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.

Cuidado: bactérias não identificados a partir de qualquer ambiente pode ser patogênica e devem ser manuseados com cuidado, utilizando protocolos de biossegurança adequadas. Trabalhar com culturas vivas deve ser realizada em uma cabine de segurança biológica Classe II utilizando Biológica nível de segurança 2 (BSL-2) procedimentos. Mais informações sobre BSL-2 procedimentos está disponível no manual do CDC / NIH intitulado, &quot;Biossegurança em microbiológicos e Biomédicas Laboratories&quot;, páginas 33-38. O documento está disponível online em <a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a> . Equipamento adequado de proteção individual (EPI), incluindo jalecos / vestidos, óculos de segurança e de borracha nitrílica ou luvas de látex, deve ser usado. Práticas microbiológicos padrão e precauções devem ser seguidas, e os resíduos de risco biológico deve ser descartado de forma adequada. As bactérias usadas nesta demonstração foram isolados a partir de Kartchner,AZ, EUA, a partir de quatro ambientes, incluindo speleothem seco, pedra fluxo, espeleotemas úmido e gotejamento estalactite (Tabela 1). Todos os isolados foram identificados por sequenciação de 16S rDNA e mantidos a -80 ° C em glicerol a 25% 2-B R médio. Todas as experiências foram concluídas à TA. Nota: Recomenda-se utilizar o mesmo método de preparação da amostra para adquirir espectros de massa para a construção do banco de dados e de massa de incógnitas. Método de preparação da amostra foi mostrado anteriormente para afetar a qualidade de espectro e reprodutibilidade 3. Usando um método diferente preparação da amostra pode causar a identificação incorrecta de desconhecidos, especialmente quando maior resolução taxonómica (por exemplo, no nível de tensão) é desejado. 1. Deposição sobre o alvo de MALDI Cuidado: Vários protocolos para obter extractos de proteínas requerem a utilização de ácidos e solventes orgânicos, que deve ser utilizado de acordo com guidElines e informações contidas em suas respectivas Fichas de Dados de Segurança de Materiais (FDSP). Apropriada PPE deve ser usado e vai variar de acordo com tipo e volume de produtos químicos usados ​​(por exemplo, jalecos / batas, luvas, óculos de segurança e de proteção respiratória devem ser utilizados ao trabalhar com quantidades significativas de solventes inflamáveis, tóxicos, tais como acetonitrilo, e ácidos corrosivos, tais como os ácidos fórmico e trifluoroacético). <ol><li> Depósito 1 ml extrato de proteína não contendo células viáveis ​​(obtidos utilizando adequadas, protocolos anteriormente descritos 11-13) sobre uma chapa de aço inoxidável alvo MALDI e deixe-a secar. Overlay o extrato de proteína seca com solução de matriz 1 ml (solução de ácido α-4-hidroxi-cinâmico-ciano), e deixe-a secar. </li><li> Para cada repetição biológica, manchar um número apropriado de repetições técnicas (5 a 20 repetições técnicas). Aqui, spot 10 repetições técnicos para cada repetição biológica e 3 repetições biológicas were preparado. Nota: Recomendamos a utilização de uma placa de MALDI-alvo de aço polido ao usar o método de preparação de amostras extração de proteínas. Usando placas de aço alvo solo pode causar difusão e de mistura não intencional das diferentes amostras fora dos poços individuais da amostra. </li><li> Depósito 1 ml de calibração padrão para a placa-alvo e deixe-a secar. Overlay com solução matriz 1 ul e deixe-a secar. </li><li> Depósito de solução de matriz 2 uL para a placa de destino como um controlo negativo. </li></ol> 2. Espectros de Massa Aquisição <ol><li> Use um espectrômetro de massa MALDI-TOF equipado com um laser de nitrogênio (λ = 337 nm) e operado usando software Bruker FlexControl. </li><li> Recolha cada espectro de massa no modo linear positiva pelo acúmulo de 500 disparos de laser em incrementos de 100 tiro. Defina a fonte de íons 1 tensão de 20 kV; fonte iónica de 2 a tensão de 18,15 kV; e a tensão de lente e 9,05 kV. Note-se que estes parâmetros são instrumento-especific e pode necessitar de reajuste em outros instrumentos para obter os melhores resultados. </li><li> Defina o intervalo de massa-carga para avaliação automática espectro de 2 a 20 kDa por carga. Use o algoritmo de detecção de pico centroid. Defina o limite mínimo de resolução a 100 Da. Defina a relação sinal-ruído (S: N) limiar a 2. Defina o limite mínimo de intensidade em 100. </li></ol> 3. Construção de banco de dados <ol><li> Design de banco de dados <ol><li> Criar um novo banco de dados no BioNumerics 7.1 usando o &quot;assistente de banco de dados New&quot;. </li><li> Criar um tipo de experimento de espectro, por exemplo, Maldi, usando os comandos no painel &quot;Tipos Experiment&quot;. </li><li> Crie os níveis usando o &quot;painel de banco de dados de design&quot;. Adicionar novos níveis usando o &quot;Nível&gt; Adicionar novo nível ...&quot; comando no menu &quot;Banco de Dados&quot;. Aqui, criar &quot;Espécies&quot; nível, nível de &quot;replicar Biológica&quot; &quot;e&quot; repetição técnica &quot;nível, respectivEly. </li></ol></li><li> Importação e pré-processamento de espectros de massa crua <ol><li> Exportar o espectro de massa crua como arquivos .txt usando FlexAnalysis clicando no comando &quot;Export&gt; Espectro de massa&quot; no menu &quot;File&quot;. </li><li> Importar o espectro de massa crua (.txt) no banco de dados no nível das repetições técnicas. </li><li> Preprocess os espectros de massa crua. <ol><li> Importação e resample (usando um algoritmo de ajuste quadrático). </li><li> Realizar uma subtracção da linha de base (com um disco do rolamento com um tamanho de 50 pontos). </li><li> Calcule o ruído [Transformation Wavelet Contínua (CWT)], liso (Janela Kaiser com um tamanho de janela de 20 pontos e beta de 10 pontos), e realizar uma segunda subtração de base (disco rolando com tamanho de 200 pontos). </li><li> Detectar picos [CWT com um sinal mínimo de ruído (S: N) de 10]. </li></ol></li><li> Depois de pré-processamento, salvar padrões característicos de cada espectro de massa, tais como listas de pico contendo picotamanhos, as intensidades dos picos, S: N, etc, no banco de dados. </li></ol></li><li> Criação de espectro de massa compósito <ol><li> Criar espectros composta a partir de espectros pré-processados ​​usando o comando &quot;Resumir ...