Blood Virosphere in Febrile Tanzanian Children

doi:10.21203/rs.3.rs-97359/v1

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Blood Virosphere in Febrile Tanzanian Children

https://doi.org/10.21203/rs.3.rs-97359/v1

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published 01 Jan, 2021

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Background: Viral infections are the leading cause of childhood acute febrile illnesses motivating consultation in sub-Saharan Africa. The majority of causal viruses are never identified in low-resource clinical settings as such testing is either not part of routine screening or available diagnostic tools have limited ability to detect new/unexpected viral variants. An in-depth exploration of the blood virome is therefore necessary to clarify the potential viral origin of fever in children. Untargeted metagenomic next-generation sequencing is a powerful tool for such broad investigations, allowing the detection of RNA and DNA viral genomes.

Results: Here, we describe the blood virome of 816 febrile children (<5 years) presenting at outpatient departments in Dar es Salaam over one-year. We show that half the population (394/816) had at least one detected virus recognized as causes of human infection/disease (13.8% enteroviruses (enterovirus A, B, C, and rhinovirus A and C), 12% rotaviruses, 11% human herpesvirus type 6). Additionally, we report the detection of a large number of viruses (related to arthropod, vertebrate or mammalian viral species) not yet known to cause human infection/disease, highlighting those who should be on the radar, deserve specific attention in the febrile paediatric population and, more broadly, for surveillance of emerging pathogens.

Conclusions: This study highlighted several DNA and RNA viral sequences on a population of young children with fever of unknown origin and/or severe outcomes living in Tanzania, and provides a large list of novel viruses that should be considered as a potential cause of human infections.

General Microbiology

Blood virome

virosphere

untargeted metagenomic next-generation sequencing

fever

children

outpatients

Humans are infected by a large number of eukaryotic DNA and RNA viruses that, together with prokaryotic phages and endogenous viral elements, comprise what we define as the human virome. Eukaryotic viruses are a diverse group of DNA and RNA viral agents replicating in a variety of human cells and tissues and engaging in a vast permutation of interactions with the human immune system. Consequently, the presentation of human viral disease depends on both viral and host factors, where a virus may only be pathological in a certain situation (e.g. following immunosuppression), whilst being considered (perhaps over simplistically) as commensal or merely an incidental finding in a “healthy” situation.

The composition of the human virome does not only vary between these populations but it also changes over time within individuals according to their personal history of exposure and immunity and thus, their geographic location, access to vaccination and their genetic make-up. Untargeted metagenomic next-generation sequencing (mNGS) has the ability to detect any viral RNA or DNA sequence in a given sample and thus, it represents the most appropriate tool with which to explore the human virome [1].

It is recognized that we have only uncovered a fraction of the so-called virosphere [2, 3]. While many human viruses have emerged from animal origin, shotgun surveillance in sentinel animals generates a lot of noise by reporting a majority of viruses unlikely to infect humans. Thus, we hypothesize that pathogen surveillance would be extremely informative to public health interventions if focused first on humans. Specifically, there is a need for investigations that could reveal not only the presence of known and expected viruses, but also divergent or unexpected ones. Characterization of the human virome could help to identify new sources of infections, possibly predict viral emergence or identify the potential deleterious effects of viruses considered as commensal.

The human virome has been described in stool, skin, nasopharyngeal swabs or breast milk [4]. Investigations were also undertaken on blood from individuals representing the general population [5], blood donors [6–9], and in subjects with various conditions such as transplant recipients [10–12], individuals living with HIV[13], and febrile adult and paediatric patients [1, 14–18]. However, most of these studies were limited by the scope of their analyses (e.g. addressing only RNA or DNA viruses, pooled samples) or in the time frame of included subjects.

