This assay was designed for use in conjunction with broad-range screening tools as a cost effective, rapid method to determine the identity of viruses from infected mosquitoes. To our knowledge, this is the most comprehensive assay to date for field surveillance of mosquito-borne viruses. Flaviviridae: Flavivirus), alphaviruses (Togaviridae: Alphavirus), orthobunyaviruses (Bunyaviridae: Orthobunyavirus), and phleboviruses (Bunyaviridae: Phlebovirus) Aedes species mosquitoes, such as dengue virus (DENV) and yellow fever virus (YFV), and those transmitted by Culex species mosquitoes, such as Japanese encephalitis virus (JEV) and West Nile virus (WNV). Additionally, flaviviruses continue to be isolated from mosquitoes without a known vertebrate host, termed arthropod-specific viruses Arthropod-borne viruses (arboviruses) are important human and veterinary pathogens that are biologically transmitted to vertebrates by hematophagous (blood feeding) arthropod vectors, such as female mosquitoes. The diverse group of mosquito-borne RNA viruses primarily includes flaviviruses  were designed for efficient PCR amplification of the viral nucleic acids isolated from infected mosquitoes and virus-specific microarray capture probes were designed to differentiate between the PCR amplicons. We previously demonstrated that a portable microarray platform, the ElectraSense 4×2K , is practical for field diagnostics Culex flavivirus (CxFV), and a Quang Binh-like virus from field-collected mosquitoes from Thailand during 2011 and 2012.In this report, we describe an oligonucleotide DNA microarray, the ArboChip5.1, which targets multiple genes from 144 mosquito-borne RNA viruses from the genera Entomological collections from private land and residences were conducted with the owners/residents permission.The assay workflow is displayed in Consensus GSPs for PCR amplification of microarray targets can be found in Probes were designed to target unique viral sequences, 30 to 45 nucleotides in length, between the GSPs using methods previously described Flavivirus NS5 group and the DENV clade, DENV-3 subgroup (DENV_DENV3). Probes that hybridized to multiple, but related viruses were classified as non-virus-specific and sorted into genus (i.e. flavivirus_generic) or clade-specific (i.e. DENV_clade) subgroups. Some very closely related viruses, such as O'nyong-nyong virus (ONNV) and Igbo Ora virus (IOV) of the Semliki Forest virus (SFV) clade, could not be differentiated. Probes for virus targets that could not be differentiated were sorted into subgroups containing virus complexes (i.e. SFV_ONNV/IOV).The virus-targeted capture probes were sorted into 12 groups, three groups for each genus based on the gene target. Each group was further sorted into subgroups based on the virus clade and probe specificity. For example, a probe specific for the DENV-3 NS5 gene would be in the Aedes albopictus) cell cultures. Mosquitoes were inoculated intrathoracically (0.3 µL/mosquito) with selected viruses at approximately 105 plaque forming units (PFU)/mL 3, and 100 units of penicillin and 100 µg of streptomycin per mL). All virus preparations were tested in duplicate using the microarray assay.The laboratory viruses used in this study are listed in TRIzol-LS  extraction of RNA and cDNA synthesis using random hexamers and SuperscriptII (Invitrogen Inc.) was completed as previously described, except that the RNA was not subjected to a second round of purification before cDNA synthesis The sequences of the GSPs are listed in Ten microarray probes  and reveThe ArboChip5.1 microarray chips were hydrated using phosphate-buffered saline (PBS) (pH 7.4) for 10 min at 65°C and then pre-hybridized for 5 min at 50°C in pre-hybridization buffer ) with rotation using a UVP HB-500 Minidizer hybridization incubator  that was modified with microarray clamps fixed onto the rotisserie wheel. Preparation of the DNA samples for microarray hybridization was completed as follows. In a PCR tube, 15 µL of asymmetric biotin-labelled PCR amplicons  and 1 µL of biotin-labelled positive hybridization control oligonucleotides were mixed with 15 µL of 2× hybridization buffer . The samples were denatured for 1 min at 95°C, cooled for 1 min at 4°C, and then added to the microarray chambers. The microarray samples were hybridized at 50°C for 2 h with rotation. The microarray chambers were rinsed with 2× PBST , re-incubated with the same solution at 50°C for 5 min with rotation, and washed two more times with 2× PBST. The chambers were blocked with ElectraSense Blocking Buffer for 15 min at room temperature (RT) and labelled with ElectraSense Biotin