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.

Attenzione: i batteri non identificati da qualsiasi ambiente possono essere patogeni e devono essere trattati con cautela utilizzando protocolli di biosicurezza adeguate. Lavora con fermenti vivi deve essere eseguita in un armadio biosicurezza Classe II utilizzando biologica Livello di sicurezza 2 (BSL-2) procedure. Maggiori informazioni su BSL-2 procedure sono disponibili nel manuale CDC / NIH intitolato &quot;biosicurezza in microbiologiche e Biomediche Laboratories,&quot; pagine 33-38. Il documento è disponibile online all&#39;indirizzo <a href="http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf">http://www.cdc.gov/biosafety/publications/bmbl5/BMBL.pdf</a> . Adeguati dispositivi di protezione individuale (DPI), tra cui camici / camici, occhiali di sicurezza e guanti di lattice o di nitrile, deve essere indossato. Pratiche microbiologiche standard e precauzioni devono essere seguite, e rifiuti biologici devono essere eliminati in modo appropriato. I batteri utilizzati in questa manifestazione sono stati isolati da Kartchner Caverns,AZ, USA, da quattro ambienti, tra cui speleothem secca, pietra flusso, speleothem umido e stalattiti gocciolatoio (Tabella 1). Tutti gli isolati sono stati identificati mediante sequenziamento del 16S rDNA e conservati a -80 ° C in 25% glicerolo-R media 2 B. Tutti gli esperimenti sono stati completati a RT. Nota: Si consiglia di utilizzare lo stesso metodo di preparazione del campione per acquisire spettri di massa per la costruzione di database e gli spettri di massa di incognite. Metodo di preparazione del campione è stato dimostrato in precedenza di influenzare la qualità dello spettro e riproducibilità 3. Utilizzando un metodo di preparazione del campione diverso può causare non corretta identificazione di incognite, soprattutto quando a risoluzione più elevata tassonomica (es, a livello del ceppo) è desiderato. 1. Deposizione sul MALDI destinazione Attenzione: Diversi protocolli per ottenere estratti di proteine ​​richiedono l&#39;uso di acidi e solventi organici che devono essere utilizzati in conformità con guidElines e le informazioni contenute nei rispettivi Schede di sicurezza dei materiali (MSDS). Adeguato DPI devono essere indossati e varia in base al tipo e il volume di prodotti chimici utilizzati (ad esempio, camici / camici, guanti, occhiali di sicurezza, e la protezione delle vie respiratorie devono essere utilizzati quando si lavora con grandi quantità di solventi infiammabili, tossiche, come acetonitrile, e acidi corrosivi, come acidi formico e trifluoroacetico). <ol><li> Deposito 1 ml estratto proteico che non contiene cellule vitali (ottenute utilizzando opportuni protocolli, descritte in precedenza 11-13) su una piastra segnale MALDI acciaio inox e lasciarlo asciugare. Sovrapporre l&#39;estratto proteico secco con una soluzione di matrice 1 ml (soluzione di acido-4-idrossi cinnamico α-ciano-), e lasciarlo asciugare. </li><li> Per ogni replica biologica, individuare un numero adeguato di repliche tecnici (da 5 a 20 replicati tecnici). Qui, individuare 10 replicati tecnici per ogni replica biologica e 3 repliche biologiche were preparati. Nota: Si consiglia di utilizzare una piastra segnale MALDI acciaio lucido quando si utilizza il metodo di preparazione del campione di estrazione di proteine. Utilizzando piastre di acciaio di destinazione terra può causare la diffusione e la miscelazione involontaria di diversi campioni di fuori dei singoli pozzetti del campione. </li><li> Deposito 1 ml calibrante di serie sulla piastra segnale e lasciare asciugare. Sovrapposizione con la soluzione di matrice 1 ml e lasciare asciugare. </li><li> Deposito soluzione di matrice 2 microlitri sulla piastra porta come controllo negativo. </li></ol> 2. Mass Spectra Acquisition <ol><li> Utilizzare uno spettrometro di massa MALDI-TOF dotato di un laser di azoto (λ = 337 nm) e gestito utilizzando il software Bruker FlexControl. </li><li> Raccogliere ogni spettro di massa in modalità lineare positiva da accumulo di colpi 500 laser con incrementi