Infections and fever in sub-Saharan Africa represent the most frequent motivation for paediatric outpatient consultation [19]. The majority of causal viruses, however, are never identified in low-resource clinical settings. In this study, we seek to characterize the blood virome in a cohort of 823 children consulting at nine outpatient departments in Dar es Salaam, Tanzania, for acute fever of unknown origin and/or severe outcomes (Fig. 1) [20]. We tested each sample by untargeted mNGS and analysed them using (a) a bioinformatic pipeline based on a novel viral reference database [21] to report vertebrate viruses and (b) by de novo assembly. We report the full diversity of their blood virome, including novel viruses and those unexpected or not previously reported in humans that deserve specific attention.

Study design

The serum samples tested by untargeted mNGS come from a larger cohort of 3192 consecutively paediatric patients (2 to 59 months of age), recruited between December 2014 and February 2016, at nine outpatient clinics in Dar es Salaam, Tanzania, with the inclusion criteria of “acute febrile illness” (axillary temperature ≥ 37.5 °C). The detailed characteristics of this cohort are described in Keitel et al. [20].

A sub-cohort of interest comprising 1233 patients was then selected (Fig. 1) based on three diagnoses which carried the highest theoretical probability of detecting blood viruses with significant clinical consequences and for which mNGS would most increase the diagnostic evidence for the aetiology of the illness. These diagnoses were obtained using by the clinical decision algorithms of the e-POCT study [20]: 1) “Fever without focus” representing children who would most benefit from further diagnostics; 2) “Severe illness”, representing children in whom the impact of blood viruses may be investigated. 3) “Malaria”, in whom blood viruses may interact with this important and frequent parasite and/or be less likely as the cause of the febrile episode.

A total of 823 patients (resulting in 823 sera samples) were then randomized from this sub-cohort of interest and submitted to the entire process of mNGS (detailed below). Only samples with a minimum of one million reads for both RNA and DNA libraries were considered for further bioinformatic analysis, resulting in the exclusion of 4 samples. Three samples were further excluded; two consecutive to a technical problem encountered during the library procedures, and one due to an overrepresentation of insect sequences after analysis of raw sequencing data [22]. Therefore, 816 of the 823 initial selected samples were considered for analysis.

Sample extraction and untargeted metagenomic next-generation sequencing

Two hundred twenty microliters (ul) of each serum sample was centrifuged 10,000 X g for 10 min and treated with 40 U of Turbo DNAse (2U/ul) (Ambion, Rotkreuz, Switzerland). Of the total resulting 244 µl, 240 µl were recovered to perform the nucleic acid extraction. RNA and DNA virus genome extractions were performed as previously described [23]. Briefly, RNA was extracted with TRIzol (Invitrogen, Carlsbad, CA, USA) and DNA with a NucliSens easyMAG magnetic bead system (bioMérieux, Geneva, Switzerland) followed by a double-stranded DNA synthesis with DNA polymerase I, Large Fragment (Klenow) (New England BioLabs, Ipswich, MA, USA). RNA and DNA pellets were then resuspended in respectively 10 ul and 5 ul of nuclease-free water (Promega, Dübendorf, Switzerland), and quantified using the Qubit 3.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and adjusted at 2 ng/ul.

For RNA virus detection, ribosomal RNA was removed using the Ribo-Zero Gold depletion kit (Illumina, San Diego, US) prior to library preparation. RNA libraries were prepared with the TruSeq total RNA preparation protocol (Illumina) before being multiplexed by four on the HiSeq 2500 platform (Illumina) using the 2 × 100 bp paired-end protocol. The mean of total number of read pairs obtained was 4.45E7 (range from 2E6 to 1.09E8).

For DNA virus detection, libraries were prepared with the Illumina Nextera XT protocol (12 PCR cycles) before being multiplexed by six on the HiSeq 4000 platform (Illumina) using the 2 × 100 bp paired-end protocol. The mean of total number of read pairs obtained was 5.46E7 (range from 2E6 to 2.52E8).

Both RNA and DNA library concentrations were measured using Qubit (Life Technologies). The size distribution of fragments was controlled using a 2200 TapeStation (Agilent, Santa Clara, CA, USA).

