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Distribution of Angiostrongylus vasorum and its gastropod intermediate hosts along the rural–urban gradient in two cities in the United Kingdom, using real time PCR

Background Angiostrongylus vasorum is a highly pathogenic metastrongylid nematode affecting dogs, which uses gastropod molluscs as intermediate hosts. The geographical distribution of the parasite appears to be heterogeneous or patchy and understanding of the factors underlying this heterogeneity is limited. In this study, we compared the species of gastropod present and the prevalence of A. vasorum along a rural–urban gradient in two cities in the south-west United Kingdom. Methods The study was conducted in Swansea in south Wales (a known endemic hotspot for A. vasorum) and Bristol in south-west England (where reported cases are rare). In each location, slugs were sampled from nine sites across three broad habitat types (urban, suburban and rural). A total of 180 slugs were collected in Swansea in autumn 2012 and 338 in Bristol in summer 2014. A 10 mg sample of foot tissue was tested for the presence of A. vasorum by amplification of the second internal transcribed spacer (ITS-2) using a previously validated real-time PCR assay. Results There was a significant difference in the prevalence of A. vasorum in slugs between cities: 29.4 % in Swansea and 0.3 % in Bristol. In Swansea, prevalence was higher in suburban than in rural and urban areas. Comparing the sampled slug fauna, Arion rufus was found in greater numbers in Swansea than Bristol, and was commonly infected (prevalence 41 %). This, alongside the timing of slug collections in summer rather than autumn, could explain low infection prevalence in the Bristol sample. In the absence of Ar. rufus as a preferred host for A. vasorum, Ar. flagellus and Limacus maculatus appear to act as versatile hosts that are present in suburban and urban areas in Swansea (prevalence in Ar. flagellus 33 %; in L. maculatus 44 %) and in Bristol (prevalence in Ar. flagellus 0.9 %). These slug species might provide A. vasorum with an alternative vehicle to reach the final host, when the main host Ar. rufus is scarce or absent. Conclusion We conclude that the composition of the slug fauna varies spatially, and that this could help explain patchiness in the prevalence of A. vasorum. A suburban peak was found in the prevalence of infection in intermediate hosts, perhaps explained by a higher density of competent intermediate and/or definitive hosts.

Background
Angiostrongylus vasorum is an emerging parasite in dogs, with frequent new reports throughout Europe and beyond [1,2]. Within several endemic Europe countries, its distribution appears to be expanding, but in a highly heterogeneous or patchy manner. In the United Kingdom (UK), A. vasorum was first reported in Cornwall in southwest England in the 1970s [3,4], later in Wales and southeast England [5][6][7], and recently in central and northern England and Scotland [8][9][10][11][12]. National questionnaire surveys of veterinary practices confirmed both northward spread and persistent heterogeneity in disease incidence in dogs [13]. Better understanding of the factors underlying this patchy distribution is needed to guide risk assessments by veterinary clinicians and other stakeholders, in order to prevent and control the severe disease that can ensue from infection [14], as well as to build on currently sparse fundamental understanding of the epidemiology of nematode parasites in gastropod intermediate hosts [15,16].
Angiostrongylus vasorum has an indirect life-cycle with canids such as domestic dogs (Canis lupus familiaris) and red foxes (Vulpes vulpes) as the definitive host, terrestrial gastropod molluscs (slug or snail) acting as intermediate hosts, and frogs acting as paratenic or intermediate hosts [17]. Heterogeneity in parasite abundance and disease risk to definitive hosts could arise from variable environmental influences on mollusc populations and infection rates. Using a recently developed real time polymerase chain reaction (PCR) assay [18], surveys of prevalence in slugs are feasible. Previous studies assessing the presence of A. vasorum in slugs and snails have reported variable prevalence. In Denmark, 29 % of molluscs sampled contained larvae and prevalence varied by locality [19]. The species found to be infected in this study were Arion lusitanicus, Ar. ater, Ar. aterrufus and Limax maximus. A survey in south Wales (UK) reported 43 % prevalence using PCR, with Ar. ater aggregate, Ar. hortensis aggregate and Deroceras sp. positive [18]. In London (UK), prevalence of larval recovery from slugs was 1.6 %, with a broad host range including two species previously not recorded as acting as intermediate hosts for A. vasorum: Ar.distinctus and Tandonia sowerbyi [20]. In a PCR survey of slugs in west Scotland, an area newly colonised by A. vasorum, the slug fauna and positive slugs were dominated by large Arion species [11], with overall prevalence of 7 %.
The aims of the present study were to determine the prevalence of infection in slugs in contrasting areas within a region endemic for A. vasorum, and to determine whether differences in slug species composition could explain differences in overall prevalence. This information is needed to better understand the role of slugs as intermediate hosts for A. vasorum, the factors underlying local risks of transmission, and the most important slug species involved as sources of infection for dogs.

Ethical approval
Ethical review details are not provided since no formal ethical approval was necessary: the work involves neither work on vertebrates nor human participants or personal data.

Study area
Slugs were collected from parks within a 20 km radius of the centres of two nearby cities, Swansea in south Wales (a known endemic hotspot for A. vasorum) (Fig. 1) and Bristol in south-west England (Fig. 2). In Swansea, 180 slugs were collected from October to November 2012, from publicly accessible areas along a rural-urban transect (Table 1). In Bristol, 338 slugs were collected from public parks in July and August 2014 ( Table 2). In each location, nine sites were sampled across three broad habitat types (urban, suburban and rural), which were categorised subjectively. Urban areas were close to the city centres, and either built-up (e.g. housing, or tarmacadam-based landscaping) or brown-field (i.e. unused and unmanaged previously built-up land). Suburban areas were composed of a mixture of housing with private gardens, and publicly accessible green space (i.e. public parks), from which slugs were sampled. Rural areas were publicly accessible amenity areas outside the city boundaries, comprising open green fields and woodland, with sampling undertaken from woodland-grassland and ecotones.

Collection and processing of slugs
Slugs were collected by actively searching the ground and surface vegetation, soon after dusk by torchlight. All slugs were stored together in labelled plastic boxes according to sampling site. In Swansea, immediate examination of slugs was not possible and so slugs were frozen at -80°C until used for DNA extraction. Slugs collected in Bristol were kept at 15°C and processed within a few days of collection: boxes were lined with moistened tissue paper and supplemented with lettuce and carrot as a food source. Individual slugs were identified using Rowson et al. [21,22] and were assigned either to genus or species level. Following identification, slugs were killed by decapitation, and 10 mg of foot tissue used for DNA extraction and real time PCR assays. Very few snails were observed and none were collected.

DNA extraction and real time PCR assays
DNA was extracted using DNEasy Blood and Tissue Kit (Qiagen, Germany) according to the animal tissue extraction protocol, with final elution volume of 100 μl. A portion of the ITS-2 (internal transcribed spacer 2) gene (180 base pairs) was amplified using the following primers: I2F2 5′-GCGTGTGTTCATGTTTGGAC-3′and I2R2 5′-CATTACTAGCATACAAGCACATG-3′ [18]. Real-time PCR assays were performed in a final reaction volume of 25 μl that consisted of 12.5 μl of 2× Quantifast SYBR Green PCR Master Mix (QIAGEN, Germany), 1 μM of each primer and 6.5 μl of dH 2 O. Cycling conditions involved an initial activation step of 95°C for 5 min followed by 40 cycles of 95°C for 10 s and 62°C for 30 s using a Mx3005P quantitative PCR system (Stratagene, California, USA). For all assays, positive (adult A. vasorum DNA) and negative (absence of DNA template) controls were included.
For samples from freshly collected slugs, higher fluorescence on 10-fold dilution of some extractions suggested the possible presence of PCR inhibitors as seen in previous studies [18]. All extractions were diluted 1:10 to surmount this. The cycle number was increased from 40 to 50 following dilution to allow sufficient amplification of PCR product.
In order to check for false negatives arising from localisation of larvae outside the 10 mg foot muscle sample, a total of 58 slugs testing negative by PCR, from all localities of Swansea [suburban (n = 16); urban (n = 21) and rural (n = 21)] were homogenised. The slugs were individually homogenised using a mortar and pestle and 25 mg of tissue randomly taken for DNA extraction and real time PCR analysis.

Statistical analysis
The prevalence of A. vasorum was compared across localities using Chi-square tests, and across slug species using Fisher's exact test, in both cases using global prevalence to calculate expected frequencies. To describe variation in slug species composition in Bristol and Swansea, Principal Components Analysis (PCA) was used, with sample site set as group and slug species as the list of variables (SPSS Statistics version 21, IBM, USA). Chi-square tests were further used to compare the relative frequency of individual slug species in samples from different localities. P values of < 0.05 were considered to be statistically significant.

