The bacterium Escherichia coli O157:H7 is a worldwide threat to public health and has been implicated in many outbreaks of haemorrhagic colitis, some of which included fatalities caused by haemolytic uraemic syndrome. Close to 75,000 cases of O157:H7 infection are now estimated to occur annually in the United States. The severity of disease, the lack of effective treatment and the potential for large-scale outbreaks from contaminated food supplies have propelled intensive research on the pathogenesis and detection of E. coli O157:H7 (ref. 4). Here we have sequenced the genome of E. coli O157:H7 to identify candidate genes responsible for pathogenesis, to develop better methods of strain detection and to advance our understanding of the evolution of E. coli, through comparison with the genome of the non-pathogenic laboratory strain E. coli K-12 (ref. 5). We find that lateral gene transfer is far more extensive than previously anticipated. In fact, 1,387 new genes encoded in strain-specific clusters of diverse sizes were found in O157:H7. These include candidate virulence factors, alternative metabolic capacities, several prophages and other new functions--all of which could be targets for surveillance.

The WGS-based assignment of iGBS resistance features and serotypes is an accurate substitute for phenotypic testing.

Our whole genome sequence (WGS) pipeline was assessed for accurate prediction of antimicrobial phenotypes. For 2316 invasive pneumococcal isolates recovered during 2015 we compared WGS pipeline data to broth dilution testing (BDT) for 18 antimicrobials. For 11 antimicrobials categorical discrepancies were assigned when WGS-predicted MICs and BDT MICs predicted different categorizations for susceptibility, intermediate resistance or resistance, ranging from 0.9% (tetracycline) to 2.9% (amoxicillin). For β-lactam antibiotics, the occurrence of at least four-fold differences in MIC ranged from 0.2% (meropenem) to 1.0% (penicillin), although phenotypic retesting resolved 25%-78% of these discrepancies. Non-susceptibility to penicillin, predicted by penicillin-binding protein types, was 2.7% (non-meningitis criteria) and 23.8% (meningitis criteria). Other common resistance determinants included mef (475 isolates), ermB (191 isolates), ermB + mef (48 isolates), tetM (261 isolates) and cat (51 isolates). Additional accessory resistance genes (tetS, tet32, aphA-3, sat4) were rarely detected (one to three isolates). Rare core genome mutations conferring erythromycin-resistance included a two-codon rplD insertion (rplD69-KG-70) and the 23S rRNA A2061G substitution (six isolates). Intermediate cotrimoxazole-resistance was associated with one or two codon insertions within folP (238 isolates) or the folA I100L substitution (38 isolates), whereas full cotrimoxazole-resistance was attributed to alterations in both genes (172 isolates). The two levofloxacin-resistant isolates contained parC and/or gyrA mutations. Of 11 remaining isolates with moderately elevated MICs to both ciprofloxacin and levofloxacin, seven contained parC or gyrA mutations. The two rifampin-resistant isolates contained rpoB mutations. WGS-based antimicrobial phenotype prediction was an informative alternative to BDT for invasive pneumococci.

Zinc fingers are the most abundant DNA-binding motifs encoded by eukaryotic genomes and one of the best understood DNA-recognition domains. Each zinc finger typically binds a 3-nt target sequence, and it is possible to engineer zinc-finger arrays (ZFAs) that recognize extended DNA sequences by linking together individual zinc fingers. Engineered zinc-finger proteins have proven to be valuable tools for gene regulation and genome modification because they target specific sites in a genome. Here we describe ZiFDB (Zinc Finger Database; http://bindr.gdcb.iastate.edu/ZiFDB), a web-accessible resource that compiles information on individual zinc fingers and engineered ZFAs. To enhance its utility, ZiFDB is linked to the output from ZiFiT—a software package that assists biologists in finding sites within target genes for engineering zinc-finger proteins. For many molecular biologists, ZiFDB will be particularly valuable for determining if a given ZFA (or portion thereof) has previously been constructed and whether or not it has the requisite DNA-binding activity for their experiments. ZiFDB will also be a valuable resource for those scientists interested in better understanding how zinc-finger proteins recognize target DNA.