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.

Precaución: las bacterias no identificados de cualquier entorno pueden ser patógenos y deben ser manejados con precaución el uso de protocolos de bioseguridad adecuadas. Trabaja con cultivos vivos debe realizarse en una cabina de seguridad biológica Clase II utilizando procedimientos de seguridad biológica de nivel 2 (BSL-2). Más información acerca de BSL-2 procedimientos está disponible en el manual CDC / NIH titulado, &quot;Bioseguridad en laboratorios microbiológicos y biomédicos&quot;, páginas 33-38. El documento está disponible en línea en <a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a> . Equipos de protección personal (PPE), incluyendo batas de laboratorio / batas, gafas de seguridad y guantes de nitrilo o látex, se debe usar. Prácticas y precauciones análisis microbiológico de base deben seguir, y residuos biológicos peligrosos deben desecharse adecuadamente. Las bacterias utilizadas en esta demostración se aislaron de las Cavernas Kartchner,AZ, EE.UU., a partir de cuatro ambientes, incluyendo espeleotema seco, piedra flujo, espeleotemas húmedo y goteo estalactita (Tabla 1). Todos los aislamientos fueron identificados por secuenciación de 16S rDNA y se mantuvieron a -80 ° C en 25% de glicerol-R medio 2 B. Todos los experimentos se realizaron a temperatura ambiente. Nota: Se recomienda utilizar el mismo método de preparación de muestras para adquirir espectros de masas para la construcción de bases de datos y los espectros de masas de incógnitas. Método de preparación de la muestra se ha demostrado previamente para afectar a la calidad del espectro y reproducibilidad 3. Utilizando un método de preparación de la muestra diferente puede causar una incorrecta identificación de incógnitas, especialmente cuando mayor resolución taxonómica (por ejemplo, a nivel de cepa) que se desea. 1. Deposición en el Target MALDI Precaución: Varios protocolos para obtener extractos de proteínas requieren el uso de ácidos y disolventes orgánicos que deben utilizarse de acuerdo con GUIDElines y la información contenida en sus respectivas Hojas de Datos de Seguridad de Materiales (MSDS). Apropiada PPE debe usar y puede variar en función del tipo y volumen de los productos químicos utilizados (por ejemplo, batas de laboratorio / batas, guantes, gafas de seguridad y de protección respiratoria deben ser usados ​​cuando se trabaja con cantidades significativas de disolventes inflamables, tóxicos, tales como acetonitrilo, y ácidos corrosivos, tales como ácidos fórmico y trifluoroacético). <ol><li> Depósito 1 l extracto de proteína que no contiene células viables (obtenidos mediante su caso, los protocolos descritos anteriormente 11-13) en una placa de MALDI objetivo acero inoxidable y permitir que se seque. Superposición del extracto de proteína seca con solución de matriz 1 l (α-ciano-4-hidroxi-cinámico solución de ácido), y deje que se seque. </li><li> Para cada biológica replicar, detectar un número apropiado de repeticiones técnica (de 5 a 20 repeticiones técnicos). Aquí, detectar 10 repeticiones técnica para cada biológica replicar y 3 repeticiones biológica were preparado. Nota: Se recomienda el uso de una placa de acero objetivo MALDI pulido cuando se utiliza el método de preparación de la muestra de extracción de proteínas. El uso de placas de puntería de acero de tierra puede causar la difusión y mezcla involuntaria de las diferentes muestras fuera de pocillos de muestras individuales. </li><li> Depósito 1 l estándar de calibrador en el plato blanco y deje que se seque. Superposición con solución de matriz 1 l y deje que se seque. </li><li> Depósito de solución de matriz 2 l en la placa del objetivo como un control negativo. </li></ol> 2. Los espectros de masas Adquisición <ol><li> Utilice un espectrómetro de masas MALDI-TOF equipado con un láser de nitrógeno (λ = 337 nm) y operado utilizando software Bruker FlexControl. </li><li> Recoge cada espectro de masas en modo lineal positiva por acumulación de 500 disparos de láser en incrementos de 100 tiro. Ajuste el voltaje de la fuente de iones de 1 a 20 kV; fuente de iones 2 voltaje a 18.15 kV; y la tensión de la lente a 9.05 kV. Tenga en cuenta que estos parámetros son instrumento específic y podría requerir un ajuste en otros instrumentos para obtener resultados óptimos. </li><li> Ajuste el rango de masa-carga para la evaluación automatizada del espectro de 2 a 20 kDa por carga. Utilice el algoritmo de detección de pico centroide. Ajuste el umbral mínimo de resolución de 100 Da. Establecer la relación señal a ruido (S: N) umbral en 2. Establezca el umbral mínimo de intensidad a 100. </li></ol> 3. Base de datos de construcción <ol><li> Diseño de base de datos <ol><li> Crear una nueva base de datos en BioNumerics 7.1 utilizando el &quot;Asistente para nueva base de datos&quot;. </li><li> Cree un tipo de experimento espectro, por ejemplo, Maldi, mediante los comandos del &quot;Tipos de experimentos&quot; del panel. </li><li> Crear los niveles utilizando el &quot;panel de diseño de base de datos&quot;. Añadir nuevos niveles utilizando el &quot;Nivel&gt; Añadir nuevo nivel ...&quot; comando en el menú &quot;Base de datos&quot;. Aquí, crear &quot;especies&quot; de nivel, el nivel de &quot;réplica biológica&quot; &quot;y&quot; técnica de reproducir &quot;nivel, respectively. </li></ol></li><li> Importación y procesamiento previo de los espectros de masa cruda <ol><li> Exportación de los espectros de masa cruda como archivos .txt utilizando FlexAnalysis haciendo clic en el &quot;Exportar&gt; Espectro de masas&quot; comando en el menú &quot;Archivo&quot;. </li><li> Importar los espectros de masa cruda (archivos .txt) en la base de datos en el nivel de los técnicos repeticiones. </li><li> Preproceso los espectros de masa cruda. <ol><li> Importación y volver a muestrear (utilizando un algoritmo de ajuste cuadrática). </li><li> Realice una sustracción de línea de base (con un disco de rodadura con un tamaño de 50 puntos). </li><li> Calcule el ruido [continua transformación Wavelet (CWT)], lisa (Kaiser ventana con un tamaño de ventana de 20 puntos y beta de 10 puntos), y llevar a cabo una segunda base de referencia resta (disco rodando con tamaño de 200 puntos). </li><li> Detectar picos [CWT con un mínimo de señal a ruido (S: N) de 10]. </li></ol></li><li> Después de preprocesamiento, guardar patrones característicos de cada espectro de masas, como las listas de pico que contienen picotamaños pico de intensidad, S: N, etc., en la base de datos. </li></ol></li><li> Creación de los espectros de masas de material compuesto <ol><li> Crear espectros compuesto a partir de espectros previamente con el comando &quot;Resumir ...