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Research Article | Host-Microbe Biology

Deep Sequencing of RNA from Blood and Oral Swab Samples Reveals the Presence of Nucleic Acid from a Number of Pathogens in Patients with Acute Ebola Virus Disease and Is Consistent with Bacterial Translocation across the Gut

Miles W. Carroll, Sam Haldenby, Natasha Y. Rickett, Bernadett Pályi, Isabel Garcia-Dorival, Xuan Liu, Gary Barker, Joseph Akoi Bore, Fara Raymond Koundouno, E. Diane Williamson, Thomas R. Laws, Romy Kerber, Daouda Sissoko, Nóra Magyar, Antonino Di Caro, Mirella Biava, Tom E. Fletcher, Armand Sprecher, Lisa F. P. Ng, Laurent Rénia, N’faly Magassouba, Stephan Günther, Roman Wölfel, Kilian Stoecker, David A. Matthews, Julian A. Hiscox
W. Paul Duprex, Editor
Miles W. Carroll
a Public Health England, Porton Down, Salisbury, United Kingdom
b NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Porton Down, United Kingdom
c University of Southampton, South General Hospital, Southampton, United Kingdom
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Sam Haldenby
d Centre for Genomic Research Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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Natasha Y. Rickett
e Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
f NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
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Bernadett Pályi
g National Public Health Institute, National Biosafety Laboratory, Budapest, Hungary
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Isabel Garcia-Dorival
e Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
f NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
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Xuan Liu
d Centre for Genomic Research Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
f NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
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Gary Barker
h School of Biological Sciences, University of Bristol, Bristol, United Kingdom
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Joseph Akoi Bore
i European Mobile Laboratory, Hamburg, Germany
j Institut National de Sante Publique, Conakry, Guinea
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Fara Raymond Koundouno
i European Mobile Laboratory, Hamburg, Germany
j Institut National de Sante Publique, Conakry, Guinea
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E. Diane Williamson
k Defence Science Technology Laboratories, Porton Down, United Kingdom
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Thomas R. Laws
k Defence Science Technology Laboratories, Porton Down, United Kingdom
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Romy Kerber
l Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
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Daouda Sissoko
m Bordeaux Hospital University Center, INSERM U1219, Bordeaux University, Bordeaux, France
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Nóra Magyar
g National Public Health Institute, National Biosafety Laboratory, Budapest, Hungary
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Antonino Di Caro
i European Mobile Laboratory, Hamburg, Germany
n National Institute for Infectious Diseases, Lazzaro Spallanzani IRCCS, Rome, Italy
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Mirella Biava
i European Mobile Laboratory, Hamburg, Germany
n National Institute for Infectious Diseases, Lazzaro Spallanzani IRCCS, Rome, Italy
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Tom E. Fletcher
o Liverpool School of Tropical Medicine, Liverpool, United Kingdom
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Armand Sprecher
p Médecins Sans Frontières, Brussels, Belgium
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Lisa F. P. Ng
e Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
f NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
q Singapore Immunology Network, Agency for Science, Technology and Research, Singapore
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Laurent Rénia
q Singapore Immunology Network, Agency for Science, Technology and Research, Singapore
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N’faly Magassouba
r Université Gamal Abdel Nasser de Conakry, Laboratoire Des Fievres Hemorragiques en Guinee, Conakry, Guinea
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Stephan Günther
i European Mobile Laboratory, Hamburg, Germany
l Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
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Roman Wölfel
s Bundeswehr Institute of Microbiology, Munich, Germany
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Kilian Stoecker
s Bundeswehr Institute of Microbiology, Munich, Germany
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David A. Matthews
t School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
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Julian A. Hiscox
e Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
f NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom
q Singapore Immunology Network, Agency for Science, Technology and Research, Singapore
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W. Paul Duprex
Boston University School of Medicine
Roles: Editor
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Christopher F. Basler
Georgia State University
Roles: Solicited external reviewer
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Christina Spiropoulou
Centers for Disease Control and Prevention
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DOI: 10.1128/mSphereDirect.00325-17
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Figures

  • Tables
  • Supplemental Material
  • FIG 1
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    FIG 1

    Mean time between symptom onset and sample procurement for two of the three different patient categories, hospitalized survivors and hospitalized fatalities.

  • FIG 2
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    FIG 2

    Illustrative examples for the total number of reads mapped to species identified in the blood samples from patients with acute EVD.

  • FIG 3
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    FIG 3

    Heat map showing the bacterial species (y axis) identified by mapping to nucleic acids by comparing all patient cohorts who had a blood sample taken when they had acute EVD. The x axis shows individual patients from the convalescent control group (green) (n = 16), hospitalized survivors group (yellow) (n = 44), and hospitalized fatalities group (orange) (n = 118); data for these groups are also separated by vertical lines. The colors represent the arbitrary read depth of the sequence, with yellow indicating a greater sequence read depth.

  • FIG 4
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    FIG 4

    Heat map showing the bacterial species (y axis) identified mapping to nucleic acids in a swab sample taken from an individual patient. All patients in this category were deceased at the time of sampling (n = 24). The color represents the arbitrary read depth of the sequence, with yellow indicating a greater sequence read depth.

  • FIG 5
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    FIG 5

    Relative abundance of transcripts mapping to host transcripts associated with pathways involved in the acute-phase response, blood clotting, innate immune response, and inflammation in patients with EVD (positive by RNA-seq and qRT-PCR) who were confirmed positive (n = 22; gray) or negative (n = 9; black) for at least one bacterial coinfection (by RNA-seq). Relative gene expression levels were measured as fragments per kilobase of transcript per million mapped reads (FPKM). Each of the graphs presented is based on the average FPKM values. Statistical analysis was performed using a two-way ANOVA with Sidak’s multiple-comparisons adjustment (Prism 7; GraphPad, CA).

  • FIG 6
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    FIG 6

    Effect of an increased number of reads mapping to P. falciparum. The green line, associated with the right-hand y axis, shows the proportion of patients who tested negative for malaria according to the RDT but for whom reads to P. falciparum were detected during RNA-seq analysis. The red line, also associated with the right-hand y axis, shows the patient fatality rate according to different levels of reads to P. falciparum. The left-hand y axis shows the abundance of patients in each bin, i.e., the number of individuals who were grouped into each category. The graph was created using Prism (GraphPad, CA).

  • FIG 7
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    FIG 7

    Relative abundance of transcripts mapping to host transcripts associated with malaria and the innate immune response in patients with EVD who were confirmed positive (gray; n = 10) or negative (black; n = 13) for P. falciparum (by RNA-seq and RDT). Relative gene expression levels were measured as the fragments per kilobase of transcript per million mapped reads (FPKM). Each set of graphs (left and right) is based on average FPKM values. Two analyses of the data are presented, and means and standard deviations (left) or medians and total ranges (right) are shown.

  • FIG 8
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    FIG 8

    Relative abundance of transcripts mapping to host transcripts associated with coagulation in patients with EVD and confirmed positive (gray; n = 10) or negative (black; n = 12) for P. falciparum (by RNA-seq and RDT). Relative gene expression levels were measured as fragments per kilobase of transcript per million mapped reads (FPKM). The graphs are based on average FPKM values. Two analyses of the data are presented, showing the means and standard deviations (left) and medians and total ranges (right).

  • FIG 9
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    FIG 9

    The presence of nucleic acids from certain viruses varies with patient age. Patients were grouped according to their immunological maturity (≤1 year [n = 8], 1.5 to 4 years [n = 13], 5 to 45 years [n = 120], and 46+ years [n = 39]), and the proportions of each group with reads mapping to P. falciparum, HHV4, and GBV-C are shown.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    Summary details of patients from which RNA-seq data were selected for this studya

    Patient traitCommunity deathsHospitalized fatalitiesHospitalized survivorsConvalescent controls
    n 241184416
    % male29.140.129.593.75
    Age range1–70 yr2 mo–80 yr10 mo–68 yr18–40 yr
    Mean age (yrs)33.3528.4433.1030
    EBOV CT
        Range12.37–31.8512.08–26.5515.77–31.60NA
        Mean18.8117.2321.67NA
    • ↵a Samples with similar threshold cycle (CT) values were chosen, as this would indicate similar viral loads. It was not possible to directly correlate CT values to viral load. NA, not applicable.

  • TABLE 2

    Exemplary data on the variety of microorganisms identified in pooled RNA-seq data from 179 patients positive for EBOV based on qRT-PCR

    OrganismProtein% identityAlignmentE value
    Escherichia coli G58-1Glycosyl transferase family 21005830.00E+0
    Lactobacillus fermentum DNA-directed RNA polymerase subunit beta1007220.00E+0
    Wohlfahrtiimonas chitiniclastica Hypothetical protein, partial1005830.00E+0
    Plasmodium falciparum UGT5.1Hypothetical protein C923_003281005114.50E−298
    Candida albicans WO-1Acetyl coenzyme A carboxylase1005042.50E−293
    Streptococcus pneumoniae EU-NP04Transposase DDE domain protein1004932.90E−292
    Myroides spp.GMP synthase1005083.20E−292
    Haemophilus influenzae Phosphoribosylformylglycinamidine synthase1005001.80E−289
    Acinetobacter baumannii AB307-02942-Isopropylmalate synthase1004791.20E−276
    Providencia rettgeri DSM 1131DNA polymerase III subunit alpha1004839.70E−276
    Plasmodium falciparum Vietnam Oak-KnollHypothetical protein PFFVO_045021004762.20E−270
    Ureaplasma urealyticum Cell division protein FtsH1004788.20E−264
    Lactobacillus fermentum 6-Phosphogluconate dehydrogenase1004573.70E−260
    Plasmodium falciparum Tanzania (2000708)Hypothetical protein PFTANZ_033151004505.70E−260
    Streptococcus sanguinis Collagen-binding protein1004392.80E−251
    Acinetobacter baumannii Putrescine/spermidine ABC transporter1004445.10E−251
    Haemophilus haemolyticus Catalase1004158.40E−250
    Plasmodium falciparum 3D7Elongation factor 1 alpha1004334.60E−249
    Plasmodium falciparum MaliPS096_E11Histidine-tRNA ligase1004031.10E−235
    Myroides spp.Multispecies collagenase1004136.20E−232
    Plasmodium falciparum Tanzania (2000708)ATP-dependent protease HslVU, ATPase subunit1004102.10E−229
    Campylobacter spp.Multispecies GTP-binding protein1004075.10E−228
    Streptococcus parasanguinis Amino acid transporter1004028.80E−228
    Haemophilus parainfluenzae l,d-Transpeptidase1003933.80E−224
    Escherichia coli Leucyl-tRNA synthetase1003839.40E−224
    Myroides spp.Diaminopimelate decarboxylase1003889.30E−223
    Myroides odoratimimus TonB-dependent receptor1003891.80E−222
  • TABLE 3

    Virus sequences identified in pooled RNA-seq data from 179 patients positive for EBOV based on qRT-PCR and clinical diagnosis

    VirusProtein% identityAmino acid lengthE value
    EBOVL protein96.82,2120.00E+0
    GB-CPolyprotein99.13281.50E−190
    HHV-4BALF21001001.9E−51
    LF2 protein1001664.9E−91
    BALF598.81724.4E−88
    BALF41002576.6E−142
    DNA polymerase catalytic subunit99.21312.3E−71
    BHRF11001217.4E-66
    BARF099.21206.5E−62
    Early antigen D1001182E−60
    gp110 precursor100951.3E−49
    A73 protein100946.1E−50
    LF198.9921.1E−48
    Putative BHLF1 protein98.7754.8E−42
    BFRF1100714.1E−32
    BMRF2100687.6E−31
    dUTPase98.5683E−32
    BFRF2100674.2E−32
    LF3 protein100450.0000000000000002
    K15100370.00000000000056
    HHV-5 (cytomegalovirus)IRL41001073.1E−53
    IRL394.11011.20E−46
    IRL7100821.60E−37
    RL5A63.6669.30E−15
    IRL555.1693.50E−12
    UL10968.1472.10E−10
    Hepatitis A virusPolyprotein100915.5E−45
    Hepatitis B virusPolymerase92.85033.30E−278
    Pepino mosaic virusReplicase99.41634.60E−87
    Sewage-associated gemycircularvirus-4Replication-associated protein1001053.60E−57
    Rotavirus AVP698.8868.1E−41
    VP1100751.1E−33
    NSP31692511.5E−21
    PapillomavirusMinor structural protein- interacting protein100563.40E−24
    L297.2721.50E−34
    Regulatory protein98.5675E−30
    Torque Teno virus 20Unnamed protein product100329.10E−9
    Tobacco mosaic virusReplicase97.21,5210.00E+0
    Capsid protein1001027.2E−49
    126-kDa protein993142.30E−181
    Movement protein97.42688.60E−145
  • TABLE 4