colpo 100. Impostare la tensione della sorgente di ioni 1 a 20 kV; sorgente di ioni 2 tensione 18.15 kV; e la tensione lenti a 9,05 kV. Si noti che questi parametri sono strumento di specific e potrebbe richiedere una regolazione su altri strumenti per ottenere risultati ottimali. </li><li> Impostare l&#39;intervallo di massa-a-charge per la valutazione dello spettro automatica da 2 a 20 kDa per carica. Utilizzare l&#39;algoritmo di rilevamento di picco baricentro. Impostare la soglia minima risoluzione a 100 Da. Impostare il rapporto segnale rumore (S: N) soglia 2. Impostare la soglia minima di intensità a 100. </li></ol> 3. Database di costruzione <ol><li> Progettazione di database <ol><li> Creare un nuovo database in BioNumerics 7.1 utilizzando la &quot;procedura guidata Nuovo database&quot;. </li><li> Creare un tipo di esperimento spettro, ad esempio, Maldi, utilizzando i comandi nel pannello &quot;Tipi di esperimento&quot;. </li><li> Creare i livelli utilizzando il &quot;pannello di progettazione del database&quot;. Aggiungere nuovi livelli utilizzando il &quot;Livello&gt; Aggiungi nuovo livello ...&quot; comando nel menu &quot;Database&quot;. Qui, creare &quot;specie&quot; di livello, &quot;replica biologica&quot; &quot;e&quot; replica tecnici &quot;livello, respectively. </li></ol></li><li> Importazione e pre-elaborazione spettri di massa grezza <ol><li> Esportare gli spettri di massa crudo come file .txt utilizzando FlexAnalysis cliccando sul pulsante &quot;Esporta&gt; Spettro di massa&quot; comando nel menu &quot;File&quot;. </li><li> Importare gli spettri di massa grezza (.txt) nel database del livello dei replicati tecnici. </li><li> Pre-elaborazione spettri di massa grezza. <ol><li> Importazione e resample (utilizzando un algoritmo quadratico raccordo). </li><li> Eseguire una sottrazione della linea di base (con un disco di rotolamento con una dimensione di 50 punti). </li><li> Calcola rumore [continua Wavelet Transformation (CWT)], liscia (Finestra Kaiser con una dimensione della finestra di 20 punti e beta di 10 punti), ed effettuare una seconda sottrazione di base (disco a rotazione con dimensioni di 200 punti). </li><li> Rileva picchi [CWT con un segnale minimo di rumore (S: N) di 10]. </li></ol></li><li> Dopo la pre-elaborazione, salvare modelli caratteristici di ogni spettro di massa, come le liste contenenti picco piccodimensioni, intensità dei picchi, S: N, ecc, nel database. </li></ol></li><li> Creazione di spettri di massa composita <ol><li> Creare spettri composito da spettri già preparati usando il comando &quot;Riassumere ...&quot; nel menu &quot;Analisi&quot;. Scegliere la &quot;replica biologica&quot; come livello target. </li><li> Qui, unire spettri di massa di 10 replicati tecnici della stessa colonia per produrre uno spettro di massa composito per quella colonia, con conseguente tre composito spettri di massa per isolare che a livello &quot;replica biologica&quot;. </li><li> Qui, riassumere i tre spettri composito per creare uno spettro composito per isolare che a livello &quot;Specie&quot;. Nota: Lo spettro composito è la media punto per punto dei replicati tecnici. Replica con una similitudine (Pearson di correlazione) alla media di inferiore al 95% (impostazione di default) sono esclusi dal composito. Peaks sul spettri compositi sono chiamati solo se sono presenti nel 75% (impostazione di default) dei replicati inclusi. Per le repliche biologiche, queste impostazioni sono state 90 e 60%, rispettivamente. </li></ol></li></ol> Analisi 4. Messa Spectrum dati <ol><li> Selezionare le voci nel database e creare un confronto facendo clic sul comando &quot;Crea nuovo confronto&quot; nel pannello &quot;Confronti&quot;. </li><li> Qui, utilizzare gli spettri di massa al &quot;replicare tecnici&quot; e / o livelli &quot;replicati biologica&quot; per visualizzare i confronti e analisi. </li><li> Analisi cluster basato somiglianza e scalabilità multi-dimensionale (MDS) <ol><li> Creare gruppi di colori. Selezionare i tre biologica spettri di massa composita e fare clic su &quot;Crea nuovo gruppo dalla selezione&quot; comando