No abstract

Microbially produced alkanes are a new class of biofuels that closely match the chemical composition of petroleum-based fuels. Alkanes can be generated from the fatty acid biosynthetic pathway by the reduction of acyl-ACPs followed by decarbonylation of the resulting aldehydes. A current limitation of this pathway is the restricted product profile, which consists of n-alkanes of 13, 15, and 17 carbons in length. To expand the product profile, we incorporated a new part, FabH2 from Bacillus subtilis, an enzyme known to have a broader specificity profile for fatty acid initiation than the native FabH of Escherichia coli. When provided with the appropriate substrate, the addition of FabH2 resulted in an altered alkane product profile in which significant levels of n-alkanes of 14 and 16 carbons in length are produced. The production of even chain length alkanes represents initial steps toward the expansion of this recently discovered microbial alkane production pathway to synthesize complex fuels. This work was conceived and performed as part of the 2011 University of Washington international Genetically Engineered Machines (iGEM) project.

Wastewater monitoring for SARS-CoV-2 has been suggested as an epidemiological indicator of community infection dynamics and disease prevalence. We report wastewater viral RNA levels of SARS-CoV-2 in a major metropolis serving over 3.6 million people geographically spread over 39 distinct sampling sites. Viral RNA levels were followed weekly for 22 weeks, both before, during, and after a major surge in cases, and simultaneously by two independent laboratories. We found SARS-CoV-2 RNA wastewater levels were a strong predictive indicator of trends in the nasal positivity rate two-weeks in advance. Furthermore, wastewater viral RNA loads demonstrated robust tracking of positivity rate for populations served by individual treatment plants, findings which were used in real-time to make public health interventions, including deployment of testing and education strike teams.

As the COVID-19 pandemic continues to affect communities across the globe, the need to contain the spread of the outbreaks is of paramount importance. Wastewater monitoring of the SARS-CoV-2 virus, the causative agent responsible for COVID-19, has emerged as a promising tool for health officials to anticipate outbreaks. As interest in wastewater monitoring continues to grow and municipalities begin to implement this approach, there is a need to further identify and evaluate methods used to concentrate SARS-CoV-2 virus RNA from wastewater samples. Here we evaluate the recovery, cost, and throughput of five different concentration methods for quantifying SARS-CoV-2 virus RNA in wastewater samples. We tested the five methods on six different wastewater samples. We also evaluated the use of a bovine coronavirus vaccine as a process control and pepper mild mottle virus as a normalization factor. Of the five methods we tested head-to-head, we found that HA filtration with bead beating performed the best in terms of sensitivity and cost. This evaluation can serve as a guide for laboratories establishing a protocol to perform wastewater monitoring of SARS-CoV-2.

Recent advances in synthetic biology have led to a wealth of well-characterized genetic parts. As parts libraries grow, so too does the potential to create novel multi-input promoters that integrate disparate signals to determine transcriptional output. Our ability to construct such promoters will outpace our ability to characterize promoter performance, due to the vast number of input combinations. In this study, we examine the input-output relations of recently developed synthetic multi-input promoters and describe two methods for predicting their behavior. The first method uses 1-dimensional induction data obtained from experiments on single-input systems to predict the n-dimensional induction responses of systems with n inputs. We demonstrate that this approach accurately predicts Boolean (on/off) responses of multi-input systems consisting of novel chimeric transcription factors and hybrid promoters in Escherichia coli. The second method uses only a small amount of multi-input response data to accurately predict analog system response over the entire landscape of input combinations. Taken together, these methods facilitate the design of synthetic circuits that utilize multi-input promoters.