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This paper was submitted to the Bulletin of the World Health Organization and was posted to the Zika open site, according to the protocol for public health emergencies for international concern as described in Christopher Dye et al. (http://dx.doi.org/10.2471/BLT.16.170860).

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RECOMMENDED CITATION

Corman VM, Rasche A, Baronti C, Aldabbagh S, Cadar D, Reusken CBEM et al. Clinical comparison, standardization and optimization of Zika molecular detection [Submitted]. Bull World Health Organ E-pub: 19 Apr 2016. doi: http://dx.doi.org/10.2471/BLT.16.175950 .

Clinical comparison, standardization and optimization of Zika virus molecular detection Victor M. Corman, a Andrea Rasche, a Cecile Baronti, b Souhaib Aldabbagh, a Daniel Cadar, c Chantal B.E.M. Reusken, d Suzan D. Pas, d Abraham Goorhuis, e Janke Schinkel, f Richard Molenkamp, f Beate M. Kuemmerer, a Tobias Bleicker, a Sebastian Brünink,a Monika Eschbach- Bludau, a Anna M. Eis-Hübinger, a Marion P. Koopmans, d Jonas Schmidt- Chanasit, c Martin P. Grobusch, e Xavier de Lamballerie, b Christian Drosten a & Jan Felix Drexler a aInstitute of Virology, University of Bonn Medical Centre, Bonn 53127, Germany bAix Marseille Université, IRD French Institute of Research for Development, EHESP French School of Public Health, EPV UMR_D 190 "Emergence des Pathologies Virales", France cBernhard Nocht Institute for Tropical Medicine, WHO Collaborating Centre for and Hemorrhagic Fever Reference and Research, Hamburg, Germany dErasmus MC, Department of Viroscience, 3000 CA, Rotterdam, the Netherlands eCenter of Tropical Medicine and Travel Medicine, Department of Infectious Diseases, Academic Medical Center, University of Amsterdam, 1100 DD Amsterdam, the Netherlands fClinical Virology Laboratory, Department of Medical Microbiology, Academic Medical Center, Amsterdam, the Netherlands (JS, RM) Correspondence to Jan Felix Drexler (e-mail: [email protected] ) (Submitted: 18 April 2016 – Published online: 19 April 2016)

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One sentence summary: Very low Zika virus loads in blood and urine from patients and low sensitivity of several published assays imply invalid test results during the current outbreak and demand highly accurate diagnostics.

What was already known about the topic concerned: The Zika virus (ZIKV) has been known since the 1950ies. There are six published real time RT-PCR-based protocols (qPCR), several of which are widely used for virus detection in the context of the current outbreak in the Americas. Data on analytical sensitivity and compatibility with current ZIKV outbreak strains is not available for most of these assays and the comparability of these assays between laboratories remains unknown.

What new knowledge the manuscript contributes: Several assays may be of limited utility for patient diagnostics during the current outbreak because of low sensitivity and incompatibility with ZIKV outbreak strains. ZIKV loads were low irrespective of sample type, implying patients may go undiagnosed during the current outbreak due to limited assay sensitivity. The novel control RNA generated in this study allowed uniform ZIKV quantification and will prove useful for patient characterization in multicentric studies on ZIKV pathogenesis.

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Abstract Objective Molecular Zika virus (ZIKV) detection is key to patient diagnostics during the current outbreak. Here, we address standardization and diagnostic performance of widely used real- time RT-PCR (qPCR) protocols for ZIKV detection. Methods Two novel qPCR protocols covering the currently known ZIKV genetic variability were analyzed together with all six published qPCR protocols. The performance of all assays was compared using a newly constructed universal control RNA (ucRNA) that contains the target regions of all compared assays on one strand of synthetic RNA. Findings Up to 10 oligonucleotide mismatches with ZIKV outbreak strains existed in published qPCR protocols. The analytical sensitivity of most assays was around 5 copies per reaction, whereas three assays showed a 3-250-fold decreased sensitivity. The novel ucRNA enabled uniform

ZIKV quantification, whereas comparisons of PCR threshold cycles (C T-values) resulted in up to 20-fold misquantification between protocols. Mean ZIKV loads in 33 outbreak samples were 10 4 RNA copies/mL of blood (range; 10 2-4x10 5) and 5x10 3 RNA copies/ mL of urine (range; 4x10 2-5.9x10 4) within two weeks after symptom onset. Conclusion Several ZIKV qPCR protocols show limited sensitivity and incompatibility with ZIKV outbreak strains. ZIKV infection results in low virus concentrations close to the technical limit of detection irrespective of sample type, implying that 20%-80% of patients may go undiagnosed due to limited sensitivity of molecular tests. We provide updated protocols for ZIKV detection that are suitable for all ZIKV strains. The ucRNA will enable coordinated implementation of ZIKV molecular diagnostics across regions and within multicentric clinical trials.

