# Investigation of the viral causes of febrile jaundice in Burkina Faso through metagenomic sequencing

Karine Bolloré, Bachirou Tinto, Florian Charriat, Amandine Pisoni, Antoni

Exbrayat, Serafin Gutiérrez, Yannick Simonin, Edouard Tuaillon

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Karine Bolloré, Bachirou Tinto, Florian Charriat, Amandine Pisoni, Antoni Exbrayat, et al. Investigation of the viral causes of febrile jaundice in Burkina Faso through metagenomic sequencing. Journal of Infection, 2025, 91 (2), pp.106541. 10.1016/j.jinf.2025.106541. hal-05415770

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Viruses and Viral Diseases

# Investigation of the viral causes of febrile jaundice in Burkina Faso through metagenomic sequencing

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Karine Bolloré<sup>a</sup>, Bachirou Tinto<sup>b,c</sup>, Florian Charriat<sup>d</sup>, Amandine Pisoni<sup>a,e</sup>, Anthony Exbrayat<sup>d</sup>, Serafin Gutierrez<sup>d,e,f,g</sup>, Yannick Simonin<sup>a,*,f,g</sup>, Edouard Tuillon<sup>a,e,***,1</sup>

## ARTICLE INFO

**Article history:**
Accepted 17 June 2025
Available online 27 June 2025

**Keywords:**
Yellow Fever
Metagenomics
Hepatitis A (HAV)
Hepatitis B (HBV)
Viral surveillance
Next Generation Sequencing (NGS)

## SUMMARY

**Objectives:** Yellow fever virus (YFV) is a key concern in West Africa, often associated with febrile jaundice. However, many patients with this syndrome test negative for YFV, raising questions about other potential viral etiologies. This study aimed to characterize the viral landscape in YFV-negative febrile jaundice cases using metagenomic next-generation sequencing (mNGS).

**Methods:** Serum samples were collected in 2019 from 152 febrile jaundice patients in Burkina Faso who tested negative for both YFV and malaria. A random subset of 100 samples was analyzed by mNGS to identify viral sequences. Bioinformatic pipelines were applied to classify viral reads, and findings were validated by quantitative PCR (qPCR) and serological assays. Additional specimens from the same cohort, as well as from febrile patients without jaundice, were tested to assess the prevalence of the key viral findings.

**Results:** mNGS revealed 58 viral species, including four human pathogens: HIV-1 (n=1), YFV (n=1), hepatitis A virus (HAV, n=16), and hepatitis B virus (HBV, n=18). qPCR confirmed 75% of HAV and 67% of HBV cases. Serological testing on additional samples confirmed comparable HAV and HBV prevalence among jaundiced patients, but significantly lower rates among those without jaundice.

**Conclusion:** mNGS uncovered undiagnosed viral infections, demonstrating its value for differential diagnosis and surveillance in resource-limited settings.

© 2025 The Author(s). Published by Elsevier Ltd on behalf of The British Infection Association. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

## Introduction

Yellow fever virus (YFV) represents a significant health challenge in West Africa, where it is a major cause of febrile jaundice.<fnref n="1" /> In Burkina Faso, the yellow fever surveillance system is a critical component of public health efforts aimed at the early detection and prevention of outbreaks.² Coordinated by national health authorities

in collaboration with the WHO, this surveillance system involves laboratory testing of suspected cases presenting with fever and jaundice.

However, febrile jaundice may result from a wide range of viral, bacterial, or parasitic infections, making accurate diagnosis particularly challenging. The percentage of YFV-positive cases among patients tested with febrile jaundice remains very low in endemic areas.³ A deeper understanding of the underlying causes of febrile jaundice in West Africa is crucial to inform in prevention, treatment, and control. In previous studies, we reported that leptospirosis and hepatitis E contribute to febrile jaundice in Burkina Faso.⁴,⁵ Moreover, other arboviruses besides yellow fever virus, which in some cases can cause febrile jaundice, are circulating in the country (⁶,⁷).

