Name: identification of rare bacterial pathogens by 16s rrna gene sequencing and maldi-tof ms
Uploaded: 2016-07-11T13:00:00.000+00:00
Duration: 6 min 34 s
Description: Watch this Scientific Journal Video about Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS at JoVE.com

The authors would like to thank Prof. Enno Jacobs for his continuing support.

1. Extraction of Bacterial DNA
<ol>
	<li>Preparation of PBS Solution
	<ol>
		<li>Weigh 1.65 g Na2HPO4 x 2H2O, 0.22 g NaH2PO4 x 2H2O and 8.80 g NaCl in a flask and fill with distilled water to a final volume of 1,000 ml. Adjust the pH to 7.4. For final use filter the solution through a bacteria-proof (0.22 &#181;m) filter.</li>
	</ol>
	</li>
	<li>DNA Extraction of Gram-negative Bacteria
	<ol>
		<li>Streak the patient material on appropriate culture media (e.g., Columbia blood agar), identify and isolate potential pathogens.</li>
		<li>Prepare pure culture and perform Gram-staining in order to determine morphotype and to confirm purity and of the culture 49.</li>
		<li>Pick a single bacterial colony and transfer it with a 1 &#181;l single use inoculation loop into a sterile 2.0 ml reaction tube which contains 1 ml PBS solution.</li>
		<li>Incubate this suspension in a thermomixer at 95 &#176;C for 10 min. After incubation, let the bacterial extract (containing the dissolved DNA) cool to room temperature and either use it directly for amplification or store at -20 &#176;C for further use (Figure 1a).</li>
	</ol>
	</li>
	<li>DNA Extraction of Gram-positive Bacteria
	<ol>
		<li>Streak the patient material on appropriate culture media (e.g., Columbia blood agar), identify and isolate potential pathogens. Perform Gram-staining in order to confirm morphotype of the culture.</li>
		<li>Prepare pure culture and perform Gram-staining in order to determine morphotype and to confirm purity of the culture.</li>
		<li>Pick a single bacterial colony with a sterile 1 &#181;l inoculation loop and suspend it in 500 &#181;l PBS solution in a 2.0 ml reaction tube.</li>
		<li>Add 500 &#181;l glass-beads and transfer the tube to an oscillating homogenizer, operated at maximum frequency and amplitude (50 Hz), for 5 min. Use glass-beads of 1.0 mm diameter to disrupt the bacterial cell walls.</li>
		<li>Incubate this tube for 10 min at 95 &#176;C and cool to room temperature. Use the extract either directly for the PCR reaction or store at -20 &#176;C for further use.</li>
	</ol>
	</li>
</ol>
2. 16S rDNA PCR
<ol>
	<li>Preparation of Amplification Primers
	<ol>
		<li>Use the primers TPU1 (5&#180;-AGA GTT TGA TCM TGG CTC AG-3&#39; [M = A/C]) as forward primer and RTU4 (5&#180;-TAC CAG GGT ATC TAA TCC TGT T-3&#180;) as reverse primer 25. Prepare a primer stock solution with DNAse and RNAse free water, having a concentration of 100 pmol/&#181;l (100 &#181;M) and dilute it further to a working solution at 10 pmol/&#181;l. Store the primer stock and working solution at -20 &#176;C or use directly.</li>
	</ol>
	</li>
	<li>Preparation of the PCR Reaction Mix
	<ol>
		<li>Pipette 1 &#181;l of each primer, 1 &#181;l dNTP-mix (100 mM), 2.5 &#181;l of the DNA extract, 5 &#181;l 10x PCR buffer (containing 25 mM MgCl2), 0.25 &#181;l Taq-polymerase (5 units/&#181;l) and 39.25 &#181;l DNAse- and RNAse free PCR water into a sterile 200 &#181;l reaction tube.</li>
	</ol>
	</li>
	<li>PCR Protocol
	<ol>
		<li>Start the PCR program built as follows: First, an initial incubation step at 95 &#176;C for 5 min which is necessary to activate the Taq-polymerase. Second, 35 amplification cycles, containing a 1 min. denaturation step at 95 &#176;C, a 1 min. primer annealing step at 50 &#176;C, and a 1.5 min primer extension step at 72 &#176;C. Third, a final extension for 10 min at 72 &#176;C. Finally, the PCR reaction is cooled to 4 &#176;C. 
		NOTE: Staphylococcus aureus and H2O serve as positive and negative control (Figure 1b).</li>
		<li>Place the tube containing the PCR reaction mix in the thermocycler and start the program.</li>
	</ol>
	</li>
	<li>Purification of the PCR Product 
	NOTE: In order to purify the PCR product, several protocols either enzyme based or with a silica adsorption matrix may be used. The single-step PCR cleanup used here utilizes two hydrolytic enzymatic reactions. While Exonuclease I (Exo I) removes single stranded DNA, shrimp alkaline phosphatase (SAP) hydrolyses unincorporated dNTPs. The second procedure is based on a silica membrane adsorption technique with a high salt fast binding - wash - low salt elution cycle.
	<ol>
		<li>Add 1.5 &#181;l of an enzymatic mix containing both Exo I and SAP to 25 &#181;l PCR product, mix thoroughly and transfer to a thermocycler applying a simple protocol of first 15 min at 37 &#176;C followed by 15 min at 80 &#176;C. Store the purified PCR product either at -20 &#176;C or use it directly as target for labeling PCR.</li>
	</ol>
	</li>
</ol>
3. DNA Agarose Gel Electrophoresis
<ol>
	<li>Preparation of the Electrophoresis Buffer (TBE-buffer)
	<ol>
		<li>Titrate 54.0 g (445 mM) TRIS base, 27.5 g (445 mM) boric acid and 20 ml of a 0.5 M EDTA (10 mM) solution at pH 8.0 with NaOH in a flask and fill it with distilled water to a total volume of 1,000 ml. Then dilute this 5x buffer 1:10 with distilled water (0.5x TBE) for electrophoresis.</li>
	</ol>
	</li>
	<li>Preparation of the Loading Buffer
	<ol>
		<li>Mix 250 mg bromophenol blue, 250 mg xylene cyanol FF and 15 g of polysucrose (Materials Table) into a flask. Then fill it with 0.5x TBE buffer to a total volume of 100 ml. Aliquot the loading dye to 1 ml portions and store at -20 &#176;C for further usage.</li>
	</ol>
	</li>
	<li>Casting of the Agarose Gel
	<ol>
		<li>Weigh 6 g standard electrophoresis agarose to 300 ml 0.5x TBE buffer (see section 3.1.1) in a flask, to prepare a 2% (w/v) agarose gel. Then put the agarose mixture in a microwave oven, heat it near-boiling point until the agarose is completely dissolved and the solution appears clear.</li>
		<li>Let the molten agarose cool sufficiently and add 10 &#181;l of a 1% (w/v) ethidium bromide stock solution. Pour the solution into a cast and place a gel comb (allowing for 30 wells) in the cast. Remove the comb when the gel is completely solidified and put the gel into the electrophoresis chamber containing 0.5x TBE buffer. 
		NOTE: Ethidium bromide is toxic and mutagenic. Therefore it should be handled with caution. It is advisable to use nitrile gloves. There are alternative intercalating nucleic acid stains which can be used as a non-toxic alternative.</li>
	</ol>
	</li>
	<li>Agarose Gel Electrophoresis of the PCR products 
	NOTE: In order to verify the correct size of the PCR amplicons and to estimate the DNA concentration the purified PCR product is separated in an agarose gel.
	<ol>
		<li>Pipette 2 &#181;l of the loading buffer into a PCR reaction tube and add 8 &#181;l of the PCR product. Pipette this mix into the wells of the gel. Add 8 &#181;l of a molecular-weight size marker (DNA ladder) in a separate well to estimate the size of the PCR product.</li>
		<li>Apply a constant voltage at 10 V/cm and stop the electrophoresis as soon as the bromophenol blue marker has reached about &#190; of the total length of the gel. Visualize the separated PCR fragments using an UV-transilluminator (wavelength 302 nm) and document them using a gel-documentation system. Estimate the quantity of DNA by visual comparison with the DNA ladder. Use approximately 10 ng as target for the labeling PCR.</li>
	</ol>
	</li>
</ol>
4. Sanger Sequencing
<ol>
	<li>Cycle Sequencing PCR 
	NOTE: Perform the PCR labeling in a 10 &#181;l reaction using a commercial kit (Materials Table).
	<ol>
		<li>Add 2 &#181;l of 5x reaction mastermix, 2 &#181;l of the DNA preparation, 1.5 &#181;l of TPU1 or RTU4 working solution (10 pmol/&#181;l) and 4.5 &#181;l of DNAse and RNAse free water.</li>