&quot; en el menú &quot;Análisis&quot;. Elija la opción &quot;réplica biológica&quot; como nivel objetivo. </li><li> Aquí, se combinan los espectros de masas de 10 repeticiones técnica de la misma colonia para producir un espectro de masas de material compuesto para esa colonia, resultando en tres espectros de masas de material compuesto para que aíslan en el nivel &quot;biológica replicar&quot;. </li><li> Aquí, un resumen de los tres espectros compuesto para crear un espectro compuesto para que aíslan a nivel &quot;Especies&quot;. Nota: El espectro compuesto es el medio punto por punto de los técnicos repeticiones. Replica con una similitud (correlación de Pearson) con la media de inferior al 95% (ajuste predeterminado), se excluyen del compuesto. Picos en los espectros de material compuesto sólo se llaman si están presentes en el 75% (ajuste por defecto) de las réplicas incluidos. Para las réplicas biológicas, estos valores fueron 90 y 60%, respectivamente. </li></ol></li></ol> Análisis 4. Espectro de masas de datos <ol><li> Seleccione las entradas de la base de datos y crear una comparación haciendo clic en el comando &quot;Crear nueva comparación&quot; en el &quot;comparaciones&quot; del panel. </li><li> En este caso, utilizar los espectros de masas en la &quot;técnica de reproducir&quot; y / o niveles &quot;biológica replicar&quot; para mostrar comparaciones y análisis. </li><li> Análisis cluster basado en la similitud y escalamiento multidimensional (MDS) <ol><li> Crear grupos con colores. Seleccione los tres biológica espectros de masas compuesto y haga clic en &quot;Crear nuevo grupo de selección&quot; comando en el menú &quot;Grupos&quot; para crear un grupo para el aislamiento correspondiente. Designar un color que se utiliza automáticamente para estos tres espectros de masas. </li><li> Alternativamente, definir estados de campo con los colores correspondientes utilizando los comandos en el &quot;Base de datos de entradas del panel &quot;de tal manera que cualquier agrupación basado en este campo definido utiliza el mismo color definido para este grupo. </li><li> Realizar análisis de conglomerados. Haga clic en el comando &quot;Calcular el análisis cluster&quot; en el menú &quot;Clustering&quot;. En la configuración de página de comparación 1, seleccione la &quot;correlación de Pearson&quot; y dejar otros parámetros por defecto. En la página 2, seleccione &quot;UPGMA&quot;. Luego haga clic en &quot;Finalizar&quot;. </li><li> Obtener una parcela MDS usando el &quot;escalamiento multidimensional ...&quot; comando en el menú &quot;Estadísticas&quot;. </li></ol></li><li> Juego Pico <ol><li> Haga clic en el tipo de espectro &quot;Maldi&quot; en el &quot;experimentos&quot; del panel. A continuación, seleccione &quot;Diseño&gt; Mostrar imagen&quot;. Los espectros se muestran como bandas de gel. </li><li> Realizar juego pico con el comando &quot;Haz coincidente pico&quot; en el menú de &quot;espectros&quot;. </li></ol></li><li> Identificación de clases de pico <ol><li> Realizar un análisis de componentes principales (PCA). Resalta el &quot;Maldi &quot;tipo de experimento en el&quot; Experimental &quot;panel y utilizar el&quot; Análisis de Componentes Principales ... &quot;comando en el menú&quot; Estadística &quot;para realizar PCA. </li><li> Realizar bidireccional agrupación. Haga clic en el &quot;Estadísticas&gt; minería Matrix&quot; ... en la ventana &quot;Comparación&quot;. La intensidad de los picos adaptados a las clases de pico se representa usando diferentes colores (mapa de calor). </li></ol></li></ol> 5. Las bacterias de identificación con una base de datos personalizada <ol><li> Método basado en el coeficiente de similitud <ol><li> Crear una comparación y generar un dendrograma basado en los espectros de masas a nivel de &quot;réplica técnicos&quot; como se describe en el paso 4.3.3. Guarde el dendrograma para la comparación de similitud. </li><li> Seleccione un espectro de masa desconocida, y haga clic en el &quot;Análisis&gt; Identificar las entradas seleccionadas&quot;. Aparecerá el cuadro de diálogo de identificación. </li><li> Seleccione el tipo de clasificador &quot;en base de comparación&quot; (o una classif almacenadaier) y haga clic en &quot;Siguiente&quot;. En la página siguiente, elija el dendrograma guardado como una comparación de referencia y haga clic en &quot;Siguiente&quot;. </li><li> Elija la opción &quot;similitud básica&quot; como un método de identificación y luego haga clic en &quot;Siguiente&quot;. </li><li> Elija la opción &quot;similitud máxima&quot; como método de puntuación. Escriba los valores mínimos apropiados y valores mínimos de diferencia para cada parámetro y haga clic en &quot;Siguiente&quot;. </li><li> Una vez terminados los cálculos, aparecerá la ventana de identificación. En la &quot;Resultados&quot; del panel, se enumeran los miembros de la base de datos que mejor se adapten a lo desconocido. </li><li> Guarde el proyecto de identificación y validación de la identificación mediante el comando &quot;validación cruzada análisis&quot; en el panel de &quot;proyecto de identificación&quot;. </li></ol></li><li> Método basado en el potencial biomarcador <ol><li> Definir las clases altas. En la ventana &quot;Matrix Minería&quot;, seleccionar conjuntos de picos que comparten características comunes y definir estos picos como especíclases pico fic (biomarcadores potenciales) utilizando &quot;Espectros&gt; Administrar tipos de clase pico ...&quot; en la &quot;ventana de comparación&quot;. </li><li> Aquí, definir clases pico específicas para cada aislamiento para todos los 15 aislamientos. </li><li> Seleccionar los espectros de masas de incógnitas y combinar los picos de estos espectros a las clases Pico definido como se describe anteriormente. </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.

Esta demostración mostró procedimientos detallados de caracterización e identificación de bacterias usando MALDI-TOF MS y una base de datos personalizada. En comparación con los métodos moleculares tradicionales, por ejemplo, los métodos de secuenciación de 16S rDNA de huellas digitales, basados ​​en MS MALDI-TOF facilitan más rápida identificación de diversas bacterias. Debido a su robustez, esta técnica se utiliza ampliamente para caracterizar las bacterias, los virus, los hongos y la levadura del medi...

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.

Las bases de datos construidos en esta demostración tenían cuatro niveles, de mayor a menor nivel, incluyendo &quot;Todos los niveles&quot;, &quot;Especies&quot;, &quot;réplica biológica&quot; y &quot;técnica de reproducir&quot;, respectivamente (Figura 1). La &quot;técnica de reproducir&quot; nivel contenía todos los espectros preprocesado de técnicos repeticiones. El &quot;biológica replicar&quot; y los niveles de &quot;especies&quot; contenían el compuesto (resumen) espectros. &quot;Todos l...

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.

El uso de MALDI-TOF espectrometría de masas y una base de datos personalizada para caracterizar bacterias indígenas a una cueva entorno único (Kartchner Cavernas, AZ, EE.UU.)

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

Research

JoVE Journal

Biology

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

13.9K vistas.  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).

MALDI preparación de la muestra: el método de ultra capa fina

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

Investigación

Biología

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.

La cuantificación de proteínas mediante el enriquecimiento de inmunoafinidad péptido acoplado a espectrometría de masas

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.

Amida de hidrógeno / deuterio intercambio y MALDI-TOF análisis de espectrometría de masas de Pak2 activación

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.

Captura compuesto Espectrometría de Masas - Una herramienta de gran alcance para identificar nuevos c-di-GMP efectoras Proteínas

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.

El uso de Correlación Digital de Imágenes para caracterizar cepas locales en muestras de tejido vascular

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.

Identificación de MyoD Interactome Usando Tandem Affinity purificación Acoplada a la Espectrometría de Masas

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.

Flujo de trabajo completo para el análisis de las histonas modificaciones post-traduccionales El uso de espectrometría de masas de abajo hacia arriba: A partir de la histona Extracción de Análisis de Datos

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.

Programas de propagación cautiva de mariposas en riesgo para mejorar el conocimiento de la historia de la vida y técnicas eficaces de conservación ex situ

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.