GMOCU: Digital Documentation, Management, and Biological Risk Assessment of Genetic Parts.

The continuous evolution of molecular biology and gene synthesis methods paired with an ever-increasing potential of synthetic biology approaches and genome engineering toolkits enables the rapid design of genetic bioparts and genetically modified organisms. Although various software solutions assist with specific design tasks and challenges, lab internal documentation and ensuring compliance with governmental regulations on biosafety assessment of the generated organisms remain the responsibility of individual academic researchers. This results in inconsistent and redundant documentation regimes and a significant time and labor burden. GMOCU (GMO documentation) is a standardized semi-automatic user-oriented software approach -written in Python and freely available- that unifies lab internal data documentation on genetic parts and genetically modified organisms (GMOs). It automatizes biological risk evaluations and maintains a shared up-to-date inventory of bioparts for team-wide data navigation and sharing. GMOCU further enables data export into customizable formats suitable for scientific publications, official biosafety documents, and the research community.

Better research by efficient sharing: evaluation of free management platforms for synthetic biology designs.

Synthetic biology aims to introduce engineering principles into biology, for example, the construction of biological devices by assembling previously-characterized, functional parts. This approach demands new resources for cataloging and sharing biological components and designs, in order to accelerate the design-build-test-learn cycle. We evaluated two free, open source software platforms for managing synthetic biology data: Joint Bioenergy Institute-Inventory of Composable Elements (JBEI-ICE) and SynBioHub. We analyzed the systems from the perspective of experimental biology research groups in academia, which seek to incorporate the repositories into their synthetic biology workflow. Here, we define the minimal requirements for a repository in this context and develop three usage scenarios, where we then examine the two platforms: (i) supporting the synthetic biology design-build-test-learn cycle, (ii) batch deposit of existing designs into the repository and (iii) discovery and reuse of designs from the repository. Our evaluation of JBEI-ICE and SynBioHub provides an insight into the current state of synthetic biology resources, might encourage their wider adoption and should guide future development to better meet the needs of this user group.

Expanding the bioluminescent reporter toolkit for plant science with NanoLUC.

BACKGROUND: Protein data over circadian time scale is scarce for clock transcription factors. Further work in this direction is required for refining quantitative clock models. However, gathering highly resolved dynamics of low-abundance transcription factors has been a major challenge in the field. In this work we provide a new tool that could help this major issue. Bioluminescence is an important tool for gathering data on circadian gene expression. It allows data collection over extended time periods for low signal levels, thanks to a large signal-to-noise ratio. However, the main reporter so far used, firefly luciferase (FLUC), presents some disadvantages for reporting total protein levels. For example, the rapid, post-translational inactivation of this luciferase will result in underestimation of protein numbers. A more stable reporter protein could in principle tackle this issue. We noticed that NanoLUC might fill this gap, given its reported brightness and the stability of both enzyme and substrate. However, no data in plant systems on the circadian time scale had been reported. RESULTS: We tested NanoLUC activity under different scenarios that will be important for generating highly quantitative data. These include enzyme purification for calibration curves, expression in transient plant systems, stable transgenic plants and in planta time series over circadian time scales. Furthermore, we show that the difference in substrate use between firefly luciferase and NanoLUC allows tracking of two different reporters from the same samples. We show this by exploring the impact of a BOAp:BOA-NanoLUC construct transformed into a Col-0 CCA1p:FLUC background. CONCLUSIONS: We concluded that NanoLUC reporters are compatible with established instrumentation and protocols for firefly luciferase. Overall, our results provide guidelines for researchers gathering dynamic protein data over different time scales and experimental setups.