Fast Identification and Quantification of Uropathogenic E. coli through Cluster Analysis

Development of efficient techniques to discriminate the sources of E. coli in aquatic environments is essential to improve the surveillance of fecal pollution indicators, to develop strategies to identify the sources of fecal contamination, and to implement appropriate management practices to minimize gastrointestinal disease transmission. In this study the robustness of five different rep-PCR methods, such as REP-PCR, ERIC-PCR, ERIC2-PCR, BOX-PCR and (GTG)5-PCR were evaluated to discriminate 271 E. coli strains isolated from two watersheds (Lakelse Lake and Okanagan Lake) located in British Columbia, Canada. Cluster analysis of (GTG)5-PCR, BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR profiles of 271 E. coli revealed 43 clusters, 35 clusters, 28 clusters, 23 clusters and 14 clusters, respectively. The discriminant analysis of rep-PCR genomic fingerprints of 271 E. coli isolates yielded an average rate of correct classification (watershed-specific) of 86.8%, 82.3%, 78.4%, 72.6% and 55.8% for (GTG)5-PCR, BOX-PCR, REP-PCR, ERIC-PCR and ERIC2-PCR, respectively. Based on the results of cluster analysis and discriminant function analysis, (GTG)5-PCR was found to be the most robust molecular tool for differentiation of E. coli populations in aquatic environments.

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A Simple and Effective Isocratic HPLC Method for Fast Identification and Quantification of Surfactin

Sains Malaysiana ◽

10.17576/jsm-2015-4401-16 ◽

2015 ◽

Vol 44 (1) ◽

pp. 115-120 ◽

Cited By ~ 8

Author(s):

Muhammad Qadri Effendy Mubarak ◽

Abdul Rahman Hassan ◽

Aidil Abdul Hamid ◽

Sahaid Khalil ◽

Mohd. Hafez Mohd. Isa

Keyword(s):

Hplc Method ◽

Fast Identification ◽

Isocratic Hplc ◽

Identification And Quantification

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Statistical Analyses: Possible Reasons for Unreliability of Source Tracking Efforts

Applied and Environmental Microbiology ◽

10.1128/aem.71.8.4690-4695.2005 ◽

2005 ◽

Vol 71 (8) ◽

pp. 4690-4695 ◽

Cited By ~ 17

Author(s):

Clarivel Lasalde ◽

Roberto Rodríguez ◽

Gary A. Toranzos

Keyword(s):

Cluster Analysis ◽

Discriminant Function ◽

Discriminant Function Analysis ◽

Function Analysis ◽

Microbiological Quality ◽

Statistical Analyses ◽

Indicator Organisms ◽

E Coli ◽

Quality Of Water

ABSTRACT Analyses for the presence of indicator organisms provide information on the microbiological quality of water. Indicator organisms recommended by the United States Environmental Protection Agency for monitoring the microbiological quality of water include Escherichia coli, a thermotolerant coliform found in the feces of warm-blooded animals. These bacteria can also be isolated from environmental sources such as the recreational and pristine waters of tropical rain forests in the absence of fecal contamination. In the present study, E. coli isolates were compared to E. coli K12 (ATCC 29425) by restriction fragment length polymorphism using pulsed-field gel electrophoresis. Theoretically, genomic DNA patterns generated by PFGE are highly specific for the different isolates of an organism and can be used to identify variability between environmental and fecal isolates. Our results indicate a different band pattern for almost every one of the E. coli isolates analyzed. Cluster analysis did not show any relations between isolates and their source of origin. Only the discriminant function analysis grouped the samples with the source of origin. The discrepancy observed between the cluster analysis and discriminant function analysis relies on their mathematical basis. Our validation analyses indicate the presence of an artifact (i.e., grouping of environmental versus fecal samples as a product of the statistical analyses used and not as a result of separation in terms of source of origin) in the classification results; therefore, the large genetic heterogeneity observed in these E. coli populations makes the grouping of isolates by source rather difficult, if not impossible.

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Cluster analysis of resistance combinations in Escherichia coli from different human and animal populations in Germany 2014-2017

10.21203/rs.3.rs-30292/v2 ◽

2020 ◽

Author(s):

Beneditta Suwono ◽

Tim Eckmanns ◽

Heike Kaspar ◽

Roswitha Merle ◽

Benedikt Zacher ◽

...