To assess the presence of potential mNGS contaminants (i.e. false positive results), a “no-template” control (NTC) submitted to the whole procedure was included to each sequencing run. In order to assess the entire process efficiency (i.e. from sample preparation to bioinformatics analysis) each sequencing run included a virus-spiked positive control (PC). This latter is also useful in addition to the NTC samples to evaluate the presence of potential sporadic mNGS contaminants. For the RNA virus procedure, the PC consists of a canine distemper virus (CDV)-spiked positive control (RNA-PC), whereas for the DNA virus procedure, a baculovirus (GenScript, Piscataway, NJ, USA) harboring 793 nucleotides of the CDV fusion gene (corresponding to positions 5505 to 6297 from the GenBank KY971529 reference) was used as spiked-positive controls (DNA-PC).

Bioinformatic analysis

For each sample, the reads generated by the Illumina platforms (RNA and DNA libraries) were processed for virus detection using a bioinformatics pipeline (FeVir) designed to detect all vertebrate viruses and a de novo assembly sequencing approach.

Bioinformatics pipeline for vertebrate virus detection

Paired reads were quality filtered and mapped against Virosaurus (version V90v_2018_11) [21], a curated database for all known vertebrate viruses, using virusscan 1.0 [24]. The mapping was performed allowing up to 12 mismatches per tag but at most 15 mismatches for the pair, ultimately retaining the lowest possible mismatch. Sequence pairs whose lowest mismatch count mapped equally well onto more than 512 distinct viruses entries were discarded. Non-redundant mapping results were used to compute metrics for mapped reads and coverage. Then for each virus species (or genus for anelloviridae) detected, results with the best total genome coverage value were kept. Only results with coverage ≥ 300 nucleotides were considered as positive.

Index hopping/lane cross-contamination was removed using a 1% threshold. An important challenge in the interpretation of mNGS data is to distinguish clinically significant viruses from those that are known to be medically irrelevant and also to differentiate novel viruses (not previously described in humans) from those that are more likely to be environmental or mNGS reagent contaminants [25]. Therefore, results for viruses detected in processes controls and endogenous retrovirus were not reported.

For segmented viruses, results for each segment were summed. For Herpesviridae, each gene’s results were summed using a non-redundant and non-overlapping list of genes.

Viral hits were classified into 3 groups:

1) Those of “recognized clinical significance” including viruses known to infect humans and well recognized in clinical practice as disease-causing agents. As for most pathogens the spectrum of associated illness associated with this group of viruses ranges from mild symptoms (or even asymptomatic infection) to life-threatening disease.

2) Commensal viruses of “undetermined clinical significance” that comprise viruses that infect human but for which the association with a specific disease has not yet been established. Most of these viruses however have shown to interact with the human immune response and have been referred to by some as commensal agents. The two main representatives are Anelloviridae and human pegivirus-1.

3) Those of “unknown significance” that represent viral agents not usually recognized as infecting humans and/or have never fulfilled Koch’s postulates that would attribute a theorical pathogenic role [26]. Thus, it could reflect transient or aborted infections, environmental spill over, contaminants from skin or other body fluids or spurious mNGS reagent contaminants. We consider this group of particular interest to surveillance efforts, since it could signal contact with potential emerging human pathogens and guide targeted screening in the future.

De novo analysis

The de novo analysis was performed as previously published [27]. First, reads were trimmed to remove low-quality and adapter sequences using Trimmomatic (v0.33). Human reads were removed by mapping reads against the human genome and transcriptome (hg38, gencode.V23) using the SNAP nucleotide aligner program [28]. Then, de novo assembly was performed using IDBA-UD (v.1.1.3) [29] and the generated contigs of greater than 2000 nucleotides were blasted (blastx, v.2.3.0+) [30] against the U-RVDBv12.2 viral database [31]. Anelloviridae contigs were filtered out and processed separately. The newly obtained sequences were then cross-checked by BLAST (blastn, blastx) against GenBank (Nucleotid collection (nr/nt), Non-redundant protein sequences (nr)) and NCBI conserved domain tool. Novel virus contigs with a presumed bacterial or fungi host were not considered for further analyses.

