New approaches to preventing and treating infections, particularly of the respiratory tract, are needed. One promising strategy is to reconfigure microbial communities (microbiomes) within the host to improve defense against pathogens. Probiotics and prebiotics for gastrointestinal (GI) infections offer a template for success. We sought to develop comparable countermeasures for respiratory infections. First, we characterized interactions between the airway microbiome and a biodefense-related respiratory pathogen ( Burkholderia thailandensis ; Bt), using a mouse model of infection. Then, we recovered microbiome constituents from the airway and assessed their ability to re-colonize the airway and protect against respiratory Bt infection. We found that microbiome constituents belonging to Bacillus and related genuses frequently displayed colonization and anti-Bt activity. Comparative growth requirement profiling of these Bacillus strains vs Bt enabled identification of candidate prebiotics. This work serves as proof of concept for airway probiotics, as well as a strong foundation for development of airway prebiotics.
This report describes research conducted to use data science and machine learning methods to distinguish targeted genome editing versus natural mutation and sequencer machine noise. Genome editing capabilities have been around for more than 20 years, and the efficiencies of these techniques has improved dramatically in the last 5+ years, notably with the rise of CRISPR-Cas technology. Whether or not a specific genome has been the target of an edit is concern for U.S. national security. The research detailed in this report provides first steps to address this concern. A large amount of data is necessary in our research, thus we invested considerable time collecting and processing it. We use an ensemble of decision tree and deep neural network machine learning methods as well as anomaly detection to detect genome edits given either whole exome or genome DNA reads. The edit detection results we obtained with our algorithms tested against samples held out during training of our methods are significantly better than random guessing, achieving high F1 and recall scores as well as with precision overall.
We present the draft genome sequences of three Burkholderia thailandensis strains, E421, E426, and DW503. E421 consists of 90 contigs of 6,639,935 bp and 67.73% GC content. E426 consists of 106 contigs of 6,587,853 bp and 67.73% GC content. DW503 consists of 102 contigs of 6,458,767 bp and 67.64% GC content.