The financial support for this study was provided by S&#227;o Paulo Research Foundation - FAPESP (2019/10564-5, 2014/12727-5 and 2014/50249-8 to L.G.O; 2013/12598-8 and 2015/01013-4 to R.S.; and 2019/08853-9 to C.F.F.A). B.S.P, C.F.F.A., and L.G.O. received fellowships from the National Council for Scientific and Technological Development - CNPq (205729/2018-5, 162191/2015-4, and 313492/2017-4). L.G.O. is also grateful for the grant support provided by the program For Women in Science (2008, Brazilian Edition). All authors acknowledge CAPES (Coordination for the Improvement of Higher Education Personnel) for supporting the post-graduation programs in Brazil.

1. Genome mining for biosynthetic gene clusters
<ol>
	<li>Perform whole genome sequencing (WGS) as the first step to electing a biosynthetic gene cluster (BCG) for MS-guided genome mining. The whole genome draft of the strain of interest (bacteria) can be obtained by Illumina MiSeq technology using the following with high quality genomic DNA: shotgun TruSeq PCR-Free library prep and Nextera Mate Pair Library Preparation Kit33. 
	NOTE: After sequencing, the Illumina shotgun library and Illumina mate pair library can be assembled using the Newbler v3.0 (Roche, 454) assembler program (found at &#60;https://ngs.csr.uky.edu/Newbler&#62;) and annotated using a pipeline based on FgeneSB (found at &#60;http://www.softberry.com/berry.phtml?topic=fgenesb_annotator&#38;group=help&#38;subgroup=pipelines&#62;), as described previously33. Microbiology Resource Announcements (MRA) is a fully open access journal with articles publishing the availability of any microbiological resource deposited in an available repository (found at &#60;https://mra.asm.org&#62;). The candidate protein-coding genes are identified using the RAST server annotation34, and the Whole Genome Shotgun (WGS) project is deposited in the DDBJ/ENA/GenBank (found at &#60;https://www.ncbi.nlm.nih.gov/genbank/&#62;) and Gold (found at &#60;https://gold.jgi.doe.gov&#62;) sequence databases.</li>
	<li>To obtain in silico information about secondary metabolism gene clusters annotations from a complete sequenced genome, submit the sequence file (GenBank/EMBL or FASTA format) to an antiSMASH platform (found at &#60;https://antismash.secondarymetabolites.org/&#62;).</li>
	<li>Select the gene cluster of interest from output data (Figure 2) based on the most similar known cluster. 
	NOTE: First, it is routine to explore gene-by-gene and conduct individual searches (blastp) to evaluate which functions are associated with the desired biosynthetic gene groups. This procedure can also help to determine which BGC is likely associated with the production of a desired compound, even if it is a low percentage. An antiSMASH prediction considers all genes within a cluster to make percentage coverage, which can represent a global low percentage of similarity for the aimed BGC. However, when analyzing gene-by-gene, it is possible to obtain more accurate information using the most similar known cluster. Second, antiSMASH has two options to refine a search: 1) detection strictness: the degree of strictness to which the biosynthetic gene cluster must be to be considered a hit. For this option, the user should use the following parameters: a) strict: detects exclusively well-defined clusters containing all required regions, insusceptible to errors about genetic information; b) relaxed: detects partial clusters missing one or more functional region, which also works for detecting the strict feature; or c) loose: detects poorly defined clusters and clusters that likely match primary metabolites, which can lead to appearance of false positives or poorly defined BGCs. The other option is 2) extra features: the type of information the platform must search for and show in the output. In general, these two options can save time after the prediction. However, the antiSMASH job requires a longer time period.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60825/60825fig2.jpg" /> 
Figure 2: Output from antiSMASH platform. Secondary metabolism in silico analysis from whole genome sequence annotation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="4">
	<li>Based on DNA sequence information of the BGC, design primers (20&#8211;25 nt) flanking the gene cluster for ESAC (E. coli/Streptomyces Artificial Chromosome) library screening. 
	NOTE: Different methods35,36 can be used to capture the whole biosynthetic gene cluster from DNA. Here, the method used is construction of a representative ESAC library37,38 from Streptomyces sp. CBMAI 2042 containing clones with average size fragments of ~95 kb.</li>
</ol>
2. Heterologous expression of whole biosynthetic gene cluster from the ESAC library
<ol>
	<li>Move the ESAC vector from E. coli DH10B to E. coli ET12567 by triparental conjugation32.
	<ol>
		<li>Inoculate E. coli ET12567 (CamR), TOPO10/pR9604 (CarbR), DH10B/ESAC4H (AprR) in 5 mL of Luria-Bertani (LB) medium containing chloramphenicol (25 &#181;g/mL), carbenicillin (100 &#181;g/mL), and apramycin (50 &#181;g/mL).</li>
		<li>Incubate the culture overnight at 37 &#176;C and 250 rpm.</li>
		<li>Inoculate 500 &#181;L of the overnight culture in 10 mL of LB medium containing a half-concentration of antibiotics.</li>
		<li>Incubate the culture at 37 &#176;C and 250 rpm until reaching an A600 of 0.4&#8211;0.6.</li>
