Name: mass spectrometry-guided genome mining as a tool to uncover novel natural products
Uploaded: 2020-03-12T13:00:00
Description: Watch this Scientific Journal Video about Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products at JoVE.com

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 Illum...

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 profil...

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/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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#197;, 2.1 x 50 mm, 1.8 &#181;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 cultivat...

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 ...

Whole genome sequencing, genome mining and molecular networking constitutes the base for the mass spectrometry guide that genome mining protocol that we describe here. And are the most contemporary approach in natural product discovery. Genome mining and molecular networking match in cluster, in coded annotations in whole genome sequencing with chemical restructure signatures from crude extracts providing massive prospects for uncovering navel by active molecules.In principle, genome mining targets a single or a small group of molecules per experiment. Thereby resulting in a slow discovery rate. In this sense, using genome mining along with molecular networking represents an important advance for natural products research.Mass spectrometry guide-genome mining is a versatile method which can structure information of several chemotypes contained in the large amount of data from a crude extract simultaneously. Today it is high range methodology capable to explore bacterial, fungal or plants sequence providing. Chemotype to genotype and genotype to chemotype bioinformatics links between biosynthetic clusters and are small molecules products.Hearing the protocol is demonstrated to characterize navel cycle depths Peptide products observed in metabolic extracts of streptomyces species. But it's applicable to fungal and plants sequence and extracts as well. It's very important to have nicely curated genome dataset and nowadays fragmented draft sequences have increasingly being supplanted by near finished sequence data.Also, different strategies have been developed to access new bioactive natural products and certainly combining them with MS-Guide approach. We will lead to improvement in natural products isolation and structure elucidation. As here we are connecting genotype to chemotype approach, bioinformatics links between biosynthetic clusters and there's small molecule products are crucial.to estimate the variety and class of compounds and encode by genome. Which means the replication. Later, clustering based on ion similarities can be preformed by using GNPS.Demonstrating the procedure will be Dr Renata Sigrist and Professor Angolini my collaborators in this study. To obtain in silico information about secondary metabolism gene clusters annotations from a complete sequence genome. Submit the sequence file, gene bank E-M-B-L or faster format, to an anti-smash platform.So like the gene clusters of interest from the output data based on the most similar known cluster. Based on the DNA sequence information of the BGC, design primaries between 20 to 25 nucleotides flanking the gene cluster for ecoli streptomyces. Artificial chromosome library screening.After obtaining the recombinant heterologous organism according to the manuscript, inoculate one 100th of the strains preculture in appropriate fermentation media. Such as liquid ISP2 media. Place the culture on an incubator at 30 degree Celsius and 220 RPM, for seven days.After inoculation, centrifuge the cultures at 2200 times G for 10 minutes. Discard the cells and transfer the supernatant into a separatory funnel. Add one to one volume of ethyl acetate and shake.Wait one to three minutes for the organic and acquiesced layers to separate. And collect the organic layer in an Erlenmeyer flask. To acquire MS/MS data.Program suitable HPLC and mass spectrometry methods using the control software. It will be noted that MS/MS networking is the detectable molecular network under the given mass Spectrometric conditions. Convert mass spectra to mzXML format using MS Convert from protea wizard.Adjust the input parameters for the conversion. Upload the converted LC-MS/MS files into the GNPS database. Two options are available.Using a file transfer protocol or directly in a browser through the online platform. After creating an account in GNPS, log in to the created account and select create a molecular network. Add a job title.To perform basic options. Select the mzXML files to perform the molecular network. Organize them in up to six groups.So like the libraries for the dereplication routine. Select the precursor ion mass tolerance and fragment Da on mass tolerance of 0.02 Dalton and 0.05 Dalton respectively. To perform advanced network options, select the parameters which directly influenced the network cluster size and form.Advanced parameters are in GNPS documentation. Choose an email address to receive an alert when the work is done and submit the job. To analyze GNPS results.Log into GNPS. Select jobs, published job, done to open a job. A webpage opens with all results obtained from molecular networking displayed.Select view spectral families in browser network visualizer to see all network clusters. A list is displayed with all generated molecular networking clusters. Tentative molecular identification is displayed in all IDs.Select show to visualize them. To analyze the molecular network cluster, select visualize network. In the node labels box, select parent mass.In the edge labels box, select co-sign or Delta Emz. To observe node similarity or mass difference between nodes respectively. In the case of multi-group analysis, click draw pies in the node coloring box.To observe the frequency at which each node appears in each group. To see all libraries, select view, all library, hits. Open the fragmentation spectra directly in the GNPS platform.Or in original raw files to manually confirm dereplication compounds and structure elucidation of related compounds. The protocol was successfully exemplified using a combination of genome mining, heterologous expression and MS-guided code approaches. To access new specialized Valinomycin and analog molecules.The genome to molecule workflow for the target valinomycin is presented here. The chromatogram showed that valinomycin, montanastatin and five analogs were produced by valinomycin, biosynthetic gene clusters expression in a heterologous host. Molecular networking ions corresponding to valinomycin, an already known compound with the corresponding biosynthetic gene clusters annotated in streptomyces species, CBM AI two zero four two genome.Are clustered with ions related to analogs, firstly described for valinomycin biosynthetic gene cluster. The MS spectra and chemical structures for valinomycin and related analogs, are shown here. Perform whole genome sequencing and proper dataset curating is the first step to electing a biosynthetic cluster for MS-guide the genome mining.The whole procedure can be performed with the wild types string crude extract, but you can opt to select the gene cluster of your interest and promote heterologous expression. Different methods are described the to capture the whole biosynthetic gene cluster from a DNA sample. The strongest advantage of this protocol is its ability to rapidly replicates metabolic profiles and breed genomic information with MS data.In order to elucidate these such as of new molecules. And mass guide-genome mining was firstly described in the field of peptide genomics and glycogenomics by Doris Chime and colleagues. And after the introduction of the NPS integrated metabolomics and genome mining approach have become the most versatile avenue to connect molecular networks with biosynthetic capabilities.

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