The contents of this section have been extracted and modified from the article Disentangling host–microbiota complexity through hologenomics published in Nature Reviews Genetics in 2022 by the authors of the Holo-omics Workbook.
Controlling the complexity of hologenomic variables is essential for addressing specific research questions. Broadly speaking, the more detailed and mechanistic the question under study, the greater the required control. For instance, research on specific biomolecular processes using laboratory models will require a higher level of control than studying biogeographical patterns of host–microbiota interactions in wild organisms. The control of hologenomic variables can be achieved through a number of strategies.
The control over host genomic complexity largely depends on the model organisms studied and the technical approaches employed. In laboratory organisms that can reproduce asexually, such as water fleas (Daphnia, Crustacea) and Lamiaceae plants, absolute control over host genotypes can be achieved by using clonal organisms . When clones cannot be used, inbred laboratory animals can provide a high level of genomic homogeneity. The use of groups of genetically homogeneous hosts allows the effects of contrasting environmental conditions or specific microbial comunities to be compared. Clonal and inbred models also enable the effects of a specific host genetic factor to be studied in a controlled genomic background through the application of targeted techniques for modulating gene expression (such as RNA-mediated interference) or for genomic engineering (such as CRISPR–Cas9). Working with humans and wild organisms does not enable such a degree of control over the genotypes studied unless in vitro models, such as organ-on-a-chip co-cultures of animal tissues and microbial communities, are generated62. When this level of control is not possible, coarse control over host genotypes can be achieved through contrasting animals from different populations or from closely related species63, while greater control can be achieved through comparing individuals across different degrees of kinship, such as monozygotic versus dizygotic twins38, and family members to other individuals64.
Control over microbial metagenomic complexity is usually achieved through modulating microbial communities. Some strategies, such as modification of dietary regimes or the administration of microbiota-targeted additives or prebiotics, aim to modify microbial ecosystems by changing nutrient availability. However, unless compounds that match unique enzymatic capabilities of specific microorganisms are used, it is difficult to accurately modulate the microbiota owing to the complexity of ecological relationships among microorganisms. Alternative approaches to modify microbial communities include inoculation of target bacteria (such as probiotics) and faecal microbiota transplantation. The efficacy and accuracy of these methods is also variable; there is no guarantee that inoculated bacteria will establish or modulate the microbiota, while transplantation does not enable accurate control over the microbial community introduced or the secondary elements that are transplanted along with bacteria. These issues complicate interpretation of results; for example, bacteriophages transferred alongside bacteria may severely impact the gut microbiota composition. A higher level of control could potentially be achieved through transplanting synthetic microbial communities. While this approach has been successfully implemented in diverse in vitro setups the complexity of microbial communities still hinders its efficient use as a routine scientific procedure in live animals.
In most laboratory studies, environmental complexity is reduced so that no, or very few, environmental parameters (usually only experimental treatments) vary among groups and subjects. Climate chambers and aquaria enable experiments by providing absolute control of abiotic conditions, such as light/dark cycles, humidity and temperature variations. Outdoor common garden experiments do not provide full control over environmental factors, but they ensure the effect on the systems being compared is identical. Some natural systems can also provide special conditions that enable environmental features to be controlled, such as cuckoo nestlings that are bred by other birds or salmon populations that breed in the same rivers in alternating years. Research on wild organisms usually incorporates more complex and dynamic environmental conditions. When controlling them is not possible, collection of relevant environmental metadata to be incorporated as covariates in the statistical analyses is useful. A century of ecological research has revealed the advantages of each of these approaches. On the one extreme, laboratory microcosms allow the most reductive control. On the other extreme, studies in the macrocosm of the real world provide perspective on emergent properties of natural ecosystems that cannot be anticipated solely based on microcosms.