To the National Council of Science and Technology of Mexico (CONACyT by its acronym in Spanish) for the Doctoral scholarship awarded to Jos&#233; Antonio Maga&#241;a-Liz&#225;rraga [No. 481143].

NOTE: The E. coli strain ACM5 was recovered by processing and culturing the fish sample for fecal coliform (FC) determination12. During the fish sampling, fish did not show clinical signs of disease, bacterial, or fungal infection, and a mean temperature of 22.3 &#176;C prevailed. After isolation, the E. coli isolate was subjected to biochemical testing and cryopreserved in brain heart infusion (BHI) broth with DMSO (8% v/v) as a cryoprotective agent.
1. Reactivation of frozen E. coli ACM5 stock culture
<ol>
	<li>From the frozen bacterial stock, open the tube and use a sterile loop, pipette tip, or toothpick to scrape the surface of the frozen bacterial culture.</li>
	<li>Streak the bacteria onto a Luria-Bertani (LB) agar plate and incubate at 37 &#177; 2 &#176;C for 24 h.</li>
</ol>
2. Determination of antibacterial susceptibility
NOTE: The antimicrobial susceptibility testing described here corresponds to the disk diffusion method based on the Clinical and Laboratory Standards Institute (CLSI) guidelines (M02 Ed13:2018)31. E. coli ATCC 25922 strain is required for quality control purposes.
<ol>
	<li>Inoculum preparation by colony suspension method
	<ol>
		<li>From the LB plate incubated in step 1.2, streak one or two colonies onto a Mueller-Hinton (MH) agar plate and incubate at 37 &#177; 2 &#176;C for 18-24 h.</li>
		<li>Use a sterile loop to pick two or three colonies from the freshly sub-cultured E. coli isolate on MH agar plate and resuspend them in 3 mL of sterile MH broth or 0.85% (w/v) saline solution, mixing thoroughly by vortexing to obtain a uniform bacterial suspension.</li>
		<li>Using a UV-visible spectrophotometer (see Table of Materials), adjust the bacterial suspension to turbidity comparable to a 0.5 MacFarland standard (equivalent to approximately 1-2 x 108 cells/mL). If the bacterial suspension is too light or too heavy, add more E. coli colonies or MH broth as appropriate. Use the prepared inoculum within 15 min of preparation.</li>
	</ol>
	</li>
	<li>Inoculation of the test agar plates
	<ol>
		<li>Dip a sterile cotton swab into the adjusted bacterial suspension. Rotate the swab several times and press it firmly on the inside wall of the tube to remove excess fluid from the swab.</li>
		<li>Inoculate an MH agar plate by streaking the swab 3x over the entire agar surface, rotating the plate 60&#176; each time. Swab the rim of the agar as well to ensure an even distribution of the inoculum.</li>
		<li>Leave the Petri dish lid ajar for 3-5 min to allow moisture to evaporate.</li>
	</ol>
	</li>
	<li>Application of the antimicrobial disk onto inoculated agar plates
	<ol>
		<li>Distribute evenly and press each antimicrobial disk (see Table of Materials) down onto the surface of the inoculated agar plates to ensure complete contact. Invert the plates within 15 min after disk application and incubate at 35 &#177; 2 &#176;C for 16-18 h. 
		NOTE: A maximum of 12 and six disks must be used in Petri dishes of 150 mm and 100 mm, respectively.</li>
	</ol>
	</li>
	<li>After incubation, measure the zone of inhibition sizes using a Vernier caliper and interpret the resulting diameters according to the CLSI breakpoint criteria (M100 - Table 2A)32.</li>
</ol>
3. Genomic DNA (gDNA) extraction and quantification
<ol>
	<li>Genomic DNA extraction
	<ol>
		<li>Resuspend a loopful of freshly cultured E. coli colonies in 5 mL of LB broth and incubate overnight in a shaking incubator (180 rpm) at 37 &#177; 2 &#176;C.</li>
		<li>Centrifuge the bacterial suspension at 3,500 x g for 5 min and carefully discard the supernatant.</li>
		<li>Extract gDNA following the DNA extraction kit guidelines (see Table of Materials).</li>
		<li>Check the purity of the gDNA by measuring optical density at 260/280 nm (ratio: &#62;1.8) and 260/230 nm (ratio: 2.0-2.2) using a UV-visible spectrophotometer. Perform 0.8% agarose gel electrophoresis to verify the gDNA integrity.</li>
	</ol>
	</li>
	<li>Genomic DNA quantification 
	NOTE: Use only thin-wall, clear, 0.5 mL PCR tubes approved by the fluorescence assay kit manufacturer (see Table of Materials).
	<ol>
		<li>Set up the required number of tubes for samples and assay standards. Prepare the working assay solution by mixing component A and component B provided in the kit, following the manufacturer&#39;s guidelines.</li>
		<li>Prepare the assay standards by adding 190 &#181;L of the working solution and 10 &#181;L of each standard to the appropriate tube (two standards are required).</li>
		<li>Add 198 &#181;L of the working solution and 2 &#181;L of the DNA sample to the appropriate tube. Vigorously mix by vortexing all the tubes for 5 s. Be careful not to create bubbles.</li>
		<li>Incubate all tubes for 2 min at room temperature, protected from light. Measure the fluorescence of all tubes based on the manufacturer&#39;s guidelines using the fluorometer (see Table of Materials).</li>
		<li>Properly adjust the gDNA sample concentration for sequencing proceeding.</li>
	</ol>
	</li>
</ol>
4. DNA library preparation
NOTE: DNA library preparation and sequencing were performed following the manufacturer&#39;s guidelines and protocols (see Table of Materials). The starting gDNA concentration is 4.0 ng.
<ol>
	<li>Tagmentation, PCR amplification, and indexing
	<ol>
		<li>In a 0.2 mL tube, add 2.5 &#181;L of tagmentation buffer and 2 &#181;L of input gDNA (2.0 ng/&#181;L). Mix gently by pipetting.</li>
		<li>Add 1 &#181;L of amplification buffer and mix gently by pipetting. Spin down at 280 x g for 1 min at room temperature.</li>
		<li>Place the samples in a thermocycler (see Table of Materials) and run the following PCR program: 55 &#176;C for 5 min, then hold at 10 &#176;C. When the samples reach 10 &#176;C, proceed immediately to neutralize the reaction.</li>
		<li>Add 1 &#181;L of neutralizing buffer to the sample tubes and mix gently by pipetting. Spin down at 280 x g for 1 min and incubate for 5 min at room temperature.</li>
		<li>Add 1.7 &#181;L of each index adapter (i.e., indexes i7 and i5) and 3 &#181;L of indexing PCR master mix. Mix gently by pipetting. Spin down at 280 x g for 1 min at room temperature.</li>