Labeling Solution for 15 min at RT. The chambers were washed twice with ElectraSense Biotin Wash Solution, incubated for 10 min at RT, washed a third time with the Biotin Wash Solution, and rinsed with ElectraSense TMB Rinse Solution. The samples were developed using ElectraSense TMB Substrate and the ECD signals were measured in picoamps using the ElectraSense Reader within 1 min of adding the substrate. Data were recorded using the ElectraSense application software. All ElectraSense products were purchased from CustomArray, Inc. and the hybridization and detection methods were based on their recommendations, except that the 16 h hybridization was reduced to 2 h.The data were transformed into text files and transferred to Microsoft Excel  for analysis. Each capture probe was sorted into one group, consisting of the virus genus and gene or segment, and one subgroup, consisting of the virus clade and specific target. The virus clades were based on observed phylogenetic analysis and did not always correspond to other reported clades or serogroups. The specific targets were single viruses or groups of related viruses.The ECD signals were transformed into standard scores (z-scores) by subtracting the average signal of the negative background controls from each measured probe and dividing the difference by the standard deviation of the negative controls. Aforementioned, the probes were sorted into groups and subgroups for analysis. Subgroups with average z-scores greater than 10 were considered positive and used for viral RNA identification. Z-scores greater than 10 indicate that the measured probe values were greater than 10 standard deviations above the background, therefore significant. Graphs for each group expressing the subgroups' average z-scores, maximum individual probe z-scores, and the positive cut-off (10) were created for visual analysis. Example data analysis is shown in In order for the microarray chips to be re-hybridized with new PCR amplicons, the previous amplicon:probe hybrids were denatured and then the amplicons were washed off. This was accomplished by washing the chip with Stripping Solution , incubating the chip in the same solution at 65°C for 1 h, and then washing the chips with 95% EtOH and nuclease-free water. The microarray chambers were filled with PBS until re-use. The microarray chips were not used more than five times.Ex Taq DNA Polymerase , 0.4 µL of 10 µM forward primer, 0.4 µL of 10 µM reverse primer, 7.2 µL of nuclease-free water, and 2 µL of cDNA template using the following primer sets: mFU1 and cFD2 primers for the detection of flaviviruses The microarray lower limit of detection (LLOD) for virus infected mosquitoes was evaluated by using 10-fold serial dilutions of RNA extracted from one infected mosquito pooled with 24 uninfected mosquitoes. Isolation of RNA and synthesis of cDNA was completed using the methods described. The mosquito pool dilutions were tested using the described microarray amplification and detection methods and compared to results obtained using corresponding real-time PCR (qPCR) and convectional PCR assays. Real-time PCR was completed as follows: a 20-µL reaction contained 10 µL of SYBR Premix Mosquitoes were collected from locations near Lopburi and Kamphaeng Phet, Thailand, during March and April of 2011 and near Kamphaeng Phet and Ranyong, Thailand, during April and May of 2012. The mosquitoes were identified, processed, tested for the presence of viral RNA using the broad-range conventional PCR assays for flaviviruses, alphaviruses, and phleboviruses described above. In addition, BSC82V and BSC332V primers (250 bp amplicons) were used to screen for the Bunyamwera virus (BUNV) group The GSP sets did not PCR amplify viruses from a different genus, uninfected cells , or uninfected mosquitoes , CHIKV (strain INDO23574), and RVFV (strain ZH501) were PCR amplified using the GSP sets and analyzed by the microarray four times independently. The z-scores from the virus-specific probes were averaged for each replicate. The mean, standard deviation, 95% confidence interval and coefficient of variance was calculated for each virus. The results are summarized in Microarray identification of PCR amplicons using the consensus flavivirus GSPs was evaluated using 20 flaviviruses propagated in cell culture . A visuaAe. albopictus were faint but they were still accurately identified by all three genes on the microarray. The E gene was not PCR amplified from either stain of TMUV infected Culex tarsalis and could not be detected on the microarray. To mimic field samples, single mosquitoes infected with DENV-1, TMUV, and WNVL1 were pooled