This work was supported by the New College of Interdisciplinary Arts and Sciences at Arizona State University, Applied Maths NV, and by the National Science Foundation (ROA Supplement to Award No. MCB0604300). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Caution: Unidentified bacteria from any environment may be pathogenic and must be handled with caution using appropriate biosafety protocols. Work with live cultures must be performed in a Class II biosafety cabinet using Biological Safety Level 2 (BSL-2) procedures. More information about BSL-2 procedures is available in the CDC/NIH manual titled, &#34;Biosafety in Microbiological and Biomedical Laboratories,&#34; pages 33-38. The document is available online at <a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a>. Appropriate personal protective equipment (PPE), including lab coats/gowns, safety glasses, and nitrile or latex gloves, must be worn. Standard microbiological practices and precautions must be followed, and biohazardous waste must be discarded appropriately.
Bacteria used in this demonstration were isolated from Kartchner Caverns, AZ, USA, from four environments, including dry speleothem, flow stone, moist speleothem and stalactite drip (Table 1). All isolates were identified by 16S rDNA sequencing and kept at -80 &#176;C in 25% glycerol-R2B medium. All experiments were completed at RT.
Note: We recommend using the same sample preparation method to acquire mass spectra for database construction and mass spectra of unknowns. Sample preparation method has been shown previously to affect spectrum quality and reproducibility3. Using a different sample preparation method may cause incorrect identification of unknowns, especially when higher taxonomic resolution (e.g., at the strain level) is desired.
1. Deposition on the MALDI Target
Caution: Several protocols to obtain protein extracts require use of acids and organic solvents that must be utilized in accordance with guidelines and information contained in their respective Materials Safety Data Sheets (MSDS). Appropriate PPE must be worn and will vary based upon type and volume of chemicals used (e.g., lab coats/gowns, gloves, safety glasses, and respiratory protection must be used when working with significant quantities of toxic, flammable solvents, such as acetonitrile, and corrosive acids, such as formic and trifluoroacetic acids).
<ol>
	<li>Deposit 1 &#181;l protein extract containing no viable cells (obtained using appropriate, previously described protocols11-13) onto a stainless steel MALDI target plate and allow it to dry. Overlay the dried protein extract with 1 &#181;l matrix solution (&#945;-cyano-4-hydroxy-cinnamic acid solution), and allow it to dry.</li>
	<li>For each biological replicate, spot an appropriate number of technical replicates (5 to 20 technical replicates). Here, spot 10 technical replicates for each biological replicate and 3 biological replicates were prepared. 
	Note: We recommend using a polished MALDI steel target plate when using the protein extraction sample preparation method. Using ground steel target plates may cause spreading and unintentional mixing of the different samples outside of individual sample wells.</li>
	<li>Deposit 1 &#181;l calibrant standard onto the target plate and allow it to dry. Overlay with 1 &#181;l matrix solution and allow it to dry.</li>
	<li>Deposit 2 &#181;l matrix solution onto the target plate as a negative control.</li>
</ol>
2. Mass Spectra Acquisition
<ol>
	<li>Use a MALDI-TOF mass spectrometer equipped with a nitrogen laser (&#955; = 337 nm) and operated using Bruker FlexControl software.</li>
