The river water samples were taken at six different locations along the river Veshaw, study sites include Kongwattan and Aharbal (upstream), Nihama and Kulgam (middle stream), Khudwani and Sangam (down stream during the all season from February to January 2020-2022 in order to do a quantitative assessment of the Total Coliform (TC) load in the River Veshaw flowing the TC values in the river water sample ranged from 0.00 - 72.600 (CFU 10x6) during all the four seasons. The faecal contamination of drinking water was the primary emphasis of this research. E. coli was used as an indirect way to discover pollution by faecal matter, which is associated with severe hazards to one's health (WHO 2008; Pantha et al.2021; Kongprajug et al. 2021). The bacterial research's findings revealed highest Coliform count was recorded at Sangam (72.600 CFU 10x6) followed by Khudwani (47.567 CFU 10x6) down stream of the river during summer and in spring season highest Coliform count was also found at Sangam (61.133 CFU 10x6) and Khudwani (39.567 CFU 10x6) [table1 and figure2] showed significant E. coli and coliform contamination in down stream of the river Veshaw . Related research has been done by Sharma and his colleagues (2010) and Bhat and his colleagues (2015), as well as Dimri and Bhat et al. (2021) who focused on surface water (rivers and lakes) in the SNP. It was determined that E. coli and coliform bacteria were present in every sample of surface water examined, with contamination rising dramatically as altitude decreased. This is in line with the findings of other researchers who found that contamination levels in the Ganges River decreased as one descended from the higher elevations of the Gangotri Glacier (Uttarakhand, India) to the lower altitudes of the Ganges River (Americo-Pinheiro et al.2021; Selvam et al.2022). The findings of Baghel et al. (2005), Sharma et al. (2010), and Bowes et al. (2020) imply that the rise in bacterial contamination in their study regions' lower altitudes was caused by an increase in anthropogenic and socio-cultural activity. Total coliform and E. coli were also found to be at their highest in the summer months, which researchers linked to an rise in the number of pilgrims and hikers in the area. A preliminary study by Nicholson et al. (2016) supports the concept that water pollution is highest at lower altitudes and may be linked to the tourist and/or resident populations there. Our findings support those of Baghel et al. 2005; Sharma et al. 2010;Nicholson et al. 2016) relived the correlation between bacterial counts and additional measures of water quality with altitude in the river from upstream to down stream. Bacterial contamination has a direct correlation with water quality (Figure 7).This indicates the pollution level in the river increase with decrease in altitude and there is positive correlation between coliform and the chemical parameters of the river water (figure 7). Reports of large influences on Nakdong River water quality by Cho and Um, 2005, were found in the upstream to midstream section of the Nakdong River. The river water quality may be harmed by excessive pollution amounts from discharges, according to a number of researches (Jung et al. 2016; Oh et al. 2017). Polluted tributaries to the Nakdong River have been linked to an increase in coliform bacteria, according to Lee et al. (2016). The water quality of the mid- and downstream part of the Geumho River, where the Nongong station is located, may be harmed by secondary tributaries that reach the river from severely contaminated sources (Yu et al.2015; Na et al.2016; Elassass et al.2022). While faecal coliform levels were low in the upstream stations, they steadily rose as the study moved downriver (Table 2 and Figure 1). Its hilly upstream region is home to the Veshaw River, which is also home to pollution sources such as agricultural land and animal sheds as well as industries like as agriculture and tourism. The river's midstream and downstream regions are also home to important tributary inflows. High coliform concentrations at downstream sites could be the result of such a complicated interaction between point and non-point pollution sourcesThroughout the months of spring and summer, both total and faecal coliforms had significant population densities and broad distribution ranges (Figure 2 and Table S1). Similarly, the frequency status of total coliform bacterial species like absent, present and rare, common, dominant and abundant are mentioned in Table 1 This may have been a contributing cause due to the impact of rainfall, non-point pollution sources, and rising water temperatures over the summer. According to the findings of this study, coliform bacteria populations in the Nakdong River were reported to be high throughout the summer and fall due to rainfall in the middle to downstream part of the river. This finding is similar with the findings of the previous study (Ramteke et al.1992; Baek et al. 2014; Seo et al.2019; Bisimwa et al. 2022).
