Energy, Ecology and Environment

, Volume 3, Issue 2, pp 110–125 | Cite as

Effect of vegetative filter strips on the dynamics of E. coli biofilm-building potential and expression of virulence factors at Mau, Kenya

  • Casianes Owino Olilo
  • Anastasia Wairimu Muia
  • Japheth Ogalo Onyando
  • Wilkister Nyaora Moturi
Original Article
  • 655 Downloads

Abstract

Escherichia coli (E. coli) populations could become tolerant to extreme environmental temperatures to become resident in soil and surface water habitats creating public health problems. The objective of this study was to understand the interaction between dynamics of E. coli genetic diversity, physiology, and vegetative filter strips (VFS) in overland flows and soil habitats. Pulse-field gel electrophoresis was used to establish the genetic diversity of the isolates (n = 4). Genotypic analysis showed that the runoff isolates do not form a single genetic strain, but that multiple genotype strains were capable of surviving and proliferating in these habitats. High overall unique genotypic diversity was observed in VFS II (38.5%) as compared to VFS III (9.5%) and VFS I (1.5%). Approximately 8.5% E. coli genotypes observed in the surface water and 5.5% observed in soil habitat were shared between different sampling sites, suggesting diffuse sources of E. coli in these habitats. Approximately 32.5% genotypic overlap and a limited sharing (72.5%) between soil and surface water habitats were observed. These findings inferred that certain E. coli strains might have the ability to colonize and adapt to soil and runoff surface water habitats through production of biofilms. Thus, these results confirm that biofilm formation confers competitive advantage to the E. coli environmental isolates including hardiness, versatility, higher resistance to ecological and physical impacts and higher resource availability.

Keywords

Vegetated filter strip design Genetic and physiological adaptations E. coli populations Overland flow Soil habitat Biofilm formation 

1 Introduction

Faecal coilform loading could pose serious and wide array of pathogenic infection of concern to human health, including bacteria, protozoa, viruses and helminths (Tate et al. 2006; Gerba and Smith 2004; Savichtcheva and Okabe 2006; Tran et al. 2015). Pathogenic faecal coliforms such as E. coli are some of the world’s major microbial contaminants of surface water bodies posing risks to the management agencies and water users’ associations. Some of the main sources of contamination include animal feedlots, impervious surfaces, and overland flow from agricultural land, failing septic systems, birds/wildlife, domestic pets, and raw sewage as described by Pachepsky et al. (2006). Source tracking methods have emerged as a means to determine key contributors to surface waters in order to refine monitoring and evaluate potential interventions (American Public Health Association 1992; Savichtcheva and Okabe 2006; Baertsch et al. 2007; Dick et al. 2010; Lalancette et al. 2014; Tran et al. 2015). E. coli diversity in different VFS conservation systems and rainfall seasons has different genetic and physiological set-up (Escherich 1988; Shulman et al. 2007; Zimmer 2008; Brennan et al. 2010; Blount 2015). Klein and Casida Jr (1967) reported that E. coli cells sampled after staying in soil for some time had a lower growth rate, which subsequently changed to a higher growth rate after acclimatization to laboratory media. E. coli could grow and survive for long periods of time in tertiary habitats such as aquatic vegetation, silty and sandy environments, sediment deposits and soil raising its validity for use as an indicator organisms (Hood and Ness 1982; Byappanahalli and Fujioka 1998; Byappanahalli et al. 2003a, b, c; Whitman and Nevers 2003; Anderson et al. 2005; Byappanahalli et al. 2006; Ksoll et al. 2007; Ishii et al. 2007; Yamahara et al. 2007; Byappanahalli et al. 2006, Ishii et al. 2006; Goto and Yan 2011). Different environmental conditions including VFSs favour the growth of E. coli (Rauch and Bar-Yam 2004).

Understanding of E. coli DNA isolated from primary and secondary hosts has been enhanced by the new DNA screening methods such as repetitive extragenic palindromic techniques of polymerase chain reaction (rep_PCR), ribotyping, and pulsed-field gel electrophoresis (Versalovic et al. 1991; Delbès et al. 2000; Dombek et al. 2000; Ribot et al. 2006; Tamura et al. 2007). Genetic diversity isolated from various environments including aquatic and soil ecosystems shows different unique genotypes, and virulence factors (Saitou and Nei 1987; Anderson et al. 2005; Carson et al. 2001), but have hardly been differentiated (Kaper et al. 2004; Gordon et al. 2008; Brennan et al. 2010; Clermont et al. 2000, 2013; Blount 2015; Quero et al. 2015). Understanding the interaction of vegetative filter strips of E. coli genetic and physiology is important in interpreting the biofilms dynamics of this microbe. The E. coli biofilms studies have revealed numerous information concerning primary and secondary host adaptations of this microbe (Wang et al. 2006; Beloin et al. 2008; Nesse et al. 2014; Vogeleer et al. 2014; Blount et al. 2012). The dynamics of the genetic and physiological microbial adaptations and antibiotic resistance behaviour of E. coli biofilms have been revealed (Beloin et al. 2008; Ponciano et al. 2009; Tyerman et al. 2013; Blount 2015).

Biofilms refer to microorganisms that attach and grow on the surfaces (Muia et al. 2011). These biofilm microorganisms produce sticky exopolymeric substances (EPS) that have revealed numerous information concerning primary and secondary host adaptations of these microbes (Wang et al. 2006; Beloin et al. 2008; Nesse et al. 2014; Vogeleer et al. 2014; Blount et al. 2012). Biofilms can act as a catalogue of horizontal gene transfer processes that could reveal the antibiotic resistance behaviour, genetic, and physiological microbial adaptations by these microorganisms (Beloin et al. 2008; Ponciano et al. 2009; Tyerman et al. 2013; Blount 2015) (Beloin et al. 2008). Biofilm building microbial organisms could initiate numerous organism membrane infections that could be difficult to eliminate completely (Hassan et al. 2011). Antibiotics can be ineffective in biofilm environments as a result of microbial reduced growth rates, limited infiltration of antibiotics into biofilms, and expression of resistance genes in their vicinity (Kim 2001).

