Terminology summary
Transducer: For the purpose of defining biased signaling
initiated by the GPCRs, transducers are defined as proteins that bind
directly to an activated receptor to initiate downstream signaling
events. This includes G proteins, GRKs and arrestins. Some also use
‘primary’ effector to denote a transducer, although this word can be
confusing for these protein families as they typically are engaged
consecutively in a signaling cascade (although all bind to the
receptor).
Effector: Downstream protein that is a node of the
pathway/signaling cascade, i.e., following the transducer. Some also use
‘secondary’ transducer to denote an effector.
Modulator: Proteins or molecules that interact with the
receptor, transducer or effectors to modify the signaling response
without mediating it. Examples include RAMPs, GEFs, GAPs, RGSs, NO,
cholesterol, other lipids etc.
Second messenger: Small molecules or ions directly
controlled by the effectors. Changes in second messenger homeostasis
mediate cellular responses and can serve as a quantifiable measurement
of GPCR activation. Examples include cAMP, calcium, etc.
Pathway: A pathway is named after a transducer protein,
or family thereof, that binds to GPCRs and elicits a distinct downstream
signaling cascade or cellular response profile. This includes G proteins
and their families – i.e., the Gs,
Gi/o, Gq/11, and G12/13.
It also includes the arrestin and GPCR kinase (GRK) families, which can
be recruited to activated GPCRs either dependent or independent of
functionally active G protein heterotrimers.
Ligand bias definition and distinction from receptor and
system
bias
This paper focuses on ‘ligand-dependent bias’ i.e.,
cases where a receptor’s signaling pathway engagement changes as a
function of the addition of a given ligand. Quantification of bias
typically compares only two transducer pathways at a time and includes
the pathway with the strongest signaling. An exhaustive quantitative
comparison of all pathways would therefore be constituted by a profile
of pairwise comparisons. Quantified bias measures the change in
transducer-pathway preference relative to a reference ligand (Table 1)
and is therefore a comparison of both pathways and ligands (like a
quantitative rank order). In contrast, ‘non-quantitative
bias’ (previously termed ‘perfect bias’ or ‘full bias’) entails a
single ligand’s selective signaling through one pathway while the other
pathway(s) display no detectable signaling or signaling with another
modality (see section “Special recommendation for agonism versus
antagonism…”).
All ligand-independent mechanisms that may result in functional
selectivity are covered by the term “system bias” .
Functional selectivity due to system bias is independent from the
specific identity of the ligand (i.e. applies equally to all ligands of
the same modality), but depends on the properties of the system (i.e.
experimental setup, cell type, tissue, receptor reserve etc.). System
differences span e.g., constitutive selectivity of receptors for
different transducers, spatiotemporal expression levels of signaling
proteins (including receptor, transducers, effectors and other members
of the signaling pathways), presence or absence of proteins acting on
the receptor as allosteric modulators (like RAMPs (Hay & Pioszak, 2016)
or other modulators like kinases (Strachan, Sciaky, Cronan, Kroeze &
Roth, 2010), and finally, presence of intra- or inter-pathway feedbacks
are all determinants of system bias. The impact of signaling efficiency
of different pathways, on the manifestation of full or partial agonism
with agonists of different efficacy is another example of system bias.
In general, ‘functional selectivity’ is a combination of
ligand and system bias. Physiologically, this is exemplified by an
endogenous agonist regulating alternative physiological functions in
different cells/tissues often differentially expressing signaling
components. Some GPCRs lack the inherent capability to elicit G protein
coupling while exhibiting robust arrestin interaction (Rajagopal et al.,
2010; Shubhi Pandey, 2021). This gives all ligands functional
selectivity towards arrestin responses through system bias rather than
ligand bias. In drug discovery, this provides an opportunity to elicit
predominantly one of several physiological effects that a given receptor
can mediate by designing drugs that are transducer- or pathway-selective
(i.e. adjusting ligand bias on the background of system bias in the
tissues of interest) (Figure 1).
Experimental studies can suffer from so called
‘observational bias’ , which is an artificial bias
introduced by the experimental setup. For example, stronger signal
amplification in one of two compared pathways when measuring different
signaling processes at different levels (Figure 1). Another example
would be the use of different cells with different protein expression.
Another reason of observation bias is that the readout signals of the
studied ligand are below the assay’s sensitivity for one pathway, but
detectable in the other pathway. This case may be overcome by using more
sensitive assays or by increasing expression levels of the involved
signaling partners, if feasible. Moreover, the actual signal plateau may
be missed if the signal detection tools saturate prematurely or if the
measurement time point does not match the ligand binding kinetics. This
gives rise to an assay-dependent (nonlinear) amplification in the
observed signal(s). Nevertheless, the latter effect is taken care of by
bias-quantitation strategies in exactly the same way as the
“system-bias” is handled. To test for observational bias, it is
recommendable to use an independent ‘orthogonal’ assay to validate each
pathway. Furthermore, it is necessary to ensure that at least one assay
for each pathway has sufficient sensitivity (preferred) or to increase
expression levels of the involved signaling partners (alternative) to
overcome sensitivity problems of a particular assay.
Disclaimer: In some cases, it can be difficult to cleanly
separate ligand bias and system bias. Furthermore, the use of
recombinant and/or overexpressed receptor, transducer or effector
proteins may not fully reflect the bias in a native system.