Current gaps in understanding the etiology of yellow fever are largely due to limited testing for globally significant and emerging pathogens. Advanced molecular technologies offer opportunities to better identify causative agents. In this context, metagenomic

<footnote><sup>a</sup> Université de Montpellier, INSERM, Pathogenèse et Contrôle des Infections Chroniques et Émergentes, PCCEI U1058, 34394 Montpellier, France</footnote>

<footnote><sup>b</sup> Institut de Recherche en Sciences de la Santé, Centre National de la Recherche Scientifique et Technologique, Ouagadougou, Burkina Faso</footnote>

<footnote><sup>c</sup> Centre Muraz, Institut National de Santé Publique, Bobo-Dioulasso, Burkina Faso</footnote>

<footnote><sup>d</sup> ASTRE, Cirad, INRAe, Université de Montpellier, Montpellier, France</footnote>

<footnote><sup>e</sup> University Hospital of Montpellier, Montpellier, France</footnote>

<footnote>* Corresponding author at: ASTRE, Cirad, INRAe, Université de Montpellier, Montpellier, France.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>*** Corresponding author at: Université de Montpellier, INSERM, Pathogenèse et Contrôle des Infections Chroniques et Émergentes, PCCEI U1058, 34394 Montpellier, France.</footnote>

<footnote>E-mail addresses: serafin.gutierrez@cirad.fr (S. Gutierrez), yannick.simonin@umontpellier.fr (Y. Simonin), e-tuillon@chu-montpellier.fr (E. Tuillon).</footnote>

<footnote n="1">¹ Authors contributed equally.</footnote>
approaches based on next-generation sequencing (mNGS) have emerged as powerful tools for untargeted pathogen detection, particularly in surveillance or research settings where conventional diagnostics are limited or unavailable. Unlike molecular diagnostic approaches that target specific genomic sequences, mNGS theoretically allows for the detection of virtually all pathogens in a sample, including unknown ones.⁸ For instance, mNGS has been used to explore the full taxonomic diversity of viruses, even novel ones, in clinical samples.⁹,¹⁰

In this study, we employed mNGS to investigate the viral diversity in blood samples collected from individuals exhibiting symptoms of febrile jaundice but testing negative for YFV. We subsequently used a combination of serological and molecular tests to validate mNGS findings. Finally, we analyzed an additional set of samples, from individuals presenting with febrile jaundice or non-malarial fever (the “suspected-dengue cohort”), using targeted diagnostic tests to assess the robustness of the prevalence rates observed in the first sample set.

## Material and methods

### Sample collection

Serum was sampled from 152 subjects of various ages and from different regions (Supplementary Data) with febrile jaundice (≥38 °C) who tested negative for malaria and yellow fever in 2019. Specimens were collected between day 5 and day 7 after symptom onset, in accordance with the national yellow fever surveillance protocol that allows collection up to 14 days post-onset. Then, they were sent and stored at the Centre Muraz, Bobo-Dioulasso, which serves as a central reference laboratory for the national surveillance of yellow fever. As part of this surveillance program, all patients are orally informed that their blood samples may be used for further epidemiological investigations, including the detection of pathogens other than yellow fever virus. This consent procedure is integrated into routine surveillance activities coordinated by the national health authorities. The study protocol was reviewed and approved by the Burkina Faso Health Research Ethics Committee (ref. 2020-3-049) and was conducted in accordance with the Declaration of Helsinki. From these samples, 100 sera, collected from patients aged 1 to 70 years (59 males and 41 females) from various regions of Burkina Faso, were randomly selected for mNGS (see Supplementary Data). The number of sera included was chosen to ensure sufficient sequencing depth per patient (target: 5 million reads per serum). The remaining 52 samples were tested using serological assays targeting the infections most frequently identified through mNGS (Fig. 1). Additionally, the same immunological assays were performed on samples collected from a cohort of patients suspected of dengue, presenting with an acute febrile illness (> 39 °C) lasting 2 to 7 days, associated with at least two of the following symptoms: headache, retro-orbital pain, myalgia, arthralgia, skin rash, hemorrhagic manifestations, or signs of shock. All patients had non-malarial fever and tested negative for dengue (n=147).