    Details of bacterial isolates from throat swab samples identified by mass spectrometry approaches

    EBOV CTAgeSexMALDI-TOF result(s)a
    2422 yrMale Proteus mirabilis (2.537)
    1933 yrFemale Rothia mucilaginosa (2.181)
    2560 yrNDb Proteus mirabilis (2.427)
    144 moMale Staphylococcus aureus (2.378) Acinetobacter baylyi (2.054)
    2663 yrMale Streptococcus salivarius (2.117) Candida albicans (2.141)
    • ↵a Scoring for MALDI-TOF results was as follows: 3.000 to 2.300, highly probable species identification; 2.2999 to 2.000, secure genus identification.

    • ↵b ND, no data.

Supplemental Material

  • Figures
  • Tables
  • TABLE S1

    Evidence of genetic material belonging to other pathogens. Download TABLE S1, XLS file, 12.5 MB.

    Copyright © 2017 Carroll et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • FIG S1

    Phylogenetic tree showing alveolates identified through Diamond BLASTX querying of Trinity-assembled contigs against the nonredundant NCBI database. An E value cutoff of 1 × 10−10 was applied with no additional filtering. Plasmodium species are indicated with red branches, and yellow and red bars indicate the relative abundance levels of sequences associated with species, based on the length of BLAST hits; yellow branches represent log values and red branches represent linear values. Many of the BLAST hits to species with apparently very low hit lengths may have been artifactual but have been retained to provide a contextual background. Download FIG S1, PDF file, 0.4 MB.

    Copyright © 2017 Carroll et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

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Deep Sequencing of RNA from Blood and Oral Swab Samples Reveals the Presence of Nucleic Acid from a Number of Pathogens in Patients with Acute Ebola Virus Disease and Is Consistent with Bacterial Translocation across the Gut
Miles W. Carroll, Sam Haldenby, Natasha Y. Rickett, Bernadett Pályi, Isabel Garcia-Dorival, Xuan Liu, Gary Barker, Joseph Akoi Bore, Fara Raymond Koundouno, E. Diane Williamson, Thomas R. Laws, Romy Kerber, Daouda Sissoko, Nóra Magyar, Antonino Di Caro, Mirella Biava, Tom E. Fletcher, Armand Sprecher, Lisa F. P. Ng, Laurent Rénia, N’faly Magassouba, Stephan Günther, Roman Wölfel, Kilian Stoecker, David A. Matthews, Julian A. Hiscox
mSphere Aug 2017, 2 (4) e00325-17; DOI: 10.1128/mSphereDirect.00325-17

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Deep Sequencing of RNA from Blood and Oral Swab Samples Reveals the Presence of Nucleic Acid from a Number of Pathogens in Patients with Acute Ebola Virus Disease and Is Consistent with Bacterial Translocation across the Gut
Miles W. Carroll, Sam Haldenby, Natasha Y. Rickett, Bernadett Pályi, Isabel Garcia-Dorival, Xuan Liu, Gary Barker, Joseph Akoi Bore, Fara Raymond Koundouno, E. Diane Williamson, Thomas R. Laws, Romy Kerber, Daouda Sissoko, Nóra Magyar, Antonino Di Caro, Mirella Biava, Tom E. Fletcher, Armand Sprecher, Lisa F. P. Ng, Laurent Rénia, N’faly Magassouba, Stephan Günther, Roman Wölfel, Kilian Stoecker, David A. Matthews, Julian A. Hiscox
mSphere Aug 2017, 2 (4) e00325-17; DOI: 10.1128/mSphereDirect.00325-17
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    • ABSTRACT
    • INTRODUCTION
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KEYWORDS

Ebola
Ebola virus disease
informatics
Plasmodium falciparum
RNA-seq
bioinformatics
coinfection
filovirus
gene expression
host-pathogen interactions
intracellular parasites
malaria

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