Despite some limitations, E. coli continues to advance as a versatile organism for water quality monitoring including influences by nitrogen in water bodies (D’Elia et al. 1977; Eisakhani and Malakahmad 2009). In recent years, there has been considerable advances in the application of E. coli as a model organism or an index (Odonkor and Ampofo 2013). The rapid detection techniques for specific microbial organisms and diagnostics specific to disease-causing strains of E. coli have a good correlation with standardized indicator techniques for E. coli and faecal coliform which depend upon microorganisms replication reduced time considerably (Noble and Weisberg 2005; Savichtcheva and Okabe 2006; Shelton et al. 2006; Ahmed et al. 2007; Liu et al. 2009; Bonetta et al. 2011; Ram et al. 2011; Shih et al. 2015). The outbreak incidents with protozoa and viruses and environmental detection data have shown that traditional coliforms and FIB could not predict the presence of these microorganisms adequately Savichtcheva and Okabe 2006; Karanis et al. 2007; Lalancette et al. 2014). The Sigma factor RpoS (ςS) is a stress response regulator that could control rpoS gene expression that provides these bacteria with high resistance to environmental factors in their microhabitats in some Gram-negative bacteria (Suh et al. 1999). In developed overland flow models, most pathogens adsorb to particulates at sizes larger than 15.9 µm that help them at faster rates in developed overland flows models (Haydon and Deletic 2006). The growing body of knowledge in the biology of most of these microorganisms need to be incorporated into the predictive models because life strategies could impact microbial survival, pathogenicity, and fate (Jamieson et al. 2002; Moreira et al. 2006; Muirhead et al. 2006). Algae and VFSs vegetation are some of the most important secondary habitats where biofilm formations by E. coli adapt themselves to withstand different severe environmental conditions (Byappanahalli et al. 2003a, b, c; Moreira et al. 2006; Quero et al. 2015). During biofilm formation, pathogenic strains of faecal coliforms such as E. coli utilize the attaching and effacing histopathology that is induced through selective expression of virulence genes for entrohaemorrhagic E. coli (EHEC) and enteropathogenic E. coli (EPEC) (Kaper et al. 2004; Kaper 2005). The development and the removal of overland flow of pathogenic strains of faecal coliforms such as E. coli utilize the attaching and effacing histopathology that is induced through these processes (Kaper et al. 2004; Moreira et al. 2006). During biofilm development, biofilm-forming (autoaggregation process) pili are responsible for E. coli and other microbial aggregations, because virulence traits of EPEC pathotype such as biofilm-forming pili (BFP) and encoding secreted proteins (Esp A, Esp B, and Esp D) are entwined in biofilm (Kaper et al. 2004).

The transport and adsorption of microorganism are influenced by three factors, namely the partitioning of E. coli between overland flow and sediment, secondly between soil micropore solution and solid phases, and finally between stream bottom and bank sediments as described by Pachepsky et al. (2006). The prediction of human health risk requires incorporation of more nuanced approaches to the modelling. It is important to understand the adaptive capabilities than enhance enteric bacterial activities and persistence and growth in soils, VFSs, and grass morphology as well as river–lake interfaces (Kaper et al. 2004; Pachepsky et al. 2006; Brennan et al. 2010). E. coli that causes diarrhoea is usually transmitted through contacts with food, water, animal, or human faeces in the contaminated environments. Extra intestinal-related diseases, intestinal numerous cellular processes in humans and other animals are caused by E. coli strains that have virulence factors (Gordon et al. 2008; Kaper et al. 2004; Clermont et al. 2013, Blount 2015; Quero et al. 2015). These E. coli in biofilm-building process and virulent behaviour result from pathotypes that include: (1) enteroinvasive E. coli (EIEC); (2) diffusely adherent E. coli (DAEC); (3) enteropathogenic E. coli (EPEC); (4) enteroaggregative E. coli (EAEC); (5) enterotoxigenic E. coli (ETEC); foodborne outbreak-associated pathotypes such as: (6) enterohaemorrhagic E. coli (EHEC); (7) verocytotoxin-producing E. coli (VTEC); and (8) Shiga toxin-producing E. coli (STEC), which are associated with infections such as diarrhoea in humans (Kaper et al. 2004) at eastern Mau, Njoro River Watershed (Mavura et al. 2006; Shivoga and Moturi 2009; Kiruki et al. 2011). It is considered that little information and understanding is available on the factors influencing microbial survival in manure, the soil matrices, and aquatic systems (Ferguson et al. 2003a, b; Brennan et al. 2010). The objective of this study was to evaluate the effect of VFS in encouraging development of E. coli biofilm-building potential and expression of virulence factors at Mau, Njoro River Watershed, Kenya. This study hypothesized that there was no significant effect of VFS in encouraging development of E. coli biofilm-building potential and expression of virulence factors.

2 Materials and methods

2.1 Description of the study site

The study site was located at Tatton Demonstration Unit (TDU) field 18, Livestock Farm, Njoro campus, Egerton University, located 25 km from Nakuru town and 175 km west of Nairobi in the East African Rift Valley. The farm was situated between 0022′00″–00°22′22″S and 35°55′33.3″–35°66′15″E, at an elevation of 2314 m above sea level. Topography at Egerton University is hilly with land slopes ranging from 5 to 15%. It was located down slope from Mau Forest and slightly above Njoro River and accompanying underlying shrubby vegetation (Fig. 1). It was located in the eastern escarpments of Mau Ranges. The study area experiences long rainfall seasons from April to August, short rains from September to December and hot, dry seasons from January to March each year. Mean annual precipitation is 1000 mm, with approximately 90% falling in April through August. The mean air temperature ranges from a minimum of 11.8 °C to a maximum of 25.5 °C. The mean radiation ranges from a minimum value of 500 to a maximum value of 655 cal cm−2 day−1. The mean evaporation ranges from 3.2 to 5.6 mm day−1. The mean daily humidity ranges from 35 to 80%. The mean wind speed recorded ranges from 3.4 to 7.5 km per hour. On the study site, the predominant vegetation consists of indigenous grasses such as African couch grass (Digitaria abyssinica L.), African bristle grass (Setaria sphacelata (Schumach) Stapf and C.E. Hubb ex M. B. Moss), Buffel grass (Cenchrus ciliaris) and Rescue grass (Bromus catharticus Vahl), Wire grass (Eleusine indica (L.) Gaertin), Couch grass (Cynodon dactylon) (L.), Pers. The study site was designed as described by Kirk (1982) with slight modifications.
Fig. 1