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Introduction The arthropod-borne Zika virus (ZIKV, genus , family ) was first identified in 1947 in Uganda (1). Only sporadic human cases were reported prior to the 2007 outbreak in the Micronesian Yap islands from which 49 confirmed and 59 probable cases were reported (2, 3). ZIKV infections are frequently asymptomatic or show only mild clinical symptoms, including fever, arthralgia and rash (2, 4). However, severe neurological complications, including the Guillain-Barré syndrome were reported from previous outbreaks (5, 6). The current outbreak is additionally associated with fetal malformations (7-10). In Latin America and the Caribbean, ZIKV infection cannot be reliably diagnosed by clinical presentation because the co-circulating (DENV) and virus (CHIKV) cause similar symptoms. Serology is challenging because of the cross-reactivity of antibodies caused by endemic including DENV, St Louis encephalitis, and (WNV) (4, 11, 12). Reliable detection of ZIKV is key to investigations of ZIKV epidemiology and pathogenesis (13). Because of potential association with neurological fetopathies, ZIKV infection should ideally be diagnosed already in the first trimester of pregnancy when neurological development takes place (14). Direct detection of ZIKV is also key to investigations of alternative transmission routes such as semen and blood donations. There are six widely used real-time RT-PCR assays for ZIKV detection (11, 15-17). An additional novel real-time RT-PCR assay has been recommended by the Pan American health association (PAHO) for the current outbreak (13). It is unclear which type of clinical specimens is most suitable for ZIKV detection. Investigations of small series of patients suggest that ZIKV is present in blood only a few days after infection. According to these studies, a generally low level of viral loads may further complicate ZIKV detection (11, 18). ZIKV detection in saliva may be more sensitive than detection in blood, but shedding in saliva and blood appeared to be equally short-lived (19). Urine, semen and saliva were reported to be positive for ZIKV RNA for 2 weeks and longer, and could thus be useful non-invasive materials for diagnosis and clinical studies (18, 20, 21). Here we determined viral load profiles in blood and urine, provide comparative laboratory data for published real-time RT-PCR tests, generate quantitative controls and project a high risk of false negative ZIKV test results.

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Materials and Methods Clinical specimens Clinical specimens were obtained from routine diagnostics sent for investigation of ZIKV or DENV to the University of Bonn Medical Centre, Bonn, Germany, the Bernhard-Nocht Institute for Tropical Medicine, Hamburg, Germany, the Academic Medical Centre, Amsterdam, the Netherlands and the Erasmus Medical Centre, Rotterdam, the Netherlands. Virus quantification and characterization DENV RNA quantification and flavivirus typing were done as described previously (22)(23). Quantitative controls were generated as described previously (24). The universal control RNA (ucRNA) was custom designed as a gBlocks fragment with a T7 promotor sequence (Integrated DNA Technologies, Leuven, Belgium) and in-vitro transcribed as described before (24). All individual IVT and the ucRNA allowed highly comparable quantification of ZIKV RNA with a mean 2-fold deviation of results (maximum deviation, 6-fold), suggesting the ability to use all of these controls to generate comparable results even upon usage of different real-time RT-PCR methods in different laboratories. For all other experiments, ZIKV RNA was generally quantified using reaction conditions exemplified in Supplementary Figure S1 . The ucRNA offers advantages to laboratories operating different real-time RT-PCR assays, but bears the same risk of laboratory contamination as full viral RNA. In contrast to full viral RNA, potential cases of laboratory contamination with the ucRNA can be proven by two highly sensitive real-time RT-PCR marker assay variants designed to specifically detect the ucRNA at lower limits of detection that were comparable to ZIKV-specific assays with 4.3 (95% confidence interval (CI), 2.9-10.9) and 3.3 (95% CI, 2.4-6.5) copies per reaction, respectively. These marker assays contain detection probes that target the overlap of two joined genomic target domains, which do not naturally occur in the full ZIKV genome. Accordingly, the two assays showed no detection of full ZIKV RNA even upon using high- titred cell culture isolates (10 6-10 9 copies/mL). Oligonucleotide sequences of the two marker assays were: Marker1-rtF, GCATCCAGCCAGAGAATCTG; Marker1-rtR, CAATAACGGCTGGATCACACTC; Marker1-rtP, TGCTGTCAGTTCACTCAAGGTTAGAGA-Black Hole Quencher-1 (BHQ-1) and Marker2- rtF, CTTGACAATATTTACCTCCAAGATG; Marker2-rtR, GTTGCTTTTCGCTCCAGAGAC; Marker2-rtP, FAM- CATAGCCTCGCTCTCTACACATGAGA-BHQ1.

