A substrate-based ontology for human solute carriers

Abstract

Solute carriers (SLCs) are the largest family of transmembrane transporters in the human genome with more than 400 members. Despite the fact that SLCs mediate critical biological functions and several are important pharmacological targets, a large proportion of them is poorly characterized and present no assigned substrate. A major limitation to systems-level de-orphanization campaigns is the absence of a structured, language-controlled chemical annotation. Here we describe a thorough manual annotation of SLCs based on literature. The annotation of substrates, transport mechanism, coupled ions, and subcellular localization for 446 human SLCs confirmed that ~30% of these were still functionally orphan and lacked known substrates. Application of a substrate-based ontology to transcriptomic datasets identified SLC-specific responses to external perturbations, while a machine-learning approach based on the annotation allowed us to identify potential substrates for several orphan SLCs. The annotation is available at https://opendata.cemm.at/gsflab/slcontology/. Given the increasing availability of large biological datasets and the growing interest in transporters, we expect that the effort presented here will be critical to provide novel insights into the functions of SLCs.

Keywords: SLCs; annotation; de-orphanization; ontology; solute carriers.

Figure 1. Manually curated annotation of 446 human SLC transporters

1. A

   Frequencies of unknown annotations for 446 SLCs in four annotation categories.

2. B–E

   Distribution of annotated terms for substrate class, coupled ions, transport mechanism, and subcellular localization. Terms annotated to less than ten SLCs were summarized as “Other” in (C) and (E).

Figure 2. Construction of an SLC substrate‐specific ontology from ChEBI ontology

1. Individual steps in ontology creation workflow.

2. Distribution of the number of SLCs per ontology term. Red line indicates the median value.

3. Distribution of the number of ontology terms associated with one SLC. Red line indicates the median value.

4. Exemplified visualization of term “L‐alpha amino acid (aa)” and its sub‐terms. This is a sub‐graph and SLC substrates (gray) are connected to more terms in the full ontology. Please refer to Fig EV1 for an extended example of the term “amino acid” and its sub‐terms.

5. Visualization of the resulting SLC‐specific ontology: role sub‐ontology (left) and chemical entity sub‐ontology (right).

Figure EV1. Exemplified visualization of term “amino acid” and sub‐terms

The proteinogenic amino acids can be found in the lower left area of the network and are grouped metabolically (blue nodes on the left) as well as physicochemically (blue nodes in the right). Please note that this is a sub‐graph of the full ontology and SLC substrates (gray) might be connected to more terms in the full ontology.

Figure EV2. Upregulation of amino acid transporter gene expression in HEK293T and MCF7 cells after amino acid deprivation conditions

1. A, B

   Number of (A) differentially expressed genes and of (B) up‐ and downregulated SLC genes for different amino acid depletion conditions in HEK293T cells.

2. C, D

   Number of (C) differentially expressed genes and of (D) up‐ and downregulated SLC genes for different amino acid depletion conditions in MCF7 cells.

Figure 3. Application of SLC substrate ontology and prediction of substrates for orphan SLCs

1. A, B

   Ontology term enrichment analysis in set of SLCs upregulated in (A) HEK293T and (B) in MCF7 cells after amino acid (aa) deprivation conditions using SLC ontology terms and GO terms (TMT: transmembrane transporter). Enrichments were calculated using Fisher's exact test. For simplification, only enrichment of the most specific terms is shown in (A,B), for complete version see Fig EV3.

2. C

   Area under the receiver operating characteristic (AUROC) and under the precision recall curve (AUPRC) derived from out‐of‐bag (OOB) error estimates for random forest classifiers for the 18 selected SLC substrate terms.

3. D

   Statistical performance measures for the binary classifiers from OOB estimates.

4. E

   Predicted substrate term probabilities for orphan SLCs, normalized to a decision threshold of 0.5.

Figure EV3. Unfiltered SLC ontology enrichment results

1. A, B

   SLC ontology term enrichment analysis in upregulated SLCs (all terms) in (A) HEK293T cells and (B) in MCF7 cells. Enrichments were calculated using Fisher's exact test.

Figure EV4. Substrate probabilities for orphan SLCs

Probabilities were normalized to a decision threshold of 0.5.