Tatton Agriculture Park, eastern escarpment of the Mau Forest, Njoro River Watershed, Kenya

2.2 Application of manure

Cowpat manure was collected through scooping it off a cattle pen mud floor that included no urine. The manure was collected one day before the experiment and refrigerated (1 °C) until applied to the fields. Prior to every rainfall event, for the simulation of grazed pasture, manure was applied to each of the fields at the rate of 40 kg N ha−1 (with the gross application of 5.2 kg manure) at 14 m above the edge of VFSs. The area under manure was 300 m2 of the total experimental field, so the gross application was 1200 g of manure. This rate of manure application was equivalent to the manure that would be produced from a stocking density of six 450 kg animal units/ha for a seven-day grazing duration, which would represent a heavily grazed condition. Using the Kenya Meteorological Department weather forecasts or predictions of the research area, the tropical rainfall events were accurately predicted, and records were made of the rainfall patterns, cloud type and cover, air temperature pattern and humidity, and the tropical wind patterns per season in the area. The error component of the rainfall occurrence was at positive or negative three days. Natural rainfall was used to generate overland flow. Rainfall intensity was recorded per event at a mean of 54 mm. The cattle manure used originated from free range grazing dairy cattle within confines of Egerton University feeding on native grass (Couch grass–Buffel grass), Kikuyu grass and Napier grass hay.

2.3 Interaction of E. coli strains in overland flow, grass morphology, and subsoil particles horizon

E. coli isolates from soil particles, overland flow, and grass morphology were separately incubated as described by Yao et al. (2014) and references therein to perform tests for the interaction along the VFS ecosystems once every rainfall event totalling 18 sampling times during long and short rainy seasons from August 2013 to December 2014. The control had sterilized deionized water (DD) instead of E. coli cells. Predicted survival rate of E. coli was compared to the observed survival rate by fitting the experimental data onto the Weibull survival model as described by American Public Health Association (1992):
$$ \log 10\left( {N_{\text{td}} } \right) = \log 10\left( {N_{0} } \right) - \left( {t/\delta } \right)^{p} $$
(1)
where p is the shape parameter, δ is the scale parameter factor representing time (t, days) required for decimal reductions (Greenberg et al. 1992; Eaton et al. 2005), N0 is the size of E. coli inoculum, and N t is the number of remaining cells surviving at a given time t, and at time td, N t reaches the detection limit of 100 CFUg−1 (Yao et al. 2014). E. coli isolate samples from overland flow; epiphytic to Couch grass, Buffel grass, Kikuyu grass, and Napier grass leaves, stems, roots, and subsoil horizon were collected from the samples that had been uninoculated and inoculated with E. coli after 24 h of incubation and were subjected to particulate organic carbon (POC) and dissolved organic carbon (DOC) analysis as described by Maciolek (1962). The extractions of phospholipid fatty acids (PLFA) were carried out using the original methods (Bligh and Dyer 1959; Strickland and Parsons 1972; Sharpley 1993; American Public Health Association 1992; Yao et al. 2014). The attachment of E. coli on the surfaces of soil particles, leaves, stems, and roots horizons were visualized using fluorescence microscopy as described in the following section.

2.4 The biofilm-building potential of the isolated E. coli strains in overland flow, plants morphology epiphytes, and subsoil particles horizon

The isolates were also tested for their ability to form biofilm at 37, 20 and 12 °C once every rainfall event totalling 18 sampling times during long and short rainy seasons from August 2013 to December 2014. Forty-two isolates were tested for biofilm formation where 12 isolates were from Couch grass–Buffel grass, 12 isolates from Kikuyu grass, 12 isolates from Napier grass, and 6 isolates from overland flow. The biofilm production of E. coli was determined by methods described by Quero et al. (2015) using fluorescent microscopic examination. Briefly, at KMFRI microbiology laboratory, the E. coli organisms were isolated from cowpat and inoculated on fresh Luria–Bertani (LB) agar supplemented with 0.2% glucose. Ten mL of E. coli isolate cultures were grown at 37 °C for 24 h, in sterile 96-well flat-bottomed polystyrene tissue culture-treated plates without shaking. These wells were filled with 200 µL of the diluted cultures. Whereas negative control wells contained inoculated sterile broth, the control E. coli isolates were also added to tissue culture plate after incubating and diluting. After incubation, contents of each well were removed by gentle tapping. Free-floating E. coli was removed by washing with 0.2 mL of phosphate buffer saline (pH 7.2) more than three times. A 2% sodium acetate fixated biofilm was formed by E. coli adherent to the wells and stained by crystal violet (CV) (0.1%) stain. Excess stain was removed by using deionized water and plates were kept for drying. The experiment was performed in triplicate and repeated twice. The cover slips were mounted on the microscope slides and the biofilm cells of E. coli were detected using a 40× objective lens and a Zeiss Axioplan fluorescence microscope with a blue excitation filter set (Hagstrom et al. 1979; Noor et al.2013).

2.5 The process of environmental E. coli strains becoming virulent and their growth rates in overland flow, epiphytic to plants morphology and subsoil horizon

The virulence behaviour of E. coli strains was tested by demonstration of secondary metabolites production by performing biochemical assays on the isolates once every rainfall event totalling 18 sampling times during long and short rainy seasons from August 2013 to December 2014. These tests included E. coli isolation, the morphological and biochemical identification, polymerase chain reaction (PCR), and pyocyanin, pyoverdine and alginate, catalase, rpoS gene and exotoxin A, adenosine diphosphate (ADP)-ribosyltransferase, elastase, Las A protease, and casein-degrading protease isolation and determination were performed as described by Suh et al. (1999), Ribot et al. (2006), and Tamura et al. (2007).

