Joint BioEnergy Institute

Trucking is critical for New Zealand’s economy as it is responsible for most of the country’s freight movement. However, the reliance on diesel-powered trucks disproportionately contributes to carbon dioxide (CO 2 ) and air pollutant emissions. Two different technologies have the potential to electrify heavy freight: battery-electric and fuel cell electric trucks; however, it remains unclear which technology will be used to reach New Zealand’s goal of net-zero freight transportation emissions by 2050. In this study, we develop an integrated assessment framework that quantifies present-day heavy truck emissions in New Zealand and compares the energy requirement of decarbonization through battery-electric versus fuel cell trucks in 2035 and 2050. This framework includes freight demand, vehicle powertrain, truck operation and charging, and diesel emission models. Further, we quantify the electricity grid infrastructure requirements of the shift to battery-electric and fuel cell truck fleets using the REMix-NZ capacity expansion model. Results show that the current fleet of heavy diesel trucks in New Zealand emits 2.4 million tonnes of CO 2 eq. annually, which could be fully mitigated by 2050 through battery-electric or fuel cell fleets. A full fleet of battery-electric trucks in 2050 would consume 7.2% of New Zealand’s current electricity generation compared to 13.5% for fuel cell trucks. A sensitivity analysis shows that improved truck design and efficiency can reduce this electricity requirement. Up to 3.6 GW additional capacity would need to be built by 2050, primarily through solar power, to satisfy the energy demand of a battery-electric truck fleet compared to 5.3 GW for a fuel cell fleet. • Electrifying the heavy vehicle fleet in New Zealand would avoid 2.4 million tonnes of on-road CO 2 eq. emissions annually. • A full fleet of battery-electric trucks in 2050 would consume 7.2% of current electricity generation compared to 13.5% for fuel cell trucks. • Policy strategies should target improving aerodynamic truck design, which has the greatest impact on truck energy consumption and can reduce electricity requirements by 13%. • New Zealand should invest in refueling infrastructure for battery-electric and hydrogen vehicles to enable the large-scale deployment of zero-emission trucks. • Coordinating grid and transportation planning will ensure that the appropriate capacity is built to meet the added demand caused by electrifying heavy trucks.

Isoprenyl acetate, a volatile ester derived from isoprenol, is a key biosynthetic intermediate for the advanced aviation fuel candidate, 1,4-dimethylcyclooctane. Here, we engineered Pseudomonas putida KT2440 for the production of isoprenyl acetate from mixed sugar substrates. We first generated isoprenyl acetate by introducing a heterologous alcohol acetyltransferase (ATF1) and deleting three promiscuous native esterases to reduce product degradation. Then, we engineered efficient glucose and xylose co-utilization by integrating a heterologous xylose isomerase pathway and deleting global regulators crc and hexR to alleviate catabolite repression. Additionally, intracellular acetyl-CoA flux was reinforced through the expression of auxiliary carbon-conserving routes, including non-oxidative glycolysis and acetate assimilation. Culture conditions were systematically optimized by adjusting medium composition, induction, and overlay solvent to maximize product yields and titers. These cumulative efforts achieved isoprenyl acetate titers of 1.5 g/L in shake flasks and 1.9 g/L in fed-batch bioreactor cultures from mixed sugars, corresponding to a yield of 0.067 g/g of total sugar consumed. Our work demonstrates the potential of P. putida as a robust microbial chassis for scalable biosynthesis of ester-based biofuels from lignocellulosic feedstocks.

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Understanding molecular motion in diffusion-driven complex environments is critical for designing sustainable materials and improving chemical processes. Here, we introduce a multidimensional nuclear magnetic resonance (NMR) method that captures how molecular populations exchange across different dynamic regimes. By extending the modulated gradient spin-echo technique to include frequency-frequency correlations, our approach reveals diffusion pathways that are otherwise obscured in heterogeneous systems. Implemented on a unilateral NMR magnet, the method eliminates gradient pulsing constraints and accesses dynamics in the kilohertz regime. We apply this technique to swelling and acid-catalyzed deconstruction of cross-linked and linear polymers to observe how structural heterogeneity evolves over time. By linking molecular motion to topology and chemical state, we extract physical metrics such as fractal surface dimensionality and reaction wavefront velocity, properties inaccessible with standard diffusion measurements. This work expands the capabilities of NMR for probing soft matter, with implications for polymer recycling and materials design.

Agrobacterium-mediated transformation (AMT) is the primary means of genetic engineering in plants and many fungi, but the factors that control transformation outcomes—efficiency, transgene insertion number, and transgene integrity—remain poorly characterized. Although transformation outcomes dictate an event’s potential utility in both industrial and academic contexts, AMT remains largely unoptimized for these metrics. Here, we systematically analyze the impact of the transgene-harboring binary vector on transformation outcomes across plant and fungal species. Through a comparison of different plasmid origin of replication (ORI) families and engineered copy number variants, our results reveal that the ORI family—not plasmid copy number—dictates T-DNA insertion number, backbone inclusion, and transformation efficiency, while plasmid copy number tuning alters efficiency without changing ORI family-specific signatures. Independent of plasmid copy number across kingdoms, the most widely used pVS1 ORI-based vectors (e.g. pCambia) result in significantly more insertions per transformant and high levels of transgene silencing compared to the less-utilized pSa ORI family, which enriches for more uniform single insertion events. Furthermore, we demonstrate that ORI-dependent transformation outcomes in yeast predictably reflect those in Arabidopsis. Together, these results lay the foundation for future binary vector design aimed at achieving more predictable, controllable, and optimized transformation outcomes across diverse eukaryotic hosts.

Lignin valorization is restricted by the stability of its methoxy groups, creating a critical need for efficient demethylation strategies. Here, density functional theory (DFT) is employed to dissect the mechanistic pathways of demethylation in lignin model compounds, guaiacol and syringol, using protic ionic liquids (PILs) that act as both solvent and catalyst. Conductor-like Screening Model for Real Solvents (COSMO-RS) analysis identifies monoethanolammonium acetate ([MEOA][Ace]) as the most promising medium, attributed to its strong hydrogen bonding network and solvation ability. By integrating implicit and explicit solvation models, it is revealed that an acid-catalyzed hydrolytic mechanism governs demethylation, with PILs stabilizing crucial transition states and intermediates. Complementary electronic structure evaluations, including highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap analysis, charge distribution, and electrostatic potential mapping, demonstrate how PILs lower energetic barriers and enhance reactivity. To simulate realistic environments, this study is extended to lignin dimer complexes with varying water content, uncovering how water-bridged solvation reverses demethylation preference from guaiacyl to syringyl units. This mechanistic shift aligns with experimental observations showing faster S-unit reactivity in hydrated systems. Together, these findings provide atomic-level insight into lignin demethylation dynamics and highlight how tuning acid concentration in PILs can accelerate kinetics, enabling a rational pathway toward next-generation biomass conversion technologies.

Exploring the potential of advanced distillable solvents as efficient biomass pretreatment agents is critical for biorefineries, enhancing fermentable sugar yields while enabling solvent recovery and recycling without suffering significant losses. Here, we employ distillable amine-based solvents for pretreating a wide range of lignocellulosic feedstocks, aiming to facilitate the industrial release of fermentable sugars from diverse feedstocks through enzymatic hydrolysis. Twenty-two diverse feedstocks, sourced from different geographical regions and representing various biomass categories, were surveyed for chemical (mainly carbohydrates and lignin) and lignin (S, G, and H units) profiles. Several solvents, including ethanolamine, ethanolammonium acetate, butylamine, butylammonium acetate, and triethylamine, were tested for the pretreatment of eight selected biomasses. Among these solvents, butylamine emerged as the most effective due to its favorable sugar release, excellent solvent removal rate, and low boiling point, facilitating solvent recovery and recycling. Extending butylamine pretreatment to all 22 feedstocks demonstrated desirable sugar yields and highly efficient solvent removal in the majority of the biomass sources tested. Agricultural residues and their mixtures showed particularly favorable sugar release. Despite minimal changes in cellulose crystallinity, XRD characterization of sorghum, poplar, and pine before and after butylamine pretreatment showed a decrease in intensity and a slight shift of certain peaks, indicating alterations in cellulose structure. Fourier-transform infrared spectroscopy and thermogravimetric analysis analyses suggested disruption of biomass linkages in hemicellulose and lignin, enhancing enzymatic digestibility. Scale-up experiments of the mixed agricultural feedstocks in a 1 L Parr reactor achieved over 90% glucose liberation and more than 99% butylamine removal, highlighting the scalability of the method. The resulting hydrolysates supported the growth of diverse bacterial and fungal strains, indicating downstream compatibility with commercial fermentation processes. This study presents butylamine as an effective, recoverable pretreatment solvent for a wide range of lignocellulosic feedstocks, offering a promising solution to key biorefinery challenges. The demonstrated scalability and compatibility with various biomass types and blends underscore its potential for industrial application, advancing sustainable biofuel and biochemical production.

MotivationMetabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge.ResultsHere, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets.Availability and implementationFluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.