Keyword(s):

Escherichia Coli ◽

Cluster Analysis ◽

Average Distance ◽

Companion Animals ◽

Clinical Isolates ◽

Human Populations ◽

E Coli ◽

Animal Populations ◽

Routine Surveillance ◽

Animal Isolates

Abstract Recent findings on Antibiotic Resistance (AR) have brought renewed attention to the comparison of data on AR from human and animal sectors. This is however, a major challenge since the data is not harmonized. This study performs a comparative analysis of data on resistance combinations in Escherichia coli (E. coli) from different routine surveillance and monitoring systems for human and different animal populations in Germany. Data on E. coli isolates were collected between 2014 and 2017 from human clinical isolates, non-clinical animal isolates from food-producing animals and food, and clinical animal isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Sixteen possible resistance combinations to four antibiotics - ampicillin, cefotaxime, ciprofloxacin and gentamicin – for these populations were used for hierarchical clustering (Euclidian and average distance). All analyses were performed with the software R 3.5.1 (Rstudio 1.1.442). Data of 333,496 E. coli isolates and forty-one different human and animal populations were included in the cluster analysis. Three main clusters were detected. Within these three clusters, all human populations (intensive care unit (ICU), general ward and outpatient care) showed similar relative frequencies of the resistance combinations and clustered together. They demonstrated similarities with clinical isolates from different animal populations and most isolates from pigs from both non-clinical and clinical isolates. Isolates from healthy poultry demonstrated similarities in relative frequencies of resistance combinations and clustered together. However, they clustered separately from the human isolates. All isolates from different animal populations with low relative frequencies of resistance combinations clustered together and likewise separately from the human populations. Cluster analysis has been able to demonstrate the linkage among human isolates and isolates from various animal populations based on the resistance combinations. Further analyses based on these findings might promote a better one-health approach for AR in Germany.

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Fast Identification and Quantification of Graphene Oxide in Aqueous Environment by Raman Spectroscopy

Nanomaterials ◽

10.3390/nano10040770 ◽

2020 ◽

Vol 10 (4) ◽

pp. 770 ◽

Cited By ~ 2

Author(s):

Shengnan Yang ◽

Qian Chen ◽

Mengyao Shi ◽

Qiangqiang Zhang ◽

Suke Lan ◽

...

Keyword(s):

Raman Spectroscopy ◽

Graphene Oxide ◽

Environmental Risk ◽

Large Scale ◽

Carbon Nanomaterials ◽

Aqueous Environment ◽

Fast Identification ◽

Aqueous Environments ◽

Potential Applications ◽

Identification And Quantification

Today, graphene nanomaterials are produced on a large-scale and applied in various areas. The toxicity and hazards of graphene materials have aroused great concerns, in which the detection and quantification of graphene are essential for environmental risk evaluations. In this study, we developed a fast identification and quantification method for graphene oxide (GO) in aqueous environments using Raman spectroscopy. GO was chemically reduced by hydrazine hydrate to form partially reduced GO (PRGO), where the fluorescence from GO was largely reduced, and the Raman signals (G band and D band) were dominating. According to the Raman characteristics, GO was easily be distinguished from other carbon nanomaterials in aqueous environments, such as carbon nanotubes, fullerene and carbon nanoparticles. The GO concentration was quantified in the range of 0.001–0.6 mg/mL with good linearity. Using our technique, we did not find any GO in local water samples. The transport of GO dispersion in quartz sands was successfully quantified. Our results indicated that GO was conveniently quantified by Raman spectroscopy after partial reduction. The potential applications of our technique in the environmental risk evaluations of graphene materials are discussed further.

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Fast identification and quantification of BTEX coupling by Raman spectrometry and chemometrics

The Analyst ◽

10.1039/c5an00035a ◽

2015 ◽

Vol 140 (10) ◽

pp. 3535-3542 ◽

Cited By ~ 8

Author(s):

J. Moreau ◽

E. Rinnert

Keyword(s):

Raman Spectrometry ◽

Fast Identification ◽

Chemical Pollutants ◽

Monoaromatic Hydrocarbons ◽

Identification And Quantification

Monoaromatic hydrocarbons (MAHs) monitoring is of environmental interest since these chemical pollutants are omnipresent.