### Preanalytical treatment

Two hundred microliters of serum per patient were used to build libraries for shotgun sequencing as previously described.¹¹ Briefly, the samples were ground with 3-mm tungsten balls twice for 1 min at 30 Hz to lyse cells and expose non-viral nucleic acids to nucleases (see below).

For each serum, two samples of nucleic acids were generated. First, the sample to be analyzed with mNGS was a 150-µl aliquot that was treated with a nuclease cocktail to enrich for nuclease-protected viral genomes. The cocktail included 20 U/L of Exonuclease I (Thermofisher), 5 U/L of RNase I (Thermofisher), 25 U/L of Benzonase

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Flowchart of experimental design. In the suspected YFV cohort, 100 samples were screened using mNGS, followed by specific qPCR for confirmation tests. The other patients were screened by conventional diagnostic tests, similarly to those in the DENV cohort.</figcaption></figure>

(Merck Chemical) and 20 U/L of Turbo DNase (Ambion). Digestion took place at 37 °C for 60 min in 1× Turbo DNase buffer (Ambion). Then, nucleic acids in the resulting suspension were isolated using the Nucleospin RNA virus kit (Macherey-Nagel) according to the manufacturer's protocol with slight modifications. Specifically, the RNA carrier was replaced in the RAV1 buffer with 5 µg of linear acrylamide (Ambion). Nucleic acids were eluted in 50 µl of RNase-free water and stored at -80 °C. In parallel, a 150-µl aliquot of each serum was processed as above but without the nuclease digestion. This total RNA sample was used for virus detection by PCR.

### Library preparation and Illumina sequencing

Custom library preparation for NGS was conducted from nucleic-acid suspensions enriched in nuclease-protected molecules as described.¹¹ Briefly, cDNA was generated from those suspensions in a reverse transcription reaction using the RevertAid First Strand cDNA synthesis kit (Thermofisher) and the 454-E-8N primers. Double-stranded DNA (dsDNA) was then generated using the Klenow fragment polymerase (Thermofisher) and the 454-E-8N primers. The dsDNA fragments were amplified in a PCR reaction with the 454-E primer and the Phusion High-Fidelity DNA Polymerase kit (Thermofisher). Amplicons were purified with the NucleoSpin gel and PCR Clean-up (Macherey-Nagel). Then, adapters were ligated to the amplicons in a PCR reaction using P5 and P7-bearing adapter primers¹¹ and the Phusion High-Fidelity DNA Polymerase kit (Thermofisher). Amplicons were sized and purified using AMPure XP magnetic beads (Agencourt). The size of the resulting libraries (expected 500-600 bp) was validated in a capillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies). Library concentration was estimated with the Library Quantification kit (Takara Bio) according to the manufacturer's protocol. All libraries were pooled together in equimolar concentrations, and the resulting sample was sequenced in a full run with a HiSeq 2500 sequencer (Illumina; 250-bp paired reads) using specific sequencing primers.¹¹

### Bioinformatic analysis

The bioinformatic analysis of reads was carried out with the Snakevir pipeline.¹¹ Snakevir and the associated documentation are freely available at https://github.com/FlorianCHA/snakevir. Briefly, removal of adapter and low-quality sequences from reads was done with Cutadapt 1.6.¹² Then, rRNA-derived reads were removed from the dataset. The identification of rRNA-derived reads was done by
mapping them with BWA 0.7.15.¹³ Then, reads from all libraries were pooled and used as input in a de-novo assembly to generate a non-redundant set of contigs using Megahit v1.1.2.¹⁴ The contigs and non-assembled reads were used in a second *de-novo* assembly with CAP3.¹⁵ The resulting metagenome was screened for virus-derived contigs in a homology search using Diamond against the NCBI nr database (e-value cutoff = 10⁻³). Potential host and virus taxonomies of each best-hit species were automatically collected with the taxonomy retrieval tool of Snakevir and manually verified. All contigs with the same best-hit species were pooled together into viral operational taxonomic units (vOTUs).¹⁶ The number of reads per virus-like contig and library was quantified through mapping with BWA 0.7.15. Duplicate reads had been previously removed with the markdup tool in Samtools. A threshold of ten reads per sample was used to consider a vOTU as detected in a given library.¹⁶