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Results All published real-time RT-PCR assays used in ZIKV studies until April 1 st were used in this study. These published assays target the membrane (M), envelope (E), NS1, NS2b, NS3 and NS5 domains of the ZIKV genome ( Figure 1A ) (11, 13, 15-17). Two additional assays targeting the E and NS1 domains were designed for this study. To enable comparative testing of all existing assays, we generated individual quantified in-vitro transcripts (IVT) for the genomic target regions of all assays (IVT I-V, Figure 1A ). In addition, we joined all target domains into a quantitative universal calibrator RNA (ucRNA, Figure 1A ). This quantifiable genome mimic enables stoichiometrically exact analyses of the lower limits of detection of all assays in comparison, without the need to rely on full viral RNA that cannot be quantified. All controls generated for this study are based on a current ZIKV outbreak strain from Brazil (GenBank accession no. KU321639). Table 1 provides details on oligonucleotide sequences and the IVT to be used for each assay. All quantitative controls can be acquired free of charge for non-commercial purposes via the European Virus Archive (EVA, see Acknowledgment for details) Real-time RT-PCR sensitivity can be affected by nucleotide mutations in the binding sites of primers and probes (28). So far, the genetic variability of the ZIKV Asian lineage, including virus strains causing the current American outbreak is lower than that of the African lineage. Genomic variation within the known Asian lineage ZIKV strains so far is limited to around 2% nucleotide differences across the genome ( Figure 1B ). However, mutations do not occur evenly across viral genomes and up to 10 nucleotide mismatches between the sequences of the published assay oligonucleotides and the Asian lineage already exist, with up to 5 mismatches in individual primers or probes ( Figure 2A ). Due to the various mismatches observed with published assays ( Table 1 ), we designed two novel assays covering the currently known ZIKV genetic variability in two different genomic domains. These novel assays showed only up to 3 potential mismatches per assay ( Figure 2B ) and were designed to avoid mismatches in the most critical 3’-terminal regions of oligonucleotides which affect primer binding most (28). The novel NS1-based assay was additionally designed to allow cross-detection of Spondweni virus, the closest relative of ZIKV. This is because regions conserved between related virus taxa can be expected to allow less variation than other genomic regions. Specific detection of ZIKV is crucial to avoid false-positive test results. We evaluated all assays on 37 high-titered flavivirus cell culture isolates, covering the majority of the mosquito-borne flaviviruses ( Supplementary Figure S2 and Table S1 ). None of the 7 published assays detected the co-circulating alphavirus CHIKV or any other flavivirus tested. Despite very high concentrations of viral RNA an Asian ZIKV strain-specific E-based assay did not detect the African ZIKV lineage likely because of nucleotide mismatches, (16). As intended, the novel NS1-based assay presented in this study cross-detected the Spondweni virus, as well as Kokobera and Jugra virus , which do not occur in humans affected by the current outbreak (12). The limit of detection (LOD) is a standardized measure to assess each assay´s performance for qualitative detection. Data on analytical sensitivity including LOD are not available for most of the published assays. To determine exactly comparable LODs based on the present outbreak strain, the ucRNA was used to assess the sensitivity of all assays. As shown in Table 2 and Supplementary Figure S3 , all but three assays showed comparably high analytical sensitivities of around 5 copies per reaction for this standardized target molecule. An NS1- and an NS2b-based assay showed about two- to three-fold higher LODs (13, 16), whereas an NS3-based assay showed a high LOD of 1,373 copies per reaction (17). To exemplify the clinical impact of the LODs of real-time RT-PCR assays, we extrapolated the assay sensitivity to viral loads in clinical samples. Even highly sensitive assays with an LOD of 5 copies per reaction reach a detection limit in the range of 103 copies per mL, whereas an LOD of 1,000 copies per reaction implies a detection limit in the range of 10 6 copies per mL ( Table 3 ). In addition to the qualitative detection of viral RNA, virus quantification is an important tool to investigate the risk of transmission through different biological specimens, viral response to antiviral therapy and the impact of viral concentrations on pathogenesis. Several previous studies reported ZIKV viral load data in form of threshold cycle (C T) values (15, 18, 29).