		<li>Harvest the cells by centrifugation at 2,200 x g for 5 min.</li>
		<li>Wash the cells twice with 20 mL of LB medium.</li>
		<li>Resuspend the cells in 500 &#181;L of LB medium.</li>
		<li>Mix 20 &#956;L of each strain in a microcentrifuge tube and drip into an agar plate with LB medium lacking antibiotics.</li>
		<li>Incubate the plates at 37 &#176;C overnight.</li>
		<li>Streak the grown cells onto a fresh LB agar plate containing antibiotics and incubate at 37 &#176;C overnight.</li>
	</ol>
	</li>
</ol>
3. Streptomyces/E. coli conjugation
<ol>
	<li>To obtain the recombinant heterologous organism, perform conjugation32 between E. coli ET12567 containing the ESAC vector, helper plasmid pR9604, and Streptomyces coelicolor M1146 or another selected host strain39.</li>
	<li>Day 1: Inoculate isolated colonies of S. coelicolor M1146 in 25 mL of TSBY medium in a 250 mL Erlenmeyer flask fitted with an inox-spring at 30 &#173;&#176;C and 200 rpm for 48 h.</li>
	<li>Day 2/3: Inoculate ET12567/ESAC/pR9604 in 5 mL of LB medium containing chloramphenicol (25 &#181;g/mL), carbenicillin (100 &#181;g/mL), and apramycin (50 &#181;g/mL) overnight at 37 &#176;C and 250 rpm.</li>
	<li>Day 3/4: Inoculate 500 &#181;L of the overnight culture in 10 mL of 2TY (in a 50 mL conical tube) containing half-working concentrations of antibiotics. Incubate at 37 &#176;C and 250 rpm until reaching an A600 of 0.4&#8211;0.6.</li>
	<li>Centrifuge the cultures (ET12567/ESAC/pR9604 and M1146) at 2200 x g for 10 min.</li>
	<li>Wash the pellets 2x in 20 mL of 2TY medium and resuspend in 500 &#181;L of 2TY.</li>
	<li>Aliquot 200 &#181;L of the S. coelicolor M1146 suspension and dilute in 500 &#181;L of 2TY (suspension A).</li>
	<li>Aliquot 200 &#181;L of suspension A and dilute in 500 &#181;L of 2TY (suspension B).</li>
	<li>Aliquot 200 &#181;L of suspension B and dilute in 500 &#181;L of 2TY (suspension C).</li>
	<li>Aliquot 200 &#181;L of the ET12567/ESAC/pR9604 suspension and mix with 200 &#181;L of suspension C.</li>
	<li>Plate 150 &#181;L of the conjugation mixture on an SFM agar plate lacking antibiotics.</li>
	<li>Incubate at 30 &#176;C for 16 h.</li>
	<li>Cover plates with 1 mL of antibiotic solution (according to plasmid resistance). After drying, incubate at 30 &#176;C for 4&#8211;7 days. 
	NOTE: Here, a solution containing 1.0 mg/mL thiostrepton and 0.5 mg/mL nalidixic acid was prepared.</li>
	<li>Streak putative exconjugants onto SFM agar plates containing thiostrepton (50 mg/mL) and nalidixic acid (25 mg/mL). Incubate at 30 &#176;C.</li>
	<li>Streak exconjugants onto an SFM agar containing only nalidixic acid.</li>
	<li>Perform PCR analysis with isolated colonies to confirm that the entire gene cluster has been transferred to the S. coelicolor M1146 host.</li>
</ol>
4. Strain cultivation
<ol>
	<li>To obtain the metabolic profile, inoculate 1/100 of the strain&#39;s pre-culture in appropriate fermentation media and under the appropriate culture conditions.</li>
	<li>Centrifuge cultures at 2200 x g for 10 min.</li>
	<li>Perform the extraction according to the class of the compound of interest40.</li>
</ol>
5. Acquiring mass spectra and preparation for GNPS analysis
<ol>
	<li>To acquire MS/MS data, program suitable HPLC and mass spectrometry methods using the control software. Both high and low resolution data-dependent mass spectrometry analysis (DDA) can be analyzed. 
	NOTE: Generally, a 1 mg/mL solution of complex crude extract samples is ideal. Dilutions are needed for less complex extracts. It should be noted that MS/MS networking is the detectable molecular network under the given mass spectrometric conditions.</li>
	<li>Convert mass spectra to .mzXML format using MSConvert from Proteowizard (found at &#60;http://proteowizard.sourceforge.net/&#62;). The input parameters for the conversion are illustrated in Figure 3. Data from software of almost all companies are compatible.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60825/60825fig3.jpg" /> 
Figure 3: Using MsConvert to convert MS files to mzXML extension. The correct parameter for GNPS analysis is displayed. The instructions are as follows: add all MS files in box 1 and add the filter Peak Picking in box 2; for this filter, use the algorithm vendor; press start and the processes of conversion will follow. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Upload the converted LC-MS/MS files into the GNPS database. Two options are available: using a file transfer protocol (FTP) or directly in a browser through the online platform. 
	NOTE: Detailed information on how to install and transfer data to GNPS is available at &#60;https://ccms-ucsd.github.io/GNPSDocumentation/fileupload/&#62;.</li>
</ol>
6. GNPS analysis
<ol>
	<li>After creating an account in GNPS (found at &#60;https://gnps.ucsd.edu/&#62;), log in to the created account select Create Molecular Network. Add a job title.</li>
	<li>Basic options: select the mzXML files to perform the molecular network. They can be organized into up to six groups. Select the libraries for the dereplication routine (Figure 4). 
	NOTE: These groups do not interfere with molecular network construction. This information will be used only for the graphical representation.</li>
</ol>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60825/60825fig4b.jpg" /> 