&quot; no menu &quot;Análise&quot;. Escolha a opção &quot;replicar Biológica&quot;, como nível de alvo. </li><li> Aqui, combinar os espectros de massa de 10 repetições técnicos da mesma colónia, para se obter um espectro de massa compósito para que colónia, resultando em três espectros de massa compósito para esse isolado no nível &quot;réplica biológica&quot;. </li><li> Aqui, resumir os três espectros combinados para criar um espectro composto para que o isolado no nível &quot;Espécies&quot;. Nota: O espectro de compósito é a média de ponto-a-ponto das repetições técnicas. Replica com uma similaridade (correlação de Pearson) com a média de 95% menor do que (configuração padrão) são excluídos do compósito. Os picos nos espectros compósito só são chamados, se estiverem presentes em 75% (configuração padrão) das repetições incluídos. Para as réplicas biológicas, estas definições foram de 90 e 60%, respectivamente. </li></ol></li></ol> 4. Massa de Análise de Dados do Espectro <ol><li> Selecione as entradas no banco de dados e criar uma comparação clicando no comando &quot;Criar nova comparação&quot; no painel &quot;As comparações&quot;. </li><li> Aqui, use o espectro de massa na &quot;repetição técnica&quot; e / ou níveis de &quot;reproduzir Biológica&quot; para mostrar comparações e análises. </li><li> Análise de cluster baseada em similaridade e de escala multi-dimensional (MDS) <ol><li> Crie grupos com cores. Selecione o espectro de massa compósito biológica três e clique no botão &quot;Criar novo grupo da seleção&quot; comando no menu &quot;Grupos&quot; para criar um grupo para o isolado correspondente. Designar uma cor automaticamente utilizado para estes três espectros de massa. </li><li> Como alternativa, definir estados de campo com as cores correspondentes, utilizando os comandos no &quot;Base de dados entradas &quot;painel de tal modo que qualquer agrupamento com base neste campo definido usa a mesma cor definida para este grupo. </li><li> Realizar análise de cluster. Clique no comando &quot;Calcular análise de cluster&quot; no menu &quot;Clustering&quot;. No configurações Comparação Page 1, selecione a opção &quot;correlação de Pearson&quot; e deixar outros parâmetros como padrão. Na página 2, selecione &quot;UPGMA&quot;. Em seguida, clique em &quot;Finish&quot;. </li><li> Obter um enredo MDS usando o &quot;dimensionamento Multi-dimensional ...&quot; comando no menu &quot;Estatísticas&quot;. </li></ol></li><li> Coincidindo Peak <ol><li> Clique no tipo de espectro &quot;Maldi&quot; no painel &quot;Experiências&quot;. Em seguida, selecione &quot;Layout&gt; Mostrar imagem&quot;. Os espectros são apresentados como bandas de gel. </li><li> Executar a correspondência de pico usando o comando &quot;Faça a correspondência de pico&quot; no menu &quot;Spectra&quot;. </li></ol></li><li> Identificação de classes de pico <ol><li> Realizar análise de componentes principais (PCA). Realce o &quot;Maldi &quot;tipo de experimento no&quot; Experimental &quot;painel e use a&quot; Análise de Componentes Principais ... &quot;comando no menu&quot; Estatística &quot;para realizar PCA. </li><li> Execute bidirecional clustering. Clique no botão &quot;Estatísticas&gt; mineração Matrix&quot; ... na janela &quot;Comparação&quot;. A intensidade dos picos correspondentes às classes de pico é representado usando cores diferentes (mapa de calor). </li></ol></li></ol> 5. As bactérias Identificação com um banco de dados personalizada <ol><li> Método baseado em coeficiente de similaridade <ol><li> Criar uma comparação e gerar um dendrograma baseado no espectro de massa no nível &quot;replicar técnicos&quot;, como descrito no passo 4.3.3. Salve o dendrograma para comparação da similaridade. </li><li> Selecione um espectro de massa desconhecida, e clique no botão &quot;Análise&gt; Identificar as entradas selecionadas&quot;. A caixa de diálogo identificação aparece. </li><li> Selecione a opção &quot;Comparação baseada&quot; tipo de classificador (ou um classif armazenadoier) e clique em &quot;next&quot;. Na página seguinte, escolha o dendrograma salvo como uma comparação de referência e, em seguida, clique em &quot;next&quot;. </li><li> Escolha a opção &quot;semelhança Basic&quot; como um método de identificação e clique em &quot;next&quot;. </li><li> Escolha a opção &quot;semelhança máxima&quot; como método de pontuação. Escreva em valores limiares adequados e os valores mínimos de diferença para cada parâmetro e clique em &quot;next&quot;. </li><li> Uma vez que os cálculos são concluídas, aparece a janela de identificação. No &quot;Resultados&quot; do painel, os membros do banco de dados que melhor combinam com o desconhecido são listados. </li><li> Salve o projeto de identificação e validar a identificação usando o comando &quot;de validação cruzada análise&quot; no painel &quot;projeto de Identificação&quot;. </li></ol></li><li> Método baseado biomarcador potencial <ol><li> Definir classes de pico. Na janela &quot;Matrix Mining&quot;, selecionar os conjuntos de picos com características comuns e definir esses picos como especiaulas fic de pico (de potenciais biomarcadores) usando &quot;Spectra&gt; Gerenciar tipos de classe de pico ...&quot; na &quot;janela de comparação&quot;. </li><li> Aqui, definir classes específicas de pico de cada isolado para todos os 15 isolados. </li><li> Escolher os espectros de massa de desconhecidos e combinar os picos destes espectros para as classes de pico definidos como descrito 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 demonstração mostrou procedimentos detalhados de caracterização e identificação de bactérias que utilizam MALDI-TOF MS e um banco de dados personalizado. Em comparação com os métodos tradicionais moleculares, por exemplo, métodos de impressão digital de 16S rDNA, baseados em MS MALDI-TOF facilitar a identificação mais rápida de diversas bactérias. Devido à sua robustez, esta técnica é muito utilizada para caracterizar bactérias, vírus, fungos e leveduras do ambiente e em ambientes clínicos <su...

