Survival of Escherichia coli O157:H7 in soils along a natural pH gradient

doi:10.21203/rs.3.rs-4911988/v1

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Survival of Escherichia coli O157:H7 in soils along a natural pH gradient

https://doi.org/10.21203/rs.3.rs-4911988/v1

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Persistence of E. coli O157:H7 (EcO157) in soils from different places was widely reported, while its survival behavior in soils over a pH gradient was yet to be investigated. In the current study, a total of 24 soil samples were collected along a natural pH gradient, and the soils were classified into weak acidic soil (pH < 6.5), neutral soil (6.5 < pH < 7.5), weak basic soil (7.5 < pH < 8.5), and strong basic soil (8.5 < pH < 10). Soil physical and chemical properties were determined using standard methods, and bacterial communities were characterized by next generation high throughput sequencing protocol. EcO157 cells were inoculated into those soils and the survival profiles were investigated. The influencing factors affecting the survival behavior of EcO157 were analyzed by multivariate statistical analysis. The results showed that the average survival time of EcO157 in weak acidic, neutral, weak basic and strong basic soils was 61.08, 72.05, 76.85 and 18.54 days, respectively. The survival time in strong basic soils was significantly less than those in the other three soil groups. Results of both stepwise multiple regression and Mental tests revealed that soil physicochemical properties such as NO₃^--N, clay content, and NH₄⁺-N negatively linked to the survival of EcO157, while TP was positively correlated to the survival of EcO157 (P<0.05). Stepwise multiple regression showed that microbial community diversity was negatively correlated with the survival of EcO157, while relative abundances of Proteobacteria and Acidobacteria were positively and negatively correlated to the survival of EcO157, respectively. Our study highlighted the role of pH in the survival of EcO157 in soils. Both co-occurrence networks analysis and structural equation model results showed that pH was a key factor that could directly and indirectly via bacterial community influence the survival of EcO157. Our data coupled with the findings of others might be of great helpful in evaluation, control, and reduction of the potential health risk associated with EcO157 in soils along a natural pH gradient.

Biological sciences/Microbiology/Environmental microbiology/Soil microbiology

Earth and environmental sciences/Environmental sciences

E. coli O157:H7

survival

pH gradient

soil

bacteria community

structural equation model

E.coli O157:H7 (EcO157) was a bacterial pathogen that could infect humans with a very low infectious dose¹. Once infected by this pathogen, the patients could potentially manifest life-threatening symptoms including hemolytic uremic syndrome². EcO157 was generally associated closely to public health due to its foodborne and waterborne modes of transmission³. EcO157 could contaminate agricultural soils via irrigation and organic fertilizer application⁴. Several foodborne outbreaks due to contaminated vegetables in the past decades attracted great public concern, such as the ones occurred in Japan and United States, which were thought to be caused by the radish sprout and bagged spinach and lettuce⁵, respectively. The health risk of EcO157 was largely linked to its survival potential in then environment. EcO157 could survive in soils for a significant period ranged from weeks to months⁶. Previous studies showed that it survived for about 30 days in soils from Salinas, USA and for 70 days in soils from Northeastern China⁷.

Natural and anthropogenic activities resulted environmental gradient, along which striking variation of fate and transport of pollutants were reported, e.g. brominated flame retardants at altitude gradient⁸, halogen at moisture gradient⁹, and antibiotic resistance genes at slope gradient¹⁰. It was also documented that such environmental gradient also significantly affected the indigenous plant and microbial communities^11,12. Recently, it was found that the soil pH was gradually decreasing from west to east of Jilin Province, China¹³, obviously a pH gradient was observed. The soil pH of Baicheng City in western Jilin was significantly higher than that of other areas, with an average of 8.1¹³. The soil pH of Changchun and Siping City in the central region of Jilin Province was at a neutral level, averaging at 6.5 and 6.8, respectively. The soil pH of Yanbian Prefecture in the eastern part of Jilin was obviously low, with an average pH being 5.6^13,14. The main reason for pH gradient formation lied the reduction of annual mean temperature and precipitation from west to each of Jilin Province. The land use type might also contribute the pH gradient¹⁵. Soils from western Jilin (Baicheng and Songyuan) was in the agricultural and pastoral transition zone, EcO157 entered the soils with livestock manure. Farmland was the dominant soil type in central Jilin (Changchun and Siping), and the application of organic fertilizers could introduce human pathogens into the soils. While the wild animals could be the major source of EcO157 in soils in Yanbian Prefecture where the soil was largely covered by artificial and natural forest. Since soil properties might vary at different pH, and such changes would significantly affect the environmental behaviors of pollutants, including chemical and biological contaminants. It would be interesting to investigate the fate of pathogens, such as EcO157 in soils over such pH gradient.

