Majalah Far Eastern Agriculture

Widespread, frequent testing is essential for curbing the ongoing COVID-19 pandemic. Because its simplicity makes it ideal for widely distributed, high throughput testing, RT-LAMP provides an attractive alternative to RT-qPCR. However, most RT-LAMP protocols require the purification of RNA, a complex and low-throughput bottleneck that has often been subject to reagent supply shortages. Here, we report an optimized RT-LAMP-based SARS-CoV-2 diagnostic protocol for saliva and swab samples. In the protocol we replace RNA purification with a simple sample preparation step using a widely available chelating agent, as well as optimize key protocol parameters. When tested on clinical swab and saliva samples, this assay achieves a limit of detection of 105 viral genomes per ml, with sensitivity close to 90% and specificity close to 100%, and takes 45 minutes from sample collection to result, making it well suited for a COVID-19 surveillance program

Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen’s susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning–personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens

Evolthon: A community endeavor to evolve lab evolution.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation