Beyond Mendelian Inheritance: Genetic Buffering and Phenotype Variability

Understanding the way genes work amongst individuals and across generations to shape form and function is a common theme for many genetic studies. The recent advances in genetics, genome engineering and DNA sequencing reinforced the notion that genes are not the only players that determine a phenotype. Due to physiological or pathological fluctuations in gene expression, even genetically identical cells can behave and manifest different phenotypes under the same conditions. Here, we discuss mechanisms that can influence or even disrupt the axis between genotype and phenotype; the role of modifier genes, the general concept of genetic redundancy, genetic compensation, the recently described transcriptional adaptation, environmental stressors, and phenotypic plasticity. We furthermore highlight the usage of induced pluripotent stem cells (iPSCs), the generation of isogenic lines through genome engineering, and sequencing technologies can help extract new genetic and epigenetic mechanisms from what is hitherto considered ‘noise’.

Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens

Major efforts on pooled library CRISPR knockout screening across hundreds of cell lines have identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens are very low compared to the number of constitutively expressed genes in a cell, raising the question of why there are so few essential genes. Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observed that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. We investigated paralog buffering through systematic dual-gene CRISPR knockout screening by testing algorithmically defined ~400 candidate paralog pairs with the enCas12a multiplex knockout system in three cell lines. We observed 24 synthetic lethal paralog pairs which have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions were present in at least two out of three cell lines and 14 of 24 (58%) were present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Together these observations strongly suggest that paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes due to genetic buffering in monogenic CRISPR-based mammalian functional genomics approaches.

Distinguish between Duplication of Essential Genes and Duplication of Dispensable Genes

When a dispensable gene is duplicated (ancestral dispensability), genetic buffering and duplicate compensation together maintain the gene dispensability, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (ancestral essentiality). To explore the distinct pattern of genetic robustness between these evolutionary scenarios, we formulated a probabilistic model with some biologically reasonable assumptions for analyzing a set of duplicate pairs with three possible states: double-dispensable (DD), semi-dispensable (one dispensable one essential, DE) or double-essential (EE). A computational pipeline is then developed to predict the distribution of three states (DD, DE and EE) conditional of ancestral dispensability or essentiality, respectively. This model was applied to yeast duplicate pairs from a whole-genome duplication, revealing that the process of essentiality of those duplicated from essential genes could be significantly higher than that of those duplicated from dispensable genes. We thus proposed a hypothesis that the process of sub-functionalization may be faster than neo-functionalization. Our analysis may provide some new insights about the role of duplicate compensation on genetic robustness.

Robust cullin-RING ligase function is established by a multiplicity of poly-ubiquitylation pathways

The cullin-RING ligases (CRLs) form the major family of E3 ubiquitin ligases. The prototypic CRLs in yeast, called SCF enzymes, employ a single E2 enzyme, Cdc34, to build poly-ubiquitin chains required for degradation. In contrast, six different human E2 and E3 enzyme activities, including Cdc34 orthologs UBE2R1 and UBE2R2, appear to mediate SCF-catalyzed substrate polyubiquitylation in vitro. The combinatorial interplay of these enzymes raises questions about genetic buffering of SCFs in human cells and challenges the dogma that E3s alone determine substrate specificity. To enable the quantitative comparisons of SCF-dependent ubiquitylation reactions with physiological enzyme concentrations, mass spectrometry was employed to estimate E2 and E3 levels in cells. In combination with UBE2R1/2, the E2 UBE2D3 and the E3 ARIH1 both promoted SCF-mediated polyubiquitylation in a substrate-specific fashion. Unexpectedly, UBE2R2 alone had negligible ubiquitylation activity at physiological concentrations and the ablation of UBE2R1/2 had no effect on the stability of SCF substrates in cells. A genome-wide CRISPR screen revealed that an additional E2 enzyme, UBE2G1, buffers against the loss of UBE2R1/2. UBE2G1 had robust in vitro chain extension activity with SCF, and UBE2G1 knockdown in cells lacking UBE2R1/2 resulted in stabilization of the SCF substrates p27 and CYCLIN E as well as the CUL2-RING ligase substrate HIF1α. The results demonstrate the human SCF enzyme system is diversified by association with multiple catalytic enzyme partners.

Genetic buffering and potentiation in metabolism

Cells adjust their metabolism in response to mutations, but how this reprogramming depends on the genetic context is not well known. Specifically, the absence of individual enzymes can affect reprogramming and thus the impact of mutations in cell growth. Here, we examine this issue with an in silico model of Saccharomyces cerevisiae’s metabolism. By quantifying the variability in the growth rate of 10000 different mutant metabolisms that accumulated changes in their reaction fluxes, in the presence, or absence, of a specific enzyme, we distinguish a subset of modifier genes serving as buffers or potentiators of variability. We notice that the most potent modifiers refer to the glycolysis pathway and that, more broadly, they show strong pleiotropy and epistasis. Moreover, the evidence that this subset depends on the specific growing condition strengthens its systemic underpinning, a feature only observed before in a simple model of a gene-regulatory network. Some of these enzymes also modulate the effect that biochemical noise and environmental fluctuations produce in growth. Thus, the reorganization of metabolism triggered by mutations has not only direct physiological implications but also changes the influence that other mutations have on growth. This is a general result with implications in the development of cancer therapies based on metabolic inhibitors.

Epistasis contributes to the genetic buffering of plasma HDL cholesterol in mice

Stressful environmental factors, such as a high-fat diet, can induce responses in the expression of genes that act to maintain physiological homeostasis. We observed variation in plasma concentrations of high-density lipoprotein (HDL) cholesterol across inbred mouse strains in response to high dietary fat intake. Several strains, including C57BL/6J, have stable levels of plasma HDL independent of diet, whereas other strains, including DBA2/J, show marked changes in plasma HDL. To explore this phenomenon further, we used publicly available data from a C57BL/6J × DBA/2J intercross to identify genetic factors that associate with HDL under high-fat diet conditions. Our analysis identified an epistatic interaction that plays a role in the buffering of HDL levels in C57BL/6J mice, and we have identified Arl4d as a candidate gene that mediates this effect. Structural modeling further elucidates the interaction of genetic factors that contribute to the robustness of HDL in response to high-fat diet in the C57BL/6J strain.