Both biotic and abiotic factors could exert influence on the survival of EcO157 in soils¹⁶. Previous research indicated that pH was one of the crucial abiotic factors influencing the survival of pathogen in soils and it had a positive correlation with the survival of EcO157^17–19. It has also been reported that it was more difficult for EcO157 to survive in sandy soils than in clay soils¹⁰. Higher salinity might produce shorter survival time of EcO157 in soils¹⁴. Organic carbon and nitrogen content in soils were positively correlated with the survival of EcO157 since they could provide necessary nutrient substances for pathogens^18,20. On the other hand, it was well established that microbial diversity was negatively correlated with the survival of EcO157²¹. As an invading pathogen, EcO157 might suffer from the resistant from indigenous species. It has been certified that Proteobacteria, especially the subclass γ-Proteobacteria could negatively affect the survival of EcO157 to a great extent²². However, Actinobacteria and Acidobacteria displayed a positive correlation to the survival of EcO157²⁰. Meanwhile, EcO157 might pose a threat to the stability of the microbial environment and cause subsequent changes of the composition and diversity of bacterial community²². Besides, there were a large number of factors determining soil bacterial community^23–25. However, there is no research has been conducted to systematically investigate the interactions between EcO157 and soils properties in soils over a natural pH gradient.

In the current study, we collected and characterized soil samples along the pH gradient as mentioned above and the EcO157 cells were inoculated in those samples to test its survival potential. The objectives of this study were to: 1) investigate the survival behavior of EcO157 in soils along a natural pH gradient and 2) identify key factors influencing the survival parameters. Our study would provide insights into the mechanisms on the survival behavior of EcO157 in soils with distinct pH.

1. Soil sample collection and characterization

As was shown in Fig. 1, a total of 24 soil samples were collected, and those samples were almost evenly distributed in the western, central and eastern parts of Jilin Province. The latitude of 24 soils was between 43°50′06.96″ and 45°38′12.14″, and the longitude was between 123°33′01.74″and 127°19′34.59″. Top soils (0-20cm) were sampled, and each sample was a composite of 3 individual soil cores spaced at 5 m intervals. The soil was mixed, homogenized and sifted (< 2 mm) to remove plant roots, debris and stones. The samples were divided into two parts, one part was air-dried and used for the determination of physicochemical properties, the other part was stored in a -80°C refrigerator for DNA extraction. Soil particle content was determined using a laser particle size analyzer (Bettersize 2000, Dandong, China). A pH meter (FE20, Mettler, Shanghai,

China) and a digital conductivity meter (DDS-11A, Rex, Shanghai, China) were used to determine soil pH and electrical conductivity salinity (EC, µS/cm, soil-water ratio of 1:2.5), respectively. Total phosphorus (TP, mg/kg), ammonium nitrogen (NH₄⁺-N, mg/kg), and nitrate nitrogen (NO₃⁻-N, mg/kg) were measured using a UV-Vis spectrophotometer (MapData, Shanghai, China). Water soluble organic carbon (WSOC, mg/kg, soil-water = 1:2.5) was determined using a TOC instrument (TOC- vcph, Shimadzu, Kyoto, Japan). The soil sample physicochemical properties were shown in Table S1.

2. Bacterial strains

EcO157 EDL931 (ATCC 35150) was used in the soil survival assays. EDL931 was a human feces isolate conferring stx₁, stx₂, and eas genes²⁶. In order to facilitate the enumeration of EDL931 on Sorbital MacConkey agar supplemented with BCIG (5-bromo-4-chloro-3-indoxyl-β-D-glucuronide) (Lab M, UK), the EDL931 wild type was tagged with nalidixic acid and rifampicin resistance, and its growth in LB (Luria − Bertani) broth and survival in rich media was found to be identical to that of the non-tagged wild-type strain²⁷.

3. Survival of E. coli O157:H7 in Soils

Inoculum preparation and inoculation procedure was reported previously⁷. In brief, early stationary phase cells were used, after washing (0.9% saline water) and starvation (1 h in dark) the cells were inoculated into the soil to a final concentration of 5 ×10⁶ colony forming units per gram of soil dry weight (CFU/gdw). All experiments were conducted under room temperature (22 ± 1°C). Triplicate plastic bags containing the soils inoculated with EcO157 were prepared. Moisture content of the soil sample was maintained constantly (60% of WHC) during the course of experiment by adding additional deionized water to make up for evaporation. Soils without EcO157 inoculation were used as control. At days 0, 1, 2, 4, 6, 8, 11, 13, 15, 19, 25, 32, 37, 47, 57, 68, 80, 96, the inoculated soils were sampled. Cells were extracted, plated on the selective agar plates, and finally, the CFU was counted according to the methods described previously⁷.

4. Soil bacterial communities’ characterization.

The hypervariable V4 region of the 16S rRNA gene was amplified using the primers: 16S-F (5ʹ-AYTGGGYDTAAAGNG-3ʹ) and 16S-R (5ʹ-TACNVGGGTATCTAATCC-3ʹ)²⁸. Sequencing was performed on an Illumina MiSeq instrument using a paired-end 150-bp sequence read run with the Miseq Reagent Kit v3 at the Personal Biotechnology Company (Shanghai, China). Each DNA sample was individually PCR-amplified in triplicated 25-µ L reactions. The cycling conditions were an initial denaturation at 94°C for 5 min, followed by 25 cycles of 94°C for 30 s, 50°C for 30, 72°C for 30 s, and a final 7 min extension at 72°C. Each reaction contained 1 × PCR buffer, 2.5 mM dNTPs, 0.625 U of Taq DNA polymerase (Takara Bio, Otsu, Shiga, Japan), 10µM of each primer, and 20 ng of target DNA. Sequences were processed by using MOTHUR v.1.20.1²⁹.

Briefly, any sequences of length < 150 bp or > 300 bp, mean quality < 30, ambiguous bases > 1, homopolymer length > 6, maximum primer mismatch > 0 were removed from further analysis³⁰. The remaining sequences were aligned to a reference alignment, and those sequences that did not align to the correct region were eliminated. Further, any non-bacterial ribosome sequences and chimeras were removed using Black Box Chimera Check software (B2C2) according to a previously used procedure³¹. Sequences were split into groups according to their taxonomy and assigned to operational taxonomic units (OTUs) at a 3% dissimilarity level. OTUs containing 1 read (singleton) were not used to avoid or at least reduce possible sequencing errors. For the following data analyses, we used a randomly selected subset of 27582 sequences from each sample to standardize sequencing effort across samples. We then used Bayesian classifier to classify those sequences against the Ribosomal Database Project (RDP) 16S rRNA gene training set (version 9, http://rdp.cme.msu.edu) ³¹. We required an 80% pseudo-bootstrap confidence score³². BLAST searches based on the NCBI GenBank database (www.ncbi.nlm.nih.gov/blast) were also performed to provide information on the OTUs that were not classified by RDP. Dominant OTUs were defined as those with a representation ≥ 5% within a sample, abundant OTUs were defined as those with a representation ≥ 1% within a sample, and rare OTUs were defined as having an abundance < 0.01% within a sample. The sequence data has been deposited to NCBI BioProject under accession number PRJNA530627.

