Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils

Abstract

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters.

Malaria intensity in Colombia by regions and populations

“…In recent years, TDA has been used to find subgroups of individuals with unique genetic and prognostic profiles of breast cancer [ 30 , 72 ], political alliances in the congress [ 30 ], profiles of basketball players [ 30 ], pathogen persistence in soil [ 73 ], novel patterns in spinal and brain injury [ 74 ], subgroups of individuals with different complications from type 2 diabetes [ 75 ], among others. We use TDA to find synchronic outbreak clusters among the data.…”

Survivability of Salmonella and shiga-toxigenic Escherichia coli (STEC) O157 in microwave heated ready-to-eat (RTE) foods

“…Most RTE food was observed to have some raw vegetable garnishing placed on the food which presumably became the vehicle of contamination for E. coli. Pathogenic E. coli O157: H7 is frequently found in soil (Ibekwe et al, 2014) and water sources if contaminated with faeces from infected humans or animals. The presence of E. coli on food indicates faecal contamination, which can lead to the possible presence of other harmful microorganisms, viral or helminthic of protozoal parasites (Jay, 1997).…”

Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets

“…The most important tool of TDA is the persistent homology method [12,13], which is proven as useful in many real-world applications. The abundance of applications covers a broad range of phenomena in biological and medical science, like breast cancer research [14], brain science [15][16][17][18][19][20][21], biomolecules [22][23][24], evolution [25] and bacteria [26], followed by the applications in sensor networks [27,28], signal analysis [29], image processing [30], musical data [31], text mining [32], phase space reconstruction of dynamical systems [33,34], as well as complex networks related to either dynamics taking place on networks [35] or structural properties [36,37].…”