Prevalence of Angiostrongylus vasorum in slugs
In Swansea, of the 180 slugs examined for A. vasorum infection, 53 (29.4 %) were positive by real time PCR assay. More than half of all infected slugs were recorded in suburban environments (Fig. 3). The prevalence of A. vasorum was significantly different between the habitats sampled (urban, suburban and rural; χ 2 = 14.78, d.f. = 2, p < 0.001). There was no significant difference in prevalence between suburban and urban habitats (p = 0.15); however, infection was significantly more common in suburban than in rural slugs (χ 2 = 14.16, d.f. = 1, p < 0.001). There was also substantially higher prevalence in urban areas compared to rural areas (χ 2 = 14.54, d.f. = 1, p < 0.001). In Bristol, only one of the 338 slugs tested positive for A. vasorum, and this was collected from a suburban location (Table 3), giving a prevalence of 1.3 % for that park and a prevalence of 0.3 % overall for the sites in Bristol. All slugs homogenised (n = 58) for identification of false negatives gave a negative result.

Comparison of slug species across locations
Principal components analysis (PCA) revealed differences in the composition of the slug fauna in different locations (Table 4), but no clear overall pattern along the urban-rural gradient or between the two cities (Fig. 4).
Using univariate Chi-square tests, however, Ar. rufus was found to be more common in all habitats sampled in Swansea compared with Bristol (Table 5). By contrast, Ar. flagellus was more common at sub-urban and rural habitats in Bristol than in Swansea. Deroceras reticulatum and L. maximus were also more common in Bristol than in Swansea. Arion flagellus was significantly more common at combined suburban and urban than rural habitats in Bristol (Table 6), while Ar. owenii and T. sowerbyi were more common in rural locations. In Swansea, L. maculatus was found only in the urban habitats.

Discussion
These results demonstrate for the first time a ruralurban gradient in A. vasorum prevalence in intermediate hosts in the UK. Higher prevalence of A. vasorum in suburban areas is consistent with studies in Denmark in gastropods [19] and in foxes [23]. Peak prevalence in suburban areas could be explained by overlap of high fox population density and high density of suitable intermediate hosts, as proposed for the fox tapeworm, Echinococcus multilocularis, in Europe [24]. Wide variation in the prevalence of A. vasorum at different sites and between the cities of Swansea and Bristol demonstrates a high level of spatial heterogeneity in parasite distribution among intermediate host populations. Previous studies have noted marked geographical patchiness of angiostrongylosis in definitive hosts [25][26][27][28]. The reasons for this patchy distribution are unknown [30],  The prevalence of 29.4 % in Swansea in the current study is considerably higher than the 1.6 % recorded by Patel et al. [20] in London, by extraction and identification of larvae from gastropod tissues. Larval extraction methods are likely to underestimate true infection prevalence, through low sensitivity and because pre-infective stage larvae are usually not recovered [Abdul-Aziz, unpublished observations]. Conversely, prevalence estimated from PCR does not accurately reflect the presence of mature larvae able to propagate infection in definitive hosts, since earlier larval stages and, potentially, residual DNA from non-viable larvae can also be detected [11].
Prevalence estimates using these different methods are, therefore, not comparable. Low prevalence in a large sample size in the present study in Bristol suggests that false positive results are not common using this PCR assay.
In the present study, slug species were identified by external and internal morphology based on Rowson et al. [22], a new comprehensive guide to British and Irish slugs validated by PCR amplification and sequencing of mitochondrial (16S, COI) and nuclear (ITS-1) DNA. Previous work on A. vasorum in slugs [18][19][20] examined 'aggregates' of superficially similar species. The present study therefore provides more accurate information on species distribution of A. vasorum based on more specific identification.
The present study found broadly similar slug species composition in the nearby cities of Swansea and Bristol. Together with the wide host range observed here and in previous studies, this suggests that the overall prevalence of A. vasorum is not closely related to the presence or absence of particular intermediate host species. Nevertheless, some differences in slug fauna were noted. Sampling in Swansea yielded fewer species overall, even allowing for lower sample size, and less variability in slug species between sites compared with Bristol. Arion rufus was relatively more common across locations in Swansea, and Ar. flagellus and some of the smaller slug species were more common in Bristol. Reasons for these differences could include ecological factors (e.g. climate, soil, habitat, or predation) or a history of accidental introduction (perhaps especially for those species found at single sites). However, the consistent differences in abundance of widespread species present at all sites (Ar. rufus and Ar.   flagellus) seem more likely to be due to ecological rather than historical factors. Arion rufus, widely found and commonly infected in the Swansea samples, has been acknowledged to act as an efficient host of A. vasorum and to be a major potential source of infection in various locations [18,33,34]. It appears that in urban areas of Swansea, L. maculatus acts as additional or secondary host. In Bristol, the lower availability of Ar. rufus as the preferred or optimal host of A. vasorum in Bristol might explain the low overall infection prevalence. Nevertheless, Ar. flagellus and L. maculatus were abundant and could be important alternative hosts in such areas.
The broad intermediate host range observed in this study is in agreement with previous data on experimental and natural infections [18][19][20]35]. Around 25 species have been successfully infected in an experimental  Table 4 for component loadings and eigenvalues    [42,43] and A. costaricensis [44,45].
The slugs most commonly infected in this study were larger Arion spp., which have been previously found to have higher larval burdens than smaller slugs [41,46]. Rosen et al. [38] reported average infection levels in Ar. rufus of 750 L3 and over 1000 in some cases. Arion flagellus and L. maculatus, alternative hosts for A. vasorum in the present study, are also among the larger slug species. It is possible that these larger slugs are more commonly infected than others through greater coprophagic tendencies. Arion flagellus was found to be a new host record for A. vasorum. There is evidence of ongoing distributional spread of both Ar. flagellus and L. maculatus in the UK and elsewhere [21,22].
The current study demonstrated infection of slugs with A. vasorum in summer and autumn, confirming previous records in summer [18], autumn [19] and summer and winter [20]. Thus, slugs are likely to present a source of infection to dogs all year round. The much lower prevalence in Bristol in summer, compared with that in Swansea in autumn, might stem from a seasonal rather than a spatial effect, and results are therefore not comparable between locations. Higher prevalence in autumn might be expected based on accumulation of infection and maturation of larvae through the summer months. More information is needed on seasonal variation in A. vasorum prevalence in intermediate hosts, to contribute to better understanding of the seasonal epidemiology and risks of infection to dogs, and also to guide efficient sampling for surveillance or spatial studies.

Conclusions
In conclusion, the results presented here demonstrate heterogeneity in the distribution of A. vasorum infection in slugs in two nearby cities, and variation in prevalence along the rural-urban gradient. The composition of the slug fauna appears to play a role in parasite distribution at these scales. Further studies are needed to account for ecological and host factors underlying species differences in intermediate host infections, and to separate spatial from seasonal drivers of prevalence.


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
Time-space analysis of highly pathogenic avian influenza H5N2 outbreak in the US

Background In early 2015, highly pathogenic avian influenza H5N2 caused outbreaks in commercial poultry farms in Minnesota and neighboring states where more than 48 million birds were affected. To date, the origin and transmission pathways of HPAI H5N2 have not been conclusively established. Methods In this study, we analyzed forty-six samples from turkeys and their environment that were collected at different time-points of the outbreak to identify origins and within outbreak evolutionary changes. We performed de-novo whole genome sequencing from primary samples and the most recent common ancestors of the PB2, PA, HA5, M and NS segments were traced back to Japanese HPAI H5N8 isolates. These segments appeared to have diverged from the ancestor around June and November 2014. Results The time to most recent common ancestor analysis for PB1, NP and NA2 segments suggest two likely possibilities of reassortant HPAI H5N2 origin - either a reassortment in Alaska area or multiple reassortments with North American low pathogenic avian influenza strains, before the HPAI H5N2 outbreak strain emerged. Within the outbreak, viruses clustered into two and three subgroups suggesting high substitution rates of 0.702x10-2 - 1.665x10-2 (subs/site/year), over the 5-month outbreak period. Conclusions Data are suggestive of a fast evolving HPAI strain within an outbreak that should be taken into consideration in developing appropriate control strategies in the future. Electronic supplementary material The online version of this article (doi:10.1186/s12985-016-0605-4) contains supplementary material, which is available to authorized users.