Table 1: Seasonal variation in the presence of Coliform populations at selected sites of River Veshaw
*Frequency status: (-, 0%) Absent; (+, < 20%) Present and Rare; (++, >20-50%) Common; (+++, >50%) Dominant; (++++, 100%) Abundant
From upstream to downstream of the river Veshaw there were important differences (p 0.05) in the following parameters: TC, pH, EC, BOB, and COD. Water from Sangam downstream had the highest pH, EC, BOB, and COD readings during the summer (Figure 3-6). In order to survive, aquatic fauna required a pH of 7.4, although photosynthesis and respiration are the primary metabolic pathways that influence pH in aquatic habitats (Hamid et al., 2020). As shown in Figure 3 and Table S2, there was a significant difference (p 0.05) in the alkaline pH of the Vishav stream across the sampling sites, with the highest pH (8.947) discovered in summer at Sangam (downstream) and the lowest pH (6.847) found in winter at Kongwattan (upstream). While testing the pH of the Doodhganga stream in the Kashmir valley, the same kind of supportive evidence were found (Dar et al., 2020). As the temperature rises, the pH likewise rises, which can be linked to a change in the process of photosynthesis, which is also affected by the rise in pH (mean value of 7.90.3). The high concentration of buffering chemicals (calcium and magnesium carbonates) in the riverside area and downstream can be blamed for the study's overall alkaline pH. (Kang et al., 2001). Analyses of the pH of the Doodhganga stream in the Kashmir Valley produced comparable supporting evidence (Husaini, 2020). Also, the pH indicated a significant difference between seasons (p0.05), and the higheer value (mean value of 7.90.3) during summer and autumn can be linked to the rising temperature, which also governs the photosynthetic activity. Carbonates of calcium and magnesium, which contribute to the high alkaline pH, are found in the riverside area downstream due to a significant quantity of human activity (Kilonzo et al.2014;Indu et al., 2015; Adesakin et al. 2020; Chakraborty et al. 2022).
Temperature variations have a significant impact on electrical conductivity (EC), which can rise by 2 to 3 percent for every 1 degree Celsius increase (Yilmaz & Koc, 2014). Water's electrical conductivity (Rana et al. 2016) is directly related to the amount of total dissolved solid in the water (sodium, potassium, calcium, carbonate, bicarbonate, chloride, and sulphate). While it has long been believed that water flowing through granite bedrock has less of an EC than water flowing through clay soils, this theory has recently been challenged (Gupta & Paul, 2013). They argue that this is because granite is composed of inert material, which does not conduct electricity when washed away by water (Gupta & Paul, 2013). (Bhateria & Jain, 2016; Denise et al. 2022;). During the present study, average mean EC varied significantly, from (71.620 -253.680dS/m), highest EC (253.680dS/m) was recorded during summer at Sangam (down stream) of the river and lowest EC (71.620 dS/m) was recorded at Kongwattan (up stream) during winter season showed in (figure 4 & Table S3) all of which may be the result of agricultural runoff and/or enhanced bank erosion during food preparation in the downstream regions, a rise in total dissolved solids, and an increase in temperature, The Doodhganga stream in the Kashmir Himalaya has also shown a close similarity in the spatial variance of EC (Husaini et al., 2020;Dar et al. 2020). An increase in water conductivity due to temperature fluctuations can be attributed to the winter low EC (mean of 80.622.9 s cm-1) and the summer high (mean of 176.876.4 s cm-1) reported during the seasons of winter and summer, respectively. Also in Kashmir Himalaya's Jhelum basin, summer and winter seasons show the same trend ( Indu et al.2015; Khanday et al., 2021).
