We thank Dr. Moutusee Jubaida Islam and Dr. Luke Joyce for their contributions to this protocol. We would also like to recognize the University of Texas at Dallas Genome Center for their feedback and support. This work was funded by the Welch Foundation, award number AT-2030-20200401 to N.J.D., by the National Institutes of Health, award number R01AI116610 to K.P., and by the Felecia and John Cain Chair in Women&#39;s Health, held by P.E.Z.

The authors have nothing to disclose.

The comprehensive hybrid genome assembly protocol described here offers a streamlined approach for the successful culturing of diverse urinary microbiota and uropathogens, and the complete assembly of their genomes. Successful WGS of bacterial genomes begins with the isolation of diverse and sometimes fastidious microbes in order to extract their genomic DNA. To date, existing urine culture protocols either lack the necessary sensitivity to detect many urinary species or involve lengthy and extensive approaches that require extended time and resources11. The Modified Enhanced Urine Culture approach described offers a simplified yet comprehensive protocol for the successful isolation of bacteria belonging to 17 common urinary genera, including potentially pathogenic or beneficial commensal species, and both facultative and obligate aerobic or anaerobic bacteria. This in turn provides the necessary starting material for accurate sequencing and assembly of bacterial genomes and for critical phenotypic experiments, which contribute to the understanding of urinary health and disease. Furthermore, this modified culture approach provides for a more defined clinical diagnosis of viable microorganisms found in urine specimens and allows for their biobanking for future genomic studies. However, this protocol is not without limitations. It may require long incubation times depending on the organism as well as use of resources such as a hypoxia chamber or controlled incubators that may not be readily available. The use of anaerobic GasPaks offers an alternative solution but these are costly and do not always produce a sustained and controlled environment. Finally, culture bias as well as sample diversity may allow for particular organisms and uropathogens to outcompete fastidious bacteria. Despite these limitations, a culture of diverse urinary bacteria is made possible by this approach.
Genomic sequencing has gained popularity with the advancement of Next Generation Sequencing technologies which tremendously increased both the yield and accuracy of sequencing data14,15. Coupled with the development of algorithms for data processing and de novo assembly, complete genome sequences are at the fingertips of novice and expert scientists alike15,45. Knowledge of overall genome organization provided by complete genomes offers important evolutionary and biological insights, including gene duplication, gene loss, and horizontal gene transfer14. Additionally, genes important to antimicrobial resistance and virulence are often localized on mobile elements, which are typically not resolved in draft genome assemblies15,16.
The protocol herein follows a hybrid approach for the combination of sequencing data from short-read and long-read platforms to generate complete genome assemblies. While focused on urinary bacterial genomes, this procedure may be adapted to diverse bacteria from various isolation sources. Critical steps in this approach include following adequate sterile technique and utilizing appropriate media and culture conditions for the isolation of pure urinary bacteria. Furthermore, the extraction of intact, high-yield gDNA is essential for generating sequencing data free of contaminating reads that may hamper assembly success. Subsequent library preparation protocols are critical for the generation of quality reads of sufficient length and depth. Therefore, it is of utter importance to handle gDNA with care during library preparation for long-read sequencing in particular, as this technology&#39;s greatest benefit is the generation of long reads with no theoretical upper length limit. Also outlined are sections for the appropriate quality control (QC) of sequencing reads that eliminates noisy data and improves assembly outcome.
Despite successful DNA isolation, library preparation, and sequencing, the nature of genomic architecture of some species may still provide an obstacle for the generation of a closed genome assembly45,46. Repetitive sequences often complicate assembly computation and despite long read data, these regions may be resolved with low confidence, or not at all. Long reads thus have to be on average longer than the largest repeat region in the genome or coverage must be high (&#62;100x)19. Some genomes may remain incomplete and require manual approaches for completion. Nevertheless, hybrid assembled incomplete genomes are typically composed of fewer contigs than short-read draft genomes. Adjusting default parameters of the assembly algorithm or following more stringent cutoffs for read QC may help. Alternatively, one suggested approach is to map long reads to the incomplete regions in search of evidence for the most likely assembly path, and then confirm the path utilizing PCR and Sanger sequencing of the amplified region. Mapping reads using Minimap2 is suggested and Bandage offers a useful tool for the visualization of mapped reads along assembled contigs providing evidence for contig linkage47.
An additional challenge to generating complete genomes lies in familiarity and comfort with command-line tools. Many bioinformatic tools are developed to offer computational opportunities to any user; however, their utilization relies on an understanding with the basics of UNIX and programming. This protocol aims to provide sufficiently detailed instructions to enable individuals without prior command-line experience to generate closed genome assemblies and annotate them.