However, C T values are highly variable and may cause misleading comparisons of viral loads between studies (30, 31). Laboratory conditions such as PCR instruments and reagents can greatly influence C T values. We explored the variability of C T values by testing our two new assays under diverse reaction conditions involving reagents by different suppliers on different real-time PCR instruments. Even upon a variation of only two variables, the same virus target concentration could yield C T values that differed from each other by up to 4.3 cycles, which corresponds to about 20-fold deviations in viral load results ( Figure 3 ). To obtain more information on the relative utility of blood or urine for diagnostics of acute ZIKV infection, the novel assays were used to quantify ZIKV loads in 33 clinical materials from patients sampled during the current outbreak (24 patients in total). Matched urine and blood specimens taken on the same day from the same patient were available from six patients sampled 2-12 days after symptoms onset. In three patients, urine viral loads were equivalent 8 to or lower than those in blood, whereas in the other three patients urine viral loads were 10- 100-fold higher than those in blood ( Figure 4 ). ZIKV loads in all available samples (12 blood and 21 urine specimens) showed low viral loads ( Figure 5A and Supplementary Figure S4 ) and were comparable to viral loads in blood from patients sampled during the ZIKV outbreak in Micronesia in 2007 (11) ( Figure 5A ). A combined dataset comprising the data from the 2007 outbreak provided by Lanciotti et al. (11) and this study resulted in mean ZIKV loads of 10 4 RNA copies per mL of blood (range; 10 2-4x10 5) and 5x10 3 RNA copies per mL of urine (range; 4x10 2-5.9x10 4). These viral loads were determined in clinical specimens sampled during comparable intervals with 11 days after symptom onset for urine and 12 days after symptom onset for blood specimens, respectively and did not differ significantly (t-test, p=0.26). Within the combined ZIKV dataset, nine of 41 specimens contained viral loads of 2.5x10 3 RNA copies per mL or lower, leading to an estimated risk of false-negative test results of 20% even with highly sensitive assays. Using assays with an LOD of 100 copies per reaction, the rate of false-negative test results grows up to 80% ( Figure 5B ). Many of the laboratories conducting ZIKV testing in countries affected by the outbreak have long-standing experience with detection and quantification of the co-circulating DENV. To compare the risk of false-negative test results between ZIKV and DENV, we additionally quantified 38 DENV clinical specimens. Here, mean viral loads were 5x10 5 RNA copies per ml (range; 5x10 2-5x10 8), i.e., DENV loads in blood were generally about 100-fold higher virus than ZIKV loads (t-test, p=0.03) ( Figure 5C ). Accordingly, the technical risk of false-negative results can be estimated to be 10-fold lower for highly sensitive DENV assays with an LOD of 5 copies per reaction and 4-fold lower for assays with an LOD of 100 copies per reaction compared to ZIKV ( Figure 5D ).

Discussion Highly sensitive molecular testing is a key element in the response to the current ZIKV outbreak. The present study provides guidance for the choice of methodology and introduces optimized ZIKV detection assays along with an available molecular calibrator that enables the comparison of results between laboratories and studies. The finding of low ZIKV loads forecasts a high proportion of false-negative test results during clinical application of real- time RT-PCR. The results of assay comparisons suggest that several published real-time RT-PCR assays may be of limited utility for clinical diagnostics during the current ZIKV outbreak. One NS3- based assay that was intended for virus typing and differentiation should not be used for 9 clinical ZIKV diagnostics because of its low sensitivity (17). Several other assays present features potentially limiting their utility within the current outbreak, including limited access to specific probe formats (15), relatively lower analytical sensitivity (13) and high numbers of potential mismatches with members of the Asian ZIKV lineage (16). Our novel assays may be more robust against genetic variation in ZIKV, but real-time monitoring of all assay oligonucleotide binding regions is required during the current situation. In summary, the Lanciotti E-, the Bonn E- and the Bonn NS1-based assays are highly sensitive according to our data and show limited mismatched genomic positions. These assays can thus be reliably used at the present knowledge of ZIKV variability, preferably in combination of at least two assays to increase clinical sensitivity. The low ZIKV loads we detected in urine and blood samples are in agreement with the few previous studies reporting quantitative data (11, 18). Contrary to preliminary data from 6 patients from French Polynesia (18), no significant difference in virus loads between urine and blood specimens could be observed. Our data thus did not support urine as the generally more suitable clinical specimen to detect ZIKV. However, ZIKV RNA seems to remain detectable in urine and semen for a longer time period than in blood (18, 20, 21). Therefore, our results support real-time RT-PCR testing of at least two different clinical specimens for ZIKV diagnostics, ideally including blood and urine. Unfortunately, we could not comparatively evaluate saliva specimens in this study. While commercial diagnostic real-time RT-PCR reagents for ZIKV detection are becoming available, in-house formulations are still widely used in the affected region because of limited resources (13, 24). These assays are difficult to standardize and compare, but will often constitute the only technical resource available in laboratories. Transfer of essential reagents with coordinated implementation of protocols and practical skills can permit accurate real- time RT-PCR diagnostics in resource-limited settings (24, 25, 32, 33). Among the most essential contributions to technology transfer is the provision of standardized RNA reagents that can be shipped internationally without biosafety concerns. Research consortia and public health structures can use these reagents to establish a technical basis for test implementation, as demonstrated, e.g., for SARS and MERS coronavirus (34, 35). These were novel at the time of emergence, and diagnostic tests could be defined along with the provision of reagents. In the case of ZIKV, a long-known agent invading a new region, a multitude of test formulations has already been available, so that assay standardization can only work by provision of a reference reagent that is universally applicable in all assays. Our ucRNA reagent can be used with any current published in-house assay to continuously control for 10 sensitivity, without the need to rely on full virus RNA. Quantitative comparability between studies will enable relative estimates of transmission risks associated with blood donations as well as solid organ transplants and other body fluids than blood, e.g., semen or saliva. These data may also assist studies on ZIKV pathogenesis, since higher viral loads have been associated with severe clinical courses of DENV infection (36) and with prolonged severe arthralgia in CHIKV infection (37). Low ZIKV loads imply a high proportion of false-negative test results in countries affected by the outbreak. To date, only 3% of 199,922 suspected ZIKV cases could be confirmed in the PAHO region (38). Certainly, the difficulties to manage the high number of diagnostic requests in resource-limited settings contribute most to the low number of confirmed cases, consistent with the results of a recent study conducted in Puerto Rico, in which 20% of suspected ZIKV cases could be confirmed using molecular and serologic tools (39). However, our data imply that several thousand patients presenting with low ZIKV loads may have gone undiagnosed by molecular testing, contributing to the low rate of confirmed cases and highlighting the need of combined ultrasensitive molecular and serologic testing. The need for ultrasensitive molecular ZIKV detection additionally applies to blood safety in endemic countries. ZIKV has been detected in 3% of blood donors in previous outbreaks (40) and transfusion-associated transmission has already been reported from Brazil (41). Our comparison of blood viral loads and real-time RT-PCR sensitivity suggest a risk of false- negative results during pooled and even individual blood donor screening. This estimated risk is consistent with several cases of transfusion- and solid organ transplantation-associated transmission of WNV in the US, another mosquito-borne flavivirus showing relatively low viral loads that led to false-negative real-time RT-PCR results in pooled blood donor testing previously (42). Most importantly, the observed association of ZIKV and fetal malformations demand reliable ZIKV diagnostics of pregnant women. A comparison of the current sensitivity and viral load data suggest that molecular testing during pregnancy may preferentially diagnose highly viremic women, which may influence estimates of the manifestation index of congenital disease, if congenital disease correlates with viral load. The low PCR sensitivity caused by low ZIKV loads implies a limited capacity of molecular protocols to exclude ZIKV detection in highly affected regions, requiring additional serological testing to exclude ZIKV infection during pregnancy in multicentric cohort studies investigating ZIKV pathogenesis. In summary, our data emphasize the need for highly sensitive protocols in molecular diagnostic testing for ZIKV infection. In addition to an appropriate choice of methodology, 11 clinical sensitivity can be increased by testing several specimens per patient, by using more than one real-time RT-PCR target in the laboratory and combined molecular and serological testing (11). The novel assays provided through this study, as well as the ucRNA reagent used as a universal quantitative calibrator and positive control can ensure high sensitivity and good comparability of qualitative and quantitative diagnostic results in public health laboratories and clinical studies.

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Acknowledgement The controls described in this paper can be ordered at the following links to the European Virus Archive (EVA): Zika virus IVT I, http://www.european-virus- archive.com/Portal/produit.php?ref=1598&id_rubrique=9 ; Zika virus IVT II, http://www.european-virus-archive.com/Portal/produit.php?ref=1599&id_rubrique=9 ; Zika virus IVT III, http://www.european-virus- archive.com/Portal/produit.php?ref=1600&id_rubrique=9 ; Zika virus IVT IV, http://www.european-virus-archive.com/Portal/produit.php?ref=1601&id_rubrique=9 ; Zika virus IVT V, http://www.european-virus- archive.com/Portal/produit.php?ref=1602&id_rubrique=9 ; Zika virus universal calibrator RNA 1.0, http://www.european-virus- archive.com/Portal/produit.php?ref=1603&id_rubrique=9 . We thank Janett Wieseler, Sandra Junglen, and Annemiek van der Eijk for their support. This study was supported by funding from the European Commission through the Horizon 2020 project EVAg (European Virus Archive goes global), grant agreement number 653316, and the framework program (FP)7 project PREPARE (Platform for European Preparedness Against (Re-)emerging Epidemics), grant agreement number 602525.