		<li>Put this sequencing reaction mix in a thermocycler and run another PCR. In total, process 25 cycles consisting of a denaturation step (96 &#176;C, 10 sec), an annealing step (45-60 &#176;C, 5 sec) and an extension step (60 &#176;C, 2 min). Finally, cool the reaction to 4 &#176;C.</li>
	</ol>
	</li>
	<li>Purification of the Sequencing Reaction
	<ol>
		<li>Purify the labeling reaction mix using commercial spin columns for PCR purification (Materials Table).</li>
		<li>Load 10 &#181;l of the sequencing reaction onto the pre-hydrated gel-filtration matrix and perform a centrifugation step at 750 x g for 3 min. 
		NOTE: By the use of this procedure unincorporated dye terminators are retained in the gel matrix.</li>
		<li>Then dry the aqueous eluate in a vacuum concentrator. Centrifuge at 2,500 x g for 20 to 30 min at 40 &#176;C to complete dryness.</li>
		<li>Add 10 &#181;l of highly deionized formamide then denature at 90 &#176;C for 2 min and cool the mixture on ice. Pipette 10 &#181;l of the denatured samples on a 96 well &#181;l plate. Store the dried and cleaned PCR product at -20 &#176;C if the analysis is to be carried out at a later time.</li>
	</ol>
	</li>
	<li>16S rRNA Gene Sequencing and Analysis of the DNA Sequence 
	NOTE: Perform sequence analysis on an automated sequencer (Materials Table). The sequencer used here is an automated fluorescence-based capillary electrophoresis system that analyzes 4 samples simultaneously (50 cm 4-capillary array) with a liquid polymer 67 (Figure 1c).
	<ol>
		<li>Place the microtiter plate in the sequencer. Write a sample sheet (plate record) as laid out in 2, save it and start the sequencing run/analysis following the steps given in 1. 
		NOTE: Duration of a sequence run is 2 hr and provides data from 800 to 1,000 bp.</li>
		<li>Open the sequencing analysis software (Materials Table). Sequence data are given as a text file (.seq) and a file containing the electropherogram including quality values (QV) (.ab1). 
		NOTE: Base calling is automatically performed by the sequencing analysis software and the underlying electropherogram is checked visually (e.g., peak height, peak separation) before the sequence is used for further applications.</li>
		<li>Compare the stored DNA sequence file to the NCBI nr database using BLAST algorithm (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Nucleotide).</li>
	</ol>
	</li>
</ol>
5. MALDI-TOF MS
NOTE: The mass spectrometer uses a 337 nm N2 laser and is operated by a specific control software (Materials Table). Spectra are recorded in linear mode and a mass range between 2,000 to 20,000 Da is covered. Data interpretation and allocation of scores to the samples is carried out in real time by an analysis software (Materials Table) (Figure 1d).
<ol>
	<li>Preparation of Bacteria for MALDI-TOF MS Analysis
	<ol>
		<li>Grow the bacteria of interest (e.g., Myroides odoratimimus) on Columbia blood agar containing 5% horse blood at 37 &#176;C for 18 to 24 hr, depending on the bacterial strain (Figure 2a). Perform Gram-staining to verify the morphotype of the organism under investigation as well as purity of the culture.</li>
		<li>Use a wooden toothpick for transferring a single bacterial colony to a well of a 96-well steel target (Figure 2b and 2c). Spot 1 &#181;l of 70 % formic acid on top of the air dried organism on the steel target and leave to dry for approximately 2-3 min.</li>
		<li>Then, overlay the spot with 1 &#181;l matrix solution, containing 50 mg/ml CHCA (&#945;-Cyano-4-hydroxycinnamic acid) in an organic solvent (50% acetonitrile, 2.5% trifluoroacetic acid, 47.5% H2O). 
		NOTE: The acid overlay method (on plate preparation method) may be used to improve the quality of the mass spectra. It has been shown that for Gram-positive bacteria such an &#34;acid attack&#34; increases the score values of the measured spectra 45. 
		NOTE: Alternatively an automated sample preparation system (Materials Table) may be used in which contact free spots of 1 &#181;l 70% formic acid solution and, after a first drying cycle, 1 &#181;l matrix solution are applied on the bacterial smear. The samples are then dried under standardized conditions at 60% relative humidity and are ready to use for analysis.</li>
		<li>Let the smear with matrix overlaid air-dry again for about 5 min in a hood. This bacterial smear with the matrix applied is now stable for many hours. 
		NOTE: Bacterial smears without matrix may not be stable due to protein degradation.</li>
	</ol>
	</li>
	<li>Performing the MALDI-TOF MS Analysis
	<ol>
		<li>Introduction of samples
		<ol>
			<li>Open the control software of our mMIS (Materials Table).</li>
			<li>Aerate the sample loading port of the mass spectrometer by pressing the IN/OUT button of the instrument.</li>
			<li>Open the loading port after a click and insert the MALDI-TOF MS target plate with the dried bacterial smear plus matrix.</li>
			<li>Close the port and evacuate again. Observe vacuum and when it reaches 4.5 x 10-6 mbar start measurement.</li>
		</ol>
		</li>
		<li>Sample Analysis
		<ol>
			<li>Open the analysis software of our mMIS (Materials Table) and click the &#34;File&#34; menu. Select &#34;New classification&#34; and a new window &#34;MALDI biotyper real time classification wizard&#34; opens. Type the project name, for instance &#34;Myroides measurement project_1, in the field &#34;Project name&#34; and press the button &#34;New&#34;. Under the new dialog box named &#34;New project&#34; check project name and proceed by pressing the button &#34;OK&#34;.</li>
			<li>In the &#34;MALDI biotyper real time classification wizard&#34; observe the &#34;Analyte placement&#34; window open. Select target positions (e.g., A1, A2, A3&#8230;), right-click any selected position (indicated by a blue square) and select &#34;Add samples&#34;. In the automatically opened table view type in sample name, sample ID and optionally a comment. Click the button &#34;Next&#34; to proceed.</li>
			<li>In the &#34;MALDI biotyper real time classification wizard&#34; observe the &#34;Selection of MALDI biotyper methods&#34; window open. Select &#34;Bruker Taxonomy&#34; from &#34;MSPs from taxonomy trees&#34; and click &#34;Next&#34; to continue.</li>
			<li>In the &#34;MALDI biotyper real time classification wizard&#34; observe the &#34;Project summary&#34; window open. Check the inserted entries given above for the actual classification project. Start the classification run by pressing the button &#34;Finish&#34; (Figure 2d-f). Measurement starts automatically when the appropriate vacuum is reached.</li>
			<li>Follow the classification run until the measurement is successfully completed. View the results table in the &#34;MALDI Biotyper Realtime Classification Project Myroides measurement project_1&#34; open the menu &#34;View&#34; and click &#34;Results&#34;.</li>
			<li>View the &#34;Bruker Daltonics MALDI Biotyper Classification Results&#34; as HTML file in the default web browser. Print and save the results.</li>
		</ol>
		</li>
		<li>Measure a test standard daily prior to sample analysis to calibrate the instrument and perform instrument validation weekly (instrument self-test). 
		NOTE: During measurement MALDI-TOF spectra are analyzed in real time by the analysis software (Materials Table). A list of ten species with their respective scores is given and is fed into the laboratory information system (LIS) for reporting.</li>
		<li>Critically evaluate the MALDI-TOF MS results (see representative results) and, if appropriate, include other tests, such as biochemical- and antibiotic susceptibility profiles or 16S rRNA gene sequencing and, if necessary, Gram-staining for assigning a bacterial species to the microbiological report.</li>
	</ol>
	</li>
</ol>