The urinary microbiome is an emerging area of research that has shattered a decades long misconception that the urinary tract is sterile in healthy individuals. Members of the urinary microbiota may serve to balance the urinary environment and prevent urinary tract infection (UTI)1,2. Uropathogenic bacteria invade the urinary tract and employ diverse virulence mechanisms to displace the resident microbiota, colonize the urothelium, evade immune responses and counteract environmental pressures3,4. Urine is a relatively nutrient-limited medium characterized by high osmolarity, limited nitrogen and carbohydrate availability, low oxygenation, and low pH5,6,7. Urine is also considered to be antimicrobial, composed of high concentrations of inhibitory urea and antimicrobial peptides such as the human cathelicidin LL-378. Investigating mechanisms employed by both resident bacteria and uropathogens to colonize the urinary tract is critical to further understanding urinary tract health and developing new strategies for UTI treatment. Furthermore, as the failure of front-line antimicrobial therapies becomes more common, it is increasingly important to monitor the dissemination of mobile genetic elements carrying antimicrobial resistance determinants within populations of urinary bacteria9,10.
To investigate genotypes and phenotypes of urinary bacteria, their successful culture and subsequent whole genome sequencing (WGS) is imperative. Culture-dependent methods are necessary to detect and identify viable microbes in urine samples11. Standard clinical urine culture involves plating urine onto 5% sheep blood agar (BAP) and MacConkey agar and incubating aerobically at 35 &#176;C for 24 h12. However, with a detection threshold of &#8805;105 CFU/mL13, many members of the urinary microbiota are not reported by this method. Improved culturing techniques such as Enhanced Quantitative Urine Culture (EQUC)11 employ various combinations of different urine volumes, incubation times, culture media, and atmospheric conditions to identify microbes commonly missed by standard urine culture. Described in this protocol is a modified version of EQUC, termed here Modified Enhanced Urine Culture protocol, that enables culturing of diverse urinary bacteria and uropathogens using selective media and optimal atmospheric conditions but is not inherently quantitative. The successful isolation of urinary bacteria enables the extraction of genomic DNA (gDNA) for downstream WGS and genome assembly.
Genome assemblies, complete assemblies in particular, enable the discovery of genetic factors that may contribute to colonization, niche maintenance, and virulence among both resident microbiota and uropathogenic bacteria. Draft genome assemblies contain a diverse number of contiguous sequences (contigs) that may contain sequencing errors and lack orientation information. In a complete genome assembly, both the orientation and the accuracy of every base pair have been verified14. Furthermore, obtaining complete genome sequences provides insight into genome structure, genetic diversity, and mobile genetic elements15. Short reads alone may identify the presence or absence of important genes but may not pinpoint their genomic context16. With enabling long-read sequencing technologies such as Oxford Nanopore and PacBio, generating&#160;closed de novo assemblies of bacterial genomes no longer requires strenuous methods such as manual closing of de novo assemblies by multiplex PCR17,18. The combination of Next Generation short-read sequencing and Nanopore long-read sequencing technologies allows the facile generation of accurate, complete, and closed bacterial genome assemblies at relatively low costs19. Short-read sequencing produces accurate yet fragmented genome assemblies generally consisting of an average of 40-100 contigs, while Nanopore sequencing generates long reads of about 5-100 kb in length that are less accurate but can serve as scaffolds to join contigs and resolve genomic synteny. Hybrid approaches utilizing both short-read and long-read technologies can produce accurate and complete bacterial genomes19.
Described here is a comprehensive protocol for the isolation and identification of bacteria from human urine, genomic DNA extraction, sequencing, and complete genome assembly using a hybrid assembly approach. This protocol provides a special emphasis on the steps necessary to properly modify reads generated by short-read and long-read sequencing for the accurate assembly of a closed bacterial chromosome and extrachromosomal elements such as plasmids.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Equipment:</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bioanalyzer 2100</td>
