A grand promise of modern biology is to predict phenotypes from genotypes. The most notable example is the Human Genome Project which aimed to dramatically accelerate diagnosis, prevention, and treatment of diseases based on genome sequence and gene functions. Cures for complex diseases such as heart disease, schizophrenia, and cancer were supposed to be within reach, but 20 years later we are far from that goal. Another major implication of genotype-to-phenotype mapping is to predict evolution, which is crucial for preventing antibiotic resistance, controlling the spread of infectious diseases (as illustrated by the current pandemic), and anticipating micro-organism adaptations to climate change. However, predicting phenotypes from genomes has revealed to be much more challenging than expected, has halted the outcome of the Human Genome Project, and remains a fully open question. Indeed, the “one gene, one function, one disease” model does not match observations of complex genetic interactions. The factors underlying such complexity are: (1) Epistasis (Genotype-by-Genotype interactions); (2) Genotype-by-Environment interactions; (3) Pleiotropy. Epistasis has been measured and classified in several model system, but the origin of epistasis has been explained only in small synthetic gene networks, notably by the Philippe Nghe’s lab. Pleiotropy remains poorly characterized in general. Environmental dependences are essentially studied in the context of regulatory responses, but little as a function of genotypes. Overall, the interactions described above are widespread, but there is no generic framework to explain them from a priori knowledge in biology. Inferring these interactions from human studies is highly challenging due to incomplete information and biases in the sampled population. Instead, this project aims to address complex genetic interactions in a model organism, amenable to systematic experimental investigation, with the ambition to extract generic rules and push the frontiers of understanding major societal issues like diseases, adaptation to changing environment, and evolution in general.
The overall ambition of this project is to: (1) develop technology to measure the transcriptomes and growth of up to 104 E. coli strains at a very high-throughput; (2) apply this technology to exhaustively decipher the genetic interactions between global regulators, and between global and local regulators, as a model of epistasis and pleiotropy, in different environments; (3) study the relationship between the known gene network structure and the observed genetic interactions. This project was divided into three work packages (WP). WP1 builds a novel cutting-edge method based on microfluidics and spatial transcriptomics for bacteria. In WP1 we proposed to grow up to 104 colonies each of a different strain, each colony comprising ~103 bacteria, on a microfabricated polyacrylamide (hydrogel) pad coupled with spatial transcriptomics, dubbed “Bacterial Evolution Adapted Spatial Transcriptomic Sequencing” or BEAST-Seq. By locally capturing mRNAs on spatially barcoded DNA primers, the transcriptome of each clonal colony is associated to its position on the pad and to its growth rate measured by time-lapse microscopy. Additionally, the hydrogel pad used as a membrane allows us to vary the growth medium in time. WP2 studies genome wide regulatory logic of global regulators and gene hierarchies. Using the technology developed in WP1, we proposed to characterize high-order gene interactions between global regulators using a combinatorial CRISPR/dCas9 knock-downs (KDs) library of the host lab. WP3 uncovers the impact of mutations on global regulators. We proposed to use Tenaillon’s lab (INSERM Bichat) newly developed CRISPR/Cas9 tool to create mutations libraries of the global regulators from WP2. The fine-grained view provided by mutations compared to KDs in WP2 will allow us to test mathematical models of gene networks that predict epistasis and pleiotropy.