with 24 negative mosquitoes. Compared to single infected mosquitoes, the identification results were the same but had overall lower z-scores. A WNVL1-infected and a JEV-infected Cx. pipiens were pooled with 23 negative mosquitoes to assess the amplification and detection of two distinct viruses that could be found in a field collected pool of mosquitoes. All three gene targets were amplified using the flavivirus GSP sets but the E genes of JEV and WNVL1 and the NS5 gene of WNVL1 were not detected using the microarray. Three of the five JEV-specific NS5 gene probes had z-scores greater than 10, but the average z-score for the subgroup was less than 10 (9.13). However, all of the JEV- and WNVL1-specific  and two of the four WNVL1/KUNV NS3 probes had z-scores greater than 10  were evaluated individually and pooled with 24 uninfected mosquitoes , SINV (strain Ken07-611), and RVFV (strain ZH501). Pools of Ae. aegypti, were screened for the presence of alphaviruses, phleboviruses, and BUNV group viruses, however, no positive pools were detected. The flavivirus RNA positive pools were further analyzed using the microarray (Mosquitoes collected in Thailand were used to determine the applicability of the ArboChip5.1 microarray for identifying virus RNA present in mosquito pools (n = 1–25). From1,445 mosquito pools (642 pools were collected in 2011 and 803 pools were collected in 2012) that were screened for the presence of flaviviruses by conventional PCR, 13 pools were confirmed as containing flavivirus RNA. Selected medically important mosquito pools, such as those containing croarray . Visual Cx. vishnui, Cx. tritaeniorhynchus, Cx. quinquefasciatus, Cx. gelidus, Cx. terzii, Ae. albopictus, Ae. vexans, Mansonia uniformis, Armigeres subalbatus, and Anopheles peditaeniatus (n = 19–25 mosquitoes/pool) were processed and used to confirm that the microarray identifications were not from cross-hybridizing mosquito RNA or DNA. All mosquito pools that tested negative by PCR also produced negative microarray results.Virus-negative pools of Flavivirus, Alphavirus, Orthobunyavirus, and Phlebovirus. For most of the selected viruses, capture probes were designed to multiple genes to increase the odds of detection, identification, and confirmation. The assay also utilized consensus GSPs for broad-range PCR amplification of viral RNA, even in the presence of an abundance amount of mosquito nucleic acids. The microarray platform described here, and as we previously discussed, is portable and rugged enough to endure travel and field conditions The ArboChip5.1 consists of 2,097 oligonucleotide probes targeting 144 different mosquito-borne RNA viruses across the genera Previously developed and newly defined assays were created or adapted to advance and to streamline the process for using microarrays in field surveillance. RNA extraction and the PCR field-based procedures previously described The limiting factors for creating the viral diagnostic DNA microarray presented here were the availability of viral sequences present in publically available databases and the sequence diversity in the targeted nucleotide regions. Flaviviruses and alphaviruses are generally well characterized, and their entire genomic sequences are available for most known species. As a result, unique probes targeting all three genes of diagnostic interest for most flaviviruses and alphaviruses were created. We were able to detect and identify all of the flaviviruses and alphaviruses tested from both cell culture and infected mosquitoes, except for a few closely related alphaviruses as discussed below. Closely related flaviviruses, such as the four serotypes of DENV and WNVL1, WNVL2, and KUNV were clearly differentiated using the microarray. Additionally, JEV and WNVL1, both JEV clade flaviviruses, were detected from a pool of laboratory infected mosquitoes, showcasing the ability of the microarray to differentiate between closely related viruses that might be found in the same mosquito pool. Though we were able to differentiate between many closely related alphaviruses, we were not able to differentiate between SINV, OCKV, and BBKV and between ONNV and IOV. This was because there was not enough sequence diversity between the listed alphaviruses in the targeted nucleotide regions to create probes that would not cross-hybridize using the described assay conditions. Instead, the viruses were identified as either part of the SINV or ONNV clades and other data, such as the mosquito species and collection location, could be used to distinguish the specific alphavirus.Orthobunyavirus), LACV (Orthobunyavirus), and RVFV (Phlebovirus), are