			<td>Agilent</td>
			<td>G29398A</td>
			<td>Optional but recommended</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Eppendorf</td>
			<td>--</td>
			<td>Any centrifuge for spinning conicals and microcentrifuge tubes (e.g. Models 5810R/5424R)</td>
		</tr>
		<tr>
			<td>Electrophoresis</td>
			<td>BioRad Laboratories</td>
			<td>1645070</td>
			<td></td>
		</tr>
		<tr>
			<td>Gel Imaging System</td>
			<td>BioRad Laboratories</td>
			<td></td>
			<td>ChemiDoc models</td>
		</tr>
		<tr>
			<td>Incubator</td>
			<td>ThermoFisher Scientific</td>
			<td>--</td>
			<td>Any CO2 Incubator (e.g. Thermo Forma model 3110)</td>
		</tr>
		<tr>
			<td>Magnetic Rack</td>
			<td>New England BioLabs</td>
			<td>S15095</td>
			<td>12-tube rack</td>
		</tr>
		<tr>
			<td>MinION</td>
			<td>Oxford Nanopore Technologies</td>
			<td>--</td>
			<td></td>
		</tr>
		<tr>
			<td>Nanodrop</td>
			<td>ThermoFisher Scientific</td>
			<td>ND-ONE-W</td>
			<td></td>
		</tr>
		<tr>
			<td>NextSeq 500</td>
			<td>Illumina</td>
			<td>SY-415-1002</td>
			<td>Other Illumina models are acceptable</td>
		</tr>
		<tr>
			<td>Plate Reader</td>
			<td>BioTek</td>
			<td>--</td>
			<td>Synergy H1</td>
		</tr>
		<tr>
			<td>Qubit fluorometer</td>
			<td>ThermoFisher Scientific</td>
			<td>Q33238</td>
			<td></td>
		</tr>
		<tr>
			<td>Rotator</td>
			<td>Benchmark Scientific</td>
			<td>H2024</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermocycler</td>
			<td>ThermoFisher Scientific</td>
			<td>--</td>
			<td>Any thermocycler for PCR reactions (e.g. ProFlex PCR system)</td>
		</tr>
		<tr>
			<td>Materials:</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>10X Phosphate Buffered Saline (PBS)</td>
			<td>Fisher Scientific</td>
			<td>BP3991</td>
			<td></td>
		</tr>
		<tr>
			<td>10X TBE buffer</td>
			<td>--</td>
			<td>--</td>
			<td>1M Tris,1M Boric Acid,0.2M EDTA (pH 8.0)</td>
		</tr>
		<tr>
			<td>1429R primer</td>
			<td>Sigma Aldrich (Custom oligos)</td>
			<td>--</td>
			<td>GGTTACCTTGTTACGACTT</td>
		</tr>
		<tr>
			<td>1kb Ladder</td>
			<td>VWR</td>
			<td>101228-494</td>
			<td></td>
		</tr>
		<tr>
			<td>1M Tris-Cl (pH 7.5)</td>
			<td>ThermoFisher Scientific</td>
			<td>15567027</td>
			<td></td>
		</tr>
		<tr>
			<td>6x Loading dye</td>
			<td>Fisher Scientific</td>
			<td>NC0783588</td>
			<td></td>
		</tr>
		<tr>
			<td>8F primer</td>
			<td>Sigma Aldrich (Custom oligos)</td>
			<td>--</td>
			<td>AGAGTTTGATCCTGGCTCAG</td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>Fisher Scientific</td>
			<td>BP1423-2</td>
			<td></td>
		</tr>
		<tr>
			<td>Agarose</td>
			<td>BioRad Laboratories</td>
			<td>63001</td>
			<td></td>
		</tr>
		<tr>
			<td>AMPure XP Beads</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>Anaerobe Pouch System - GasPak EZ</td>
			<td>BD Diagnostic Systems</td>
			<td>B260683</td>
			<td></td>
		</tr>
		<tr>
			<td>Boric Acid</td>
			<td>Fisher Scientific</td>
			<td>A73-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Brain Heart Infusion Broth</td>
			<td>BD Diagnostic Systems</td>
			<td>212304</td>
			<td></td>
		</tr>
		<tr>
			<td>CDC Anaerobe 5% Sheep Blood Agar</td>
			<td>BD Diagnostic Systems</td>
			<td>L007357</td>
			<td></td>
		</tr>
		<tr>
			<td>CHROMagar Orientation</td>
			<td>BD Diagnostic Systems</td>
			<td>PA-257481.04</td>
			<td></td>
		</tr>
		<tr>
			<td>DNeasy Blood &#38; Tissue</td>
			<td>QIAGEN</td>
			<td>69504</td>
			<td></td>
		</tr>
		<tr>
			<td>DreamTaq Master Mix</td>
			<td>ThermoFisher Scientific</td>
			<td>K1081</td>
			<td></td>
		</tr>
		<tr>
			<td>Dry Anaerobic Indicator Strips</td>
			<td>BD Diagnostic Systems</td>
			<td>271051</td>
			<td></td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>Fisher Scientific</td>
			<td>S311-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol 200 Proof</td>
			<td>Sigma Aldrich</td>
			<td>E7023</td>
			<td>For molecular biology</td>
		</tr>
		<tr>
			<td>Ethidium Bromide</td>
			<td>ThermoFisher Scientific</td>
			<td>BP130210</td>
			<td></td>
		</tr>
		<tr>
			<td>Flow cell priming kit</td>
			<td>Oxford Nanopore Technologies</td>
			<td>EXP-FLP002</td>
			<td></td>
		</tr>
		<tr>
			<td>Flow cell wash kit</td>
			<td>Oxford Nanopore Technologies</td>
			<td>EXP-WSH003</td>
			<td></td>
		</tr>
		<tr>
			<td>Gel Extraction Miniprep Kit</td>
			<td>BioBasic</td>
			<td>BS654</td>
			<td></td>
		</tr>
		<tr>