Sample and control FASTQ files were additionally filtered for low-complexity sequences, using tagdust (v2.31). Remaining reads were mapped against assembled genomes that were not already detected by the previously described FeVir pipeline, using SNAP. For each new hit, mapped reads and coverage metrics were then computed.

Only results with ≥ 300 nucleotides of coverage were considered. Results for viruses detected in processes controls were not reported.

Anelloviridae sequence analysis

Anelloviridae contigs with a complete ORF1 larger than 600 amino acids were separated into Alpha-, Beta- and Gamma-torquevirus genera using multiple alignment (muscle v3.8.31) with the reference sequences of ORF1. Alternative “ACG” initiation codon was also used to detect Alphatorquevirus ORF1.

To estimate the proportion of assembled contigs that was “new” compared to reported sequences in GenBank (Query: alphatorquevirus[Organism] OR betatorquevirus[Organism] OR gammatorquevirus[Organism] OR unclassified Anelloviridae[Organism], downloaded in March 2019), we first excluded non-primate sequences, then filtered as above and added to the new assembled sequences. For the three genera, complete ORF-1 sequences were separated in clusters of 80% nucleotide identity, using cdhit-est (CD-HIT v4.6). For each cluster, sequences of 90% identity were selected using cdhit-est. Then representative sequences were aligned two by two using muscle (v3.8.1551), when two clusters contain reference sequences that shared more than 80% nucleotide identity, clusters were merged.

To determine the number of distinct Anelloviridae of the same genus present in each sample, the filtered FASTQ files were mapped to the ORF1 representative sequence using SNAP, for each cluster, positive detection was considered if 50% or more of the ORF1 was covered.

Additionally, for each of the assembled Anelloviridae contigs that contained a complete ORF1, circular genomes were linearized starting after the GC rich region and incomplete genomes were trimmed the same way. Finally, representative genomes were selected at 90% nucleotide identity with cdhit-est. Then filtered FASTQ files were mapped against the representative genomes with SNAP. Metrics were computed and results were processed as described above (≥ 300 nucleotides, 1% index-hopping/lane cross-contamination threshold). Results with the best total genome coverage value for each genus were reported if previously found negative with the FeVir pipelin

In this large population of febrile paediatric outpatients recruited in East Africa, we report the detection of a large diversity of RNA and DNA virus sequences that we classify into 3 groups (Supplementary Fig. 1, https://www.unige.ch/virology/en/krona/); those known to cause disease in humans and of “recognized clinical significance” (Supplementary Fig. 1a), those known to be present in humans and of “undetermined clinical significance” (Supplementary Fig. 1b) and a third group of viruses not usually described in humans and of “unknown significance” (Supplementary Fig. 1c).

Viruses of “recognized clinical significance”

Of the 816 children, 394 (48.3%) harboured RNA and/or DNA sequences specific for a virus recognized as causing human diseases (Supplementary Table 1). This group was dominated by RNA viruses, predominantly from members of the Picornaviridae family such as human enteroviruses (EV) (13.8% of all cases, 113/816) and rotaviruses (12% of all cases, 98/816) (Supplementary Fig. 1a).

Species from various genera of the Picornaviridae family were detected, including EV-A, B and C (n = 39, 31 and 1, respectively), rhinovirus (RV)-A and C (n = 3 and 39, respectively), hepatovirus A (n = 10) and parechovirus A (n = 9) (Fig. 2a). Within the different EV species, various coxsackievirus A and echovirus genotypes were observed (Supplementary Table 1), as well as one EV-A71 case that may represent a new EV-A71 genogroup (Supplementary Fig. 2, SAfia-57/TZA/2016 MN727165.1). Among EV-C, poliovirus-related sequences were not observed. A large diversity of RV-C were detected (Supplementary Fig. 3), nine of which were only detectable by de novo analysis (Fig. 2b), highlighting the absence of similar sequences in any available database. The 17 RV-C for which a complete VP1 sequence was recovered, could be clustered into 9 distinct RV types (including one that may, tentatively, be categorised as a new type). One of the genomes (SAfia-94/TZA/2015 MN727266.1) is an RV-C45/RV-C11 recombinant similar to a variant described in a wild chimpanzee in Uganda [32].