		<li>Place the samples in the thermocycler and perform a second PCR reaction as follows: 72 &#176;C for 3 min, 95 &#176;C for 30 s, 18 cycles of 95 &#176;C for 10 s, 55 &#176;C for 30 s, 72 &#176;C for 30 s, 72 &#176;C for 5 min, and hold at 10 &#176;C.</li>
	</ol>
	</li>
	<li>Amplified library clean up
	<ol>
		<li>Mix by vortexing the commercial magnetic bead solution (see Table of Materials) based on the manufacturer&#39;s guidelines.</li>
		<li>Transfer the tagmented/indexed gDNA sample to a new 1.5 mL tube and add 0.6 &#181;L of magnetic beads for each &#181;L of the final volume of gDNA sample (&#8776;13 &#181;L). Mix gently by pipetting and incubate for 5 min at room temperature.</li>
		<li>Place the sample tubes on a magnetic rack (see Table of Materials) for 2 min until the supernatant has cleared. Remove and discard the supernatant carefully without disturbing the beads.</li>
		<li>Add 200 &#181;L of freshly prepared 80% ethanol without mixing. Incubate for 30 s until the eluate clears and carefully remove and discard the supernatant without disturbing the beads.</li>
		<li>Perform a second wash step. Add 200 &#181;L of 80% ethanol to the beads and incubate for 30 s. After the eluate clears, remove and discard the supernatant and air-dry the beads for 10 min.</li>
		<li>Add 15 &#181;L of 10 mM tris buffer (pH 8) to the beads and mix gently by pipetting. Incubate for 2 min at room temperature. Place the sample tubes again on the magnetic rack for 2 min, allowing the supernatant to clear.</li>
		<li>Carefully transfer 14 &#181;L of the supernatant from the sample tube to a new 0.2 mL tube. This is the cleaned-up library. 
		NOTE: At this point, the cleaned-up library can be stored at -20 &#176;C for up to 7 days.</li>
	</ol>
	</li>
	<li>Library normalization
	<ol>
		<li>Proceed to quantify the cleaned-up libraries by fluorescence assay as described in step 3.2, except for step 3.2.5.</li>
		<li>Visualize the cleaned-up libraries on a 1% agarose gel electrophoresis to determine the average fragment sizes.</li>
		<li>Calculate the molarity value of the library using the following equation: 
		<img alt="Equation 1" src="/files/ftp_upload/64404/64404eq01.jpg" style="margin: auto;" /></li>
		<li>Using the molarity value, calculate the proper volumes of resuspension buffer (RSB) and cleaned-up libraries to dilute the individual library at a starting concentration of 10 nM.</li>
	</ol>
	</li>
</ol>
5. Library pooling, denaturalizing, and sequencer initiating
<ol>
	<li>Thaw the reagent cartridge following the manufacturer&#39;s directions. Extract the flow cell from the refrigerator (4 &#176;C) and bring it to room temperature before sequencing.</li>
	<li>Pool 5 &#181;L of each normalized library (10 nM) into a low-bind 1.5 mL tube. Dilute the pooled library at 4 nM with the proper volume of RSB.</li>
	<li>Add 5 &#181;L of the pooled library (4 nM) and 5 &#181;L of 0.2 N NaOH in a new low-bind 1.5 mL tube. Briefly mix by vortexing, spin down at 280 x g for 1 min, and incubate for 5 min at room temperature to denature the pooled library into single strands.</li>
	<li>Gently mix 10 &#181;L of denatured pooled library and 990 &#181;L of prechilled hybridization buffer by pipetting and place on ice until the final dilution for sequencer loading is performed. The concentration of the denatured pooled library is 20 pM.</li>
	<li>Thaw and prepare a control library (see Table of Materials) at a concentration of 4 nM by mixing 2 &#181;L of control library (10 nM) and 3 &#181;L of nuclease-free water.</li>
	<li>Mix 5 &#181;L of the control library (4 nM) and 5 &#181;L of freshly prepared 0.2 N NaOH. Briefly mix by vortexing, spin down at 280 x g for 1 min, and incubate for 5 min at room temperature to denature the control library into single strands.</li>
	<li>Gently mix 10 &#181;L of denatured control library and 990 &#181;L of prechilled hybridization buffer by pipetting and place on ice until the final dilution for sequencer loading is done. The concentration of the denatured control library is 20 pM.</li>
	<li>In a new low-bind 1.5 mL tube, combine 594 &#181;L of the denatured pooled library at 20 pM and 6 &#181;L of the denatured control library at 20 pM. Mix properly.</li>
	<li>Dilute the final library mix (20 pM) to a final loading concentration of 1.2 pM in a volume of 600 &#181;L using the prechilled hybridization buffer.</li>
	<li>Before loading the final library mix onto the reagent cartridge, perform an additional thermal pretreatment to achieve efficient loading into the flow cell. Incubate the final library mix for 2 min at 96 &#176;C. Invert the tube to mix, and place the tube on ice for 5 min.</li>
	<li>Load 500 &#181;L of the final library mix into the designed reservoir on the reagent cartridge. Initiate sequencing following the guidelines. Load the flow cell and the reagent cartridge and set up the sequencing run.</li>
</ol>
6. Sequence data analysis
NOTE: Check Supplementary File 1 for further description of the general WGS data preprocessing, software, parameter settings, and sequence analysis of the E. coli genome.
<ol>
	<li>Connect to the sequencing data server and download the FASTQ files.</li>
	<li>Evaluate the initial quality of raw sequence data with third-party software. Remove remnant adapter sequences, low-quality bases (&#60;Q30), and short reads (&#60;50 bp) from the raw sequencing data (see Supplementary File 1).</li>
	<li>Assemble the quality-checked sequencing data into contig- or scaffold-level using third-party software (see Supplementary File 1).</li>
	<li>Perform genome annotation by submitting the FASTA file containing the assembled genome to the RAST server (https://rast.nmpdr.org/).</li>
	<li>Upload the FASTA file to the Center for Genome Epidemiology (CGE) (http://www.genomicepidemiology.org/services/) and ClermonTyping (http://clermontyping.iame-research.center/index.php) web platforms to identify epidemiological features, ARGs, VAGs, and plasmids (see&#160;Supplementary File 1).</li>
	<li>Upload the FASTA file to the PHASTER server to identify prophage sequences (https://phaster.ca/).</li>
</ol>

The authors have nothing to disclose.