The authors declare that they have no competing financial interests.

Both MALDI-TOF MS and 16S rRNA gene sequencing offer the possibility to identify large numbers of different bacteria. MALDI-TOF MS is a fast and inexpensive method, which is easy to handle and large databases of bacterial mass spectra are available. For this reason, MALDI-TOF MS is a rapid, cost effective and reliable method to conduct screening studies focused on rare bacterial pathogens 17,20,39,51. In a prospective study comparing MALDI-TOF MS with other phenotypic identification methods, Seng et al. demonstrated cost effectiveness and speed of MALDI-TOF MS 59 and Tan et al. reported a reduction of reagent and labor costs for bacterial identification in their laboratory setting by 56.9% annually 62. Such cost reductions are supported by a note of Gaillot et al. 28
On the other hand, 16S rDNA sequencing is more time consuming, laborious and warrants specialized personnel to perform the analysis 34. Nevertheless, both approaches are suitable for routine diagnostics and it could be demonstrated that the combination of these two methods leads to a higher reliability and more secure identification accuracy, which is especially beneficial in case of doubtful results 56. Therefore, we propose the combination of both methods as the best approach to verify the identification of rare occurring bacteria in routine diagnostics. For reliable species identification, it is absolutely imperative to use pure bacterial cultures because mixed cultures prevent species determination. As a plausibility check, species identification obtained with either MALDI TOF MS or 16S rDNA sequencing has to be in accordance with the results from Gram-staining, biochemical characterization and the clinical presentation 49.
Mass spectrometry as an analytical tool for bacterial identification was first proposed in 1975 51. However, it took until the late 1980s to establish a practicable approach for protein analysis. The &#34;soft desorption ionization&#34; technology was introduced by Koichi Tanaka in 1985 63, who received the Nobel Prize for chemistry in 2002, allowing analysis of intact proteins. At the same time the matrix-assisted ionization time of flight mass spectrometry was introduced by Hillenkamp and Karas as they were the first to use an organic acid to analyze biomolecules 37 and this is the method which is still used today. Although the MALDI technology has been used sporadically 23,30,41, it took until 2004 when Bruker Daltonics introduced its microflex MALDI Biotyper system (Pittcon Conference &#38; Expo, 2004, press release), that MALDI-TOF MS based microbial identification became a routine diagnostic technique 46. Recent approaches in MALDI-TOF MS techniques gave the opportunity to identify yeasts, detect multiresistant bacteria and perform antimicrobial susceptibility testing 17,51. Moreover, certain procedures offer the possibility to directly analyze bacteria from primary samples, such as urine or blood cultures 17,51.
Although the use of this system is principally easy, there are some pitfalls which may influence the results. The age of the microorganisms for instance affects the bacterial protein expression and therefore the reproducibility of the results. First, growth conditions may be a problem. As an example, enterobacteria such as Escherichia coli grow faster than non-fermenting bacteria (e.g., Pseudomonas aeruginosa) and consequently have to be analyzed earlier 17. Second, the matrix used for MALDI-TOF MS consists of small organic acid molecules that have a strong laser optical absorption for the wavelength of the laser used. Prior to analysis the matrix is added to the sample and both components undergo a crystallization process forming a solid solution.
This explains why changes in the matrix may affect the accuracy of the bacterial identification 17. Therefore, freshly prepared matrix solutions, not older than 7 days, should be used. Third, the medium from which bacteria are picked may influence the identification results 17,68. For instance, crystal violet, which is a component of MacConkey agar, interferes with mass spectra 17. Additionally, too many bacteria applied on the steel target will lead to lower scores and therefore affect the identification accuracy. Therefore, it is advisable to define clear criteria as to how an analysis is carried out. However, misleading results performed by MALDI-TOF MS are mostly caused by insufficient reference spectra contained in the database. (Currently the data base contains &#62;6,000 entries.) This is especially still the case for the identification of anaerobic bacteria 17. However, additional spectra can be added by the user. MALDI-TOF MS library for instance contains 98 additional reference spectra of isolates which are not adequately addressed by the original database, such as Mycoplasma sp., Myroides sp., Legionella sp., Roseomonas sp., Comamonas sp. and Chryseobacterium sp. Consequently, additional reference spectra will lead to a higher identification accuracy 59,69. Finally, in some cases the spectra of different species are very similar. This can lead to misidentification in cases such as the discrimination of Escherichia coli and Shigella spp. or Streptococcus pneumoniae and Streptococcus mitis 17,51.
Sequencing of 16S rDNA and additional genes such as rpoB have simplified the molecular identification of rare or unknown bacteria. These genes are common in most bacteria and their individual function is identical. However, since they possess enough genetic variability to produce results which permit a differentiation on genus and species level, they can be used for bacterial identification 34. According to Stackebrandt and G&#246;bel, homologies &#60;97% represent different species 61. However, homologies &#62;97% do not necessarily lead to a secure species identification 34,47. These uncertainties have several reasons.
The quality of the databases which are used to calculate the homologies is sometimes questionable 34. In cases where high homologies at the 16S rDNA level exist, uncertainties may result in species identification. A combination of different genetic regions may therefore lead to more secure results. Moreover, there are no general guidelines for the interpretation of 16S rDNA sequencing data 22,34,61. However, increasing the reliability of the existing databases will lead to a higher accuracy for bacterial identification using this molecular approach 34. Regarding the sequencing reaction we need to mention that the accuracy of the sequencing results mostly relies on the quality of the underlying PCR. Furthermore, since the results are being gained by comparing them to the entries listed in public databases, quality of sequence entries and maintenance of the data is of crucial importance.
In general, although both approaches, MALDI-TOF MS and 16S rRNA gene sequencing, have advantages they have also weaknesses. The combination of both methods, however, leads to a high accuracy for bacterial identification. For instance, in a previous study we could demonstrate that MALDI-TOF MS is able to distinguish between Myroides odoratimimus and Myroides odoratus 56. In a few cases the MALDI score suggested unreliable species identification, while the results obtained by 16S rDNA sequencing could confirm the species identity. Mass spectral fingerprints of these isolates were then created and introduced into our MALDI reference spectra database. Future research focusing on other rare bacteria may demonstrate the applicability of the proposed strategy.