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CleanRecomb, a quick tool for recombination detection in SNP based cluster analysis

10.1101/317131 ◽

2018 ◽

Cited By ~ 3

Author(s):

Mark Østerlund ◽

Kristoffer Kiil

Keyword(s):

Cluster Analysis ◽

Genome Sequences ◽

E Coli ◽

Recombination Detection

AbstractWe present CleanRecomb, a tool to quickly filter a SNP matrix for likely recombination events.MethodThe method evaluates segments with identical SNP profiles over the genome, based on the assumption that SNPs in the absense of recombination events are uniformly distributed across the genome. The method is evaluated on a set of 9 ST200 E. coli genome sequences.ResultsThe detected recombination events coincide with regions of elevated SNP density.

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Cluster analysis of resistance combinations in Escherichia coli from different human and animal populations in Germany 2014-2017

10.21203/rs.3.rs-30292/v1 ◽

2020 ◽

Author(s):

Beneditta Suwono ◽

Tim Eckmanns ◽

Heike Kaspar ◽

Roswitha Merle ◽

Benedikt Zacher ◽

...

Keyword(s):

Escherichia Coli ◽

Cluster Analysis ◽

Average Distance ◽

Companion Animals ◽

Clinical Isolates ◽

Human Populations ◽

E Coli ◽

Animal Populations ◽

Routine Surveillance ◽

Resistance Patterns

Abstract Background Recent findings on Antibiotic Resistance (AR) have brought renewed attention to the comparison of data on AR from human and animal sectors. This is however, a major challenge since the data is not harmonized. This study performs a comparative analysis of phenotypical AR data from different routine surveillance and monitoring systems in Germany. Escherichia coli data were used as a model to describe the similarities based on the resistance patterns in human and different animal populations in Germany. Method: Data on E. coli isolates were collected from 2014 to 2017 from human clinical isolates, non-clinical isolates from food-producing animals and food, and clinical isolates from food-producing and companion animals from national routine surveillance and monitoring for AR in Germany. Four antibiotics - ampicillin, cefotaxime, ciprofloxacin and gentamicin - were chosen for the analysis. Resistant isolates were defined according to EUCAST clinical breakpoints for humans. Based on the 16 possible resistance combinations to these four antibiotics, cluster analysis was performed using hierarchical clustering with Euclidian and average distance. All analyses were performed with the software “R”. Result Data of 333,496 E. coli isolates were included in this study. Forty-one different human and animal populations were included in the cluster analysis. Three main clusters were detected. Within these three clusters, all human populations (intensive care unit (ICU), general ward and outpatient care) showed similar relative frequencies of the resistance combinations and clustered together. They demonstrated similarities with clinical isolates from different animal populations and most isolates from pigs from both non-clinical and clinical isolates. Isolates from healthy poultry demonstrated similarities in relative frequencies of resistance combinations and clustered together. However, they clustered separately from the human isolates. All isolates from different animal populations with low relative frequencies of resistance combinations clustered together and likewise separately from the human populations. Conclusion Cluster analysis facilitated the comparison of phenotypical AR data across human and animal sectors. It indicated linkage among human isolates and with isolates from various animal populations based on the resistance combinations in E. coli. Further analyses based on these findings might promote a better one-health approach for AR in Germany.

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Complete Genome Sequence of Escherichia Coli Strain S9922 Isolated from Meningitis in Calf and Comparative Genomic Analysis with Other E. Coli Pathotypes

10.21203/rs.3.rs-846112/v1 ◽

2021 ◽

Author(s):

Beibei Li ◽

Jingjing Ren ◽

Xun Ma ◽

Qian Qin ◽

Xinyu Wang ◽

...