Background
Avian influenza virus (AIV) is an enveloped virus that contains eight negative sense single strand RNA segments which encode at least ten functional proteins [1]. Hemagglutinin (HA) and neuraminidase (NA) are surface proteins that are used to classify AIV into subtypes. At present, 16 HA and 9 NA subtypes have been identified in aquatic wild birds which are a natural reservoir of these viruses [2]. AIV can be divided into low pathogenic avian influenza viruses (LPAI) and highly pathogenic avian influenza viruses (HPAI) by its ability to cause disease in poultry which is identified by intravenous pathogenicity index (IVPI) test or by it's possession of poly-basic amino acid feature at the HA cleavage site [3].
Outbreaks HPAI H5 in Guangdong, China in 1996 have likely origins from goose Guangdong lineage HPAI viruses [4]. Since 1997, HPAI H5N1 dispersed in more than 50 countries in Africa, Asia, Europe, and the Middle East. In 2003, a re-emergence of HPAI H5N1 in China and South East Asia was reported. Since its emergence, the H5 gene has continued to evolve and has been arbitrarily classified in the OIE nomenclature system into clades identified from 0 to 9 [5]. The latest clade, 2.3.4.4, was designated in January 2015 and replaced the provisional clade 2.3.4.6. The first isolation of H5 clade 2.3.4.4 viruses was from domestic mallard ducks (Anas platyrhynchos) in China in 2008 [6]. The H5 clade 2.3.4.4 has demonstrated an ability to reassort with multiple neuraminidase subtypes including N1, N2, N3, N5, N6 or N8 [7].
In January 2014, HPAI H5N8 caused outbreaks in 161 commercial poultry flocks in South Korea and led to culling of 14,000,000 birds [8]. In early November 2014, HPAI H5N8 was detected in a turkey flock in Germany followed by an outbreak in a duck farm in England and a chicken farm in the Netherlands. Wild birds may play an important role in generating novel reassortant subtypes of clade 2.3.4.4 and carrying viruses across continents [9]. In late November 2014, HPAI H5N2 caused outbreaks in turkey and broiler breeder flock in British Columbia, Canada. In the United States, the first case of HPAI H5 was detected in a captive gyrfalcon (Falco rusticolus) in Washington in early December and subsequently the first detection of the reassortant HPAI H5N2 was reported in a northern pintail duck (Anas acuta) in Washington. In December 2014 and January 2015, HPAI H5 viruses associated with various NA subtypes were detected in backyard poultry flocks in Oregon and Washington, USA [10]. In January 2015, an HPAI H5N8 virus was detected in a commercial turkey flock in California [11]. On March 2 nd , 2015, the first case of HPAI H5N2 in the Midwestern USA was confirmed in Pope County, Minnesota. On June 17th, 2015, last confirmed case in this outbreak was reported in Iowa. In the Midwest region, more than 200 confirmed cases were reported and more than 48,000,000 birds were affected [12].
In this study, we investigated the origin and within outbreak evolution of this new emergent HPAI H5N2 that caused outbreaks in Midwestern states.

Sample collection
Samples were collected from turkeys and the environment at locations involved in outbreaks of HP clade 2.3.4.4 H5N2. Sampling was performed over a 106-day period during the outbreak. A total of 46 tissue or oropharyngeal or environmental samples were collected. Samples were divided into three phases representing an approximate 35-day interval each -early phase (March 4th-April 7th) [n = 18], mid phase (April 8th-May 12th) [n = 19] and late phase (May 13th-June 17th) [n = 9].
All samples were identified by RT-PCR testing water and bird samples from all barns of turkey flocks to identify infected samples per established methods [13]. All RT-PCR positive samples (water in infected barns, tracheal and cloacal swabs from birds or air) collected at early, mid and late phases of the outbreak were genome sequenced directly from primary samples ( Table 1).

Whole genome sequencing
Forty-six samples from turkeys, drinker biofilm, air and environment submitted to the University of Minnesota Mid Central Research and Outreach Center (Willmar, MN) were used in this study. RNA was extracted with the Mag-MAX™-96 Viral RNA Isolation Kit (Ambion) using a magnetic particle processor (Kingfisher, model 700) according to manufacturer's instructions. All RNA samples were tested by RT-PCR for matrix gene as described [13]. The positive RNA samples were subjected to amplify all eight segments of virus simultaneously using a one-step RT-PCR as described [14]. Briefly, SuperScript® III one-Step RT-PCR system with Platinum® Taq DNA polymerase (Invitro-gen™; CA, USA) was used with 1.6 mM and 0.2 μM final concentration of Magnesium and primers, respectively. PCR products were imaged by electrophoresis in 1.5 % agarose gel and subsequently purified by QIAquick PCR Purification Kit (Qiagen®; Hilden, Germany). Purified PCR products were submitted to University of Minnesota Genomics Center (UMGC) for illumina paired-end 250 cycles sequencing by using Nextera XT DNA kit for library generation. Sequences were assembled by mapping all reads to a reference and whole-or partialgenome sequences were extracted via CLC genomics workbench module available on Minnesota supercomputing institute (MSI) resources at the University of Minnesota.

Phylogenetic analysis
The reference nucleotide sequences were obtained from Influenza Research Database (http://www.fludb.org/) and GISAID (http://gisaid.org/) in May 2016. Reference sequences were selected to represent previous and recent avian influenza strains from varying geographic areas including North America and Eurasia. The following approach was applied to select reference sequences: 1) include an ancestral strain of clade 2.3.4; 2) use of sequences of isolates selected from 3 well characterized H5 (2.3.4.4 lineage) outbreaks in Asia, Europe and North America; and 3) use of a double selection criteria to identify the top 30 hits to the current outbreak isolates by BLAST and develop a tree. Subsequently, these closely related sequences were reanalyzed by BLAST to expand the reference database and used to reconstruct phylogeny. LPAI North American strains that were isolated during 2012-2015 were also included. Reference and sample sequences from the current study were aligned using Muscle v.3.6 [15] subsequently any extra sequences beyond and after start and stop codon were trimmed. Maximum clade credibility (MCC) tree of each gene segments were generated by BEAST 1.8 with Bayesian Markov Chain Monte Carlo (BMCMC) algorithm. Strict clock model with coalescent constant population and HKY with gamma 4 substitution was used as model parameters [16,17].
Mean substitution rate was estimated by Bayesian coalescent with constant population size [16] and strict clock model was applied. The Bayesian MCMC chain lengths were 10,000,000 generations with sampling every 10,000 generations and the effective sample size (ESS) value was assessed by using Tracer (v1.6.0) (Molecular evolution, phylogenetics and epidemiology, Edinburgh, Scotland, UK) [18]. Every gene segment analysis had ESS value greater than 200 to suggesting minimal standard error. The resulting tree of each iteration was summarized for a Table 1 Details of samples in this study Note: White and grey box indicates available and unavailable sequencing data on each segment, respectively. TS, CS and Envi stand for tracheal swab, cloacal swab and environmental sample, respectively representative clustering pattern by using a tree annotator with 10 % discarding of the chains as burn-in and the resulting maximum clade credibility tree was visualized with FigTree software (v1.4.2) (Molecular evolution, phylogenetics and epidemiology, Edinburgh, Scotland, UK).

Results
First case of this series of outbreaks in the Midwest area was confirmed on March 4th, 2015 in Pope county, Minnesota and last case on June 17th, 2015 in Iowa [12]. A 106-day period was divided into three phases representing an approximate 35-day interval -early phase (March 4th-April 7th), mid phase (April 8th-May 12th) and late phase (May 13th-June 17th). Eighteen, nineteen and nine samples were collected during early, mid and late phases of the outbreak, respectively ( Table 1).

Origin of HPAI H5N2
Forty samples were successfully whole genome sequenced and six provided whole segment sequences of some gene segments as shown in Table 1. Phylogenetic analysis of HPAI H5N2 (HPAI H5N2 EA/NA) showed that it was a reassortant between Eurasian (EA) HPAI H5N8 and North American (NAm) LPAI. HPAI H5N2 EA/NA genetic constellation is composed of five gene segments (PB2, PA, HA, M and NS) from EA HPAI H5N8 and the remaining three segments (PB1, NP and NA) from NAm LPAI (Fig. 1a,

Within outbreak evolution
The mean substitution rate of all segments over the 106day interval of this study was estimated to be between 0.702 and 1.665 × 10 −2 . The NS gene segment was the most divergent segment while NP gene segment was the most conserved segment ( Table 2). Bayesian Coalescent analysis showed that every gene segment of samples in this study formed three clades (I, II and III) except M and NS segment that form two major clades (I and II) (Fig. 1a-c and Additional file 1a-e). Members in subgroup I of PB2, PB1, PA, HA5, NP and NA2 were consistent in terms of clustering by time within the outbreak period with some minor variations. For example two samples (E15 and E16) fell into cluster II for M while the same samples clustered in clade I for NS. The HA5 segment of Korean H5N8 clustered within groups A and B [8]. Group A is a predominant cluster and appears to have dispersed to other continents. In addition, this cluster was subdivided into 3 clades (A1, A2 and A3) [19]. HPAI EA/NAm H5N2 belongs to the A2 subgroup which same subgroup as the two isolates H5N8 (Japan) and HPAI H5N2 (Canada) lineages (Fig. 1a). These results of high polymorphism rate and diversification of gene segments over the course of the epidemic indicate rapid evolution of viruses within this outbreak. These analyses assume evolution from a single introduction. Thus, that this high rate of change could likely be explained by multiple introductions cannot be ruled out.

Genetic analysis for amino acid variability
Genetic analysis of the HA5 segment indicated multiple basic amino acid at HA cleavage site. Samples from early and mid phase of the outbreak contain the motif PLRERRRKR/GLF that is a characteristic of H5 clade 2.3.4, while samples collected during late phases of the outbreak carried the PQRERRRKR/GLF motif Glutamine (Q) is the major amino acid of other H5 clades [20]. Amino acid substitutions were analyzed by comparing with A/Northern pintail/Washington/40964/2014 (A/NP/ WA/14), the first isolate of HPAI H5N2 EA/NA in the US. At position 141 (H5 numbering system) on the HA gene, A/NP/WA/14 and sub-cluster I contained serine (S) while 17 samples, sub-cluster II and III, contain proline (P). S141P mutations are related to surface protein stability and reduced host immune response [21]. The deduced amino acid sequence of the HA stalk region of all samples were identical.
All samples contained S at position 31 on M2 that has been shown to confer reduced susceptibility to amantadine and rimantadine antiviral drugs. However, analysis of resistance to neuraminidase inhibitors (NAI) at position 119, 151, 222, 224, 276, 292 and 371 on NA2 gene segment [22] showed that all samples in this study contain the conserved amino acid at every position which are expected to maintain NAI susceptibility. The E627K and D701N substitutions in the PB2 gene segment that are expected to increase virulence in mammals were examined. The results showed that all samples contain E and D in respective amino acid sites suggesting low mammalian virulence in this group of viruses.