Secure identification of rare pathogens in routine diagnostics is hampered by the fact that classical cultural and biochemical methods are cumbersome and sometimes questionable. Furthermore, a diagnostic microbiology laboratory has to process a large number of pathogens, ranging from a few hundred to several thousands, daily, which requires the use of automated systems. In addition to the management of a high daily throughput, the precise identification of bacterial species is needed. This is warranted since they differ in their antimicrobial susceptibility pattern and therefore correct identification provides the clinician with essential information to choose appropriate antibiotics (e.g., Enterococcus spp., Acinetobacter spp.) 12,43.
Automated microbial identification systems (aMIS) apply standardized sets of enzymatic reactions to characterize the metabolic properties of bacterial isolates 13,15,16,26,27. Although the cartridges used in these systems utilize a large number of different biochemical reactions, e.g., 47 in the GN card of the aMIS used in this study 52, this strategy permits secure identification only for a limited set of bacteria. Furthermore, the database, an advanced expert system, is clearly focused on detection of relevant and highly relevant bacteria of medical importance 13,15,16,36. Two further systems, widely used in laboratories, also apply this biochemical approach for bacterial identification. Recent studies demonstrate a comparable identification accuracy between the aMIS used in this study and one of the competitors (93.7% and 93.0% respectively), while the 3rd aMIS has an identification accuracy of only 82.4% on species level 35. Such discrepancies may be explained by the quality of the underlying identification data references, the versions of kits and software, differences in metabolism and proficiency of technical personnel 35,36.
Two automated MALDI-TOF MS systems (MALDI-TOF microbial identification system, mMIS) are mainly used. These systems allow for detection of a large number of bacterial species based on their protein fingerprint mass spectra. For instance, the database of the mMIS used contains 6,000 reference spectra. Identification systems based on mass spectrometry offer fast and reliable detection of a great variety of microorganisms including rare pathogens 11,48,51. To date only a few direct comparisons are available between the mMIS used in this study and its competitor 19,33. According to Daek et al. both systems provide a similar high rate of identification accuracy, but the mMIS used in this study seems to be more reliable in species identification 19.
Similarly, molecular techniques addressing well conserved but also distinct genes (e.g., 16S rDNA or rpoB) permit a clear species identification 3,22,61. Among these, the 16S rDNA is the most widely used housekeeping gene because of its presence in all bacteria 34. Its function remains unchanged and finally, with roughly 1,500 bp, it is long enough to be suitable for bio-informatics 14,34. Many researchers regard 16S rRNA gene analysis as the &#34;gold-standard&#34; for bacterial identification 21. This is due to the fact that few laboratories use DNA-DNA hybridization techniques to date for identification of rare or new bacteria 14,34. Additionally, more and more databases are available which can be used for 16S rRNA gene analysis 50. However, it has to be taken into account that 16S rDNA based detection systems have a limited sensitivity compared to standard PCR protocols. Moreover, the molecular approach is sophisticated, time consuming and requires highly trained personnel as well as dedicated laboratory facilities and is, therefore, not easily implemented into routine diagnostics 55. Furthermore, it has been shown that the combination of at least two different methods of bacterial identification leads to highly accurate strain identification. The combination of MALDI-TOF MS and 16S rDNA sequencing permits the identification of large numbers of different bacterial species with high accuracy. Recently the combination of MALDI-TOF MS and 16S rRNA gene analysis was presented for bacterial identification studying epidemiological questions and rare pathogens 56.

