Elucidating and Validating Functional Roles of Genes, Gene Families, and Associated Pathways
Biofeedstock. [Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign]
Ongoing developments in ‘omics technologies have enabled unprecedented views of plants across scales, from the molecular to ecosystem level. When integrated with computational modeling and data analysis, these technologies could greatly enhance understanding of critical plant processes such as metabolism, development, and inter/intraspecies signaling and communications. By elucidating the properties of individual components within their molecular, cellular, and organismal contexts, a better understanding can be gained of the living systems as a whole, enabling prediction of the organism’s behavior under fluctuating and sometimes adverse conditions. Omics technologies have generated immense amounts of data that can be harnessed toward this goal. However, a substantial amount of computational systems biology analyses lack experimental validation of gene function and are purely correlative or computationally inferred. Current limitations in understanding of the individual functional components of a given biological system represent a major obstacle to answering fundamental questions in plant biology. They also impede development of computational models that accurately predict the behavior of such systems in natural environments.
All sequenced genomes, both eukaryotic and prokaryotic, contain a significant fraction of genes of “unknown function” or with functional assignments based solely on comparative genomic analyses or bioinformatic predictions (i.e., “hypothetical”). However, gene expression, particularly in eukaryotes, is often influenced by the complex interactions between a gene(s) and the organism’s broader genetic background. Without a full understanding of the role(s) of the many predicted and hypothetical genes that comprise this background, accurately predicting phenotype from genotype is impossible, and currently the necessary physiological and biochemical data exist for only a very small subset of genes and species. Furthermore, most existing experimental studies have been confined to a relatively small number of model organisms that are often genetically distant from economically important species, and are therefore of limited use in these species for defining gene function. Thus, the ability to accurately assign and validate gene function in nonmodel organisms is severely limited.
Characterization and validation of gene function in plants presents several unique challenges due to an extensive evolutionary history of segmental and whole genome duplications. While such events have increased adaptability of plants to diverse environments, they also have resulted in an inherently more complex regulatory network that creates multiple challenges to identifying and characterizing the functional role(s) of individual genes in determining plant phenotypes. Additionally, the mode of reproduction (i.e., inbreeding versus outcrossing) and the creation of genetic bottlenecks during domestication of cultivated species present further challenges due to confounding epistatic effects on genetic analyses and interpretation of the effect of mutations in different genetic backgrounds.
For most biological systems, including plant species relevant to the sustainable production of bioenergy and bioproducts, research to determine gene function has focused on traditional experimental studies on individual genes or classes of similar genes. As the speed of omics data acquisition increases, the ability to experimentally characterize gene functions continues to be a huge bottleneck. To overcome this obstacle, more efficient, high-throughput methods for generating experimental evidence must be developed for accurate functional determination that enables more rapid scientific advancement. While experimental approaches are unlikely to scale with advances in omics technologies, concentrated development and application of new approaches are needed to tackle this critical knowledge gap of genomic space.
In 2019, the Department of Energy’s (DOE) Office of Biological and Environmental Research (BER) solicited applications for systems biology research on plant genomes, gene systems, and molecular processes relevant to DOE missions in bioenergy and the environment. Innovative combinations of omics (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics) and computational methodologies to deduce gene function were strongly encouraged.
Specific topics of interest include:
By gaining insight into the individual parts of plants and how they function in situ, BER hopes to enable predictive biology at a systems level.
Catherine M. Ronning