Water from Sangam (downstream) had the highest BOD (4.963 mg/l) during summer seasion and the lowest BOD (1.120 mg/l) was recordrd during winter at Kongwattan (upstream) figure 5 & Table S4. Organic wastes must be oxidised aerobically in order for bacteria to meet their biochemical oxygen demand (BOD) (Masters and Ela, 2014). The domestic sewage and wastewater from industries are laden with a variety of biodegradable organic pollutants. When this waste is discharged into the water body, it is readily decomposed by the microorganisms into the simpler organic and inorganic compounds. This decomposition process consumes a lot of dissolved oxygen, thereby decreasing the DO level in the water body upto a critical minimum level (hypoxia) which ultimately disrupts the biota and causes the death of fish (Zaghloul et al. 2019; Mudau, 2021). Therefore, BOD is considered as an important tool in the determination of the extent of dissolved oxygen required for the stabilization of organic waste from domestic and industrial settings. High BOD value reveals the higher organic pollution load in the stream (Lkr et al. 2020; Lkr et al. 2022; Krishan et al. 2022). In the presence of ample oxygen, the end product of the microbial decomposition of the organic waste is non-objectionable substances like carbon dioxide, sulphate, phosphate and nitrate. Whereas, under anaerobic conditions, potentially objectionable and odorous substances such as hydrogen sulphide, ammonia and methane are generated (Watson and Juttner , 2017). We observed a significant increase (p < 0.05) in the mean BOD values of River Veshaw stream from least polluted upstream sites to more polluted downstream sites during all the seasons. For each of the four seasons studied, BOD exhibited significant differences (p 0.05). The highest mean BOD was observed during the summer season when DO was less which was followed by summer and the least mean BOD was observed during the winter when the oxygen level was fairly high as compared to spring. The BOD values in the present study showed a positive correlation with the pH and TC. It is pertinent to mention here that the mean BOD values of the Veshaw stream were most of the time < 2 mgL-1 in the upper reaches revealing oligosaprobic nature (unpolluted to slightly polluted) of the stream (Taylor et al. 2007; Fernandez et al. 2018; Yorulmaz et al. 2021). The mean BOD in the middle and lower reaches were however > 2 mgL-1 and < 4 mgL-1 during spring and autumn seasons which shows the β-mesosaprobic nature (moderately polluted) of the Veshaw River downstream in these seasons. In the summer season, the mean values of BOD were > 4 mgL-1 revealing β-α-mesoosaprobic nature (critical levels of pollution) in the lower reaches of the stream (Taylor et al., 2007; Fernandez et al. 2018; Yorulmaz et al. 2021). The highest mean BOD during the summer season is related to increased biological activities at higher temperatures and low dissolved oxygen available in this season. The minimum BOD during the winter season is related to the ample availability of oxygen due to high phytoplanktonic photosynthesis in less discharge and slow flow. A decrease in water temperature also increases the solubility of dissolved oxygen in water resulting in low BOD. Our findings are in agreement with Venkatesharaju et al. 2010; Huang et al. 2022; Zhang et al. 2022; George et al.2022) who also reported the high DO and less BOD as an outcome of plentiful phytoplanktonic growth in lean water flow resulting in high photosynthesis. The gradual increase in BOD concentration from upstream to downstream indicates that the BOD is most significantly contributed by the discharge of sewage, agricultural runoff and organic wastes from the catchment area into the stream (Indu et al. 2015;Hamil et al. 2018; Lkr et al. 2020) reported a similar trend of BOD from upstream to a downstream gradient of the Gravatai river. They observed the increased BOD with the increase in the organic contamination gradient. Further, they found high BOD values and severe eutrophication in the spring and summer and related it to the prevalence of the high temperature in these seasons.
The average mean COD varied significantly, from (24.637 to 51.440 mg/l) maximum COD 51.440 mg/l was recorded at Sangam downstream during summer season and the minimum COD 24.637 mg/l was found at Kongwattan upstream during winter stream figure 6 & Table S5. In order to convert all physiologically accessible and inert organic matter into carbon dioxide and water, a metric known as the chemical oxygen demand (COD) must be used (Kaur and Kaur, 2015; Azer et al. 2022; Cai et al. 2022).Some fraction of the organic matter such as cellulose, phenols, benzene resists biodegradation and others such as pesticides and industrial pollutants are toxic to microorganisms and thus not degraded easily. COD is a measurable quantity in which whole organic matter (biodegradable plus inert) gets oxidized chemically and independently without relying on the type of substance or microorganism present. Therefore, COD gives quicker and higher estimates of organic waste and its values are always higher than BOD (Watson and Juttner , 2017). COD test is frequently employed in combination with the BOD test for the quantification of the total of non-biodegradable organic material in a water body (Elsheikh et al. 2012; Choi et al. 2022). In the Veshaw stream, like BOD, mean COD values also exhibited significant (p < 0.05) downstream increase with the highest values observed at Sangam Downstreamin all the seasons. The COD values increased with the increase in temperature and its highest mean value was recorded during the summer season. The higher value of COD downstream is attributed to the increasing anthropogenic activities such as discharge of the agricultural waste, domestic waste and untreated municipal sewage into the stream and is an indication of high pollution from various organic wastes. Our results are in agreement with (Indu et al. 2015; Hamil et al. 2018) who reported a similar spatial variation in the COD as a result of anthropogenic activities in and around Ghrib Dam, Algeria. The high COD in summer may be related to increased water temperature coupled with low discharge resulting in the high rate
