A design of experiments approach for the rapid formulation of a chemically defined medium for metabolic profiling of industrially important microbes

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Control points for design of taxonomic composition in synthetic human gut communities

Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states. Furthermore, systematically varied initial abundances drive variation in community assembly and enable inference of pairwise inter-species interactions via a dynamic ecological model. These interactions are overall consistent with conditioned media experiments, demonstrating that specific perturbations to a high-richness community can provide rich information for building dynamic ecological models. This model is subsequently used to design low-richness communities that display low or high temporal taxonomic variability over an extended period. A record of this paper's transparent peer review process is included in the supplemental information.

Insights into the rapid metabolism of Geobacillus sp. LC300: unraveling metabolic requirements and optimal growth conditions

This study investigated the metabolism of Geobacillus sp. LC300, a promising biorefinery host organism with high substrate utilization rates. A new defined medium was designed and tested that allows for exponential growth to elevated cell densities suitable for quantitative physiological studies. Screening of the metabolic requirements of G. sp. LC300 revealed prototrophy for all essential amino acids and most vitamins and only showed auxotrophy for vitamin B12 and biotin. The effect of temperature and pH on growth rate was investigated, adjusting the optimal growth temperature to several degrees lower than previously reported. Lastly, studies on carbon source utilization revealed a capability for fast growth on several common carbon sources, including monosaccharides, oligosaccharides, and polysaccharides, and the highest ever reported growth rate in defined medium on glucose (2.20 h-1) or glycerol (1.95 h-1). These findings provide a foundation for further exploration of G. sp. LC300's physiology and metabolic regulation, and its potential use in bioproduction processes.

Rational search of genetic design space for a heterologous terpene metabolic pathway in Streptomyces

Modern tools in DNA synthesis and assembly give genetic engineers control over the nucleotide-level design of complex, multi-gene systems. Systematic approaches to explore genetic design space and optimize the performance of genetic constructs are lacking. Here we explore the application of a five-level Plackett-Burman fractional factorial design to improve the titer of a heterologous terpene biosynthetic pathway in Streptomyces. A library of 125 engineered gene clusters encoding the production of diterpenoid ent-atiserenoic acid (eAA) via the methylerythritol phosphate pathway was constructed and introduced into Streptomyces albidoflavus J1047 for heterologous expression. The eAA production titer varied within the library by over two orders of magnitude and host strains showed unexpected and reproducible colony morphology phenotypes. Analysis of Plackett-Burman design identified expression of dxs, the gene encoding the first and the flux-controlling enzyme, having the strongest impact on eAA titer, but with a counter-intuitive negative correlation between dxs expression and eAA production. Finally, simulation modeling was performed to determine how several plausible sources of experimental error/noise and non-linearity impact the utility of Plackett-Burman analyses.

Optimizing Soluble Cues for Salivary Gland Tissue Mimetics Using a Design of Experiments (DoE) Approach

The development of therapies to prevent or treat salivary gland dysfunction has been limited by a lack of functional in vitro models. Specifically, critical markers of salivary gland secretory phenotype downregulate rapidly ex vivo. Here, we utilize a salivary gland tissue chip model to conduct a design of experiments (DoE) approach to test combinations of seven soluble cues that were previously shown to maintain or improve salivary gland cell function. This approach uses statistical techniques to improve efficiency and accuracy of combinations of factors. The DoE-designed culture conditions improve markers of salivary gland function. Data show that the EGFR inhibitor, EKI-785, maintains relative mRNA expression of Mist1, a key acinar cell transcription factor, while FGF10 and neurturin promote mRNA expression of Aqp5 and Tmem16a, channel proteins involved in secretion. Mist1 mRNA expression correlates with increased secretory function, including calcium signaling and mucin (PAS-AB) staining. Overall, this study demonstrates that media conditions can be efficiently optimized to support secretory function in vitro using a DoE approach.

Rational Design of CRISPR/Cas12a-RPA Based One-Pot COVID-19 Detection with Design of Experiments

Simple and effective molecular diagnostic methods have gained importance due to the devastating effects of the COVID-19 pandemic. Various isothermal one-pot COVID-19 detection methods have been proposed as favorable alternatives to standard RT-qPCR methods as they do not require sophisticated and/or expensive devices. However, as one-pot reactions are highly complex with a large number of variables, determining the optimum conditions to maximize sensitivity while minimizing diagnostic cost can be cumbersome. Here, statistical design of experiments (DoE) was employed to accelerate the development and optimization of a CRISPR/Cas12a-RPA-based one-pot detection method for the first time. Using a definitive screening design, factors with a significant effect on performance were elucidated and optimized, facilitating the detection of two copies/μL of full-length SARS-CoV-2 (COVID-19) genome using simple instrumentation. The screening revealed that the addition of a reverse transcription buffer and an RNase inhibitor, components generally omitted in one-pot reactions, improved performance significantly, and optimization of reverse transcription had a critical impact on the method’s sensitivity. This strategic method was also applied in a second approach involving a DNA sequence of the N gene from the COVID-19 genome. The slight differences in optimal conditions for the methods using RNA and DNA templates highlight the importance of reaction-specific optimization in ensuring robust and efficient diagnostic performance. The proposed detection method is automation-compatible, rendering it suitable for high-throughput testing. This study demonstrated the benefits of DoE for the optimization of complex one-pot molecular diagnostics methods to increase detection sensitivity.

