Gene machines

Abstract:

During transcription elongation, NusG aids RNA polymerase by inhibiting pausing, promoting anti-termination on rRNA operons, coupling transcription with translation on mRNA genes, and facilitating Rho-dependent termination. Despite extensive work, the in vivo functional allocation and spatial distribution of NusG remain unknown. Using single-molecule tracking and super-resolution imaging in live E. coli cells, we found NusG predominantly in a chromosome-associated population (binding to RNA polymerase in elongation complexes) and a slowly diffusing population complexed with the 30S ribosomal subunit; the latter provides a "30S-guided" path for NusG into transcription elongation. Only ∼10% of NusG is fast diffusing, with its mobility suggesting non-specific interactions with DNA for >50% of the time. Antibiotic treatments and deletion mutants revealed that chromosome-associated NusG participates mainly in rrn anti-termination within phase-separated transcriptional condensates and in transcription-translation coupling. This study illuminates the multiple roles of NusG and offers a guide on dissecting multi-functional machines via in vivo imaging.

Abstract:

<jats:p>We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable...</jats:p>

Abstract:

The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.