5. Survival data modeling

We analyzed the survival of EcO157 by fitting the experimental data to the Weibull survival model by using GInaFiT v1.5^33,34. The Weibull survival model was constructed based on the hypothesis that the deactivation kinetics of the EcO157 population followed a Weibull distribution²¹. The size of the surviving population could be calculated using Eq. 1,

\(\:\text{log}\left({N}_{t}\right)=\text{log}\left({N}_{0}\right)-{\left(t/\delta\:\right)}^{p}\) Eq. 1

where N_t was number of survivors, N₀ was inoculum size, t is time (days) post inoculation, δ was scale parameter representing the time (days) needed for first decimal reduction, and p was nonunit shape parameter. When p > 1, < 1, and = 1, a convex, concave, and linear curve was expected³⁵. Time needed to reach detection limit (ttd) could also be calculated by modeling the data. The detection limit was 100 CFU per gram soil dry weight, gdw^− 1.

6. Statistics analysis

Stepwise multiple regression analysis of ttd with soil physicochemical properties and bacterial community structure (Shannon-Wiener Index(H), total OTU count and the relative abundances of dominant phyla) was performed by using SPSS v22.0 (IBM, Chicago, USA). Detrended Correspondence Analysis (DCA), Mantel and Partial Mantel Tests among ttd, soil physicochemical properties, and bacterial community structure were conducted by R 3.5.2 with vegan package. The DCA first axis score of each sample was used to represent the community structure. Co-occurrence network analysis were conducted by R 3.5.2 with Hmisc package and were visualized using Gephi (https://gephi.org/). Structural Equation Modeling (SEM) was performed using SPSS coupled with using AMOS 20.0 (IBM, Chicago, USA).

1. Soil characterization and grouping

Results of soil characterization showed that soil pH was between 5.61 and 9.92. EC ranged from 0.04 to 8.57 mS/cm. NH₄⁺-N varied from 0.55 to 40.79 mg/kg, while NO₃⁻-N varied from 0.12 to 240.02 mg/kg; total phosphorus was between 2.05 and 36.10. Clay was from 2.8 to 21.07 and WSOC was in a range of 26.65 to 2182.40 mg/kg (Table S1).

In order to highlight the differences between soil pH, we divided the soil samples into four groups according to the pH value. The soils with pH < 6.5 were classified as weak acidic soils, including soil samples T1-T8; soils with 6.5 < pH < 7.5 were classified as neutral soils, including soil samples T9-T13; soils with 7.5 < pH < 8.5 were classified as weak basic soils, including soil samples T14-T18; soils with pH > 8.5 were classified as strong basic soils, including soil samples T19-T24.

2. Survival behavior of E .coli O157:H7 in soils

Survival profiles of EcO157 in soils were showed in Fig. 2. It was showed that all survival curves were displayed in convex shape, which was consistent with the results that p > 1, and the shortest persistence was observed for strong basic soils. Among all the 24 soil samples, ttd had an average of 56.02 days, ranging from 9.99 to 100.70 days (Fig. 3A). Weak acidic soils had an average ttd of 61.08 days, ranging from 27.40 to 100.70 days. Neutral soils had an average ttd of 72.05 days, ranging from 52.00 to 92.50 days. Weak basic soils had an average ttd of 76.85 days, ranging from 66.85 to 91.10 days. Strong basic soils had an average ttd of 18.54 days, ranging from 9.99 to 28.40 days.

The δ had an average of 18.83 days, ranging from 4.00 to 47.54 days in all the soil samples

(Fig. 3B). Weak acidic soils had an average δ of 20.36 days, ranging from 5.79 to 41.46 days. Neutral soils had an average δ of 18.18 days, ranging from 8.05 to 31.84 days. Weak basic soils had an average δ of 28.63 days, ranging from 15.24 to 47.54 days. Strong basic soils had an average δ of 9.16 days, ranging from 4.00 to 12.24 days.

Among all the 24 soil samples, p had an average of 1.52, ranging from 0.78 to 2.67 (Fig. 3C). Weak acidic soils had an average p of 1.27, ranging from 0.78 to 1.64. Neutral soils had an average p of 1.08, ranging from 0.78 to 1.33. Weak basic soils had an average p of 1.65, ranging from 1.04 to 2.44. Strong basic soils had an average p of 2.12, ranging from 1.32 to 2.67.

