Temporal and spatial analysis of the 2014–2015 Ebola virus outbreak in West Africa - Nature

We used a deep sequencing approach to gain insight into the evolution of Ebola virus (EBOV) in Guinea from the ongoing West African outbreak. This was an approach based on analysis pipelines developed for a guinea-pig model of EBOV infection and Hendra virus infection of human and bat cells^4,5. Here we use this approach to derive consensus EBOV genomes from individual patient samples that can be used to study viral genome evolution during the course of the outbreak. Viral genomes were derived primarily from blood samples that had been taken from patients in Guinea and sent to the European Mobile Laboratory (EMLab), deployed by the World Health Organisation within the Médecins Sans Frontières Ebola Treatment Centre Guéckédou in March 2014 to aid the diagnostic effort. With the permission of Guinean authorities a biobank of samples was assembled which had known provenance of EBOV infection. Linked to each sample were the following data: patient location (to district level), sample collection date, disease onset and outcome. The collection dates were a median of 4 days after the date of onset of symptoms. Baseline data was cleaned, formatted and imported into the Geographic Information System, ESRI ArcGIS. Statistical tools were used to generate tabular output and to join the numeric case data with the district level boundaries of Guinea, Liberia and Sierra Leone (district geometries freely available from http://www.gadm.org/) (Fig. 1a).

a, Geographical location of patient samples. The origin of the sequenced samples (one sample per patient) from Guinea, Sierra Leone, and Liberia processed by EMLab Guéckédou are plotted as numbers of cases by district. EMLab data are overlaid on an Ebola outbreak distribution map where cumulative cases are plotted as a heat map (low (yellow) to high (brown)) of confirmed cases from March 2014 to January 2015. Case data sourced from World Health Organization (WHO) Ebola response situation reports (http://apps.who.int/ebola/en/ebola-situation-reports); Geographic Information Systems (GIS) data sourced from Environmental Systems Research Institute (ESRI) and Database of Global Administrative Areas (GADM; http://www.gadm.org/). b, Sequence depth per nucleotide position. The number of reads for each nucleotide position was plotted across the full length of the virus genome for each of the 179 virus isolates we analysed. In red is shown the uniformity of the depth across individual genomes, although the median number of reads per nucleotide position had a variation spanning over four log₁₀ units. c, Linear regression of the log₁₀ median sequence depth of each virus isolate versus the C_t value of the viral load as determined by qRT–PCR. Red dots indicate samples taken from patients who went on to survive EBOV infection and grey shaded dots are from patients who records suggest died from EBOV infection.

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The viral genome sequence was derived from RNA sequencing analysis of the patient samples with no pre-amplification of the viral genome. In general we selected a range of samples from both males and females of different ages and a fair representation of sequences for each month (Extended Data Fig. 1), and with C_t values less than 20 for EBOV RNA. In this selected patient cohort, with a relatively high viral load, there was approximately 80% mortality. The read depth mapping to the EBOV genome varied between samples and regions in the genome (Fig. 1b) and in general the number of sequence reads obtained for each genome correlated with the amount of viral load as determined by quantitative reverse-transcription PCR (qRT–PCR) (Fig. 1c).

Phylogenetic analysis revealed the dynamic nature of the epidemic and molecular change in the viral sequence (Fig. 2a). Several distinct lineages were identified, with an initial lineage A (Figs 2a, 3 and Extended Data Fig. 2) linked to early Guinean cases dating from March 2014 including the three original viruses published by Baize et al.². A second lineage, B, emerged in May and June and comprises all the sequences from Gire et al.⁶ and the remainder of those described here. As the epidemic expanded, lineage A remained confined in Guinea from March to June 2014, except for one sequence from 18 July 2014. A single Liberian sequence from March 2014 grouped within this lineage. No further EBOV genomes that we sequenced from samples taken after July 2014 belonged to lineage A. This clade was likely to have been associated with the original outbreak in Guinea and was almost successfully contained in May 2014 by the interventions of the multi-agency response. Two clusters of Sierra Leone viruses described by Gire et al.⁶ (denoted by the authors as clusters SL1 and SL2), both of which contain later viruses from Guinea and Liberia, suggest continued spread across the border during this time. Early cases in SL1 and SL2 were both associated with a single funeral⁶, so it is possible that this event may have reignited the epidemic. Thereafter, lineage B spread into Guinea, Liberia and Sierra Leone. This lineage is associated with the large epidemics in these three countries and persisted into 2015. The spatiotemporal spread of these viruses based on the phylogenetic analysis presented in Figs 2a and 3 was summarized (Extended Data Fig. 3) and indicated how the virus may have spread between the neighbouring countries. There was no evidence from the data that increases or decreases in mortality were associated with any particular virus cluster (Extended Data Fig. 4).

a, Phylogenetic relatedness of EBOV isolates. Phylogenetic tree inferred using MrBayes¹¹ for full-length EBOV genomes sequenced from 179 patient samples obtained between March 2014 and January 2015. Displayed is the majority consensus of 10,000 trees sampled from the posterior distribution with mean branch lengths. Posterior support is shown for selected key nodes. Twenty-two samples originated in Liberia and were collected between March and August 2014 and six samples from Sierra Leone were obtained in June and July 2014. In our analysis we also included published sequences, including the three early Guinean sequences² and 78 sequences described by Gire et al.⁶. A number of lineages predominantly circulating in Guinea are denoted as GN1–4 along with a uniquely Sierra Leone lineage (SL3) recognised in Gire et al.⁶. b, EBOV nucleotide sequence divergence from root of the phylogeny in Fig. 2a plotted against time of collection of each virus. The date of the first documented case near Meliandou in eastern Guinea is indicated by the red triangle.

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Shown is a maximum clade credibility tree constructed from 10,000 trees sampled from the posterior distribution with mean node ages. Clades described in Gire et al.⁶ are identified here (SL1, SL2 and SL3) as well as a number of lineages predominantly circulating in Guinea and posterior probability support is given for these. For certain key node ages, 95% credible intervals are shown by horizontal bars.

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The Bayesian time-scaled phylogenetic analysis estimated an average rate of evolution over the genome of 1.42 × 10⁻³ substitutions per site per year with 95% credible intervals of 1.22 × 10⁻³ and 1.62 × 10⁻³. Details of the model assumptions are given in the Methods section. This rate is lower than that initially described for the West African outbreak by Gire et al.⁶ but still higher than the long-term, between-outbreak rate of 0.8 × 10⁻³ estimated using viruses back to the 1976 Yambuku outbreak⁶. This apparent drop in rate of evolution between these two studies is consistent with the explanation provided by Gire et al.⁶ that the short sampling interval (March to June) provided insufficient time for the action of purifying selection. However, the much longer sampling interval in the present study may simply be providing a more precise estimate of the rate. It should be noted, however, that the between-outbreak rate will exclusively reflect transmission and evolution that has occurred in the non-human reservoir species, so may not be directly comparable to the rate within a human outbreak. We observed no evidence of a change in evolutionary rate over the course of the epidemic with the accumulation of genetic change having a linear relationship with time (Fig. 2b), confirming that the apparent decline in rate between the two studies is an observational phenomenon⁷ rather than a change in the virus.

The estimate of the date of the most recent common ancestor of the sampled viruses is mid-January 2014 (95% credible intervals 12 December 2013, 18 February 2014). Although this is an estimate of first transmission event that resulted in more than one lineage in our sample, this provides an upper bound on the date of emergence of the virus into the human population. This date estimate is consistent with the epidemiological tracing of the first suspected cases to December 2013².

Given the error-prone nature of EBOV genome replication we examined the potential amino acid variation in EBOV proteins from the start of our sample collection in March 2014 to January 2015. The location of amino acid changes on EBOV proteins and their relative representation in the 179 assembled genomes were compared to an isolate identified in March 2014 (ref. 2) (Fig. 4). While there is amino acid variation in all of the genomes sampled, there were very few changes in viral protein 30 (VP30), viral protein 40 (VP40) and viral protein 24 (VP24), and these changes are only in less than ∼2% of the genomes sampled. However, a single amino acid substitution in VP24 is associated with adaptation to a new host^4,8, and this may be due to interactions with host-cell proteins^9,10. While some of the variation may be attributed to a purely random molecular clock pattern, in GP, VP35, NP and L there are some amino acid variations that are present in over ∼15% of the genomes sampled. For example, in GP there is an A to V substitution in ∼70.5% of the genomes sampled compared to the reference genome. Implications of the mutations within GP in relation to immune escape of therapeutics and vaccines will need to be assessed in pseudotype neutralization assays using EBOV monoclonal antibodies and serum from people who have been vaccinated.

Shown is the frequency of all amino acid positions that had variability and the substitution that occurred with the first single letter position indicating the reference sequence and the second position showing the variation. The percentage frequency in the 179 genomes is shown on the y axis. GP, glycoprotein; NP, nucleoprotein; L, RNA polymerase; VP, viral protein.

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No statistical methods were used to predetermine sample size. There was no randomization or blinding in selection of samples for sequencing.

Ethics statement

The National Committee of Ethics in Medical Research of Guinea approved the use of diagnostic leftover samples and corresponding patient data for this study (permit no. 11/CNERS/14). As the samples had been collected as part of the public health response to contain the outbreak in Guinea, informed consent was not obtained from patients.

Genome sequencing and consensus building

Viral genome sequence was derived from the RNA extracted for diagnostic purposes from blood samples in the field with no pre-amplification of the viral genome. These samples were processed by the EMLab and are detailed in Supplementary Table 1, which indicates sample name, geographical location, date of onset of symptoms, date sample was collected, and the C_t value of EBOV RNA at the date of test. The clinical status is also indicated as well as malaria co-infection where known. Extracted RNA was DNase treated with Turbo DNase (Ambion) using the rigorous protocol. RNA sequencing libraries were prepared from the resultant RNA using the Epicentre ScriptSeq v2 RNA-Seq Library Preparation Kit. Following 10–15 cycles of amplification, libraries were purified using AMPure XP beads. Each library was quantified using Qubit and the size distribution assessed using the Agilent 2100 Bioanalyzer. These final libraries were pooled in equimolar amounts using the Qubit and Bioanalyzer data with 9–10 libraries per pool. The quantity and quality of the pool was assessed by Bioanalyzer and subsequently by qPCR using the Illumina Library Quantification Kit from Kapa on a Roche Light Cycler LC480II according to manufacturer’s instructions. Each pool of libraries was sequenced on one lane of a HiSeq2500 at 2 × 125-bp paired-end sequencing with v4 chemistry.

The trimmed fastq files were first aligned to a copy of the human genome using Bowtie2 (ref. 12) and the unaligned reads were then mapped with Bowtie2 to a list of 3731 known viral genomes excluding EBOV genomes. The reads that were still unmapped were then aligned to the EBOV genome—either the prototype strain isolated in Zaire in 1976 (AF086833.2) or a strain isolated during the current outbreak (KJ660348.2). For this step we again used Bowtie2 and the resultant alignment files were filtered with samtools to remove unmapped reads and reads with a mapping quality score below 11, followed by filtering with markdup to remove PCR duplicates. The resultant BAM file was then analysed by Quasirecomb¹³ to generate a phred-weighted table of nucleotide frequencies which were parsed with a custom perl script to generate a consensus genome in fasta format. This consensus genome was then used as a reference genome to which we remapped the sequence reads which did not map to the human genome or other viruses in order to generate a second consensus. In this way we were able to manually determine if the reference genome used by Bowtie2 influenced the process of calling a consensus genome. In addition, we used FreeBayes to independently call and identify SNPs and indels. The pipeline is entirely open source and implemented in the Galaxy environment¹⁴, a Galaxy compatible workflow, novel scripts and XML wrappers needed for implementation in Galaxy are freely available and included in Supplementary Data File 1. Sequence alignment maps were manually inspected and curated over regions with consistent low coverage (for example, at the 5′ ends).

Phylogenetic analysis

Phylogenetic analysis comprised the 179 EBOV genomes from this study, 78 genomes from Sierra Leone⁶, three sequences from Guinea² and two sampled from Mali¹⁵. The genomes were partitioned into four sets of sites—1st, 2nd and 3rd codon positions of the protein-coding regions and the non-coding intergenic regions—with each partition being assigned a generalized time reversible substitution model¹⁶, gamma distributed rate heterogeneity¹⁷ and a relative rate of evolution. This model was used to construct a Bayesian nucleotide divergence tree (Fig. 2) using MrBayes¹¹ and a time-scaled phylogenetic analysis (Fig. 3) using BEAST¹⁸ with a log-normal distributed relaxed molecular clock¹⁹, and the ‘Skygrid’ non-parametric coalescent tree prior²⁰. The alignments and control files for both analyses are available in Supplementary Data Files 2 and 3 and provide documentation of all model parameters.

Primary accessions

GenBank/EMBL/DDBJ

Data deposits

The 179 consensus genome sequences described in this study have been assigned the GenBank accession numbers KR817067–KR817245. Further information is provided in Supplementary Table 1.

• 05 August 2015

  Spelling of author M.D.F.-G. was corrected.

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Author notes

1. David A. Matthews, Julian A. Hiscox, Michael J. Elmore, Georgios Pollakis, Andrew Rambaut and Stephan Günther: These authors contributed equally to this work.

Authors and Affiliations

1. Public Health England, Porton Down, SP4 0JG, Wiltshire, UK

   Miles W. Carroll, Michael J. Elmore, Roger Hewson, Babak Afrough, Barry Atkinson, Simon Bate, Andrew Bosworth, Simon Clark, Lisa Jameson, Eeva Kuisma, Christopher H. Logue, James McCowen, Edmund N. C. Newman, Didier Ngabo, Thomas Pottage, Catherine Pratt, Ruth Thom, Stephen Thomas, Howard Tolley, Inês Vitoriano, Yper Hall & Francis Senyah

2. The European Mobile Laboratory Consortium, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, D-20359, Germany

   Miles W. Carroll, Roger Hewson, Joseph Akoi Bore, Raymond Koundouno, Saïd Abdellati, Babak Afrough, John Aiyepada, Patience Akhilomen, Danny Asogun, Barry Atkinson, Marlis Badusche, Amadou Bah, Simon Bate, Jan Baumann, Dirk Becker, Beate Becker-Ziaja, Anne Bocquin, Benny Borremans, Andrew Bosworth, Jan Peter Boettcher, Angela Cannas, Fabrizio Carletti, Concetta Castilletti, Simon Clark, Francesca Colavita, Sandra Diederich, Adomeh Donatus, Sophie Duraffour, Deborah Ehichioya, Heinz Ellerbrok, Maria Dolores Fernandez-Garcia, Alexandra Fizet, Erna Fleischmann, Sophie Gryseels, Antje Hermelink, Julia Hinzmann, Ute Hopf-Guevara, Yemisi Ighodalo, Lisa Jameson, Anne Kelterbaum, Zoltan Kis, Stefan Kloth, Claudia Kohl, Miša Korva, Annette Kraus, Eeva Kuisma, Andreas Kurth, Britta Liedigk, Christopher H. Logue, Anja Lüdtke, Piet Maes, James McCowen, Stéphane Mély, Marc Mertens, Silvia Meschi, Benjamin Meyer, Janine Michel, Peter Molkenthin, César Muñoz-Fontela, Doreen Muth, Edmund N. C. Newman, Didier Ngabo, Lisa Oestereich, Jennifer Okosun, Thomas Olokor, Racheal Omiunu, Emmanuel Omomoh, Elisa Pallasch, Bernadett Pályi, Jasmine Portmann, Thomas Pottage, Catherine Pratt, Simone Priesnitz, Serena Quartu, Julie Rappe, Johanna Repits, Martin Richter, Martin Rudolf, Andreas Sachse, Kristina Maria Schmidt, Gordian Schudt, Thomas Strecker, Ruth Thom, Stephen Thomas, Ekaete Tobin, Howard Tolley, Jochen Trautner, Tine Vermoesen, Inês Vitoriano, Matthias Wagner, Svenja Wolff, Constanze Yue, Maria Rosaria Capobianchi, Romy Kerber, Tatjana Avšič-Županc, Andreas Nitsche, Marc Strasser, Giuseppe Ippolito, Stephan Becker, Kilian Stoecker, Martin Gabriel, Hervé Raoul, Antonino Di Caro, Roman Wölfel & Stephan Günther

3. University of Southampton, South General Hospital, Southampton, SO16 6YD, UK

   Miles W. Carroll

4. Department of Cellular and Molecular Medicine, School of Medical Sciences, University of Bristol, Bristol, BS8 1TD, UK

   David A. Matthews

5. Institute of Infection and Global Health, University of Liverpool, Liverpool, L69 2BE, UK

   Julian A. Hiscox, Georgios Pollakis, Isabel García-Dorival, Andrew Bosworth & Natasha Y. Rickett

6. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 2FL, UK

   Andrew Rambaut & Gytis Dudas

7. Fogarty International Center, National Institutes of Health, Bethesda, 20892, Maryland, USA

   Andrew Rambaut

8. Centre for Immunology, Infection and Evolution, University of Edinburgh, Edinburgh, EH9 2FL, UK

   Andrew Rambaut

9. London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK

   Roger Hewson

10. Université Gamal Abdel Nasser de Conakry, Laboratoire des Fièvres Hémorragiques en Guinée, Conakry, Guinea

    Joseph Akoi Bore, Raymond Koundouno & N’Faly Magassouba

11. Institut National de Santé Publique, Conakry, Guinea

    Joseph Akoi Bore, Raymond Koundouno & Lamine Koivogui

12. Institute of Tropical Medicine, Antwerp, B-2000, Belgium

    Saïd Abdellati & Tine Vermoesen

13. Institute of Lassa Fever Research and Control, Irrua Specialist Teaching Hospital, Irrua, Edo State, Nigeria

    John Aiyepada, Patience Akhilomen, Danny Asogun, Adomeh Donatus, Yemisi Ighodalo, Jennifer Okosun, Thomas Olokor, Racheal Omiunu, Emmanuel Omomoh & Ekaete Tobin

14. Bernhard Nocht Institute for Tropical Medicine, Hamburg, D-20359, Germany

    Marlis Badusche, Jan Baumann, Beate Becker-Ziaja, Sophie Duraffour, Deborah Ehichioya, Britta Liedigk, Lisa Oestereich, Elisa Pallasch, Martin Rudolf, Romy Kerber, Martin Gabriel & Stephan Günther

15. German Centre for Infection Research (DZIF), Braunschweig, 38124, Germany

    Marlis Badusche, Dirk Becker, Beate Becker-Ziaja, Sandra Diederich, Erna Fleischmann, Anne Kelterbaum, Britta Liedigk, Anja Lüdtke, Marc Mertens, Benjamin Meyer, Peter Molkenthin, César Muñoz-Fontela, Doreen Muth, Lisa Oestereich, Elisa Pallasch, Martin Rudolf, Gordian Schudt, Thomas Strecker, Matthias Wagner, Svenja Wolff, Romy Kerber, Stephan Becker, Kilian Stoecker, Martin Gabriel, Roman Wölfel & Stephan Günther

16. Swiss Tropical and Public Health Institute, University of Basel, Basel, CH-4002, Switzerland

    Amadou Bah

17. Institute of Virology, Philipps University Marburg, Marburg, 35043, Germany

    Dirk Becker, Anne Kelterbaum, Gordian Schudt, Thomas Strecker, Svenja Wolff & Stephan Becker

18. National Reference Center for Viral Hemorrhagic Fevers, Lyon, 69365, France

    Anne Bocquin, Alexandra Fizet & Stéphane Mély

19. Laboratoire P4 Inserm-Jean Mérieux, US003 Inserm, Lyon, 69365, France

    Anne Bocquin, Stéphane Mély & Hervé Raoul

20. Department of Biology, University of Antwerp, Antwerp, B-2020, Belgium

    Benny Borremans & Sophie Gryseels

21. Robert Koch Institute, Berlin, 13353, Germany

    Jan Peter Boettcher, Heinz Ellerbrok, Antje Hermelink, Julia Hinzmann, Ute Hopf-Guevara, Stefan Kloth, Claudia Kohl, Andreas Kurth, Janine Michel, Martin Richter, Andreas Sachse, Kristina Maria Schmidt, Constanze Yue & Andreas Nitsche

