Bioinformatic challenges in metagenomic next generation sequencing data analysis while unravelling a case of uncommon campylobacteriosis

Abstract

Objective

This study aimed to employ advanced bioinformatics and modern sequencing approaches to solve a diagnostic problem of persistent Campylobacter spp. molecular detection yet negative culture results from four consecutive stool samples of a previously healthy patient with newly diagnosed selective IgA deficiency and prolonged diarrhoea.

Methods

Metagenomic next-generation sequencing (mNGS) based on short-paired end reads with basic bioinformatic read classification analysis was used at first. Due to ambiguous results, advanced bioinformatics involving contigs construction and classification, reference genome mappings and reads filtering with BBSplit, additionally coupled with metagenomic long-reads sequencing and Full-length 16S rRNA metabarcoding were employed to further elucidate the results. Virulence factors were analysed using the Prokka Genome Annotation tool. Modified classical bacteriology methods were finally used for further clarification.

Results

Short-pair end reads analysis identified several Campylobacter species in all four samples. After advanced bioinformatic approaches were applied, candidatus C. infans was suspected as the putative pathogen. This result was further supported by metagenomic long-reads sequencing and Full-length 16S rRNA metabarcoding. Nevertheless, after modifying the culture conditions based on mNGS results, a mixed culture of candidatus C. infans and C. ureolyticus was obtained. Sequencing of the mixed culture resulted in an 87.48% and 73.47% genome coverage of candidatus C. infans and C. ureolyticus, respectively. In the candidatus C. infans genome more virulence factors hits were found than in the C. ureolyticus genome thus supporting the first as the most probable cause of symptoms.

Conclusion

This study shows the pivotal role and strengths of mNGS in unravelling an unusual case of diarrhoea and demonstrates how mNGS can guide established microbiological methods to improve on current limitations. However, it also emphasises the need for careful interpretation of sequencing data, particularly for closely related bacterial species from clinical samples that are known to support complex microbial communities.