			<td>Ligation sequencing kit</td>
			<td>Oxford Nanopore Technologies</td>
			<td>SQK-LSK109</td>
			<td></td>
		</tr>
		<tr>
			<td>Lysozyme</td>
			<td>Research Products International Corp</td>
			<td>L381005.05</td>
			<td></td>
		</tr>
		<tr>
			<td>Mutanolysin</td>
			<td>Sigma Aldrich</td>
			<td>M9901-5KU</td>
			<td></td>
		</tr>
		<tr>
			<td>Native barcoding expansion 1-12</td>
			<td>Oxford Nanopore Technologies</td>
			<td>EXP-NBD104</td>
			<td></td>
		</tr>
		<tr>
			<td>NEB Blunt/TA Ligase Master Mix</td>
			<td>New England BioLabs</td>
			<td>M0367L</td>
			<td></td>
		</tr>
		<tr>
			<td>NEBNext FFPE DNA Repair Mix</td>
			<td>New England BioLabs</td>
			<td>M6630L</td>
			<td></td>
		</tr>
		<tr>
			<td>NEBNext quick ligation buffer</td>
			<td>New England BioLabs</td>
			<td>B6058S</td>
			<td></td>
		</tr>
		<tr>
			<td>NEBNext Ultra II End repair / dA-tailing module</td>
			<td>New England BioLabs</td>
			<td>E7546L</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera DNA CD Indexes</td>
			<td>Illumina</td>
			<td>20018708</td>
			<td></td>
		</tr>
		<tr>
			<td>Nextera DNA Flex Library Prep - (M) Tagmentation</td>
			<td>Illumina</td>
			<td>20018705</td>
			<td></td>
		</tr>
		<tr>
			<td>Nuclease-free water</td>
			<td>Sigma Aldrich</td>
			<td>W4502</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit 1X dsDNA HS Assay Kit</td>
			<td>ThermoFisher Scientific</td>
			<td>Q33230</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit Assay Tubes</td>
			<td>ThermoFisher Scientific</td>
			<td>Q32856</td>
			<td></td>
		</tr>
		<tr>
			<td>Quick T4 DNA Ligase</td>
			<td>New England BioLabs</td>
			<td>E6056L</td>
			<td></td>
		</tr>
		<tr>
			<td>R9 Flow cell</td>
			<td>Oxford Nanopore Technologies</td>
			<td>FLO-MIN106D</td>
			<td></td>
		</tr>
		<tr>
			<td>RNase A</td>
			<td>ThermoFisher Scientific</td>
			<td>EN0531</td>
			<td></td>
		</tr>
		<tr>
			<td>Sheep Blood</td>
			<td>Hemostat Laboratories</td>
			<td>DS13250</td>
			<td></td>
		</tr>
		<tr>
			<td>TE buffer</td>
			<td>--</td>
			<td>--</td>
			<td>10mM Tris, 1mM EDTA (pH 8.0)</td>
		</tr>
		<tr>
			<td>Triton X-100</td>
			<td>Sigma Aldrich</td>
			<td>T8787</td>
			<td></td>
		</tr>
		<tr>
			<td>Tryptic Soy Broth</td>
			<td>BD Diagnostic Systems</td>
			<td>211825</td>
			<td></td>
		</tr>
		<tr>
			<td>Software &#38; Bioinformatic Tools:</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bandage</td>
			<td>--</td>
			<td>--</td>
			<td>https://rrwick.github.io/Bandage/</td>
		</tr>
		<tr>
			<td>Center for Genomic Epidemiology</td>
			<td>--</td>
			<td>--</td>
			<td>http://www.genomicepidemiology.org/</td>
		</tr>
		<tr>
			<td>CLC Genomics Workbench 12</td>
			<td>QIAGEN</td>
			<td>--</td>
			<td></td>
		</tr>
		<tr>
			<td>CRISPRcasFinder</td>
			<td>--</td>
			<td>--</td>
			<td>https://crisprcas.i2bc.paris-saclay.fr/</td>
		</tr>
		<tr>
			<td>FastQC</td>
			<td>--</td>
			<td>--</td>
			<td>https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td>
		</tr>
		<tr>
			<td>Geneious Prime</td>
			<td>Geneious</td>
			<td>--</td>
			<td></td>
		</tr>
		<tr>
			<td>gVolante (BUSCO)</td>
			<td>--</td>
			<td>--</td>
			<td>https://gvolante.riken.jp/</td>
		</tr>
		<tr>
			<td>Kbase Prokka Wrapper</td>
			<td>--</td>
			<td>--</td>
			<td>https://kbase.us/applist/apps/ProkkaAnnotation/annotate_contigs/release</td>
		</tr>
		<tr>
			<td>Minimap2</td>
			<td>--</td>
			<td>--</td>
			<td>https://github.com/lh3/minimap2</td>
		</tr>
		<tr>
			<td>MinKNOW</td>
			<td>Oxford Nanopore Technologies</td>
			<td>--</td>
			<td></td>
		</tr>
		<tr>
			<td>NanoFilt</td>
			<td>--</td>
			<td>--</td>
			<td>https://github.com/wdecoster/nanofilt</td>
		</tr>
		<tr>
			<td>NanoStat</td>
			<td>--</td>
			<td>--</td>
			<td>https://github.com/wdecoster/nanostat</td>
		</tr>
		<tr>
			<td>PHASTER</td>
			<td>--</td>
			<td>--</td>
			<td>https://phaster.ca/</td>
		</tr>
		<tr>
			<td>Prokka</td>
			<td>--</td>
			<td>--</td>
			<td>https://github.com/tseemann/prokka</td>
		</tr>
		<tr>
			<td>QUAST</td>
			<td>--</td>
			<td>--</td>
			<td>http://quast.sourceforge.net/quast</td>
		</tr>
		<tr>
			<td>Trim Galore</td>
			<td>--</td>
			<td>--</td>
			<td>https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/</td>
		</tr>
		<tr>
			<td>Trimmomatic</td>
			<td>--</td>
			<td>--</td>
			<td>http://www.usadellab.org/cms/?page=trimmomatic</td>
		</tr>
		<tr>
			<td>Unicycler</td>
			<td>--</td>
			<td>--</td>
			<td>https://github.com/rrwick/Unicycler#necessary-read-length</td>
		</tr>
	</tbody>
</table>