Keyword(s):

Escherichia Coli ◽

Cluster Analysis ◽

Large Scale ◽

Virulent Strain ◽

Evolutionary Relationship ◽

Intestinal Flora ◽

Genomic Analysis ◽

Comparative Genomic ◽

E Coli ◽

Wide Range

Abstract Background: Extraintestinal pathogenic Escherichia coli (ExPEC) exists in the normal intestinal flora, but can invade and colonize extraintestinal sites and cause a wide range of infections. Genomic analysis of ExPEC has mainly focused on isolates of human, poultry and pig. In recent years, some large-scale dairy farms in Xinjiang broke out cases characterized by neurological symptoms and acute death in newborn calves. To better understand the genomic attributes underlying the pathogenicity of bovine-source ExPEC, a highly virulent strain, which named E. coli S9922 was isolated from cerebral effusion in a calf that died of meningitis, was sequenced and analyzed.Results: Using single-molecule sequencing technology on PacBio and then assembled, the genes were predicted and annotated. The whole genome of E.coli S9922 was consisted of a chromosome and three plasmids containing 5055 genes, and the total length was 5269374 bp and the average G+C content was 50.82%. In addition, 291 host-, 204 virulence-, and 185 resistance-related genes, and 182 T3SS effector proteins were found by comparison with related databases. Comparison of this genome to 16 representative strains of pathogenic E.coli genomic sequences showed that E.coli S9922 had the greatest co-linearity with E.coli 90-9272. In addition, Core genes obtained by cluster analysis of E.coli S9922 homologous genes were classified, a total of 2570, 2780, and 2188 genes were obtained via COG, KEGG, and GO comparisons, respectively. The unique genes identified by homologous cluster analysis were classified 204, 550, 239 genes in COG, KEGG, and GO comparisons, respectively. Evolutionary tree analysis revealed a close evolutionary relationship between E.coli S9922 and E.coli 90-9272, and a distant relationship between E.coli S9922 and UTI89.Conclusions: The study provide dgenomics of E.coli S9922 strain from the cattle that had died of meningitis. It enriched the genome data of E.coli and laid a theoretical foundation for further experimental study of ExPEC. Comparative genomics analysis showed that E.coli S9922 had a close evolutionary relationship with E.coli 90-9272, but far from that of UTI89.

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Preliminary Microbiological Coastal Water Quality Determination along the Department of Atlántico (Colombia): Relationships with Beach Characteristics

Journal of Marine Science and Engineering ◽

10.3390/jmse9020122 ◽

2021 ◽

Vol 9 (2) ◽

pp. 122

Author(s):

Zamira E. Soto-Varela ◽

David Rosado-Porto ◽

Hernando José Bolívar-Anillo ◽

Camila Pichón González ◽

Bertha Granados Pantoja ◽

...

Keyword(s):

Water Quality ◽

Cluster Analysis ◽

Urban Areas ◽

Microbiological Quality ◽

Salmonella Spp ◽

E Coli ◽

International Regulations ◽

Rural And Remote Areas ◽

Beach Water Quality ◽

Beach Water

Beach water quality is an important factor concerning public health and tourism linked to the “Sun, Sea and Sand” market and is usually assessed in international regulations by the quantification of Escherichia coli and enterococci counts. Despite Salmonella spp. detection not being included in international normative, the presence/absence of this bacteria is also an indicator of seawater quality. The objective of this study was to determine microbiological quality of beach water at 14 beaches along the Department of Atlántico (Colombia) and its relationship with beach characteristics as beach typology (i.e., urban, village, rural and remote areas), presence of beach facilities (e.g., bars, restaurants, etc.) and streams outflowing into the coastline. Sampling program aimed to analyse E. coli and Salmonella spp., by culture-based and real time PCR methods, respectively. Microbiological outcomes were compared with beach characteristics, and a cluster analysis was performed. E. coli and Salmonella spp. were detected in 70% and 20% of samples, respectively. Highest E. coli counts were observed at beaches classified as urban and at Sabanilla, a rural beach with presence of numerous beach restaurants/bars. Salmonella spp. presence was associated with streams that lack wastewater treatment systems. Cluster analysis clearly evidenced the relationship between E. coli and Salmonella spp. and beach characteristics, allowing to obtain indications to implement management programs. According to data obtained, monitoring programs have to be especially carried out in urban areas and at places with beach facilities. This could enhance microbiological water quality and consequently, beachgoers safety and touristic beach attractiveness to international visitors.

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