Samuel Deutsch

Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome

Ruminant livestock represent the single largest anthropogenic source of the potent greenhouse gas methane, which is generated by methanogenic archaea residing in ruminant digestive tracts. While differences between individual animals of the same breed in the amount of methane produced have been observed, the basis for this variation remains to be elucidated. To explore the mechanistic basis of this methane production, we measured methane yields from 22 sheep, which revealed that methane yields are a reproducible, quantitative trait. Deep metagenomic and metatranscriptomic sequencing demonstrated a similar abundance of methanogens and methanogenesis pathway genes in high and low methane emitters. However, transcription of methanogenesis pathway genes was substantially increased in sheep with high methane yields. These results identify a discrete set of rumen methanogens whose methanogenesis pathway transcription profiles correlate with methane yields and provide new targets for CH4 mitigation at the levels of microbiota composition and transcriptional regulation.

Development of an orthogonal fatty acid biosynthesis system in E. coli for oleochemical production

Here we report recombinant expression and activity of several type I fatty acid synthases that can function in parallel with the native Escherichia coli fatty acid synthase. Corynebacterium glutamicum FAS1A was the most active in E. coli and this fatty acid synthase was leveraged to produce oleochemicals including fatty alcohols and methyl ketones. Coexpression of FAS1A with the ACP/CoA-reductase Maqu2220 from Marinobacter aquaeolei shifted the chain length distribution of fatty alcohols produced. Coexpression of FAS1A with FadM, FadB, and an acyl-CoA-oxidase from Micrococcus luteus resulted in the production of methyl ketones, although at a lower level than cells using the native FAS. This work, to our knowledge, is the first example of in vivo function of a heterologous fatty acid synthase in E. coli. Using FAS1 enzymes for oleochemical production have several potential advantages, and further optimization of this system could lead to strains with more efficient conversion to desired products. Finally, functional expression of these large enzyme complexes in E. coli will enable their study without culturing the native organisms.

Phylogenomically Guided Identification of Industrially Relevant GH1 β-Glucosidases through DNA Synthesis and Nanostructure-Initiator Mass Spectrometry

Harnessing the biotechnological potential of the large number of proteins available in sequence databases requires scalable methods for functional characterization. Here we propose a workflow to address this challenge by combining phylogenomic guided DNA synthesis with high-throughput mass spectrometry and apply it to the systematic characterization of GH1 β-glucosidases, a family of enzymes necessary for biomass hydrolysis, an important step in the conversion of lignocellulosic feedstocks to fuels and chemicals. We synthesized and expressed 175 GH1s, selected from over 2000 candidate sequences to cover maximum sequence diversity. These enzymes were functionally characterized over a range of temperatures and pHs using nanostructure-initiator mass spectrometry (NIMS), generating over 10,000 data points. When combined with HPLC-based sugar profiling, we observed GH1 enzymes active over a broad temperature range and toward many different β-linked disaccharides. For some GH1s we also observed activity toward laminarin, a more complex oligosaccharide present as a major component of macroalgae. An area of particular interest was the identification of GH1 enzymes compatible with the ionic liquid 1-ethyl-3-methylimidazolium acetate ([C2mim][OAc]), a next-generation biomass pretreatment technology. We thus searched for GH1 enzymes active at 70 °C and 20% (v/v) [C2mim][OAc] over the course of a 24-h saccharification reaction. Using our unbiased approach, we identified multiple enzymes of different phylogentic origin with such activities. Our approach of characterizing sequence diversity through targeted gene synthesis coupled to high-throughput screening technologies is a broadly applicable paradigm for a wide range of biological problems.

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. We have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. The herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.

Development of a High Throughput Platform for Screening Glycoside Hydrolases Based on Oxime-NIMS

Cost-effective hydrolysis of biomass into sugars for biofuel production requires high-performance low-cost glycoside hydrolase (GH) cocktails that are active under demanding process conditions. Improving the performance of GH cocktails depends on knowledge of many critical parameters, including individual enzyme stabilities, optimal reaction conditions, kinetics, and specificity of reaction. With this information, rate- and/or yield-limiting reactions can be potentially improved through substitution, synergistic complementation, or protein engineering. Given the wide range of substrates and methods used for GH characterization, it is difficult to compare results across a myriad of approaches to identify high performance and synergistic combinations of enzymes. Here, we describe a platform for systematic screening of GH activities using automatic biomass handling, bioconjugate chemistry, robotic liquid handling, and nanostructure-initiator mass spectrometry (NIMS). Twelve well-characterized substrates spanning the types of glycosidic linkages found in plant cell walls are included in the experimental workflow. To test the application of this platform and substrate panel, we studied the reactivity of three engineered cellulases and their synergy of combination across a range of reaction conditions and enzyme concentrations. We anticipate that large-scale screening using the standardized platform and substrates will generate critical datasets to enable direct comparison of enzyme activities for cocktail design.