hybrid de novo genome assembly for the generation of complete genomes of urinary bacteria using short- and long-read sequencing technologies

Complete genome sequences provide valuable data for the understanding of genetic diversity and unique colonization factors of urinary microbes. These data may include mobile genetic elements, such as plasmids and extrachromosomal phage, that contribute to the dissemination of antimicrobial resistance and further complicate treatment of urinary tract infection (UTI). In addition to providing fine resolution of genome structure, complete, closed genomes allow for the detailed comparative genomics and evolutionary analyses. The generation of complete genomes de novo has long been a challenging task due to limitations of available sequencing technology. Paired-end Next Generation Sequencing (NGS) produces high quality short reads often resulting in accurate but fragmented genome assemblies. On the contrary, Nanopore sequencing provides long reads of lower quality normally leading to error-prone complete assemblies. Such errors may hamper genome-wide association studies or provide misleading variant analysis results. Therefore, hybrid approaches combining both short and long reads have emerged as reliable methods to achieve highly accurate closed bacterial genomes. Reported herein is a comprehensive method for the culture of diverse urinary bacteria, species identification by 16S rRNA gene sequencing, extraction of genomic DNA (gDNA), and generation of short and long reads by NGS and Nanopore platforms, respectively. Additionally, this method describes a bioinformatic pipeline of quality control, assembly, and gene prediction algorithms for the generation of annotated complete genome sequences. Combination of bioinformatic tools enables the selection of high quality read data for hybrid genome assembly and downstream analysis. The streamlined approach for the hybrid de novo genome assembly described in this protocol may be adapted for the use in any culturable bacteria.

