Combinations of Antibiotics Effective against Extensively- and Pandrug-Resistant Acinetobacter baumannii Patient Isolates.

Infections due to drug-resistant Acinetobacter baumannii strains are increasing and cause significant morbidity and mortality, especially in hospitalized and critically ill patients. A. baumannii rapidly develops resistance to numerous antibiotics, and antibiotics traditionally used against this deadly pathogen have been failing in recent years, highlighting the need to identify new treatment strategies. Treatment options that have shown promise include revisiting common antibiotics not typically used against A. baumannii, evaluating new antibiotics recently introduced to market, and identifying combinations of antibiotics that display synergistic interactions. In this study, we characterized the antibiotic susceptibility profiles of extensively (XDR) and pandrug-resistant (PDR) A. baumannii patient isolates. We examined the potency of 22 standard-of-care antibiotics and the newer antibiotics eravacycline, omadacycline, and plazomicin against these strains. Furthermore, we examined combinations of these antibiotics against our collection to identify synergistic effects. We found that this collection is highly resistant to most or all standard-of-care antibiotics, except for minocycline and rifampin. We show that eravacycline and omadacycline are effective against these strains based on minimum inhibitory concentrations. We also identified two highly effective combinations, cefepime and amikacin and cefepime and ampicillin-sulbactam, which exhibited high rates of synergy against this collection. This information is valuable in our battle against highly drug resistant and virtually untreatable A. baumannii infections.

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we address in this study. We applied an evolutionarily informed machine learning approach to predict phenotypes based on transcriptome responses shared both within and across species. Specifically, we exploited the phenotypic diversity in nitrogen use efficiency and evolutionarily conserved transcriptome responses to nitrogen treatments across Arabidopsis accessions and maize varieties. We demonstrate that using evolutionarily conserved nitrogen responsive genes is a biologically principled approach to reduce the feature dimensionality in machine learning that ultimately improved the predictive power of our gene-to-trait models. Further, we functionally validated seven candidate transcription factors with predictive power for NUE outcomes in Arabidopsis and one in maize. Moreover, application of our evolutionarily informed pipeline to other species including rice and mice models underscores its potential to uncover genes affecting any physiological or clinical traits of interest across biology, agriculture, or medicine.