<table><tbody><tr><td>CHROMASOLV, HPLC grade water, 1 L</td><td>Sigma-Aldrich Chemie, M&amp;uuml;nchen, Germany</td><td>270733</td><td></td></tr><tr><td>Tissue Lyser LT</td><td>Qiagen, Hilden, Germany</td><td>85600</td><td>Oscillating homogenizer</td></tr><tr><td>Glass-beads 1.0 mm</td><td>VWR International, Darmstadt, Germany</td><td>412-2917</td><td></td></tr><tr><td>Thermomixer 5436</td><td>Eppendorf, Hamburg, Germany</td><td>2050-100-05</td><td></td></tr><tr><td>QIAamp DNA Mini Kit (250)</td><td>Qiagen, Hilden, Germany</td><td>51306</td><td></td></tr><tr><td>Taq PCR Core Kit (1,000 U)</td><td>Qiagen, Hilden, Germany</td><td>201225</td><td></td></tr><tr><td>Forward Primer TPU1 (5&amp;acute;-AGA GTT TGA TCM TGG CTC AG-3&amp;rsquo;)</td><td>biomers.net, Ulm, Germany&amp;nbsp;</td><td>-</td><td></td></tr><tr><td>Reverse Primer RTU4 (5&amp;acute;-TAC CAG GGT ATC TAA TCC TGT T-3&amp;acute;)</td><td>biomers.net, Ulm, Germany&amp;nbsp;</td><td>-</td><td></td></tr><tr><td>Mastercycler&amp;nbsp;</td><td>Eppendorf, Hamburg, Germany</td><td>-&amp;nbsp;</td><td>Thermocylcer</td></tr><tr><td>Reaction tube 1.5 ml</td><td>SARSTEDT, N&amp;uuml;mbrecht, Germany</td><td>72,692</td><td></td></tr><tr><td>Reaction tube 2 ml</td><td>SARSTEDT, N&amp;uuml;mbrecht, Germany</td><td>72,693,005</td><td></td></tr><tr><td>PCR 8er-CapStrips</td><td>Biozym Scientific, Hessisch Oldendorf, Germany</td><td>711040X</td><td></td></tr><tr><td>PCR 8er-SoftStrips</td><td>Biozym Scientific, Hessisch Oldendorf, Germany</td><td>711030X</td><td></td></tr><tr><td>Sharp R-ZV11&amp;nbsp;</td><td>Sharp Electronics, Hamburg, Germany</td><td>-</td><td>Microwave</td></tr><tr><td>Titriplex III (EDTA Na&lt;sub&gt;2&lt;/sub&gt;-salt dehydrate; 1 kg)</td><td>Merck, Darmstadt, Germany</td><td>1084211000</td><td></td></tr><tr><td>SeaKem LE Agarose</td><td>Biozym Scientific, Hessisch Oldendorf, Germany</td><td>849006</td><td></td></tr><tr><td>SmartLadder SF - 100 to 1,000 bp</td><td>Eurogentec, L&amp;uuml;ttich, Belgium</td><td>MW-1800-04</td><td></td></tr><tr><td>Bromophenol blue (25 g)</td><td>Sigma-Aldrich Chemie, M&amp;uuml;nchen, Germany</td><td>B0126</td><td></td></tr><tr><td>Xylene cyanol FF (10 g)</td><td>Sigma-Aldrich Chemie, M&amp;uuml;nchen, Germany</td><td>X4126</td><td></td></tr><tr><td>ComPhor L Maxi&amp;nbsp;</td><td>Biozym, Hessisch Oldendorf, Germany</td><td>-</td><td></td></tr><tr><td>Ethidium bromide solution 1% (10 ml)</td><td>Carl Roth, Karlsruhe, Germany</td><td>2218.1</td><td></td></tr><tr><td>Gel Doc 2000</td><td>Bio-Rad Laboratories, M&amp;uuml;nchen, Germany</td><td>-</td><td>Gel-documentation system&amp;nbsp;</td></tr><tr><td>ExoSAP-IT (500 reactions)</td><td>Affymetrix UK, Wooburn Green, High Wycombe, United Kingdom</td><td>78201</td><td></td></tr><tr><td>Buffer (10x) with EDTA&amp;nbsp;</td><td>Life Technologies, Darmstadt, Germany</td><td>402824</td><td></td></tr><tr><td>BigDye Terminator Kit v1.1</td><td>Life Technologies, Darmstadt, Germany</td><td>4337450</td><td></td></tr><tr><td>Hi-Di formamide (25 ml)</td><td>Life Technologies, Darmstadt, Germany</td><td>4311320</td><td></td></tr><tr><td>DyeEx 2.0 Spin Kit (250)</td><td>Qiagen, Hilden, Germany</td><td>63206</td><td></td></tr><tr><td>3130 Genetic Analyzer</td><td>Life Technologies, Darmstadt, Germany</td><td>-</td><td>Sequencer</td></tr><tr><td>MicroAmp optical 96-well reaction plate with barcode</td><td>Life Technologies, Darmstadt, Germany</td><td>4306737</td><td></td></tr><tr><td>3130 Genetic Analyzer, plate base 96-well</td><td>Life Technologies, Darmstadt, Germany</td><td>4317237</td><td></td></tr><tr><td>3130 Genetic Analyzer, plate retainer 96-well</td><td>Life Technologies, Darmstadt, Germany</td><td>4317241</td><td></td></tr><tr><td>3130 Genetic Analyzer, well plate septa</td><td>Life Technologies, Darmstadt, Germany</td><td>4315933</td><td></td></tr><tr><td>3130 Genetic Analyzer, POP-7 Polymer, 7 ml</td><td>Life Technologies, Darmstadt, Germany</td><td>4352759</td><td></td></tr><tr><td>3130 Genetic Analyzer, 4-Capillary Array, 50 cm</td><td>Life Technologies, Darmstadt, Germany</td><td>4333466</td><td></td></tr><tr><td>Sequencing Analysis Software 5.4</td><td>Life Technologies, Darmstadt, Germany</td><td>-</td><td></td></tr><tr><td>microflex</td><td>Bruker Daltonik, Bremen, Germany</td><td>-</td><td>Our mMIS</td></tr><tr><td>VITEK MS</td><td>bioM&amp;eacute;rieux, N&amp;uuml;rtingen, Germany&amp;nbsp;</td><td></td><td>2&lt;sup&gt;nd&lt;/sup&gt; mMIS</td></tr><tr><td>flexControl 3.4</td><td>Bruker Daltonik, Bremen, Germany</td><td>-</td><td>Device control software</td></tr><tr><td>Biotyper Realtime Classification 3.1 (RTC)</td><td>Bruker Daltonik, Bremen, Germany</td><td>-</td><td>Analysis software</td></tr><tr><td>&amp;alpha;-cyano-4-hydroxycinnamic acid, HCCA, 1&amp;nbsp;g</td><td>Bruker Daltonik, Bremen, Germany</td><td>201344</td><td></td></tr><tr><td>Peptide Calibration Standard II</td><td>Bruker Daltonik, Bremen, Germany</td><td>222570</td><td></td></tr><tr><td>MSP 96 target polished steel</td><td>Bruker Daltonik, Bremen, Germany</td><td>8224989</td><td></td></tr><tr><td>peqGREEN</td><td>peqlab&amp;nbsp;</td><td>37-5010</td><td></td></tr><tr><td>MALDI Biotyper Galaxy&amp;nbsp;</td><td>Bruker Daltonik, Bremen, Germany</td><td>Part No. 1836007&amp;nbsp;</td><td>Automated sample preparation system</td></tr><tr><td>Vitek 2&amp;nbsp;</td><td>bioM&amp;eacute;rieux, N&amp;uuml;rtingen, Germany&amp;nbsp;</td><td></td><td>Our aMIS</td></tr><tr><td>MicroScan&amp;nbsp;</td><td>Beckman Coulter, Krefeld, Germany</td><td></td><td>2&lt;sup&gt;nd&lt;/sup&gt; aMIS</td></tr><tr><td>BD Phoenix&amp;trade; Automated Microbiology System</td><td>BD, Heidelberg, Germany</td><td></td><td>3&lt;sup&gt;rd&lt;/sup&gt; aMIS</td></tr><tr><td>&lt;i&gt;Staphylococcus aureus&lt;/i&gt;&amp;nbsp;subsp.&amp;nbsp;aureus&amp;nbsp;Rosenbach (ATCC&amp;reg;&amp;nbsp;25923&amp;trade;)</td><td>American Type Culture Collection (ATCC), LGC Standards, Molsheim, France</td><td></td><td>Positive control for PCR&amp;nbsp;</td></tr></tbody></table>

