Piscine orthoreovirus (PRV) is ubiquitous in farmed Atlantic salmon and sometimes associated with disease – most notably, Heart and Skeletal Muscle Inflammation (HSMI). However, PRV is also widespread in non-diseased fish, particularly in Pacific Canada, where few cases of severe heart inflammation have been documented. To better understand the mechanisms behind PRV-associated disease, this study investigated the infection dynamics of PRV from Pacific Canada and the potential for experimental passage of putatively associated heart inflammation in Pacific-adapted Mowi-McConnell Atlantic salmon. Regardless of the PRV source (fish with or without HSMI-like heart inflammation), infections led to high-load viremia that induced only minor focal heart inflammation without significant transcriptional induction of inflammatory cytokines. Repeated screening of PRV dsRNA/ssRNA along with histopathology and gene expression analysis of host blood and heart tissues identified three distinct phases of infection: (1) early systemic dissemination and replication without host recognition; (2) peak replication, erythrocyte inclusion body formation and load-dependent host recognition; (3) long-term, high-load viral persistence with limited replication or host recognition sometimes accompanied by minor heart inflammation. These findings contrast previous challenge trials with PRV from Norway that induced severe heart inflammation and indicate that strain and/or host specific factors are necessary to initiate PRV-associated disease. Salmo salar)1. This disease represents one of the most significant infectious diseases currently affecting Norwegian Atlantic salmon production2. Alternative PRV subtypes have also been identified in association with erythrocytic inclusion body syndrome (EIBS) in farmed Japanese Coho salmon (Oncorhynchuskisutchi)3 as well as with an HSMI-like disease in farmed Norwegian rainbow trout (Oncorhynchus mykiss)4; both conditions have been associated with high morbidity/mortality in some situations.Piscine orthoreovirus (PRV) infections of ocean-farmed salmon are widespread, and most farmed populations probably become infected at some point during a production cycle. These infections usually occur in the absence of overt disease; however, in some farming instances PRV has been associated with the development of serious disease syndromes. Specifically, a PRV subtype from Norway has been demonstrated to be the etiological agent of a disease known as Heart and Skeletal Muscle Inflammation (HSMI) of farmed Atlantic salmon . However, the condition is generally rare and not all fish diagnosed with these HSMI-like lesions were infected with PRV7 – a circumstance that has also been described previously9. Further, clinical presentation of disease as originally used in the case definition of HSMI as it occurs in Norway11 has never been reported in Pacific Canada.In contrast, PRV is also prevalent in many asymptomatic farmed salmon, particularly along the Pacific coast of North America, which often have high viral loads without associated lesions12. In Canada, comparable PRV exposure trials using both intra-peritoneal injection and cohabitation have resulted in systemic PRV loads that were similar (if not greater) than those achieved in Norway, but no heart or skeletal muscle inflammation occurred during a similar time frame in either Atlantic or Sockeye (Oncorhynchus nerka) salmon13. Thus, it is currently unclear whether the HSMI-like lesions observed in Atlantic salmon of Pacific Canada are indeed HSMI; i. e., initiated primarily as a result of PRV.A juxtaposition in virulence following PRV infection has also been highlighted in laboratory exposure trials. In Norway, exposure of Atlantic salmon to PRV by intra-peritoneal injection, cohabitation, and anal intubation have all resulted in moderate to severe heart lesions between 4–8 weeks post challenge14. However, the HSMI-like disease reported in PRV-infected Atlantic salmon from Pacific Canada supports an alternate hypothesis that at least one PRV variant from Pacific Canada may have caused severe heart inflammation in farmed Atlantic salmon of western North America8.One notable difference between Norwegian and Pacific Canada PRV exposure trials concerns the disease state of the donor fish from which PRV was sourced. Where Norwegian-based experiments have used PRV collected from Atlantic salmon with HSMI, Pacific Canada studies have used virus from non-diseased fish owing to the lack of regional availability for HSMI-diseased specimens. Thus, the previous inability of PRV from Pacific Canada to cause HSMI or any other disease symptoms in Atlantic salmon following challenge might be a result of genetic differences that have rendered at least some PRV variants of Pacific Canada less virulent. The fact that PRV sequenced from Pacific Canada to date appears phylogenetically distinct relative to PRV from Norway, which encompasses multiple amino-acid substations on various genomic segments, provides evidence in support of such a hypothesisIn this study, we compared PRV of Pacific Canada derived from two sources: (1) non-diseased fish and (2) fish with significant inflammation in the heart and skeletal muscle characteristic of HSMI. Our goal was to identify (i) if differences in virulence could be explained by genetic differences in the virus, and (ii) if, like in Norwegian studies, HSMI-like lesions as they have been recently diagnosed in Pacific Canada could be demonstrated as an infectious and transmissible disease within farmed Atlantic salmon. Further, we sought to improve upon the general understanding of PRV infection dynamics within farmed Atlantic salmon of Pacific Canada in the hope of identifying differences from Norway challenge trials that might help to explain the mechanisms for PRV virulence and pathogenesis.11. Currently, histolopathology is still used to diagnose clinical HSMI and also to differentially diagnose subclinical cases of HSMI, cardiomyopathy syndrome (CMS) and pancreas disease (PD) in farmed Atlantic salmon of Norway15. Although these diagnoses are made solely upon histological evaluations, it is generally accepted that the primary agent responsible for each disease is a unique virus: PRV is the primary agent responsible for HSMI1, piscine myocarditis virus (PMCV) is the primary agent responsible for CMS16, and salmon alpha virus (SAV) is the primary agent responsible for PD17. Indeed, although environmental and/or host contributing factors may explain the often exacerbated severity of HSMI in a field relative to laboratory setting, PRV appears to be the sole infectious agent associated with the unique set of histopathological criteria that defines HSMI in Norway18 and to our knowledge HSMI has never been used to classify a disease state in Norway where PRV has been confirmed to be absent.The original case definition of HSMI in Norway was founded on a set of clinical disease, gross pathology, and histological characteristics that could be differentially diagnosed from other common transmissible muscular disorders  using histopathology8. Although the presumed commonality for the heart and skeletal muscle lesions in these Canadian studies relative to HSMI diagnosed in Norway is the causation by PRV, there is far less evidence in Canada to support that PRV is indeed the key component for initiating this disease state. Particularly given that these modified definitions based on heart pathology alone have occasionally been observed in the absence of PRV7. Consequently, if HSMI diagnosis is based solely on histopathological heart lesions which can occur in the absence of PRV, then PRV cannot be assumed to be the causative agent of the disease, but rather one of multiple stand-alone or synergistic putative factors.Two recent studies from Pacific Canada have also used the term HSMI to classify subclinical heart disease of farmed Atlantic salmon based on histopathology in accordance with their own definitions that are similar to those previously reported in Norway – namely, moderate to severe heart inflammation sometimes accompanied by skeletal muscle inflammation15 – presence of cellular epicarditis; moderate-to-severe inflammation and necrosis especially in the ventricle (inflammation predominant); inflammation of the red skeletal muscle a supportive finding – for which PRV is the likely primary etiologic agent. We use the term HSMI-like for cases which fit the definition of HSMI as presented above but have questionable etiology, and use the term idiopathic cardiopathy in all other instances of heart associated pathology for which PRV may or may not be a contributing cause. In a laboratory setting where external factors are controlled, we adopt the modified diagnosis of HSMI previously used in assessing most experimental challenge trials in Norway; namely, histological visualization of moderate to severe heart inflammation that may or may not be accompanied by skeletal muscle inflammation but is clearly associated with a PRV infection, e.g., absent from the control population22.For this study, we reserve the designation of HSMI in field environments to cases as defined by Wiik-Nielsen and colleagues26. More recently, Fisheries and Oceans Canada Aquaculture Management Division conducts a regulatory Fish Health Auditing and Surveillance Program that from 2014–2017 sampled 2,960 moribund or recently deceased farmed Atlantic salmon for histopathology (https://open.canada.ca). These fish were collected during 407 audits of Atlantic salmon marine farms. Various types of idiopathic cardiopathy were diagnosed as a cause or marker of death in 62 (2.1%) of the sampled fish, and one of these fish  also had moderate skeletal muscle inflammation. Although some of the idiopathic cardiopathy observed in these samples (as well in samples from the early 1990s) might represent HSMI caused by PRV, a definitive diagnosis of HSMI is impossible to provide with any degree of certainty in these isolated occurrences.Idiopathic cardiopathy of a severity to cause morbidity or death (for which putative HSMI or HSMI-like lesions would be encompassed as a subset) is uncommon among British Columbia farmed Atlantic salmon and has historically been diagnosed in 1–3% of surveillance samples taken from moribund or recently deceased fish on farm sites since the early 1990sth, 2016, microscopic lesions consistent with the recent diagnosis of HSMI in Pacific Canada8 were identified during a pre-transfer government audit of an Atlantic salmon sea-pen cohort in the Johnstone Strait off the eastern coast of Vancouver Island. Of the 40 fish sampled, 11 (28%) had inflammatory heart lesions within the atrium and ventricle that were moderate (10 fish) to severe (1 fish); two fish (5%) also had moderate inflammation within the red skeletal muscle. Between July 29th and August 7th, 2016, 5 of 11 fish (45%) from the same farm were also diagnosed with moderate to severe heart inflammation characteristic of HSMI, although these samples did not have skeletal muscle inflammation. In a final sampling taken specifically for this study on August 19th, 2016, 3 of 20 fish (15%) had moderate to severe heart inflammation  and all 20 were qPCR positive for PRV L1 RNA with a mean load of 9.0e8 (±6.9e8 SEM) reverse transcribed copies per mL blood. The pathologists (HNS and GDM) who initially assessed these samples, although blinded to PRV status, reported that the lesions were similar to descriptions of HSMI as it is currently diagnosed in Norway, which was confirmed by a third pathologist  with long experience diagnosing HSMI in farmed Atlantic salmon in Norway. A summary for the histopathology scoring of heart and muscle tissues from all three pathologists was highly comparable . The first sampling and diagnosis of HSMI-like lesions was 2–3 months post seawater entry. At sea, and specific to the farm and period where the lesions were