	<li>Collect each mass spectrum in positive linear mode by accumulation of 500 laser shots in 100 shot increments. Set the ion source 1 voltage to 20 kV; ion source 2 voltage to 18.15 kV; and the lens voltage to 9.05 kV. Note that these parameters are instrument-specific and might require adjustment on other instruments to obtain optimal results.</li>
	<li>Set the mass-to-charge range for automated spectrum evaluation from 2 to 20 kDa per charge. Use the centroid peak detection algorithm. Set the minimum resolution threshold at 100 Da. Set the signal to noise ratio (S:N) threshold at 2. Set the minimum intensity threshold at 100.</li>
</ol>
3. Database Construction
<ol>
	<li>Database design
	<ol>
		<li>Create a new database in BioNumerics 7.1 using the &#34;New database wizard&#34;.</li>
		<li>Create a spectrum experiment type, e.g., Maldi, using commands in the &#34;Experiment Types&#34; panel.</li>
		<li>Create the levels using the &#34;Database design panel&#34;. Add new levels using the &#34;Level &#62; Add new level&#8230;&#34; command in the &#34;Database&#34; menu. Here, create &#8220;Species&#8221; level, &#8220;Biological replicate&#8221; level&#8221; and &#8220;Technical replicate&#8221; level, respectively.</li>
	</ol>
	</li>
	<li>Importing and preprocessing raw mass spectra
	<ol>
		<li>Export the raw mass spectra as .txt files using FlexAnalysis by clicking the &#34;Export &#62; Mass spectrum&#34; command in the &#34;File&#34; menu.</li>
		<li>Import the raw mass spectra (.txt files) into the database in the level of the technical replicates.</li>
		<li>Preprocess the raw mass spectra.
		<ol>
			<li>Import and resample (using a quadratic fitting algorithm).</li>
			<li>Perform a baseline subtraction (with a rolling disc with a size of 50 points).</li>
			<li>Compute noise [Continuous Wavelet Transformation (CWT)], smooth (Kaiser Window with a window size of 20 points and beta of 10 points), and perform a second baseline subtraction (rolling disc with size of 200 points).</li>
			<li>Detect peaks [CWT with a minimum signal to noise ratio (S:N) of 10].</li>
		</ol>
		</li>
		<li>After preprocessing, save characteristic patterns of each mass spectrum, such as peak lists containing peak sizes, peak intensities, S:N, etc., in the database.</li>
	</ol>
	</li>
	<li>Creating composite mass spectra
	<ol>
		<li>Create composite spectra from preprocessed spectra using the &#34;Summarize&#8230;&#34;command in the &#34;Analysis&#34; menu. Choose the &#34;Biological replicate&#34; as target level.</li>
		<li>Here, combine mass spectra of 10 technical replicates of the same colony to yield a composite mass spectrum for that colony, resulting in three composite mass spectra for that isolate at the &#34;Biological replicate&#34; level.</li>
		<li>Here, summarize the three composite spectra to create one composite spectrum for that isolate at the &#34;Species&#34; level. 
		Note: The composite spectrum is the point-by-point average of the technical replicates. Replicates with a similarity (Pearson correlation) to the average of lower than 95% (default setting) are excluded from the composite. Peaks on the composite spectra are only called if they are present in 75% (default setting) of the included replicates. For the biological replicates, these settings were 90 and 60%, respectively.</li>
	</ol>
	</li>
</ol>
4. Mass Spectrum Data Analysis
<ol>
	<li>Select the entries in the database and create a comparison by clicking the &#34;Create new comparison&#34; command in the &#34;Comparisons&#34; panel.</li>
	<li>Here, use the mass spectra at the &#34;Technical replicate&#34; and/or &#34;Biological replicate&#34; levels to show comparisons and analyses.</li>
	<li>Similarity-based cluster analysis and multi-dimensional scaling (MDS)
	<ol>
