KEY TAKEAWAYS:
- A persistent subtype of E. coli O157:H7, dubbed REPEXH02, has been linked to several leafy green foodborne outbreaks.
- Researchers are conducting sampling and laboratory studies to look at both genetic and environmental facotrs that may influence REPEXH02's persistence and unique fitness traits.
- Their goal is to develop an online predictive tool to help growers gauge potential risks in their fields.
Referenced CPS Research:
About a decade ago, scientists discovered that the pathogen responsible for several leafy green–related foodborne outbreaks wasn’t the classic E. coli O157:H7. Using whole genome sequencing (WGS), they identified it as a recurring, persistent subtype they labeled E. coli O157:H7 REPEXH02.
Teresa Bergholz, Ph.D., of Michigan State University, said this subtype, while apparently persistent, does not appear to be widespread in the environment. Nevertheless, understanding the biological and environmental factors that allowed REPEXH02 to emerge in the past remains an important goal.
Many other E. coli O157:H7 outbreaks involving different foods, however, have been associated with another subtype — REPEXH01 — which is both persistent, geographically widespread, and more genetically diversified than REPEXH02.
As part of her two-year Center for Produce Safety (CPS)–funded project, Bergholz aims to identify the genetic fitness traits and environmental conditions that may contribute to REPEXH02’s ability to persist. The resulting data will support development of a user-friendly online tool that leafy greens growers can use to assess potential risks posed by this and other subtypes with similar WGS-defined characteristics.
Joining Bergholz as co-principal investigators are Shannon Manning, Ph.D., and Jiyoon Yi, Ph.D., both from Michigan State University. Manning brings expertise in E. coli bioinformatics, while Yi specializes in applying artificial intelligence (AI) and machine learning (ML) for data analytics and predictive modeling.
Michelle Carter, Ph.D., with the U.S. Department of Agriculture’s Agricultural Research Service (USDA-ARS) in Albany, California, also serves as a co-PI. The project is titled “Genomic and Phenotypic Assessment of E. coli O157:H7 REPEXH02 Strains.”
In March, Bergholz and her team collected their first set of baseline soil, sediment, water, and wildlife fecal samples from a leafy greens production region. They did not detect E. coli O157:H7. Given this pre-season baseline sampling, unusually dry late Spring and before crop irrigation began this was not an unusual outcome.
The team plans to collect samples monthly beginning in November. Each sample is being analyzed for key characteristics, including texture, pH, soluble salts, organic matter, and heavy metal content. Heavy metals are of particular interest because the Centers for Disease Control and Prevention (CDC) has identified regional REPEXH02 strains with a specific WGS variation that may confer increased tolerance to arsenic. Arsenic tolerance resides in what is termed the accessory genome and may be passed from one E. coli subtype to another and may also confer other stress-tolerance traits.
In parallel, Bergholz is collaborating with Carter, who maintains a large collection of E. coli O157:H7 isolates at the USDA-ARS facility. By sequencing both newly collected and historical isolates, the researchers will compare genetic profiles across the pathogen’s family tree—and evolutionary branches or clades—to trace when, how, and where the REPEXH02 subtype may have originated.
The team will use several analytical tools, including AI and ML, to identify environmental and possible crop-associated fitness traits that distinguish REPEXH02 from other subtypes.
Ultimately, the researchers plan to develop an online predictive model that allows growers to input field data—such as soil test results or observations of wildlife scat—and receive a risk assessment indicating the potential likelihood (low, medium, or high) of REPEXH02 presence.
Before launching the tool publicly, Bergholz and her colleagues will validate it using results from known positive and negative samples to ensure its accuracy and reliability.