The role of bioinformatics in target validation

Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis

BACKGROUND

Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks.

RESULTS

According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets.

CONCLUSIONS

Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.

Probing orientations of single fluorescent labels on a peptide reversibly binding to the human delta-opioid receptor

We report the first in-depth study of single-molecule polarization behavior of a species that is undergoing reversible binding with its biological receptor. We examine the utility of the information in single-molecule polarization measurements for investigations of binding. The human delta-opioid receptor, which is a G protein-coupled receptor, was incorporated into a supported lipid bilayer. A Cy3 label was covalently attached by a hydrophilic linker to a peptide agonist, Deltorphin II (5,6 Ile-Ile). The fluorescence excitation was alternated between s- and p-polarization using a microscope having the capability of total internal reflectance fluorescence (TIRF) excitation. The polarization behavior reveals that nonspecific binding events for this system give emission that is mostly s-polarized, while binding to the receptor gives emission that has a strong component of p-polarization. The results show that a high signal-to-noise ratio is achievable with single-molecule polarization measurements. The experiment detected 37 binding events of short duration (<30 s) and 35 binding events of long duration (from 30 s to 500 s). The polarization studies indicate that the receptors in the bilayer do not freely rotationally diffuse in the plane of the bilayer when the peptide is bound. The system exhibits two types of polarization behavior. One type has the dye label with fixed orientation, which sometimes abruptly switches. The other type has the dye orientation continuously fluctuating over time, typically exhibiting occasional periods of fixed orientation. For a long binding event of fixed orientation, it is established through analysis of the variance that the orientation actually is fluctuating through a range of angles on the order of 6 degrees. It is shown that precise measurements of reorientation are achievable, with a detection limit of 1.3 degrees for a typical single-molecule signal.

Asymmetric functioning of dimeric metabotropic glutamate receptors disclosed by positive allosteric modulators

The recent discovery of positive allosteric modulators (PAMs) for G-protein-coupled receptors open new possibilities to control a number of physiological and pathological processes. Understanding the mechanism of action of such compounds will provide new information on the activation process of these important receptors. Within the last 10 years, a number of studies indicate that G-protein-coupled receptors can form dimers, but the functional significance of this phenomenon remains elusive. Here we used the metabotropic glutamate receptors as a model, because these receptors, for which PAMs have been identified, are constitutive dimers. We used the quality control system of the GABA(B) receptor to generate metabotropic glutamate receptor dimers in which a single subunit binds a PAM. We show that one PAM/dimer is sufficient to enhance receptor activity. Such a potentiation can still be observed if the subunit unable to bind the PAM is also made unable to activate G-proteins. However, the PAM acts as a non-competitive antagonist when it binds in the subunit that cannot activate G-proteins. These data are consistent with a single heptahelical domain reaching the active state per dimer during receptor activation.