		<li>Create groups with colors. Select the three biological composite mass spectra and click the &#34;Create new group from selection&#34; command in the &#34;Groups&#34; menu to create a group for the corresponding isolate. Designate a color automatically used for these three mass spectra.</li>
		<li>Alternatively, define field states with corresponding colors using the commands in the &#34;Database entries&#34; panel such that any grouping based on this defined field uses the same color defined for this group.</li>
		<li>Perform cluster analysis. Click the &#34;Calculate cluster analysis&#34; command in the &#34;Clustering&#34; menu. On the Comparison settings Page 1, select the &#34;Pearson correlation&#34; and leave other parameters as default. On page 2, select &#34;UPGMA&#34;. Then click &#34;Finish&#34;.</li>
		<li>Obtain an MDS plot using the &#34;Multi-dimensional scaling&#8230;&#34; command in the &#34;Statistics&#34; menu.</li>
	</ol>
	</li>
	<li>Peak matching
	<ol>
		<li>Click on the spectrum type &#34;Maldi&#34; in the &#34;Experiments&#34; panel. Then select &#34;Layout &#62; Show image&#34;. Spectra are shown as gel bands.</li>
		<li>Perform peak matching using the &#34;Do peak matching&#34; command in the &#34;Spectra&#34; menu.</li>
	</ol>
	</li>
	<li>Identification of peak classes
	<ol>
		<li>Perform principal component analysis (PCA). Highlight the &#34;Maldi&#34; experiment type in the &#34;Experimental&#34; panel and use the &#34;Principal Components Analysis&#8230;&#34; command in the &#34;Statistical&#34; menu to perform PCA.</li>
		<li>Perform two-way clustering. Click the &#34;Statistics &#62; Matrix mining&#34;&#8230; in the &#34;Comparison&#34; window. The intensity of the peaks matched to the peak classes is represented using different colors (heat map).</li>
	</ol>
	</li>
</ol>
5. Bacteria Identification with a Custom Database
<ol>
	<li>Similarity coefficient-based method
	<ol>
		<li>Create a comparison and generate a dendrogram based on mass spectra at the &#34;Technical replicate&#34; level as described in step 4.3.3. Save the dendrogram for comparison of similarity.</li>
		<li>Select an unknown mass spectrum, and click the &#34;Analysis &#62; Identify selected entries&#34;. The identification dialog box appears.</li>
		<li>Select the &#34;Comparison based&#34; classifier type (or a stored classifier) and click &#34;next&#34;. On the next page, choose the saved dendrogram as a reference comparison and then click &#34;next&#34;.</li>
		<li>Choose the &#34;Basic similarity&#34; as an identification method and then click &#34;next&#34;.</li>
		<li>Choose the &#34;Maximum similarity&#34; as scoring method. Type in appropriate threshold values and minimum difference values for each parameter and then click &#34;next&#34;.</li>
		<li>Once the calculations are completed, the identification window appears. In the &#34;Results&#34; panel, the members of the database that best match the unknown are listed.</li>
		<li>Save the identification project and validate the identification using the &#34;cross-validation analysis&#34; command in the &#34;Identification project&#34; panel.</li>
	</ol>
	</li>
	<li>Potential biomarker-based method
	<ol>
		<li>Define peak classes. In the &#34;Matrix Mining&#34; window, select sets of peaks sharing common characteristics and define these peaks as specific peak classes (potential biomarkers) using &#34;Spectra &#62; Manage peak class types&#8230;&#34; in the &#34;Comparison window&#34;.</li>
		<li>Here, define peak classes specific for each isolate for all the 15 isolates.</li>
		<li>Select the mass spectra of unknowns and match the peaks of these spectra to the defined peak classes as previously described.</li>
	</ol>
	</li>
</ol>

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Authors Vranckx and Janssens are employees of Applied Maths NV, the manufacturer of data analysis software used in this video. Applied Maths NV provided select software modules highlighted in this video as well as a portion of the publication costs associated with this video.

This demonstration showed detailed procedures of characterization and identification of bacteria using MALDI-TOF MS and a custom database. In comparison to traditional molecular methods, for example, 16S rDNA sequencing, MALDI-TOF MS-based fingerprint methods facilitate more rapid identification of diverse bacteria. Because of its robustness, this technique is widely used to characterize bacteria, viruses, fungi and yeast from the environment and in clinical settings1,14-16. Moreover, MALDI-TOF MS has been rep...

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been shown to be a rapid and reliable tool for identification of bacteria at the genus, species, and in some cases, strain levels1-4. MALDI-TOF MS ionizes biological molecules (typically proteins) that originate from cell surfaces, intracellular membranes, and ribosomes from bacterial whole cells or protein extracts1,5. The resulting peaks form characteristic patterns or &#8220;fingerprints&#8221; of the bacteria analyzed1. Identification of bacteria is based on these mass-to-charge &#8220;fingerprints&#8221;. 
Two of the most commonly used identification strategies are library-based and bioinformatics-based strategies1. Library-based approaches involve comparing the mass spectra of unknowns to previously collected mass spectra of known bacteria in databases/libraries for identification. Commercially available software, such as BioNumerics, Biotyper, and SARAMIS software packages, as well as open source software tools, such as SpectraBank6, are available to facilitate the comparison and quantification of similarity between mass spectra of unknowns and reference bacteria. Bioinformatics-based approaches usually rely on fully sequenced genomes of bacteria for identification. In contrast to library-based approaches which do not involve identification of the biological nature of particular peaks, bioinformatics-based approaches involve protein identification1. 
The majority of recent MALDI fingerprint-based studies have used library-based approaches to identify bacteria1. Library-based approaches require construction of databases and comparison of the similarity between mass spectra. Studies show that many experimental procedures, such as medium3,7, cultivation time8, sample preparation method3, and matrix used9, affect the mass spectra obtained. Furthermore, some closely-related species and strains generate spectra with only subtle differences. Thus, library-based approaches require rigorously standardized procedures to generate highly reproducible mass spectra between replicates. Minor variations in protocols may compromise the efficacy of identification, especially at the subspecies and strain levels1,3,10. However, neither manufacturer-provided reference databases nor reported custom databases include visually documented procedures for database construction and/or application of a data analysis pipeline. For this reason, the objective of this work was to develop, apply, and demonstrate a comprehensive and detailed procedure for library-based bacterial identification using MALDI-TOF MS. 
In this demonstration, mass spectra of 15 bacteria isolated from a karstic environment (Kartchner Cavern, AZ, USA) were collected and imported into software to construct a model database. Data processing and the analysis pipeline were detailed using the model database. Finally, mass spectra of blind-coded bacteria which were randomly selected from these 15 bacteria were collected again and compared to the reference spectra in the model database for identification. Results show that bacteria can be correctly identified either based on similarity coefficients or potential biomarkers/peak classes.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>&#945;-cyano-4-hydroxy-cinnamic acid</td>
			<td>ACROS Organics</td>
			<td align="right">163440050</td>
			<td>&#8805; 97%, CAS 28168-41-8</td>
		</tr>
		<tr>
			<td>MALDI calibration kit</td>
			<td>Aigma-Aldrich</td>
			<td>MSCAL1-1KT</td>
			<td>This kit also contains acetonitrile, trifluoroacetic acid , sinapinic acid, etc.</td>
		</tr>
		<tr>
			<td>MALDI target plate</td>
			<td>Bruker Daltonics</td>
			<td align="right">280800</td>
			<td>Polished Steel</td>
		</tr>
		<tr>
			<td>Bruker Microflex LRF MALDI-TOF mass spectrometer</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Bruker FlexControl software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bruker FlexAnalysis software</td>
			<td>Bruker Daltonics</td>
			<td></td>
			<td>version 3.0</td>
		</tr>
		<tr>
			<td>Bionumerics software</td>
			<td>Applied Maths</td>
			<td></td>
			<td>version 7.1</td>
		</tr>
	</tbody>
</table>

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.

The databases constructed in this demonstration had four levels, from highest to lowest level, including &#8220;All levels&#8221;, &#8220;Species&#8221;, &#8220;Biological replicate&#8221; and &#8220;Technical replicate&#8221;, respectively (Figure 1A). The &#8220;Technical replicate&#8221; level contained all the preprocessed spectra of technical replicates. The &#8220;Biological replicate&#8221; and &#8220;Species&#8221; levels contained the composite (summary) spectra. &#8220;All levels&#8221; contain...

Watch this Scientific Journal Video about Use of MALDI-TOF Mass Spectrometry and a Custom Database to Characterize Bacteria Indigenous to a Unique Cave Environment Kartchner Caverns, AZ, USA at JoVE.c

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.

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, ...

use-maldi-tof-mass-spectrometry-custom-database-to-characterize

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13.9K Views.  Arizona State University. 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. 

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

MALDI Sample Preparation: the Ultra Thin Layer Method

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) 1,2. The ultra-thin layer method involves the production of a substrate layer of matrix crystals (alpha-cyano-4-hydroxycinnamic acid) on the sample plate, which serves as a seeding ground for subsequent crystallization of a matrix/analyte mixture. Advantages of the ultra-thin layer method over other sample deposition approaches (e.g. dried droplet) are that it provides (i) greater tolerance to impurities such as salts and detergents, (ii) better resolution, and (iii) higher spatial uniformity. This method is especially useful for the accurate mass determination of proteins. The protocol was initially developed and optimized for the analysis of membrane proteins and used to successfully analyze ion channels, metabolite transporters, and receptors, containing between 2 and 12 transmembrane domains 2. Since the original publication, it has also shown to be equally useful for the analysis of soluble proteins. Indeed, we have used it for a large number of proteins having a wide range of properties, including those with molecular masses as high as 380 kDa 3. It is currently our method of choice for the molecular mass analysis of all proteins. The described procedure consistently produces high-quality spectra, and it is sensitive, robust, and easy to implement.

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS).

This video demonstrates the preparation of an ultra-thin matrix/analyte layer for analyzing peptides and proteins by Matrix-Assisted Laser Desorption ...

Quantification of Proteins Using Peptide Immunoaffinity Enrichment Coupled with Mass Spectrometry

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in quantitation, are associated with a high cost, long lead time, and are fraught with drawbacks (e.g. heterophilic antibodies, autoantibody interference, 'hook-effect').1 An alternative technique is affinity enrichment of peptides coupled with quantitative mass spectrometry, commonly referred to as SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies).2 In this technique, affinity enrichment of peptides with stable isotope dilution and detection by selected/multiple reaction monitoring mass spectrometry (SRM/MRM-MS) provides quantitative measurement of peptides as surrogates for their respective proteins. SRM/MRM-MS is well established for accurate quantitation of small molecules 3, 4 and more recently has been adapted to measure the concentrations of proteins in plasma and cell lysates.5-7 To achieve quantitation of proteins, these larger molecules are digested to component peptides using an enzyme such as trypsin. One or more selected peptides whose sequence is unique to the target protein in that species (i.e. "proteotypic" peptides) are then enriched from the sample using anti-peptide antibodies and measured as quantitative stoichiometric surrogates for protein concentration in the sample. Hence, coupled to stable isotope dilution (SID) methods (i.e. a spiked-in stable isotope labeled peptide standard), SRM/MRM can be used to measure concentrations of proteotypic peptides as surrogates for quantification of proteins in complex biological matrices. The assays have several advantages compared to traditional immunoassays. The reagents are relatively less expensive to generate, the specificity for the analyte is excellent, the assays can be highly multiplexed, enrichment can be performed from neat plasma (no depletion required), and the technique is amenable to a wide array of proteins or modifications of interest.8-13 In this video we demonstrate the basic protocol as adapted to a magnetic bead platform.

Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution mass spectrometry (MRM-MS) to provide quantitative measurement of peptides as surrogates for their respective proteins. Here we describe the protocol using magnetic particles in a partially automated format.

There is a great need for quantitative assays in measuring proteins. Traditional sandwich immunoassays, largely considered the gold standard in ...

Amide Hydrogen/Deuterium Exchange &amp; MALDI-TOF Mass Spectrometry Analysis of Pak2 Activation

Amide hydrogen/deuterium exchange (H/D exchange) coupled with mass spectrometry has been widely used to analyze the interface of protein-protein interactions, protein conformational changes, protein dynamics and protein-ligand interactions. H/D exchange on the backbone amide positions has been utilized to measure the deuteration rates of the micro-regions in a protein by mass spectrometry1,2,3. The resolution of this method depends on pepsin digestion of the deuterated protein of interest into peptides that normally range from 3-20 residues. Although the resolution of H/D exchange measured by mass spectrometry is lower than the single residue resolution measured by the Heteronuclear Single Quantum Coherence (HSQC) method of NMR, the mass spectrometry measurement in H/D exchange is not restricted by the size of the protein4. H/D exchange is carried out in an aqueous solution which maintains protein conformation. We provide a method that utilizes the MALDI-TOF for detection2, instead of a HPLC/ESI (electrospray ionization)-MS system5,6. The MALDI-TOF provides accurate mass intensity data for the peptides of the digested protein, in this case protein kinase Pak2 (also called &gamma;-Pak). Proteolysis of Pak 2 is carried out in an offline pepsin digestion. This alternative method, when the user does not have access to a HPLC and pepsin column connected to mass spectrometry, or when the pepsin column on HPLC does not result in an optimal digestion map, for example, the heavily disulfide-bonded secreted Phospholipase A2 (sPLA2). Utilizing this method, we successfully monitored changes in the deuteration level during activation of Pak2 by caspase 3 cleavage and autophosphorylation7,8,9.

MALDI-TOF mass spectrometry was successfully utilized to monitor the amide hydrogen/deuterium exchange in protein kinase Pak2 activation.

Amide hydrogen/deuterium exchange (H/D exchange) coupled with mass spectrometry has been widely used to analyze the interface of protein-protein ...

A Fluorescence-based Exonuclease Assay to Characterize DmWRNexo, Orthologue of Human Progeroid WRN Exonuclease, and Its Application to Other Nucleases

WRN exonuclease is involved in resolving DNA damage that occurs either during DNA replication or following exposure to endogenous or exogenous genotoxins. It is likely to play a role in preventing accumulation of recombinogenic intermediates that would otherwise accumulate at transiently stalled replication forks, consistent with a hyper-recombinant phenotype of cells lacking WRN. In humans, the exonuclease domain comprises an N-terminal portion of a much larger protein that also possesses helicase activity, together with additional sites important for DNA and protein interaction. By contrast, in Drosophila, the exonuclease activity of WRN (DmWRNexo) is encoded by a distinct genetic locus from the presumptive helicase, allowing biochemical (and genetic) dissection of the role of the exonuclease activity in genome stability mechanisms. Here, we demonstrate a fluorescent method to determine WRN exonuclease activity using purified recombinant DmWRNexo and end-labeled fluorescent oligonucleotides. This system allows greater reproducibility than radioactive assays as the substrate oligonucleotides remain stable for months, and provides a safer and relatively rapid method for detailed analysis of nuclease activity, permitting determination of nuclease polarity, processivity, and substrate preferences.

Exonucleases play critical roles in ensuring genome stability. Loss of WRN exonuclease function results in premature aging. Studying substrates and other requirements of the nuclease in vitro can help elucidate its role in vivo. Here we demonstrate a rapid and reproducible fluorescence-based assay to measure its nuclease activity.

WRN exonuclease is involved in resolving DNA damage that occurs either during DNA replication or following exposure to endogenous or exogenous genotoxins. ...

Capture Compound Mass Spectrometry - A Powerful Tool to Identify Novel c-di-GMP Effector Proteins

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis (diguanylate cyclases) and degradation (phosphodiesterases) of the second messenger c-di-GMP. In contrast, little information is available regarding the molecular mechanisms and cellular components through which this signaling molecule regulates a diverse range of cellular processes. Most of the known effector proteins belong to the PilZ family or are degenerated diguanylate cyclases or phosphodiesterases that have given up on catalysis and have adopted effector function. Thus, to better define the cellular c-di-GMP network in a wide range of bacteria experimental methods are required to identify and validate novel effectors for which reliable in silico predictions fail.
We have recently developed a novel Capture Compound Mass Spectrometry (CCMS) based technology as a powerful tool to biochemically identify and characterize c-di-GMP binding proteins. This technique has previously been reported to be applicable to a wide range of organisms1. Here we give a detailed description of the protocol that we utilize to probe such signaling components. As an example, we use Pseudomonas aeruginosa, an opportunistic pathogen in which c-di-GMP plays a critical role in virulence and biofilm control. CCMS identified 74% (38/51) of the known or predicted components of the c-di-GMP network. This study explains the CCMS procedure in detail, and establishes it as a powerful and versatile tool to identify novel components involved in small molecule signaling.

The ubiquitous second messenger c-di-GMP controls growth and behavior of many bacteria. We have developed a novel Capture Compound Mass Spectrometry based technology to biochemically identify and characterize c-di-GMP binding proteins in virtually any bacterial species.

Considerable progress has been made during the last decade towards the identification and characterization of enzymes involved in the synthesis ...

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and product development. Stress-strain relationships are typically obtained from mechanical testing data to enable comparative assessment among samples and in some cases identification of constitutive mechanical properties. However, errors may be introduced through the use of average strain measures, as significant heterogeneity in the strain field may result from geometrical non-uniformity of the sample and stress concentrations induced by mounting/gripping of soft tissues within the test system. When strain field heterogeneity is significant, accurate assessment of the sample mechanical response requires measurement of local strains. This study demonstrates a novel biomechanical testing protocol for calculating local surface strains using a mechanical testing device coupled with a high resolution camera and a digital image correlation technique. A series of sample surface images are acquired and then analyzed to quantify the local surface strain of a vascular tissue specimen subjected to ramped uniaxial loading. This approach can improve accuracy in experimental vascular biomechanics and has potential for broader use among other native soft tissues, engineered soft tissues, and soft hydrogel/polymeric materials. In the video, we demonstrate how to set up the system components and perform a complete experiment on native vascular tissue.

We describe the use of digital image correlation to characterize the local surface strain field on vascular tissue samples subjected to uniaxial tensile testing. These measurements facilitate precise quantification of the sample mechanical response and the generation of constitutive stress-strain relations.

Characterization of the mechanical behavior of biological and engineered soft tissues is a central component of fundamental biomedical research and ...

Identification of MyoD Interactome Using Tandem Affinity Purification Coupled to Mass Spectrometry

Skeletal muscle terminal differentiation starts with the commitment of pluripotent mesodermal precursor cells to myoblasts. These cells have still the ability to proliferate or they can differentiate and fuse into multinucleated myotubes, which maturate further to form myofibers. Skeletal muscle terminal differentiation is orchestrated by the coordinated action of various transcription factors, in particular the members of the Muscle Regulatory Factors or MRFs (MyoD, Myogenin, Myf5, and MRF4), also called the myogenic bHLH transcription factors family. These factors cooperate with chromatin-remodeling complexes within elaborate transcriptional regulatory network to achieve skeletal myogenesis. In this, MyoD is considered the master myogenic transcription factor in triggering muscle terminal differentiation. This notion is strengthened by the ability of MyoD to convert non-muscle cells into skeletal muscle cells. Here we describe an approach used to identify MyoD protein partners in an exhaustive manner in order to elucidate the different factors involved in skeletal muscle terminal differentiation. The long-term aim is to understand the epigenetic mechanisms involved in the regulation of skeletal muscle genes, i.e., MyoD targets. MyoD partners are identified by using Tandem Affinity Purification (TAP-Tag) from a heterologous system coupled to mass spectrometry (MS) characterization, followed by validation of the role of relevant partners during skeletal muscle terminal differentiation. Aberrant forms of myogenic factors, or their aberrant regulation, are associated with a number of muscle disorders: congenital myasthenia, myotonic dystrophy, rhabdomyosarcoma and defects in muscle regeneration. As such, myogenic factors provide a pool of potential therapeutic targets in muscle disorders, both with regard to mechanisms that cause disease itself and regenerative mechanisms that can improve disease treatment. Thus, the detailed understanding of the intermolecular interactions and the genetic programs controlled by the myogenic factors is essential for the rational design of efficient therapies.

MyoD is a myogenic transcription factor with a strong capacity to induce myogenic transdifferentiation of many fully differentiated non-muscle cell lines. The epigenetic mechanisms involved in this transdifferentiation are largely unknown. Here we describe a double-affinity purification method followed by mass spectrometry to exhaustively characterize MyoD partners.

Skeletal muscle terminal differentiation starts with the commitment of pluripotent mesodermal precursor cells to myoblasts. These cells have still the ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

Improving knowledge of ex situ best practices for at-risk butterflies is important for generating successful conservation and recovery program outcomes. Research on such captive populations can also yield valuable data to address key information gaps about the behavior, life history, and ecology of the target taxa. We describe a protocol for captive propagation of the federally endangered Cyclargus thomasi bethunebakeri that can be used as a model for other at-risk butterfly ex situ programs, especially those in the family Lycaenidae. We further provide a simple and straightforward protocol for recording various life history metrics that can be useful for informing ex situ methodologies as well as adapted for laboratory studies of other lepidoptera.

Here, we present protocols for 1) the laboratory captive propagation of the federally endangered Miami blue butterfly (Cyclargus thomasi bethunebakeri), and 2) assessing basic life history information such as immature development time and number of larval stadia. Both methods can be adapted for use with other ex situ conservation programs.

Improving knowledge of ex situ best practices for at-risk butterflies is important for generating successful conservation and recovery program outcomes. ...

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer progression. How the chromosomal location and function of a centromere (i.e., centromere identity) are determined and participate in accurate chromosome segregation is a fundamental question. CENP-A is proposed to be the non-DNA indicator (epigenetic mark) of centromere identity, and CENP-A ubiquitylation is required for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability.
Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-CENP-A K124R mutant suggesting that ubiquitylation at a different lysine is induced because of the EYFP tagging in the CENP-A K124R mutant protein. Lysine 306 (K306) ubiquitylation in EYFP-CENP-A K124R was successfully identified, which corresponds to lysine 56 (K56) in CENP-A through mass spectrometry analysis. A caveat is discussed in the use of GFP/EYFP or the tagging of high molecular weight protein as a tool to analyze the function of a protein. Current technical limit is also discussed for the detection of ubiquitylated bands, identification of site-specific ubiquitylation(s), and visualization of ubiquitylation in living cells or a specific single cell during the whole cell cycle.
The method of mass spectrometry analysis presented here can be applied to human CENP-A protein with different tags and other centromere-kinetochore proteins. These combinatory methods consisting of several assays/analyses could be recommended for researchers who are interested in identifying functional roles of ubiquitylation.

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A EYFP-CENP-A

CENP-A ubiquitylation is an important requirement for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability. Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A) protein.