diagnosed, the site veterinarian reports that the population did not have clinical signs of disease. Total farm monthly mortality  was between 0.2–0.5%, which is below the industry average of ~0.83%, and feeding behavior was normal with mean growth rates in accordance with company-targeted projections.All fish on the affected site were progeny of North America-adapted Mowi-McConnell brood stockth described above (designated 16-011D), and (2) three non-diseased fish infected by experimental challenge with PRV originating from an alternate freshwater facility with at least a three year history of PRV presence without documentation of heart or muscle inflammation; this PRV had not generated HSMI in previous laboratory challenge trials28 .Naïve juvenile Atlantic salmon originating from the same brood stock and rearing facility as the fish with HSMI-like lesions described above were exposed in parallel under controlled laboratory conditions to PRV from one of two sources: (1) three fish with HSMI-like heart lesions collected August 196 for 16-005ND and 3.7e6 for 16-011D  between 10-15 wpc. In contrast, infection with PRV 16-011D was slower to develop, with peak loads not occurring until 5 wpc and to a fivefold lesser degree (mean quantity of 6.0e7 PRV L1 copies per ug RNA) than observed for PRV 16-005ND. Further, although the PRV 16-011D challenge generated large quantities of virus with persistent high-load systemic infections – 3.2e6 mean copies per µg RNA, representing >1.3e9 copies per mL blood between 10–15 wpc – this persistent load was only about 5% of PRV 16-005ND.Following intra-peritoneal (i.p.) injection of similar quantities of PRV, virus replication dynamics were slightly different for the two PRV inoculates. The total quantity of PRV L1 copies per injection was 2.0eRNA Fig. . This wade novo transcript assembly using Trinity29 which identified all 10 genomic segment of PRV for both isolates. The longest Trinity assembled PRV transcripts were deposited in Genbank; accession MH347359 – MH347378  and 98.9% (16-011D) concatenated coverage across all segments with respect to two previously published PRV genomes – one isolated from diseased Atlantic salmon in British Columbia during the first reported outbreak of HSMI in the region (isolate B5690)8, the other from HSMI diseased Atlantic salmon in Norway which was used to demonstrate laboratory passage of HSMI and the causal relationship of PRV in HSMI disease within Norway (NOR2012-V3621)1. This coverage incorporated 99.5% (16-005ND) and 100% (16-011D) of the protein coding sequence for these two published genomes where the only notable loss in coverage was the putative 128 bp (41 aa) missing 5′ end of the L1 segment for 16-005ND.RNA extracted and purified from the blood of both PRV 16-005ND and 16-011D challenged fish at 7 wpc (n = 2 per treatment) were subjected to next-generation RNA-sequencing (RNA-seq) to independently obtain the genomic sequences of PRV 16-005ND and 16-011D. Following rRNA depletion and Illumina sequencing, 27–40 million reads with Phred scores >33 were generated from the RNA of each fish and deposited in the NCBI Sequence Read Archive, study SRP145317. Individual SRA library accession numbers are provided in Fig. 378 Fig. . De novoGenomic sequences of 16-005ND and 16-011D have 99.96% nucleotide identity with only 10 nucleotide substitutions over the approximate 23 kb concatenated genome. Both isolates are also highly similar (>99.9% nucleotide identity) to the B5690 isolate previously identified in BC, but are comparatively divergent (~98.9% nucleotide identity) to the Norwegian V3621 isolate Fig. . This paAt 10 wpc, a portion of blood collected from three 16-005ND PRV infected fish was separated into plasma, leukocyte, and erythrocyte fractions. By qPCR screening, virtually all PRV L1 transcripts were associated with the erythrocyte fraction during this persistent phase of infection Fig. . Further30. To better understand the timing and degree of PRV protein production versus gRNA synthesis, we developed a new method to differentially quantify PRV single-stranded (mRNA) and double-stranded (gRNA) RNA using qPCR. Because recombinant reverse transcriptases such as MultiScribe™ selectively target ssRNAs during cDNA synthesis, we hypothesized that PRV dsRNA segments would not be detected by qPCR unless they were denatured into single-strands prior to reverse transcription of cDNA and that this would require temperatures above 90 °C based on sequence-specific melting point estimations. By this reasoning, exposure to low temperatures  for the removal of secondary structure prior to reverse transcription would result in only single-stranded PRV (mRNA) detection by qPCR. Experimental testing confirmed this hypothesis to be correct, as the dsRNA genomic component was only measureable in those samples denatured at 95 °C and not following exposure to either 55 or 80 °C  °C Fig. . Capital °C Fig. . Subsequ °C Fig. . This pa4 fold change over this time , peak (4 wpc) and persistent (10 wpc) phases of infection. This identified that relative quantities of all 10 segments of PRV 16-005ND were not significantly different at any of the three time points analyzed even though the total systemic PRV RNA load encompassed an approximate 10eime Fig. ; indicatime Fig. .Figure 5Selective ssRNA targeting techniques previously validated for L1 were also applied to each of the other 9 segments in this dataset. In a similar pattern to L1 transcription, the ssRNA component of all 10 PRV segments was relatively high (~10–90%) during early and peak infection but became reduced  following the transition to late-stage persistent infections Fig. . Neverth7 mean copies per µg RNA), were slightly less at 9 wpc, and stabilized at reduced but still substantial loads at 13, 17, 19 and 23 wpc (4.9e6 mean copies per µg RNA). Passage of virus to cohabitants introduced soon after the primary injection exposure (1wpc) provides evidence that natural shedding might be minimal in this early period of infection as PRV L1 RNA could not be detected in sentinel fish after 4 weeks of cohabitation. Even after 8 weeks of cohabitation, when 5 of the 6 sampled fish were positive for PRV L1 RNA, the systemic loads were still relatively low (<1e3 copies per µg RNA) in all but one fish  were injected with PRV 16-005ND from which viral passage was attempted by either cohabitation (1:1 shedder to naïve fish ratio) or by i.p. injection  to naïve recipients introduced at either 1, 9, or 15 weeks post challenge  systemic blood loads following PRV 16-005ND and 16-011D injection challenges, neither PRV 16-005ND nor 16-011D caused a notable reduction in hematocrit  Mx, an antiviral response element triggered by cellular recognition of interferon33. Both genes were transcriptionally down-regulated during early infection (1-2 wpc) in both PRV 16-005ND and 16-011D infected fish relative to time-matched controls; however, as PRV infections progressed, significant induction of both IFNa and Mx occurred in 16-005ND but not 16-011D challenged fish (approximately 4-5 fold) when virus reached at or near peak loads (3-6 wpc)  in either 16-005ND or 16-011D challenges relative to time-matched controls. Further, monitoring of IL-1β transcription, an important cytokine involved in the inflammatory process of vertebrate and non-vertebrate animals including salmon34, also did not demonstrate significant systemic induction in blood of either PRV 16-005ND or 16-011D infected fish at any time point following challenge  pc) Fig. . Neithernge Fig. . Rather,35 . In all instances, pathologists were blinded to PRV exposure status and lesions were scored according to severity  similar to methods applied in previous HSMI studiesEndocardial tissues provided a clearer association between PRV and heart inflammation, where both 16-005ND and 16-011D challenged fish had significantly greater median severity scores for lymphohistiocytic endocarditis compared to controls Fig. . This ma11. One fish sampled during this study at 8 wpc  had both moderate epicarditis and endocarditis within the ventricle  developed greater prevalence of heart inflammation than fish challenged with PRV 16-011D  Fig. . Howevercle Fig. . HoweverOrthoreovirus genus18. Although Orthoreoviruses are almost certainly the most well-studied within the Reoviridae family, there is considerable diversity in pathogenicity and disease association within the genus for which much is still unknown36. PRV adds yet further complexity by associating functional and genetic commonalities to both the Aquareoviruses and Orthoreoviruses while remaining phylogenetic and functionally distinct38.First identified in 2010, PRV represents the newest member assigned to the 39, and laboratory challenge trials have demonstrated a clear ability for PRV to cause severe heart lesions1. Indeed, laboratory challenges trials in Norway routinely generate severe heart lesions in accompaniment with occasional skeletal muscle lesions that are similar to lesions observed in diseased salmon on farms40.In Norway, most commercial Atlantic salmon become PRV positive, but only some develop HSMI. This does not appear to be dependent on systemic PRV load, and it is not clear why some farms experience high losses due to HSMI while others do not. Nevertheless, clinical outbreaks of HSMI in farmed Atlantic salmon of Norway are reasonably common5; yet, only rare subclinical cases of farm-level HSMI-like pathology have been reported and a clinical outbreak of HSMI as described in Norway11 has never been described. Here we diagnosed one case of an HSMI-like disease state from a pre-transfer government farm site audit, with up to five additional cases being identified between 2011 and 20138. Even if all isolated cases of idiopathic cardiopathy observed during these audits were presumed to be HSMI, it would constitute approximate 2% prevalence within dead and dying farmed Atlantic salmon of British Columbia. Because annual mortality among farmed Atlantic salmon in Pacific Canada is about 10–15%, HSMI-like lesions would thus (at most) be associated with only about 0.3% annual mortality across the industry. In a laboratory setting, PRV from Pacific Canada has failed to cause severe heart lesions or any severity of skeletal muscle inflammation despite establishing high-load blood infections13. Here we confirm these findings using PRV from two different commercial sources in Pacific Canada which similarly replicated to high loads following experimental infection.The results of our study expand our understanding of a strikingly divergent relationship regarding PRV and its association with disease in Pacific Canada. PRV also appears to be highly prevalent in farmed Atlantic salmon of Pacific Canada41 43, and it is possible that one or more of the three putative amino acid changes unique to the PRV isolated from non-diseased fish used in this study could be held responsible for an increase in virulence. However, as pointed out previously13, altered virulence associated with genetic viral variation is almost always accompanied by distinct tropisms and/or altered replication kinetics. For example, HPR0 variants of ISAV have altered tissue tropisms (gill specific rather than systemic)44 and low-virulent IPNV variants are correlated with reduced in vivo loads compared to high virulence stains45. Similar differential tropisms or replication kinetics do not appear to be evident when comparing PRV sourced from cohorts with (16-011D) and without (16-005ND) HSMI-like lesions in our study. Further, HSMI-like disease could not be passed from field-infected fish into a virtually identical cohort of Atlantic salmon held in a laboratory setting. We therefore hypothesize that at least in Pacific Canada, the ability for PRV to cause HSMI or any form of severe heart lesions cannot be attributed to a genetic variation of the virus alone.Small genomic changes can be