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Abstract Host mucosal barriers possess an arsenal of defense molecules to maintain host-microbe homeostasis such as antimicrobial peptides and immunoglobulins. In addition to these well-established defense molecules, we recently reported small RNAs (sRNAs)-mediated interactions between human oral keratinocytes and Fusobacterium nucleatum (Fn), an oral pathobiont with increasing implications in extra-oral diseases. Specifically, upon Fn infection, oral keratinocytes released Fn-targeting tRNA-derived sRNAs (tsRNAs), an emerging class of noncoding sRNAs with gene regulatory functions. To explore potential antimicrobial activities of tsRNAs, we chemically modify the nucleotides of the Fn-targeting tsRNAs and demonstrate that the resultant tsRNA derivatives, termed MOD-tsRNAs, exhibit growth inhibitory effect against various Fn type strains and clinical tumor isolates without any delivery vehicle in the nanomolar concentration range. In contrast, the same MOD-tsRNAs do not inhibit other representative oral bacteria. Further mechanistic studies uncover the ribosome-targeting functions of MOD-tsRNAs in inhibiting Fn. Taken together, our work provides an engineering approach to targeting pathobionts through co-opting host-derived extracellular tsRNAs. 

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.