This protocol has been optimized for the culture and sequencing of urinary bacteria belonging to the genera listed in Figure 1. Not all urinary bacteria are culturable by this method. Culture media and conditions are specified by the genus in Figure 1. Exemplary gel electrophoresis assessments of gDNA integrity are depicted in Figure 2. An overview of the bioinformatics pipeline for sequencing read processing, genome assembly, and annotation is described in Figure 3. A guide for computational directory structure is provided in Figure 4 to both simplify protocol understanding and provide framework for successful organization. Furthermore, included are representative complete genomes of two Klebsiella spp., K. pneumoniae and K. oxytoca, that were generated by this protocol. A representation of these assemblies is provided in Figure 5 and also includes an additional incomplete example K. pneumoniae genome. A detailed overview of each fully annotated complete genome is shown in Figure 6. Finally, a summary of sequencing read statistics is provided in Table 1 to offer a broad understanding of raw and trimmed data sufficient for the generation of high-quality closed genome assemblies. Additionally, key parameters of the two representative complete Klebsiella spp. genomes are listed. Genomes and raw data were deposited in Genbank under the BioProject PRJNA683049.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62872/62872fig01.jpg" /> 
Figure 1: Modified enhanced urine culture of diverse urinary genera. Chart for the agar and liquid broth that may be used to culture diverse urinary genera. All culturing is suggested to be performed at 35 &#176;C as described in subsection 1.1. Circles represent media appropriate for culturing a particular genus, colors were arbitrarily selected to distinguish one media type from another. CDC-AN BAP (red), CDC Anaerobe Sheep Blood Agar; 5% Sheep-BAP (orange), Sheep Blood Agar; BHI (green), Brain Heart Infusion; TSB (yellow), Tryptic Soy Broth; CHROMagar Orientation (blue). aGardnerella vaginalis should be cultured on HBT Bilayer G. vaginalis Selective agar in microaerophilic atmosphere and under special broth culture requirements44. bLactobacillus iners should be cultured on 5% Rabbit-BAP plates and NYCIII broth in microaerophilic atmosphere. cLactobacillus spp. may be cultured on MRS in microaerophilic conditions. <a href="https://www.jove.com/files/ftp_upload/62872/62872fig01large.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/62872/62872fig02.jpg" /> 
Figure 2: Genomic DNA extraction agarose gel images. Representative gel images depicting gDNA extraction outcomes. (A) Lane 1: 1 kb ladder, Lane 2: intact gDNA representing successful extraction, Lane 3: smearing indicating fragmented gDNA. (B) Lane 1: 1 kb ladder, Lanes 2 &#38; 3: rRNA contamination denoted by two bands between 1.5 kb and 3 kb. <a href="https://www.jove.com/files/ftp_upload/62872/62872fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62872/62872fig03.jpg" /> 
Figure 3: Hybrid genome assembly workflow. Schematic of steps from read quality control and pre-processing to assembly annotation. Read trimming removes ambiguous and low-quality reads. Q-score and length parameters are indicated and represent the reads that are retained. Assembly utilizes both short and long reads to generate a hybrid de novo genome assembly. Assembly quality is evaluated based on completeness and correctness using specified tools and parameters. The final genome assembly is annotated for all genes and specific loci of interest. <a href="https://www.jove.com/files/ftp_upload/62872/62872fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62872/62872fig4v2.jpg" /> 
Figure 4: Bioinformatics directory structure guide. A schematic of recommended directory and file organization for the processing of short and long reads, hybrid assembly, and genome annotation and QC. Key command-line data processing steps are highlighted next to corresponding files and directories. Eliciting commands and flags (bold), input files (blue), output files or directories (red), user input such as file naming convention (magenta). <a href="https://www.jove.com/files/ftp_upload/62872/62872fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62872/62872fig05.jpg" /> 
Figure 5: Genome assembly graphs by Bandage. Representative complete genome assembly graphs of (A) Klebsiella oxytoca KoPF10 and (B) Klebsiella pneumoniae KpPF25 and incomplete genome assembly of (C) Klebsiella pneumoniae KpPF46. The complete genome of KoPF10 demonstrates a single closed chromosome and the complete genome of KpPF25 consists of a closed chromosome and five closed plasmids. The incomplete chromosome of KpPF46 consists of two interconnected contigs. Unicycler hybrid de novo assembly generates an assembly graph that is visualized by Bandage. The assembly graph provides a simplistic schematic of the genome, indicating closed chromosome or plasmids by a linker connecting two ends of a single contig. The presence of more than one interconnected contig indicates incomplete assembly. Contig size and depth can be noted in Bandage as well. <a href="https://www.jove.com/files/ftp_upload/62872/62872fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62872/62872fig06.jpg" /> 
Figure 6: Complete genome maps of annotated hybrid assemblies. Assembly maps generated by Geneious Prime for the complete genome of (A) K. oxytoca KoPF10 and (B) K. pneumoniae KpPF25 showing annotated genes denoted by colored arrows along plasmid backbones. Chromosomes only show rRNA and tRNA genes for simplicity. Genome annotations were performed using Prokka as indicated in section 10 of this protocol. <a href="https://www.jove.com/files/ftp_upload/62872/62872fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" class="xfigimg" src="/files/ftp_upload/62872/62872table1.jpg" /> 
Table 1: Representative Klebsiella spp. complete assembly characteristics. Assembly parameters of K. oxytoca strain KoPF10 and K. pneumoniae strain KpPF25. Accession numbers for the deposited data on NCBI are provided. Number of reads both prior to and after trimming are specified for both sequencing technologies. N50 is provided for long reads only since short reads are of a controlled length. Plasmid replicon predicted using PlasmidFinder v2.1 Enteroebacteriaceae database with parameters set to 80% identity and 60% length.&#160;a MLST, Multilocus Sequence Type. b CDS, Coding Sequences. c Plasmid replicon predicted using PlasmidFinder v2.1 Enterobacteriaceae database with parameters set to 80% identity and 60% length. d Oxford Nanopore Technologies (ONT) deposited read data. e Illumina deposited read data.&#160;<a href="https://www.jove.com/files/ftp_upload/62872/Table 1_Sharon_JoVE_Revised.xlsx" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies at JoVE.com

