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Identification of Microbial Pathogens Using Metagenomic Next-Generation Sequencing and ID

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Identification of Microbial Pathogens Using Metagenomic Next-Generation Sequencing and ID bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 Etiology of fever in Ugandan children: identification of microbial pathogens using 2 metagenomic next-generation sequencing and IDseq, a platform for unbiased metagenomic 3 analysis 4 5 Akshaya Ramesh1, 2, † *, Sara Nakielny3 *, Jennifer Hsu4, Mary Kyohere5, Oswald 6 Byaruhanga5, Charles de Bourcy6, Rebecca Egger6, Boris Dimitrov6, Yun-Fang Juan6, Jonathan 7 Sheu6, James Wang6, Katrina Kalantar3, Charles Langelier4, Theodore Ruel7, Arthur 8 Mpimbaza8, Michael R. Wilson1, 2, Philip J. Rosenthal4, Joseph L. DeRisi3, 6 † 9 * 10 - These authors contributed equally to this work 11 † - Corresponding authors 12 13 1 – Weill Institute for Neurosciences, University of California, San Francisco, CA, USA 14 2 – Department of Neurology, University of California, San Francisco, CA, USA 15 3 – Department of Biochemistry and Biophysics, University of California, San Francisco, CA, 16 USA 17 4 – Division of Infectious Diseases, Department of Medicine, University of California, San 18 Francisco, CA, USA 19 5 – Infectious Diseases Research Collaboration, Kampala, Uganda 20 6 – Chan Zuckerberg Biohub, San Francisco CA bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 21 7 – Division of Pediatric Infectious Diseases and Global Health, Department of Pediatrics, 22 University of California, San Francisco, CA, USA 23 8 – Child Health and Development Centre, Makerere University, Kampala, Uganda 24 25 e-mail address for authors: 26 Akshaya Ramesh: [email protected] 27 Sara Nakielny: [email protected] 28 Jennifer Hsu: [email protected] 29 Mary Kyohere: [email protected] 30 Oswald Byaruhanga: [email protected] 31 Charles de Bourcy: [email protected] 32 Rebecca Egger: [email protected] 33 Boris Dimitrov: [email protected] 34 Yun-Fang Juan: [email protected] 35 Jonathan Sheu: [email protected] 36 James Wang: [email protected] 37 Katrina Kalantar: [email protected] 38 Charles Langelier: [email protected] 39 Theodore Ruel: [email protected] 40 Arthur Mpimbaza: [email protected] 41 Michael R. Wilson: [email protected] 42 Philip J. Rosenthal: [email protected] 43 Joseph L. DeRisi: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 44 Abstract 45 Background: Febrile illness is a major burden in African children, and non-malarial causes of 46 fever are uncertain. We built and employed IDseq, a cloud-based, open access, bioinformatics 47 platform and service to identify microbes from metagenomic next-generation sequencing of 48 tissue samples. In this pilot study, we evaluated blood, nasopharyngeal, and stool specimens 49 from 94 children (aged 2-54 months) with febrile illness admitted to Tororo District Hospital, 50 Uganda. 51 52 Results: The most common pathogens identified were Plasmodium falciparum (51.1% of 53 samples) and parvovirus B19 (4.4%) from blood; human rhinoviruses A and C (40%), 54 respiratory syncytial virus (10%), and human herpesvirus 5 (10%) from nasopharyngeal swabs; 55 and rotavirus A (50% of those with diarrhea) from stool. Among other potential pathogens, we 56 identified one novel orthobunyavirus, tentatively named Nyangole virus, from the blood of a 57 child diagnosed with malaria and pneumonia, and Bwamba orthobunyavirus in the nasopharynx 58 of a child with rash and sepsis. We also identified two novel human rhinovirus C species. 59 60 Conclusions: This exploratory pilot study demonstrates the utility of mNGS and the IDseq 61 platform for defining the molecular landscape of febrile infectious diseases in resource limited 62 areas. These methods, supported by a robust data analysis and sharing platform, offer a new tool 63 for the surveillance, diagnosis, and ultimately treatment and prevention of infectious diseases. 64 65 Keywords: Metagenomic Next-Generation Sequencing, Febrile illness, surveillance, pathogen 66 discovery, IDseq, cloud computing bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 67 Introduction 68 The evaluation of children with fever is challenging, particularly in the developing world. 