Discussion
A study addressing the spread of HPAI H5N8 from South Korea to Europe and Japan have suggested that these outbreaks originated from a single source population [23]. Dispersion of the virus to different geographic localities coincides with wild bird migratory seasons strongly suggesting their important role in the transmission of this pathogen and has been suggested as a mechanism by which H5N8 entered South Korea [24]. The most likely flyway involved in the recent spread of HPAI EA H5N8 to Japan was the East Asian Australian (EAA) Flyway [23]. The EAA flyway extends from north-eastern Asia and western Alaska to the southern extent of Australia and New Zealand and Mongolia, western China and eastern India [25]. On the North America continent, Alaska is an area where there is major overlap between migratory birds from the EAA and Pacific Flyways [25] but there exists no evidence of HP H5 clade 2.3.4.4 in Alaska.
The existence of PB1, NP and NA2 ancestor that related to HP H5N2 in Alaska arise the possibility that HPAI H5N2 EA/NA originated in a location that coincides with Alaska and subsequently spread to British Columbia, Canada and Washington, USA. However, the mechanism and timing of viral transfer from the Pacific Flyway to the Mississippi Flyway in NA has not been established. The ancestral strain and the overlapping of interval between TMRCA estimates of PB1, NP and NA2 suggest two hypothesis of HPAI EA H5N2 origin. First, it is possible that reassortant event occurred in Alaska. This finding is concordant with the report of avian influenza surveillance in Alaska during spring and summer 2015 that found reassortant LPAI between EA and NAm [26]. Second, the outbreak viral genome constellation likely occurred via multiple reassortment events with an LPAI before addition of genes of HPAI EA H5N2 origin due to the earlier divergence time of PB1, NP and NA2. Furthermore, HA5 clade 2.3.4.4 has the potential to reassort  Fig. 1 Time-scaled Bayesian maximum clade credibility tree inferred for the HA5 a, NA2 b and NS gene c. Trees were generated by Bayesian Markov Chain Monte Carlo algorithm in Bayesian evolutionary analysis by sampling trees. The TMRCAs representing the estimated timing of viral divergence from their ancestor are provided in parentheses. Red dot at each node represents the posterior probability above 0.7. A/AGWT/AK/ 472/14, group of A/mallard/SAK/14 and non-H5 LPAI NAm strain were labeled by pink, green and blue branch. Clustering is shown with reference strains of recent ancestry and within outbreak viruses clearly cluster in 2 or 3 clades separated by 35-day intervals. The TMRCA with multiple NA subtypes including NA1, NA2, NA5, NA6 and NA8 [27]. For example, reassortant H5N1 (EA/NA H5N1) containing PB2, HA5, NP and M from EA H5N8 and the rest of the gene segments (PB1, PA, NA1 and NS) from AIV North American lineage was detected in a green wing teal and an American wigeon in December 2014, with no subsequent case reports. This finding indicates that EA/NA H5N1 was likely unable to persist in the population while EA/NA H5N2 provided a compatible constellation for maintenance. Alternately, this may indicate that there was insufficient surveillance to detect the fate of that reassortment.
The nucleotide substitution rate of all segments within the outbreak are greater than 5.000 × 10 −3 substitution/ site/year which are higher than the rates estimated in long term analysis [28]. These results indicated that viruses were evolving rapidly during the current outbreak. Host jump from wild birds to domestic poultry including turkeys and chickens is a factor that provoked this rapid evolution. Therefore, identification of bridge of interspecies transmission is an important process to prevent the further outbreaks. While the HA gene had an extremely high substitution rate, the HA stalk region of all samples were identical at the amino acid level. The HA stalk region, PA and NP proteins were highly conserved, opening avenues for a universal subunit vaccine concept and development.
At present, no human case has been reported due to HPAI H5N2 EA/NA and it is considered as low health risk to public health [29]. However, three human cases with severe respiratory disease caused by HPAI H5N6 HA5 clade 2.3.4.4 have been reported in China [30]. These cases confirmed that HA5 clade 2.3.4.4 is capable of causing human infection and thus ongoing monitoring of viral evolution should be performed.

Conclusion
Wild bird surveillance in United States has been performed annually during September and November, which is a migratory season and only fecal or cloacal swabs are traditionally collected. After HPAI H5 clade 2.3.4.4 was detected in December 2014, USDA/APHIS wildlife services responded by enhancing surveillance in Pacific Flyway and detected more viruses. Furthermore, multiple introductions of this clade of viruses cannot be ruled out. Therefore, there is a need for more comprehensive wild bird surveillance effort to address a complex wild-domestic animal-human interface to capture variations in subtype specific organ predilection and varying patterns of shedding by different host species.

Additional file
Additional file 1: Alignment and phylogeny of PB2 segment sequences also captures the 3 stages in evolution amon the outbreak isolates. (ZIP 2258 kb)


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Psychology

text:
Exogenous dopamine reduces GABA receptor availability in the human brain

Abstract Background While it has recently been shown that dopamine release stimulates conscious self‐monitoring through the generation of gamma oscillations in medial prefrontal/anterior cingulate cortex, and that the GABAergic system is effective in producing such oscillations, interaction of the two transmitter systems has not been demonstrated in humans. We here hypothesize that dopamine challenge stimulates the GABA system directly in the medial prefrontal/anterior cingulate region in the human brain. Methods Positron emission tomography (PET) with the GABA receptor α1/α5 subtype ligand [11C] Ro15‐4513 was used to detect changes in GABA receptor availability after clinical oral doses of levodopa in a double blind controlled study. Results We here provide the first direct evidence for such coupling in the cerebral cortex, in particular in the medial prefrontal anterior cingulate region, by showing that exogenous dopamine decreases [11C] Ro15‐4513 binding widely in the human brain compatible with a fall in α1 subtype availability in GABA complexes due to increased GABA activity.

Introduction
Cognition depends on paralimbic medial prefrontal/anterior cingulate and medial parietal hubs (Lou et al. 2004;Luber et al. 2012) and their rich network of highly oxygen-demanding, fast-spiking GABAergic interneurons (Tomasi et al. 2013). The network is regulated by dopamine, which in humans stimulates medial prefrontal/anterior cingulate cortex and insula, thereby generating gamma oscillations (Joensson et al. 2015) essential for cognitive function (Voss et al. 2014). The high oxygen requirement of these hubs (Tomasi et al. 2013) makes the system vulnerable in penuria, and dysfunction is associated with disorders like ADHD (Castellanos et al. 2008), autistic spectrum disorder (Assaf et al. 2010), schizophrenia (Gallinat et al. 2004), addiction (Romer et al. 2013), and dementia (Liu et al. 2014), and can follow traumatic brain injury (Ham et al. 2014). It is, therefore, important to determine how the information gathered in animal models on the molecular arrangement of dopamine/GABA interaction (Sesack et al. 2003) translates into the human brain. This is particularly the case for the medial prefrontal/anterior cingulate region which facilitates attention and self-awareness. With that aim we here examined the effect of oral L-dopa, a dopamine prodrug, on the activation of the GABA system in the human living cerebral cortex. The idea that dopamine release regulates cognition, including conscious experience, via the GABAergic system is based on the finding that dopamine increases confidence and accuracy of seeing rapidly presented words (Lou et al. 2011), and on the fact that GABAergic interneurons are instrumental in mediating cognition in rodents (Sohal et al. 2009). The hypothesis is further strengthened by the recent demonstration that dopamine release in the medial prefrontal/anterior cingulate cortex activates self-monitoring and metacognition (Joensson et al. 2015). Theoretical aspects of interaction between dopamine and GABA function have been studied in detail in a recent neurocomputational model by Lew and Tseng (2014).
Direct experimental approaches have been scarce, but recently it has been demonstrated that extracellular striatal dopamine elicits opposing effects on the GABA system in rodents: dopamine release activates synaptically located GABA A receptor subtypes, including alpha 5, but inhibits tonic GABA A currents via extrasynaptically located tonic level GABA A receptors (Hoerbelt et al. 2015). These effects of dopamine mimic similar opposing effects of extracellular GABA (Ransom et al. 2013), and are in accordance with the concept that tonic GABA currents are a homeostatic regulator responding to localized synaptic stimulation of GABA receptors by decreasing excitability in the network (Stokes et al. 2014). Here, we provide the first direct in vivo evidence for such interaction in humans by showing that an exogenous dopamine challenge in physiological relevant dosage decreases GABA receptor availability, reflecting increased GABA activity.