identification of rare bacterial pathogens by 16s rrna gene sequencing and maldi-tof ms

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in immunocompromised patients. In most cases only few data, mostly published as case reports, are available which investigate the role of such pathogens as an infectious agent. Therefore, in order to clarify the pathogenic character of such microorganisms, it is necessary to conduct epidemiologic studies which include large numbers of these bacteria. The methods used in such a surveillance study have to meet the following criteria: the identification of the strains has to be accurate according to the valid nomenclature, they should be easy to handle (robustness), economical in routine diagnostics and they have to generate comparable results among different laboratories. Generally, there are three strategies for identifying bacterial strains in a routine setting: 1) phenotypic identification characterizing the biochemical and metabolic properties of the bacteria, 2) molecular techniques such as 16S rRNA gene sequencing and 3) mass spectrometry as a novel proteome based approach. Since mass spectrometry and molecular approaches are the most promising tools for identifying a large variety of bacterial species, these two methods are described. Advances, limitations and potential problems when using these techniques are discussed.

MALDI-TOF MS is a novel, fast and inexpensive method for microbiological routine diagnostics. Bacterial species identification by MALDI-TOF MS produces spectra mainly composed of ribosomal proteins but also other &#34;very conserved proteins with house-keeping functions affected to a minimal extent by environmental conditions&#34; 17.The database of this mMIS contains a large set of reference spectra and even bacteria which are rarely found in clinical isolates can be securely identified 7,56,57. Score values show the reliability of the identified species. Scores above 2.300 represent a highly probable species identification, a score between 2.000 and 2.300 indicates a secure species identification, a score between 1.700 and 2.000 stands for a probable species identification and a score below 1.700 is non-reliable. In the case of more than one species being identified with a score above 2.000, additional tests have to be applied and this is the reason why we have combined different methods such as 16S rDNA sequencing, API and techniques addressing the bacterial morpho- and/or phenotype such as Gram stain, motility etc. In the case where the score is below 1.700 the above mentioned tests have to be used to achieve reliable species identification. A combination of MALDI-TOF MS and 16S rDNA sequencing has been demonstrated recently 56-58. It should be pointed out that clear guidelines for species determination based on 16S rDNA sequence homologies are still lacking 22,61. For practical interpretation, homologies of &#8805;97%, as proposed by Stackebrandt and G&#246;bel 61, are used 56. Advances in DNA sequencing allow the determination of whole bacterial genomes by next generation techniques at relatively low cost and, therefore, in principle identification of bacteria based on their genome sequence. This technique has already been used in genome based outbreak management or in epidemiology 9,29. However, the strongest limitation today is the lack of easy to use software packages for the end user 65.
Seven examples of rare occurring bacteria, isolated from patient samples during routine diagnostics and analyzed by both MALDI-TOF MS and 16s rRNA gene sequencing are listed in Table 1. MALDI-TOF spectra of strain #1 to #6 are shown in Figure 3 and a spectrum of strain #7, Sphingobacterium spiritivorum, is shown in Figure 1e. All isolates show high scores in MALDI-TOF MS analysis and 99 to 100% 16S rDNA sequence homologies. Thus, both techniques lead to secure genus and species identification. However, as pointed out in a recent publication on comparing identification methods of Myroides sp., the combination of MALDI-TOF MS and 16S rRNA gene sequencing led to more reliable results 56. These data illustrate that a single method used may not always be sufficient to achieve a reliable identification result. The combination of two independent methods leads to a higher accuracy and expands the commercial MALDI-TOF MS database with in-house entries resulted in an unequivocal species identification 56.
Strain #1 was identified as Chryseobacterium gleum (MALDI-TOF MS score 2.490, sequence homology 99%). Chryseobacteria are Gram-negative, non-fermenting rods and related to Myroides spp. Chryseobacterium spp. are regarded as emerging pathogens, associated with septicemia, pneumonia and urinary tract infections. The genus Chryseobacterium comprises a large number of species and the best studied species is Chryseobacterium indologenes. Similar to infections caused by Myroides sp., mostly immunocompromised patients are affected 6,10,60. Strain #2 was identified as Myroides odoratimimus (MALDI-TOF MS score 2.436, sequence homology 99%) and strain #3 as Myroides odoratus (MALDI-TOF MS score 2.237, sequence homology 99%) Myroides sp. are Gram-negative, nonfermenting rods which are associated with severe diseases such as sepsis, cellulitis or pneumonia. Mostly immunocompromised patients with underlying hematologic or oncologic diseases are affected 8,18,42. Strain #4 was identified as Sphingobacterium multivorum32,71 (MALDI-TOF MS score 2.092, sequence homology 99%).
The bacteria are Gram-negative non-fermenting rods, which may cause septicemia in immunocompromised patients 5,24,44,53. In addition, a case of fatal meningoencephalitis has been reported 70. The stains #5 (MALDI-TOF MS score 2.282, sequence homology 100%) and #6 (MALDI-TOF MS score 2.289, sequence homology 100%) were identified as Wohlfahrtiimonas chitiniclastica. These bacteria are short, non-motile, Gram-negative rods which were first isolated from larvae of the parasitic fly Wohlfahrtia magnifica and described in 2008 66. These zoonotic bacteria are regarded as emerging pathogens and causative agents for bacteremia, septicemia or soft tissue infections 4,40,64. Interestingly, most patients reported were either homeless and/or suffered from alcoholism and, therefore, the occurrence of these rare pathogens may be explained by the higher rate of ectoparasites in the homeless population 4,54. Strain #7, Sphingobacterium spiritivorum (MALDI-TOF MS score 2.269, sequence homology 100%), is a rare pathogen. It may cause infections in immunocompromised patients and first human isolates were described in 1982 31,71. A recent report identifies Sphingobacterium spiritivorum as causative agent for a fatal case of bacteremia and sepsis in a patient with acute myeloid leukemia 38.
<img alt="Figure 1" src="/files/ftp_upload/53176/53176fig1.jpg" /> 
Figure 1: Workflow for nucleic acid and/or mass spectrometry based detection of clinically relevant bacteria in the medical microbiology laboratory. Starting from a pure culture (a), target DNA is amplified in a thermocycler (b). PCR amplicons are sequenced in a four capillary sequencer using Sanger technology (c). Sequencing results are determined as percent homologies of query sequences to database entries by computer assisted comparison. A second, proteomics based method, uses mass spectrometry (d) and in the center a mass spectroscopic result of strain #1 in Table 1, Sphingobacterium spiritivorum, including its MALDI-TOF MS score, is given (e). A combination of mass spectrometry and 16S rRNA gene sequencing may be necessary, in order to achieve a more reliable and accurate species identification. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/53176/53176fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53176/53176fig2.jpg" /> 
Figure 2: Analysis procedure using MALDI-TOF MS for identification of clinically relevant bacteria. An operator takes whole cell bacterial material (a) with a toothpick from a pure culture (b) and places bacterial smears on a steel target (c). The steel target is inserted into the loading port of the mass spectrometer. When the proper vacuum of 5 x 10-6 mbar is reached, the target will be moved to the ionization chamber. Using an N2-laser, ions are created by soft desorption which are then accelerated in an electrostatic field (d) and separated in the flight tube (e). The time of flight (TOF) needed for the ions to reach the detector of the flight tube (f) is directly related to their mass and forms the basis of subsequent calculations of mass peaks. Specific identification software (Materials Table) then assigns score values to the mass spectrum fingerprint of ionized bacterial proteins. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/53176/53176fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53176/53176fig3.jpg" /> 
Figure 3: MALDI-TOF spectra and assigned scores of rare pathogens. In this figure, representative MALDI-TOF spectra of the first six rare pathogens listed in Table 1 are shown. Species name and corresponding MALDI-TOF MS score are noted in each spectrum. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/53176/53176fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Strain</td>
			<td>MALDI identification</td>
			<td>MALDI score</td>
			<td>16s rDNA result</td>
			<td>BLAST homology</td>
		</tr>
		<tr>
			<td>#1</td>
			<td>Chryseobacterium gleum</td>
			<td>2.211</td>
			<td>Chryseobacterium gleum</td>
			<td>99% identity</td>
		</tr>
		<tr>
			<td>#2</td>
			<td>Myroides odoratimimus</td>
			<td>2.397</td>
			<td>Myroides odoratimimus</td>
			<td>99% identity</td>
		</tr>
		<tr>
			<td>#3</td>
			<td>Myroides odoratus</td>
			<td>2.237</td>
			<td>Myroides odoratus</td>
			<td>99% identity</td>
		</tr>
		<tr>
			<td>#4</td>
			<td>Sphingobacterium multivorum</td>
			<td>2.093</td>
			<td>Sphingobacterium multivorum</td>
			<td>99% identity</td>
		</tr>
		<tr>
			<td>#5</td>
			<td>Wohlfahrtiimonas chitiniclastica</td>
			<td>2.282</td>
			<td>Wohlfahrtiimonas chitiniclastica</td>
			<td>100% identity</td>
		</tr>
		<tr>
			<td>#6</td>
			<td>Wohlfahrtiimonas chitiniclastica</td>
			<td>2.289</td>
			<td>Wohlfahrtiimonas chitiniclastica</td>
			<td>100% identity</td>
		</tr>
		<tr>
			<td>#7</td>
			<td>Sphingobacterium spiritivorum</td>
			<td>2.269</td>
			<td>Sphingobacterium spiritivorum</td>
			<td>100% identity</td>
		</tr>
	</tbody>
</table>
Table 1: Representative MALDI-TOFs results of rare occurring bacteria in comparison to 16s rRNA gene sequencing.

Watch this Scientific Journal Video about Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS at JoVE.com

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

There are a number of rare and, therefore, insufficiently described bacterial pathogens which are reported to cause severe infections especially in ...

identification-rare-bacterial-pathogens-16s-rrna-gene-sequencing

Universidad San Sebastian

Research

JoVE Journal

Immunology and Infection

17.7K Views.  TU Dresden. Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and molecular techniques (16S rRNA gene sequencing) permit the identification of rare bacterial pathogens in routine diagnostics. The goal of this protocol lies in the combination of both techniques which leads to more accurate and reliable data.

Video: Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics (MSPP)

MALDI-TOF MS offers the possibility to differentiate some bacteria not only at the species and subspecies level but even below, at the strain level. Allelic isoforms of the detectable biomarker ions result in isolate-specific mass shifts. Mass spectrometry-based phyloproteomics (MSPP) is a novel technique that combines the mass spectrometric detectable biomarker masses in a scheme that allows deduction of phyloproteomic relations from isolate specific mass shifts compared to a genome sequenced reference strain. The deduced amino acid sequences are then used to calculate MSPP-based dendrograms.
Here we describe the workflow of MSPP by typing a Campylobacter jejuni ssp. doylei isolate collection of seven strains. All seven strains were of human origin and multilocus sequence typing (MLST) demonstrated their genetic diversity. MSPP-typing resulted in seven different MSPP sequence types, sufficiently reflecting their phylogenetic relations.
The C. jejuni ssp. doylei MSPP scheme includes 14 different biomarker ions, mostly ribosomal proteins in the mass range of 2 to 11 kDa. MSPP can in principle, be adapted to other mass spectrometric platforms with an extended mass range. Therefore, this technique has the potential to become a useful tool for strain level microbial typing.

Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics MSPP

Mass spectrometry-based phyloproteomics (MSPP) was used to type a collection of Campylobacter jejuni ssp. doylei isolates at the strain level in comparison to multilocus sequence typing (MLST).

MALDI-TOF MS offers the possibility to differentiate some bacteria not only at the species and subspecies level but even below, at the strain level. ...

Genetics

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, we developed IDBac&#8212;a low-cost and high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) bioinformatics pipeline. IDBac software is designed for non-experts, is freely available, and capable of analyzing a few to thousands of bacterial colonies. Here, we present procedures for the preparation of bacterial colonies for MALDI-TOF MS analysis, MS instrument operation, and data processing and visualization in IDBac. In particular, we instruct users how to cluster bacteria into dendrograms based on protein MS fingerprints and interactively create Metabolite Association Networks (MANs) from specialized metabolite data.