enough to drastically alter the virulence of a virus, such as in the HPR0 variant within infectious salmon anemia virus (ISAV)It is nevertheless possible that the previously reported instances of HSMI-like disease in Pacific Canada, although sharing similar microscopic lesions, were not generated by the same mechanism(s) as in Norway. For example, perhaps PRV was contributing to and exacerbating HSMI-like disease in farmed Atlantic salmon of Pacific Canada that was initiated by alternate or synergistic factors. By this reasoning, any of the 27 putative amino acid changes observed in this study between the Canadian and Norwegian PRV variants could act alone or in concert to produce an altered state of virulence responsible for the rather striking prevalence discrepancy for HSMI in these two countries. It therefore becomes important to consider the phenotypic characteristics of PRV following analogous laboratory challenge trials conducted in Norway and Canada which may support this hypothesis.40. Second, there is a considerable difference in scale regarding host recognition of PRV. Although direct comparisons between Canadian and Norwegian studies are limited because Canadian studies have assessed gene expression relative to time-matched controls whereas Norwegian studies have referenced expression to time zero, it is nevertheless conspicuous that mean systemic and heart-specific antiviral responses increased no more than fivefold in our study whereas in Norwegian challenges these genes increased 10–50 fold in the blood46 and more than 100 fold in the heart21. The comparative lack of antiviral response to Pacific Canada PRV compared to Norwegian PRV is further supported by the relative protection PRV has afforded to fish challenged with a secondary virus (IHNV) in Norway47 but not in Pacific Canada28. Lastly, in addition to the discrepancies concerning the severity of heart inflammation, the timing of PRV associated heart inflammation is also different between challenges conducted with PRV from these two countries. Specifically, by either injection or cohabitation exposure of PRV, heart inflammation  in Norwegian studies consistently begins around the time of peak systemic PRV load, reaches high severity 1-2 weeks later, and thereafter diminishes20. In contrast, increased prevalence of heart inflammation in our challenge trials did not occur until approximately 4 weeks after peak PRV systemic loads were reached and maintained high prevalence  for the remaining 6-7 weeks of the trial.The phenotypic characteristics of both Canadian and Norwegian PRV during infection have at least three potentially significant dissimilarities Fig. . First, 48 and also has high genetic diversity between Norwegian and Canadian PRV14, it presents a likely candidate for further investigation as a virulence factor for PRV. However, the involvement of cytotoxic T-cells and a robust type-II interferon response generated in heart tissues of HSMI diseased Atlantic salmon in Norway21 almost certainly enhances or even causes the observed tissue damage. PRV from Pacific Canada also elicits a specific cytotoxic T-cell response in heart tissues of Pacific-adapted Mowi-McConnell Atlantic salmon, but to far less of a degree49. This presents the possibility that HSMI might result (or be enhanced) by a host-associated hypersensitivity heightened in the Norwegian-adapted Atlantic salmon farmed in Norway relative to the Pacific-adapted Mowi-McConnell strain farmed in Pacific Canada. This hypothesis is supported by the development of an HSMI resistant strain of Atlantic salmon in Norway that is somewhat resistant to disease but not PRV infection51. Ultimately we believe that both host and virus specific factors likely play a role in the development of HSMI, and further investigations are needed to pinpoint the mechanisms responsible for generating this disease.Taken together, these findings suggest that phenotypic differences in infection kinetics between PRV from Norway and Pacific Canada stem at least in part from genetic variation between the two regional variants. Although a specific virulence factor (or set of factors) remains unclear, we hypothesize that the increased prevalence of Norwegian PRV outside red blood cells  might help to explain the heightened recognition of PRV by Norwegian fish and that this recognition could contribute to the elevated inflammatory immune responses observed. Because the S1 segment is responsible for the spread of mammalian orthoreovirus52; and the recent detection of PRV in intestinal enterocytes7 indicates that a similar course of infection might be followed by PRV. Regardless, it is expected that this early replicative phase dictates the overall severity of infection53, as it likely accounts for how many erythrocytes ultimately become infected. This is supported by the discrepancy in total virus production following challenge with PRV 16-005ND compared to 16-011D in this study, where an initial lag in 16-011D replication appeared to be the major difference between otherwise identical replication dynamics within the two challenges. What caused  this poor primary infection rate following challenge with PRV 16-011D compared to 16-005ND is unclear; however, the lack of PRV transmission via fish cohabitation at this early stage of infection suggests that whatever cell type the virus is infecting during this period, it is not likely being shed into the environment to a high degree.The kinetics of North American PRV in Atlantic salmon as observed in this study indicates three distinct phases of infection: early, peak, and persistence. In the first (early) phase of infection which lasts 2-3 weeks at 10-12 °C, initial replication and dissemination of the virus into the blood cells of the host occurs. During this period, viral ‘factories’ (inclusions) were not observed in erythrocytes and there seemed to be no systemic recognition of virus by host cells. This indicates that the virus might be replicating by an alternative process relative to later time points or it might be initially infecting a different cell type. Mammalian orthoreoviruses first infect epithelial cells of the small intestine or lung prior to hematogenous dissemination54 as well as to those that develop during mammalian reovirus infection of well-established cell lines55. The highest systemic loads of PRV RNA occur during this period; which appeared sufficient in some instances to initiate mild systemic host recognition of virus. As host recognition was only observed following 16-005ND challenge and not following PRV 16-011D challenge in this study, we speculate that this recognition was load dependent since PRV 16-005ND reached higher transcriptional quantities during this period. From previous cohabitation challenges, it is also concluded that substantial shedding of virus occurs at this time13.In the second (peak) phase of infection that lasts 2-3 weeks  at 10-12 °C, substantial PRV replication within erythrocytes occurs along with the formation of cytoplasmic viral inclusions. The large spherical inclusions observed here were similar to those previously reported in Norway13. Nevertheless, infectious virus was still present in the blood of infected fish in this study for at least 15 wpc and it is presumed that the rather substantial quantities of RNA detected in blood at 63 wpc by Garver et al.13 also represented at least a moderate amount of infectious PRV particles.In the third (persistent) phase of infection, viral inclusions within erythrocytes disappear. No systemic host recognition of virus occurs, and a marked reduction in viral protein production (as measured by mRNA concentration) occurs even though large quantities of genomic PRV material remain associated with the erythrocyte cell fraction. This supports the hypothesis that infectious PRV is retained in the cytoplasm of infected erythrocytes but is in a reduced or non-replicative state. The ability to recapitulate infectious replication of PRV from this late stage of infection was readily accomplished by injecting lysed blood cell material into naïve fish which generated comparable temporal infection dynamics to virus that had been harvested during the peak phase of infection Fig. . However58. However, some studies have targeted alternate segments of the PRV genome for relative quantification. Specifically, Haatveit et al. identified differential temporal expression of PRV S1 compared to M2 and M3 during the persistent phase of infection in a Norwegian challenge trial, where the relative quantities of S1 were approximately 9 Ct less than M2 and M3 46. In our study, the relative quantity of all 10 genomic segments was comparable during all three phases of infection with less than fourfold proportional divergence between any two given segments  became significantly reduced during the final persistent phase of infection. This is in line with previous observations of λ1, μ1, σ1 and σ3 PRV protein production as noted in late stage infections of Atlantic salmon in Norway59. It is unknown as to whether antibodies were generated against PRV in our current study; however, given the high viral loads, relative ease for horizontal transmission, and mild but significant innate antiviral recognition observed during peak PRV infections in this study, we speculate that at least some PRV specific antibodies were produced. If so, there may be future potential in exploring the avirulent characteristics of the Pacific Canada PRV used here to potentially protect against Norwegian isolates of PRV that have been associated with causing HSMI, particularly since both formalin killed PRV and DNA plasimids expressing PRV µNS have demonstrated at least partial protection against HSMI in Norway60.One important aspect of the host immune response to PRV that was not addressed in this study was the putative development of antibodies. In Norway, host Atlantic salmon have been demonstrated to generate detectable antibodies specific to PRV µ1c and µNS in the plasma that began approximately two weeks after peak systemic PRV loads were reached and were maintained at detectable levels until the end of the study one month laterIn conclusion, although we were able to identify an HSMI-like disease state in farmed Atlantic salmon of Pacific Canada, this regionally rare condition could not be effectively transmitted via injection of PRV infected blood material into naïve fish as has been accomplished in Norway. This study also revealed genotypic and phenotypic differences between PRV from Pacific Canada compared to what has been reported from PRV challenge trials in Norway. These differences suggest that virus and/or host specific factors are likely needed for the development of HSMI in farmed Atlantic salmon and that currently PRV infections of Atlantic salmon in Pacific Canada are of low virulence. Further research is needed to determine the potential cause or causes for HSMI-like lesions in Pacific Canada. Also, because interactions between Atlantic salmon, PRV, and the environmental conditions of Pacific Canada do not appear conducive to HSMI development, the low virulence associated with Pacific Canada PRV provides a useful model for comparative studies to investigate the requirements for initiating PRV-associated disease and exploring possible protection mechanisms against HSMI.27. Once at PBS, fish were maintained in UV treated municipal freshwater (10° ± 1 °C) for 3 months prior to smoltification to undiluted sand-filtered UV-irradiated seawater . A natural photoperiod was used during culture and fish were fed dry pellets (EWOS) at 1-2% body weight per day prior to challenge. This cohort was used as a source for PRV negative control inoculum, provided the naïve recipients for the primary PRV 16-005ND and 16-011D i.p. injection challenges, and also provided the naïve recipients for the subsequent i.p. and cohabitation viral passage experiments using PRV 