2.6 Data analysis

Analysis of variance (ANOVA) was used to test statistical significance at α ≤ 0.05. The differences were determined by the least squares means test (Snedecor and Cochran 1980; Zar 1996) for both independent (environmental factors) and dependent variables (E. coli CFU 100 mL−1). Statistical data analyses were performed as described by Zar (1996) and were performed by PAST (Hammer et al. 2005) and Systat (SYSTAT Institute Inc. 2007) to test the significant difference at α = 0.05.

3 Results and discussion

3.1 Genetic diversity of E. coli in overland flows and VFS morphology

The 36 E. coli isolates recovered from overland flows, grass morphology of VFS and subsoil horizon were analysed and subsequently grouped into 18 unique genotypes. E. coli in subsoil horizon exhibited spatial variation in the genotype group isolated from Napier grass VFS in field II and Kikuyu grass VFS in field III, but not Couch–Buffel grass VFS in field I (Fig. 2). The different VFS combinations significantly (p < 0.05) affected the genotypic diversity. Eight E. coli genotypes (0.09% of 828) were detected at more than one root zone system of Napier grass VFS in field II sampling site, while there was no genotypes shared between the subsoil horizons in the VFS and controls sampling sites. This was due to the differences in the diversity and genotypes of E. coli in the sampled fields (Kingsley and Bohlool 1981; Gotelli and Entsminger 2004; Hay et al. 2006; Eisakhani and Malakahmad 2009).
Fig. 2

Spatial variations of E. coli isolates in each genotype in overland flow, grass morphology, and subsoil horizon in VFS III, II, and I from August 2013 to December 2014

3.2 Factors influencing E. coli in overland flow and soil

The physical chemical factors (Table 1) that influenced the genetic diversity of the E. coli in the study site included soil moisture, total suspended solids, turbidity, temperature, dissolved organic carbon (DOC), particulate organic carbon (POC), nitrates, and phosphorus. Among the 102 unique genotypes, 40 genotypes were shared among the VFS. The E. coli genotypes detected in root zone system and overlying litters of the Napier grass VFS appeared to have slightly higher detection frequencies than those in overland flow water and subsoil horizon. Generally, 58% of genotypes were represented by four or fewer isolates. Some of the genotypes were likely present in the subsoil horizon at high levels as indicated by high isolation frequencies.
Table 1

Mean (SEM ± σ\({\bar{\text{x}}}\)) spatial variation in microbial concentration and environmental factors in soil and overland flow along the length of VFS from August 2013 to December 2014

Variable

VFS I

Couch grass–Buffel grass

VFS II

Kikuyu grass

VFS III

Napier grass

E. coli CFU (100 mL−1)

50 × 107 ± 3.2 × 102

22. × 104 ± 3.2 × 102

30 × 105 ± 3.2 × 102

Total suspended solids (mg L−1)

53 ± 3.2

50 ± 3.2

51 ± 3.2

Temperature °C

23.2 ± 1.05

21 ± 1.05

20 ± 1.04

Turbidity (NTU)

230 ± 12

177 ± 13

199 ± 11

SSC (mg L−1)

153 ± 2.4

78 ± 2.3

212 ± 4.5

Particulate organic carbon (µg L−1)

1022.6 ± 121.3

1323.3 ± 120.3

1351.180 ± 2.3

Dissolved organic carbon (µg L−1)

1555 ± 120.3

2234 ± 140.3

3012 ± 130.3

Ammonium nitrogen (µg L−1)

12.6 ± 1.2

13.32 ± 1.2

15.7 ± 1.2

Nitrate nitrogen (µg L−1)

25.4 ± 2.1

26.25 ± 2.12

26.12 ± 2.11

Total nitrogen (µg L−1)

41.24 ± 2.4

42.25 ± 2.3

42.25 ± 2.4

Soluble reactive phosphorus (µg L−1)

22.5 ± 1.2

21.26 ± 1.2

23.25 ± 1.3

Total phosphorus (µg L−1)

52.25 ± 5.2

53.25 ± 5.1

52.25 ± 5.12

3.3 Temporal variation of E. coli population

The temporal variation of E. coli isolates were obtained from overland flow, plant morphology and subsoil horizon at four sampling sites in each of the nine VFS fields including the Napier grass VFS in field II, Kikuyu grass in field III and Couch grass–Buffel grass in field I for a duration of sixteen rainy events from August 2013 to December 2014. Season variation of E. coli relative to the temperature changes showed that the survival rates of E. coli isolated from the VFS overland flow decreased with temperature from 12 to 20 up to 37 °C. This suggests that at 12 °C the ability of E. coli to survive was significantly higher than at 20 and 37 °C, respectively. This suggests further that the E. coli vigour to survive is enhanced at lower temperatures than it is at higher temperatures. At 12, the ability of E. coli to decline was significantly lower than at 20 and 37. This suggests that at lower temperatures the rate of decline of E. coli was significantly (p < 0.05) lower than at higher temperatures (20 and 37) during both short (September–December) rains and wet (April–August) season as well as dry (January–March) season. Lower temperatures could increase the ability of E. coli to survive in VFS. Thus, temperature determines the persistency levels and survival of E. coli in VFSs. Temperature was associated with different phases of E. coli growth in each medium, during wet season. Significant correlations (p < 0.05) were identified between E. coli survival rates and temperatures: r = 0.87 at 37 °C, r = 0.45 at 12 °C, and r = 0.68 at 20 °C. The estimated E. coli pathogens CFU 100 mL−1 survival rates at lower temperatures and decrease in survival rates at higher temperatures suggests a relative risks increase with higher ambient temperatures from 12 to 20 and 37 °C during dry season (January–March) and both short rains (September–December) and long rains (April–August). This phenomenon could influence pathogenic risks resulting from: (1) point source outbreaks; (2) travel outside the hosts’ domicile; (3) dietary and food behaviour of individual hosts; (4) warmer ambient temperatures associated with increase in the risk of E. coli population during dry and hot seasons; (5) E. coli is associated and sensitive to higher ambient temperatures; (6) the season variations in ambient temperature could influence the growth of E. coli in the media; (7) this suggests that the occurrence of enteric E. coli-associated diseases are potentially impacting and resulting from climate change; (8) This study can be applied in the solving social issues of the society despite the new decreasing trends in enteric diseases and infections, the public health facilities need to take precautionary measures and focus on public education programs, including policies to vulnerable groups and to new occupational groups; (9) to enable setting up new policies in anticipation of new cases of foodborne diseases because of climate change (Samson et al. 2006; Olilo et al. 2016a, b, c; 2017).