The ttd of weak basic soils was the longest, followed by neutral, weak acidic and strong basic soils. Correspondingly, δ of neutral group was the largest, followed by neutral, weak acidic and strong basic soils. As for p, there was no significant difference (P < 0.05) among weak acidic soils, neutral soils and weak basic soils.

3. Soil bacterial community characterization

From the high-throughput sequencing analysis, a total of 22022 OTUs were obtained from the 24 soil samples after normalization. Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi and Gemmatimonadetes were the dominant phyla among the bacterial communities in all soils samples (Fig. S1 and S2). Further analysis showed that in weak acidic soils, Proteobacteria was the most abundant phyla and the abundance of Acidobacteria was much more than the rest three soils. Instead, Actinobacteria was the most abundant phyla and Acidobacteria was the least abundant phyla in strong basic soils. In neutral and weak basic soils, the abundance of major phyla was familiar, exhibiting that Proteobacteria and Actinobacteria were the two abundant phyla (Fig. 4A-C). Besides, we found that the relative abundance of Proteobacteria and Acidobacteria were positively related to ttd while the relative abundance of Actinobacteria was negatively related to ttd (Fig. 4D-F).

4. Stepwise multiple regression, mantel and partial mantel tests analysis

Stepwise multiple regression equation showed that pH, NO₃⁻-N, Proteobacteria, Acidobacteria and H are major factors influencing the survival of EcO157 in soils (Table 1). To be more specific, pH, NO₃⁻-N and Acidobacteria were negatively correlated with ttd and Proteobacteria, H were positively correlated with ttd.

To further discuss the effects of various factors on ttd, mantel and partial mantel tests were conducted. The results of Mantel test showed that H, bacterial community, NO₃⁻-N, pH, Acidobacteria, WSOC, EC, Proteobacteria, NH₄⁺-N and Actinobacteria were all correlated with ttd (Table 2). We also carried out partial mantel test to figure out the net effect of each factor on ttd, and the results showed that H, bacterial community, NO₃⁻-N and pH were still correlated with ttd after partial out other factors.

5. Co-occurrence network of survival time, soil properties and microbial community composition and structure

Co-occurrence network analysis was able to visualize the factors affecting ttd, including both abiotic factors, such as various soil physicochemical properties (pH, WSOC, EC, NO₃⁻-N, NH₄⁺-N, clay, TP), and biotic factors, such as the structure of the bacterial community, including Shannon Wiener index (H), relative abundances of Proteobacteria, Acidobacteria, Actinobacteria, α-Proteobacteria, β-Proteobacteria, γ-Proteobacteria, and δ-Proteobacteria (Fig. 5). In Fig. 5, red dots represented ttd, blue dots indicated soil physicochemical properties affecting EcO157 survival, and green dots represented biological factors. The larger the circle and the darker the color, the more significant the effect of the factors on ttd. The major influencing factors included pH, WSOC, NO₃⁻-N, bacterial community, H, Proteobacteria, and Acidobacteria.

6. Structural equation model of survival data, soil properties and bacterial community

The results of SEM showed (Fig. 6) that NO₃⁻-N and bacterial community structure had a direct effect on the survival of EcO157 and both were negatively correlated with ttd. NH₄⁺-N, clay and TP were indirectly affecting ttd by influencing the bacterial community structure. Clay and NH₄⁺-N showed positive correlation with bacterial community structure and TP showed negative correlation with bacterial community structure. As seen in Fig. 6, NH₄⁺-N and clay were negatively correlated with EcO157 survival, while TP was positively correlated with the survival of EcO157. The pH was both correlated with ttd directly and indirectly linked to ttd via bacterial community.

Soil texture as indicated by sand, silt and clay contents may promote the flourish of some species while restrain the others³⁶. In the current study, clay displayed and indirect effect the EcO157 survival via bacterial community. Although clay positively correlated with bacterial community, which was negatively linked ttd. This could be explained that clay minerals were able to secure the water content and provide binding sites for organic materials required for bacterial community, and the clay particles might also offer protection from desiccation, gas diffusion, toxic exogenous compounds, and predation by protozoa^37,38, while the effect of bacterial community on ttd was largely depend on its composition and structure as discussed below.