## PCR and serology

Virus detection with mNGS was compared to in-house or commercial diagnostic PCRs. The PCRs used were: HIV (Alinity HIV-1 Amp kit, Abbott), HBV (Generic HBV Charge Virale, Biocentric), HAV⁶ and YFV.¹⁷

HAV and HBV diagnosis with serological tests was carried out with samples from the febrile-jaundice collection that had not been tested with mNGS (n=52) or from another collection of dengue-suspected patients (n=147). Tests were carried out in the University Hospital of Montpellier using the Alinity HAVAB IgM and HBsAg Reagent Kit (Abbott). All HBV-positive samples obtained with mNGS or serology were tested for the presence of anti-HBc IgM antibodies

<table><caption>**Table 1**

Detection and confirmation of pathogenic viruses on mNGS cohort (mNGS = number of mNGS positives, nd = not determined).</caption><thead><tr><th>Virus</th><th>mNGS</th><th>qPCR confirmation</th><th>Serological confirmation</th></tr></thead><tbody><tr><td>YFV</td><td>1</td><td>0</td><td>nd</td></tr><tr><td>HIV</td><td>1</td><td>1</td><td>nd</td></tr><tr><td>HAV</td><td>16</td><td>12</td><td>nd</td></tr><tr><td>HBV</td><td>18</td><td>12</td><td>11</td></tr></tbody></table>

(Monolisa Anti-HBc IgM, Bio-Rad). All HBV and HAV serologically positive samples were tested by in-house PCR.

## Phylogenetic analysis

Genotyping for HAV and HBV viral sequences was done with the online tool NGphylogeny. Contig sequences (with a minimum coverage of 5–10 reads all along the contig) and reference genomes, from NCBI Genbank, were aligned using MUSCLE aligner with default settings. Graphical representation and editing of the phylogenetic tree were performed with the NGphylogeny tool.¹⁸

## Results

We performed mNGS in sera from 100 individuals with febrile jaundice who tested negative for yellow fever virus by PCR. Sequencing generated 462 million reads (mean read depth per sample = 4.6 million). Reads assigned to viruses accounted for 0.12% of the total reads (mean viral reads per sample = 6607 reads), although the number of viral reads varied widely per sample (range =

