Salmonella mansoni, , Parasite, Parasite

Jutzeler, Kathrin S., Platt, Roy N., Li, Xue, Morales, Madison, Diaz, Robbie, Clec’h, Winka Le, Chevalier, Frédéric D. & Anderson, Timothy J. C., 2024, Molecular dissection of laboratory contamination between two schistosome populations, Parasites & Vectors (528) 17 (1), pp. 1-13 : 2

KeyWords Schistosoma mansoni , Parasite, Laboratory populations, Contamination, SmBRE, SmLE, Population genomics, Pool-sequencing

Background

Laboratory research with pathogen populations or cell lines requires rigorous safeguards to prevent contamination and to ensure repeatability of results from different laboratories. Nevertheless, a growing body of literature suggests that contamination (or mislabeling) of laboratory pathogens is surprisingly common. For example, phylogenetic studies of laboratory-adapted malaria parasite lines reveal widespread evidence for these issues [ 1 –3]. Contamination from positive control samples has resulted in extensive false-positive diagnoses in hospital diagnostic laboratories working with Mycobacterium tuberculosis , Salmonella spp. , and enterococci [ 4 – 7]. Finally, methods like isozyme analysis, human leukocyte antigen (HLA) identity testing, and DNA fingerprinting have exposed misidentification of lymphoma, hematopoietic, and ovarian carcinoma cell lines as a result of cross-contamination [ 8 – 10]. In many cases, the contamination may go unnoticed, particularly when no change is observed in pathogen phenotypes or when changes are subtle. As a result, the National Institutes of Health (NIH) and other funding agencies now require provision of protocols for validating the identity of the pathogens under study.

A second process—rapid evolution—can also result in genomic and phenotypic changes in pathogen populations over a short time period [ 11]. Rapid evolution of microbial populations in response to drug pressure, or to avoid immune attack, is ubiquitous. Evolution can also be surprisingly rapid in helminth parasites such as schistosomes. For example, selection for drug resistance [12, 13] or cercarial shedding number [ 14] can substantially alter parasite phenotypes in <10 generations.

Te life cycle of schistosome parasites can be maintained in the laboratory using freshwater snail intermediate hosts and rodents as definitive hosts. Our laboratory maintains several populations of Schistosoma mansoni , including two parasite populations originating from Brazil, SmLE and SmBRE. We have previously investigated these two populations in great detail, and we have reported striking differences in virulence, sporocyst growth, cercarial shedding, and immunopathology between them [ 15 – 18]. SmBRE exhibited lower fitness than SmLE for multiple life history traits in both the intermediate and definitive host. However, we noticed a drastic change in phenotypes typical for the SmBRE population starting in 2021. Over time, we noticed increased snail infectivity, higher cercarial shedding, and increased worm burden in SmBRE, while SmLE phenotypes remained relatively unchanged. Tese observations led us to speculate that the changes observed in the low-fitness SmBRE parasites could have resulted from two processes: (i) laboratory contamination with the more efficient SmLE population or (ii) selection of de novo mutations within the SmBRE population leading to increased fitness.

To evaluate these alternative scenarios, we sequenced pools of male and female worms from SmBRE and SmLE parasites collected at 10 time intervals over a 7-year period (2016–2023). We monitored allele frequency changes across the genome over time, both within and between the SmBRE and SmLE populations, to answer the following questions: (i) How stable are allele frequencies in laboratory schistosome populations? (ii) Do phenotypic changes in SmBRE reflect the selection of de novo mutations or laboratory contamination? (iii) If contamination occurred, what can we learn about the dynamics of genomic changes following admixture? (iv) Can we develop molecular approaches to verify laboratory schistosome populations and detect contamination?