This study presents an adaptation of the bacterial WGS workflow using a benchtop sequencer and a pipeline for genomic characterization of a pathogenic E. coli variant. Depending on the sequencing platform used, the turnaround times (TATs) for wet laboratory procedures (bacterial culturing, gDNA extraction, library preparation, and sequencing) and sequence analysis could vary, particularly if slow-growing bacteria are studied. Following the protocol for WGS described above, the TAT was within 4 days, which is comparable to what the literature currently states (&lt;5 days) 34.
The theoretical depth of coverage for genome sequencing of E. coli ACM5 was calculated at 30x. Nonetheless, the sequencing generated empirical data for the genome assembly at 38x depth. As previously recommended35, this was sufficient to get an entire genome representation and follow downstream data analysis regarding the antimicrobial resistance, virulence, and mobilome features of E. coli ACM5. Respecting antimicrobial resistance, only the acquired determinants were investigated. E. coli ACM5 is an ampicillin-resistant isolate, but only the MdfA multidrug efflux pump encoding gene was observed. However, this does not confer resistance to &#946;-lactams36, and no other acquired determinant involved in &#946;-lactam resistance was identified. Hence, it is likely that a combination of other molecular mechanisms, such as decreased cell membrane permeability and increased expression of distinct efflux pump systems, is underlying the observed ampicillin resistance37.
Regarding virulence traits, the sole presence of the intimin-encoding gene (eae) is the hallmark genetic marker of aEPEC strains. The aEPEC pathotype is a subset of intestinal pathogenic E. coli strain carrying the locus of enterocyte effacement (LEE) pathogenicity island, where a T3SS and associated effector/translocator proteins are encoded, including Cif, EspA/B/D, EspF, intimin, and the translocated intimin receptor (Tir). The interplay between T3SS and its effectors is directly involved in the ability to promote the attaching and effacing (A/E) lesions on intestinal epithelial cells by typical enteropathogenic E. coli (EPEC) and EHEC, both pathotypes known as human foodborne illness causative agents17,19. The astA gene encodes a heat-resistant enterotoxin named EAST1, and although it was first recognized and associated with EAEC strains, the EAST1 toxin is widely distributed among E. coli pathotypes38. Here, WGS demonstrated that E. coli ACM5 possesses the astA gene, and despite the fact that EAST1-producing E. coli isolates have been associated with diarrheal illness in humans and animals, its pathogenic role in eliciting diarrhea remains controversial39.
The key limitation of this methodological approach should be acknowledged; the initial contig-level assembly resulted in a fragmented draft genome, and therefore was further subjected to scaffolding against a closer reference genome. The implemented sequencing read length configuration (2 x 150 bp) and the presence of vast, repetitive elements throughout the genome of E. coli are the most presumable explanations for the fragmented contig-level assembly. Short-read datasets cannot correctly resolve, and therefore reconstruct large repetitive elements, causing potential misassemblies and several breakpoints during the assembly process. Hence, the implementation of long-read sequencing technologies would aid in overcoming these limitations and obtaining closed genomes40.
Several critical steps must be considered throughout the WGS protocol. High-quality gDNA is the first checkpoint required to ensure high-quality sequencing data. Secondly, in the library preparation, extra caution must be taken during the tagmentation process, especially in the handling of multiple samples and the addition of index primers, to avoid cross-contamination, and in controlling the incubation time, to properly fragment the DNA samples. Moreover, quantification and normalization steps are essential to eliminate any bias that DNA libraries could introduce into the final sequencing data, since an incorrect equimolar ratio in the pooled library can favor a substantially unequal distribution in the number of reads per sample. The method presented here is straightforward and represents a cost-effective alternative, principally, for the optimal usage of the reagents contained in the DNA library preparation kits. The protocol for WGS described above has been applied not only to E. coli genome sequencing but also to other unrelated bacterial species such as Vibrio parahaemolyticus41. According to the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO), NGS methodologies can play a determining role in food safety by providing rapid identification and characterization of microorganisms and antimicrobial resistance (AMR) with accuracy that was previously not possible42. Therefore, these methodologies constitute a tool for surveillance and source tracking of pathogens, not only in the clinical context but also in the risk assessment of foodborne pathogens, revealing insights into the ecological and physiological properties of these microorganisms43.

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/64404">Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing</a>. The Authors section was updated from:
Jos&#233; Antonio Maga&#241;a-Liz&#225;rraga1, 
Bruno G&#243;mez-Gil2, 
Julissa Enciso-Ibarra2, 
Mar&#237;a Elena B&#225;ez-Flores1 
1Unidad de Investigaciones en Salud P&#250;blica &#8220;Dra. Kaethe Willms&#8221;, Facultad de Ciencias Qu&#237;mico Biol&#243;gicas, Universidad Aut&#243;noma de Sinaloa, Ciudad Universitaria 
2Centro de Investigaci&#243;n en Alimentaci&#243;n y Desarrollo, A. C. (CIAD), Unidad Mazatl&#225;n en Acuicultura y Manejo Ambiental
to:
Jos&#233; Antonio Maga&#241;a-Liz&#225;rraga1, 
Bruno G&#243;mez-Gil2, 
Julissa Enciso-Ibarra2, 
Mar&#237;a Elena B&#225;ez-Flores1 
1Unidad de Investigaciones en Salud P&#250;blica &#8220;Dra. Kaethe Willms&#8221;, Facultad de Ciencias Qu&#237;mico Biol&#243;gicas, Universidad Aut&#243;noma de Sinaloa 
2Centro de Investigaci&#243;n en Alimentaci&#243;n y Desarrollo, A. C. (CIAD), Unidad Mazatl&#225;n en Acuicultura y Manejo Ambiental

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/64404">Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing</a>. The Authors section was updated.