Former study found that survival of EcO157 were positively correlated with microbial diversity in soils³⁹, which was in agreement with our findings. It seemed that a minimum number of species were essential for ecosystem functioning and a larger number of other species were probably necessary for maintaining the stability of an ecosystem⁴⁰. As a result, EcO157 was easier to survive in a stable ecosystem with abundant energy source and less competition⁴¹. Previous study showed that EcO157 would interact with the local microbial communities and produced a negative correlation, which was identical with our outcomes⁴². The adverse effect of bacterial community on EcO157 survival was believed to be the result of predation, substrate competition and antagonism^,43.

Furthermore, we found that Proteobacteria was correlated with survival of EcO157 positively while Acidobacteria was correlated with the survival of EcO157 negatively. The relative abundance of β-Proteobacteria was most strongly related to carbon availability, a higher relative abundance of Proteobacteria may imply more carbon in soils, which was essential for pathogen survival²⁵. The α-Proteobacteria may contribute to Fe²⁺ production, which was necessary for pathogen survival⁴⁴. As for Acidobacteria, previous analysis indicated that the response of the different subgroups of Acidobacteria to soil pH was varied. The relative abundance of subgroups 1, 2, 3, 12, 13 and 15 were negatively correlated with soil pH, whereas, subgroups 4, 6, 7, 10, 11, 16, 17, 18, 22 and 25 were positively correlated with soil pH⁴⁵. We assumed that in our soil samples, the subgroups of Acidobacteria which were positively associated with pH were more dominant, resulting in the negative correlation between Acdiobacteria and ttd. Furthermore, researchers believed that the relative abundance of Acidobacteria was negatively related with soil carbon availability^46,47. So a lower abundance of Acidobacteria equaled to more carbon content and the survival would be promoted.

The pH was one of the major factors influencing bacterial community structures²³, it may impose physiological constraint on soil bacteria, alter competitive outcomes or reduce the net growth of individual taxa²⁵. Previous study revealed that the relative abundance of δ-Proteobacteria decreased as pH increased, but α-Proteobacteria increased³⁶. In the current study, it was found that pH was an important factor influencing the survival of EcO157 and was negatively associated with ttd (Table 1, Fig. 6). On the one hand, pH could restrain the survival of EcO157 directly^48,49. Although previous study showed that EcO157 can survive longer in lower pH soils. Any change of the soil characteristics may alter soil pH⁵⁰, i.e., soil characteristics may vary a lot in soils with different pH, as a result, pH and other soil properties may display a complex influence on the survival of EcO157^25,51. In neutral and alkaline soils, EcO157 lost its preferred acidic environment, resulting in decreased viability. Notably, EcO157 was rapidly inactivated in strongly alkaline soils, with a reduction in ttd to about 20 days, which was significantly lower than the survival time of EcO157 in the other three group soils. Therefore, it was concluded that ttd and pH were negatively correlated.

NO₃⁻-N was another key factor affecting ttd and it was negatively linked to ttd (Table 1, Fig. 6). As usual, NO₃⁻-N could provide nutrition for pathogen and it may promote the survival of EcO157⁵². Instead, our results showed that NO₃⁻-N was negatively correlated with the survival of EcO157 and it could be explained by the specific characteristics of soils from western Jilin Province. Due to unreasonable management, excessive fertilization and great differences in seasonal precipitation, soils in western Jilin Province suffered from salinization, exhibiting extreme soil physicochemical properties like high content of NO₃⁻-N and high pH^14,53. At this aspect, the inhibiting effect of NO₃⁻-N on ttd was coincident with that high pH would shorten ttd. Besides, we found that soils from central and eastern Jilin Province may support the survival of EcO157 by providing moderate content of NO₃⁻-N as nutrient substance. However, the resistance effect of basic soils was so strong that NO₃⁻-N presented a negative correlation on the whole. The N source in soil may mainly come from fertilization. Some researchers have found that the influence of soil fertilization was closely related to soil pH variation (generally increase). The survival of EcO157 was significantly increased in acidic soils when N source was applied. However, the promotion effect of N source on the survival of EcO157 was greatly reduced in alkaline soil, which was consistent with the conclusion of our study. Experiments showed that compared with chicken manure, pig manure application in soils has a more positive effect on the survival of EcO157⁵⁴. As mentioned above, NO₃⁻-N might indirectly affect EcO157 by affecting the survival of other microorganisms in the soil. Appropriate input N source in soil might be helpful in maintaining the stability of soil microbial community, which would hinder the invasion and survival of EcO157:H7⁶. Therefore, excessive fertilization behavior could lead to excessive NO₃⁻-N content in soil, which could inhibit the survival of EcO157.