<table><caption>**Table 2**

mNGS findings for pathogenic viruses. Demographic data and results for all 36 patients with a pathogenic virus.</caption><thead><tr><th>Sample</th><th>mNGS finding</th><th>Age</th><th>Sex</th><th>Reads</th><th>Read proportion (% virus reads/total viral reads)</th><th>qPCR confirmation</th><th>Serological confirmation</th><th>Genotype</th></tr></thead><tbody><tr><td>18_OUA</td><td>HIV-1</td><td>26</td><td>M</td><td>111659</td><td>99,79</td><td>pos</td><td>nd</td><td>nd</td></tr><tr><td>12_BOB</td><td>YFV</td><td>36</td><td>M</td><td>35</td><td>0,11</td><td>neg</td><td>nd</td><td>nd</td></tr><tr><td>12_BOB</td><td>HBV</td><td>36</td><td>M</td><td>30896</td><td>95,87</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>47_DED</td><td>HBV</td><td>14</td><td>M</td><td>5909</td><td>46,58</td><td>pos</td><td>IgM HBc +</td><td>nd</td></tr><tr><td>71_KAY</td><td>HBV</td><td>27</td><td>M</td><td>3678</td><td>63,24</td><td>pos</td><td>IgM HBc +</td><td>nd</td></tr><tr><td>91_FAD</td><td>HBV</td><td>27</td><td>F</td><td>1380</td><td>53,68</td><td>pos</td><td>IgM HBc +</td><td>nd</td></tr><tr><td>75_BOB</td><td>HBV</td><td>17</td><td>F</td><td>217</td><td>8,92</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>59_BOB</td><td>HBV</td><td>17</td><td>F</td><td>139</td><td>1,27</td><td>pos</td><td>IgM HBc -</td><td>E</td></tr><tr><td>78_KAY</td><td>HBV</td><td>19</td><td>F</td><td>122</td><td>1,17</td><td>neg</td><td>IgM HBc -</td><td>nd</td></tr><tr><td>26_DED</td><td>HBV</td><td>15</td><td>M</td><td>104</td><td>1,03</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>80_OUH</td><td>HBV</td><td>7</td><td>M</td><td>82</td><td>0,73</td><td>neg</td><td>IgM HBc -</td><td>nd</td></tr><tr><td>5_DED</td><td>HBV</td><td>22</td><td>F</td><td>51</td><td>37,23</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>90_FAD</td><td>HBV</td><td>21</td><td>M</td><td>46</td><td>8,78</td><td>neg</td><td>IgM HBc -</td><td>nd</td></tr><tr><td>96_BOB</td><td>HBV</td><td>12</td><td>M</td><td>46</td><td>2,12</td><td>neg</td><td>IgM HBc -</td><td>nd</td></tr><tr><td>53_BOB</td><td>HBV</td><td>17</td><td>F</td><td>42</td><td>0,38</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>14_MAN</td><td>HBV</td><td>17</td><td>M</td><td>27</td><td>0,19</td><td>neg</td><td>IgM HBc -</td><td>E</td></tr><tr><td>45_TEN</td><td>HBV</td><td>47</td><td>F</td><td>26</td><td>2,97</td><td>pos</td><td>IgM HBc +</td><td>nd</td></tr><tr><td>94_OUA</td><td>HBV</td><td>25</td><td>F</td><td>26</td><td>1,00</td><td>neg</td><td>IgM HBc -</td><td>nd</td></tr><tr><td>3_BOB</td><td>HBV</td><td>18</td><td>M</td><td>24</td><td>0,06</td><td>pos</td><td>IgM HBc +</td><td>E</td></tr><tr><td>55_OUA</td><td>HBV</td><td>60</td><td>F</td><td>24</td><td>4,80</td><td>pos</td><td>IgM HBc +</td><td>nd</td></tr><tr><td>35_BOB</td><td>HAV</td><td>2</td><td>F</td><td>15765</td><td>57,27</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>77_BOB</td><td>HAV</td><td>31</td><td>M</td><td>6667</td><td>83,25</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>9_DED</td><td>HAV</td><td>4</td><td>M</td><td>2134</td><td>82,62</td><td>neg</td><td>nd</td><td>1B</td></tr><tr><td>31_KOU</td><td>HAV</td><td>3</td><td>F</td><td>1981</td><td>63,25</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>15_ZIN</td><td>HAV</td><td>51</td><td>M</td><td>1621</td><td>68,22</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>20_GAO</td><td>HAV</td><td>64</td><td>M</td><td>418</td><td>6,66</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>69_BOB</td><td>HAV</td><td>40</td><td>M</td><td>228</td><td>5,22</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>98_KAY</td><td>HAV</td><td>35</td><td>M</td><td>152</td><td>4,69</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>54_OUH</td><td>HAV</td><td>2</td><td>F</td><td>90</td><td>4,10</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>48_FAD</td><td>HAV</td><td>4</td><td>M</td><td>62</td><td>2,27</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>85_BOB</td><td>HAV</td><td>12</td><td>M</td><td>55</td><td>4,13</td><td>neg</td><td>nd</td><td>1B</td></tr><tr><td>80_OUH</td><td>HAV</td><td>7</td><td>M</td><td>36</td><td>0,32</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>10_BOB</td><td>HAV</td><td>15</td><td>F</td><td>21</td><td>4,09</td><td>neg</td><td>nd</td><td>nd</td></tr><tr><td>78_KAY</td><td>HAV</td><td>19</td><td>F</td><td>16</td><td>0,15</td><td>pos</td><td>nd</td><td>1B</td></tr><tr><td>34_BAN</td><td>HAV</td><td>26</td><td>M</td><td>12</td><td>1,01</td><td>neg</td><td>nd</td><td>1B</td></tr><tr><td>50_GAO</td><td>HAV</td><td>4</td><td>F</td><td>11</td><td>2,18</td><td>pos</td><td>nd</td><td>1B</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Phylogenetic analysis of the YFV contig (named k141_878195). YFV sequences are shown with their country of isolation and GenBank accession numbers. Analysis was processed through the French phylogeny website (http://www.phylogeny.fr). Nucleotide sequences were aligned using the MUSCLE software (https://www.ebi.ac.uk/Tools/msa/muscle/). The phylogenetic tree was constructed using the maximum-likelihood method in PhyML (http://www.atgc-montpellier.fr/phyml/). Scale bar indicates nucleotide substitutions per site.</figcaption></figure>