nel menu &quot;gruppi&quot; di creare un gruppo per la corrispondente isolato. Designare un colore usato automaticamente per questi tre spettri di massa. </li><li> In alternativa, definire gli stati di campo con i colori corrispondenti utilizzando i comandi del &quot;Database voci pannello &quot;tale che qualsiasi raggruppamento basato su questo campo definito utilizza lo stesso colore definito per questo gruppo. </li><li> Eseguire l&#39;analisi cluster. Fare clic sul comando &quot;Calcola cluster analysis&quot; nel menu &quot;Clustering&quot;. In Impostazioni di confronto Page 1, selezionare il &quot;Pearson di correlazione&quot; e lasciare gli altri parametri di default. A pagina 2, selezionare &quot;UPGMA&quot;. Quindi fare clic su &quot;Fine&quot;. </li><li> Ottenere una trama MDS utilizzando il &quot;ridimensionamento Multi-dimensionale ...&quot; comando nel menu &quot;Statistica&quot;. </li></ol></li><li> Corrispondenza Peak <ol><li> Cliccate sul tipo di spettro &quot;Maldi&quot; nel pannello &quot;Esperimenti&quot;. Quindi selezionare &quot;Layout&gt; Immagine&quot;. Spectra sono presenti come bande gel. </li><li> Eseguire corrispondenza picco con il comando &quot;Do corrispondente picco&quot; nel menu &quot;Spectra&quot;. </li></ol></li><li> Individuazione delle classi di punta <ol><li> Effettuare analisi delle componenti principali (PCA). Evidenziare il &quot;Maldi &quot;tipo di esperimento nel&quot; sperimentale &quot;del pannello e utilizzare la funzione&quot; Analisi delle Componenti Principali ... &quot;comando nel menu&quot; statistico &quot;per eseguire PCA. </li><li> Eseguire due vie clustering. Fare clic su &quot;Statistiche&gt; mining Matrix&quot; ... nella finestra &quot;Confronto&quot;. L&#39;intensità dei picchi corrispondenti alle classi di punta è rappresentata utilizzando colori diversi (mappa di calore). </li></ol></li></ol> 5. Batteri identificazione con un database personalizzato <ol><li> Metodo coefficiente a base di somiglianza <ol><li> Creare un confronto e generare un dendrogramma basato su spettri di massa a livello di &quot;replicare tecnici&quot; come descritto al punto 4.3.3. Salvare il dendrogramma per il confronto di similarità. </li><li> Selezionare uno spettro di massa sconosciuta, e fare clic su &quot;Analisi&gt; Identificare le voci selezionate&quot;. Verrà visualizzata la finestra di dialogo di identificazione. </li><li> Selezionare la &quot;base di confronto&quot; tipo classificatore (o un classif memorizzataier) e cliccare su &quot;Avanti&quot;. Nella pagina successiva, scegli il dendrogramma salvato come un confronto di riferimento e quindi fare clic su &quot;Avanti&quot;. </li><li> Scegliere la &quot;somiglianza di base&quot; come un metodo di identificazione e quindi fare clic su &quot;Avanti&quot;. </li><li> Scegliere la &quot;somiglianza Massimo&quot; come metodo di punteggio. Digitare appropriati valori soglia e valori minimi di differenza per ogni parametro e quindi fare clic su &quot;Avanti&quot;. </li><li> Una volta che i calcoli sono stati completati, viene visualizzata la finestra di identificazione. Nel &quot;Risultati&quot; pannello, vengono elencati i membri del database che meglio corrispondono l&#39;ignoto. </li><li> Salvare il progetto di identificazione e validare l&#39;identificazione con il &quot;analisi cross-validazione&quot; comando nel pannello &quot;progetto di identificazione&quot;. </li></ol></li><li> Potenziale metodo basato biomarcatore- <ol><li> Definire le classi di picco. Nella finestra &quot;Matrix Mining&quot;, selezionare serie di cime che condividono caratteristiche comuni e definire questi picchi come speciclassi fic picco (potenziali biomarkers) usando &quot;Spectra&gt; Gestisci tipi di classe picco ...&quot; nella &quot;finestra di confronto&quot;. </li><li> Qui, definire classi di picco specifici per ogni isolano per tutti i 15 isolati. </li><li> Selezionare spettri di massa di incognite e abbinare i picchi di questi spettri alle classi picchi definiti come precedentemente descritto. </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.