Figure 7 depicts the results of an investigation into the relationship between coliform bacteria and various aspects of water quality. A positive correlation has been found between pH, EC, BOD, and COD, as well as between faecal coliforms and total coliforms. This is the degree of association among the following water quality factors: In this order, TC, pH, BOD, and COD. It's shown here in Figure 6. Positive relationships were found between BOD, pH, EC, and COD, as well as moderate correlations between pH and COD. In addition to coliform bacteria, total coliforms had high correlation values of more than 0.5. Rainfall's impact on coliform concentration was discussed by Seo et al.2019. There is a significant association between rainfall and coliform bacteria abundance in rivers due to increased suspended particles, agricultural wastes, and phosphorus that has a strong potential to adsorb. Point pollution sources located along the Veshaw River had a favourable impact on the link between COD and BOD and coliform bacteria in terms of organic matter. A prior investigation of the Nakdong River and its major tributaries proved successful that resembled those of the present study, therefore these findings appeared to be reliable (Hong et al. 2015;Jung et al. 2016 ;Seo et al.2019). A rise in nitrite nitrogen was shown to be inversely proportional to coliform concentration in Beck and Sohn (2006), as well. The correlations between pH and coliforms in the river's mid-and downstream stations could be explained by a combination of the previously mentioned factors. Other research have identified favourable correlations between organic matter and coliform bacteria, but this was not the case with BOD (Beck and Sohn 2006;Kim et al. 2006; Mamun et al. 2022). According to Cho and Song, 2008, in a river system, coliform bacteria dropped significantly after an average of three days. Rather than upstream stations, it was thought that such natural die-offs (Van der et al.2000; Im and Mostaghimi,2004; Saraswat et al.2022) were responsible for the negative connection. In the Nakdong River's main stem, Lee et al. 2016 found a weak negative connection between BOD and coliform bacteria. Eutrophication and the consequent spread of Chl-a, an indication of algal blooms, could result from the influx of contaminants and slow flow velocity downstream. Increased production of Chl-a as self-replicating organic matter may limit the growth of coliform bacteria. Ahmad et al. (2014) and Seo et al. (2019) looked on the removal of coliform bacteria by a freshwater algae species (see references). It's possible that rainfall or the discharges of nearby streams contribute to the EC's positive association with coliform bacteria. There are numerous tributaries flowing into the Veshaw River's middle and lower regions. Kim et al. 2007; Mamun et al. 2022) found negative associations between pollutants and precipitation, as well as between contaminants and discharges. Electrolyte loss and the EC drop that follows could be the result of pollutant dilution. Correlations between the presence of coliform bacteria and EC were shown to be negative (Yun et al. 2017).
Outcomes of Regression Analysis Total coliform level for all locations was impacted by BOD, COD, EC and pH. Total coliforms showed proportionate correlations with pH, EC, BOD and COD. The relative influence of the factors ranks as follows: precipitation pH >EC>BOD >COD As for faecal coliforms, EC was included along with the abovementioned five water quality parameters, and these factors accounted up to the concentration of faecal coliforms (figure 7 and 8). The regression statistics of the physicochemical data set of the Vishav stream is summarised in Figure8. The connection with water quality variables was equal to that of the total coliforms. EC exhibited similar proportionate association with faecal coliforms figure 7 and 8. The relative influence of the components evaluated as follows: pH >BOD >COD>EC. In general, organic matter had a dominant impact on total coliforms, whereas pH and EC and BOD have this effect on faecal coliforms. BOD was the most influencing factor for the proportion of both types of coliforms. The regression findings using all station data indicated the significant water quality characteristics at each location. At each site, the water quality parameters affecting the level of coliforms were BOD, COD, EC and pH (figure 8). However, the water quality parameters could be defined at the up and midstream locations and the mid- and downstream locations beneath Vehaw River, depending on dominating patterns. (Hong et al. 2015;Jung et al. 2016;Seo et al. 2019) similarly spatially categorised the Nakdong River as the up- and midstream and mid- and downstream portions at a non-weir station between Dasa (Gangjeong-Goryeong weir) and Nongong (Dalseong weir) locations using cluster analysis.
