While often obvious for macroscopic organisms, determining whether a microbe is dead or alive is fraught with complications. Fields such as microbial ecology, environmental health, and medical microbiology each determine how best to assess which members of the microbial community are alive, according to their respective scientific and/or regulatory needs. Many of these fields have gone from studying communities on a bulk level to the fine-scale resolution of microbial populations within consortia. For example, advances in nucleic acid sequencing technologies and downstream bioinformatic analyses have allowed for high-resolution insight into microbial community composition and metabolic potential, yet we know very little about whether such community DNA sequences represent viable microorganisms. In this review, we describe a number of techniques, from microscopy- to molecular-based, that have been used to test for viability (live/dead determination) and/or activity in various contexts, including newer techniques that are compatible with or complementary to downstream nucleic acid sequencing. We describe the compatibility of these viability assessments with high-throughput quantification techniques, including flow cytometry and quantitative PCR (qPCR). Although bacterial viability-linked community characterizations are now feasible in many environments and thus are the focus of this critical review, further methods development is needed for complex environmental samples and to more fully capture the diversity of microbes (e.g., eukaryotic microbes and viruses) and metabolic states (e.g., spores) of microbes in natural environments.
Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway.
Widespread testing for the presence of the novel coronavirus SARS-CoV-2 in individuals remains vital for controlling the COVID-19 pandemic prior to the advent of an effective treatment. Challenges in testing can be traced to an initial shortage of supplies, expertise and/or instrumentation necessary to detect the virus by quantitative reverse transcription polymerase chain reaction (RT-qPCR), the most robust, sensitive, and specific assay currently available. Here we show that academic biochemistry and molecular biology laboratories equipped with appropriate expertise and infrastructure can replicate commercially available SARS-CoV-2 RT-qPCR test kits and backfill pipeline shortages. The Georgia Tech COVID-19 Test Kit Support Group, composed of faculty, staff, and trainees across the biotechnology quad at Georgia Institute of Technology, synthesized multiplexed primers and probes and formulated a master mix composed of enzymes and proteins produced in-house. Our in-house kit compares favorably to a commercial product used for diagnostic testing. We also developed an environmental testing protocol to readily monitor surfaces across various campus laboratories for the presence of SARS-CoV-2. Our blueprint should be readily reproducible by research teams at other institutions, and our protocols may be modified and adapted to enable SARS-CoV-2 detection in more resource-limited settings.
Widespread testing for the presence novel coronavirus SARS-CoV-2 in patients remains vital for controlling the COVID-19 pandemic prior to the advent of an effective treatment. The early testing shortfall in some parts of the US can be traced to an initial shortage of supplies, expertise and/or instrumentation necessary to detect the virus by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Here we show that academic biochemistry and molecular biology laboratories equipped with appropriate expertise and infrastructure can produce the RT-qPCR assay and backfill pipeline shortages. The Georgia Tech COVID-19 Test Kit Support Group synthesized multiplexed primers and probes and formulated a master mix composed of enzymes and proteins produced in-house. We compare the performance of our in-house kit to a commercial product used for diagnostic testing and describe implementation of environmental testing to monitor surfaces across various campus laboratories for the presence of SARS-CoV-2.
The synthesis capabilities of the translation system (TS) provide engineering and directed evolution potential as well as a system for generating novel polymers. Orthogonal translation systems offer a parallel platform for translation engineering, enabling primary and secondary translation systems to operate independently without interfering with each other. However, current translation platforms for engineering lack complete orthogonality between the primary and secondary systems. Using a combination of indirect modifications through antibiotics, truncations of mito-rProteins mL50 and uL23, and replacement of mito-rProtein uL2, we develop and test the mitochondrial translation system in Saccharomyces cerevisiae as a fully orthogonal platform for in vivo directed evolution of translation. Our results show that continuous, comprehensive, creative, directed-evolution of a fully orthogonal TS in vivo appears to be a useful approach for technical control of the TS. This study emphasizes the power of system-wide evolutionary approaches over rational design. Evolution is creative and non-linear and can find unexpected solutions to problems.
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