A total of 98/816 (12%) samples had detectable rotavirus sequences, and only one corresponded to the vaccine G1P[8] strain (Supplementary Fig. 1a). Human astroviruses (HAstV) were the 3rd most frequently detected among RNA viruses from this group. The classical HAstV (i.e. HAstV serotypes 1–8) that are a well-known cause of gastroenteritis, represented only a minority of cases (n = 7) whereas the novel HAstV (i.e. HAstV MLB/VA) were observed in 17 cases (4 MLB1, 2 MLB2, 5 VA1 and 6 VA3). We also identified Norwalk virus (n = 12), human immunodeficiency virus 1 (n = 7), Sapporo virus (n = 4), human coronavirus OC43 (n = 2), human parainfluenza virus type 4a (n = 1) and the Wistar RA 27/3 rubella vaccine strain in one case. Finally, only one patient had detectable sequences of dengue virus (serotype 2), which corroborates clinical diagnoses and on-site rapid testing, that the study did not occur during a dengue virus outbreak.

Concerning the DNA viruses, by far the most frequent ones were members of the Betaherpesvirinae subfamily followed by mastadenovirus species. Notably, a significant proportion of these agents were also recovered at the RNA level.

Among Betaherpesvirinae (Supplementary Fig. 1a), human herpesvirus type 6 (HHV-6) was the most frequent (n = 90; 63 belonging to the HHV-6 B species and 27 remaining untyped), followed by human herpesvirus type 7 (HHV-7) (n = 36) and cytomegalovirus (CMV) (n = 15) (Fig. 2c-d). In addition, the following herpesviruses were identified in a few samples: varicella zoster virus (n = 4), herpes simplex type 1 virus (n = 1), and human herpesvirus 8 (n = 1). Among all HHV-6, HHV-7 and CMV positive samples listed above, a large proportion (73.3%, 75.7% and 33.3%, respectively) were detected in both DNA and RNA libraries suggesting the presence of ongoing genome replication. Of note, 7 HHV-6, 2 HHV-7 and 5 CMV positive cases were detected in the RNA libraries only, which means that, overall, 81.1% of HHV-6 (n = 73/90), 83.3% of HHV-7 (n = 30/36) and 66.6% of CMV (n = 10/15) positive cases were detected in the RNA libraries.

Specific sequences for human mastadenovirus (HAdV) were recovered in 28 samples and encompassed species A, C, E and F. Human bocavirus, parvovirus B19, trichodysplasia spinulosa-associated polyomavirus and BK virus were recovered in 10 or less samples each. (Fig. 2c). A fraction of these DNA viruses was also detected in the RNA libraries only (five HAdVs (one each for species A, E and F, two for the species C) and one human bocavirus) (Fig. 2c).

Commensal viruses of “undetermined clinical significance”

Anelloviridae sequences were detected in all except one clinical sample (Supplementary Fig. 1b and Fig. 3). Co-infection with multiple genera was the rule (99.0%), with 752/816 samples found positive for all 3 genera (Fig. 4a). Torque teno mini virus (TTMV, n = 803) was the most frequent, followed by Torque teno virus (TTV, n = 795), and Torque teno midi virus (TTMDV, n = 776) (Fig. 3c-d and Fig. 4a). Sequence analyses revealed a very high genetic heterogeneity (Fig. 4b) with an extremely large number of divergent and/or different genotypes from the same genus present in a single sample (Fig. 4c). As an example, up to 69 different TTV, 259 TTMV and 189 TTMDV genotypes were observed in a single clinical sample (Supplementary Table 1, sample ID 25161).

In addition, we detected sequences corresponding to adeno-associated dependoparvovirus A (n = 69), human pegivirus-1 (HPgV-1) (n = 42), cardiovirus B (n = 2), and human polyomavirus 10 (n = 1) (Supplementary Fig. 1b and Fig. 3a-d).

