Experimental infection with the hookworm, Necator americanus, is associated with stable gut microbial diversity in human volunteers with relapsing multiple sclerosis

Published: 12-02-2021| Version 1 | DOI: 10.17632/pkk4vtc57r.1
Timothy Jenkins


There is growing evidence that gastrointestinal (GI) parasitic worms can alter human gut microbiota during infection and it is thought that this is part of the mechanisms via which these parasites suppress immune responses. However, the diseases that have been studied to date involve the GI tract and have clear links with pre-existing dysbiosis, making it challenging to disentangle causality. Multiple sclerosis is a Th1-mediated disease of the nervous system whose pathogenesis has been recently linked to alterations in gut microbiome. Therefore, in the present study we investigated, for the first time, the changes in gut microbial profiles of human volunteers with relapsing-remitting multiple sclerosis (RRMS) prior to and following experimental infection with 25 hookworm stage 3 larvae (Necator americanus; N+), and following administration of anthelmintic treatment, then compared the findings with data obtained from a cohort of RRMS patients subjected to placebo treatment (PBO). Bacterial 16S rRNA high-throughput sequencing data revealed significantly increased microbial alpha diversity in the gut microbiota of N+ compared to PBO subjects over the course of infection, which is indicative of a healthier gut environment. Furthermore, significant differences in the abundance of several bacterial taxa, such as the immune-modulatory bacterial taxon Tenericutes/Mollicutes, were observed between samples from N+ and PBO subjects. Overall, these data demonstrate a significant impact of N. americanus infection on the human gut microbiota and lend support to the hypothesis of a contributory role of parasite-associated changes in gut microbial composition to the therapeutic properties of hookworm parasites. Amongst others the data set includes: 1. Compressed raw paired end read files - 2 per sample (demultiplexed): e.g. 463_MS_L001_R2_001.fastq.gz 2. OTU table created using Qiime: feature-table.biom and as text file feauture-table.txt 3. Taxonomy file including all of the taxonomic information of the samples: taxonomy.tsv 4. Metadata file containing the relevant contextual information for each sample: Metadata.xlsx 5. Weighted unifrac distance file to explore beta diversity: Weighted-unifrac.tsv