Unique genotypes were identified. Significant (p < 0.05) temporal variation of E. coli genotypes diversity was observed among the isolates recovered from overland flow water, sediment and plant morphology samples at 36 sampling sites during a 16-month period. The E. coli isolates were fingerprinted and clustered to identify unique genotypes. The small percentages of E. coli genotypes detected during more than one sampling date in overland flow water or in sediment at the same site (2.8–12.1%) indicate a strong temporal variation of the observed E. coli genotypes in the VFS at Tatton Agriculture Park (Fig. 3).
Fig. 3

Percentage variations of E. coli genotype isolates detected in overland flow, grass morphology, and subsoil horizon at VFS III, II, and I from August 2013 to December 2014

3.4 Spatial variations of E. coli population in overland flow

Spatial variation of E. coli genotypes in VFS was observed with significant differences (p < 0.05) between the Couch grass–Buffel grass (VFS I), Napier grass (VFS II) and Kikuyu grass (VFS III) taken from experimental blocks A, B and C in overland flow water. Using cluster analysis, the 540 E. coli isolates from overland flow water were analysed by rep-PCR DNA fingerprinting and then grouped into 86 unique genotypes (Fig. 4). Spatial variations of E. coli population genetic diversity were evidenced in overland flow in Napier grass VFS of the watershed. There was a significant (p < 0.05) temporal variation of E. coli genotypes in different soils of Napier grass VFS. Large concentrations of E. coli in soil in the eastern escarpment of the Mau Forest, Njoro River Watershed could be attributed to three reasons; first E. coli is an autochthonous resident of microbial community with mechanistic linkage with genetic diversity as suggested by Goto and Yan (2011); secondly, sufficient and abundant nutrients to help enhance growth of E. coli in the soil as suggested by Byappanahali and Fujioka (1998), and Eisakhani and Malakahmad (2009); and thirdly, warm optimum temperatures in the soil microbiome promoted the E. coli growth (Ishii et al. 2007). Spatial variations of E. coli populations’ genetic diversity were observed in grass morphology including the overlying surface vegetation such as leaf, litter, and root zone system in Napier grass VFS. Spatial variations of E. coli populations’ genetic diversity were observed in subsoil particle horizons in Napier grass VFS. Significant (p < 0.05) spatial variations of E. coli population genetic diversity was evidenced in overland flow in Kikuyu grass VFS of the watershed. Spatial variations of E. coli populations genetic diversity were also observed in grass morphology including the overlying surface vegetation such as leaf, litter, and root zone system in Kikuyu grass VFS. Spatial variations of E. coli populations genetic diversity were computed (Gotelli and Entsminger 2004) in subsoil particle horizons in Kikuyu grass VFS.
Fig. 4

Number of rank abundance for the pooled E. coli isolates in overland flow, grass morphology, and root zone samples (n = 432) collected from VFS III, II, and I from August 2013 to December 2014

3.5 Interaction of E. coli strains in overland flow

Percentage (%) proportions of phospholipid fatty acids associated with E. coli in root zones of VFS (Fig. 5). The ratio of phospholipids fatty acids associated with E. coli in the root zones of VFS field plots (Fig. 6). The abundance of E. coli in overland flow water and sediment was determined on 432 samples overland flow water and sediment samples collected from 18 sites over a 12-month period. The overland flow water and sediment samples were collected prior to the edge of VFS and at the exit of VFS. Counts ranged from undetected (detection limit 1 CFU 100 mL−1) in the overland flow to 3.5 × 106 CFU 100 mL−1 in the sediment. Sixty water samples (12.5%) had E. coli counts at or below the United States of America Environmental Protection Agency water quality Standards of 120 CFU 100 mL−1 for all the controls (VFS I) in each of the blocks A, B, and C studied. E. coli isolates recovered from the sediment and water were from three blocks of nine fields with three controls VFS. Temporal variation was determined by sampling during storm events from August 2013 to December 2014. Thirty water-sampling events that yielded a total of 540 isolates from water samples and 828 isolates from 216 samples of sediment were obtained and used for the study. Comparison of E. coli concentrations of sediment and overland flow surface water using F test showed that overland flow concentrations were significantly (p < 0.05; df = 29) higher than sediment concentration samples.
Fig. 5

Proportions (%) of phospholipid fatty acids associated with E. coli in root zones of VFS from August 2013 to December 2014

Fig. 6

The ratio of phospholipids fatty acids associated with E. coli in the root zones of VFS from August 2013 to December 2014

The interaction of genotypes between sediment and overland flow was significantly related to VFS Napier grass–Kikuyu grass combination types (p < 0.05), but Couch grass–Buffel grass (control). The 20-m Kikuyu grass combination influenced the interaction more significantly (p < 0.05) than the 20-m Napier grass combination of VFS provided the grass was not submerged by the overland flow. VFSs significantly encourage development of E. coli biofilm-building potential and expression of virulence factors. E. coli (CFU 100 mL−1) isolated from soil particles were attached to the roots of Napier grass VFS. There was a significant interaction (p < 0.5) between E. coli in the roots’ soil particles attached to the roots of Napier grass VFS. The survival and growth (occurrence and abundance) of E. coli in the roots of these grasses were determined by carbon, and the tolerance of the physical–chemical factors such as temperature, pH, and moisture. These environmental factors did not exceed the minimum or maximum tolerance threshold of E. coli that supports Shelford’s law of tolerance (Atlas 1984). E. coli is known to die at temperatures beyond 45.5 °C when exposed for a period of between 25 and 40 min (Klein and Casida 1967; Khaleel et al. 1979). This finding shows that (1) E. coli phylo-typing method improves specificity that detects new phylo-groups, (2) genetically ultraviolet (UV) light acts by interfering with DNA replication of E. coli, and (3) the presence of redundant DNA information prevents lethal damage and Liebig’s law of minimum (Atlas 1984; Hay et al. 2006; Clermont et al. 2013).