Participants and Procedures
The study was approved by the local ethical board (Region Midtjylland). After information given orally and in writing, and consent, nine healthy males were studied. This provides 80% power to detect a 20% reduction of anterior cingulate Ro 15-4513 binding at P = 0.05. The participants were without current medication. None had a history of mental or neurological disorders, or a history of addiction, with the exception that two had been smokers 10 and 15 years ago, respectively. Median age was 29 years, range 24-47. The PET ligand [ 11 C]Ro15-4513 is sensitive to interstitial GABA levels evidenced as a fall in availability of a1 subtype sites on the GABA complex for its binding (Semyano et al. 2004;Stokes et al. 2014). While [ 11 C]Ro15-4513 binds to both the a1 and a5 receptor subtypes, the a1 subtype is present at a greater density and increases in brain GABA have been shown to reduce its availability, while a5 binding site availability is either unaffected or mildly elevated. In the present study, it was not possible to separate signals from a1 and a5 subunits of the GABA complex as we did not have an arterial input curve so we have assumed that any decreased Ro 15-4513 binding seen represented competitive occupancy of a1 sites by endogenous GABA (Lingford-Hughes et al. 2002).
Each participant was studied twice in counterbalanced order with an interval of at least several days. We used PET following an oral L-dopa/dopamine challenge or placebo in identical capsules given orally containing either sinemet (MSD, 100 mg L-dopa plus 25 mg carbidopa) or placebo (starch). The medication was unknown to the participants and experimenters responsible for PET injection and calculation of binding potentials. The administration of L-dopa or placebo was done between 30 and 45 min prior to intravenous injection of [ 11 C]Ro15-4513. This interval is based on the interval for obtaining peak plasma concentrations and clinical effects on parkinsonism after oral sinemet medication (Joensson et al. 2015).

Results and Discussion
The distribution of [ 11 C]Ro15-4513 binding with placebo is shown in Figure 1. After placebo administration, the ligand is bound primarily in medial prefrontal/anterior cingulate cortex and left and right insula. The cerebellum provides a reference for nonspecific tissue binding of the tracer. After L-dopa, [ 11 C]Ro15-4513 binding is reduced, which can be seen more clearly by subtraction. There is a reduction of between 5% and 20% throughout most of gray matter regions (BP 100x [placebo minus dopamine]/ placebo). Small foci of BP increases were, however, present in the hippocampi, possibly reflecting the high concentration of D4 dopamine receptors there which inhibit interneuron GABA release when activated (Romo-Parra et al. 2005). After placebo, the ligand was bound primarily in medial prefrontal/anterior cingulate cortex and left and right insula. After dopamine challenge ligand binding is reduced. This is seen more clearly by subtraction, with a general reduction of between 5% and 20% throughout gray matter (BP 100x [placebo minus dopamine]/placebo). In the hippocampal regions there were small foci of increased BP.
Brain and Behavior, doi: 10.1002/brb3.484 (2 of 5) Quantification using parametric modeling is shown in Figure 2 for medial prefrontal/anterior cingulate region, left and right insula, cerebellum, and whole gray matter. For a composite gray matter region, a one-sample t-test showed that L-dopa was associated with an 11% mean decrease in [ 11 C]Ro15-4513 BP (t = À2.29, 9 degrees of freedom, P = 0.048). There was a highly significant effect of regions (linear mixed model, P = 0.000049). Because dopamine has previously been shown to stimulate electromagnetic activity and self-awareness mainly in the medial prefrontal/anterior cingulate and insula regions (Joensson et al. 2015), we a priori calculated BP changes in these regions separately and compared these with cerebellum and whole gray matter, using two-tailed t tests with Bonferroni corrections and a cut off at P = 0.01. The BP of the GABA ligand decreased significantly with L-dopa in the medial prefrontal/anterior cingulate and right insula regions (by 18%, t = À3.63 P = 0.0054, and 18%, t = 3.85 P = 0039, respectively), with no significant effect in the other regions.
The present results account for the effect of dopamine in its regulation of self-awareness via direct action on the GABA system in the medial prefrontal/anterior cingulate cortex. Similarly, the direct effect of dopamine on GABA in right insula, rather than the left, may explain the preferential role of right hemisphere in self-recognition (Keenan et al. 2001). The close cortical interaction of the two transmitter systems broadens the scope for pharmacological intervention in the many human disorders where the medial prefrontal region is dysfunctional. It may open new perspectives for treatment.

Magnetic resonance imaging
All participants were scanned with a 3T MR system preregistration with PET (Siemens Trio, Erlangen, Germany). A T1 MPRAGE scan (TR/TE 2420/3.7 msec, 1 mm isotropic resolution, scan time 5½ min) was performed.

Positron emission tomography
Participants were placed in the scanner with the orbitomeatal line parallel to the transaxial plane of the tomograph. Head position was monitored via laser crosshairs and video camera. In order to correct for attenuation of emitted radiation by skull and tissues, a transmission scan was acquired using a single rotating photon point source of 150 MBq of 137 Cs. Participants were pretreated with placebo or 100 mg L-dopa + 25 mg carbidopa given orally to block peripheral metabolism of L-dopa, thereby increasing tracer delivery to the brain. This was done 30-45 min prior to injection of 400 (range 375-425) MBq [ 11 C]Ro15-4513 through a cubital vein in 10 mL of normal saline over 30 sec. Three-dimensional PET was recorded over 60 min, using an ECAT EXACT HR++ (CTI/Siemens 966; Siemens PET/CT Biograph, Erlangen, Germany) camera, which covers an axial field of view of 23.4 cm and provides 95 transaxial planes. The tomograph has a spatial resolution of 4.8 + 0.2 mm FWHM (transaxial, 1 cm off axis) and 5.6 mm + 0.5 mm (axial, on axis) after image reconstruction. PET and MRI were coregistered to a common MR T1 atlas from Montreal Neurological Institute (MNI) (Collins et al. 1998) using PMOD (PMOD Technologies Ltd., Zurich, Switzerland). The kinetic parameters of [ 11 C]Ro15-4513 were determined by the SRTM2 method using cerebellum as reference (Wu and Carson 2002). No FDR or cluster corrections were used as the regions were predefined.


text:
Assessing mutant p53 in primary high-grade serous ovarian cancer using immunohistochemistry and massively parallel sequencing

The tumour suppressor p53 is mutated in cancer, including over 96% of high-grade serous ovarian cancer (HGSOC). Mutations cause loss of wild-type p53 function due to either gain of abnormal function of mutant p53 (mutp53), or absent to low mutp53. Massively parallel sequencing (MPS) enables increased accuracy of detection of somatic variants in heterogeneous tumours. We used MPS and immunohistochemistry (IHC) to characterise HGSOCs for TP53 mutation and p53 expression. TP53 mutation was identified in 94% (68/72) of HGSOCs, 62% of which were missense. Missense mutations demonstrated high p53 by IHC, as did 35% (9/26) of non-missense mutations. Low p53 was seen by IHC in 62% of HGSOC associated with non-missense mutations. Most wild-type TP53 tumours (75%, 6/8) displayed intermediate p53 levels. The overall sensitivity of detecting a TP53 mutation based on classification as ‘Low’, ‘Intermediate’ or ‘High’ for p53 IHC was 99%, with a specificity of 75%. We suggest p53 IHC can be used as a surrogate marker of TP53 mutation in HGSOC; however, this will result in misclassification of a proportion of TP53 wild-type and mutant tumours. Therapeutic targeting of mutp53 will require knowledge of both TP53 mutations and mutp53 expression.

TP53 is widely acknowledged as the most frequently mutated gene in human malignancy, with mutations identified in at least 50% of human cancers [1][2][3][4][5] . In the absence of cellular stress, wild-type p53 is maintained at low levels by the E3 ubiquitin ligase MDM2 that polyubiquitinates p53, marking it for proteasomal degradation. In response to stress, numerous mechanisms act to disrupt the MDM2-p53 association, resulting in stabilisation and activation of p53 6 . Activated wild-type p53 promotes processes consistent with tumour suppression, including cell cycle inhibition, apoptosis, senescence, DNA repair and autophagy, as well as processes that oppose oncogenic metabolic reprogramming [reviewed in 2 ]. Mutant p53 (mutp53) results in loss of these tumour suppressive functions.
Human TP53 maps to chromosome 17p13.1 and is composed of 19,198 nucleotides spanning eleven exons, with the coding sequence commencing in exon 2 [reviewed in 7 ]. Mutations in TP53 can occur throughout the coding sequence; however, around 80% are located in the DNA binding domain (DBD) encoded by amino acid residues 102 to 292 and encompassing six 'hot-spot' amino acids [7][8][9] . TP53 mutations include single base substitutions leading to missense or nonsense mutations, deletions or insertions leading to frameshifts, in-frame deletions or insertions, as well as mutations that affect splicing. Pathogenic mutations result in loss or abrogation of wild-type p53 activity, a major mechanism likely being the inability of mutp53 to bind to response elements in DNA, preventing its function as a transcription factor. Along with the loss of wild-type function, many TP53 mutations result in gain of an oncogenic function for the mutp53 protein. These 'gain-of-function' (GOF) TP53 mutations cause mutp53 to accumulate at high levels in cells, contributing to tumorigenesis and the development of drug resistance 1 . Not all p53 mutants that lead to accumulation of p53 are proven GOF mutations; however, certain mutants such as p.Arg175His, p.Gly245Ser, p.Arg248Trp, p.Arg248Gln and p.Arg273His have been shown to actively promote tumorigenesis [reviewed in 2 ]. Furthermore, a number of GOF mutations, including p.Ar-g175His and p.Arg273His have been linked to the development of chemoresistance 10,11 .
Given the broad role of p53 in malignancy, there is intense focus on its potential as a therapeutic target. Strategies include restoring normal transcriptional activity of mutp53 by restoration of wild-type protein folding using drugs such as APR-246 that are currently recruiting for Phase Ib/II clinical trials for recurrent high-grade serous ovarian cancer (HGSOC) 12 or zinc metallochaperones 13 . Alternatively, gene replacement therapy to reintroduce wild-type p53 into mutp53 cells, most often with an adenoviral vector (Adp53), has also been shown to suppress tumour growth 14 . It is clear that success of these different approaches will be influenced by the type of TP53 mutation present, and whether it results in accumulation of mutp53 in the cell, severe truncation of the p53 protein, or low to absent levels of p53.
HGSOC is characterised by a high frequency of TP53 mutation. Worldwide, HGSOC is the eighth most frequent cause of cancer-related deaths in women 15 . More than 80% of women present with advanced disease (Stage III-IV), where 5 year survival is around 25% 16 . The mainstay treatment is surgical debulking followed by combinations of platinum drugs and paclitaxel; however, the majority of women relapse within two years, ultimately develop broad chemoresistance and succumb to their disease 17 .
With the advent of massively parallel sequencing (MPS) and analyses of large data sets through The Cancer Genome Atlas, TP53 mutations have been identified at a frequency of approximately 96% in large HGSOC cohorts 18,19 . It has recently been suggested that 100% of HGSOC are in fact TP53 mutant 20 . These figures are higher than first thought from reports that used less sensitive methods such as Sanger sequencing and/or only focussed on mutational 'hot-spot' regions of this gene [21][22][23][24] . TP53 is in fact the only gene that is frequently mutated at the somatic level in HGSOC, this malignancy otherwise being characterised by low prevalence (2-6%) of recurrently mutated genes such as BRCA1, BRCA2, RB1 and NF1, and a long tail of infrequently mutated genes 18 . TP53 mutations are believed to occur early in tumorigenesis, likely in precursor lesions of ovarian cancer, highlighting the importance of mutp53 as a driver of this malignancy [25][26][27][28] .
Immunostaining for p53 has been used as a surrogate marker for the presence of a TP53 mutation in HGSOC, although precise validation of this practice by comparison to detailed sequencing data has been limited, especially in the context of MPS 21,24,29 . Here, we report the use of MPS to characterise TP53 mutations in a cohort of HGSOC in parallel with p53 immunohistochemical analyses. We note cut-offs based on our data related to percent positive p53 nuclei that should be used to increase the accuracy of p53 immunohistochemistry (IHC) as a surrogate marker of mutp53 and suggest that both p53 IHC and MPS should be considered to inform therapies targeting this protein. Further, we report TP53 mutations in previously uncharacterised cell line models of HGSOC.

Results
TP53 mutations identified using massively parallel sequencing in high-and low-grade serous ovarian cancer. Depth of coverage for data generated was approximately 850 reads per amplicon per sample.
A mutation in TP53 was identified in 94% (68/72) of HGSOC. Four HGSOC reported to be wild-type for TP53 underwent second review of archived tissue by an experienced pathologist [AJG] and remained classified as HGSOC. An additional four samples contained clear cell components and were excluded from this study (Fig. 1). The four low-grade serous ovarian cancer (LGSOC) samples contained wild-type TP53 (Table 1; Supplementary  Table S1). The majority of mutations identified (62%, 42/68) were missense, with all except one of these mutations located in the DBD of p53 (Fig. 2). The single missense mutation occurring outside of the DBD, p.Leu344Gln, was located in exon 10 of TP53. All missense mutations had been previously reported in The Cancer Genome Atlas ovarian study 18 and/or the International Agency for Research on Cancer (IARC) TP53 database (Supplementary  Table S1). TP53 mutations that had not been previously reported are indicated (Supplementary Table S1). Overall, 82% (56/68) of mutations occurred within the DBD. Frameshift, nonsense, in-frame insertion or splice mutations (38%, 26/68) were identified in exons or flanking intronic sequence between exons 4-10.
Arginine 248 (Arg248) was the most frequently mutated amino acid, with approximately 9% (6/68) of mutations (p.Arg248Gln and p.Arg248Trp) occurring at this codon, followed by Arg175 with approximately 6% (4/68) of mutations (p.Agr175His) and Arg342 with approximately 4% (3/68) of mutations (p.Arg342* and p.Arg-342Glufs*2) occurring at this amino acid (Fig. 2). The frequency of mutation type we observed in our cohort was not dissimilar to that reported in recent studies 18,19 . The presence or location of a TP53 mutation did not influence overall patient survival ( Supplementary Fig. S1).

Validation of TP53 by Sanger sequencing in HGSOC.
Of the 68 TP53 mutations identified by MPS, Sanger sequencing of the identical DNA sample analysed in a blinded manner was able to identify 61 (~90%). Of the seven samples that were incorrectly determined to be TP53 wild-type by Sanger sequencing, re-analysis with knowledge of the mutation that was identified by MPS identified small variant peaks in five samples consistent with presence of the identified mutation (Fig. 3). In the remaining two samples, no variant was detected by Sanger sequencing alone (Fig. 3). This data therefore suggests that allele frequencies of less than approximately 25% are unable to be reliably detected by Sanger sequencing.
Representative images of these staining patterns are shown in Fig. 4b and Supplementary Figs S2 and S3. The DO-7 antibody used was suitable for detecting all variants identified in our study by MPS.
All missense mutations were scored as 'High' for expression of the p53 protein by IHC, indicating that these mutations were associated with increased levels of mutp53. Considerable variability was observed in p53 staining levels in samples containing stop, frameshift, in-frame insertion or splice mutations (N = 26) (Fig. 4a). While the majority of samples in each non-missense mutation category were scored as 'Low' for p53 IHC (7 of 9 frameshift mutations, 6 of 11 nonsense mutations and 3 of 5 splice mutations), a single nonsense mutation, p.E298* (exon 8),

Figure 1. Flow diagram summarising inclusion and exclusion criteria from the primary tumour cohort.
Eighty-eight primary ovarian tumours underwent MPS for TP53. Two samples were excluded as paired p53 IHC was unable to be obtained. Upon pathological reassessment, 4 samples were excluded due to the identification of clear cell components. A further 6 samples were excluded as they contained < 5% tumour cells based on hematoxylin and eosin staining. Of the 76 remaining samples, 72 were HGSOC and 4 were LGSOC.
TP53 mutations and p53 protein expression in previously uncharacterized HGSOC and clear cell ovarian cancer cell lines. Among the cell lines examined, Western immunoblotting showed the greatest basal p53 expression in OV202, followed by OV207. OV167 expressed a lower level of basal p53 compared to the wild-type p53 A2780 cells (Fig. 7a). Only OV202 displayed a statistically significant difference in p-p53 abundance from the other cell lines (p = 0.000005-0.000016; Fig. 7b). At the transcript level, all cell lines showed a significant difference between TP53 transcripts, OV167 < A2780 < OV202 < OV207 (p = 0.000002-0.046; Fig. 7c). OV202 and OV207 were found to have the TP53 missense mutations c.722C > T (p.Ser241Phe) and c.818G > A (p.Arg273His) respectively. OV167 had the nonsense mutation c.892G > T (p.Glu298*) (Fig. 7d). Further, immunostaining of a cell pellet of OV167 showed an "Intermediate" staining pattern similar to the tumour sample #353-09 shown to have the identical mutation ( Supplementary Fig. S4). The mutant allele frequency for each of these cell lines was ≥ 99% as determined by MPS and our analysis pipeline.

