The network and hierarchical structure of fatty acid traits
The above examples illustrate the varied ways in which consumers adapt
to the heterogeneous distributions of n-3 LC-PUFA in nature (Fig. 2),
including via the evolution of metabolic capacity for biosynthesis of FA
and/or the foraging behaviors underlying the dietary acquisition of n-3
LC-PUFA. In light of this complexity, we suggest an integrative approach
that includes both investigating the individual enzymes and processes
involved in fatty acid synthesis within the metabolic network (Fig. 4;
Table 1), and situating these metabolic traits within a hierarchical
structure of functional traits leading to fitness variation (Fig. 5;
Table 2).
Although all organisms share core metabolic processes for fatty acid
synthesis (Fig. 4), consumer species vary widely in capacity to convert:
1) MUFA to PUFA (Module C, Fig. 4), 2) n–6 to n–3 PUFA (Module D, Fig.
4), and 3) C18 n-6 and n-3 PUFA to LC-PUFA (Modules E
and F, Fig. 4). Each step within the n-3 LC-PUFA biosynthesis pathway is
governed by the presence and activity level of particular enzymes, as
well as by the presence and expression levels of specific genes.
Saturated fatty acids (SFA), such as stearic acid (18:0), are
synthesised de novo through the fatty acid synthase (fasn )
and SFA elongase system (Module A, Fig. 4). Stearoyl-CoA desaturase
(Scd ) can then introduce a double bond at the Δ9 position of the
fatty carbon chain, producing monounsaturated fatty acids, such as oleic
acid (OA, 18:1n-9) (Module B, Fig. 4). All eukaryotes appear to be able
to synthesize OA. In contrast, the biosynthesis from MUFA (OA; Module C,
Fig. 4) to PUFA, with multiple double bonds like linoleic acid (LIN,
18:2n-6), only exists in a limited number of consumers with the
methyl-end (ωx) desaturase enzyme, Δ12 desaturase (Blomquist et al.
1991). Most consumers neither possess the related methyl-end desaturase
enzyme (∆15 desaturase) that is necessary to produce ALA from LIN, nor
the ∆17 and ∆19 desaturases to convert n-3 LC-PUFA from their n-6
LC-PUFA counterparts (Module D, Fig. 4). These enzymes introduce an
additional double bond between the terminal methyl group of a fatty acyl
chain and a pre-existing double bond, allowing the synthesis of PUFA
from MUFA, and, importantly, n-3 PUFA from n-6 PUFA. The methyl-end
desaturases were historically thought to exist only in plants, algae,
protists, fungi and a nematode (i.e. Caenorhabditis elegans ), but
a recent study suggests that this gene family also occurs in cnidarians,
additional nematode species, lophotrochozoans (molluscs, annelids,
rotifers), and arthropods (copepods and at least two species of insects)
(Kabeya et al. 2018; Garrido et al. 2019; Kabeya et al. 2020). A much
greater number of consumers are able to elongate and desaturate n-6 and
n-3 C18PUFA into corresponding n-6 and n-3 LC-PUFA
(Modules E and F, Fig. 4). Network modules E and F involve several
front-end desaturases as well as fatty acid elongases (elongation of
very long-chain fatty acids protein, Elovl ) and exist, with
varying efficiency, in consumers ranging from molluscs and some
arthropods (Monroig and
Kabeya 2018) to chickens (Gregory and James 2014; Boschetti et al.
2016) and humans
(Leonard et al. 2002;
Nakamura and Nara 2004), suggesting that these pathways have evolved
multiple times.
In light of the complexity of fatty acid metabolic networks, identifying
a set of modules and component traits can be a useful approach. As
illustrated in Fig. 4, we identify six core modules based on important
functional metabolic capacities (Fig. 4A-F, Table 1A), and further break
these down into constituent traits that define the reaction rates
between specific FA substrates and products (e.g., ALA to EPA conversion
capacity and efficiency, Table 1B). There is some value to such
simplifications because they reveal broad-scale patterns in metabolic
capacity across the tree of life. However, there is also substantial
pleiotropy, in that single genes can modify the activity of numerous
reaction rates across the overall metabolic network
(Table
1B). For example, in many teleosts, Fads2 gene products can
influence conversion rates of LIN to a series of n-6 LC-PUFA including
ARA (Fig. 4, Module E), as well as ALA to a series of n-3 LC-PUFA
including EPA and DHA (Fig. 4, Module F). Nevertheless, treating both
modules and their component pathways as metabolic traits permits us to
document heritable variation within metabolic network modules (Box 1),
and to identify both the ecological and genetic mechanisms underlying
their adaptation. This is an important step for understanding the
complex evolution of metabolic networks (Olson-Manning et al. 2012;
Watson et al. 2014; Melián et al. 2018) and the role that metabolism
plays in evolutionary diversification more broadly.
The metabolic traits we summarize in Table 1 are also embedded within a
hierarchy of other potentially fitness-relevant consumer traits
(Table
2). Natural selection acts upon the heritable intraspecific metabolic
traits in the context of other subordinate and emergent functional
traits in the hierarchy (Fig. 5; Henshaw et al. 2020; Laughlin et al.
2020). Where there is a heritable basis for metabolic traits, there is
the potential for adaptive evolution of consumer metabolism in response
to natural selection. Such evolution might involve fatty acid synthesis
and internal regulation, and/or of behavioral traits related to resource
acquisition (e.g., selective foraging) and/or life history traits (e.g.,
migration and phenology) (see references for
Table
2; Fig. 5). The evolution of metabolic traits might evolve
independently, or as a correlated response to other heritable traits,
and culminate in changes in physiological performance, immunocompetence,
and cell membrane fluidity
(Table
1, Fig. 5). Such trait change has the potential to influence numerous
processes ranging from those affecting individual molecules to those
affecting an individual’s lifetime reproductive Darwinian fitness
(Table
2, Fig. 5).