Figure 4: Using online GNPS platform to perform molecular network analysis. Selection of mzXML files is done by clicking in box 1. In the open dialog box, the files can be selected from personal folder (box 2) or be uploaded in the second tab using the drag-and-drop file uploader (less than 20 MB). The files can be grouped into up to six groups. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig4alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>Select the precursor ion mass tolerance and fragment ion mass tolerance of 0.02 Da and 0.05 Da, respectively. 
	NOTE: GNPS has different types of strictness available based on 1) how accurate the MS/MS data is and 2) how accurate the association must be. Basic options: in this folder, it is possible to set Precursor Ion Mass Tolerance and Fragment Ion Mass Tolerance. These parameters are used as a guide to determine how precise the precursor ion and fragment ion must be. The selected mass tolerances depend on the resolution and accuracy of the mass spectrometer that is used.</li>
	<li>Advanced network options: select the parameters according to Figure 5. These parameters directly influence the network cluster size and form. Another parameter in the remaining tabs section are for advanced users; thus, leave the default values. 
	NOTE: Advanced parameters can be read in GNPS documentation (found at &#60;https://ccms-ucsd.github.io/GNPSDocumentation/&#62;).</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60825/60825fig5.jpg" /> 
Figure 5: Using GNPS to perform molecular network analysis (advanced options). Min Pair Cos will directly influence the size of clusters, as high values will result in combining closely-related compounds and low values in combining distantly-related compounds. Using values that are too low should be avoided. Minimum matched fragment ions represent the number of shared fragments between two fragmentation spectra to be linked in the network. Together, both parameters guide the network format; lower values will cluster more distantly-related compounds and vice-versa. Using the proper values will greatly help the compound elucidation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="5">
	<li>Choose an e-mail address to receive an alert when the work is done, and submit the job.</li>
</ol>
7. Analysis of GNPS results
<ol>
	<li>Log in to GNPS. Select Jobs &#62; Published job &#62; Done to open the job. A webpage will open as illustrated in Figure 6. All results obtained from molecular networking will be displayed.</li>
	<li>Select View Spectral Families (In Browser Network Visualizer) to see all network clusters (red box, Figure 6).</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/60825/60825fig6.jpg" /> 
Figure 6: Using GNPS to visualize molecular network results. All related compound clusters can be seen in view spectral families (red box). To visualize only library hits, &#34;view all library hits&#34; (blue box) should be selected. For better graphical representation of molecular network results, &#34;Direct Cytoscape Preview&#34; (yellow box) should be downloaded, and the latest version of Cytoscape should be used. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="3">
	<li>A list will be displayed with all generated molecular networking clusters. If a library search was selected to generate the findings, tentative molecules identification will be displayed in AllIDs. Select Show to visualize them. 
	NOTE: The data analyses can be driven for other results (i.e., genome mining, biological assays, library dereplication molecules, etc.).</li>
	<li>To analyze the molecular network cluster, select Visualize Network. 
	NOTE: Each cluster is composed of nodes (circles) and edges, which represents molecules and molecular similarity, respectively. Dereplicated molecules will be highlighted as a blue node in the online browser network visualizer.</li>
	<li>In the node labels box, select parent mass (red box, Figure 7).</li>
	<li>In the edge labels box, select Cosine or DeltaMZ to observe node similarity or mass difference between nodes, respectively (yellow box, Figure 7).</li>
	<li>In the case of multigroup analyses, click Draw pies in the node coloring box to observe the frequency at which each node appears in each group (blue box, Figure 7). 
	NOTE: Other choices are possible, but those suggested above are optimal for annotating cluster nodes and unraveling their structures.</li>
</ol>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/60825/60825fig7a.jpg" /> 
Figure 7: Using GNPS to visualize molecular cluster results. After opening the molecular clusters for better data visualization, the following should be chosen: &#34;Parent mass&#34; as node labels (red box); &#34;DeltaMZ&#34; as edge labels (yellow box); and &#34;Draw pies&#34; as node coloring (blue box). Navigate through the molecular cluster and try to annotate all nodes. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="8">
	<li>To see all library hits, select View all library hits (blue box, Figure 7). 
	NOTE: Also, the MNW can be downloaded in &#34;Direct Cytoscape Preview/Download&#34; (yellow box, Figure 7), and the file can be opened in the Cytoscape platform (found at &#60;https://cytoscape.org/&#62;) for more options in graphical structure.</li>
	<li>Manual confirmation of dereplicated compounds and structure elucidation of related compounds are needed. Open the fragmentations spectra directly in the GNPS platform or in original raw files.</li>
</ol>