In addition, ttd was correlated with other soil physicochemical properties like WSOC, NH₄⁺-N and EC. Soil WSOC and NH₄⁺-N could guarantee sufficient energy sources and adjust soil water content to provide a favorable environment for EcO157 persistence⁵⁵. Consequently, the competitive pressure between indigenous microorganisms would be decreased for pathogen survival^18,56. Researchers found that EC may influence the survival of EcO157 by a general osmotic effect or by specific ion toxicity, which led to a decrease of activity in some enzymes that were essential for cell metabolism, e.g. K⁺ transport⁷. Furthermore, it was also possible that geographic location was responsible for the majority of alterations in microbial community structure that impacted the survival of EcO157. Previous research has indicated that soils situated at greater distances exhibited reduced similarity, implying that the number of species shared between soils sampled at greater distances was lower. Investigations into large-scale bacterial biogeography have demonstrated that bacterial community structure did indeed undergo changes with increasing geographic distance.

In summary, the current research revealed that pH was an important factor influencing the survival of EcO157 in soils. pH can be not only correlated with the survival of EcO157 directly, but also be correlated with the survival indirectly by influencing other soil physicochemical properties and bacterial community. Besides, NO₃^--N was also an important influencing factor. Biotic factors like microbial diversity and bacterial community played a crucial role in shaping the survival pattern of EcO157. Among them, bacterial community was affected by several soil physicochemical properties, such as pH, clay, NH₄⁺-N and TP. To sum up, the survival of EcO157 differed greatly in different areas of Jilin Province over a pH gradient and it has a weak persistence in basic soils compared to acidic and neutral soils. This research may help us to better understand the survival activity of EcO157 and prevent the infection outbreak associated with this pathogen.

Data availability

The sequence data has been deposited to NCBI BioProject under accession number PRJNA530627. The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Acknowledgements

The research was financed by National Natural Science Foundation of China (No. 41571304).

Author contributions

Guangze Lyu: Conceptualization, Methodology, Formal analysis, Writing-Original draft preparation; Huiru Li: Methodology; Jiayang Hu: Writing-Reviewing and Editing; Jincai Ma: Funding acquisition, Writing- Reviewing and Editing. All authors reviewed and approved the manuscript.

Funding

The research was supported by National Natural Science Foundation of China (No. 41571304).

Competing interests

The authors declare no competing interests.

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Table 1. Stepwise multiple regression equation predicting ttd using soil properties and relative abundances of dominant phyla.

Regression Equation

R²

r and T value of partial regression coefficient

ttd = -177.079 - 2.845 × pH - 3.912 × NO₃^--N + 25.409 × Proteobacteria - 111.826 × Acidobacteria + 28.432 × H

0.861

8.155

0.000

NO₃^--N

Proteobacteria

Acidobacteria

-0.152

-0.274

0.074

-0.396

+0.825

-0.430^**

-1.629^**

-0.335^**

-1.136^**

+3.748^**

** denotes significant at 0.01 level.

Table 2. Results of Mantel and partial Mantel tests.

	Mantel	Partial Mantel
H	***	***
Bacterial Community	***	***
NO₃^--N	***	**
pH	***	*
Acidobacteria	***	-
WSOC	***	-
EC	**	-
Proteobacteria	**	-
NH₄⁺-N	*	-
Actinobacteria	*	-

***, ** and * denotes significant at 0.001, 0.01, and 0.05, respectively.

No competing interests reported.

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Survival of Escherichia coli O157:H7 in soils along a natural pH gradient

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