0.001–2.47%). The low percentage of viral reads was similar to that found in previous studies.⁹

Bioinformatics analysis detected 58 vOTUs, including 24 vOTUs related to vertebrate viruses. The remaining vOTUs were mainly associated with bacteria, fungi and plants, as commonly observed using mNGS.¹⁹ We focused on human pathogens, as the goal was to test mNGS for infectious disease diagnosis. Among vertebrate viruses, four pathogenic viruses were detected: HIV-1 in one case, YFV for another, HAV in 16 cases, and HBV in 18 cases (Table 1). Eight out of 16 HAV-positive cases (50%) were under 15 years old, whereas three out of 18 HBV-positive cases (17%) were under 15 years old.

We performed conventional diagnostic PCRs on mNGS-positive samples for HIV-1, YFV, HBV, and HAV. These analyses confirmed 12 out of 16 HAV mNGS-positive results (75%), and 12 out of 18 HBV mNGS-positive results (67%). Among the HBV qPCR-positive samples, 11 tested positive for anti-HBc IgM, suggesting acute infections (Table 2). The mNGS-positive YFV result was not confirmed by in-house qPCR. mNGS provided a 700-bp fragment of the YFV genome and a phylogenetic analysis showed that the YFV sequence clustered

with strains from West Africa (Fig. 2), with 98% sequence similarity to the closest reference strain.

Interestingly, we observed a strong (R²=0.85) correlation between viral load measured with qPCR and the number of reads detected with mNGS for HAV-positive samples. Viral loads in HBV-positive samples were always high, irrespective of the number of reads detected with mNGS (Fig. 3). Our data suggest that detecting at least 100 reads by mNGS increases the likelihood of confirming an infection via qPCR (14/16 for ≥100 reads vs. 10/18 for ≤100 reads).

To further validate our findings, we tested 52 additional samples from subjects suspected of YFV infection and 147 subjects suspected of dengue using serological tests against HAV and HBV (Fig. 1). Fig. 4 shows similar hepatitis infection rates in the two groups of the YFV cohort: HBV was detected in 18% of the mNGS cohort and in 28% of the newly tested group, while HAV was found in 16% and 21% of cases, respectively. In contrast, in the dengue cohort, we observed 0.7% HBV-positive cases and no HAV-positive cases.

We performed phylogenetic analyses of HAV and HBV sequences obtained via mNGS (Fig. 5). Ten HAV sequences from our dataset were compared to reference sequences from different genotypes and
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Correlations between the number of reads detected by mNGS and hepatitis viral load measured by qPCR. Samples identified as infected with Hepatitis A virus (HAV) or Hepatitis B virus (HBV) are indicated with either red circles or blue squares, respectively, as shown in the legend. The correlation coefficients between read number and viral load are displayed for each virus (red = HAV, blue = HBV). IU/ml = international units per milliliter.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Hepatitis seropositive cases in three groups of patients. mNGS cohort is a selection of 100 patients from a suspected-YFV cohort. These positive samples represent all positive tests, including molecular and serological diagnostic. * indicates a statistically significant difference according to Tukey's test between cases in suspected-YFV and suspected DENV cohorts (p=0.0265 for HBV cases and p=0.0440 for HAV cases).</figcaption></figure>

regions. The analysis indicated that all HAV sequences in our dataset belonged to the genotype 1B (Fig. 5A). Twelve HBV sequences from our dataset were compared to HBV reference sequences from Niger, Liberia, Vietnam, and France. The analysis revealed that all HBV sequences identified in our study belonged to the genotype E (Fig. 5B).

Discussion

This study illustrates the potential of mNGS as an exploratory tool to identify overlooked viral pathogens in patients with unexplained febrile jaundice in low-resource settings. We do not propose mNGS as a routine diagnostic tool, but rather as an exploratory approach to uncover overlooked infections in cases where conventional diagnostics have failed or are not systematically applied. We found that a large fraction of YFV-suspected patients from Burkina Faso were infected with HAV or HBV. Diagnostic tests against these two viruses are not routinely carried out during the analysis of yellow fever cases in Burkina Faso. Our study strongly suggests that tests against HAV and HBV could improve the diagnosis of yellow

fever cases in Burkina Faso and countries with a similar routine surveillance of febrile jaundice cases.²⁰

HAV and HBV cases with low viral loads, as shown with qPCR, were often detected with mNGS, suggesting the analytical robustness of our approach. Moreover, the ability of mNGS to recover viral sequences enhances its diagnostic potential, particularly in retrospective investigations and epidemiological studies. Providing genome sequences also allows for tracing the geographical origin of viruses, which is an advantage over qPCR. For example, we detected YFV with mNGS in a patient who tested negative by qPCR. Phylogenetic analysis showed that the mNGS-generated sequence clustered with YFV strains from western Africa. These results suggest that the patient may be infected with YFV, but the sample was too degraded to allow robust analysis with qPCR. This illustrates how mNGS, despite lower sensitivity in theory, can occasionally detect viral sequences in samples where PCR fails due to degradation, fragmentation, or inhibitor presence. However, no additional arboviruses were identified in our dataset. Although sera were obtained within the recommended 14-day window after symptom onset, all were collected between days 5 and 7. This timeframe, while still within the general diagnostic window, may be suboptimal for arbovirus detection by mNGS, as viremia typically declines sharply after day 5. This factor may have contributed to the lack of detection of other arboviral infections.

The strong correlation between HAV reads and qPCR viral loads suggests that mNGS could be used for the quantification of the viral load in HAV infections. However, the lack of correlation for HBV, where high viral loads were observed regardless of read numbers, highlights the complexity of viral genome coverage in mNGS and the need for cautious interpretation of quantitative data.²¹

Taken together, all diagnostic approaches indicate that HAV infections predominantly affected children under 15 years old, aligning with expected transmission patterns in endemic regions. However, the detection of HAV infections in older patients raises questions about delayed exposure, incomplete immunity, or potential reinfection in highly endemic settings. Further research is needed to clarify these dynamics and their implications for long-term immunity.

For HBV, the high proportion of acute infections, evidenced by anti-HBc IgM antibodies, underscores the need for strengthened vaccination programs. The prevalence of HBV in children under 15 years old suggests potential gaps in vaccination coverage or efficacy. Ensuring the timely administration of the birth dose and improving immunization strategies remain IG.
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** A and B: Phylogenetic analysis of HAV(A) and HBV(B) contigs. HAV and HBV sequences are shown with their country of isolation, genotype, and corresponding GenBank accession numbers. Analysis was processed through the French phylogeny website (http://www.phylogeny.fr). Nucleotide sequences were aligned by using MUSCLE software (https://www.ebi.ac.uk/Tools/msa/muscle/). The phylogenetic tree was constructed by using the maximum-likelihood method in PhyML (http://www.atgc-montpellier.fr/phyml/). Scale bar indicates nucleotide substitutions per site.</figcaption></figure>
Despite its advantages for diagnosis, implementing mNGS in low-resource settings remains challenging due to high costs, specialized equipment, and the need for bioinformatics expertise. High costs and the complexity of library preparation and data analysis limit its widespread use. Addressing these barriers will require investments in cost-effective sequencing technologies, simplified library preparation, procedures and bioinformatics pipelines,¹¹ and local capacity-building programs.

Our findings demonstrate that mNGS can uncover infections undetected by routine diagnostics, improving pathogen screening and surveillance. While its role in routine diagnostics remains to be defined, mNGS holds promise for investigating febrile jaundice and other infectious diseases in sub-Saharan Africa. Reducing costs and enhancing accessibility of mNGS will be essential to unlock its full potential for public health applications, ultimately improving health outcomes in resource-limited settings.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jinf.2025.106541.

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