IDBac is an open-source mass spectrometry-based bioinformatics pipeline that integrates data from both intact protein and specialized metabolite spectra, collected on cell material scraped from bacterial colonies. The pipeline allows researchers to rapidly organize hundreds to thousands of bacterial colonies into putative taxonomic groups, and further differentiate them based on specialized metabolite production.

In order to visualize the relationship between bacterial phylogeny and specialized metabolite production of bacterial colonies growing on nutrient agar, ...

Biochemistry

Isolation, Propagation, and Identification of Bacterial Species with Hydrocarbon Metabolizing Properties from Aquatic Habitats

Hydrocarbon pollutants are recalcitrant to degradation and their accumulation in the environment is toxic to all life forms. Bacteria encode numerous catalytic enzymes and are naturally capable of metabolizing hydrocarbons. Scientists harness biodiversity in aquatic ecosystems to isolate bacteria with biodegradation and bioremediation potential. Such isolates from the environment provide a rich set of metabolic pathways and enzymes, which can be further utilized to scale up the degradation process at an industrial scale. In this article, we outline the general process of isolation, propagation, and identification of bacterial species from aquatic habitats and screen their ability to utilize hydrocarbons as the sole carbon source in vitro using simple techniques. The present protocol describes the isolation of various bacterial species and their subsequent identification using the 16S rRNA analysis. The protocol also presents steps for characterizing the hydrocarbon degrading potential of bacterial isolates. This protocol will be useful for researchers trying to isolate bacterial species from environmental habitats for their biotechnological applications.

We present the process of isolating, propagating, and characterizing hydrocarbon-degrading bacteria from aquatic habitats. The protocol outlines bacterial isolation, identification by the 16S rRNA method, and testing of their hydrocarbon-degrading potential. This article would help researchers in characterizing microbial biodiversity in environmental samples, and specifically screen for microbes with bioremediation potential.

Hydrocarbon pollutants are recalcitrant to degradation and their accumulation in the environment is toxic to all life forms. Bacteria encode numerous ...

Environment

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 ...

Rapid Identification of Gram Negative Bacteria from Blood Culture Broth Using MALDI-TOF Mass Spectrometry

An important role of the clinical microbiology laboratory is to provide rapid identification of bacteria causing bloodstream infection. Traditional identification requires the sub-culture of signaled blood culture broth with identification available only after colonies on solid agar have matured. MALDI-TOF MS is a reliable, rapid method for identification of the majority of clinically relevant bacteria when applied to colonies on solid media. The application of MALDI-TOF MS directly to blood culture broth is an attractive approach as it has potential to accelerate species identification of bacteria and improve clinical management. However, an important problem to overcome is the pre-analysis removal of interfering resins, proteins and hemoglobin&#160;contained in blood culture specimens which, if not removed, interfere with the MS spectra and can result in insufficient or low discrimination identification scores. In addition it is necessary to concentrate bacteria to develop spectra of sufficient quality. The presented method describes the concentration, purification, and extraction of Gram negative bacteria allowing for the early identification of bacteria from a signaled blood culture broth.

The application of matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (MS) directly to blood culture broth expedites the identification of bacteria. The presented method is a rapid and reliable method for identification of Gram negative bacteria directly from blood culture broth.

An important role of the clinical microbiology laboratory is to provide rapid identification of bacteria causing bloodstream infection. Traditional ...

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

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the long-lasting gold standards is to sequence 16S ribosomal RNA (rRNA) gene amplicons generated by domain-level PCR reactions amplifying from genomic DNA. Traditionally, this was performed by cloning and Sanger (capillary electrophoresis) sequencing of PCR amplicons. The advent of next-generation sequencing has tremendously simplified and increased the sequencing depth for 16S rRNA gene sequencing. The introduction of benchtop sequencers now allows small labs to perform their 16S rRNA sequencing in-house in a matter of days. Here, an approach for 16S rRNA gene amplicon sequencing using a benchtop next-generation sequencer is detailed. The environmental DNA is first amplified by PCR using primers that contain sequencing adapters and barcodes. They are then coupled to spherical particles via emulsion PCR. The particles are loaded on a disposable chip and the chip is inserted in the sequencing machine after which the sequencing is performed. The sequences are retrieved in fastq format, filtered and the barcodes are used to establish the sample membership of the reads. The filtered and binned reads are then further analyzed using publically available tools. An example analysis where the reads were classified with a taxonomy-finding algorithm within the software package Mothur is given. The method outlined here is simple, inexpensive and straightforward and should help smaller labs to take advantage from the ongoing genomic revolution.

Characterizing microbial community has been a longstanding goal in environmental microbiology. Next-generation sequencing methods now allow for the characterization of microbial communities at an unprecedented depth with minimal cost and labor. We detail here our approach to sequence bacterial 16S ribosomal RNA genes using a benchtop sequencer.

One of the major questions in microbial ecology is &#8220;who is there?&#8221; This question can be answered using various tools, but one of the ...

Biology

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, strain levels. Commercially available and open source software tools have been developed to facilitate identification; however, no universal/standardized data analysis pipeline has been described in the literature. Here, we provide a comprehensive and detailed demonstration of bacterial identification procedures using a MALDI-TOF mass spectrometer. Mass spectra were collected from 15 diverse bacteria isolated from Kartchner Caverns, AZ, USA, and identified by 16S rDNA sequencing. Databases were constructed in BioNumerics 7.1. Follow-up analyses of mass spectra were performed, including cluster analyses, peak matching, and statistical analyses. Identification was performed using blind-coded samples randomly selected from these 15 bacteria. Two identification methods are presented: similarity coefficient-based and biomarker-based methods. Results show that both identification methods can identify the bacteria to the species level.

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA

This work details procedures for rapid identification of bacteria using MALDI-TOF MS. The identification procedures include spectrum acquisition, database construction, and follow up analyses. Two identification methods, similarity coefficient-based and biomarker-based methods, are presented.

MALDI-TOF mass spectrometry has been shown to be a rapid and reliable tool for identification of bacteria at the genus and species, and in some cases, ...

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. ...

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome&#8217;s role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Described here is a simplified standard operating procedure for microbiome profiling using 16S rRNA metagenomic sequencing and analysis using freely available tools. This protocol will help researchers who are new to the microbiome field as well as those requiring updates on methods to achieve bacterial profiling at a higher resolution.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the ...

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Summary

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

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment (Kartchner Caverns, AZ, USA)

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

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing