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Pseudomonas putida has been engineered to produce medium chain length oleochemicals, including medium chain length free fatty acids and their ester forms, compounds which may be useful as biodiesel blending agents.

Summary: Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.

Abstract Sugarcane (Saccharum sp.) is a tropical and subtropical C4 plant with a high photosynthetic and carbon assimilation efficiency that stores sucrose. Culm biomass is also composed of bagasse fibre, a by‐product of the sugarcane industry. This high‐yielding grass, high in sucrose and lignocellulosic biomass, is considered an optimal feedstock as an alternative to fossil fuels and to produce a broad range of high‐value biomaterials. The ideal sugarcane production system would optimise the relative production of sugar and these new products. Multi‐omics correlation analysis was used to generate a global view of the essential metabolic pathways identifying critical genes involved in carbon partitioning during different stages of development. This research employed an unprecedented metabolic and transcriptomic dataset of 360 samples from a selection of 1440 culms of 24 genotypes at five different development stages. Chemical composition and metabolome analysis showed an increase through the culm development of lignin, sucrose, carbon, and amino acids such as aspartic acid, serine, alanine, methionine, threonine 3‐cyano‐L‐alanine, and citric acid. Transcriptome analysis revealed functionalities such as transcription, nucleotide transport and metabolism, and the biosynthesis of amino acids that are highly activated during the immature stage and highly down‐regulated during the most mature age.

Abstract Background Many microbes used for the rapid discovery and development of metabolic pathways have sensitivities to final products and process reagents. Isopentenol (3-methyl-3-buten-1-ol), a biogasoline candidate, has an established heterologous gene pathway but is toxic to several microbial hosts. Reagents used in the pretreatment of plant biomass, such as ionic liquids, also inhibit growth of many host strains. We explored the use of Corynebacterium glutamicum as an alternative host to address these constraints. Results We found C. glutamicum ATCC 13032 to be tolerant to both the final product, isopentenol, as well to three classes of ionic liquids. A heterologous mevalonate-based isopentenol pathway was engineered in C. glutamicum. Targeted proteomics for the heterologous pathway proteins indicated that the 3-hydroxy-3-methylglutaryl-coenzyme A reductase protein, HmgR, is a potential rate-limiting enzyme in this synthetic pathway. Isopentenol titers were improved from undetectable to 1.25 g/L by combining three approaches: media optimization; substitution of an NADH-dependent HmgR homolog from Silicibacter pomeroyi; and development of a C. glutamicum ∆poxB ∆ldhA host chassis. Conclusions We describe the successful expression of a heterologous mevalonate-based pathway in the Gram-positive industrial microorganism, C. glutamicum, for the production of the biogasoline candidate, isopentenol. We identified critical genetic factors to harness the isopentenol pathway in C. glutamicum. Further media and cultivation optimization enabled isopentenol production from sorghum biomass hydrolysates.

The conversion of biomass to biofuels presents a solution to one of the largest global challenges of our era, climate change. A critical part of this pipeline is the process of breaking down cellulosic sugars from plant matter to be used by microbes containing biosynthetic pathways that produce biofuels or bioproducts. In this inquiry-based course, students complete a research project that isolates cellulase-producing bacteria from samples collected from the environment. After obtaining isolates, the students characterize the production of cellulases. Students then amplify and sequence the 16S rRNA genes of confirmed cellulase producers and use bioinformatic methods to identify the bacterial isolates. Throughout the course, students learn about the process of generating biofuels and bioproducts through the deconstruction of cellulosic biomass to form monosaccharides from the biopolymers in plant matter. The program relies heavily on active learning and enables students to connect microbiology with issues of sustainability. In addition, it provides exposure to basic microbiology, molecular biology, and biotechnology laboratory techniques and concepts. The described activity was initially developed for the Introductory College Level Experience in Microbiology (iCLEM) program, a research-based immersive laboratory course at the US Department of Energy Joint BioEnergy Institute. Originally designed as an accelerated program for high-potential, low-income, high school students (11th–12th grade), this curriculum could also be implemented for undergraduate coursework in a research-intensive laboratory course at a two- or four-year college or university.

This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R2 > 0.80. The use of REPs failed to accurately predict LAI (R2 < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches ( < 1 m) found on the sites.