3.6 Biofilm-building potential of the E. coli strains in VFS morphology and subsoil horizon

When the PCR analysis of genotypic similarity in overland flow in VFS II was performed, it indicated a strong correlation coefficient (r = 0.87) between the genotypic isolates. Approximately 22.2 2% of the genotypes were negatively correlated with the rest of the isolates, when Rho similarity index was used to compare the genotypic isolates; there was a correlation (r = 0.79). There was low to high production of biofilms (Fig. 7). The E. coli isolates produced biofilm at various temperature levels, indicating that biofilm condition was common phenomenon among attached populations of E. coli, which shows that environmental E. coli could potentially become virulence. The biofilm-building potential of the E. coli strains isolated were estimated in overland flows, overlying epiphytic grass vegetation morphology and subsoil horizon of Napier grass VFS. The E. coli isolates in the rangeland and watershed grass species have the capability to build biofilm at varying temperatures and substrates. This suggests that: (1) biofilm could be a lifestyle that could be usually found between attached E. coli; (2) this indicates that these entero-bacterial strains could potentially become virulent in the environment; (3) freshwater periphyton and macroalgae (Cladophora spp.) E. coli isolates have been identified to provide substrates to E. coli that attach and form biofilm than the human E. coli strains (Lim et al. 1998; Hay et al. 2006; Moreira et al. 2006; Quero et al. 2015); (4) this shows that biofilm-building behaviour enhances environmental persistence of E. coli (Kaper et al. 2004).
Fig. 7

Percentage of biofilm producers in culture medium among the E. coli isolated from the overland flow, grass morphology, and subsoil horizon at 37, 20, and 12 °C in VFS III, II, and I from August 2013 to December 2014

3.7 The virulent of E. coli in grass morphology and subsoil horizon

This study showed the results of percentage (%) action of E. coli exotoxin in the root zones of VFS (Fig. 8). This study also revealed the potential of E. coli strains becoming virulent in behaviour (Table 2), by isolating E. coli rpoS gene to evaluate the effect of Sigma factor RpoS defect on the microhabitat stress responses and the synthesis of virulence factors in VFS in Njoro River Watershed. This suggests: (1) RpoS regulated the biosynthesis of catalases genes, kat E and kat G for detoxifying (H2O2); (2) quorum-sensing behaviour of E. coli-controlled KatA, which was maximal during stationary phase of cell cycle; (3) sigma factor RpoS controls rpoS gene expression directly and quorum-sensing behaviour may be controlling kat A gene expression during the stationary phase of the cell cycle; (4) extracellular toxins were produced significantly in the presence of sigma factor RpoS; (5) early studies indicated that E. coli xcp operons and other enterocyte bacteria encoding type II secretion systems for proteins were regulated by the quorum-sensing systems, which regulated expression of rpoS (Suh et al. 1999); (6) this shows that quorum-sensing system, which requires few cells on the surface, is the basic requirement for the synthesis of alginate, but not necessarily high levels of sigma factor RpoS. However, sigma factor RpoS is required to trigger rpoS gene expression (Suh et al. 1999).
Fig. 8

Percentage (%) action of E. coli exotoxin in the root zones of VFSs from August 2013 to December 2014

Table 2

Incidence of estimated virulence factors in E. coli isolates from VFS

Virulence factor

Number of positive isolates

Ratio from total number of determined virulent factors (%) (n = 18)

F

p < 0.05

Couch–Buffel grass

Kikuyu grass

Napier grass

VT 1

0

0

0

0

0.9685

< 0.05

VT 2

0

0

0

0

0.9683

< 0.05

VT 2e

2

0.1

13.1

14.1

0.9676

< 0.05

EaeA

4

30.3

31.3

33.3

0.9225

< 0.05

Einv

0

0

0

0

0.9754

< 0.05

Eagg

0

0

0

0

0.9756

< 0.05

CNF 1

4

32.3

30.3

29.3

0.8984

< 0.05

CNF 2

0

0

0

0

0.8141

< 0.05

ST I

3

14.1

11.1

0.1

0.0823

< 0.05

ST II

2

11.1

0.1

12.1

0.0907

< 0.05

LT I

2

12.1

14.1

11.1

0.8142

< 0.05

Enteroxigenic E. coli—ETEC (ST I, ST II, LT I) gene; enteropathogenic E. coli—EPEC (EaeA) gene; enteroaggregative E. coli—EAEC—(Eagg) gene; enteroinvasive E. coli—EIEC (Einv) gene; uropathogenic E. coli—UPEC (CNF 1, CNF 2) gene; verotoxigenic E. coli—VTEC E. coli—VTEC (VT 1, VT 2, VTe) gene

This indicates that: (1) cells in stationary phase of development rpoS gene expression may have been regulated by the sigma factor RpoS; (2) the sigma factor RpoS regulated the microhabitat response, production and accumulation of virulence factors and quorum sensing; (3) the environmental E. coli virulent factors were estimated in overland flows, overlying grass morphology including leaf, stem, and root zone system and subsoil horizon of Kikuyu grass VFS in field III; (4) the environmental E. coli virulent factors were estimated in overland flows, overlying grass morphology including leaf, stem, and root zone system and subsoil horizon of Couch–Buffel grasses VFS (Table 3); (5) important biochemical analyses of presumptive E. coli isolates in different grass types of VFS were analysed (Table 4); and (6) the triple sugar iron test and motility indole test were made for acidic reaction and acidic reaction and gas emission, respectively; (7) in these tests, E. coli reacted positively in motility and indole, but negatively in H2S, urea, citrate and oxidase.
Table 3

Incidence of estimated virulence factors in E. coli isolates from VFS

Virulence factor

Number of positive isolates

Ratio from total number of determined virulent factors (%) (n = 18)