A strong tool in environmental studies, Metgenomics analysis can be used to determine the microbial diversity at a specific area and may be useful for understanding how microbial-communities communicate and interact (Yoo, et al. 2017; Chen et al. 2022). Table 2 and figure 9 provided a summary of the sequence results. In National Center for Biotechnology Information (NCBI) gene bank result showed that total of 27 accession numbers were developed from NCBI gene bank. The Coliform bacterial species identified with accession numbers include Escherichia coli strain Rather Veshaw61 (accession number ON197775), Escherichia coli strain Rather Veshaw68 (accession number ON227007), Escherichia coli strain Rather Veshaw69 accession number (ON227008), Escherichia coli strain Rather Veshaw70 accession number (ON227009), Escherichia coli strain Rather Veshaw71 accession number (ON227010), Escherichia coli strain Rather Veshaw72 (accession number ON227011), Escherichia coli strain Rather Veshaw73(accession number ON227012), Escherichia coli strain Rather Veshaw74 (accession number ON227013), Escherichia coli strain Rather Veshaw75 accession number (ON227014), Escherichia coli strain Rather Veshaw76 accession number (ON227015), Escherichia coli strain Rather Veshaw77 accession number (ON227016), Escherichia coli strain Rather Veshaw78 accession number (ON227017), Escherichia coli strain Rather Veshaw79 accession number (ON227018), Escherichia coli strain Rather Veshaw80 accession number (ON227019), Escherichia fergusonii strain Rather Veshaw1 accession number (ON614213), Escherichia fergusonii strain Rather Veshaw2 accession number (ON622781), Escherichia fergusonii strain Rather Veshaw3 accession number (ON622786), Escherichia fergusonii strain Rather Veshaw4 accession number (ON622784), Escherichia fergusonii strain Rather Veshaw5 accession number (ON622780), Escherichia fergusonii strain Rather Veshaw6 accession number (ON622785), Escherichia fergusonii strain Rather Veshaw7 accession number (ON622783), Escherichia fergusonii strain Rather Veshaw8 accession number (ON622782), Escherichia albertii strain Rather Veshaw9 accession number (ON622787), Klebsiella grimontii strain Rather Veshaw10 accession number (ON631203), Klebsiella grimontii strain Rather Veshaw11 accession number (ON631204), Klebsiella grimontii strain Rather Veshaw12 accession number (ON631205) and Shigella dysenteriae strain Rather Veshaw13 accession number (ON631212) mentioned in (Table 2 & Fiure S1).
Figure 9 showed the results of a Multiple Sequence Analysis (MSA) using MEGA 11 to determine the evolutionary relationships among the 27 Coliform genomes that have so far been identified for phylogenetic analysis. Biological connections were established using the MSA. Escherichia coli species found in both cluster 1 and 2 with the highest bootstrap values in clusters 1 and 2 of the three clusters studied. Cluster 3 contains Escherichia fergusonii , Escherichia alberti , Klebsiella grimontiand, Shigella dysenteriae species that have a close relationship with each other (Figure 2). These total 27 coliform species of bacteria are all genetically connected to each other, as depicted in Figure 9 by their phylogenetic tree. Phenotypic traits have been traditionally used to identify bacteria, but genotypic characteristics, which are more recently discovered, are more accurate and dependable. The ribosomal RNA sequence based analysis is an implicit and special way for understanding microbial diversity within and across a group and also for identifying novel strains of micro-organism (Magray et al., 2011; Matsuo et al., 2021). The 16S rRNA gene, which has highly conserved and hyper variable sections and is used to identify novel strains of bacteria, is present in all bacterial species. There has been an increase in use of genetic techniques that use comparisons of bacteria's 16SrRNA gene sequences to known bacteria in databases. Many studies have relied on the 16S rRNA gene sequence for inferring evolutionary relationships among bacteria (Sujatha et al., 2012; Dueholm et al., 2022). As a result of our analysis, we were able to identify the species with certainty. This revealed that all the 27 coliform species were of different genus. While 14 bacterial species were identified as E. coli, eight were identified as E. fergusonii as new emerging water polluting indictors. Besides this three species of Klebsiella grimontii, one species of each Escherichia albertii and species of Shigella dysenteriae were identified in the water as pollution indictors and new reported bacterial species in Veshaw River of Kashmir Himalaya figure 2. Another investigation by Wragg et al., in 2009 and Ori et al., in 2019 found that E. fergusonii and Escherichia albertii could not be distinguished from E. coli. It has been shown that both the morphological and genotypic characteristics of E. fergusonii and Escherichia albertii Coliform bacterial species are similar to those found in the pathogenic strains of E. coli, as well as the evidence that these strains may be opportunistic pathogens. Using molecular tools, researchers from Walk et al., 2009, Olowe et al., 2017, and Lin et al., 2022 were able to distinguish and identify closely related bacterial species belonging to the same genus that had previously been considered cryptic lineages of the genus Escherichia.