Viruses of “unknown significance”

A large number of sequences related to arthropod, vertebrate or mammalian viruses (DNA and RNA-based) that are not recognized as a cause of infection in humans were detected using either the FeVir pipeline and/or de novo analysis (Supplementary Fig. 1c and Fig. 5). We describe a tentative novel Dicistroviridae genus (a family of viruses known to infect arthropods) identified in 84 samples and reported separately in Cordey et al [33]. Porcine parvoviruses 4–6 (n = 50) were also detected, including two nearly complete sequences (Fig. 5c-d). Ambidensoviruses 1–3, known to infect insects and other non-vertebrates, were present (n = 44) with a significant number of reads detected in the DNA libraries (Fig. 5c-d).

Several additional viruses for which no RNA or DNA sequences were detected in the NTC and PC samples were observed. At the RNA level, several viruses were detected that could be reported as “-like” known viruses, without however matching closely to them. This included sequences for picorna-like virus (n = 27), providence-like viruses 1–2 (n = 24), dicistro-like viruses 1–2 (n = 12), bunya-like virus 1–3 (n = 3), and phlebovirus-like virus (n = 1) (Fig. 5a-b). Others were rotavirus D (n = 2), avian coronavirus (n = 1), iflavirus (n = 1), and mammalian orthoreovirus (n = 1) (Fig. 5a-b). Among DNA viruses, sequences were also detected for human associated gemykibivirus (n = 29), geminivirus (n = 11), human associated cycloviruses (n = 5), chicken stool-associated gemycircularvirus (n = 2), CRESS virus (n = 2), cyclovirus ZM36a (n = 2), avian gyrovirus 2 (n = 1, RNA library only), bat associated cyclovirus 16 (n = 1), human associated gemyvongvirus 1 (n = 1, RNA library only), human CSF-associated densovirus (n = 1), and taupapillomavirus 3 (n = 1) (Fig. 5c-d).

We provide a comprehensive characterization of the blood virome in a population of young children with fever of unknown origin and/or severe outcomes who are living in Tanzania. Beyond the well-recognised human viruses known to cause diseases and for some of them screened in routine clinical practice, or widely considered as “commensal”, we identify numerous novel, divergent and unexpected viruses that show untargeted mNGS to be a powerful tool for the surveillance of unrecognized or emerging viral infections. Among these unexpected viruses, were a large proportion never before described in humans, of mammalian, arthropod or other invertebrate origin. Further matched studies on a broader geographic, demographic and clinical scope would be required to better differentiate between background noise, abortive infections or novel causes of clinical disease.

Many previous investigations have surveyed viruses in the animal kingdom, including non-vertebrates, in order to extrapolate a probabilistic list of viruses that could emerge in humans. However, this generates noise by reporting a vast majority of viruses which would not succeed in the stochastic process of a zoonotic transfer event or have not yet acquired the mutations with which to do. We propose that such surveillance should be complemented by large sentinel human virome populations that is much more likely to identify viral signatures of interest, that could guide future extended investigations.

We conducted two bioinformatics analyses in parallel for each sample: In addition to traditional de novo analysis, the raw data were analysed with the FeVir pipeline (specifically designed for this study and including the novel and particularly exhaustive Virosaurus reference database). The untargeted mNGS analysis reported the detection of a large diversity of RNA and DNA virus sequences. Of the 816 children, 394 (48.3%) were found positive for at least one virus of “recognized clinical significance”. Anelloviridae were detected in all except one sample and up to 92.2% of samples were found co-infected by all three anellovirus genera (TTV, TTMV and TTMDV). In addition, a large number of sequences were detected that related to 33 arthropod, vertebrate or mammalian virus species that are so far unknown to cause human infections; the most frequent being dicistroviruses, porcine parvoviruses 4–6 and ambidensoviruses.