The authors have nothing to disclose.

The strongest advantage of this protocol is its ability to rapidly dereplicate metabolic profiles and bridge genomic information with MS data in order to elucidate the structures of new molecules, especially structural analogues2. Based on genomic information, different natural products chemotypes can be investigated, such as polyketides (PK), nonribosomal peptides (NRP), and glycosylated natural products (GNP), as well as cryptic BGCs. Metabolomic screening yields evidence of activated BGC profiles and chemical diversity produced by a specific strain under laboratory conditions. Thus, a BGC can be cloned to direct production of a new compound or unknown analogues related to an already known BGC, facilitated by similarities discovered by molecular networking. Therefore, this procedure helps to distinguish valuable compounds produced by natural sources and can be used as a guide for future isolation steps, which are common in natural product pipelines.
MS-guided genome mining was firstly described in the fields of peptidogenomics41 and glycogenomics42. To estimate the extent of peptide natural product chemical diversity, Dorrestein and colleagues developed an automated method using MS and genomics to visualize the connection between expressed natural products (chemotype) and their gene clusters (genotype). The concept of MS-guided genome mining was then described while using peptide specialized metabolites. Here, a method for the identification of microbial glycosylated natural products (GNP) using a GM approach and tandem MS was applied as tool to rapidly connect GNP chemotypes (from microbial metabolomes) with their corresponding biosynthetic genotypes following sugar footprints.
The concept of peptidogenomics has been applied to reveal stenothricin gene clusters in Streptomyces roseosporus, providing the first insights into the broad utility of GNPS as a platform43. Pattern-based genome mining and molecular networking was finally combined with the GNPS platform26 to facilitate the dereplication of new compounds, known compounds, detection of new analogs, and structure elucidation of 35 Salinispora strains. This led to the isolation and characterization of retimycin A, a quinomycin-type depsipeptide44. After the introduction of GNPS, integrated metabolomics and genome mining approaches have become the most versatile avenue to connect molecular networks with biosynthetic capabilities45,46,47,48,49,50.
This protocol reinforces the feasibility of using genomic and metabolomic analyses to investigate the production of known and unknown chemically analogous compounds in a few steps while consuming low levels of materials. The model presented here is related to valinomycin analogue identification from crude extracts through molecular networking dereplication. The structure of analogues is deduced by MS/MS fragmentation and follows the biosynthetic logic of cloned VLM BGCs.
Different software is available for mining secondary metabolite biosynthetic gene clusters51 and for metabolite elucidation, but open source options have the advantages because of constant updates, and they are open to the scientific community. In this sense, antiSMASH and the GNPS platform are the most popular choices.
This general procedure can be modified for other extraction methodologies based on the natural source explored. More than one method of extraction can also be combined according to metabolite properties (i.e., polarity, hydrophobicity, the capability to form micelles), and even similar properties, different solvents, or resin can achieve enhanced results. Usually, extracts are prepared from liquid medium cultivation, but there is a plethora of extraction methods available to isolate enriched extracts and screen any biological sample of interest.
When acquiring MS data, data dependent acquisition (DDA) analysis should be used. This issue is important when a larger number of compounds are being evaluated in a single injection. While performing DDA, the maximum number of MS/MS spectra of each precursor ion and maximum number of different precursor ion should be compensated. When using fast scan rate equipment, this can be achieved with higher scan rates (~6&#8211;10 MS/MS scans per cycle). However, in lower scan rate equipment, MN performance can be only increased with better chromatographic resolution. The most comprehensive data to populate the molecular networking should be obtained. For MS data acquisition, fixed collision energy is possible, but ramp energies are suitable to yield improved results. There are no optimal conditions that will perfectly work for all samples. Achieving sufficient MS analysis is crucial to the following steps. Henceforth, the molecular network clusters should be generated and dereplicated according to the procedure.
A frequent troubleshooting error is missing intensities for masses. Normally, this can be solved by introducing higher collision energy during analysis. Sometimes, no correlations are observed between the spectra and GNPS library, which is very uncommon. In this case, ensure that the folder opens properly in the previsualization MS software as errors can sometimes be created during the conversion step to .mzXML files.
Regarding genome mining, the most precise output from gene cluster annotation platforms will be provided for higher quality whole genome sequencing for both, single strain, or culture independent mining. High quality sequencing will generate high quality bioinformatic insights for dereplication of biosynthetic pathways. In contrast, although BGC prediction bioinformatics software has been rapidly developing, exact predictions of gene function and putative products is still difficult, especially when investigating novel biosynthetic pathways and features that cannot be predicted in silico. Also, some biosynthetic machinery is strikingly conserved, while enzymology that is involved in hybrid systems, trans-AT modular PKs, and NRPSs are recognized as exceptions of the colinearity rule. In this sense, heterologous expression and refinements in bioinformatic output software can help elucidate unpredictable enzyme functions and unusual biochemistry52,53,54. The enrichment of public databases will lead to more precise predictions and discovery of novel specialized metabolites, as the cost for WGS does not represent the handicap for genome mining.
Finally, the strongest advantages of integrated metabolomic and genome mining approaches are related to their feasibility to perform genotype and chemotype dereplication via automated and high throughput analysis connecting genomic, transcriptomic, and metabolomic data to efficiently connect genes with molecules.

