Back

A mixed amplicon metabarcoding and sequencing approach for surveillance of drug resistance to levamisole and benzimidazole in Haemonchus spp.

Francis, E. K.; Antonopoulos, A.; Westman, M. E.; McKay-Demeler, J.; Laing, R.; Slapeta, J.

2023-05-15 microbiology
10.1101/2023.05.14.540727 bioRxiv
Show abstract

Anthelmintic resistant parasitic nematodes present a significant threat to sustainable livestock production worldwide. The ability to detect the emergence of anthelmintic resistance at an early stage, and therefore determine which drugs remain most effective, is crucial for minimising production losses. Despite many years of research into the molecular basis of anthelmintic resistance, no molecular-based tools are commercially available for the diagnosis of resistance as it emerges in field settings. We described a mixed deep amplicon sequencing approach to determine the frequency of the levamisole (LEV) resistant single nucleotide polymorphism (SNP) within arc-8 exon 4 (S168T) in Haemonchus spp., coupled with benzimidazole (BZ) resistance SNPs within {beta}-tubulin isotype-1 and ITS-2 nemabiome. This constitutes the first multi-drug and multi-species molecular diagnostic developed for helminths of veterinary importance. Of the ovine, bovine, caprine and camelid Australian field isolates we tested, S168T was detected in the majority of Haemonchus spp. populations from sheep and goats, but rarely at a frequency greater than 16%; an arbitrary threshold we set based on whole genome sequencing of LEV resistant H. contortus GWBII. Overall, BZ resistance was far more prevalent in Haemonchus spp. than LEV resistance, confirming that LEV is still an important anthelmintic class for small ruminants in New South Wales. The mixed amplicon metabarcoding approach described herein, paves the way towards the use of large scale sequencing as a surveillance technology in the field, the results of which can be translated into evidence-based recommendations for the livestock sector.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
International Journal for Parasitology
21 papers in training set
Top 0.1%
22.2%
2
PLOS Neglected Tropical Diseases
378 papers in training set
Top 1%
10.0%
3
Journal of Clinical Microbiology
120 papers in training set
Top 0.4%
6.3%
4
Malaria Journal
48 papers in training set
Top 0.4%
4.8%
5
PLOS ONE
4510 papers in training set
Top 34%
4.2%
6
Scientific Reports
3102 papers in training set
Top 32%
3.9%
50% of probability mass above
7
Nature Communications
4913 papers in training set
Top 42%
3.0%
8
Emerging Infectious Diseases
103 papers in training set
Top 0.8%
2.8%
9
Parasites & Vectors
57 papers in training set
Top 0.5%
2.6%
10
mSphere
281 papers in training set
Top 3%
1.9%
11
PLOS Pathogens
721 papers in training set
Top 5%
1.9%
12
The Journal of Infectious Diseases
182 papers in training set
Top 3%
1.7%
13
Antimicrobial Agents and Chemotherapy
167 papers in training set
Top 1%
1.7%
14
Microbial Genomics
204 papers in training set
Top 1%
1.6%
15
One Health
29 papers in training set
Top 0.8%
1.3%
16
Gigabyte
60 papers in training set
Top 1.0%
1.2%
17
The American Journal of Tropical Medicine and Hygiene
60 papers in training set
Top 3%
1.1%
18
Pathogens
53 papers in training set
Top 1%
1.1%
19
Ticks and Tick-borne Diseases
11 papers in training set
Top 0.2%
0.9%
20
Microbiology
57 papers in training set
Top 1%
0.8%
21
eLife
5422 papers in training set
Top 56%
0.8%
22
Peer Community Journal
254 papers in training set
Top 4%
0.8%
23
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 5%
0.8%
24
Microbiology Spectrum
435 papers in training set
Top 6%
0.7%
25
PLOS Biology
408 papers in training set
Top 22%
0.7%
26
mBio
750 papers in training set
Top 13%
0.6%
27
Journal of Infection
71 papers in training set
Top 4%
0.6%