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).