Antimicrobial resistance (AMR) is a serious threat to global public health, with approximately 5 million deaths associated with bacterial AMR in 2019. Tackling AMR requires a multifaceted and cohesive approach that ranges from increased understanding of mechanisms and drivers at the individual and population levels, AMR surveillance, antimicrobial stewardship, improved infection prevention and control measures, and strengthened global policies and funding to development of novel antimicrobial therapeutic strategies. In this rapidly advancing field, this Review provides a concise update on AMR, encompassing epidemiology, evolution, underlying mechanisms (primarily those related to last-line or newer generation of antibiotics), infection prevention and control measures, access to antibiotics, antimicrobial stewardship, AMR surveillance, and emerging non-antibiotic therapeutic approaches. The Review also discusses the potential roles of artificial intelligence in addressing AMR, including antimicrobial susceptibility testing, AMR surveillance, antimicrobial stewardship, diagnosis, and antimicrobial drug discovery and development. This Review highlights the urgent need for addressing the global effects of AMR and for rapid advancement of relevant technology in this dynamic field.

Unculturable bacterial communities provide a rich source of biocatalysts, but their experimental discovery by functional metagenomics is difficult, because the odds are stacked against the experimentor. Here we demonstrate functional screening of a million-membered metagenomic library in microfluidic picolitre droplet compartments. Using bait substrates, new hydrolases for sulfate monoesters and phosphotriesters were identified, mostly based on promiscuous activities presumed not to be under selection pressure. Spanning three protein superfamilies, these break new ground in sequence space: promiscuity now connects enzymes with only distantly related sequences. Most hits could not have been predicted by sequence analysis, because the desired activities have never been ascribed to similar sequences, showing how this approach complements bioinformatic harvesting of metagenomic sequencing data. Functional screening of a library of unprecedented size with excellent assay sensitivity has been instrumental in identifying rare genes constituting catalytically versatile hubs in sequence space as potential starting points for the acquisition of new functions.

Directed evolution relies on iterative cycles of randomization and selection. The outcome of an artificial evolution experiment is crucially dependent on (i) the numbers of variants that can be screened and (ii) the quality of the assessment of each clone that forms the basis for selection. Compartmentalization of screening assays in water-in-oil emulsion droplets provides an opportunity to screen vast numbers of individual assays with good signal quality. Microfluidic systems have been developed to make and sort droplets, but the operator skill required precludes their ready implementation in nonspecialist settings. We now establish a protocol for the creation of monodisperse double-emulsion droplets in two steps in microfluidic devices with different surface characteristics (first hydrophobic, then hydrophilic). The resulting double-emulsion droplets are suitable for quantitative analysis and sorting in a commercial flow cytometer. The power of this approach is demonstrated in a series of enrichment experiments, culminating in the successful recovery of catalytically active clones from a sea of 1 000 000-fold as many low-activity variants. The modular workflow allows integration of additional steps: the encapsulated lysate assay reactions can be stopped by heat inactivation (enabling ready control of selection stringency), the droplet size can be contracted (to concentrate its contents), and storage (at −80 °C) is possible for discontinuous workflows. The control that can be thus exerted on screening conditions will facilitate exploitation of the potential of protein libraries compartmentalized in droplets in a straightforward protocol that can be readily implemented and used by protein engineers.

Significance: Understanding how new metabolic pathways emerge is one of the key issues in evolutionary and systems biology. The prevailing paradigm is that evolution capitalizes on the weak side activities of preexisting enzymes (i.e. underground reactions). However, the extent to which underground reactions provide novelties in the context of the entire cellular system has remained unexplored. In this study, we present a comprehensive computational model of the underground metabolism of Escherichia coli . Together with a high-throughput experimental survey across hundreds of nutrient environments we predicted and confirmed new functional states of metabolism in which underground reactions allow growth when their activity is increased. Our approach has important implications for biotechnological and medical applications, such as understanding gain-of-function mutations in tumor development.