Virulence shift in Type X <i>Toxoplasma gondii</i> : natural cross QTL identifies ROP33 as rodent Vir locus
Bibliographic record
Abstract
Abstract How virulent parasites are maintained in nature is an important paradigm of eukaryotic pathogenesis. Here we used population genetics and molecular methods to study the evolution and emergence of genetic variants of the protozoan parasite Toxoplasma gondii, referred collectively as Type X (HG12), recovered from a threatened marine mammal species. Specifically, 53 T. gondii strains were isolated from southern sea otters (SSO) that stranded between 1998-2004 with T. gondii infection (ranging from chronic incidental infections to fatal encephalitis). Over 74% of these SSO, collected throughout their geographic range, were infected with Type X, based on multi-locus PCR-DNA sequencing. Depending on the locus investigated, Type X strains possessed one of three allelic types that had independently assorted across the strains examined; either genetically distinct alleles, referred to as “ψ” or “8”, or a Type II allele. Phylogenetic incongruence among locus-specific trees, genome-wide CGH array and WGS analyses confirmed that Type X is a sexual clade of natural recombinants that resemble F1 progeny from a genetic cross between Type II and a mosaic of two distinct “ψ” or “δ” ancestries. A single Type X genotype (19/53; 36%) that largely caused subclinical chronic infections in SSO, was highly pathogenic to mice (LD 100 = 1 parasite). To determine whether murine virulence genes could be mapped within this population of natural isolates, we performed a genome scan and identified four QTLs with LOD scores greater than 4.0. Targeted disruption of ROP33, the strongest candidate from among 16 genes within the highest QTL on Chromosome VIIa established ROP33 as a murine virulence locus. The ability of this highly pathogenic mouse-virulent T. gondii clone to expand its environmental niche and infect a majority of SSO supports a virulence shift model whereby generalist pathogens like T. gondii utilize their sexual cycles to produce new strains that possess an expanded biological potential. Such a trait enables pathogens to extend their host range or be naturally selected within their vast intermediate host range to maximize transmission. Our work establishes a rationale for how virulent strains can be maintained cryptically in nature across a pathogen’s broad host range, and act as potential reservoirs for epidemic disease. Importance Waterborne outbreaks of protozoal parasites are capable of causing fatal disease in a wide range of animals, including humans. Population expansion of felids in addition to anthropogenic changes near marine estuarine environments may facilitate marine wildlife exposure to highly infectious Toxoplasma gondii oocysts shed in felid feces. Infected cats shed millions of environmentally-resistant T. gondii oocysts that can be widely dispersed by storm events. In North America T. gondii is thought to possess a highly clonal population structure dominated by 4 clonal lineages (I, II, III, and X). Population genetic analysis of 53 T. gondii isolates collected longitudinally from SSO infected with T. gondii that stranded between 1998-2004 identified 74% of otters infected with Type X T. gondii , and that Type X is not a clonal lineage, but rather a recombinant clade of at least 12 distinct strains consistent with a recent genetic cross. Importantly, one Type X haplotype was isolated from 36% of southern sea otters ( Enhydra lutris neries ) across their geographic range in California. This haplotype was highly pathogenic to mice but caused relatively benign infections in SSO. A genome scan was performed to identify a virulence locus; a secreted serine threonine kinase (ROP33) that enhanced pathogenicity in laboratory mice, but not sea otters. Our data support a virulence shift model whereby generalist pathogens like T. gondii utilize their sexual cycles to produce virulent strains that can be maintained cryptically in nature, according to their differential capacity to cause disease within the pathogen’s broad intermediate host range. This type of “host selection” has important public health implications. Strains capable of causing fatal infections can persist in nature by circulating as chronic infections in resistant intermediate host species that act as reservoirs for epidemic disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".