very well studied and genetically characterized. However, the Bunyaviridae family is expansive and diverse, and despite many characterization efforts Some bunyaviruses, such as BUNV  The intended use of the ArboChip5.1 is for arbovirus surveillance studies targeting mosquitoes. The concept is to use generic screening assays to identify mosquito pools potentially containing arboviral RNA. The screening assays should be based on the field or laboratory team's capabilities and can include the conventional and real-time assays cited in this paper or other published assays Multiplexed-PCR methods for viral RNA identification can be efficient for a small sub-set of viruses, yet when there is a need to discern between a larger set of mosquito-borne viruses, PCR approaches for virus identification can become laborious. The microarray, as described here, is a time and cost effective method for the detection of numerous viral RNA species potentially present within an infected mosquito with limited bias. Each ElectraSense 4×2K microarray has four wells to analyze four samples at a time, and when using the rotisserie hybridization incubator described here, holding four microarray chips at a time, up to 16 samples can be processed in as little as 12 hours. The major limitations of this assay are that only known viral sequences can be used for probe design, meaning that it cannot identify novel viruses, and it cannot discern between slight nucleotide changes, meaning that it cannot anticipate viral evolution. Even though previously unknown or non-targeted viral RNA maybe detected because of some sequence homology in one of the targeted regions, the microarray would not be able to identify novel viruses. On the other hand, next-generation sequencing (NGS) has the potential to provide the most amount of nucleic acid information, but they are not portable and require more time and labor to create sequencing libraries and analyze the results. Moreover, NGS systems are much more expensive to operate, generally costing over $1000 per run In summary, the ArboChip5.1 microarray can identify multiple genes from a wide range of mosquito-borne RNA viruses through broad-range PCR amplification and detection with virus-specific probes. It is vastly more multiplexed than PCR assays alone, more specific than universal microarrays, and more cost effective than most sequencing platforms. The microarray reader is small and rugged, making it field-portable. This system is an ideal tool for active surveillance or monitoring programs in regions where little is known about the circulating mosquito-borne viruses. We have demonstrated that it detects many viruses of medical importance, such as DENV, YFV, JEV, and CHIKV. In addition, the microarray targets many viruses that are also major causes of animal disease, such as RVFV, VEEV, and WNV. The ultimate goal is to provide researchers, veterinarians, and clinicians with a diagnostic tool that will allow them to recognize previously known pathogens that are emerging or re-emerging before they become major health issues.Dataset S1Example data analysis. Data analysis worksheet with example data from the field collected mosquito pool Th9-0122 Click here for additional data file.Figure S1Detection of JEV and WNVL1 from a dual-infected pool of Cx. pipiens mosquitoes. A mosquito pool (n = 25) containing two infected Cx. pipiens, one each with JEV and WNVL1, and 23 uninfected mosquitoes were processed together and PCR amplified using the Flavivirus GSP sets. The NS3 PCR amplicons hybridized with probes in the JEV-specific, WNVL1/KUNV, and WNVL1-specific subgroups. The JEV and WNVL1 probes have not been observed to cross-hybridize to the other virus when analyzed individually, thus showing the detection of RNA from two related but distinct flaviviruses in a single mosquito pool. The virus abbreviations are defined in (TIF)Click here for additional data file.Table S1Target viruses. List of the ArboChip5.1 targeted mosquito-borne RNA viruses, sorted by genera and clades (as determined by phylogenetic comparisons of the gene target sequences and not necessarily represented by taxonomic clades), with the number of virus-specific and non-specific probes.(XLSX)Click here for additional data file.Table S2Probe list. List of the ArboChip5.1 oligonucleotide probes created for the multi-gene detection of RNA from mosquito-borne viruses. For each of 2,097 probes, the probe name, group (genus and gene target), subgroup (clade and virus), sequence source (GenBank accession number), start position, design source, and sequence is listed.(XLSX)Click here for additional data file.