Discussion
Mutation of TP53 is pervasive in HGSOC; however, whether these mutations lead to accumulation of mutp53 and possible dominant-negative and/or gain-of-function effects, or low to absent levels of mutp53 is dependent upon individual mutations. Using MPS, we identified 94% (68/72) of HGSOC with TP53 mutation and 100% (4/4) of LGSOC were wild-type for TP53, similar to recent studies 18,19,24 . Pathological reassessment of the four TP53 wild-type HGSOC did not change the initial diagnosis of HGSOC. When correlated with p53 IHC data for the same specimens, we showed an overall sensitivity of 99% for detecting a TP53 mutation based on an IHC score of ≤ 5% or ≥ 70% percent p53 positive nuclei; however, this test showed a lower specificity of 75%. In a cohort of HGSOC where the expectation is that the vast majority of samples will be TP53 mutant, p53 IHC may be considered an acceptable surrogate marker for TP53 mutation. In tumour types where the frequency of somatic TP53 mutation is expected to be much lower, the lower specificity of this test may impact on its value as a surrogate marker of TP53 mutation.
There is general agreement in the literature that both high and low, or entirely absent, p53 expression is correlated with mutant p53, while expression levels between these two extremes are thought to represent wild-type p53. In studies of HGSOC, high levels of p53 positive nuclei indicative of mutp53 have been described as > 30% 30 , > 50% 31,32 and ≥ 60% 21 , although it is noted in one report that the majority of high expressors showed > 85% p53 positive nuclei 31 . In the current study, the cut-off for high p53 expression was ≥ 70%. Reduction of this cut-off to lower levels previously published would have resulted in misclassification of several wild-type TP53 tumours as mutant and lowered the overall specificity of this test. A lower cut-off for high expression would; however, have correctly classified tumour #353-09 that displayed 55% p53 positive nuclei and was misclassified under our criteria.
Low levels of p53 expression correlated with the presence of a mutation have been described as completely negative 21,31 , although tumours with wild-type TP53 have also been reported to have low p53 levels, ≤10% positive nuclei 21 , potentially complicating clear discrimination between mutant and wild-type p53. In the current study, 10 tumours showed complete absence of p53, one of which was wild-type. A further 7 tumours, all TP53 mutant, showed < 5% positive p53 nuclei (1-4%). We suggest that even with careful optimisation of p53 immunohistochemistry, this lower range of p53 expression will pose problematical for the discrimination of all wild-type from mutp53 tumours.
The majority of TP53 mutations identified in this study (82%; 56/68) were located in the p53 DBD. Hot-spot codons for mutation included p.Arg248 and p.Arg175 residing within the p53 DBD, that have previously been recognized as TP53 mutational hot-spots in human malignancy 2 , as well as p.Arg342. No differences in overall survival in HGSOC were observed that could be correlated with presence or absence of a TP53 mutation, mutation type or location; however, we could not exclude the possibility that specific mutations, or mutations at certain amino acids, may in fact influence patient survival. Sanger sequencing of samples in which a mutation had been identified by MPS proved to be less efficient at detecting mutations, failing to identify approximately 10% of the mutations that were identified by MPS when analyses were performed in a blinded fashion. The lower sensitivity of this technique in comparison to MPS may at least partly explain the lower frequency of TP53 mutations in HGSOC reported in earlier studies 21,23 . This will have impacted on the accuracy of previous studies where attempts have been made to correlate TP53 mutation and p53 IHC 21 . In the current study, Sanger sequencing was unable to robustly identify mutations where the frequency of the mutated allele determined by MPS was less than 25%. This limit of detection is supported by other studies 33,34 .
The majority (62%) of mutations identified in HGSOC were missense, all occurring in the p53 DBD with the exception of p.Leu344Gln in exon 10. All tumours with missense mutations were scored as 'High' by p53 IHC, consistent with accumulation of mutp53. This data suggests that p53 IHC is a strong surrogate marker of TP53 missense mutation; however, it was not specific for this class of mutation. Four mutations in exon 10 classified as nonsense (two tumours with p.Arg342*, p.Glu343* and p.Glu349*) and two with frameshifts (p.Phe328Hisfs*7 in exon 9 and p.Arg342Glufs*2 in exon 10) also resulted in accumulation of mutp53 and a score of 'High' for IHC. All of the nonsense and frameshift mutations associated with high levels of mutp53 would be predicted to truncate p53 towards its carboxyl-terminus. Splice site mutations (c.376-2A > G in intron 4 and c.920-1G > A in intron 8), as well as an in-frame insertion of a duplication in exon 7, also resulted in accumulation of mutp53 and a 'High' p53 IHC score. The remaining nonsense, frameshift and splice site mutations resulted in 'Low' p53 IHC scores, with the exception of a single nonsense mutation, p.Glu348* in exon 8, that demonstrated an 'Intermediate' p53 staining pattern. Two distinct clusters of non-missense mutations in the context of cellular  . While all missense TP53 mutants resulted in ≥ 70% tumour cell nuclei staining positive for p53, there was considerable variation amongst p53 staining patterns for wild-type (including both HGSOC and LGSOC) and non-missense TP53 mutations. While the majority of wild-type TP53 samples clustered between 8-65% tumour cell nuclei staining positive for p53 (denoted by the broken red line), one HGSOC sample demonstrated Low staining (#695-11; 0%) and one High (#493-10; 100%). Furthermore, there were two distinct clusters of non-missense mutations, with tumour cell nuclei staining positive for p53 between 80-100% and 0-4%, that could not further be discriminated by whether the mutations were nonsense, splice site, an in-frame insertion or frameshifts. Furthermore, a single nonsense mutation displayed 55% of tumour cell nuclei staining positive for p53 (#353-09).
Scientific RepoRts | 6:26191 | DOI: 10.1038/srep26191 accumulation of mutp53 were present in this study; however, they could not be clearly defined based on mutation type or location.
Four HGSOC were identified as wild-type for TP53. These fresh frozen specimens contained between 30-80% tumour cells. In the absence of mutation or stimulus as the result of DNA damage, wild-type p53 would be expected to be present at low levels in the cell 6 . Of the HGSOCs that were wild-type for TP53, one showed 'High' p53 IHC, two 'Intermediate' and one 'Low' . As expected, the four LGSOCs were TP53 wild-type, and all showed 'Intermediate' staining for p53 IHC. It is possible that the wild-type TP53 tumours demonstrating 'High' or 'Low' levels of accumulated p53 may have other abnormalities, such as amplification of MDM2 or MDM4 that has previously been associated with some, but not all, TP53 wild-type HGSOC 29 . The literature remains conflicted as to whether differing levels of mutp53 are associated with survival 24,29,31,32,[35][36][37] ; however, we did not find any association between overall survival and levels of mutp53 expression in this study (Supplementary Fig. S1).
Recent genomic assessment of cell line models of HGSOC has identified cell lines believed to be stronger models for studying this cancer based on their genomic similarity to primary tumours 38,39 . All cell lines ranking as strong models for HGSOC were TP53 mutant. Two HGSOC cell lines not included in these published studies, OV167 and OV202, contained a nonsense and missense TP53 mutation respectively. A third cell line established from a clear cell adenocarcinoma, OV207, also contained a missense TP53 mutation. While our reporting of TP53 mutation status in these cell lines reflects only part of the extensive genomic analyses of cell lines performed by other studies 38,39 , it does support the likelihood that these are suitable cell line models for studying this malignancy.
In summary, we suggest that p53 IHC may be used with care as a surrogate marker of TP53 mutation by designating specimens demonstrating 'Intermediate' p53 staining as most likely p53 wild-type. However, detailed knowledge of mutation effect determined by the combination of MPS, mutation location in TP53 and levels of p53 expression may be particularly important when considering options for therapeutic targeting of mutant and wild-type p53 that are currently in development 40 . The success of therapeutic targeting of mutant p53 will in large part depend on knowledge of both the level of p53 expression and the exact nature of the mutation. While this study has focussed on HGSOC, combining MPS of TP53 with p53 IHC should also be considered when investigating different therapeutic strategies for targeting p53 in other human malignancies. Sensitivities and specificities of these tests should be determined for other malignancies that demonstrate a range of TP53 mutation frequencies.

