1. Metabolite Preprocessing (Optional)

To get started with MetaBridge, you'll need to upload your list of compounds as HMDB or KEGG IDs. If you are starting with raw metabolomics data (i.e. compounds names, such as glucose), you will need to preprocess your metabolites with your method of choice to obtain their corresponding HMDB or KEGG IDs. We recommend MetaboAnalyst for metabolite preprocessing and ID mapping, available by selecting "Other Utilities" then "Compound ID Conversion."

MetaBridge mapping

2a. Upload metabolites

Navigate to MetaBridge and upload the file containing your metabolite IDs. Then, select the column to be used in the mapping and the matching ID type (HMDB or KEGG). We'll use KEGG IDs for this tutorial.

2b. Map metabolites

Choose a database to map against, and view your mapping results. Here, we've mapped our metabolites via KEGG, to maximize the number of genes MetaBridge returns.

2c. Download Results

Use the button to download your full mapping results from MetaBridge - the table below shows the first few rows from our mapped data.

Compound	KEGG	HMDB	Enzyme	Enzyme Name	Gene Name	Entrez
Urea	C00086	HMDB00294	3.5.3.1	arginase	ARG1	383
Urea	C00086	HMDB00294	3.5.3.1	arginase	ARG2	384
Urea	C00086	HMDB00294	3.5.3.4	allantoicase	ALLC	55821
Urea	C00086	HMDB00294	3.5.3.11	agmatinase	AGMAT	79814
Pyruvate	C00022	HMDB00243	1.1.1.27	L-lactate dehydrogenase	LDHAL6A	160287

3. NetworkAnalyst

3a. Upload mapped genes

Visit NetworkAnalyst and select [Gene List Input] from among the possible options.

Follow the steps below to upload your gene lists to NetworkAnalyst:

• Paste in the genes that you received from MetaBridge
• Repeat this for each gene or protein list you wish to integrate, separating each list with a `//` as instructed

For this tutorial, we've uploaded two gene lists - our mapped metabolites from MetaBridge, and the endotoxin tolerance signature.

Once you have uploaded all of your gene/protein lists, select "Homo sapiens" as the organism, and "Official gene symbol" as the ID type.

Next, click [Proceed], then choose [Generic PPI] which will open a dialog box. At the top, select "Union of all lists" to create the combined network, then choose the literature-curated IMEX Interactome as your protein-protein interaction (PPI) database. Click [OK], then [Proceed] to continue.

3b. Network creation

Now, a PPI network will be generated based on the data you have uploaded. Depending on the size of the dataset you have uploaded, you may wish to choose a different network type from the list of options on the lefthand side. Generally, we recommend using a minimum-connected PPI network. However, if your data are particularly sparse, you may need to use first-order interaction networks, whereas if your dataset is particularly large, you may wish to use a zero-order interaction network. Typically, we aim for a network containing a few hundred nodes. Try the different options and see how the number of nodes/edges change, then pick the appropriate type and [Proceed] to visualization.

Below, you will find an example of each minimum-connected network generated from the two individual gene lists, and the combined list.

1. Minimum-connected PPI network from our MetaBridge-mapped metabolites.

2. Minimum-connected PPI network from the endotoxin tolerance signature.

3. Minimum-connected PPI network from combined gene list.

3c. Analyze the Networks

For each network you create, you can extract various information and perform a few different analyses. For example, you can use the [Function Explorer] panel on the right side to perform pathway enrichment on the current network using a number of databases, such as Reactome or KEGG. On the lefthand side, you can use the [Node Explorer] to view information about individual nodes of the network, and [Save] the entire node list as a CSV file for further analysis outside of NetworkAnalyst.

You can also tweak the network appearance in a number of ways. By increasing the node size, more labels will be added to the nodes in your network. Or you can paste in your original lists to the [Batch Selection] panel on the right, and colour-code each node of the network based on its original data source (e.g. from metabolomics or transcriptomics). Then, you can download your network as an image (e.g. PNG or SVG) or network file (e.g. graphML or JSON).