Microbial pathways for advanced biofuel production

Decarbonisation of the transport sector is essential to mitigate anthropogenic climate change. Microbial metabolisms are already integral to the production of renewable, sustainable fuels and, building on that foundation, are being re-engineered to generate the advanced biofuels that will maintain mobility of people and goods during the energy transition. This review surveys the range of natural and engineered microbial systems for advanced biofuels production and summarises some of the techno-economic challenges associated with their implementation at industrial scales.

PASIV: A Pooled Approach-Based Workflow to Overcome Toxicity-Induced Design of Experiments Failures and Inefficiencies

We present here a newly developed workflow—which we have called PASIV—designed to provide a solution to a practical problem with design of experiments (DoE) methodology: i.e., what can be done if the scoping phase of the DoE cycle is severely hampered by burden and toxicity issues (caused by either the metabolite or an intermediary), making it unreliable or impossible to proceed to the screening phase? PASIV—standing for pooled approach, screening, identification, and visualization—was designed so the (viable) region of interest can be made to appear through an interplay between biology and software. This was achieved by combining multiplex construction in a pooled approach (one-pot reaction) with a viability assay and with a range of bioinformatics tools (including a novel construct matching tool). PASIV was tested on the exemplar of the lycopene pathway—under stressful constitutive expression—yielding a region of interest with comparatively stronger producers.

Removing the Bottleneck: Introducing cMatch - A Lightweight Tool for Construct-Matching in Synthetic Biology

We present a software tool, called cMatch, to reconstruct and identify synthetic genetic constructs from their sequences, or a set of sub-sequences—based on two practical pieces of information: their modular structure, and libraries of components. Although developed for combinatorial pathway engineering problems and addressing their quality control (QC) bottleneck, cMatch is not restricted to these applications. QC takes place post assembly, transformation and growth. It has a simple goal, to verify that the genetic material contained in a cell matches what was intended to be built - and when it is not the case, to locate the discrepancies and estimate their severity. In terms of reproducibility/reliability, the QC step is crucial. Failure at this step requires repetition of the construction and/or sequencing steps. When performed manually or semi-manually QC is an extremely time-consuming, error prone process, which scales very poorly with the number of constructs and their complexity. To make QC frictionless and more reliable, cMatch performs an operation we have called “construct-matching” and automates it. Construct-matching is more thorough than simple sequence-matching, as it matches at the functional level-and quantifies the matching at the individual component level and across the whole construct. Two algorithms (called CM_1 and CM_2) are presented. They differ according to the nature of their inputs. CM_1 is the core algorithm for construct-matching and is to be used when input sequences are long enough to cover constructs in their entirety (e.g., obtained with methods such as next generation sequencing). CM_2 is an extension designed to deal with shorter data (e.g., obtained with Sanger sequencing), and that need recombining. Both algorithms are shown to yield accurate construct-matching in a few minutes (even on hardware with limited processing power), together with a set of metrics that can be used to improve the robustness of the decision-making process. To ensure reliability and reproducibility, cMatch builds on the highly validated pairwise-matching Smith-Waterman algorithm. All the tests presented have been conducted on synthetic data for challenging, yet realistic constructs - and on real data gathered during studies on a metabolic engineering example (lycopene production).

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Time-Course Transcriptome of Parageobacillus thermoglucosidasius DSM 6285 Grown in the Presence of Carbon Monoxide and Air

Parageobacillus thermoglucosidasius is a metabolically versatile, facultatively anaerobic thermophile belonging to the family Bacillaceae. Previous studies have shown that this bacterium harbours co-localised genes coding for a carbon monoxide (CO) dehydrogenase (CODH) and Ni-Fe hydrogenase (Phc) complex and oxidises CO and produces hydrogen (H₂) gas via the water-gas shift (WGS) reaction. To elucidate the genetic events culminating in the WGS reaction, P. thermoglucosidasius DSM 6285 was cultivated under an initial gas atmosphere of 50% CO and 50% air and total RNA was extracted at ~8 (aerobic phase), 20 (anaerobic phase), 27 and 44 (early and late hydrogenogenic phases) hours post inoculation. The rRNA-depleted fraction was sequenced using Illumina NextSeq, v2.5, 1x75bp chemistry. Differential expression revealed that at 8 vs.. 20, 20 vs.. 27 and 27 vs.. 44 h post inoculation, 2190, 2118 and 231 transcripts were differentially (FDR < 0.05) expressed. Cluster analysis revealed 26 distinct gene expression trajectories across the four time points. Of these, two similar clusters, showing overexpression at 20 relative to 8 h and depletion at 27 and 44 h, harboured the CODH and Phc transcripts, suggesting possible regulation by O_2. The transition between aerobic respiration and anaerobic growth was marked by initial metabolic deterioration, as reflected by up-regulation of transcripts linked to sporulation and down-regulation of transcripts linked to flagellar assembly and metabolism. However, the transcriptome and growth profiles revealed the reversal of this trend during the hydrogenogenic phase.