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.

As bases de dados construídos desta manifestação teve quatro níveis, maior para o menor nível, incluindo &quot;todos os níveis&quot;, &quot;Espécies&quot;, &quot;repetição Biológica&quot; e &quot;repetição técnica&quot;, respectivamente (Figura 1A). O nível &quot;técnico replicar&quot; continha todos os espectros pré-processado de repetições técnicas. A &quot;repetição Biológica&quot; e os níveis de &quot;espécies&quot; continha o (resumo) espectros composto. &quot;Todos os níve...

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.

Uso de MALDI-TOF Espectrometria de Massa e um banco de dados personalizado para caracterizar bactérias indígenas para uma caverna ambiente único (Kartchner Caverns, AZ, EUA)

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, ...

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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 visualizações.  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. 

Vídeo: 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 preparação da amostra: o método de Camada Ultra Thin

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

Biologia

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.

Quantificação de Proteínas Usando Enriquecimento imunoafinidade Peptide Acoplado com Espectrometria de Massa

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 hidrogênio / deutério Exchange &amp; MALDI-TOF Análise Espectrometria de Massa de PAK2 Ativação

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 de Espectrometria de Massa Compound - uma ferramenta poderosa para identificar novos c-di-GMP efetoras 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.

Usando Correlação Digital de Imagem para caracterizar as cepas de locais vasculares amostras de tecido

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.

Identificação de MyoD interactome Usando purificação de afinidade em tandem Acoplado a Espectrometria de Massa

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.

Fluxo de trabalho completo para Análise de histona modificações pós-traducionais Usando Bottom-up Espectrometria de Massa: De histona Extração de Análise de Dados

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 propagação em cativeiro de borboletas em risco para melhorar o conhecimento da história da vida e técnicas eficazes de conservação 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.