Erratum: Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

Aquaculture is one of the fastest-growing food-producing sectors worldwide, and its production practices are intended to satisfy the rising food demand for human consumption. Global aquaculture production has tripled from 34 million tonnes (Mt) in 1997 to 112 Mt in 20171. The main species groups, contributing to nearly 75% of the production, were seaweed, carps, bivalves, catfish, and tilapia (Oreochromis spp.)1. However, the appearance of diseases caused by microbial entities is unavoidable because of intensive fish farming, leading to potential economic losses2.
Antibiotic usage in fish farming practices is well known for preventing and treating bacterial infections, the main limiting factor in productivity3,4. Nonetheless, residual antibiotics accumulate in aquaculture sediments and water, exerting selective pressure and modifying the fish-associated and the residing bacterial communities5,6,7,8. Consequently, the aquaculture environment serves as a reservoir for antimicrobial resistance genes (ARGs), and the further emergence and spread of antibiotic-resistant bacteria (ARB) in the surrounding milieu9. In addition to the bacterial pathogens commonly observed affecting fish farming practices, members of the Enterobacteriaceae family are often encountered, including human pathogen strains of Enterobacter spp., Escherichia coli, Klebsiella spp., and Salmonella spp.10. E. coli is the most common microorganism isolated from fish meal and water in fish farming11,12,13,14,15.
E. coli is a versatile gram-negative bacterium that inhabits the gastrointestinal tract of mammals and birds as a commensal member of their intestinal microbiota. However, E. coli possess a highly adaptive capacity to colonize and persist in different environmental niches, including soil, sediments, food, and water16. Because of the gene gain and loss through the horizontal gene transfer (HGT) phenomenon, E. coli has rapidly evolved into a well-adapted antibiotic-resistant pathogen, able to cause a broad spectrum of diseases in humans and animals17, 18. Based on the isolation origin, pathogenic variants are defined as intestinal pathogenic E. coli (InPEC) or extra-intestinal pathogenic E. coli (ExPEC). Furthermore, InPEC and ExPEC are subclassified into well-defined pathotypes according to disease manifestation, genetic background, phenotypic traits, and virulence factors (VFs)16,17,19.
Traditional culture and molecular techniques for pathogenic E. coli strains have allowed the rapid detection and identification of different pathotypes. However, they may be time-consuming, laborious, and frequently require high technical training19. Furthermore, no single method can be used to reliably study all pathogenic variants of E. coli because of the complexity of their genetic background. Currently, these drawbacks have been overcome with the advent of high-throughput sequencing (HTS) technologies. Whole-genome sequencing (WGS) approaches and bioinformatic tools have improved the exploration of microbial DNA affordably and at a large scale, facilitating the in-depth characterization of microbes in a single run, including closely related pathogenic variants20,21,22. Depending on the biological questions, several bioinformatics tools, algorithms, and databases can be used to perform data analysis. For instance, if the main goal is to assess the presence of ARGs, VFs, and plasmids, tools such as ResFinder, VirulenceFinder, and PlasmidFinder, along with their associated databases, might be a good starting point. Carri&#231;o et al.22 provided a detailed overview of the different bioinformatics software and related databases applied for microbial WGS analysis, from raw data preprocessing to phylogenetic inference.
Several studies have demonstrated the broad utility of WGS for genome interrogation regarding antimicrobial resistance attributes, pathogenic potential, and tracking of the emergence and evolutionary relationships of clinically relevant variants of E. coli sourced from diverse origins23,24,25,26. WGS has enabled the identification of molecular mechanisms underlying the phenotypic resistance to antimicrobials, including those rare or complex resistance mechanisms. This is through detecting acquired ARG variants, novel mutations in drug-target genes, or promoter regions27,28. Moreover, WGS offers the potential to infer antimicrobial resistance profiles without requiring prior knowledge about the resistance phenotype of a bacterial strain29. Alternatively, WGS has allowed the characterization of the mobile genetic elements (MGEs) carrying both antimicrobial resistance and virulence features, which has driven the bacterial genome evolution of existing pathogens. For instance, the application of WGS during the investigation of the German E. coli outbreak in 2011 resulted in uncovering the unique genomic features of an apparently novel E. coli pathotype; interestingly, those outbreak strains originated from the enteroaggregative E. coli (EAEC) group, which acquired the prophage encoding the Shiga toxin from the enterohemorrhagic E. coli (EHEC) pathotype30.
This work presents a methodological adaptation of the workflow for bacterial WGS using a benchtop sequencer. Moreover, a bioinformatics pipeline is provided using web-based tools to analyze the resulting sequences and further support researchers with limited or no bioinformatics expertise. The described methods allowed elucidation of the antimicrobial resistance, virulence, and mobilome features of a pathogenic E. coli strain ACM5, isolated in 2011 from inland farmed Oreochromis spp. in Sinaloa, Mexico12.