Investigations of secondary metabolism often consist of screening crude extracts for specific biological activities followed by purification, identification, and characterization of the constituents belonging to active fractions. This process has proved to be efficient, promoting the isolation of several chemical entities. However, nowadays this is seen as unfeasible, mainly due to the high rates of rediscovery. As the pharmaceutical industry revolutionized without knowledge of the roles and functions of specialized metabolites, their identification was carried out under laboratory conditions that did not accurately represent nature1. Today, there is a better understanding of natural signaling influences, secretion, and the presence of most targets at undetectably low concentrations. Additionally, regulation of the process will help the academic community and pharmaceutical industry to take advantage of this knowledge. It will also benefit research involving the direct isolation of metabolites related to silent biosynthetic gene clusters (BGCs)2.
In this context, advances in genomic sequencing have renewed interest in screening microorganism metabolites. This is because analyzing the genomic information of uncovered biosynthetic clusters can reveal genes encoding novel compounds not observed or produced under laboratory conditions. Many microbial whole genome projects or drafts are available today, and the number is growing every year, providing massive prospects for uncovering novel bioactive molecules through genome mining3,4.
The Atlas of Biosynthetic Gene Clusters is the current largest collection of automatically mined gene clusters as a component of the Integrated Microbial Genomes Platform of the Joint Genome Institute (JGI IMG-ABC)2. Most recently, the Minimum Information for Biosynthetic Gene Clusters (MIBiG) Standardization Initiative has promoted the manual reannotation of BGCs, providing a highly curated reference dataset5. Nowadays, plenty of tools are available to enable computational mining of genetic data and their connection to known secondary metabolites. Different strategies have also been developed to access new bioactive natural products (i.e., heterologous expression, target gene deletion, in vitro reconstitution, genomic sequence, isotope-guided screening [genomisotopic approach], manipulation of local and global regulators, resistance target-based mining, culture independent mining, and, more recently, MS-guided/code approaches2,6,7,8,9,10,11,12,13,14,15).
Genome mining as a singular strategy requires efforts to annotate a single or small group of molecules; thus, gaps in the process remain in which new compounds are prioritized for isolation and structure elucidation. In principle, these approaches target only one biosynthetic pathway per experiment, thereby resulting in a slow discovery rate. In this sense, using GM along with a molecular networking approach represents an important advance for natural product research14,15.
The versatility, accuracy, and high sensitivity of liquid chromatography-mass spectrometry (LC-MS) make it a good method for compound identification. Currently, several platforms have invested algorithms and software suites for untargeted metabolomics16,17,18,19,20. The core of these programs includes feature detection (peak picking)21 and peak alignment, which allows match of identical features across a batch of samples and searching for patterns. MS pattern-based algorithms22,23 compare characteristic fragmentation patterns and match MS2 similarities generating molecular families sharing structural features. These features can then be highlighted and clustered, conferring the ability to rapidly discover known and unknown molecules from a complex biological extract by tandem MS2,24,25. Therefore, tandem MS is a versatile method to gain structural information of several chemotypes contained in a large amount of data simultaneously.
The Global Natural Products Social Molecular Networking (GNPS)26 algorithm uses the normalized fragment ions intensity to construct multidimensional vectors, in which similarities are compared using a cosine function. The relationship between different parent ions are plotted in a diagram representation, in which each fragmentation is visualized as a node (circles), and the relatedness of each node is defined by an edge (lines). The global visualization of molecules from a single source is defined as a molecular network. Structurally divergent molecules that fragment uniquely will form their own specific cluster or constellation, whereas related molecules cluster together. Clustering chemotypes allows the hypothetical connection of similar structural features to their biosynthetic origins.
Combining both chemotype-to-genotype and genotype-to-chemotype approaches is powerful when creating bioinformatics links between BGCs and their small molecule products27. Therefore, MS-guided genome mining is a rapid method and low material-consuming strategy, and it helps bridge parent ions and biosynthetic pathways revealed by WGS of one or more strains under diverse metabolic and environmental conditions.
The workflow of this protocol (Figure 1) consists of feeding WGS data into a biosynthetic gene cluster annotation platform such as antiSMASH28,29,30. It helps estimate the variety of compounds and class of compounds encoded by the genome. A strategy to target a biosynthetic gene cluster encoding a chemical entity of interest must be adopted, and culture extracts from a wild type strain and/or heterologous strain containing the BGC can be analyzed to generate clustered ions based on similarities using GNPS26,31. Consequently, it is possible to identify new molecules that associate with the targeted BGC and are unavailable in the database (mainly unknown analogues, sometimes produced in low titers). It is relevant to consider that users can contribute to these platforms and that the availability of bioinformatics and MS/MS data is increasing rapidly, driving to a constant development and upgrade of effective computational tools and algorithms to guide efficient connections of complex extracts with molecules.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60825/60825fig1b.jpg" /> 
Figure 1: Overview of the entire workflow. Shown is an illustration of the bioinformatic, cloning, and molecular networking steps involved in the described MS-guided genome mining approach to identify new metabolites. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig1blarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
This protocol describes a rapid and efficient workflow to combine genome mining and molecular networking as starting point for the natural product discovery pipeline. Although many applications are able to visualize the composition and relatedness of MS-detectable molecules in one network, several are adopted here to visualize structurally similar clustered molecules. Using this strategy, novel cyclodepsipeptide products observed in metabolic extracts of Streptomyces sp. CBMAI 2042 are successfully identified. Guided by genome mining, the whole biosynthetic gene cluster encoding for valinomycins is recognized and cloned into the producer strain Streptomyces coelicolor M1146. Finally, following a MS pattern-based molecular networking, the molecules detected by MS are correlated with BGCs responsible for their biogenesis32.