Pseudomonadota the most common phylum in the six areas examined, with varying levels of diversity. Because of its great ability to perform biogeochemical activities in high-nutrient environments, the microbial community. Pseudomonadota, commonly known as coliforms, is well-adapted to high-nutrient environments where it may proliferate quickly (Krishna et al., 2020; Senevirathna et al. 2022). Similar research has been performed by (Alotaibi et al. 2022). (Nunez Salazar et al., 2020) that Proteobacteria was found to be the most abundant phylum in all of their research , with the maximum abundance in the area where human intervention was discovered, according to a recent metagenomic study. Proteobacteria belonging to the Alpha/B eta/Gamma-proteobacteria family like high pH conditions. . Another study found that the presence of such a phylum was significantly higher in areas with low salinity (Rath et al. 2019). There may be valid reasons for the higher abundance of Pseudomonadota, such as decreased conductivity and elevated pH (Brad et al. 2022). According to a study by Siles and Margesin (2016), alpine forest soils with high microbial diversity had lower EC values than soils with low microbial diversity. However, this phylum was shown to be the primary taxon in the water that had been contaminated by humans (Yadav and Sharma, 2019; Dubey et al., 2019). Also, in Veshaw River water samples, Pseudomonadota was the dominant phylum; however, its decreased abundance in upstream may be linked to lower anthropogenic contamination/lower pollution levels, as was found to be the case with the continuous discharge of human waste, agricultural inputs, and so on.
Table 2: Molecular identification of pollution indicating bacteria from water samples in Veshaw River based on their 16s rDNA sequences deposited in NCBI, USA.
S.No.
|
Strain
|
NCBI gene bank accession number
|
Gene size amplified (bp)
|
01
|
Escherichia coli strain Rather Veshaw61
|
ON197775
|
300
|
02
|
Escherichia coli strain Rather Veshaw68
|
ON227007
|
301
|
03
|
Escherichia coli strain Rather Veshaw69
|
ON227008
|
317
|
04
|
Escherichia coli strain Rather Veshaw70
|
ON227009
|
300
|
05
|
Escherichia coli strain Rather Veshaw71
|
ON227010
|
312
|
06
|
Escherichia coli strain Rather Veshaw72
|
ON227011
|
304
|
07
|
Escherichia coli strain Rather Veshaw73
|
ON227012
|
316
|
08
|
Escherichia coli strain Rather Veshaw74
|
ON227013
|
308
|
09
|
Escherichia coli strain Rather Veshaw75
|
ON227014
|
309
|
10
|
Escherichia coli strain Rather Veshaw76
|
ON227015
|
302
|
11
|
Escherichia coli strain Rather Veshaw77
|
ON227016
|
303
|
12
|
Escherichia coli strain Rather Veshaw78
|
ON227017
|
300
|
13
|
Escherichia coli strain Rather Veshaw79
|
ON227018
|
318
|
14
|
Escherichia coli strain Rather Veshaw80
|
ON227019
|
318
|
15
|
Escherichia fergusonii strain Rather Veshaw1
|
ON614213
|
772
|
16
|
Escherichia fergusonii strain Rather Veshaw2
|
ON622781
|
773
|
17
|
Escherichia fergusonii strain Rather Veshaw3
|
ON622786
|
773
|
18
|
Escherichia fergusonii strain Rather Veshaw4
|
ON622784
|
780
|
19
|
Escherichia fergusonii strain Rather Veshaw5
|
ON622780
|
792
|
20
|
Escherichia fergusonii strain Rather Veshaw6
|
ON622785
|
867
|
21
|
Escherichia fergusonii strain Rather Veshaw7
|
ON622783
|
764
|
22
|
Escherichia fergusonii strain Rather Veshaw8
|
ON622782
|
764
|
23
|
Escherichia albertii strain Rather Veshaw9
|
ON622787
|
1502
|
24
|
Klebsiella grimontii strain Rather Veshaw10
|
ON631203
|
301
|
25
|
Klebsiella grimontii strain Rather Veshaw11
|
ON631204
|
298
|
26
|
Klebsiella grimontii strain Rather Veshaw12
|
ON631205
|
300
|
27
|
Shigella dysenteriae strain Rather Veshaw13
|
ON631212
|
301
|