Questa dimostrazione ha mostrato le procedure dettagliate di caratterizzazione e identificazione di batteri utilizzando MALDI-TOF MS e un database personalizzato. Rispetto ai metodi tradizionali molecolari, per esempio, metodi di impronte 16S rDNA sequenziamento, MS MALDI-TOF facilitano più rapida identificazione di diversi batteri. A causa della sua robustezza, questa tecnica è ampiamente utilizzata per caratterizzare i batteri, virus, funghi e lieviti dall&#39;ambiente e in ambito clinico 1,14-16. Inoltre...

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.

Le basi di dati costruiti in questa manifestazione ha avuto quattro livelli, dal più alto al più basso livello, tra cui &quot;tutti i livelli&quot;, &quot;Species&quot;, &quot;replica biologica&quot; e &quot;replicare tecnici&quot;, rispettivamente (Figura 1A). La &quot;replica tecnici&quot; livello conteneva tutti gli spettri di pre-elaborato replicati tecnici. La &quot;replica Biologica&quot; e livelli di &quot;specie&quot; contenevano il (sommario) spettri composito. &quot;Tutti i livelli&quot; con...

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.

L&#39;uso di MALDI-TOF spettrometria di massa e un database personalizzato per caratterizzare batteri indigeni ad un unico ambiente di Cave (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

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 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).

MALDI Preparazione del campione: il metodo ultra sottile strato

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

Ricerca

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.

Quantificazione delle proteine ​​Utilizzando Arricchimento Peptide immunoaffinità Accoppiato con spettrometria di 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.

Ammide Idrogeno / Deuterio Scambio &amp; massa MALDI-TOF analisi di spettrometria di attivazione Pak2

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.

Capture spettrometria di massa Compound - un potente strumento per identificare nuovi c-di-GMP effettori Proteine

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.

Utilizzo di Digital Image Correlation per caratterizzare ceppi locali vascolari campioni di tessuto

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

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.

Flusso di lavoro completo per l&#39;analisi dell&#39;istone modificazioni post-traduzionali Utilizzando Bottom-up Spettrometria di Massa: Da istone estrazione all&#39;analisi dei dati

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.

Programmi di propagazione captive farfalla a rischio per migliorare la conoscenza della storia della vita e le tecniche efficaci di conservazione 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.

Analisi della spettrometria di massa per identificare l'ubiquilazione del CENP-A con tag EYFP (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer ...

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved. As computational miRNA identification and annotation processes are mostly error-prone processes, homology-based predictions increase prediction accuracy. We developed and have improved the miRNA annotation pipeline, SUmir, in the last decade, which has been used for several plant genomes since then. 
This study presents a fully automated, new miRNA pipeline, mirMachine (miRNA Machine), by (i) adding an additional filtering step on the secondary structure predictions, (ii) making it fully automated, and (iii) introducing new options to predict either known miRNA based on homology or novel miRNAs based on small RNA sequencing reads using the previous pipeline. The new miRNA pipeline, mirMachine, was tested using The Arabidopsis Information Resource, TAIR10, release of the Arabidopsis genome and the International Wheat Genome Sequencing Consortium (IWGSC) wheat reference genome v2.

Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.

mirMachine: uno sportello unico per l'annotazione dei miRNA vegetali

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of ...