22. National Institute for Infectious Diseases (INMI) Lazzaro Spallanzani, Rome, 00149, Italy

    Angela Cannas, Fabrizio Carletti, Concetta Castilletti, Francesca Colavita, Silvia Meschi, Serena Quartu, Maria Rosaria Capobianchi, Giuseppe Ippolito & Antonino Di Caro

23. Friedrich Loeffler Institute, Federal Research Institute for Animal Health, Greifswald, 17493, Insel Riems, Germany

    Sandra Diederich & Marc Mertens

24. KU Leuven Rega institute, Leuven, B-3000, Belgium

    Sophie Duraffour & Piet Maes

25. Redeemer’s University, Osun State, Nigeria

    Deborah Ehichioya

26. Centro Nacional de Microbiologia, Instituto de Salud Carlos III, Madrid, 28029, Spain

    Maria Dolores Fernandez-Garcia

27. Unité de Biologie des Infections Virales Emergentes, Institut Pasteur, Lyon, 69365, France

    Alexandra Fizet

28. Bundeswehr Institute of Microbiology, Munich, 80937, Germany

    Erna Fleischmann, Peter Molkenthin, Matthias Wagner, Kilian Stoecker & Roman Wölfel

29. National Center for Epidemiology, National Biosafety Laboratory, Budapest, H-1097, Hungary

    Zoltan Kis & Bernadett Pályi

30. Institute of Microbiology and Immunology, Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia

    Miša Korva & Tatjana Avšič-Županc

31. Public Health Agency of Sweden, Solna, 171 82, Sweden

    Annette Kraus

32. Heinrich Pette Institute – Leibniz Institute for Experimental Virology, Hamburg, 20251, Germany

    Anja Lüdtke & César Muñoz-Fontela

33. Institute of Virology, University of Bonn, Bonn, 53127, Germany

    Benjamin Meyer & Doreen Muth

34. Federal Office for Civil Protection, Spiez Laboratory, Spiez, CH-3700, Switzerland

    Jasmine Portmann & Marc Strasser

35. Bundeswehr Hospital, Hamburg, 22049, Germany

    Simone Priesnitz

36. Institute of Virology and Immunology, Mittelhäusern, CH-3147, Switzerland

    Julie Rappe

37. Janssen-Cilag, Sollentuna, SE-192 07, Sweden

    Johanna Repits

38. Thünen Institute, Hamburg, D-22767, Germany

    Jochen Trautner

39. Eurice - European Research and Project Office GmbH, Berlin, 10115, Germany

    Birte Kretschmer

40. Centre for Genomic Research, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK

    John G. Kenny

41. Department of Infection and Population Health, University College London, London, WC1E 6JB, UK

    Cordelia E. M. Coltart

42. Research IT, University of Bristol, Bristol, BS8 1HH, UK

    Damien Steer

43. Advanced Computing Research Centre, University of Bristol, Bristol, BS8 1HH, UK

    Callum Wright

44. Ministry of Health Guinea, Conakry, Guinea

    Sakoba Keita

45. World Health Organization, Geneva 27, 1211, Switzerland

    Patrick Drury & Pierre Formenty

46. World Health Organization, Conakry, Guinea

    Boubacar Diallo

47. Médecins Sans Frontières, Brussels, B-1050, Belgium

    Hilde de Clerck, Michel Van Herp & Armand Sprecher

48. Section Prévention et Lutte contre la Maladie à la Direction Préfectorale de la Santé de Guéckédou, Guéckédou, Guinea

    Alexis Traore

49. Université Gamal Abdel Nasser de Conakry, CHU Donka, Conakry, Guinea

    Mandiou Diakite

50. Health and Sustainable Development Foundation, Conakry, Guinea

    Mandy Kader Konde

Contributions

M.W.C., S.G., J.A.H., D.A.M and N.M. designed the study. J.A.H., D.A.M., M.J.E., A.R., G.P., S.G. and M.W.C. wrote the manuscript. D.A.M., J.A.H., M.J.E., A.R., G.P., M.W.C., S.G., Y.H. and I.G.D. analysed the data. All other authors were involved either in sample collection, processing and/or logistical support and strategic oversight for the work.

Corresponding author

Correspondence to Miles W. Carroll.

Competing interests

The authors declare no competing financial interests.

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