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

This protocol details a comprehensive approach for the culturing, sequencing, and de novo hybrid genome assembly of urinary bacteria. It provides a reproducible procedure for the generation of complete, circular genome sequences useful in studying both chromosomal and extrachromosomal genetic elements contributing to urinary colonization, pathogenesis, and antimicrobial resistance dissemination.

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Complete genome sequences provide valuable data for the understanding of genetic diversity and unique colonization factors of urinary microbes. These data ...

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JoVE Journal

Genetics

4.8K Views.  University of Texas at Dallas. This protocol details a comprehensive approach for the culturing, sequencing, and de novo hybrid genome assembly of urinary bacteria. It provides a reproducible procedure for the generation of complete, circular genome sequences useful in studying both chromosomal and extrachromosomal genetic elements contributing to urinary colonization, pathogenesis, and antimicrobial resistance dissemination.

Video: Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) refers to the experimental situation in which evolution is observed using living organisms under controlled conditions and stressors; organisms are thereby artificially forced to make evolutionary changes. Microorganisms are subject to a variety of stressors in the environment and are capable of regulating certain stress-inducible proteins to increase their chances of survival. Naturally occurring spontaneous mutations bring about changes in a microorganism's genome that affect its chances of survival. Long-term exposure to chemostat culture provokes an accumulation of spontaneous mutations and renders the most adaptable strain dominant. Compared to the colony transfer and serial transfer methods, chemostat culture entails the highest number of cell divisions and, therefore, the highest number of diverse populations. Although chemostat culture for ALE requires more complicated culture devices, it is less labor intensive once the operation begins. Comparative genomic and transcriptome analyses of the adapted strain provide evolutionary clues as to how the stressors contribute to mutations that overcome the stress. The goal of the current paper is to bring about accelerated evolution of microorganisms under controlled laboratory conditions.

