Unraveling RNA Interference-Microbiome Interactions: A Multiscale Approach for Assessing Non-Target Effects

Modern agriculture increasingly relies on RNA interference (RNAi) constructs, which protect crops by silencing specific genes in pests and pathogens. These biotechnology tools are now used around the world, and the global RNAi market is on track to reach $9 billion by 2033. However, the effects of RNAi constructs on microbes in the environment are not well understood. Microbes drive key processes like nutrient cycling, soil health, and water quality. Harm to these microbial communities can threaten both ecosystem and agricultural stability. This project will study how RNAi molecules interact with environmental bacteria. It will also build new artificial intelligence (AI) tools to predict and prevent unintended harm. Project findings will help engineers design safer, more effective RNAi products. This project will also create hands-on learning modules for K-8 students, building early science literacy and inspiring the next generation of STEM leaders. This project will employ a multiscale experimental and computational framework to identify the mechanisms by which engineered RNAi biotechnologies, including small interfering RNAs (~20 bp) and double-stranded RNAs (~200-800 bp), interact with and disrupt non-target microbiomes. First, dose-dependent impacts of an array of RNAi constructs on microbial growth and the emergence of stress phenotypes will be quantified using high-throughput microcosm experiments. Variables to be tested will include microbiome composition, RNAi structure and sequence, RNAi concentration, and external matrix chemistries. Second, transcriptomic profiling will resolve primary (direct, sequence-specific gene silencing) and secondary (generalized stress response) effects of RNAi exposure in bacteria, using RNA sequencing and bioinformatic analysis to identify functional biomarkers of non-target RNAi activity. Third, chemostat experiments with a defined synthetic soil microbial community will assess how RNAi exposure reshapes microbiome composition and biogeochemical cycling, integrating 16S amplicon sequencing, fluorescence in situ hybridization, and targeted gene expression analysis. Across all objectives, AI-enabled machine learning algorithms, including explainable boosting machines and gradient-boosted ensembles, will be trained on these multimodal datasets to identify the RNAi construct features that most strongly predict microbial responses. This framework will generate quantitative toxicity and gene-based biomarkers, establish data-supported AI tools for assessing RNAi fate in bacterial communities, and produce design principles for next-generation biotechnology constructs with enhanced ecosystem compatibility. This work will advance a shift from descriptive risk assessment to mechanistic AI-informed prediction of RNAi-microbiome interactions, ultimately enabling proactive design practices for the rapidly expanding RNA biotechnology sector. To broaden the societal impact of this research, the project will develop and deploy hands-on, inquiry-based biotechnology learning modules for K-8, connecting with students through classroom instruction, regional STEM events, and after school programs. These activities will accelerate early bioliteracy and build a pipeline of future biotechnology leaders equipped to navigate the scientific, ethical, and societal dimensions of emerging RNAi technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2600076 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Courtney Gardner | Institution: University of Texas at Austin, AUSTIN, TX | Award Amount: $352,842 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2600076 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2600076.html