F

p < 0.05

Couch–Buffel grass

Kikuyu grass

Napier grass

VT 1

0

0

0

0

0.9685

< 0.05

VT 2

0

0

0

0

0.9683

< 0.05

VT 2e

2

0.1

13.1

14.1

0.9676

< 0.05

EaeA

4

30.3

31.3

33.3

0.9225

< 0.05

Einv

0

0

0

0

0.9754

< 0.05

Eagg

0

0

0

0

0.9756

< 0.05

CNF 1

4

32.3

30.3

29.3

0.8984

< 0.05

CNF 2

0

0

0

0

0.8141

< 0.05

ST I

3

14.1

11.1

0.1

0.0823

< 0.05

ST II

2

11.1

0.1

12.1

0.0907

< 0.05

LT I

2

12.1

14.1

11.1

0.8142

<0.05

Enteroxigenic E. coli—ETEC (ST I, ST II, LT I) gene; enteropathogenic E. coli—EPEC (EaeA) gene; enteroaggregative E. coli –EAEC—(Eagg) gene; enteroinvasive E. coli—EIEC (Einv) gene; uropathogenic E. coli—UPEC (CNF 1, CNF 2) gene; verotoxigenic E. coli—VTEC E. coli—VTEC (VT 1, VT 2, VTe) gene

Table 4

Important biochemical analyses of presumptive E. coli isolates in different grass types of VFS

E. coli isolate in grass type

Triple sugar iron test

Motility indole urea test

Urea

Citrate

Oxidase

Slant

Butt

H2S

Motility

Indole

CouchBuffel

EC1

Acidic reaction

Acidic reaction and gas

+

+

EC2

Acidic reaction

Acidic reaction and gas

+

+

EC3

Acidic reaction

Acidic reaction and gas

+

+

EC4

Acidic reaction

Acidic reaction and gas

+

+

EC5

Acidic reaction

Acidic reaction and gas

+

+

EC6

Acidic reaction

Acidic reaction and gas

+

+

EC7

Acidic reaction

Acidic reaction and gas

+

+

EC8

Acidic reaction

Acidic reaction and gas

+

+

Kikuyu

EC1

Acidic reaction

Acidic reaction and gas

+

+

EC2

Acidic reaction

Acidic reaction and gas

+

+

EC3

Acidic reaction

Acidic reaction and gas

+

+

EC4

Acidic reaction

Acidic reaction and gas

+

+

EC5

Acidic reaction

Acidic reaction and gas

+

+

EC6

Acidic reaction

Acidic reaction and gas

+

+

EC7

Acidic reaction

Acidic reaction and gas

+

+

EC8

Acidic reaction

Acidic reaction and gas

+

+

Napier

EC1

Acidic reaction

Acidic reaction and gas

+

+

EC2

Acidic reaction

Acidic reaction and gas

+

+

EC3

Acidic reaction

Acidic reaction and gas

+

+

EC4

Acidic reaction

Acidic reaction and gas

+

+

EC5

Acidic reaction

Acidic reaction and gas

+

+

EC6

Acidic reaction

Acidic reaction and gas

+

+

EC7

Acidic reaction

Acidic reaction and gas

+

+

EC8

Acidic reaction

Acidic reaction and gas

+

+

+ Positive, − Negative

3.8 Influence of temperature on the E. coli population growth rates in grass morphology and subsoil horizon

Isolates 9, 20, 44, 49, and 50 were environmental isolates that were tested that grew well at different temperature ranges under the conditions in the laboratory (Table 5). They showed superior performance traits to K-12 strain at all temperatures and in all media tested. This was observed particularly at lower temperatures, in which the environmental isolates had mean specific growth rates of 2.5–4.5 times greater than those of K-12 on sediment extract and grass extract media. Statistical analyses showed that the interaction factor for strain, media, and temperature was significant (p < 0.05). The growth rate at 37 °C was significantly (p < 0.05) greater than growth rate at the lower temperatures. The growth of the environmental isolates (isolates 9, 20, 44, 49, and 50) was significantly greater than that of the K-12 laboratory strain under the laboratory conditions tested.
Table 5

Mean (SEM±σ\({\bar{\text{x}}}\))-specific growth ratesa of five environmental isolates and a laboratory strainb in an aerobic culture at 12°, 20°, and 37°

Mean specific growth rate (h−1) ± SEσ\({\bar{\text{x}}}\)

Temperature (°C)

Bacteria strain

Minimal medium

Soil extract

Napier grass extract

Kikuyu grass extract

Couch–Buffel grasses extract

37

K-12

0.369 ± 0.01a

0.392 ± 0.031a

0.455 ± 0.029a

0.422 ± 0.032a

0.399 ± 0.021a

Isolate 9

0.536 ± 0.021b

0.525 ± 0.013b

0.628 ± 0.015b

0.568 ± 0.043b

0.522 ± 0.017b

Isolate 20

0.545 ± 0.014b

0.532 ± 0.025c

0.652 ± 0.017c

0.548 ± 0.011b

0.532 ± 0.031b

Isolate 44

0.548 ± 0.027b

0.522 ± 0.023c

0.644 ± 0.031c

0.536 ± 0.031b

0.523 ± 0.031b

Isolate 49

0.568 ± 0.026b

0.556 ± 0.016c

0.639 ± 0.027c

0.555 ± 0.032b

0.548 ± 0.029b

Isolate 50

0.577 ± 0.016b

0.568 ± 0.021c

0.662 ± 0.031c

0.568 ± 0.017b

0.548 ± 0.031b

20

K-12

0.056 ± 0.015d

0.047 ± 0.015d

0.054 ± 0.029d

0.042 ± 0.027d

0.041 ± 0.017d

Isolate 9

0.188 ± 0.017e

0.176 ± 0.031e

0.198 ± 0.031e

0.168 ± 0.017e

0.166 ± 0.031d

Isolate 20

0.172 ± 0.029f

0.156 ± 0.021f

0.189 ± 0.031e

0.142 ± 0.029g

0.144 ± 0.029g

Isolate 44

0.161 ± 0.031f

0.144 ± 0.023e

0.172 ± 0.031e

0.152 ± 0.029g

0.148 ± 0.016g

Isolate 49

0.152 ± 0.021e

0.151 ± 0.032e

0.168 ± 0.017e

0.144 ± 0.029g

0.138 ± 0.029g

Isolate 50

0.165 ± 0.022e

0.166 ± 0.029f

0.188 ± 0.017f

0.148 ± 0.029g

0.146 ± 0.023g

12

K-12

0.014 ± 0.019h

0.018 ± 0.017h

0.016 ± 0.022h

0.012 ± 0.011h

0.013 ± 0.017h

Isolate 9

0.056 ± 0.031i

0.062 ± 0.014i

0.044 ± 0.021k

0.042 ± 0.021kk

0.046 ± 0.019k

Isolate 20

0.065 ± 0.013i

0.072 ± 0.019i

0.054 ± 0.022k

0.044 ± 0.031k

0.054 ± 0.031k

Isolate 44

0.044 ± 0.012i

0.056 ± 0.017i

0.031 ± 0.022l

0.052 ± 0.021k

0.034 ± 0.032l

Isolate 49

0.071 ± 0.025j

0.081 ± 0.016j

0.052 ± 0.027m

0.061 ± 0.011k

0.056 ± 0.021m

Isolate 50

0.062 ± 0.017j

0.095 ± 0.017j

0.078 ± 0.027n

0.053 ± 0.017k

0.042 ± 0.031k

aBonferroni groupings within temperature where small letters next to growth rates represented comparisons. Growth rates followed by the same letter are not significantly different

bSignificant differences between strains of E. coli, both the laboratory and environmental isolated strains

The environmental isolates 9, 20, 44, 49, and 50 had significantly (p < 0.05) smaller cell sizes at 12 °C and increased sizes from 20 to 37 °C in all the media. The small-sized cell structures increased in size and aggregated in form and in morphology with increased temperatures could be explained in terms of five reasons: (1) at 20 °C, the cells grew normally, while at 37 °C, the growth of the cells reached maximum and ceased to increase in size; (2) at 37 °C, E. coli cells grew at a constant level of CFU 100 mL−1, in all the media, which presumably depicted the existence of viable but non-culturable E. coli cells; (3) due to the high temperature and with increased temperature, there could be suppression of growth as indicated by lower oxygen levels and low levels of CFU 100 mL−1, (4) due to the presence and generation of reactive oxygen species because of the variable heat stress conditions; and (5) with increase in temperature, the E. coli cells tended to lose their cultivability (Noor et al. 2013).

There was a significantly (p < 0.05) lower growth rates for K-12 strain than the environmental isolates in all the cases. There was a significant performance of growth rates of environmental isolates of E. coli (9, 20, 44, 49, and 50) at Kikuyu grass and Napier grass, as compared to the laboratory strain (K-12) and to the isolates in Couch grass–Buffel grass. Under different media of soil extract and McIlvaine’s minimal medium, the laboratory strain (K-12) that was used did not perform better than the environmental isolates which were tested (isolates 9, 20, 44, 49, and 50 that grew well and outperformed the laboratory strain in different temperatures of 37, 20, and 12 °C. These laboratory strains of E. coli were outperformed by the isolated environmental strains at these temperatures. Soil extract medium showed a better support to the growth of environmental isolates. The better performance of soil was because of availability of nutrients in the soil. Temperature, medium, and the strain had significant interaction (p < 0.05). In overland flow, the temperature ranged from a minimum of 20 ± 1.04 to 23.2 ± 1.05 °C, and a mean of 21.33 ± 1.05 °C. Temperature influenced the environmental strains more significantly than the laboratory strains, showing that 37 °C was optimal for entero-bacterial growth (e.g. E. coli), in the soil.

Several challenges exist in the soil, in which E. coli needs to overcome for its own survival and growth including: (1) higher temperatures, (2) sun’s radiation variability, (3) low carbon, and (4) nutrient availability. In that connection, the response of microorganisms to the availability of nutrients and carbon in good time might enable them compete favourably to their own advantage when those resources become available (Saitou and Nei 1987; Lim et al. 1998; Winfield and Groisman 2003; Brennan et al. 2010; Ribot et al. 2006; Tate et al. 2006). E. coli strain environmental isolates grew faster than their laboratory counterparts (K-12, strain ATCC 25922) in McIlvaine’s minimal medium, soil extracts medium, and Luria–Bertani medium at 37 °C. Leachates supported the growth of E. coli isolated from soil, despite some competition in the soil because of four principles: (1) the growth was not exponentially sharp as expected; (2) the three areas of the growth curve such as lag phase, exponential phase, flat phase (carrying capacity), and receding phase did not conform to the expectations; (3) their troughs and flat surfaces in the curve suggested that either nutrient was lower; or (4) competition between the soil indigenous microbiota was outcompeting the E. coli (Versalovic et al. 1991; Gordon et al. 2002, 2008; Byappanahalli et al. 2003a, b; Martinez 2009; Brennan et al. 2010). Thus, vegetated filter strips design influenced genetic and physiological adaptations of E. coli populations with capability to live in soil habitats, which was supported by the state of the growth curves.

Notes

Acknowledgements

This study was designed by among others Eng. Professor Japheth O. Onyando, the chairperson, Department of Agricultural Engineering and Technology; Dr. Anastasia W. Muia, the Department of Biological Sciences; and Dr. Wilkister N. Moturi, Department of Environmental Science. Our deepest appreciation goes to the Dean Faculty of Agriculture for granting us the permission to work in field 18 of Tatton Agriculture Park (TAP). We appreciate The Nakuru County Laboratory for the use of their facility for microbial analyses. We wish to thank The Department of Water and Civil Engineering of Egerton University provided the Meteorological data from Egerton University weather station. The Director, Kenya Marine and Fisheries Research Institute (KMFRI), Prof James M. Njiru who granted me the study grant under Egerton University (EU)_KMFRI Memorandum of Understanding (MOU) Study Programme is highly appreciated. This study was funded by The Kenya National Commission of Science and Technology, the Science, Technology and Innovation Ph.D. research Grant, under Grant nos: NCST/ST and I/RCD/4th Call PhD/181.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© Joint Center on Global Change and Earth System Science of the University of Maryland and Beijing Normal University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Casianes Owino Olilo
    • 3
  • Anastasia Wairimu Muia
    • 1
  • Japheth Ogalo Onyando
    • 2
  • Wilkister Nyaora Moturi
    • 3
  1. 1.Department of Biological SciencesEgerton UniversityEgertonKenya
  2. 2.Department of Agricultural Engineering and TechnologyEgerton UniversityEgertonKenya
  3. 3.Department of Environmental ScienceEgerton UniversityEgertonKenya

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