Here, we present a protocol to obtain adaptive laboratory evolution of microorganisms under conditions using chemostat culture. Also, genomic analysis of the evolved strain is discussed.

Natural evolution involves genetic diversity such as environmental change and a selection between small populations. Adaptive laboratory evolution (ALE) ...

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics of these pathogens, in part due to a lack of well-developed genetic and genomic resources. These organisms have large, repetitive genomes, which have made genome sequencing and assembly prohibitively difficult. Here, we describe methods for the collection, extraction, purification and quality control assessment of high molecular weight genomic DNA from one powdery mildew species, Golovinomyces cichoracearum. The protocol described includes mechanical disruption of spores followed by an optimized phenol/chloroform genomic DNA extraction. A typical yield was 7 &#181;g DNA per 150 mg conidia. The genomic DNA that is isolated using this procedure is suitable for long-read sequencing (i.e., &#62; 48.5 kbp). Quality control measures to ensure the size, yield, and purity of the genomic DNA are also described in this method. Sequencing of the genomic DNA of the quality described here will allow for the assembly and comparison of multiple powdery mildew genomes, which in turn will lead to a better understanding and improved control of this agricultural pathogen.

Described here is a method for the extraction, purification, and quality control of genomic DNA from the obligate biotrophic fungal pathogen, powdery mildew, for use in long-read genome sequencing.

The powdery mildew fungi are a group of economically important fungal plant pathogens. Relatively little is known about the molecular biology and genetics ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Here we present the proteogenomic tool PoGo and protocols for fast, quantitative, post-translational modification and variant enabled mapping of peptides identified through mass spectrometry onto reference genomes. This tool is of use to integrate and visualize proteogenomic and personal proteomic studies interfacing with orthogonal genomics data.

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

Separating Bacteria by Capsule Amount Using a Discontinuous Density Gradient

Capsule is a key virulence factor in many bacterial species, mediating immune evasion and resistance to various physical stresses. While many methods are available to quantify and compare capsule production between different strains or mutants, there is no widely used method for sorting bacteria based on how much capsule they produce. We have developed a method to separate bacteria by capsule amount, using a discontinuous density gradient. This method is used to compare capsule amounts semi-quantitatively between cultures, to isolate mutants with altered capsule production, and to purify capsulated bacteria from complex samples. This method can also be coupled with transposon-insertion sequencing to identify genes involved in capsule regulation. Here, the method is demonstrated in detail, including how to optimize the gradient conditions for a new bacterial species or strain, and how to construct and run the density gradient.

We demonstrate the use of discontinuous density gradients to separate bacterial populations based on capsule production. This method is used to compare capsule amount between cultures, isolate mutants with a specific capsule phenotype, or to identify capsule regulators. Described here is the optimization and running of the assay.

Capsule is a key virulence factor in many bacterial species, mediating immune evasion and resistance to various physical stresses. While many methods are ...

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

A Method to Study de novo Formation of Chromatin Domains

The organization and structure of chromatin domains are unique to individual cell lineages. Their misregulation might lead to a loss in cellular identity and/or disease. Despite tremendous efforts, our understanding of the formation and propagation of chromatin domains is still limited. Chromatin domains have been studied under steady-state conditions, which are not conducive to following the initial events during their establishment. Here, we present a method to inducibly reconstruct chromatin domains and follow their re-formation as a function of time. Although, first applied to the case of PRC2-mediated repressive chromatin domain formation, it could be easily adapted to other chromatin domains. The modification of and/or the combination of this method with genomics and imaging technologies will provide invaluable tools to study the establishment of chromatin domains in great detail. We believe that this method will revolutionize our understanding of how chromatin domains form and interact with each other.

A Method to Study de novo Formation of Chromatin Domains

This method is designed to follow formation of PRC2-mediated chromatin domains in cell lines, and the method can be adapted to many other systems.

The organization and structure of chromatin domains are unique to individual cell lineages. Their misregulation might lead to a loss in cellular identity ...