A minimum information standard for reproducing bench-scale bacterial cell growth and productivity

Single-cell measurement quality in bits

Single-cell measurements have revolutionized our understanding of heterogeneity in cellular response. However, there is no universally comparable way to assess single-cell measurement quality. Here, we show how information theory can be used to assess and compare single-cell measurement quality in bits, which provides a universally comparable metric for information content. We anticipate that the experimental and theoretical approaches we show here will generally enable comparisons of quality between any single-cell measurement methods.

Method for reproducible automated bacterial cell culture and measurement

Microbial cell culture is one of the most commonly performed protocols for synthetic biology, and laboratories are increasingly using 96-well plates and laboratory automation systems for cell culture. Here, we describe a method for reproducible microbial culture using laboratory automation systems, including automated liquid handling, automated plate sealing and de-sealing, automated incubation and measurement of growing cultures. We discuss the key considerations that, in our experience, are important for reproducibility and present statistical analyses of data from 150 automated microbial growth experiments performed over 27 months using our automated method.

A FAIR-compliant parts catalogue for genome engineering and expression control in Saccharomyces cerevisiae

The synthetic biology toolkit for baker's yeast, Saccharomyces cerevisiae, includes extensive genome engineering toolkits and parts repositories. However, with the increasing complexity of engineering tasks and versatile applications of this model eukaryote, there is a continued interest to expand and diversify the rational engineering capabilities in this chassis by FAIR (findable, accessible, interoperable, and reproducible) compliance. In this study, we designed and characterised 41 synthetic guide RNA sequences to expand the CRISPR-based genome engineering capabilities for easy and efficient replacement of genomically encoded elements. Moreover, we characterize in high temporal resolution 20 native promoters and 18 terminators using fluorescein and LUDOX CL-X as references for GFP expression and OD600 measurements, respectively. Additionally, all data and reported analysis is provided in a publicly accessible jupyter notebook providing a tool for researchers with low-coding skills to further explore the generated data as well as a template for researchers to write their own scripts. We expect the data, parts, and databases associated with this study to support a FAIR-compliant resource for further advancing the engineering of yeasts.

Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR

The use of standardized components and processes in engineering underpins the design-build-test model, and the engineering of biological systems is no different. Substantial efforts to standardize both the components and the methods to validate the engineered biological systems is ongoing. This study has developed a panel of control materials encoding the commonly used reporter genes GFP and RFP as DNA or RNA molecules. Each panel contained up to six samples with increasingly small copy number differences between the two reporter genes that ranged from 1- to 2-fold differences. These copy number differences represent the magnitude of changes that may need to be measured to validate an engineered system. Using digital PCR (dPCR), we demonstrated that it is possible to quantify changes in both gene and gene transcript numbers both within and between samples down to 1.05-fold. We corroborated these findings using a simple gene circuit within a bacterial model to demonstrate that dPCR was able to precisely identify small changes in gene expression of two transcripts in response to promoter stimulation. Finally, we used our findings to highlight sources of error that can contributed to the measurement uncertainty in the measurement of small ratios in biological systems. Together, the development of a panel of control materials and validation of a high accuracy method for the measurement of small changes in gene expression, this study can contribute to the engineering biology "toolkit" of methods and materials to support the current standardization efforts.

Codon-Restrained Method for Both Eliminating and Creating Intragenic Bacterial Promoters

Future applications of synthetic biology will require refactored genetic sequences devoid of internal regulatory elements within coding sequences. These regulatory elements include cryptic and intragenic promoters, which may constitute up to a third of the predicted Escherichia coli promoters. The promoter activity is dependent on the structural interaction of core bases with a σ factor. Rational engineering can be used to alter key promoter element nucleotides interacting with σ factors and eliminate downstream transcriptional activity. In this paper, we present codon-restrained promoter silencing (CORPSE), a system for removing intragenic promoters. CORPSE exploits the DNA-σ factor structural relationship to disrupt σ⁷⁰ promoters embedded within gene coding sequences with a minimum of synonymous codon changes. Additionally, we present an inverted CORPSE system, iCORPSE, which can create highly active promoters within a gene sequence while not perturbing the function of the modified gene.

Intent Parser: A Tool for Codification and Sharing of Experimental Design

Communicating information about experimental design among a team of collaborators is challenging because different people tend to describe experiments in different ways and with different levels of detail. Sometimes, humans can interpret missing information by making assumptions and drawing inferences from information already provided. Doing so, however, is error-prone and typically requires a high level of interpersonal communication. In this paper, we present a tool that addresses this challenge by providing a simple interface for incremental formal codification of experiment designs. Users interact with a Google Docs word-processing interface with structured tables, backed by assisted linking to machine-readable definitions in a data repository (SynBioHub) and specification of available protocols and requests for execution in the Open Protocol Interface Language (OPIL). The result is an easy-to-use tool for generating machine-readable descriptions of experiment designs with which users in the DARPA SD2 program have collected data from 80 208 samples using a variety of protocols and instruments over the course of 181 experiment runs.

