Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions, though largely as a retrospective tool. Prospective monitoring, in which samples are continuously added and compared to previous samples, can generate more actionable information. To enable prospective pathogen comparison, genomic relatedness metrics based on single-nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e ., the core genome , is critical to obtain a good metric. We propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus , 1362 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high sensitivity: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 44 known outbreak samples, whereas the conserved-gene method confirms 38 known outbreak samples.
Antimicrobial resistance (AMR) is a risk for patients and a burden for the healthcare system. However, AMR assays typically take several days. This study develops predictive models for AMR based on easily available clinical and microbiological predictors, including patient demographics, hospital stay data, diagnoses, clinical features, and microbiological/antimicrobial characteristics and compares those models to a naive antibiogram based model using only microbiological/antimicrobial characteristics. The ability to predict the resistance accurately prior to culturing could inform clinical decision-making and shorten time to action. The machine learning algorithms employed here show improved classification performance (area under the receiver operating characteristic curve 0.88-0.89) versus the naive model (area under the receiver operating characteristic curve 0.86) for 6 organisms and 10 antibiotics using the Philips eICU Research Institute (eRI) database. This method can help guide antimicrobial treatment, with the objective of improving patient outcomes and reducing the usage of unnecessary or ineffective antibiotics.
Microbial natural products are specialized metabolites that have long been a rich source of human therapeutics. While the chemical diversity encoded in the genomes of microbes is believed to be large, the productivity of this modality has waned as traditional fermentation-based discovery methods have been plagued by high-rates of rediscovery, inefficient scaling, and incompatibility with target-based drug discovery. Here, we demonstrate a scalable discovery platform that couples dramatically improved assembly of deep-sequenced metagenomic samples with highly efficient, target-focused, in silica search strategies and synthetic biology to discover multiple novel inhibitors of human methionine aminopeptidase-1 (HsMetAP1), a validated oncology target. For one of these novel inhibitors, metapeptin B, we demonstrate sub-micromolar potency, strong selectivity for HsMetAP1 over HsMetAP2 and leverage natural congeners to rapidly elucidate key SAR elements. Our “next-gen” discovery platform overcomes many of the challenges constraining traditional methods, implies the existence of vast, untapped chemical diversity in nature, and demonstrates computationally-enabled precision discovery of modulators of human proteins of interest.
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