Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux. IMPORTANCE While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3–5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

The growing challenge of microbial resistance emphasizes the importance of new antibiotics or reviving strategies for the use of old ones. Macrolide antibiotics are potent bacterial protein synthesis inhibitors with a formidable capacity to treat life-threatening bacterial infections; however, acquired and intrinsic resistance limits their clinical application. In the work presented here, we reveal that bicarbonate is a potent enhancer of the activity of macrolide antibiotics that overcomes both acquired and intrinsic resistance mechanisms. With a focus on azithromycin, a highly prescribed macrolide antibiotic, and using clinically relevant pathogens, we show that physiological concentrations of bicarbonate overcome drug resistance by increasing the intracellular concentration of azithromycin. We demonstrate the potential of bicarbonate as a formulation additive for topical use of azithromycin in treating a murine wound infection caused by Pseudomonas aeruginosa. Further, using a systemic murine model of methicillin-resistant Staphylococcus aureus (MRSA) infection, we demonstrate the potential role of physiological bicarbonate, naturally abundant in the host, to enhance the activity of azithromycin against macrolide-resistant MRSA. In all, our findings suggest that macrolide resistance, observed in the clinical microbiology laboratory using standard culturing techniques, is a poor predictor of efficacy in the clinic and that observed resistance should not necessarily hamper the use of macrolides. Whether as a formulation additive for topical use or as a natural component of host tissues, bicarbonate is a powerful potentiator of macrolides with the capacity to overcome drug resistance in life-threatening bacterial infections.

Nuclear export of influenza A virus (IAV) mRNAs occurs through the nuclear pore complex (NPC). Using the Auxin-Induced Degron (AID) system to rapidly degrade proteins, we show that among the nucleoporins localized at the nucleoplasmic side of the NPC, TPR is the key nucleoporin required for nuclear export of influenza virus mRNAs. TPR recruits the TRanscription and EXport complex (TREX)−2 to the NPC for exporting a subset of cellular mRNAs. By degrading components of the TREX-2 complex (GANP, Germinal-center Associated Nuclear Protein; PCID2, PCI domain containing 2), we show that influenza mRNAs require the TREX-2 complex for nuclear export and replication. Furthermore, we found that cellular mRNAs whose export is dependent on GANP have a small number of exons, a high mean exon length, long 3’ UTR, and low GC content. Some of these features are shared by influenza virus mRNAs. Additionally, we identified a 45 nucleotide RNA signal from influenza virus HA mRNA that is sufficient to mediate GANP-dependent mRNA export. Thus, we report a role for the TREX-2 complex in nuclear export of influenza mRNAs and identified RNA determinants associated with the TREX-2-dependent mRNA export.

SummaryOf the ∼4,400 genes that constitute Escherichia coli's genome, ∼300 genes are indispensable for its growth in nutrient-rich conditions. These encode housekeeping functions, including cell wall, DNA, RNA, and protein syntheses. Under conditions in which nutrients are limited to a carbon source, nitrogen source, essential phosphates, and salts, more than 100 additional genes become essential. These largely code for the synthesis of amino acids, vitamins, and nucleobases. Although much is known about this collection of ∼400 genes, their interactions under nutrient stress are uncharted. Using a chemical biology approach, we focused on 45 chemical probes targeting encoded proteins in this collection and mapped their interactions under nutrient-limited conditions. Encompassing 990 unique pairwise chemical combinations, we revealed a highly connected network of 186 interactions, of which 81 were synergistic and 105 were antagonistic. The network revealed signature interactions for each probe and highlighted new connectivity between housekeeping functions and those essential in nutrient stress.

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.