<table><tbody><tr><td>Accublock Mini digital dry bath</td><td>Labnet</td><td>D0100</td><td>Dry bath for incubation of tubes</td></tr><tr><td>Agencourt AMPure XP</td><td>Beckman Coulter</td><td>A63881</td><td>Magnetic beads in solution for DNA library purification</td></tr><tr><td>DeNovix DS-11</td><td>DeNovix Inc.</td><td></td><td>UV-Vis spectophotometer to check the quality of the gDNA extracted</td></tr><tr><td>DNA LoBind Tubes</td><td>Eppendorf</td><td>0030108418</td><td>1.5 mL PCR tubes for DNA library pooling</td></tr><tr><td>DynaMag-2 Magnet</td><td>Invitrogen, Thermo Fisher Scientific</td><td>12321D</td><td>Magnetic microtube rack used during magnetic beads-based DNA purification</td></tr><tr><td>Gram-negative Multibac I.D.</td><td>Diagnostic reseach (Mexico)</td><td>PT-35</td><td>Commercial standard antibiotic disks for antimicrobial susceptibility testing</td></tr><tr><td>MiniSeq Mid Output Kit (300-cycles)</td><td>Illumina</td><td>FC-420-1004</td><td>Reagent cartdrige for paired-end sequencing (2x150)</td></tr><tr><td>MiniSeq System Instrument</td><td>Illumina</td><td>SY-420-1001</td><td>Benchtop sequencer used for Next-generation sequencing</td></tr><tr><td>MiniSpin centrifuge</td><td>Eppendorf</td><td>5452000816</td><td>Standard centrifuge for tubes</td></tr><tr><td>Nextera XT DNA Library Preparation Kit</td><td>Illumina</td><td>FC-131-1024</td><td>Reagents to perform DNA libraries for sequencing. Includes Box 1 and Box 2 reagents for 24 samples</td></tr><tr><td>Nextera XT Index Kit v2</td><td>Illumina</td><td>FC-131-2001, FC-131-2002, FC-131-2003, FC-131-2004</td><td>Index set A, B, C, D</td></tr><tr><td>PhiX Control v3</td><td>Illumina</td><td>FC-110-3001</td><td>DNA library control for sequencing</td></tr><tr><td>Precision waterbath</td><td>LabCare America</td><td>51221081</td><td>Water bath shaker used for bacterial culture</td></tr><tr><td>Qubit 1X dsDNA HS Assay Kit</td><td>Invitrogen, Thermo Fisher Scientific</td><td>Q33231</td><td>Reagents for fluorescence-based DNA quantification assay</td></tr><tr><td>Qubit 2.0 Fluorometer</td><td>Invitrogen, Thermo Fisher Scientific</td><td>Q32866</td><td>Fluorometer used for fluorescence assay&amp;nbsp;</td></tr><tr><td>Qubit Assay tubes</td><td>Invitrogen, Thermo Fisher Scientific</td><td>Q32856</td><td>0.5 mL PCR tubes for fluorescence-based DNA quantification assay&amp;nbsp;</td></tr><tr><td>SimpliAmp Thermal Cycler</td><td>Applied Biosystems, Thermo Fisher Scientific</td><td>A24811</td><td>Thermocycler used for DNA library amplification</td></tr><tr><td>Spectronic GENESYS 10 Vis</td><td>Thermo</td><td>335900</td><td>Spectophotometer used for bacterial suspension in antimicrobial susceptibility testing</td></tr><tr><td>ZymoBIOMICS DNA Miniprep Kit</td><td>Zymo Research Inc.</td><td>D4300</td><td>Kit for genomic DNA extraction (50 preps)</td></tr></tbody></table>

characterization of a pathogenic escherichia coli strain derived from oreochromis spp. farms using whole-genome sequencing

Aquaculture is one of the fastest-growing food-producing sectors worldwide and tilapia (Oreochromis spp.) farming constitutes the major freshwater fish variety cultured. Because aquaculture practices are susceptible to microbial contamination derived from anthropogenic sources, extensive antibiotic usage is needed, leading to aquaculture systems becoming an important source of antibiotic-resistant and pathogenic bacteria of clinical relevance such as Escherichia coli (E. coli). Here, the antimicrobial resistance, virulence, and mobilome features of a pathogenic E. coli strain, recovered from inland farmed Oreochromis spp., were elucidated through whole-genome sequencing (WGS) and in silico analysis. Antimicrobial susceptibility testing (AST) and WGS were performed. Furthermore, phylogenetic group, serotype, multilocus sequence typing (MLST), acquired antimicrobial resistance, virulence, plasmid, and prophage content were determined using diverse available web tools. The E. coli isolate only exhibited intermediate susceptibility to ampicillin and was characterized as ONT:H21-B1-ST40 strain by WGS-based typing. Although only a single antimicrobial resistance-related gene was detected [mdf(A)], several virulence-associated genes (VAGs) from the atypical enteropathogenic E. coli (aEPEC) pathotype were identified. Additionally, the cargo of plasmid replicons from large plasmid groups and 18 prophage-associated regions were detected. In conclusion, the WGS characterization of an aEPEC isolate, recovered from a fish farm in Sinaloa, Mexico, allows insights into its pathogenic potential and the possible human health risk of consuming raw aquacultural products. It is necessary to exploit next-generation sequencing (NGS) techniques for studying environmental microorganisms and to adopt a one health framework to learn how health issues originate.