Materials and Methods
Tumour Samples. Our cohort consisted of 88 gynecological tumours (serous ovarian, fallopian tube and peritoneal carcinomas) collected from 2004 to 2014 from three hospitals (Royal North Shore Hospital, North Shore Private and The Mater Hospital-North Sydney, Sydney, Australia). Seventy-two HGSOC and four low grade serous tumours (LGSOC) were included in the final analysis. Rationale for specimen exclusion is described in Fig. 1. Eighty-two percent (59/72) of HGSOCs were at an advanced stage, classified as Stage III or IV. Survival and other clinical data were available for the majority of patients (Supplementary Table S3). Survival times were defined as time from date of surgery to last follow up or death as recorded in hospital medical records, doctors' rooms and publicly available death notices. All snap frozen tumour samples were collected and stored in the Kolling Institute of Medical Research (KIMR) Gynecological Tumour Bank. Matched formalin fixed paraffin embedded samples were obtained from one of two diagnostic pathology laboratories (Pacific Laboratory Medicine Services, PaLMS or Douglass Hanly Moir Pathology, Sydney, Australia). Ethical approval for this project was obtained from the Northern Sydney Local Health District Human Research Ethics Committee (Protocol: 108-243 M). Written informed consent was obtained from all subjects. All methods were carried out in accordance with the approved guidelines. All tumour tissue was reviewed by a pathologist [AJG] to confirm diagnosis, histological grade and pathological stage. Sequential sections were ascertained from frozen tumours for determination of percent tumour cells following hematoxylin and eosin staining, and for DNA extraction (Supplementary Table S1). Tumours containing at least 5% of tumour cells were selected for this study.
DNA was prepared from approximately 30 mg fresh frozen tumour tissue, homogenised in phosphate buffered saline using a Retsch MM 301 Mixer Mill (MEP Instruments Pty. Ltd., NSW, Australia). Tissue was shaken three times for 90 seconds at the highest frequency until liquefied. Protein was digested overnight at 56 °C with proteinase K (Qiagen Pty Ltd, Chadstone, VIC, Australia). DNA was extracted using the DNeasy Blood and Tissue Kit and QIAcube (Qiagen Pty Ltd, Chadstone, VIC, Australia). DNA concentration was determined by Qubit Fluorometric Quantitation (Life Technologies Australia Pty. Ltd., Mulgrave, VIC, Australia) and 260:280 and 260:230 ratios determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific Australia, Scoresby, VIC, Australia).
Ovarian Cancer Cell Lines. Three ovarian cancer cell lines that had not been previously characterised for TP53 status, HGSOC cell lines OV167 and OV202, and the clear cell adenocarcinoma line OV207, were analysed (cell lines were kindly provided by Drs C. Conover and K. Kalli, Mayo Center, Rochester, MN, USA) 41 . All cell lines were cultured in RPMI 1640 (Gibco, Life Technologies Australia Pty Ltd, Mulgrave, VIC, Australia) supplemented with 10% (volume/volume) fetal bovine serum (SAFC Bioscience, Brooklyn, VIC, Australia) at 37 °C with 5% CO 2 as previously described 42 . Cells were grown to 80% confluence prior to harvesting for DNA extraction. DNA was extracted using the DNeasy Blood and Tissue Kit and QIAcube (Qiagen Pty Ltd, Mulgrave, VIC, Australia).
Cell lines were authenticated by short tandem repeat profiling using the AMPFLSTR ® Identifiler ® PCR Amplification Kit (Applied Biosystems), a 16 loci (15 STR loci plus Amelogenin) STR multiplex kit performed by CellBank Australia (Westmead, New South Wales, Australia). Authentication profiles are available for OV202 and OV167 42 and OV207 (Supplementary Table S4).
Approximately 3 × 10 6 of the TP53 missense HGSOC cell line OVCAR-3, p53 null cell line SK-OV-3, TP53 wild-type line A2780 and TP53 nonsense mutation line OV167 were harvested, washed twice with PBS and centrifuged at 400 RCF for 3 minutes. Supernatant was discarded, cells resuspended in 100 μ l of 1% agarose (made up in PBS) and left at room temperature for 5 minutes for the agarose to set. Cell plugs were fixed using 10% formalin for 6 h before being dehydrated by changes in graded alcohol and xylene and embedded in a paraffin block. Cell lines underwent immunohistochemistry for p53 as described.
Western Immunoblotting and Gene Expression. Cells were lysed in RIPA buffer (Sigma-Aldrich, Castle Hill, NSW, Australia) containing Protease Inhibitor Cocktail (Sigma-Aldrich), phenylmethylsulfonyl fluoride (PMSF; Sigma-Aldrich) and the phosphate inhibitor cocktail PhosStop (Roche Diagnostics, Castle Hill, NSW, Australia). Thirty μ g of total protein was added to 4 × NuPAGE ® LDS Sample Buffer (Thermo Fisher Scientific, Waltham, MA, USA), followed by heating at 70 °C for 10 minutes. Cell lysates were electrophoresed on 4-12% Bis-Tris gels (Life Technologies, Thornton, NSW, Australia) at 180 V for one hour followed by transfer to a nitrocellulose membrane (Amersham ™ Protran ® Supported Western Blotting Membrane, GE Healthcare, Sigma-Aldrich) using a wet transfer system at 100 V for 1.5 hours (Bio-Rad Laboratories, Gladesville, NSW, Australia). Membranes were blocked with 5% skim milk for one hour at ambient temperature, and probed overnight at 4 °C with primary antibody (p53 [1C12] mouse mAb, Phospho-p53(Ser15) rabbit polyclonal antibody, or GAPDH [14C10] rabbit mAb; all from Cell Signaling Technology, Danvers, MA, USA). Membranes were probed with peroxidase labeled secondary antibodies (anti-rabbit IgG HRP-linked whole antibody or anti-mouse IgG-HRP, both from GE Healthcare, VWR International, Murarrie, Queensland, Australia) for one hour. Chemiluminescent signal was detected by SuperSignal ECL Dura reagent (Pierce, Rockford, IL, USA) and visualised using the Fujifilm LAS-4000 imaging system (Berthold Australia Pty. Ltd., Bundoora, VIC, Australia). Quantitation was undertaken using Multi Gauge 3.0 (Fujifilm Australia Pty. Ltd., Brookvale, NSW, Australia).
Total RNA was extracted from all cell lines using the miRNeasy Mini Kit (Qiagen, Doncaster, VIC, Australia) and automated using a QIAcube (Qiagen). 500 μ g RNA was reverse transcribed using Superscript III reverse transcriptase (Life Technologies). Quantitative real-time RT-PCR was performed in triplicate using a TaqMan Gene Expression Assay for p53 (Hs01034249_m1; Life Technologies) and an endogenous reference gene (hydroxymethylbilane synthase (HMBS); Hs00609297_m1, Life Technologies), using iTaq Universal Probes Supermix (Bio-Rad Laboratories) on a 7900HT Fast Real-Time PCR System (Applied Biosystems). Reagents were aliquoted using an epMotion 5070 robot (Eppendorf South Pacific Pty Ltd., North Ryde, NSW, Australia).
Scientific RepoRts | 6:26191 | DOI: 10.1038/srep26191 p53 Immunohistochemistry. Immunohistochemistry for p53 was performed on formalin fixed paraffin embedded tissue sections using a commercially available mouse monoclonal anti-human antibody (Protein Clone DO-7, cat. #M7001, Dako, CA, USA) at a dilution of 1 in 50. Staining was performed on a representative whole section using an automated staining platform -the Leica BOND-III ™ autostainer (Leica Biosystems, Mount Waverley, Victoria, Australia). Heat Induced Epitope Retrieval (HIER) was performed for 30 minutes at 97 °C in the manufacturer's acidic retrieval solution (ER1: VBS part no: AR9961). Stained slides were examined by an experienced surgical pathologist [AJG] who was blinded to molecular data. The percentage of cells showing positive nuclear staining was estimated and reported in three categories: ≥ 70% positively stained nuclei (High); >5% and < 70% stained nuclei (Intermediate); ≤ 5% positively stained nuclei (Low). Sequencing data was received in FASTQ file format. FASTQ files were aligned to the human genome (hg19) using Burrows-Wheeler Aligner (BWA) and variant calling was performed using the Genome Analysis Toolkit (GATK), according to GATK best practice (https://www.broadinstitute.org/gatk/guide/best-practices). Annotation of variant calls was performed using ANNOVAR, version 2013 Jul 43 , while visualisation of data was performed with the Broad Institute's Integrative Genomics Viewer (IGV, v2.3, ww.broadinstitute.org).
Each sample summary was imported into Excel and filtered to display TP53 exonic and splice mutations, excluding mutations identified in intronic regions. Filtering criteria were applied to remove reads with a quality (QUAL) score less than 100. Mutations were further filtered based on their frequency in the 1000 Genome Database 44 . If a particular variant occurred at a frequency greater than 10% in this database, the mutation was deemed to be non-deleterious and excluded from our analysis. Lastly, mutations were filtered based on SIFT scores, an in silico tool for predicting functional effects of a mutation on the associated protein 45 . Variants predicted to be tolerated were excluded. All remaining variants were considered deleterious or did not have a SIFT score and were visualized using IGV. The allele frequency of each mutation was recorded upon verification of the mutation via IGV. Our analysis pipeline is summarised in Supplementary Fig. S5.
Sanger Sequencing to validate TP53 mutations. All samples in which a TP53 mutation was identified by MPS underwent Sanger sequencing using the identical DNA. Primers and PCR conditions used were those recommended by the International Agency for Research on Cancer (IARC) TP53 database (http://p53. iarc.fr/; Supplementary Table S5). PCR products were purified using a DNA Clean & Concentrator ™ Kit (Zymo Research Corporation, Irvine, CA, USA) and commercial sequencing undertaken by the Australian Genome Research Facility (AGRF; Sydney, NSW, Australia). TP53 variants were annotated against the human TP53 reference genomic sequence NC_000017.10 using Sequencher DNA Sequence Analysis Software version 4.9 (Gene Codes Corporation, Ann Arbor, MI, USA). Statistical Analyses. Specificity and sensitivity of the association between p53 IHC score and the presence or absence of a TP53 mutation were analysed using the VassarStats Statistical Computation website (http://www. vassarstats.net/). Western blot and qRT-PCR data from analyses of ovarian cancer cell lines are expressed as the mean ± S.E.M. from at least three independent experiments. Statistical significance was determined by one-way ANOVA. Kaplan-Meier survival analyses compared by the Log Rank (Mantel-Cox) test were undertaken to determine whether the type or location of TP53 mutation, or p53 staining pattern (graded as 'High' , 'Intermediate' or 'Low') influenced overall survival in HGSOC. P < 0.05 was considered statistically significant. With the exception of specificity and sensitivity calculations above, all other data analyses were undertaken using IBM SPSS software version 21.0 (SPSS Australasia Pty Ltd., Chatswood, NSW, Australia).