Concerning the viruses considered as being of “recognized clinical significance”, sequences related to Herpesviridae, Picornaviridae and the Reoviridae family members were the most frequently detected among the 4 DNA (Herpesviridae > Adenoviridae > Parvoviridae > Polyomaviridae) and 9 RNA (Picornaviridae > Reoviridae > Astroviridae, Caliciviridae > Retroviridae > Coronaviridae > Flaviviridae > Paramyxoviridae > Togaviridae) virus families observed (Supplementary Fig. 1a). Indeed, among the 35 viral species detected and considered of clinical significance, we observed a predominance of human enteroviruses (13.8%), rotaviruses (12%), HHV- 6 (11%) and HHV-7 (4.4%). Overall, rotavirus A sequences were the most frequently detected and confirm that rotavirus infections frequently result in viremia in paediatric patients [34, 35]. Similarly to HHV-6, HHV-7 was also frequently detected (n = 36) in our cohort. Although commercial or in-house molecular assays are available, HHV-7 is rarely (if ever) tested even in high-resource settings. The detection of RNA for HHV-6 and HHV-7 in the RNA libraries suggest ongoing viral replication in the majority of positive cases. Some viruses are also notable for their absence, for instance, Epstein-Barr was not detected by untargeted mNGS analysis in any children. We also show the wide circulation of EV in East Africa, and report a potential new EV-A71 genogroup H. Additional investigations on the prevalence and global distribution of this genogroup as well as its associated disease will give potentially important information about its neurotropic potential. Continuous genomic studies on the evolution of EV-A71 are important to detect new mutants or recombinants with epidemic potential [36]. Such surveillance is necessary, particularly as there is currently no treatment against this virus.

Interestingly, although its relationship with clinical pathology is currently difficult to establish, among de different Picornaviridae family members we observed two samples harbouring Cardiovirus B. This virus was isolated in cell culture from stool samples collected from a child with fever of unknown origin and named the Saffold virus [37]. Since then, the virus has shown a wide circulation in many countries [38] and has been isolated from clinical specimens of children with gastrointestinal and respiratory symptoms as well as from cerebrospinal fluid samples of patients with aseptic meningitis. Recently, Ramesh and colleagues also reported the detection of Saffold virus sequences with untargeted mNGS in the serum of a febrile paediatric patient in Uganda [17]. Among the EV genera, one RV-C45/RV-C11 recombinant was observed. This recombinant was previously reported in human respiratory specimen collected in US [39] and our data suggest its circulation across Africa. Interestingly, nine of the 39 RV-C were detected by the de novo analysis only. This demonstrates that, although more time-consuming, a de novo approach is complementary to database-oriented pipelines and recommended in such surveillance studies to detect highly divergent viruses. To support this statement, 13 RV-C and 13 RV-A genomes were de novo assembled by Ramesh A and colleagues in their mNGS study [17].

Our results further highlight that HAstV has a significant circulation in the global population, with a higher prevalence of novel HAstV (2.1%) than classical HAstV (0.9%). This observation is in agreement with previous reports and underscores the clinical pertinence of novel HAstV in the paediatric population [40–42].

The potential clinical implication of Anelloviridae is still debated [43]. Among sera samples analysed in this study, 98.4%, 97.4% and 95.1% were found positive for TTMV, TTV and TTMDV, respectively, which support a very early acquisition in life [44] with increased TTV and TTMDV prevalence in the paediatric population until 19–24 months of age before decreasing (modest age-related effect observed for TTMV) [45]. Anelloviridae are considered major members of the human blood virome and possibly one of the most abundant virus in blood samples [46]. Reflecting their genetic heterogeneity [47], anellovirus mixed populations are frequently observed in blood samples [48, 49]. In agreement with these previous studies, we report a very high intra- and inter-individual genetic heterogeneity for TTV, TTMV and TTMDV, with a co-detection rate of 99.0% (Fig. 4). This is exemplified here by the finding that up to 69 different TTV, 259 TTMV and 189 TTMDV were observed in the serum from a single individual. Similar to HPgV-1, TTV are known to interact with the immune system; yet the impact of these interactions is not well defined.

