A virologist studying how viruses infect host cells, a developmental biologist tracking birth defects in embryos, and an oncologist searching for new chemotherapeutic targets all use a common method to discover new mechanisms: explaining how a mutation exerts its effects to alter molecular or morphological characteristics.
Gain of function and loss of function are two widely used terms to describe how a specific genetic mutation changes protein structure, how it affects interactions with other molecular players and pathways, how it shapes cellular morphology, or tissue organization, or whole organism physiology. But on what basis do researchers label mutations as either gain-of-function or loss-of-function? Is there a broad consensus among researchers on the meaning of these terms?
In an article in Genetics’ Latest Perspectives, Tobias Warnecke provides a historical account of how researchers defined the term “functional benefit,” points out the considerable heterogeneity in how the term is used in practice—making it ambiguous—and asks whether a labeling system for benefit can be contextually relevant. Warnecke shows the inconsistencies in how it has been used by different scientists, emphasizing the pitfalls of using a simple descriptor for diverse, complex, and messy biological phenomena.
etymology
First, Warnecke pointed out that early geneticist Hermann Müller and other fly pushers classified mutations as hypomorphic, hypermorphic, amorphic, and neomorphic based on the increase or decrease in gene dosage and its effect on the appearance of the mutant flies. Thirty years later, progressive drosophilist Ed Lewis introduced the terms loss and gain-of-function, simplifying the myriad effects of mutations into two outcomes. These broader terms are prevalent in the literature, and all types of mutations, as well as their physiological manifestations, are described by these two terms, often giving a misleading impression of the molecular mechanisms involved.
Harm versus gain: from molecular changes to systemic realities
To illustrate how the acquired mutation label can mislead one’s intuition, Warnecke uses several examples of mutations and summarizes their mechanisms of action at the molecular, cellular, and organelle levels. While the functional effect of a mutation at the molecular and/or protein level may suggest a gain-of-function effect, the mutation may have a loss-of-function effect further down the molecular cascade or at the cellular or organismal level, and vice versa. According to Warneck, the gain-of-function label is often derived from the level of biological complexity at which researchers choose to test the effect of the mutation. From ion channels to prion proteins to immune activators, a closer look at known mutations reflects a common pattern: one researcher’s gain-of-function mutation is another’s loss-of-function mutation.
Incidentally, Warneck argues that such inconsistent labeling of mutations can complicate efforts to train machine learning algorithms, particularly in clinical applications. Warnecke discusses whether systematic relabeling of mutations—for example, providing context as to whether the mutation acts at the protein level or beyond—can be useful for reliably using machine learning methods.
Overall, Warnecke’s thoughtful approach is a reminder to geneticists, clinicians, and computational biologists that classifying mutations into a limited set of categories often removes significant complexity and is done very differently by different practitioners of science, which should be kept in mind when comparing their large-scale variation.