69 A febrile child in sub-Saharan Africa may have a mild self-resolving viral infection or may be 70 suffering bacterial sepsis or malaria—major causes of disability and death [1], [2]. In the past, 71 febrile illness in children under five years of age in most of Africa was treated empirically as 72 malaria due to the limited availability of diagnostics and the risk of untreated malaria progressing 73 to life-threatening illness. This strategy changed with new guidelines from the World Health 74 Organization (WHO) in 2010, which recommend limiting malaria therapy to those with a 75 confirmed diagnosis [3]. However, standard recommendations for management of febrile 76 children who do not have malaria are lacking. Increased knowledge about the prevalence of non- 77 malarial pathogens associated with fever is needed to inform management strategies for febrile 78 children who do not have malaria [2]. 79 80 Advances in genome sequencing hold promise for addressing global infectious disease 81 challenges by enabling unbiased detection of microbial pathogens without requirement for the 82 extensive infrastructure of modern microbiology laboratories [4], [5]. Although sequence-based 83 diagnostics have not yet replaced most traditional microbiological assays, this situation is 84 rapidly changing, as sequence-based strategies are incorporated for clinical care [6]–[10], and 85 costs have declined dramatically over the past decade [11]. A significant roadblock toward 86 implementation of sequence-based diagnostics is the extensive computational requirements and 87 bioinformatics expertise required. In fact, as sequencing costs decline, computational expenses 88 may proportionally increase due to inevitable expansion of existing genomic databases. 89 bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 90 To address these challenges, we developed IDseq, a cloud-based open-source 91 bioinformatics platform and service for detection of microbial pathogens from metagenomic 92 next-generation sequencing (mNGS) data. IDseq requires minimal computational hardware and 93 is designed to enhance accessibility and build informatics capacity in resource limited regions. 94 Here, we leveraged IDseq and mNGS data to perform an exploratory proof-of-concept molecular 95 survey of febrile children in rural Uganda to characterize pathogens associated with fever, 96 including both well-recognized and novel causes of illness. 97 98 Analyses/Results 99 Clinical presentations of children providing samples for analysis 100 From October to December, 2013, 94 children admitted to Tororo District Hospital were 101 enrolled (Table 1). Their mean age was 16.4 (IQR: 8.0-12.0) months, and 66 (70.2%) were 102 female. Chief symptoms reported in addition to fever were cough (88.3%), vomiting (56.4%), 103 diarrhea (47.9%), and convulsions (27.7%). Top admitting diagnoses were respiratory tract 104 infection (57.4%), gastroenteritis/diarrhea (29.8%), and septicemia (11.7%) (Data file 1a). 105 106 Serum and nasopharyngeal (NP) swab samples were collected from 90 children each; for 107 four children, only one of the two sample types was successfully collected. Although 45 (47.9%) 108 of the children had a presenting symptom of diarrhea, stool samples were available for only 10 109 due to logistical constraints. Blood smears identified P. falciparum in 12 of the 90 samples that 110 underwent mNGS analysis. 111 112 Metagenomic sequencing findings bioRxiv preprint doi: https://doi.org/10.1101/385005; this version posted August 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 113 mNGS was performed on 90 serum, 90 NP swab, and 10 stool samples and analyses was 114 done using the IDseq pipeline (see Methods section, Figure 1); detailed findings are reported in 115 Data file S1b. For each sample type, RNA was extracted, libraries were prepared, samples were 116 sequenced, and reads were analyzed using the IDseq Portal. A mean of 11.5 million (IQR 6.4 – 117 15.2 million) paired-end reads were obtained per sample; sequencing statistics are in Data file 118 Table S2. For one batch of serum samples, only a single read, rather than paired-end reads, was 119 produced. 120 121 mNGS of serum 122 At least one microbial species was detected in 60 (66.7%) serum samples; more than one 123 microbe was detected in 11 (12.2%) samples (Figure 2A). The most commonly identified 124 microbes were Plasmodium falciparum (46, 51.1%) and parvovirus B19 (4, 4.4%).
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