16-005ND. During all challenge trials, fish were maintained on undiluted UV-irradiated seawater  and fed a ration of EWOS pellets at 1% body weight per day.Atlantic salmon for the challenge studies were sourced from a single commercial freshwater hatchery on Vancouver Island, British Columbia and brought to the Pacific Biologic Station (PBS) in Nanaimo, British Columbia, Canada. Pre-transport screening of 20 fish via qPCR was negative for PRV. Tissue homogenates from 90 fish in the hatchery were negative for culturable agents; no cytopathic effect was observed on CHSE or EPC cell lines prior to transport. Fish were of a Pacific-adapted Mowi-McConnell strain of Atlantic salmon with at least 30 years isolation from the originating European stocks28. In summary, total RNA was extracted from 100 μL blood, 100 μL plasma, or ~50 mg heart tissue in TRIzol Reagent (Life Technologies) as per manufacturer’s instructions using 5 mm steel beads and TissueLyser II (Qiagen) which operated for 2 min at 25 Hz. A portion of eluted RNA (1.0 μg) was denatured for 5 min at 95 °C, immediately cooled to 4 °C, and reverse-transcribed using a High Capacity cDNA Reverse Transcription kit (Life Technologies) following the manufacturer’s instructions. Resulting cDNA was used directly as template for qPCR analysis in a StepOne-Plus real-time detection system (Applied Biosystems) using primers and TaqMan probe targeting the L1 fragment of the PRV genome28. Each reaction contained 400 nM primers and 300 nM TaqMan probe, 1X TaqMan Universal Master Mix and 1 μL cDNA template within each 15 μL reaction. Cycling conditions included an initial incubation of 95 °C for 10 min followed by 40 cycles of 95 °C for 10 s and 60 °C for 20 s. Samples were assayed in duplicate and were considered positive if both technical replicates reported a Ct value < 40 cycles. Absolute PRV quantification was determined in each instance by serial dilution of a 482 bp double-stranded DNA gBLOCK fragment (Integrated DNA Technologies) consisting of sequence targeted by the qPCR primer and probe13. A seven-step 10-fold dilution series of the gBLOCK fragment spanning a dynamic range of 10–107 target copies per reaction was incorporated in duplicate into each run. The limit for accurate quantitative assessment (< 20% CV) using this technique was calculated to be between 10–50 copies with a limit of detection (at >90% prevelence between replicates) of 1–3 copies per reaction determined as previously described61.PRV nucleic acid was detected from blood, heart and plasma samples by real-time qPCR with slight modification to previously described methods62 within the Geneious 9.1.7 software platform63 to homogenous protein-coding regions within four published PRV genomes previously identified from Pacific Canada: VT06062012-35841; BCJ19943_1314; and WSKFH12_1414 (Supplement ® green PCR master mix (ThermoFisher) in molecular grade water. Reactions were analyzed in a StepOne-Plus real-time qPCR detection system (Applied Biosystems) with an initial polymerase activation at 95 °C for 10 min followed by 40 cycles 5 s at 95 °C, 20 s at 60 °C, and 10 s at 72 °C with fluorescence measured at the end of the 72 °C step. Melt curve analyses were performed to ensure amplification specificity and a five-step fourfold dilution series of cDNA prepared from the blood of a highly infected individual  was performed in duplicate on each run to estimate relative quantity and amplification efficiency.Primers specific to each of the 10 PRV genome segments were designed using Primer364. Following incubation at 25 °C for 45 min, RNA (if remaining) was recovered using RNeasy MinElute Cleanup Kit (Qiagen) as per manufacturer’s instructions. Recovered RNA was heated to either 55 °C for 5 min, 80 °C for 1 min, or 95 °C for 5 min, immediately cooled to 4 °C and reverse transcribed using a High Capacity cDNA Reverse Transcription kit (Life Technologies) following the manufacturer’s guidlines. Resulting cDNA was used directly as template for PCR analysis in a StepOne-Plus detection system with 500 nM PRV L1 (this study) or Atlantic salmon β-actin13 forward and reverse primers and 1X Power SYBR® Green PCR master mix (ThermoFisher). Cycling conditions were performed as described above but were ended after 30-cycles prior to fluorescence saturation in samples for which product was amplified. A portion (5 µL) of product was then visualized by UV excitation on a 3% agarose Tris-borate-EDTA (TBE) gel containing 0.5x SYBR® Safe DNA stain after 45 min migration at 120 volts in 1X TBE running buffer (ThermoFisher).All TaqMan or SYBR qPCR analyses designed to exclusively amplify the ssRNA and not dsRNA component of the targeted PRV segment were performed as described above with the exception that RNA was heated to 80 °C for 1 min rather than 95 °C for 5 min prior to cDNA synthesis. Total RNA from PRV 16-005ND infected fish at 10 wpc was used to validate this differential detection of PRV ssRNA by exposing extracted RNA (2 µg) to either no enzyme, 2 U Pure Link™ RNAse A (ThermoFisher Scientific), or 2 U RNAse A in the presence of 0.5 M sodium chloride which selectively protects dsRNA but not ssRNA from RNAse A degradation65 and ensured to have a Bioanalyzer (Agilent) RNA Integrity Number (RIN) > 9. RNA sequencing was performed using half of one lane (8 libraries per lane) of an Illumina® HiSeq 2500 (Illumina Inc.) platform using a NEBNext® rRNA Depletion Kit (Human/Mous/Rat) with read lengths of 125 bp. Base calls were made using the Illumina CASAVA pipeline encoded in Phred 33. The two libraries generated for both 16-005ND and 16-011D challenges were pooled and de novo transcript assembly was performed on combined reads (2 libraries per assembly) following the pipeline described by Haas et al.66 based on the Trinity assembly software suite29. In brief, reads were trimmed using Trimmomatic software67 from the 3′ end with a minimal Phred score of 30 and a minimum length of 32 bp. A normalized metric of reads was generated using Trinity normalization utility and surviving paired reads were assembled using the Trinity assembler66. Putative assembled transcripts were aligned against the NCBI Viral Genomes Resource database68 using the blastn program from the NCBI BLAST family. Transcripts which aligned to PRV sequences with an Expect value  less than e−100 were aligned by segment using Geneious 9.1.7 to report the consensus sequence for the longest positive-sense genomic strand in each instance.Library construction, sequencing services and bio-informatics support was provided by the Canadian Centre for Computational Genomics and Génome Québec Innovation Centre, Montréal, Canada. RNA extracted from the blood of four fish were selected for library construction and RNA-seq analysis – two from fish challenged with PRV 16-005ND  and two from fish challenged with PRV 16-011D (sample numbers 167 and 171) collected at 7 wpc Fig. . A porti5. On July 5th, 2016, an audit of 40 fish  was conducted at a net-pen farm site in the Johnstone Strait of BC, for which skeletal muscle and heart tissues were collected for histology. The site had 12 operational pens; each containing between 30–50 thousand fish per pen with fish weighing approximately 500 g each. On July 29th and August 7th, 2016, samples of heart and skeletal muscle from 5 and 6 moribund/dead fish, respectively, were also collected for histology by a private veterinarian working for the source farm.Moribund or recently deceased net-pen farmed Atlantic salmon were collected as part of a pretransfer fish health audit conducted by the Fisheries and Oceans Canada, Aquaculture Management Division. With relevance to this study, skeletal muscle and heart tissues were preserved in 10% neutral buffered formalin and processed as previously describedth, 2016 by MPP and KAG, in which 20 fish were sampled specifically for this study – six fish were moribund/fresh dead, eight were from the general population , and six were non-performers of low body condition. Blood (0.5–3 mL) was collected from the caudle vein of each fish using 22 gauge needle and 3 mL syringe. A 100 µL subsample was immediately frozen in liquid nitrogen and used for PRV L1 TaqMan qPCR screening as described above. Remaining blood was divided into 1 mL aliquots and stored at −80 °C for use in generating challenge inoculating material described below. Heart and skeletal muscle from each fish was preserved in 10% neutral buffered formalin for histopathologic evaluation.A final sampling was conducted August 1928. PRV infected blood of hatchery fish (~25 g) which had been frozen at −80 °C prior to use was passed through ~30 g British Columbia Mowi-McConnell Atlantic salmon held in brackish water (15ppt) for three weeks at 11 °C, passed again through ~50 g Atlantic salmon held in seawater (32 ppt) for three weeks at 11 °C, and passed a third time in ~55 g Atlantic salmon held in seawater for four weeks at 11 °C prior to final collection. In each instance, blood from three infected fish was pooled, diluted 1:10 in Hank’s balanced salt solution (HBSS), sonicated on ice for 80 s in 10 s bursts with 30 s rests using a Branson Digital Sonifier 250/450 at 20% amplitude, clarified via centrifugation at 2,000 × g for 5 min at 4 °C, and administered to naïve fish by 100 µL intra-peritoneal injection. Following the third passage, blood from three fish was pooled and inoculate prepared as for previous passages.PRV 16-005ND was initially sourced from a cohort of healthy Atlantic salmon in March of 2016 held at a commercial freshwater Atlantic salmon rearing facility on Vancouver Island, Canada. The facility had no history of HSMI and PRV material collected from this site in 2013 had failed to generate HSMI in previous laboratory challenge trialsth, 2016  collected at the net-pen farm site in which fish had HSMI-like lesions on August 19Atlantic salmon (~70 g each) were anesthetized in an aqueous solution of Tricaine methanesulfonate (0.05 g/L) and given a 200 µl intra-peritoneal injection of either PRV 16-005ND inoculum, PRV 16-011D inoculum, or PRV-free vehicular control inoculum (90 fish per treatment). Fish were placed in treatment-specific 850 L circular tanks (one tank per inoculum) supplied with 30 L/min 11 °C (±1 °C) UV-irradiated seawater (32 ppt). Temperature, dissolved oxygen, feeding performance, and morbidity/mortality were monitored daily throughout the challenge trials  was transferred to a clean 2 mL microtube and immediately frozen at −80 °C prior to PRV qPCR screening as described above. An approximate 200 mg section of red/white skeletal muscle was excised from the left lateral line at approximately the mid-body and preserved in 10% neutral buffered formalin for histopathology. Hearts were bisected longitudinally and one half preserved in 10% neutral buffered formalin for histopathology while the other half was immediately frozen in liquid nitrogen for qPCR analyses. Tissues in 10% NBF were fixed for 24–48 hours, transferred to 70% isopropanol and paraffin embedded following standard methods. Sections 3 µm thick were transferred to glass slides and stained routinely with haematoxylin and eosin for light microscopy5. Photomicrographs were optimized for illumination and color balance69. To ensure inter-sample consistency in lesion scoring as well as consistency relative to published PRV studies from Norway, approximately 10% of slides (29/338) were examined by both principal pathologists (GDM and HNS) and a subset of 60 slides was also sent to a third reviewing pathologist (RJ) in Norway for further examination (Supplement Fish were anesthetized in an aqueous solution of Tricaine methanesulfonate (0.05 g/L), and blood and tissue samples were collected from six fish per treatment tank at 7 day intervals through 15 wpc. Blood (~2 mL) was collected using a 22 ga needle and 3 mL syringe. A 100 µL aliquot was immediately frozen in liquid nitrogen and subsequently used for PRV screening and gene expression analysis by qPCR as described above. Approximately 10 µL was transferred to a sodium-heparin treated Fisherbrand™ micro-hematocrit tube and spun at 15,000 × g for 10 min for hematocrit determination. A second 10 µL was smeared on a glass microscope for cytoplasmic inclusion body visualization. Slides were air dried, fixed in 100% methanol for 5 min, and stained with pinacyanol chloride7 cells per 100 µL HBSS which was used for PRV qPCR screening.At 10 wpc PRV 16-005ND, blood of three fish was separated into plasma, leukocyte, and erythrocyte components. Plasma was collected following centrifugation of the heparinized blood at 2,000 × g for 5 min at 4 °C. The cell pellet was suspended to an original blood volume using HBSS and layered over a 34%/51% isotonic percoll discontinuous gradient and centrifuged at 800 × g for 20 min at 4 °C which was allowed to come to rest without the use of the centrifuge breaking system. Peripheral blood leukocytes were harvested from the 34%/51% interphase and erythrocytes from the pelleted material below the 51% layer. Cells were washed twice with HBSS, verified for >99% purity via hemocytometer, and suspended at a final concentration of 10ePRV 16-005ND inoculate not used in the injection challenge above was thawed and administered to three sets of 15 fish (~150 g per fish) by intra-peritoneal injection, 200 µL per injection, under MS-222 anesthesia. Fish were cultured in 850 L tanks as above with the exception that the volume of 11 °C seawater was reduced to 250 L and supplied with a flow of 15 L per min. After a period of 1, 9, or 15 wpc, an equal number (n = 15) naïve cohabitants (with clipped adipose fins) were introduced to each to the three tanks. Blood samples were collected from six of both injected (shedder) and introduced (sentinel) fish after 4 and 8 weeks of cohabitation and screened for PRV L1 transcripts as described above.Trinity assembled PRV segments were concatenated and compared to two previously published PRV genomes (B5690 and V3621) by Jukes-Cantor phylogenetic relationship analysis using Geneious software. The relative quantities  of PRV L1 transcripts following differential pre-amplification denaturation were compared at 2, 4, and 10 wpc for both PRV 16-005ND and 16-011D challenged groups by one-way ANOVA and Tukey post-test of log-transformed data. The proportional change of PRV ssRNA  was as assessed over time in both PRV 16-005ND and 16-011D challenged groups by one-way ANOVA and Dunnett’s multiple comparison post-test of arcsin-transformed values. The relative quantity  of each PRV 16-005ND RNA segment as well as the single-stranded mRNA proportion of each segment was assessed at 2, 4, and 10 wpc by two-way ANOVA with Bonferroni post-tests following log-(relative quantity) and arcsin- transformation. The contributing proportion of total PRV RNA and total single-stranded mRNA by each segment were considered independent and dependent to time, respectively, by two-way ANOVA. Thus, total PRV segment expression was pooled from 2, 4, and 10 wpc and compared by one-way ANOVA and Tukey’s post-hoc test of arcsin transformed values and single-stranded mRNA proportional expression was compared separately at each time point by one-way ANOVA with Dunnett’s multiple comparison post-test relative to L1. The trend in hematocrit of both PRV 16-005ND and 16-011D challenged fish were compared to controls by two-way ANOVA with Bonferroni post-tests of arcsine transformed values. This was similarly applied to comparing the quantity of erythrocyte cytoplasmic inclusion bodies observed in PRV 16-005ND versus 16-011D infected fish. All gene expression data was normalized to β-actin transcription. Normalized quantities were scaled to the minimum value for each gene prior to analysis of log-transformed data by two-way ANOVA with Bonferroni post-tests. Heart and skeletal muscle histopathology inflammation scores for PRV 16-005ND and 16-011D infected fish estimated at each time point throughout challenge was compared to control fish by Mann Whitney U tests without multiple comparison adjustment.All work with animals was performed in strict accordance with the recommendations set out by the Canadian Council on Animal Care (CCAC) guide to the care and use of experimental animals and all live animal protocols were approved by the Pacific region animal care committee . All fish handling was performed under Aquacalm™ (Syndel Laboratories Ltd.) or tricaine methanesulfonate (MS222) anesthesia.Dataset 1


We adapted our standard collagen extraction protocol specifically for <100 mg bone material. Collagen was extracted at least twice  from each bone. Collagen aliquots containing <100 μg carbon were measured in replicate using the gas ion source of the AixMICADAS. The effect of sample size reduction in the EA-GIS-AMS system was explored by measuring 14C of collagen containing either ca. 30 μg carbon or ca. 90 μg carbon. The gas dates were compared to standard-sized graphite dates extracted from large amounts (500–700 mg) of bone material pretreated with our standard protocol. The results reported here demonstrate that we are able to reproduce accurate radiocarbon dates from <100 mg archaeological bone material back to 40,000 BP.Radiocarbon dating archaeological bone typically requires 300–1000 mg material using standard protocols. We report the results of reducing sample size at both the pretreatment and  Bone is one of the most frequently radiocarbon-dated materials recovered from archaeological sites. However, many precious archaeological bones, such as human remains or Palaeolithic bone tools, are too small or valuable for extensive destructive sampling. The reduction of sample size to enable direct dating of precious bone is therefore a key concern for the archaeological community.2. The development and utilisation of Accelerator Mass Spectrometers (AMS) in the 1980s represented a revolutionary step in the reduction of sample size and time required for dating3. Routine measurements today typically require 500–1000 micrograms of carbon (μg C) to produce a high precision date. In recent years, several AMS labs have worked on modifications to the graphitisation and AMS measurement process for smaller samples containing <500 μg C13. However, the graphitisation of small sample sizes is often time consuming and can be prone to large contamination effects15. A recent study by Cersoy, et al.16 demonstrated that graphite targets containing ca. 200 μg C from archaeological bones can be successfully produced and measured using the IonPlus Automated Graphitisation Equipment III (AGE 3)17 and MIni CArbon DAting System (MICADAS)19 developed at ETH Zurich. However, the hybrid nature of the MICADAS system offers an alternative solution to the complex process of graphitising small samples. Organic samples containing <100 μg C can be placed into an elemental analyser (EA) directly coupled to the gas ion source of the MICADAS via the gas interface system (GIS)24. The automated system reduces both sample preparation time and the risk of contamination through handling, and has been successfully utilised in environmental and climatic applications28. In our preliminary study29 we demonstrated that the gas ion source of the AixMICADAS30 is suitable for dating bone collagen CO2 samples of <100 μg C back to 35,000 BP .In the 1960s and 1970s, gas proportional counters required many grams of bone to produce a radiocarbon date14C. For example, in a bone extract ca. 40,000 BP, 1% modern carbon contamination would skew the resulting 14C age by over 7,000 years.However, as sample size is reduced the effect of contamination during pretreatment and measurement increases greatly. Sample pretreatment involves the extraction and purification of carbon endogenous to the original bone. Any contamination remaining in the sample at the time of dating can lead to erroneous results. The effects become increasingly catastrophic with the increasing age of the sample due to the minute concentrations of residual 14C measurement, generally referred to as ‘collagen’31. Although collagen forms around 22% weight of modern bone, degradation following death and burial makes collagen extraction increasingly challenging with advancing age32. Whilst the minimum threshold for reliable 14C dating is generally considered to be 1%32, it is common for the collagen portion of Palaeolithic bone to constitute <10% weight. The lower the level of collagen preservation, the more bone must be pretreated to obtain sufficient material to assess the quality of the extract  and for 14C dating. Therefore, 300–1000 mg material is commonly sampled for dating Palaeolithic bones.It is standard practice to extract the proteinaceous portion of bone for 14C labs follow collagen extraction protocols based on Longin33. This involves demineralisation of either powdered bone or bone chunks using hydrochloric acid (HCl) followed by gelatinisation of the collagen in weakly acidic water and freeze-drying of the final extract. Different labs vary in the strength of reagents used, the duration of treatments and the inclusion of further decontamination steps. Many studies have been published comparing the collagen yields and isotopic values of the various extraction protocols published in the literature38 as variations in pretreatment conditions can lead to differences in the quantity and quality of the final extracts. The addition of an ultrafiltration step, first proposed by Brown, et al.39 has in particular improved the accuracy of 14C dating of Palaeolithic bones40; gelatinised samples are filtered to concentrate large (>30 kDa) molecules to produce a ‘cleaner’ collagen extract. The technique is not unanimously agreed upon due to the risk of contamination from the humectant-coated filter41, the effectiveness of the application37 and the loss of collagen during filtration34. However, stringent cleaning steps have been established44 and in many cases the re-dating of ancient bones with ultrafiltration methods has produced much older dates than previous measurements from non-ultrafiltered extracts46. The collagen pretreatment protocol routinely applied to Palaeolithic bone at the Max Planck Institute for Evolutionary Anthropology  is based on a modified Longin plus ultrafiltration protocol36 and has a strong track record of obtaining high yields of high quality collagen from ca. 500 mg samples of Palaeolithic bone47.The majority of 30 at CEREGE  is suitable for measuring small archaeological bone samples with sufficient accuracy and precision. We investigated the effect of sample size reduction at both the pretreatment and gas measurement stages. Tests were performed on a set of eight archaeological bones ranging from 1% to >10% collagen preservation known to date from >50,000–1,400 BP. Each bone was pretreated multiple times from starting weights of 37–100 mg bone material. Each collagen extract was split and dated multiple times with the gas ion source of the AixMICADAS to test replicability. The gas dates were compared with graphite dates from collagen extracted from >500 mg material of the same bones. We further compared gas dates of ca. 30 μg C and ca. 90 μg C to explore the effect of sample size on the blank level of the EA-GIS-AMS system. The results demonstrate our ability to obtain accurate and moderately precise radiocarbon dates from <100 μg C extracted from 37–100 mg bone material back to 40,000 BP. The methods described will be used to extract and 14C date collagen from precious archaeological bone artefacts with minimal sample destruction.The aim of this study was to determine a suitable method to pretreat <100 mg bone material and further investigate if the gas ion source of the AixMICADAS36. The extracts were analysed by EA-IRMS at the MPI-EVA to assess their suitability for dating  and were measured at the Klaus-Tschira-AMS