A combinatorial approach to synthetic transcription factor‐promoter combinations for yeast strain engineering

Despite the need for inducible promoters in strain development efforts, the majority of engineering in Saccharomyces cerevisiae continues to rely on a few constitutively active or inducible promoters. Building on advances that use the modular nature of both transcription factors and promoter regions, we have built a library of hybrid promoters that are regulated by a synthetic transcription factor. The hybrid promoters consist of native S. cerevisiae promoters, in which the operator regions have been replaced with sequences that are recognized by the bacterial LexA DNA binding protein. Correspondingly, the synthetic transcription factor (TF) consists of the DNA binding domain of the LexA protein, fused with the human estrogen binding domain and the viral activator domain, VP16. The resulting system with a bacterial DNA binding domain avoids the transcription of native S. cerevisiae genes, and the hybrid promoters can be induced using estradiol, a compound with no detectable impact on S. cerevisiae physiology. Using combinations of one, two or three operator sequence repeats and a set of native S. cerevisiae promoters, we obtained a series of hybrid promoters that can be induced to different levels, using the same synthetic TF and a given estradiol. This set of promoters, in combination with our synthetic TF, has the potential to regulate numerous genes or pathways simultaneously, to multiple desired levels, in a single strain.

Chemiluminescent Biosensors for Detection of Second Messenger Cyclic di-GMP

Bacteria colonize highly diverse and complex environments, from gastrointestinal tracts to soil and plant surfaces. This colonization process is controlled in part by the intracellular signal cyclic di-GMP, which regulates bacterial motility and biofilm formation. To interrogate cyclic di-GMP signaling networks, a variety of fluorescent biosensors for live cell imaging of cyclic di-GMP have been developed. However, the need for external illumination precludes the use of these tools for imaging bacteria in their natural environments, including in deep tissues of whole organisms and in samples that are highly autofluorescent or photosensitive. The need for genetic encoding also complicates the analysis of clinical isolates and environmental samples. Toward expanding the study of bacterial signaling to these systems, we have developed the first chemiluminescent biosensors for cyclic di-GMP. The biosensor design combines the complementation of split luciferase (CSL) and bioluminescence resonance energy transfer (BRET) approaches. Furthermore, we developed a lysate-based assay for biosensor activity that enabled reliable high-throughput screening of a phylogenetic library of 92 biosensor variants. The screen identified biosensors with very large signal changes (∼40- and 90-fold) as well as biosensors with high affinities for cyclic di-GMP ( KD < 50 nM). These chemiluminescent biosensors then were applied to measure cyclic di-GMP levels in E. coli. The cellular experiments revealed an unexpected challenge for chemiluminescent imaging in Gram negative bacteria but showed promising application in lysates. Taken together, this work establishes the first chemiluminescent biosensors for studying cyclic di-GMP signaling and provides a foundation for using these biosensors in more complex systems.

An integrated workflow for phenazine-modifying enzyme characterization

Increasing availability of new genomes and putative biosynthetic gene clusters (BGCs) has extended the opportunity to access novel chemical diversity for agriculture, medicine, environmental and industrial purposes. However, functional characterization of BGCs through heterologous expression is limited because expression may require complex regulatory mechanisms, specific folding or activation. We developed an integrated workflow for BGC characterization that integrates pathway identification, modular design, DNA synthesis, assembly and characterization. This workflow was applied to characterize multiple phenazine-modifying enzymes. Phenazine pathways are useful for this workflow because all phenazines are derived from a core scaffold for modification by diverse modifying enzymes (PhzM, PhzS, PhzH, and PhzO) that produce characterized compounds. We expressed refactored synthetic modules of previously uncharacterized phenazine BGCs heterologously in Escherichia coli and were able to identify metabolic intermediates they produced, including a previously unidentified metabolite. These results demonstrate how this approach can accelerate functional characterization of BGCs.

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, it is limited by ambiguous metabolite identifications. Furthermore, interpretation is limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) addresses these challenges by generating metabolite-gene associations via biochemical reactions based on a score between probable metabolite identifications and probable gene annotations. This is calculated by a Bayesian-like method and emphasizes consensus between metabolites and genes. We applied MAGI to sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. We found that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations. Moreover, MAGI was found to make correct biochemical predictions for poorly annotated genes that were readily validated by literature searches. As metabolomics data become more widely available for sequenced organisms, this approach has the potential to improve our understanding of microbial metabolism while also providing testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi