Standardization of Fluorescent Reporter Assays in Synthetic Biology across the Visible Light Spectrum

In synthetic biology, Fluorescent reporters are frequently used to characterize the expression levels obtained from both genetic parts such as promoters and ribosome binding sites as well as from complex genetic circuits. To this end, plate readers offer an easy and high-throughput way of characterizing both the growth and fluorescence expression levels of cell cultures. However, despite the similar mode of action used in different devices, their output is not comparable due to intrinsic differences in their setup. Additionally, the generated output is expressed using arbitrary units, limiting reliable comparison of results to measurements taken within one single experiment using one specific plate reader, hampering the transferability of data across different plate readers and laboratories. This article presents an easy and accessible calibration method for transforming the device-specific output into a standardized output expressing the amount of fluorescence per well as a known equivalent fluorophore concentration per cell for fluorescent reporters spanning the visible light spectrum. This calibration method follows a 2-fold approach determining both the estimated number of cells and the equivalent chemical fluorophore concentration per well. It will contribute to the comparison of plate reader experiments between different laboratories across the world and will therefore greatly improve the reliability and exchange of both results and genetic parts between research groups.

Distinct behavior in early life stress dams predicts heterogeneity in future stress response in offspring over lifespan

Introduction

Exposure to early life stress (ELS) strongly predicts prevalent, impairing, and costly psychiatric illness throughout life including mental disorders. The reason, some individuals are more vulnerable to ELS whereas others remain resilient, is poorly understood. There is a need for better understanding of early biological changes triggered by ELS with responsibility to negative outcomes in health.

Objectives

We stratify animals after ELS according to corticosterone levels. [1] Re-challenging the animals to a second stressor, chronic social defeat (CSD) [2], in adulthood was performed to understand individual trajectories depending on corticosterone exposure during early adverse conditions.

Methods

We performed ELS as previously reported [1]. Behavior of mothers was observed during ELS. Correlation between level of corticosterone and behavior observed in dams. ELS animals were exposed to a second stress in adulthood. A battery of tests for different behavioral domains was performed. Behavioral analyses was combined with assessment of litter HPA system reactivity and observed behavior in dams.

Results

Stress dams where significantly higher in number of sorties over whole observation period, time dams spent outside the nest differed. We could correlate the number of sorties on p3 with corticosterone plasma level at p9. Control dams spent significantly more time outside in 9pm recordings than stress animals. We could show reduced interaction with social juvenile targets in sociability test for CSD mice. Light dark transition was significantly higher for control mice compared to CSD but lower for control vs ELS animals.

Conclusions

Behavior in dams during ELS correlates with chronic stress coping mechanisms in offspring’s adulthood.

Disclosure

No significant relationships.

ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli

A major goal in synthetic biology is the engineering of synthetic gene circuits with a predictable, controlled and designed outcome. This creates a need for building blocks that can modulate gene expression without interference with the native cell system. A tool allowing forward engineering of promoters with predictable transcription initiation frequency is still lacking. Promoter libraries specific for σ⁷⁰ to ensure the orthogonality of gene expression were built in Escherichia coli and labeled using fluorescence-activated cell sorting to obtain high-throughput DNA sequencing data to train a convolutional neural network. We were able to confirm in vivo that the model is able to predict the promoter transcription initiation frequency (TIF) of new promoter sequences. Here, we provide an online tool for promoter design (ProD) in E. coli, which can be used to tailor output sequences of desired promoter TIF or predict the TIF of a custom sequence.

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.

Predictive design of sigma factor-specific promoters

To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.