The antimicrobial susceptibility was determined by the disk diffusion method and interpreted by CLSI breakpoint criteria for 12 antibiotics spanning six distinct antimicrobial classes, that is, aminoglycosides, &#946;-lactams, fluoroquinolones, nitrofurans, phenicols, and folate pathway antagonists. The E. coli ACM5 exhibited sensitivity to all antibiotics except one &#946;-lactam drug. Four &#946;-lactam drugs were tested: ampicillin, carbenicillin, cephalothin, and cefotaxime. Among these, a 14 mm inhibition halo for ampicillin was measured. Therefore, according to the CLSI interpretative categories for ampicillin (susceptible: &#8805;17 mm; intermediate: 14-16 mm; resistant: &#8804;13 mm) 32, E. coli ACM5 shows an intermediate susceptibility to ampicillin.
The E. coli ACM5 was subjected to WGS using the benchtop sequencer. Therefore, the DNA sample was prepared, multiplexed, and sequenced following the protocol described. Overall, the sequencing run held with mid-output configuration yielded 3.37 Gb of data with an error rate of 0.73%, and 89.71% of the bases scored a quality above Q3033. Particularly, the sequencing of E. coli ACM5 generated a total of 1,490,594 paired end (PE) reads. The raw sequence data from this study were deposited in the sequence read archive (SRA) database at the national center for biotechnology information (NCBI) under BioProject number PRJNA715781.
After the initial quality check and filtering, 96.8% of the reads were conserved from raw sequencing data and assembled into scaffolds with a depth of coverage of 38x. The scaffold-level genome assembly generated 83 scaffolds (&#62;300 bp), 5,272,433 bp in length and with 50.61% GC content. Genome annotation revealed 5,633 encoded features, of which 5,524 are protein-coding genes (CDSs) and 109 are RNA-related sequences (Figure 1). Additionally, genome annotation grouped CDSs into collections of functionally related proteins called subsystems, especially those playing functional roles in carbohydrates, proteins, amino acids and derivative metabolisms, and membrane transport (Figure 2).
In silico typing predicted E. coli ACM5 as an O-nontypeable (ONT) strain assigned to the ONT:H21 serotype. Additionally, it showed that it belongs to the phylogroup B1 and sequence type (ST) 40. A single acquired antimicrobial resistance determinant coding for a multidrug efflux pump of broad-spectrum was identified, that is, the mdf(A) gene (Figure 1).
Regarding virulence traits, several VAGs featured in the E. coli genome under study, including genes encoding for a heat-stable enterotoxin (astA), a serum resistance protein (iss), and the type III secretion system (T3SS) along with its secreted effector proteins (Figure 1). The bundle-forming pili (BFP) operon and the perABC gene cluster were not evidenced. Consequently, according to the predicted virulence profile, E. coli ACM5 was assigned to the aEPEC pathotype.
WGS analysis also revealed two putative large plasmids belonging to different incompatibility (Inc) groups in the E. coli genome (i.e., the IncF and IncI plasmid groups). However, both lacked ARGs and VAGs. Moreover, 18 prophage-associated regions were identified; however, in contrast to plasmids, those harbor VAGs, including the iss gene, the sitABCD operon encoding for an iron/manganese transporter, and a few T3SS effector proteins (Figure 1).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64404/64404fig01.jpg" /> 
Figure 1: Circular map of the draft genome of E. coli ACM5. Data from the outermost to the innermost circles are declared and colored as follows. Circle 1: Genome size scale in base pairs. Circles 2 and 3: Annotated CDSs transcribed on the forward (blue) and reverse (red) DNA strand, respectively. Circles 4 and 5: Ribosomal RNAs (black) and transfer RNAs (green), respectively. Circle 6: 83 scaffolds of the draft genome (gray). Circle 7: Annotated features: antimicrobial resistance determinants (red), virulence-associated genes (purple), and prophage regions (aqua). Circle 8: %GC content (black). Circle 9: GC skew. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64404/64404fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64404/64404fig02.jpg" /> 
Figure 2: Subsystem distribution of aEPEC strain ACM5. The analysis is based on RAST SEED annotation. The pie chart organizes the subsystems by cellular process, and the protein-coding genes involved in the respective cellular process are indicated in parenthesis. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64404/64404fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File 1. Description of WGS data preprocessing, software, parameter settings, and sequence analysis of the E. coli ACM5 genome. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/64404/SupplementalMethods_64404_RE.zip" target="_blank">Please click here to download this File.</a>

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Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

The feasibility of whole-genome sequencing (WGS) strategies using benchtop instruments has simplified the genome interrogation of every microbe of public health relevance in a lab setting. A methodological adaptation of the workflow for bacterial WGS is described and a bioinformatics pipeline for analysis is also presented.

Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

Aquaculture is one of the fastest-growing food-producing sectors worldwide and tilapia (Oreochromis spp.) farming constitutes the major freshwater fish ...

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Nanyang Technological University

Research

JoVE Journal

Genetics

2.1K Views.  Universidad Autónoma de Sinaloa. Whole genome sequencing, WGS, kan helpen bij het beantwoorden van belangrijke vragen op antimicrobieel resistent gebied door middel van genoomondervraging en de identificatie van moleculaire mechanismen die ten grondslag liggen aan het fenotype van antimicrobiële resistentie. World genome sequencing verbetert de exploratie van DNA, bacterieel DNA op grote schaal, waardoor de diepgaande karakterisering van microben in een enkele run wordt vergemakkelijkt, inclusief nauw verwante pathogene var...

Video: Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Yersinia ruckeri is an important pathogen of farmed salmonids worldwide, but simple tools suitable for epizootiological investigations (infection tracing, etc.) of this bacterium have been lacking. A Multi-Locus Variable-number tandem-repeat Analysis (MLVA) assay was therefore developed as an easily accessible and unambiguous tool for high-resolution genotyping of recovered isolates. For the MLVA assay presented here, DNA is extracted from cultured Y. ruckeri samples by boiling bacterial cells in water, followed by use of supernatant as template for PCR. Primer-pairs targeting ten Variable-number tandem-repeat (VNTR) loci, interspersed throughout the Y. ruckeri genome, are distributed equally amongst two five-plex PCR reactions running under identical cycling conditions. Forward primers are labelled with either of three fluorescent dyes. Following amplicon confirmation by gel electrophoresis, PCR products are diluted and subjected to capillary electrophoresis. From the resulting electropherogram profiles, peaks representing each of the VNTR loci are size-called and employed for calculating VNTR repeat counts in silico. Resulting ten-digit MLVA profiles are then&#160;used to generate Minimum spanning trees enabling epizootiological evaluation by cluster analysis. The highly portable output data, in the form of numerical MLVA profiles, can rapidly be compared across labs and placed in a spatiotemporal context. The entire procedure from cultured colony to epizootiological evaluation may be completed for up to 48 Y. ruckeri isolates within a single working day.

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

The Multi-Locus Variable-number tandem-repeat Analysis (MLVA) assay presented here enables inexpensive, robust and portable high-resolution genotyping of the fish-pathogenic bacterium Yersinia ruckeri. Starting from pure cultures, the assay employs multiplex PCR and capillary electrophoresis to produce ten-loci MLVA profiles for downstream applications.

Yersinia ruckeri is an important pathogen of farmed salmonids worldwide, but simple tools suitable for epizootiological investigations (infection tracing, ...

Characterization and Functional Prediction of Bacteria in Ovarian Tissues

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, whether ovaries contain bacteria has not yet been confirmed yet. Herein, an experiment to detect bacteria in ovarian tissues was introduced. We chose ovarian cancer patients in the cancer group and noncancerous patients in the control group. 16S rRNA gene sequencing was used to differentiate bacteria in ovarian tissues from the cancer and control groups. Furthermore, we predicted the functional composition of the identified bacteria by using BugBase and PICRUSt. This method can also be used in other viscera and tissues since many organs have been proven to harbor bacteria in recent years. The presence of bacteria in viscera and tissues may help scientists evaluate cancerous and normal tissues and may be aid in the treatment of cancer.