A wide range of RNA and DNA viruses previously unknown to infect humans were observed. These deserve specific attention in further studies in the febrile paediatric population in Africa. Although extremely challenging, it would be important to perform a similar untargeted mNGS survey analysis on blood samples from an asymptomatic paediatric cohort with comparable demographic and epidemiological characteristics to better clarify any potential clinical causality.

This blood virome study on a population of young children with fever of unknown origin and/or severe outcomes living in Tanzania highlighted several DNA and RNA viral sequences, and provides a large list of novel viruses that should be considered as a potential cause of human infections, some certainly being candidates for future fever investigations.

Ethics approval and consent to participate

This study was approved by the local IRB in Tanzania by the Ifakara Health Institute and the National Institute for Medical research (paediatric cohort: IHI/IRB/EXT/16-2015 and NIMR/HQ/R.8a/Vol. IX/1789). IRB approval was also granted for analyses undertaken in Switzerland by the Ethikkommission Nordwest- und Zentralschweiz (EKNZ UBE-15/03). Written informed consent was obtained from the parent or guardian of the patients before enrolment. The trial was registered in ClinicalTrials.gov, identifier NCT02225769.

Consent for publication: Not applicable

Availability of data and material: The raw sequence data were deposited in the NCBI Sequence Read Archive under BioProject accession number PRJNA666535. Partial viral sequences ≥ 2000 nucleotides long obtained in this study were submitted to GenBank (accession no. MN727142-MN727295 and MN765166-MN780477 for RNA and DNA viruses, respectively).

The custom scripts that were used in this study are available at: https://github.com/SAFIA-HTS/VIRAL-HTS

Competing interest: The authors declare that they have no competing interests

Funding: This study was supported by the Bill and Melinda Gates Foundation (grant n°. OPP1163434) and by the Swiss National Science Foundation (grant n°. 320030_179507).

Author’s contributions: SC., MAH, JF, VD and LK conceived and designed the study. FL performed bioinformatics and phylogenetic analyses. SC, FL, TJ, NG, CI, PLM, AG, IX and LK developed and validated the bioinformatics pipeline “Fevir”. KK, JS, TM, FK, JM, ZS, HT, and VD managed and performed sample collection. MD helped design and performed untargeted mNGS. GV performed sample extractions. CT and MCZ contributed to the literature review. SC, FL and MAH designed the figures. SC, FL, MAH and LK wrote the manuscript. All authors revised and commented the manuscript. All authors read and approved the final version

Acknowledgments: First, we would like to thank the patients and their care-takers for their participation and contribution to improved medical care for febrile illness in Tanzania. The authors would like to thank Lara Turin (University Hospitals of Geneva) and Brice Petit (University of Geneva) for technical assistance.

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SupplementaryTable1.xlsx
Supplementary Table 1: Untargeted mNGS data for each patient
SupplementaryFig1.pptx
Supplementary Fig.1. Abundance of viruses detected by untargeted mNGS. Viruses are reported using a Krona chart according to the 3 following groups A) viruses “recognized of clinical significance” B) Commensal viruses of “undetermined clinical significance” C) viruses of “unknown significance”.
SupplementaryFigure2.jpg
Supplementary Fig.2. EV-A71 phylogenetic tree on the capsid protein VP1. The tree was created using a 1000-replicate Maximum Likelihood method. The viral sequences detected in this study are shown in boldface. Scale bar indicates nucleotide substitutions per site.
SupplementaryFigure3.jpg
Supplementary Fig.3. HRV-C phylogenetic tree on the capsid protein VP1. The tree was created using a 1000-replicate Maximum Likelihood method. The viral sequences detected in this study are shown in boldface. Scale bar indicates nucleotide substitutions per site.

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Blood Virosphere in Febrile Tanzanian Children

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Journal Publication

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Abstract

Figures

Background

Methods

Study design

Sample extraction and untargeted metagenomic next-generation sequencing

Bioinformatic analysis

Bioinformatics pipeline for vertebrate virus detection

De novo analysis

Results

Viruses of “recognized clinical significance”

Commensal viruses of “undetermined clinical significance”

Viruses of “unknown significance”

Discussion

Conclusions

Declarations

References

Supplementary Files

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