<table><tbody><tr><td>Acetonitrile</td><td>Tedia</td><td>AA1120-048</td><td>HPLC grade</td></tr><tr><td>Agar</td><td>Oxoid</td><td>LP0011</td><td>NA</td></tr><tr><td>Apramycin</td><td>Sigma Aldrich</td><td>A2024</td><td>NA</td></tr><tr><td>Carbenicillin</td><td>Sigma Aldrich</td><td>C9231</td><td>NA</td></tr><tr><td>Centrifuge</td><td>Eppendorf</td><td>NA</td><td>5804</td></tr><tr><td>Chloramphenicol</td><td>Sigma Aldrich</td><td>C3175</td><td>NA</td></tr><tr><td>Column C18</td><td>Agilent Technologies</td><td>NA</td><td>ZORBAX RRHD Extend-C18, 80&amp;Aring;, 2.1 x 50 mm, 1.8 &amp;micro;m, 1200 bar pressure limit P/N 757700-902</td></tr><tr><td>Kanamycin</td><td>Sigma Aldrich</td><td>K1377</td><td>NA</td></tr><tr><td>Manitol P.A.- A.C.S.</td><td>Synth</td><td>NA</td><td>NA</td></tr><tr><td>Microcentrifuge</td><td>Eppendorf</td><td>NA</td><td>5418</td></tr><tr><td>Nalidixic acid</td><td>Sigma Aldrich</td><td>N4382</td><td>NA</td></tr><tr><td>Phusion Flash High-Fidelity PCR Master Mix</td><td>ThermoFisher Scientific</td><td>F548S</td><td>NA</td></tr><tr><td>Q-TOF mass spectrometer</td><td>Agilent technologies</td><td>NA</td><td>6550 iFunnel Q-TOF LC/MS</td></tr><tr><td>Sacarose P.A.- A.C.S.</td><td>Synth</td><td>NA</td><td>NA</td></tr><tr><td>Shaker/Incubator</td><td>Marconi</td><td>MA420</td><td>NA</td></tr><tr><td>Sodium Chloride</td><td>Synth</td><td>NA</td><td>P. A. - ACS</td></tr><tr><td>Soy extract</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Sucrose</td><td>Synth</td><td>NA</td><td>P. A. - ACS</td></tr><tr><td>Thermal Cycles</td><td>Eppendorf</td><td>NA</td><td>Mastercycler Nexus Gradient</td></tr><tr><td>Thiostrepton</td><td>Sigma Aldrich</td><td>T8902</td><td>NA</td></tr><tr><td>Tryptone</td><td>Oxoid</td><td>LP0042</td><td>NA</td></tr><tr><td>Tryptone Soy Broth</td><td>Oxoid</td><td>CM0129</td><td>NA</td></tr><tr><td>UPLC</td><td>Agilent Technologies</td><td>NA</td><td>1290 Infinity LC System</td></tr><tr><td>Yeast extract</td><td>Oxoid</td><td>LP0021</td><td>NA</td></tr></tbody></table>

mass spectrometry-guided genome mining as a tool to uncover novel natural products

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation of their functions in nature and potential human benefits (e.g., for drug discovery applications) are desired. This protocol describes the combination of genome mining (GM) and molecular networking (MN), two contemporary approaches that match gene cluster-encoded annotations in whole genome sequencing with chemical structure signatures from crude metabolic extracts. This is the first step towards the discovery of new natural entities. These concepts, when applied together, are defined here as MS-guided genome mining. In this method, the main components are previously designated (using MN), and structurally related new candidates are associated with genome sequence annotations (using GM). Combining GM and MN is a profitable strategy to target new molecule backbones or harvest metabolic profiles in order to identify analogues from already known compounds.

The protocol was successfully exemplified using a combination of genome mining, heterologous expression, and MS-guided/code approaches to access new specialized valinomycin analogues molecules. The genome-to-molecule workflow for the target, valinomycin (VLM), is represented in Figure 8. Streptomyces sp. CBMAI 2042 draft genome was analyzed in silico, and the VLM gene cluster was then identified and transferred to a heterologous host. Heterologous and wild type strains were cultivated in triplicate using proper fermentation conditions, partitioned with ethyl acetate, and concentrated to generate the crude extract. From the product, MS/MS data was acquired to generate a tandem MS metabolite profile for molecular networking. Figure 9 represents the clustered ions obtained from MS/MS data from Streptomyces sp. CBMAI 2042 crude extract, in which characteristic fragmentation patterns and corresponding MS similarities suggest the occurrence of a molecular family sharing structural features2. Following known biosynthetic logic and bioinformatics insights, and supported by pattern-based MS/MS spectra, the structure of four originally reported cyclodepsipeptides were elucidated, and their origins were correlated with the same biosynthetic gene clusters responsible for VLM assembly32.
Molecular networking data (found at &#60;https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=6f97aa4addfa4d20b505fdb4328b088c&#62;) was processed in a GNPS platform and deposited in a MASSIVE repository (MSV000083709). For dereplication, two strategies were selected to populate the network with previously described compounds: 1) Dereplicator (found at &#60;https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=1a55e768d02649aaa09d78d0d4778ef3&#62;) and 2) a peptide natural product identification tool called VarQuest (found at Our previous publication provides further details32.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/60825/60825fig8a.jpg" /> 
Figure 8: Workflow from in silico genome sequence analysis to MS data acquisition. (A) A draft from Streptomyces sp. CBMAI 2042 genome is obtained by Illumina MiSeq sequencing. (B) Valinomycin BGC identification and annotation. (C) After transferring the whole gene cluster to an appropriate host, the strain is cultivated. The ethyl acetate extract from culture is analyzed by LC to obtain a profile of produced secondary metabolites. The chromatogram shows that valinomycin, montanastatin, and five analogues are produced by VLM BGC expression in a heterologous host. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig8alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/60825/60825fig9.jpg" /> 
Figure 9: Molecular networking results. (A) Molecular networking from Streptomyces sp. CBMAI 2042 extract. Molecular networking ions corresponding to valinomycin, an already known compound with the corresponding BGC annotated in Streptomyces sp. CBMAI 2042 genome, are clustered with ions related to analogues firstly described for VLM BGC. (B) MS spectra and chemical structures for valinomycin and related analogues are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60825/60825fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products at JoVE.com

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

A mass spectrometry-guided genome mining protocol is established and described here. It is based on genome sequence information and LC-MS/MS analysis and aims to facilitate identification of molecules from complex microbial and plant extracts.

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation ...

mass-spectrometry-guided-genome-mining-as-tool-to-uncover-novel

Nanyang Technological University

Research

JoVE Journal

Chemistry

10.8K Views.  University of Campinas (UNICAMP). A mass spectrometry-guided genome mining protocol is established and described here. It is based on genome sequence information and LC-MS/MS analysis and aims to facilitate identification of molecules from complex microbial and plant extracts.

Video: Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

From a Natural Product to Its Biosynthetic Gene Cluster: A Demonstration Using Polyketomycin from Streptomyces diastatochromogenes T&#252;6028

Streptomyces strains are known for their capability to produce a lot of different compounds with various bioactivities. Cultivation under different conditions often leads to the production of new compounds. Therefore, production cultures of the strains are extracted with ethyl acetate and the crude extracts are analyzed by HPLC. Furthermore, the extracts are tested for their bioactivity by different assays. For structure elucidation the compound of interest is purified by a combination of different chromatography methods.
Genome sequencing coupled with genome mining allows the identification of a natural product biosynthetic gene cluster using different computer programs. To confirm that the correct gene cluster has been identified, gene inactivation experiments have to be performed. The resulting mutants are analyzed for the production of the particular natural product. Once the correct gene cluster has been inactivated, the strain should fail to produce the compound.
The workflow is shown for the antibacterial compound polyketomycin produced by Streptomyces diastatochromogenes T&#252;6028. Around ten years ago, when genome sequencing was still very expensive, the cloning and identification of a gene cluster was a very time-consuming process. Fast genome sequencing combined with genome mining accelerates the trial of cluster identification and opens up new ways to explore biosynthesis and to generate novel natural products by genetic methods. The protocol described in this paper can be assigned to any other compound derived from a Streptomyces strain or another microorganism.

Here we present a detailed protocol of (A) the identification of a natural product with antibiotic activity, (B) the purification of the compound, (C) the first model of its biosynthesis, (D) genome sequencing/-mining and the (E) verification of the biosynthetic gene cluster.

Streptomyces strains are known for their capability to produce a lot of different compounds with various bioactivities. Cultivation under different ...

Genetics

Novel Sequence Discovery by Subtractive Genomics

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a larger genomic context. Subtractive genomics enables a researcher to isolate a target sequence of interest (T) by comprehensive sequencing and subtracting out known genetic elements (reference, R). The method can be used to identify novel sequences such as mitochondria, chloroplasts, viruses, or germline restricted chromosomes, and is particularly useful when T cannot be easily isolated from R. Beginning with the comprehensive genomic data (R + T), the method uses Basic Local Alignment Search Tool (BLAST) against a reference sequence, or sequences, to remove the matching known sequences (R), leaving behind the target (T). For subtraction to work best, R should be a relatively complete draft that is missing T. Since sequences remaining after subtraction are tested through quantitative Polymerase Chain Reaction (qPCR), R does not need to be complete for the method to work. Here we link computational steps with experimental steps into a cycle that can be iterated as needed, sequentially removing multiple reference sequences and refining the search for T. The advantage of subtractive genomics is that a completely novel target sequence can be identified even in cases in which physical purification is difficult, impossible, or expensive. A drawback of the method is finding a suitable reference for subtraction and obtaining T-positive and negative samples for qPCR testing. We describe our implementation of the method in the identification of the first gene from the germline-restricted chromosome of zebra finch. In that case computational filtering involved three references (R), sequentially removed over three cycles: an incomplete genomic assembly, raw genomic data, and transcriptomic data.

The purpose of this protocol is to use a combination of computational and bench research to find novel sequences that cannot be easily separated from a co-purifying sequence, which may be only partially known.