Immunohistochemistry staining and 16S ribosomal RNA gene (16S rRNA gene) sequencing were performed in order to discover and distinguish bacteria in cancerous and noncancerous ovarian tissues in situ. The compositional and functional differences of the bacteria were predicted by using BugBase and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt).

The theory of a "sterile" female upper reproductive tract has been encountering increasing opposition due to advancements in bacterial detection. However, ...

Cancer Research

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried out by Public Health laboratories and regulatory agencies. Large volumes of publicly available WGS data can be used to study pathogenic populations at a large scale. Recently, a freely available computational platform called ProkEvo was published to enable reproducible, automated, and scalable hierarchical-based population genomic analyses using bacterial WGS data. This implementation of ProkEvo demonstrated the importance of combining standard genotypic mapping of populations with mining of accessory genomic content for ecological inference. In particular, the work highlighted here used ProkEvo-derived outputs for population-scaled hierarchical analyses using the R programming language. The main objective was to provide a practical guide for microbiologists, ecologists, and epidemiologists by showing how to: i) use a phylogeny-guided mapping of hierarchical genotypes; ii) assess frequency distributions of genotypes as a proxy for ecological fitness; iii) determine kinship relationships and genetic diversity using specific genotypic classifications; and iv) map lineage differentiating accessory loci. To enhance reproducibility and portability, R markdown files were used to demonstrate the entire analytical approach. The example dataset contained genomic data from 2,365 isolates of the zoonotic foodborne pathogen Salmonella Newport. Phylogeny-anchored mapping of hierarchical genotypes (Serovar -&#62; BAPS1 -&#62; ST -&#62; cgMLST) revealed the population genetic structure, highlighting sequence types (STs) as the keystone differentiating genotype. Across the three most dominant lineages, ST5 and ST118 shared a common ancestor more recently than with the highly clonal ST45 phylotype. ST-based differences were further highlighted by the distribution of accessory antimicrobial resistance (AMR) loci. Lastly, a phylogeny-anchored visualization was used to combine hierarchical genotypes and AMR content to reveal the kinship structure and lineage-specific genomic signatures. Combined, this analytical approach provides some guidelines for conducting heuristic bacterial population genomic analyses using pan-genomic information.

This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory loci.

Routine and systematic use of bacterial whole-genome sequencing (WGS) is enhancing the accuracy and resolution of epidemiological investigations carried ...

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes

The development and spread of antibiotic resistance (AR) through microbiota associated with freshwater bodies is a major global health concern. In the present study, freshwater samples were collected and analyzed with respect to the total bacterial diversity and AR genes (ARGs) using both conventional culture-based techniques and a high-throughput culture-independent metagenomic approach. This paper presents a systematic protocol for the enumeration of the total and antibiotic-resistant culturable bacteria from freshwater samples and the determination of phenotypic and genotypic resistance in the culturable isolates. Further, we report the use of whole metagenomic analysis of the total metagenomic DNA extracted from the freshwater sample for the identification of the overall bacterial diversity, including non-culturable bacteria, and the identification of the total pool of different ARGs (resistome) in the water body. Following these detailed protocols, we observed a high antibiotic-resistant bacteria load in the range of 9.6 &#215; 105-1.2 &#215; 109 CFU/mL. Most isolates were resistant to the multiple tested antibiotics, including cefotaxime, ampicillin, levofloxacin, chloramphenicol, ceftriaxone, gentamicin, neomycin, trimethoprim, and ciprofloxacin, with multiple antibiotic resistance (MAR) indexes of &#8805;0.2, indicating high levels of resistance in the isolates. The 16S rRNA sequencing identified potential human pathogens, such as Klebsiella pneumoniae,&#160;and opportunistic bacteria, such as Comamonas spp., Micrococcus spp., Arthrobacter spp., and Aeromonas spp. The molecular characterization of the isolates showed the presence of various ARGs, such as blaTEM, blaCTX-M (&#946;-lactams), aadA, aac (6&#39;)-Ib (aminoglycosides), and dfr1 (trimethoprims), which was also confirmed by the whole metagenomic DNA analysis. A high prevalence of other ARGs encoding for antibiotic efflux pumps-mtrA, macB, mdtA, acrD, &#946;-lactamases-SMB-1, VIM-20, ccrA, ampC, blaZ, the&#160;chloramphenicol acetyltransferase gene catB10, and&#160;the rifampicin resistance gene rphB-was also detected in the metagenomic DNA. With the help of the protocols discussed in this study, we confirmed the presence of waterborne MAR bacteria with diverse AR phenotypic and genotypic traits. Thus, whole metagenomic DNA analysis can be used as a complementary technique to conventional culture-based techniques to determine the overall AR status of a water body.

Here, we present a detailed protocol for the isolation and identification of antibiotic-resistant bacteria from water and the molecular characterization of their antibiotic resistance genes (ARGs). The use of culture-based and non-culture-based (metagenomic analysis) techniques provides complete information about the total bacterial diversity and the total pool of different ARGs present in freshwaters from Mumbai, India.

The development and spread of antibiotic resistance (AR) through microbiota associated with freshwater bodies is a major global health concern. In the ...

Environment

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.

Here, we present phenomic approaches for the functional characterization of putative phage genes. Techniques include a developed assay capable of monitoring host anabolic metabolism, the Multi-phenotype Assay Plates (MAPs), in addition to the established method of metabolomics, capable of measuring effects to catabolic metabolism.

Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage ...

Immunology and Infection

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain using antibiotic induction, and identified by MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. The immunity protein of colicin E3 (Im3) and the immunity protein of bacteriocin (Im-Bac) were identified from prominent b- and/or y-type fragment ions generated by the polypeptide backbone cleavage (PBC) on the C-terminal side of aspartic acid, glutamic acid, and asparagine residues by the aspartic acid effect fragmentation mechanism. The software rapidly scans in silico protein sequences derived from the whole genome sequencing of the bacterial strain. The software also iteratively removes amino acid residues of a protein sequence in the event that the mature protein sequence is truncated. A single protein sequence possessed mass and fragment ions consistent with those detected for each immunity protein. The candidate sequence was then manually inspected to confirm that all detected fragment ions could be assigned. The N-terminal methionine of Im3 was post-translationally removed, whereas Im-Bac had the complete sequence. In addition, we found that only two or three non-complementary fragment ions formed by PBC are necessary to identify the correct protein sequence. Finally, a promoter (SOS box) was identified upstream of the antibacterial and immunity genes in a plasmid genome of the bacterial strain.

