Gene machines

Abstract:

ABSTRACT

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. Additionally, we were able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods, and has the potential for significant impact.

Abstract:

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min and does not require any lysis, purification, or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods and has the potential for significant impact.

Abstract:

Rho is a general transcription termination factor in bacteria, but many aspects of its mechanism of action are unclear. Diverse models have been proposed for the initial interaction between the RNA polymerase (RNAP) and Rho (catch-up and stand-by pre-terminational models); for the terminational release of the RNA transcript (RNA shearing, RNAP hyper-translocation or displacing, and allosteric models); and for the post-terminational outcome (whether the RNAP dissociates or remains bound to the DNA). Here, we use single-molecule fluorescence assays to study those three steps in transcription termination mediated by E. coli Rho. We find that different mechanisms previously proposed for each step co-exist, but apparently occur on various timescales and tend to lead to specific outcomes. Our results indicate that three kinetically distinct routes take place: (1) the catch-up mode leads first to RNA shearing for RNAP recycling on DNA, and (2) later to RNAP displacement for decomposition of the transcriptional complex; (3) the last termination usually follows the stand-by mode with displacing for decomposing. This three-route model would help reconcile current controversies on the mechanisms.