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 ...

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