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Here we present a protocol for the rapid identification of proteins produced by genomically sequenced pathogenic bacteria using MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. Metastable protein ions fragment because of the aspartic acid effect and this specificity is exploited for protein identification.

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain ...

Chemistry

A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.

Fecal-oral transmission of acute gastroenteritis occurs from time to time, especially when people who handled food and water are infected by Salmonella spp./Shigella spp. The gold standard method for the detection of Salmonella spp./Shigella spp. is direct culture but this is labor-intensive and time-consuming. Here, we describe a high-throughput platform for Salmonella spp./Shigella spp. screening, using real-time polymerase chain reaction (PCR) combined with guided culture. There are two major stages: real-time PCR and the guided culture. For the first stage (real-time PCR), we explain each step of the method: sample collection, pre-enrichment, DNA extraction and real-time PCR. If the real-time PCR result is positive, then the second stage (guided culture) is performed: selective culture, biochemical identification and serological characterization. We also illustrate representative results generated from it. The protocol described here would be a valuable platform for the rapid, specific, sensitive and high-throughput screening of Salmonella spp./Shigella spp.

A High-throughput Platform for the Screening of Salmonella spp./Shigella spp.

Salmonella spp./Shigella spp. are common pathogens attributed to diarrhea. Here, we describe a high-throughput platform for the screening of Salmonella spp./Shigella spp. using real-time PCR combined with guided culture.

Fecal-oral transmission of acute gastroenteritis occurs from time to time, especially when people who handled food and water are infected by Salmonella ...

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)1. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7, but GI information remains sparse for prokaryotes8, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10.
Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection11) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity9, 12, 13) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2 as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9.

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Systematic, large-scale synthetic genetic (gene-gene or epistasis) interaction screens can be used to explore genetic redundancy and pathway cross-talk. Here, we describe a high-throughput quantitative synthetic genetic array screening technology, termed eSGA that we developed for elucidating epistatic relationships and exploring genetic interaction networks in Escherichia coli.

Phenotypes  are determined by a complex series of physical (e.g. protein-protein) and  functional (e.g. gene-gene or genetic) interactions (GI)1. While ...

Biology

Detection of Live Escherichia coli O157:H7 Cells by PMA-qPCR

A unique open reading frame (ORF) Z3276 was identified as a specific genetic marker for E. coli O157:H7. A qPCR assay was developed for detection of E. coli O157:H7 by targeting ORF Z3276. With this assay, we can detect as low as a few copies of the genome of DNA of E. coli O157:H7. The sensitivity and specificity of the assay were confirmed by intensive validation tests with a large number of E. coli O157:H7 strains (n = 369) and non-O157 strains (n = 112). Furthermore, we have combined propidium monoazide (PMA) procedure with the newly developed qPCR protocol for selective detection of live cells from dead cells. Amplification of DNA from PMA-treated dead cells was almost completely inhibited in contrast to virtually unaffected amplification of DNA from PMA-treated live cells. Additionally, the protocol has been modified and adapted to a 96-well plate format for an easy and consistent handling of a large number of samples. This method is expected to have an impact on accurate microbiological and epidemiological monitoring of food safety and environmental source.

Detection of Live Escherichia coli O157:H7 Cells by PMA-qPCR

A qPCR assay was developed for detection of Escherichia coli O157:H7 targeting a unique genetic marker, Z3276. The qPCR was combined with propidium monoazide (PMA) treatment for live cell detection. This protocol has been modified and adapted to a 96-well plate format for easy and consistent handling of numerous samples

A unique open reading frame (ORF) Z3276 was identified as a specific genetic marker for E. coli O157:H7. A qPCR assay was developed for detection of E. ...

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. Deviations from what is considered a healthy microbiota composition (dysbiosis) may impair vital functions leading to pathologic conditions. Recent and ongoing research efforts have been directed toward the characterization of associations between microbial composition and human health and disease.
Advances in high-throughput sequencing technologies enable characterization of the gut microbial composition. These methods include 16S rRNA-amplicon sequencing and shotgun sequencing. 16S rRNA-amplicon sequencing is used to profile taxonomical composition, while shotgun sequencing provides additional information about gene predictions and functional annotation. An advantage in using a targeted sequencing method of the 16S rRNA gene variable region is its substantially lower cost compared to shotgun sequencing. Sequence differences in the 16S rRNA gene are used as a microbial fingerprint to identify and quantify different taxa within an individual sample.
Major international efforts have enlisted standards for 16S rRNA-amplicon sequencing. However, several studies report a common source of variation caused by batch effect. To minimize this effect, uniformed protocols for sample collection, processing, and sequencing must be implemented. This protocol proposes the integration of broadly used protocols starting from fecal sample collection to data analyses. This protocol includes a column-free, direct-PCR approach that enables simultaneous handling and DNA extraction of large numbers of fecal samples, along with PCR amplification of the V4 region. In addition, the protocol describes the analysis pipeline and provides a script using the latest version of QIIME (QIIME 2 version 2017.7.0 and DADA2). This step-by-step protocol is aimed to guide those interested in initiating the use of 16S rRNA-amplicon sequencing in a robust, reproductive, easy to use, detailed way.

This manuscript describes a detailed standardized protocol of high-throughput 16S rRNA-amplicon sequencing. The protocol introduces an integrated, uniformed, feasible, and inexpensive protocol starting from fecal sample collection through data analyses. This protocol enables analysis of large numbers of samples with rigorous standards and several controls.

The human intestinal microbiome plays a central role in protecting cells from injury, in processing energy and nutrients, and in promoting immunity. ...

Characterization of a Pathogenic Escherichia coli Strain Derived from Oreochromis spp. Farms Using Whole-Genome Sequencing

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