Scalable, two-stage, autoinduction of recombinant protein expression in E. coli utilizing phosphate depletion.

We report the scalable production of recombinant proteins in Escherichia coli, reliant on tightly controlled autoinduction, triggered by phosphate depletion in the stationary phase. The method, reliant on engineered strains and plasmids, enables improved protein expression across scales. Expression levels using this approach have reached as high as 55% of the total cellular protein. The initial use of the method in instrumented fed-batch fermentations enables cell densities of ∼30 gCDW/L and protein titers up to 8.1 ± 0.7 g/L (∼270 mg/gCDW). The process has also been adapted to an optimized autoinduction media, enabling routine batch production at culture volumes of 20 µl (384-well plates), 100 µl (96-well plates), 20 ml, and 100 ml. In batch cultures, cell densities routinely reach ∼5-7 gCDW/L, offering protein titers above 2 g/L. The methodology has been validated with a set of diverse heterologous proteins and is of general use for the facile optimization of routine protein expression from high throughput screens to fed-batch fermentation.

Improved two-stage protein expression and purification via autoinduction of both autolysis and auto DNA/RNA hydrolysis conferred by phage lysozyme and DNA/RNA endonuclease.

We report improved release of recombinant proteins in Escherichia coli, which relies on combined cellular autolysis and DNA/RNA autohydrolysis, conferred by the tightly controlled autoinduction of both phage lysozyme and the nonspecific DNA/RNA endonuclease from Serratia marcescens. Autoinduction occurs in a two-stage process wherein heterologous protein expression and autolysis enzymes are induced upon entry into stationary phase by phosphate depletion. Cytoplasmic lysozyme and periplasmic endonuclease are kept from inducing lysis until membrane integrity is disrupted. After cell harvest, the addition of detergent (0.1% Triton X-100) and a single 30 min freeze-thaw cycle results in >90% release of protein, green fluorescent protein. This cellular lysis is accompanied by complete oligonucleotide hydrolysis. The approach has been validated for shake flask cultures, high-throughput cultivation in microtiter plates, and larger scale stirred-tank bioreactors. This tightly controlled system enables robust growth and resistance to lysis in routine media when cells are propagated and autolysis/hydrolysis genes are only induced upon phosphate depletion.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope.

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

BioSamplr: An open source, low cost automated sampling system for bioreactors.

Autosampling from bioreactors reduces error, increases reproducibility and offers improved aseptic handling when compared to manual sampling. Additionally, autosampling greatly decreases the hands-on time required for a bioreactor experiment and enables sampling 24 h a day. We have designed, built and tested a low cost, open source, automated bioreactor sampling system, the BioSamplr. The BioSamplr can take up to ten samples from a bioreactor at a desired sample interval and cools them to a desired temperature. The device, assembled from low cost and 3D printed components, is controlled wirelessly by a Raspberry Pi, and records all sampling data to a log file. The cost and accessibility of the BioSamplr make it useful for laboratories without access to more expensive and complex autosampling systems.

A Review of the Biotechnological Production of Methacrylic Acid.

Industrial biotechnology can lead to new routes and potentially to more sustainable production of numerous chemicals. We review the potential of biobased routes from sugars to the large volume commodity, methacrylic acid, involving fermentation based bioprocesses. We cover the key progress over the past decade on direct and indirect fermentation based routes to methacrylic acid including both academic as well as patent literature. Finally, we take a critical look at the potential of biobased routes to methacrylic acid in comparison with both incumbent as well as newer greener petrochemical based processes.

Media Robustness and Scalability of Phosphate Regulated Promoters Useful for Two-Stage Autoinduction in E. coli.

A key challenge in synthetic biology is the successful utilization of characterized parts, such as promoters, in different biological contexts. We report the evaluation of the media robustness of a small library of E. coli PhoB regulated promoters that enable heterologous protein production in two-stage cultures. Expression levels were measured both in a rich Autoinduction Broth as well as a minimal mineral salts media. Expression was both media and promoter dependent. Of the 16 promoters tested, 4 were identified to have tightly controlled expression, which was also robust to media formulation. Improved promoter robustness led to more predictable scale up and consistent expression in instrumented bioreactors. This subset of PhoB activated promoters, useful for two-stage autoinduction, highlights the impact of the environment on the performance of biological parts and the importance of robustness testing in synthetic biology.