Subtractive genomics can be used in any research where the goal is to identify the sequence of a gene, protein, or general region that is embedded in a ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Environment

Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural features, many compounds within the same class undergo similar MS/MS fragmentation and have several identical product ions and/or neutral losses. The purpose of diagnostic fragmentation filtering (DFF) is to efficiently detect all compounds of a given class in a complex extract by screening non-targeted LC-MS/MS datasets for MS/MS spectra that contain class specific product ions and/or neutral losses. This method is based on a DFF module implemented within the open-source MZmine platform that requires sample extracts be analyzed by data-dependent acquisition on a high-resolution mass spectrometer such as quadrupole Orbitrap or quadrupole time-of-flight mass analyzers. The main limitation of this approach is the analyst must first define which product ions and/or neutral losses are specific for the targeted class of natural products. DFF allows for the subsequent discovery of all related natural products within a complex sample, including new compounds. In this work, we demonstrate the effectiveness of DFF by screening extracts of Microcystis aeruginosa, a prominent harmful algal bloom causing cyanobacteria, for the production of microcystins.

Diagnostic fragmentation filtering, implemented into MZmine, is an elegant, post-acquisition approach to screen LC-MS/MS datasets for entire classes of both known and unknown natural products. This tool searches MS/MS spectra for product ions and/or neutral losses that the analyst has defined as being diagnostic for the entire class of compounds.

Natural products are often biosynthesized as mixtures of structurally similar compounds, rather than a single compound. Due to their common structural ...

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Neuropeptides are signaling molecules that regulate almost all physiological and behavioral processes, such as development, reproduction, food intake, and response to external stressors. Yet, the biochemical mechanisms and full complement of neuropeptides and their functional roles remain poorly understood. Characterization of these endogenous peptides is hindered by the immense diversity within this class of signaling molecules. Additionally, neuropeptides are bioactive at concentrations 100x - 1000x lower than that of neurotransmitters and are prone to enzymatic degradation after synaptic release. Mass spectrometry (MS) is a highly sensitive analytical tool that can identify, quantify, and localize analytes without comprehensive a priori knowledge. It is well-suited for globally profiling neuropeptides and aiding in the discovery of novel peptides. Due to the low abundance and high chemical diversity of this class of peptides, several sample preparation methods, MS acquisition parameters, and data analysis strategies have been adapted from proteomics techniques to allow optimal neuropeptide characterization. Here, methods are described for isolating neuropeptides from complex biological tissues for sequence characterization, quantitation, and localization using liquid chromatography (LC)-MS and matrix-assisted laser desorption/ionization (MALDI)-MS. A protocol for preparing a neuropeptide database from the blue crab, Callinectes sapidus, an organism without comprehensive genomic information, is included. These workflows can be adapted to study other classes of endogenous peptides in different species using a variety of instruments.

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

Mass spectrometric characterization of neuropeptides provides sequence, quantitation, and localization information. This optimized workflow is not only useful for neuropeptide studies, but also other endogenous peptides. The protocols provided here describe sample preparation, MS acquisition, MS analysis, and database generation of neuropeptides using LC-ESI-MS, MALDI-MS spotting, and MALDI-MS imaging.

Neuropeptides are signaling molecules that regulate almost all physiological and behavioral processes, such as development, reproduction, food intake, and ...

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an ...

Biochemistry

Classification of Tropane Alkaloids from Plant Metabolites Using LC-MS/MS

Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study

Although many drugs utilized today are synthetic in origin, natural products still provide a rich source of novel chemical diversity and bioactivity, and can yield promising leads for resistant or emerging diseases. The challenge, however, is twofold: not only must researchers find natural products and elucidate their structures, but they must also identify what is worth isolating and assaying (and what is already known - a process known as dereplication). With the advent of modern analytical instrumentation, the pace of natural product discovery and dereplication has accelerated. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an especially valuable technique for identifying and classifying chemical structures. Tropane alkaloids (TAs) are plant-derived compounds of great medicinal and toxicological significance. In this study, we developed an LC-MS/MS-based screening workflow utilizing the multiple MS/MS configurations available on a triple-quadrupole (QQQ) mass spectrometer to annotate and classify TA structures based on their distinct fragmentation patterns. By using a combination of data-dependent (DD) product ion scans, precursor ion scans (PrIS), and neutral loss scans (NLS), we applied this method to TA-rich extracts of the nightshades Datura stramonium and Datura metel. This method is rapid, sensitive, and was successfully employed for both preliminary dereplication of complex TA-containing samples and for the discovery of a novel candidate for isolation, purification (and eventual bioassay).

Author Spotlight: Discovering New Alkaloids in Plants with Advanced Mass Spectrometry Techniques

We present a method for rapid mass spectrometry (MS)/mass spectrometry (MS)-based annotation and classification of tropane alkaloids, useful for both preliminary dereplication of tropane-containing samples and discovery of novel alkaloids for isolation.

Although many drugs utilized today are synthetic in origin, natural products still provide a rich source of novel chemical diversity and bioactivity, and ...

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

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

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

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

Biology

Galaxy-Enabled Metaproteomics: Insights into Host-Microbiome Interaction

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

Summary

Explore More Videos

Chapters in this video

From a Natural Product to Its Biosynthetic Gene Cluster: A Demonstration Using Polyketomycin from Streptomyces diastatochromogenes Tü6028

Novel Sequence Discovery by Subtractive Genomics

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Multi-Faceted Mass Spectrometric Investigation of Neuropeptides in Callinectes sapidus

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Applications of Liquid-Chromatography Tandem Mass Spectrometry in Natural Products Research: Tropane Alkaloids as a Case Study

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

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease