In this study we identified commonalities in the transcriptional responses of structurally diverse hERG inhibitors, suggesting microarrays as a novel proxy measurement correlated with conduction of potassium currents by hERG and hence liability of channel block. Perhaps as remarkably, the observed change in gene expression is not necessarily a direct result of hERG inhibition. While hERG inhibitor-enriched clusters were observed for profiles generated in all three cell lines, functional evidence for hERG expression has been reported only for MCF7 and HL60 [23
], indicating that channel expression may not be strictly required for the observed pattern. Indeed, there are also reports that channels including hERG have activities in addition to ion conductance [44
], indicating that independent molecular targets might converge on common signaling pathways or processes also modulated by hERG and leading to the observed correlation. For example, there is a tendency that hERG inhibitors or LQT-causing drugs are also antagonists of the multidrug resistance transporter (MDR) [47
]. Alternatively, hERG may be co-expressed with other channels correlated with oncogenesis which possess similar pharmacological profiles, such as hEAG [48
], thus confounding causal inference of the relationship between hERG activity and gene expression response.
We also note that the presence of inhibitors in the CMap outside the enriched clusters highlighted in our analysis indicates that this “hERG signature” is not necessarily “dominant” over other expression pattern(s), implying that other such patterns might perturb or mask the signature from being identified for some compounds. In this interpretation, the subset of clustered inhibitors highlighted in our analysis represent drugs for which this signature is dominant over or of equal strength with other expression responses of the compounds. Additionally, we note that a large portion of the compounds in this dataset exhibit silent or weak transcriptional response which prevents profiling for hERG inhibition using the proposed approach. As the signatures in CMap are uniformly generated at a 6 hour time point, it is possible that some compounds may display chronic transcriptional effects at a later time point, and thus be profiled by modifications in the original screening protocol. Indeed, previous studies of time course data from drug-induced gene expression responses have indicated that distinct expression patterns may be detected at different time points [49
], even for frequent measurements such as 3, 6, and 9 hours. We thus hypothesize that some of the ‘silent compounds’ in our study might have detectable signatures at later time points, while the hERG inhibitors outside of enriched clusters may exhibit a dominant ‘hERG signature’ at earlier time points. Taken together, these results suggest that improved sensitivity for this assay might be achieved by using time course instead of single point expression data. Additionally, we note that the sensitivity of our assay may be effected by our choice of a 10 µM IC50
threshold. While this threshold has been used in previous hERG predictive models [52
], previous studies have also reported greater accuracy with a lower threshold (e.g., IC50
40 µM) [53
]. Thus it may also be possible to improve the sensitivity of our method using inhibition measurements derived from higher drug concentrations.
Research has also suggested that the duration of action potentials at 90% repolarization (APD90), a correlate of clinical LQT which is elongated by hERG inhibition, may be dependent upon multi-channel drugs effects [33
], and thus the ability of our approach to forecast clinical endpoints may be aided by future integration of high-throughput recording data for other cardiac channels such as Nav1.5. Furthermore, despite the current lack of causal evidence linking the gene-expression profiles of the clustered hERG inhibitors in the CMap with functional modulation of the channel, this analysis does suggest an intriguing possibility that some hERG inhibitors induce a downstream signaling cascade as a consequence of current reduction that is visible as a global change in gene expression. Alternatively, these observations may indicate signaling pathways downstream of potassium channels that are not directly related to their role in conduction [44
]. A selection of these hypotheses is diagrammed in . Certainly, profiling selective inhibitors of hERG such as E4031 which are not present in the CMap might help clarify these hypotheses, though the large number of transcriptionally silent compounds in the dataset suggests these selective inhibitors may not exhibit a detectable signature at 6 hour time points.
Mechanistic hypotheses for hERG-inhibition correlated gene expression signatures.
From a practical standpoint, the observed similarity of microarray profiles among electrophysiologically confirmed but structurally diverse inhibitors argues for the potential of using such a surrogate as an informative descriptor for hERG liability complementing existing electrophysiological assays. The utility of such a platform is suggested by compounds such as the antidepressant Amoxapine [54
] which display slow on-rates beyond the temporal resolution of high throughput electrophysiological systems (which is typically less than 5 minutes), and thus appear as ‘negatives’ in our acute experimental electrophysiology data. In contrast, the microarray data utilized in this study were generated 6 hours following drug treatment, suggesting gene expression measurements may offer complementary temporal resolution not readily accessible by automated electrophysiology data, allowing high-throughput assessment of hERG inhibition in compounds with slow on-rates which have previously required manual patch clamp recordings to resolve. Furthermore, transcriptional signatures may identify false negatives from other assays, such as Sulconazole, which was labeled as inactive in ChemblDB from binding data. Because binding experiments often utilize displacement of a known ligand, they will not identify compounds binding at alternative sites(s) of action.
Gene expression measurements have additional attractive properties compared to other high-throughput technologies. Because microarray profiles represent an integrated output of multiple signaling pathways in the cell, they are potentially more sensitive than biochemical or cellular assays which are commonly designed to test one or a limited number of physiological parameters. Such expression profiles are also certainly more general in terms of measuring diverse signaling pathways and integrated biological events. Thus, assessment of hERG liability may be effectively evaluated in parallel with other endpoints of biological interest, such as inflammatory signaling, oxidative damage response, or metabolic perturbations. Additionally, the fact that our signature utilizes measurements in cancer cells derived from different tissues of origin suggests the attractive possibility of assaying the effects of hERG activity in these oncogenesis models, as previous research has linked hERG expression to tumor migration and cell volume [24
]. Admittedly, cells with cardiac lineage may be equally or more informative. Indeed, patient-derived induced pluripotent stem cell (iPSC) models of cardiac disease have proven to be attractive disease models in electrophysiology studies [17
], with additional evidence suggesting the potential for cardiac-specific transcriptional activity that may find utility in genomic drug-activity profiles [57
]. Combined with cost savings generated by custom arrays that measure only the subset of differentially expressed genes correlated with hERG risk, these aspects suggest the potential for a novel genetic platform to assess ion channel activity.
More generally, our analysis contributes to growing evidence that systems-level measurements of drug effect reveal connections and similarities often invisible from the perspective of single molecular descriptors or activity measurements [6
]. These links suggest not only the possibility of mining such connections for predictive purposes, but also that the full pharmacological complexity of even long-standing medications may not yet be appreciated. Integrated analyses are thus poised to illuminate these patterns and suggest possibly novel indications or, as in our study, liabilities of existing drugs.