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Table 1. Assay oligonucleotides and potential nucleotide mismatches with Zika virus strains

Assay Target* No. potential No. potential Forward primer Probe sequence Reverse primer Control* Reference nucleotide nucleotide sequence (5‘ →→→ 3‘) (5‘ →→→ 3‘) sequence (5‘ →→→ 3‘) mismatches mismatches (all ZIKV) (ZIKV Asian lineage) Lanciotti M M/E 19 7 TTGGTCATGATA CGGCATACAGC CCTTCCACAAA ucRNA; (11) (939- CTGCTGATTGC ATCAGGTGCAT GTCCCTATTGC IVT I 1,015) AGGAG (811-1,500)

Lanciotti E E 18 4 CCGCTGCCCAA AGCCTACCTTG CCACTAACGTTC ucRNA; (11) (1,190- CACAAG ACAAGCAGTCA TTTTGCAGACAT IVT I 1,266) GACACTCAA

Bonn E E 0 0 AGYCGYTGYCC CCTMCCTYGAY CACCARRCTCCC ucRNA; This study (1,188- AACACAAG AAGCARTCAGA YTTGCCA IVT I 1,316) CACYCAA

Pyke E E 28 7 AAGTTTGCATG ACCGGGAAGAG CAGCATTATCCG ucRNA; (16) (1,326- CTCCAAGAAAA CATCCAGCCAG GTACTCCAGAT IVT I 1,397) T A

Pyke NS1 NS1 13 7 GCACAATGCCC TTCCGGGCTAA TGGGCCTTATCT ucRNA; (16) (3,433- CCACTGT AGATGGCTGTT CCATTCCA IVT II 3,498) GGT (3,145- 3,739)

Bonn NS1 NS1 4 3 CRACYACTGCA ATGGTGCTGYA GCCTTATCTCCA ucRNA; This study (3,385- AGYGGAAGG GRGARTGCACA TTCCATACC IVT II 3,495) ATGC

PAHO NS2b NS2b/N 11 4 CTGTGGCATGA CCACGCTCCAG ATCCCATAGAG ucRNA; (13) S3 ACCCAATAG CTGCAAAGG CACCACTCC IVT IV (4,538- (4,246- 4,628) 4,882)

Tappe NS3 NS3 15 10 TGGAGATGAGT CTGATGAAGGC GGTAGATGTTGT ucRNA; (17) (6,012- ACATGTATG CATGCACACTG CAAGAAG IVT III 6106) (5,770- 6,370)

Faye NS5 NS5 6 3 AARTACACATA CTYAGACCA GC TCCRCTCCCYCT ucRNA; (15) (9,376- CCARAACAAAG TG AAR YTGGTCTTG IVT V 9,477) TGGT (9,100- 9,696) *nucleotide position according to GenBank acc. KU321639. Locked nucleic acid bases in the Faye et al. probe are underlined.

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Table 2. Analytical sensitivity of real-time PCR tests Assay 95% lower limit of detection [copies per reaction, confidence interval]

Lanciotti M 3.2 [2.2-8.3]

Lanciotti E 4.1 [2.7-11.4]

Bonn E 2.1 [1.4-8.0]

Pyke E 5.3 [3.0-25.7]

Pyke NS1 12.1 [5.9-78.5]

Bonn NS1 3.1 [2.3-5.8]

PAHO NS2b 17.0 [12.3-30.9]

Tappe NS3 1,377.3 [860-5,162]

Faye NS5 4.5 [8.0-43.9]

Table 3. Extrapolation of analytical sensitivity to clinical viral loads Viral load upon 100 µL Viral load upon 140 µL Technical sensitivity Technical sensitivity input volume eluted in input volume eluted in 70 (copies/µl eluate) (copies/reaction #) 100 µL (copies/mL) µL (copies/mL)*

1 5 5.0x10 3 2.5x10 3

5 25 2.5x10 4 1.25x10 4

10 50 5.0x10 4 2.5x10 4

20 100 1.0x10 5 5.0x10 4

250 1,000 1.0x10 6 5.0x10 5 #using 5 µL of eluted RNA per PCR reaction *corresponding to standard input and elution volumes in the commonly used Qiagen Viral RNA Mini kit (Qiagen, Hilden, Germany). 18

Figure legends Figure 1. Genomic locations of Zika virus real-time RT-PCR tests and controls

A, Zika virus genomic representation (GenBank accession no. KU321639), with real-time RT-PCR assays identified below by respective first authors or location and corresponding control in vitro transcripts (IVT) and parts of the universal control RNA (ucRNA) identified above. Genomic regions not containing published assays so far, but potentially useful for future assay design due to genomic conservation within the Asian Zika virus lineage were also included in the ucRNA. UTR, untranslated region. B, Genomic identity plot of all Zika virus polyprotein sequences characterized to at least 80% available at GenBank by April 13 th , 2016. Similarity plots were done using SSE V1.2 (27). A sliding window of 200 and a step size of 40 nucleotides were used.