lab in Mannheim, Germany (lab code: MAMS). The same collagen extracts from R-EVA 1489, R-EVA 123 and R-EVA 124 were also dated at the AixMICADAS facility to cross-check the ages29. The results were used as a reference for the preparation of small (<100 mg) aliquots of bone.Prior to this study, 500 to 700 mg of each bone had been pretreated using our standard collagen extraction protocolModifications to our standard pretreatment protocol were carried out for five bones Fig. : three rca. 500 mg material) as a solid piece. Although this method requires a large time investment , we observe much higher collagen yields using this technique compared to powdered extracts of equal starting weight. Small aliquots (<100 mg) of the test bones were initially pretreated as both fine powder and as solid chunks. For solid pieces of bone, in most cases the collagen yield from small extracts (<100 mg) equalled or exceeded the collagen yields of large extracts  and no difference was observed between aliquots of 50 mg bone compared to 70 mg or 100 mg bone material  were used to evaluate the extracts from the different methods. In addition, Fourier Transform Infrared Spectroscopy (FTIR) was used to double check the preservation of the extracted collagen, and to detect the presence of possible carbon contaminants49. Detailed pretreatment information for all extracts can be seen in Supplementary Dataset We initially applied our standard collagen extraction protocol to <100 mg bone material of the well-preserved bones. Three steps of the pretreatment protocol were then modified to see what effect this had on the collagen yield and quality of extracts from small bone aliquots , the collagen yield was higher from aliquots which were removed from the heater block as soon as solubilisation had occurred compared to those left on the heater block for 20 h as per our standard protocol for >500 mg. For all bone samples >30,000 BP, solubilisation occurred in <6 h Fig. , whereas14C dating  is shown in Fig. Of the extracts dated, two (R-EVA 548.13 and R-EVA 548.14) fell close to or under the minimum threshold (1%) for reliable ca. 30–40 μg C, run for the duration of one titanium (Ti) target (ca. 12 minutes) and for each bone >20,000 BP, a single aliquot containing ca. 80–90 μg C was measured over the duration of three targets to increase precision 51 were performed using the R_Combine feature in OxCal 4.252 using the F14C and associated error for gas replicates of each collagen extract individually and for all replicates per bone. The replicate measurements are statistically indistinguishable for R-EVA 1489, R-EVA 1905, R-EVA 1860, R-EVA 123, R-EVA 570 and R-EVA 124  were much younger than the other extracts of this bone  and type (solid/powder) measured during the same session.For background bone R-EVA 1753 , the dates from the collagen extracts  bone samples. Decreasing sample size from one Fig.  indicate54. The higher collagen yields from these extracts supports observations that gelatinised collagen is degraded by prolonged exposure to higher temperatures and acidity53.The main alteration to our standard protocol involved reduction in the duration of the gelatinisation stage, with samples removed from the heater block as soon as they had gelatinised see Fig. . Differeca. 39,400 BP), even working with larger sample sizes. The harshest demineralisation  applied to small aliquots of this bone  resulted in very low yields of ≤1 mg collagen, likely due to the solubilisation of collagen during the longer demineralisation stage. The resultant underestimated dates clearly show that these aliquots were massively affected by modern carbon contamination. Prior to dating, the consideration of the quality of the extract is crucial in order to obtain reliable dates. Given the low yield of collagen (≤1%) following pretreatment, under normal circumstances these extracts would not been dated or would have been treated with caution. This bone demonstrates the difficulty of pretreatment of poorly preserved bones at the limit of the 14C method.R-EVA 548 represents a very challenging prospect for collagen extraction and radiocarbon dating due to the exceptionally low levels of preservation (<1% weight collagen) and old age (ca. 30 µg C or ca. 90 µg C) prepared alongside every batch of samples and measured during the same measurement session to account for any C added during sample preparation and measurement. Figure ca. 25–40 μg C  and ca. 80–100 μg C (large). The large measurements  are on par with the system blank  measurements of equal size   are likewise equal to the system blank measurements of equal size   and to phthalic anhydride blanks measured at ETH Zurich reported in McIntyre, et al.24 . The results indicate the lower limit of 14C detection with the gas ion source to be around F14C = 0.004. As demonstrated by R-EVA 124, beyond this limit the minute levels of 14C can be measured but the uncertainty of the background correction dominates accuracy and precision.At such small sample sizes, the consideration of the background correction is crucial. The gas measurements of R-EVA 1489, R-EVA 1905, R-EVA 1860, R-EVA 123, R-EVA 570, R-EVA 548 and R-EVA 124 were all corrected with gas measurements of background bone collagen (R-EVA 1753) of equal size (24. The mass (Mc) and F14C (F14Cc) of the constant contamination of the EA + GIS system was deduced by least square regression of modern carbonate and blanks (IAEA-C1) with sample weights ranging between 3 and 100 µg C to be Mc = 0.55 ± 0.05 ug C and F14Cc = 0.12 ± 0.0355. The silver cups  had a consistent carbon contribution of 0.049 ± 0.02 µg C. The zeolite trap was heated (450 °C) and the system was flushed with helium between samples to minimize cross-contamination. However, small amounts of C may reside in the zeolite trap after flushing which has been demonstrated to have a large influence on samples <20 µg carbon55. With this in mind, even our ‘small’ samples were kept >20 μg carbon. To further alleviate problems of cross-talk, samples were run in order of increasing activity (oldest to youngest) according to the standard practice55. Background corrections of samples were applied according to sample size and an external error was added during the age calculation of all samples based on the long term standard deviation of standards and blanks .The system blank of the EA-GIS-AMS is affected by the carbon content of the silver cups, cross-talk of the zeolite trap and the cleanliness of the ion source at the time of the measurementca. 30 µg C with larger aliquots containing ca. 90 µg C. When taking the weighted mean and error of the three small aliquots the precision achieved is higher compared to the single large measurement of a roughly equal amount of carbon. However, as the likelihood of contamination being introduced via handling, the EA-GIS or the silver cup is increased for the smaller sample sizes, the preferred method for measuring larger samples would be to measure several targets from a single syringe, rather than splitting a sample into smaller aliquots. Although the measurement of gas samples requires more supervision than graphite targets, the direct coupling of the EA with the GIS significantly reduces sample preparation time by cutting out the graphitisation step which poses a large risk of contamination at such small sample sizes. Therefore in situations where sample size is limited the gas ion source offers an attractive solution for archaeological, as well as environmental, applications.In a real life situation, if a small bone sample yielded a high amount of collagen , dating with graphite targets would be preferentially undertaken as the precision achieved is much higher and measurements can be made routinely. However, the results of this study demonstrate that the gas ion source can produce an accurate radiocarbon date at low precision from as little as 30 µg C. The precision of the date can be improved when larger sample sizes (up to 100 µg C) are available for measurement over several targets . Bones of high patrimonial value could be sampled strategically – i.e. for older samples expected to have less than 10% collagen preservation 40 mg bone material could be sampled, whereas for well-preserved Holocene bone much smaller samples could be taken. The case of R-EVA 548 demonstrates that for very old samples  with very poor levels of preservation (1–2%), yields falling below 1 mg collagen can be subject to severe contamination issues.Even working with the assumption of 1% collagen preservation, in theory sufficient collagen could be extracted from less than 10 mg bone material to obtain a 14C measurements using the gas ion source of the MICADAS are stable, reproducible and accurate, reaching a level of precision suitable for dating archaeological samples particularly for Palaeolithic samples back to 40,000 BP. In this respect this technique will be highly useful for directly dating precious archaeological bone where limited material is available.The results presented here provide further confirmation that 14C timescale  at a range of preservation typical for archaeological bones. Collagen extracts from bones R-EVA 124, R-EVA 123 and R-EVA 1489 were previously dated using both graphite targets and the gas ion source in Fewlass, et al.29. R-EVA 124 was previously labelled as a bison bone but recent aDNA analysis has identified it as belonging to a woolly rhinoceros56. R-EVA 548 and R-EVA 570 are two faunal long bones from Teixoneres, Spain. R-EVA 1860 is a faunal long bone excavated from the site of Ranis, Germany and R-EVA 1905 is a predominantly trabecular fragment of horse bone excavated from Pietraszyn, Poland. R-EVA 1753 is a well-preserved cave bear rib known to date beyond the 14C timescale based on repeated measurements. As standard practice, an aliquot of this bone is extracted and dated alongside every batch of samples to monitor contamination introduced during sample preparation and is used in the age correction of the unknown samples. This is the referred to in the text as the ‘background bone’.Eight bones were selected to span the 36 to produce collagen for dating with graphite targets.For each bone, large aliquots  were pretreated using our standard acid-base-acid + gelatinisation + ultrafiltration protocol see Fig.  based on2 effervescence. For powdered samples, HCl was added and samples were monitored at room temperature (RT) until CO2 effervescence had stopped. Following demineralisation, samples were rinsed with ultra-pure Milli-Q water to a neutral pH. Samples were treated with NaOH (0.1 M) at RT for 10 min to remove humic acid contamination and re-acidified with HCl (0.5 M). If a considerable colour change was observed, NaOH was changed and left for another 10 min. Samples were then gelatinised in weak HCl (pH 3) on a heater block set to 60 °C, 70 °C or 75 °C. Samples were either left for 20 h (as per our standard pretreatment), or regularly monitored and removed from the heater block when the sample had fully solubilised. The resultant gelatin was filtered to remove large particles >80 µm  and ultrafiltered with Sartorius VivaSpin Turbo 15 (30 kDa MWCO) ultrafilters precleaned according to Brock, et al.43 to separate the high molecular weight fraction (>30kD) for freeze drying (48 h). For details of acid strength, duration of treatment and temperature during pretreatment of samples <100 mg, see Fig. In order to optimise our standard protocol for sample sizes <100 mg, small aliquots of each bone were pretreated multiple times to compare collagen yields and sample quality. Firstly, the outer surface of bone was removed using a sandblaster and aliquots were taken using a rotary drill. Fine diamond grit disc drill pieces were used to remove solid pieces of bone. Fine powder was drilled using round tungsten carbide burs (2.3 mm diameter). Aliquots were weighed via a microbalance into cleaned glass tubes. Solid samples were demineralised in HCl at 4 °C with regular visual and mechanical checks and monitoring of CO13C and δ15N). Collagen (ca. 400 μg) was weighed into tin cups using a microbalance and measured on a ThermoFinnigan Flash EA coupled to a Thermo Delta plus XP isotope ratio mass spectrometer (IRMS). Stable carbon isotope ratios were expressed relative to VPDB (Vienna PeeDee Belemnite) and stable nitrogen isotope ratios were measured relative to AIR (atmospheric N2), using the delta notation (δ) in parts per thousand (‰). Repeated analysis of both internal and international standards indicates an analytical error of 0.2‰ (1σ) for δ13C and δ15N. Where sufficient material was available, collagen (ca. 300 μg) was homogenized and mixed with ∼40 mg of IR grade KBr powder in an agate mortar and pestle, pressed into a pellet using a manual hydraulic press (Wasserman) and analysed with an Agilent Technologies Cary FTIR Spectrometer with a DTGS detector. Spectra were recorded in transmission mode at 4 cm−1 resolution with averaging of 34 scans between 4000 and 400 cm−1 using Resolution Pro software (Agilent Technologies). The spectra were evaluated and compared to library spectra of well-preserved collagen and bone to look for evidence of incomplete demineralisation, degraded collagen or the presence of any exogenous material in the extracts.To assess the quality of the collagen, all extracts were analysed via EA-IRMS to obtain elemental  and stable isotopic data , combusted in a vario MICRO cube EA , graphitized using the AGE 3 and dated using the AixMICADAS. Oxalic acid standards and background collagen samples measured in the same session were used to calculate the age of the samples. An external error of 1‰ was also propagated in the error calculation.Each bone was pretreated as per our standard protocol from approximately 500 mg material. From theses extracts, approximately 3–5 mg collagen was weighed into pre-cleaned tin cups at the MPI-EVA and sent to the Curt-Engelhorn-Centre for Archaeometry Klaus-Tschira-AMS facility in Mannheim, Germany (lab code: MAMS) for graphite dating. The samples were combusted in an EA and the sample COca. 25–40 μg C and a single aliquot per bone containing ca. 80–100 μg C) were measured via a microbalance into pre-cleaned silver cups . These were placed into the auto-sampler of a vario MICRO cube EA which was directly coupled to the gas ion source of the AixMICADAS via the GIS22. Following combustion, sample CO2 was adsorbed on a zeolite trap and subsequently expanded to the syringe of the GIS where it was mixed with He (5% CO2) and introduced to the gas ion source at a flow rate of ca. 2 µg C/min. The EA-GIS system was flushed with helium between samples. Pre-cleaned titanium (Ti) gas targets were pre-sputtered for approximately two minutes in the ion source to remove any remaining surface contamination before the sample CO2 injection. Around 30–40 µg C was consumed by the AMS over the duration of one Ti target55. For the large aliquots containing ca. 80–90 μg C measurements were performed over multiple targets (which can be changed during measurement). Each step was fully controlled via the gas-interface handling software.Small aliquots (<100 mg) of the same bones were pretreated to purify the collagen. Three or four aliquots of each collagen extract (containing 14C-free) CO2 samples  were then measured to purge the system and reach a stable operational level (F14C < 0.004) . In the first session, carbonate reference material (IAEA-C1) were run prior to the collagen samples to check the background level of the instrument and begin the measurement of old samples under optimal conditions. In the second measurement session, phthalic anhydride was run for the same purpose. In order to alleviate problems of memory effect, the GIS system was flushed with helium between samples and samples were measured in order of increasing activity as per standard procedure . Low energy ion currents for the gas analyses were in the range of 10–15 μA. BATS58 was used for data reduction. The uncorrected collagen background (R-EVA 1753) measurements of the corresponding type (piece/powder) and equal size were used to correct the archaeological samples measured in the same session . For all samples, the long term standard deviation of blanks (F14C = 0.001) was used as the absolute blank error and an external error of 3.5‰ was added to take into account the long-term variability of standards .The gas measurements in this study were made over two measurement sessions six months apart, both carried out shortly after the ion source had been cleaned. Each measurement session commenced with two oxalic acid II NIST standards (from a gas canister) to normalize and correct samples for fractionation. Blank (Supplementary text and figuresDataset S1Dataset S2Dataset S3Dataset S4


The vast majority of individuals with familial hypercholesterolaemia in the general population remain unidentified worldwide. Recognising patients most likely to have the condition, to enable targeted specialist assessment and treatment, could prevent major coronary morbidity and mortality. We aimed to evaluate a clinical case-finding algorithm, the familial hypercholesterolaemia case ascertainment tool (FAMCAT), and compare it with currently recommended methods for detection of familial hypercholesterolaemia in primary care.In this external validation study, FAMCAT regression equations were applied to a retrospective cohort of patients aged 16 years or older with cholesterol assessed, who were randomly selected from 1500 primary care practices across the UK contributing to the QResearch database. In the main analysis, we assessed the ability of FAMCAT to detect familial hypercholesterolaemia  and compared it with that of other established clinical case-finding approaches recommended internationally . We assessed discrimination by area under the receiver operating curve . Using a probability threshold of more than 1 in 500 , we also assessed sensitivity, specificity, positive predictive values, and negative predictive values in the main analysis.vs 1219 observed cases) and specificity of 60% (443 949 predicted vs 745 781 observed non-cases), with a corresponding positive predictive value of 0·84% and a negative predictive value of 99·2%.A sample of 750 000 patients who registered in 1500 UK primary care practices that contribute anonymised data to the QResearch database between Jan 1, 1999, and Sept 1, 2017, was randomly selected, of which 747 000 patients were assessed. FAMCAT showed a high degree of discrimination , which was higher than that of Simon Broome criteria , Dutch Lipid Clinic Network criteria , MEDPED criteria , and screening cholesterol concentrations higher than the 99th percentile . Using a 1 in 500 probability threshold, FAMCAT achieved a sensitivity of 84% (1028 predicted FAMCAT identifies familial hypercholesterolaemia with greater accuracy than currently recommended approaches and could be considered for clinical case finding of patients with the highest likelihood of having hypercholesterolaemia in primary care.UK National Institute for Health Research School for Primary Care Research. Familial hypercholesterolaemia is the commonest inherited autosomal dominant disorder and causes elevated serum LDL cholesterol from birth.Although clinical cardiovascular risk assessment routinely includes serum cholesterol measurement, it fails to effectively identify people at increased risk of familial hypercholesterolaemia.Evidence before this studyFamilial hypercholesterolaemia is one of the commonest inherited disorders in the general population, with 50% of people affected developing premature heart disease by 50 years of age. More than 80% of an estimated 320 000 individuals in the UK remain undiagnosed, resulting in lost opportunities to effectively prevent premature heart disease. We searched MEDLINE and Embase for research articles published in English between Jan 1, 1946, and Sept 15, 2018, using search terms: “familial hypercholesterol*”, “diagnos*”, “identif*”, “case finding”, “risk algorithm”, “Simon Broom*”, “Dutch Lipid*”, “MEDPED”, “FAMCAT”, and “primary care”. We identified that most previous studies have used the Simon Broome or Dutch Lipid Clinic Network criteria for identification of familial hypercholesterolaemia in primary care. Familial hypercholesterolaemia case ascertainment tool (FAMCAT) was the only case-finding algorithm to identify familial hypercholesterolaemia that has been developed from a large primary care database.Added value of this studyThis study has now externally validated FAMCAT using a separate cohort of 747 000 patients from 1500 primary medical care practices in the UK, from a different and the world's largest primary care database. FAMCAT is confirmed to have high predictive accuracy to detect patients at highest likelihood of having familial hypercholesterolaemia in the general primary care population and performs better than use of Simon Broome, Make Early Diagnosis to Prevent Early Deaths (MEDPED), Dutch Lipid Clinic Network criteria, and very high cholesterol concentrations alone.Implications of all the available evidenceThe current study underlines the poor predictive accuracy of simply using very elevated cholesterol concentrations alone to identify familial hypercholesterolaemia, and that specialist Simon Broome, MEDPED, and Dutch Lipid Clinical Network criteria have relatively lower accuracy than FAMCAT when applied to primary care. For clinical case finding of patients in primary care with highest likelihood of having familial hypercholesterolaemia, FAMCAT is more accurate than other approaches and could be used to improve detection of familial hypercholesterolaemia in the general population.The routine application of DLCN, Simon Broome, or MEDPED criteria to patients in generalist clinical practice, such as primary care, is a practical challenge given the assessments required, including collection of detailed family history. Moreover, as many as one in four patients in the general population with cholesterol concentrations of more than 7·5 mmol/L fulfil referral criteria for probable familial hypercholesterolaemia, most of whom will not transpire to have definite familial hypercholesterolaemia when further assessed by lipid specialistsA clinical case-finding algorithm, familial hypercholesterolaemia case ascertainment tool (FAMCAT), has recently been derived and validated using data from almost 3 million primary care patients, including more than 5000 cases of familial hypercholesterolaemia, from the Clinical Practice Research Datalink (CPRD) of 681 primary care practices in the UK.The majority of clinical algorithms lack well conducted and clearly reported external validation.QResearch database. We randomly selected, using a random number generator, a sample of adult patients aged 16 years or older, who registered in 1500 UK primary care practices that contribute anonymised data to the QResearch database and had at least one documented total or LDL cholesterol measurement (necessary for establishing a suspected diagnosis). The cohort comprised all patients who were actively registered and contributing data (had visited their practice up until the end date of when data were extracted). For patients who were identified with familial hypercholesterolaemia, the date of diagnosis was specified as their ending date to ensure all predictors remained temporal to their familial hypercholesterolaemia outcome.We did a retrospective cohort study in a large population of primary care patients, using the Patients aged younger than 16 years were excluded from the analysis because cholesterol thresholds for diagnosis and treatment of familial hypercholesterolaemia in children differ from those for adults.The selected baseline registration date corresponded to the same starting time used when deriving the FAMCAT algorithm using CPRD.Panel•Sex •Age in years •Highest cholesterol measurement recorded•Ideal: total cholesterol ≤5 mmol/L or LDL cholesterol ≤3·3 mmol/L•High: total cholesterol 5·1–6·5 mmol/L or LDL cholesterol 3·4–4·1 mmol/L•Very high: total cholesterol 6·6–7·5 mmol/L or LDL cholesterol 4·2–4·9 mmol/L•Extremely high: total cholesterol >7·5 mmol/L or LDL cholesterol >4·9 mmol/L•Triglycerides within 1 month of highest cholesterol measurement (mmol/L)•Ideal: <1·7 mmol/L•Borderline high: 1·7–2·2 mmol/L•High: 2·3–5·5 mmol/L•Very high: ≥5·6 mmol/L•Lipid-lowering drugs prescribed within 1 month of highest cholesterol measurement •Family history of familial hypercholesterolaemia (no or yes)•Family history of myocardial infarction (no or yes)•Family history of raised cholesterol (no or yes)•Type 1 or type 2 diabetes (no or yes)•Chronic kidney disease (no or yes)FAMCAT was developed as a multivariable logistic regression model, stratified by sex, to calculate an individual's probability of having familial hypercholesterolaemia. The panel summarises all ten predictors that were incorporated into FAMCAT. The variables were extracted from the QResearch database using UK National Health Service (NHS) read code lists  during tNHS read codes, which are a coded hierarchy of clinical terms that provide a standard way by which clinicians can record patients' findings (grouped in families of codes) and procedures in information technology systems across primary and secondary care. In the hypercholesterolaemia family of read codes, the familial hypercholesterolaemia code is a specific subcode.The primary outcome was defined as the incident diagnosis of familial hypercholesterolaemia, identified from a patient record. In the UK, diagnosis of familial hypercholesterolaemia is made by lipid specialists following clinical assessment using specific diagnostic criteria or genetic testing.Assuming a population frequency of familial hypercholesterolaemia between 1 in 500 (0·002) and 1 in 250 (0·004), to achieve an AUROC of at least 0·80, with 90% power and 5% significance requires between 46 690  and 95 190 samples .We applied the FAMCAT logistic regression equations directly to every patient in the cohort to calculate each patient's probability of having familial hypercholesterolaemia by use of the untransformed regression coefficients and constant terms provided in the We assessed prediction accuracy by examining measures of calibration and discrimination. Calibration was defined as how closely the predicted probability of familial hypercholesterolaemia agrees with the expected probability of familial hypercholesterolaemia. We assessed calibration by plotting the observed number of cases of familial hypercholesterolaemia against the predicted number of cases of familial hypercholesterolaemia for each tenth of predicted probability to ensure ten equally sized groups (deciles).c statistic (concordance). The AUROC value gave the probability that a randomly selected patient who has familial hypercholesterolaemia has a higher probability score than a patient who does not have familial hypercholesterolaemia, ranging from 0·5 (pure chance) to 1 (perfect discrimination). To generate 95% CIs for the AUROC, we used a jack-knife procedureDiscrimination was defined as the ability of the algorithm to differentiate between patients who were predicted to have familial hypercholesterolaemia and those who do not have familial hypercholesterolaemia. This measure was quantified by calculating the AUROC, which is equal to the In the primary analysis, we also compared the discrimination of FAMCAT against Simon Broome,As the predicted probabilities of familial hypercholesterolaemia varied across a continuum, case finding for familial hypercholesterolaemia in primary care requires decision makers to set a probability threshold. The FAMCAT algorithm was shown in an internal validation analysisIn a post-hoc analysis, we also calculated the sensitivity, specificity, positive predictive value, and negative predictive assuming a probability threshold of greater 1 in 250 to reflect the more recent prevalence figures for familial hypercholesterolaemia in the general population.In a sensitivity analysis to further enhance the accuracy of the FAMCAT algorithm, we assessed the benefit of using lipid measurements  and age as continuous variables, with an interaction term to specify whether the measurement was either total cholesterol or LDL cholesterol. Normality was assessed by the Kolmogorov-Smirnov test and, if found to be significant, the variables were log-transformed before inclusion in the multivariable model. Personal history of premature myocardial infarction (as this features in the DLCN criteria), which the primary FAMCAT algorithm does not include, was also included in the model. Premature myocardial infarction was defined as having an event before the age of 55 years for men and before the age of 60 years for women.We used MedCalc software, version 18.11.6, to estimate the sample size.The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all of the data and the final responsibility for the decision to submit for publication.Of the 750 000 patients who registered at participating primary care facilities between Jan 1, 1999, and Sept 1, 2017, 3000 patients were excluded because of having other inherited lipid disorders or having all of their cholesterol measurements after a diagnosis of familial hypercholesterolaemia. There were 1219 cases (0·2%) of familial hypercholesterolaemia in the study cohort .Table 1CThe mean highest total cholesterol was slightly higher in women (5·8 mmol/L [SD 1·3]) than in men (5·6 mmol/L [1·2]). The mean highest LDL cholesterol (3·2 mmol/L [SD 1·1] in men and 3·2 mmol/L [1·0] in women) was the same for both sexes. The majority of the sample were of white ethnicities (189 239 [52·2%] of 362 769 men and 210 914 [54·9%] of 384 231 women). 11 300 (3·1%) of 362 769 men and 13 499 (3·5%) of 384 231 women had a family history of myocardial infarction. Secondary causes of hypercholesterolaemia such as diabetes (54 680 [15·1%] of 362 769 men and 44 834 [11·7%] of 384 231 women) and chronic kidney disease (32 551 [9·0%] of 362 769 men and 43 911 [11·4%] of 384 231 women) were common.External validation of the FAMCAT model in QResearch showed a high level of discrimination  for the Asian, Asian British, or other Asian group (n=11 372) to 0·887 (0·827–0·947) for the mixed or multiple ethnic group  and specificity of 60% (443 949 predicted non-cases vs 745 781 observed non-cases), with a corresponding positive predictive value of 0·84% and a negative predictive value of 99·2%. Using a cutoff above 1 in 250 (0·004) in a post-hoc analysis, FAMCAT achieved a sensitivity of 72% (878 predicted cases vs 1219 observed cases) and specificity of 84% (624 349 predicted non-cases vs 745 781 observed non-cases), with a corresponding positive predictive value of 1·8% and a negative predictive value of 98·2%.In terms of case finding in primary care practice, we considered a threshold corresponding to the estimated frequency of familial hypercholesterolaemia in the general population between 1 in 500 and 1 in 250. Using a cutoff above 1 in 500 (0·002), FAMCAT achieved a sensitivity of 84%  to improve the identification of patients at highest likelihood of having familial hypercholesterolaemia in primary care. In this large retrospective cohort study, we found that FAMCAT had high predictive accuracy to identify such cases by using routinely available variables in patients' electronic health records. FAMCAT was also more accurate than currently available and recommended approaches to identify cases of possible familial hypercholesterolaemia using diagnostic criteria or very high total cholesterol . The algorithm also performed well across ethnic groups in the general primary care population, albeit with some variation in predictive accuracy among ethnic minority groups. The addition of previous personal history of premature myocardial infarction, and fitting a model with total cholesterol, LDL cholesterol, triglycerides, and age as continuous variables significantly improved FAMCAT predictive accuracy even further.The current external validation of this algorithm has been achieved in 747 000 randomly selected patients (from the QResearch database) registered with primary care practices, independent of the CPRD database from which the FAMCAT algorithm was developed. To our knowledge, this is the only algorithm for clinical case finding of familial hypercholesterolaemia in primary care to be robustly developed and stringently validated using two separate and similarly large-sized cohort studies. Including previous development (about 2·2 million patients) and internal validation (about 742 000 patients) cohorts, FAMCAT has now been applied to almost 3·7 million patients—ie, 7% of the UK adult population.The strong performance and consistent predictive accuracy of the FAMCAT algorithm, with high discrimination values of 0·83 (in this study) and 0·86Other strengths of this study include its longitudinal design, with a long duration of follow-up, representativeness, and lack of selection, recall, and respondent bias. The QResearch database used here is, to the best of our knowledge, the world's largest primary medical care database, from which several well established clinical algorithms have been developed and validated.23Nevertheless, we acknowledge limitations shared in common with these other risk algorithms and large database analyses.We note that family history, which is a prerequisite for all familial hypercholesterolaemia case-finding approaches, is often under-reported in electronic health records and more detailed free-text notes could not be accessed because of privacy laws. Although the accuracy of FAMCAT in detecting familial hypercholesterolaemia in various ethnicities in the cohort was good, the study population mainly comprised white Europeans, and therefore its applicability to other ethnic groups needs further exploration. Finally, there was a lower prevalence of familial hypercholesterolaemia (0·2%) seen in this cohort compared with more recent evidence suggesting general population prevalence of 0·4%.Three other electronic health record tools exist for familial hypercholesterolaemia case finding.With about 80% of people affected by familial hypercholesterolaemia currently undiagnosed, improving detection of this life-limiting condition should be a public health priority.In the UK, FAMCAT has become an automated tool for case finding of possible familial hypercholesterolaemia in primary care records and is being updated to reflect improvements in accuracy from using continuous lipid measurements and inclusion of personal history of premature myocardial infarction. Full release of the regression coefficients and variable code definitions will allow researchers to independently implement the algorithm within their own respective populations.Using routine search of electronic health records, FAMCAT can be applied in real time to all adult patients registered to a general practice or clinic who have had their cholesterol measured previously. Adult patients who have a high FAMCAT probability of having familial hypercholesterolaemia can be referred for specialist assessment (see clinical examples in Systematically searching electronic health records with information technology tools that identify patients at high risk could complement current opportunistic practice.In conclusion, when applied to a large independent cohort of patients, the FAMCAT algorithm performs well and with greater accuracy than currently recommended approaches to identify familial hypercholesterolaemia in primary care. FAMCAT has great potential to improve case finding for familial hypercholesterolaemia and enhance detection of undiagnosed familial hypercholesterolaemia in the general population.