Starting a new recombinant protein production project in Escherichia coli

One of the goals in recombinant protein production in Escherichia coli is to maximize productivity. High volumetric and specific yields can be reached after careful selection of expression strains and optimization of cultivation parameters. In this chapter, we review the many tools available to make the most out of this versatile microbial cell factory. Useful guidelines and options for troubleshooting production are presented.

Model-guided dynamic control of essential metabolic nodes boosts acetyl-coenzyme A-dependent bioproduction in rewired Pseudomonas putida

Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.

Proteomic and Transcriptomic Analysis of Microviridae φX174 Infection Reveals Broad Upregulation of Host Escherichia coli Membrane Damage and Heat Shock Responses

A major part of the healthy human gut microbiome is the Microviridae bacteriophage, exemplified by the model φX174 phage, and their E. coli hosts. Although much has been learned from studying φX174 over the last half-century, until this work, the E. coli host response to infection has never been investigated in detail.

Measuring host-bacteriophage dynamics is an important approach to understanding bacterial survival functions and responses to infection. The model Microviridae bacteriophage φX174 is endemic to the human gut and has been studied for over 70 years, but the host response to infection has never been investigated in detail. To address this gap in our understanding of this important interaction within our microbiome, we have measured host Escherichia coli C proteomic and transcriptomic response to φX174 infection. We used mass spectrometry and RNA sequencing (RNA-seq) to identify and quantify all 11 φX174 proteins and over 1,700 E. coli proteins, enabling us to comprehensively map host pathways involved in φX174 infection. Most notably, we see significant host responses centered on membrane damage and remodeling, cellular chaperone and translocon activity, and lipoprotein processing, which we speculate is due to the peptidoglycan-disruptive effects of the φX174 lysis protein E on MraY activity. We also observe the massive upregulation of small heat shock proteins IbpA/B, along with other heat shock pathway chaperones, and speculate on how the specific characteristics of holdase protein activity may be beneficial for viral infections. Together, this study enables us to begin to understand the proteomic and transcriptomic host responses of E. coli to Microviridae infections and contributes insights to the activities of this important model host-phage interaction.

IMPORTANCE A major part of the healthy human gut microbiome is the Microviridae bacteriophage, exemplified by the model φX174 phage, and their E. coli hosts. Although much has been learned from studying φX174 over the last half-century, until this work, the E. coli host response to infection has never been investigated in detail. We reveal the proteomic and transcriptomic pathways differentially regulated during the φX174 infection cycle and uncover the details of a coordinated cellular response to membrane damage that results in increased lipoprotein processing and membrane trafficking, likely due to the phage antibiotic-like lysis protein. We also reveal that small heat shock proteins IbpA/B are massively upregulated during infection and that these holdase chaperones are highly conserved across the domains of life, indicating that reliance on them is likely widespread across viruses.

Accelerating strain engineering in biofuel research via build and test automation of synthetic biology

Biofuels are a type of sustainable and renewable energy. However, for the economical production of bulk-volume biofuels, biosystems design is particularly challenging to achieve sufficient yield, titer, and productivity. Because of the lack of predictive modeling, high-throughput screening remains essential. Recently established biofoundries provide an emerging infrastructure to accelerate biological design-build-test-learn (DBTL) cycles through the integration of robotics, synthetic biology, and informatics. In this review, we first introduce the technical advances of build and test automation in synthetic biology, focusing on the use of industry-standard microplates for DNA assembly, chassis engineering, and enzyme and strain screening. Proof-of-concept studies on prototypes of automated foundries are then discussed, for improving biomass deconstruction, metabolic conversion, and host robustness. We conclude with future challenges and opportunities in creating a flexible, versatile, and data-driven framework to support biofuel research and development in biofoundries.

Flapjack: Data Management and Analysis for Genetic Circuit Characterization

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different laboratories and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parametrize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting, and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated with circuit part composition via SBOL (Synthetic Biology Open Language). We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale

High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).

Measurements drive progress in directed evolution for precise engineering of biological systems

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

Are synthetic biology standards applicable in everyday research practice?

The issue of standardization in synthetic biology is a recurring one. As a discipline that incorporates engineering principles into biological designs, synthetic biology needs effective ways to communicate results and allow different researchers (both academic and industrial) to build upon previous results and improve on existing designs. An aspect that is left out of the discussions, especially when they happen at the level of academic and industrial consortia or policymaking, is whether or not standards are applicable or even useful in everyday research practice. In this caucus article, we examine this particular issue with the hope of including it in the standardization discussions agenda and provide insights into a topic that synthetic biology researchers experience daily.