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Figure 2. Variable genomic positions under assay oligonucleotide sites

For the African and Asian lineage, 100% consensus sequences were generated and mapped to respective PCR primers and probes. Consensus calculations included all complete and partial Zika virus sequences available at GenBank by April 7 th , 2016 (n=259 entries). Variable sites within the oligonucleotide binding sites of the two ZIKV lineages are highlighted in orange, variable sites between the two lineages in grey, mismatches by asterisks and red color within the oligonucleotide sequences. Y=C/T, R=A/G, M=A/C, B=C/G/T, S=G/C, W=A/T, H=A/C/T, D=A/G/T, N=A/C/T/G, K=G/T, V=A/C/G. Locked nucleic acids in the probe of the Faye et al. NS5-based assay are underlined, the deletion under the forward primer boxed. Potential mismatches below oligonucleotides are indicated in red and by asterisks. Highlighted in grey, variable site African vs. Asian Zika virus lineage. 20

Figure 3. Threshold cycle variation using different reaction conditions and thermocyclers

Symbols identify reaction mix and thermocycler yielding different threshold cycle (CT) values (y-axis) compared to the standard protocol and thermocycler (x-axis). Comparison of C T values used the Bonn E- and NS1-based assays using either the Superscript III One-Step RT-PCR kit (Thermo Fischer) or the Qiagen One-Step RT-PCR kit (Qiagen) on a Roche LightCycler 480 and LightCycler 2.0 a Qiagen Rotorgene HQ and an Applied Biosystems 7500 thermocycler. Reference conditions refer to the usage of Life Technologies SuperScript III One-Step enzyme mix and a Roche LC480 thermocycler.

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Figure 4. Zika virus loads in paired clinical specimens

Zika virus loads plotted per type of clinical specimen. Each color corresponds to an individual patient. Paired urine and blood specimens were taken on same days and within first 10 days after symptom onset.

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Figure 5. Zika and Dengue virus loads and risk of false-negative test results

A, Zika virus loads in blood and urine. Lines in plots show mean viral loads. Data from Lanciotti et al. were reported in (11). All blood specimens included in the analysis were sampled on day 2-10 after symptom onset, urine specimens were sampled on day 2-12 after symptom onset. For details on time of sampling see Supplementary Figure S4 . B, Projection of false-negative test results according to different lower limits of detection for Zika virus , according to a 2:1 input vs. elution volume (e.g., 140 µL blood eluted in 70 µL) as in (11) and a 100% extraction efficacy. C, Dengue virus loads in blood. D, Projection of false-negative test results according to different lower limits of detection for dengue virus , using identical parameters as in B.

Supplementary Table S1. Viruses used for specificity testing Virus family Genus Virus Species Virus strain TCID50/mL Flaviviridae Flavivirus Aroa virus Aroa virus 1.32E+06 Aroa virus Bussuquara virus 4.18E+02 Aroa virus Iguape virus 1.32E+05 Bagaza virus Bagaza virus 2.89E+05 Banzi virus Banzi virus 1.32E+03 Bouboui virus Bouboui virus 1.32E+05 Dengue virus Dengue 1 5.78E+04 Dengue virus Dengue 2 6.30E+03 Dengue virus Dengue 3 7.09E+03 Dengue virus Dengue 4 8.93E+06 Edge Hill virus Edge Hill virus 1.32E+03 Ilheus virus Ilheus virus 1.32E+04 Ilheus virus Rocio virus 2.89E+05 Japanese encephalitis Japanese encephalitis 3.15E+06 virus virus Jugra virus Jugra virus 6.18E+04 Kedougou virus Kedougou virus 1.32E+04 Kokobera virus Kokobera virus 1.32E+04 Koutango virus Koutango virus 4.18E+05 Modoc virus Modoc not available Murray Valley Alfuy virus 1.32E+05 encephalitis virus Murray Valley Murray Valley 4.18E+04 encephalitis virus Encephalitis Virus Saboya virus Saboya virus 5.26E+04 Sepik virus Sepik virus 4.18E+04 St Louis encephalitis Saint Louis Encephalitis 1.32E+05 virus Virus Tembusu virus Tembusu virus 4.18E+04 Tick-borne Tick-borne encephalitis 1.16E+06 encephalitis virus virus Uganda S virus Uganda S virus 1.32E+04 Usutu virus Usutu virus 4.18E+05 Wesselsbron virus Wesselsbron virus 4.18E+03 West Nile virus West Nile virus 3.36E+06 Yellow fever virus Yellow fever virus 1.47E+07 Zika virus Zika virus 1.32E+04 no approved species Spondweni virus 1.32E+04 no approved species Cell fusing agent virus 3.40E+05 no approved species Culex Flavi 7.00E+04 no approved species Kamiti River Virus 3.40E+06 no approved species Lammi Virus 1.32E+04 no approved species Niénokoué 6.45E+06 Togaviridae Alphavirus Chikungunya virus Chikungunya virus 3.1E+06

Supplementary Figure S1. Bench protocol for real‐time RT‐PCR assays

Real time RT-PCR for Zika virus Professor Dr. med. Jan Felix Drexler

Fon: 0228. 287-11697 Bonn NS1 and Bonn E assay Fax: 0228. 287-19144 [email protected] Example formulation: Thermo Fisher SuperScriptIII OneStep RT-PCR System Dr. med. with Platinum Taq DNA Polymerase Victor Corman Fon: 0228. 287-13590 25µl Cycler: [email protected]

MasterMix: single rxn, μl Universitätsklinikum Bonn H2O (RNAse free) 1.4 50°C 15’ Sigmund-Freud-Str. 25 53105 Bonn MgSO4(50mM) 0.4 95°C 3’ 2x Reaction mix 12.5 BSA (1 mg/ml)* 1 95°C 15’’ Fwd primer (10 µM) 1.5 56°C 20’’ 45x Rev primer (10 µM) 1.5 72°C 15“ Probe (10 µM) 0.7 SSIII/Taq EnzymeMix* 1 40°C 30’’ 20 ' = minutes; " = seconds Template RNA 5 *non-acetylated. This component is only necessary if using glass capillary LightCycler. Can be replaced with water in plastic vessel machines such as ABI 7500, LC 480, etc.

Primers / probe: Bonn_NS1 _FWD CAACYACTGCAAGYGGAAGG Bonn_NS1 _P 6-FAM-ATGGTGCTGYAGRGARTGCACAATGC-BHQ Bonn_NS1_ REV GCCTTATCTCCATTCCATACC

Bonn_ E_FWD AGYCGYTGYCCAACACAAG Bonn _E_P 6-FAM-CCTMCCTYGAYAAGCARTCAGACACYCAA-BHQ Bonn _E_REV CACCARRCTCCCYTTGCCA

Reference:

Positive Control: We will provide controls through the European Virus Archive (EVA) http://global.european-virus-archive.com

Supplementary Figure S2. Genetic relationship of representative flaviviruses.

Maximum Likelihood phylogeny of the genus Flavivirus. Filled circles at nodes indicate bootstrap supports above 75% (1,000 replicates). Viruses are identified by name and GenBank accession number. Viruses included in specificity testing are highlighted with an asterisk. Viruses known to occur in humans are given in red, viruses co-circulating with ZIKV in the current outbreak are given in bold. Vector associations are indicated to the right. Tick-borne flaviviruses were collapsed for graphical reasons and included Gadgets Gully virus (DQ235145); Kyasanur Forest disease virus (AY323490); Alkhurma hemorrhagic fever virus (AF331718); Langat virus (AF253419); Louping ill virus, Spanish subtype (DQ235152); Louping ill virus, Turkish sheep encephalitis virus subtype (DQ235151); Louping ill virus, Greek goat encephalitis virus subtype (DQ235153); Omsk hemorrhagic fever virus (AY323489); Powassan virus (L06436); Royal Farm virus (DQ235149); Tick-borne encephalitis virus, European subtype (U27495); Tick-borne encephalitis virus, Far Eastern subtype (JN229223); Tick-borne encephalitis virus, Siberian subtype (L40361); Meaban virus (DQ235144); Saumarez Reef virus (DQ235150); Tyuleniy virus (DQ235148); Kadam virus (DQ235146) . ML phylogenies were calculated using a complete deletion option, a HKY nucleotide substitution model and 1,000 bootstrap replicates in MEGA6 (26).

Supplementary Figure S3. Analytical sensitivity of Zika virus real-time RT-PCR tests.

Probability of detection (y-axis) is plotted against ucRNA concentration per reaction in 8 parallel test samples (x-axis), with data points representing the observed fraction of positive results in parallel experiments. Solid line, predicted proportion of positive results at a given RNA input concentration; dashed lines, 95% confidence limits for the prediction. In each panel, assay name, 95% lower limit of detection and confidence intervals are indicated. Probit analyses were done using SPSS V22 (IBM, Ehningen, Germany).

Supplementary Figure S4. Zika virus loads in clinical specimens per day after symptom onset.

Zika virus loads in different types of clinical specimens plotted per day after symptom onset. Each color corresponds to an individual patient. Datum points to the right represent unknown onset of symptoms.