Quantitative Trait Locus Mapping of Swarming Motility and Germination Rate in a Bacillus subtilis Library
Authors:
John Lagergren1,3* ([email protected]), Delyana Vasileva1,3, Jared Streich1,3, Hari Chhetri1,3, Leah Burdick1, Matthew Lane1,2,3, Mirko Pavicic1,3, Peter Kruse1,2, Dawn Klingeman1, Daniel Jacobson1,3, Joshua Michener1,3, Gerald A. Tuskan1,3
Institutions:
1Oak Ridge National Laboratory; 2University of Tennessee–Knoxville; 3Center for Bioenergy Innovation, Oak Ridge National Laboratory
URLs:
Goals
The Center for Bioenergy Innovation (CBI) vision is to accelerate domestication of bioenergy-relevant, non-model plants and microbes to enable high-impact innovations along the bioenergy and bioproduct supply chain while focusing on sustainable aviation fuels (SAF). CBI has four overarching innovation targets: (1) develop sustainable, process-advantaged biomass feedstocks; (2) refine consolidated bioprocessing with cotreatment to create fermentation intermediates; (3) advance lignin valorization for biobased products and aviation fuel feedstocks; and (4) improve catalytic upgrading for SAF blendstocks certification.
Abstract
Overview. Linking genes of unknown function to relevant phenotypes in microbial systems is challenging. This necessitates the development of novel bacterial quantitative trait loci (QTL) mapping techniques enabled by genome shuffling. Bacillus subtilis is used as a proof-of-concept model for phenotyping and mapping causal genetic variants. Novel hardware and software were developed for high-throughput phenotyping, including an XY-robot for automated imaging and mathematical and statistical methods for image processing and feature extraction. Genome-wide association studies (GWAS) based on methods used in plant populations were used to identify predictive loci and validate causative genetic variants.
Approach. The approach is broken into five steps: (1) genome shuffling by protoplast fusion mimics sexual recombination in bacteria to enable QTL mapping (Vasileva et al. 2022); (2) a custom-built XY-robot with camera modularity and automated sample tracking enables high-throughput imaging; (3) convolutional neural networks (Lagergren et al. 2023) and mathematical models are used to predict morphological features of swarm assays and spore gemination dynamics; (4) genomic analysis is used to map multiple swarming and germination phenotypes using the QTL population; and (5) new strains are created with targeted modifications to confirm that genes in the QTL are causal.
Results. This work demonstrated B. subtilis as a proof-of-concept model to generate 386 recombined strains with 15,906 variants. The custom XY-robot captured high-resolution images of micro-organisms in six-well plates at 20 to 30 images per minute. Mathematical and statistical models extracted multiple phenotypes relating to swarming motility and germination. Novel software was developed to extract gene regions for alignment-based sample binning across the population. Genomic analysis revealed highly significant SNPs associated with colony area and germination. Causal regions were validated through the creation of new B. subtilis strains with targeted modifications that exhibited phenotypic differences in, for example, spore germination.
Impact. After successful demonstration in B. subtilis, researchers are now applying the same approach in Clostridium thermocellum, a model system relevant to bioprocessing and enzyme engineering. A similar approach could be widely used to connect phenotype to genotype in other bacteria important for the bioeconomy.
References
Vasileva, D., et al. 2022. “Protoplast Fusion in Bacillus Species Produces Frequent, Unbiased, Genome-Wide Homologous Recombination,” Nucleic Acids Research 50(11), 6211–23.
Lagergren, J., et al. 2023. “Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa.” Plant Phenomics 5(75).
Funding Information
Funding was provided by the Center for Bioenergy Innovation (CBI) led by Oak Ridge National Laboratory. CBI is funded as a DOE Bioenergy Research Center supported by the BER program in the DOE Office of Science under FWP ERKP886. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the DOE under contract no. DE-AC05-00OR22725.