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BackgroundAdvanced spark ignition engines require high performance fuels with improved resistance to autoignition. Biologically derived olefinic alcohols have arisen as promising blendstock candidates due to favorable octane numbers and synergistic blending characteristics. However, production and downstream separation of these alcohols are limited by their intrinsic toxicity and high aqueous solubility, respectively. Bioproduction of carboxylate esters of alcohols can improve partitioning and reduce toxicity, but in practice has been limited to saturated esters with characteristically low octane sensitivity. If olefinic esters retain the synergistic blending characteristics of their alcohol counterparts, they could improve the bioblendstock combustion performance while also retaining the production advantages of the ester moiety.ResultsOptimization of Escherichia coli isoprenoid pathways has led to high titers of isoprenol and prenol, which are not only excellent standalone biofuel and blend candidates, but also novel targets for esterification. Here, a selection of olefinic esters enhanced blendstock performance according to their degree of unsaturation and branching. E. coli strains harboring optimized mevalonate pathways, thioester pathways, and heterologous alcohol acyltransferases (ATF1, ATF2, and SAAT) were engineered for the bioproduction of four novel olefinic esters. Although prenyl and isoprenyl lactate titers were limited to 1.48 ± 0.41 mg/L and 5.57 ± 1.36 mg/L, strains engineered for prenyl and isoprenyl acetate attained titers of 176.3 ± 16.0 mg/L and 3.08 ± 0.27 g/L, respectively. Furthermore, prenyl acetate (20% bRON = 125.8) and isoprenyl acetate (20% bRON = 108.4) exhibited blend properties comparable to ethanol and significantly better than any saturated ester. By further scaling cultures to a 2-L bioreactor under fed-batch conditions, 15.0 ± 0.9 g/L isoprenyl acetate was achieved on minimal medium. Metabolic engineering of acetate pathway flux further improved titer to attain an unprecedented 28.0 ± 1.0 g/L isoprenyl acetate, accounting for 75.7% theoretical yield from glucose.ConclusionOur study demonstrated novel bioproduction of four isoprenoid oxygenates for fuel blending. Our optimized E. coli production strain generated an unprecedented titer of isoprenyl acetate and when paired with its favorable blend properties, may enable rapid scale-up of olefinic alcohol esters for use as a fuel blend additive or as a precursor for longer-chain biofuels and biochemicals.

Table of Contents I. Howh Demanding? 641 II. Rights Minimalism 643 III. Against Intuitive Answers 649 IV. The Appeal of Rights Minimalism 652 Conclusion 654 What do we owe distant [...], What do we owe distant strangers? Effective altruists, as well as philosophers, have offered differing views. A demanding answer holds that we are subject to strong moral requirements to provide aid, the satisfaction of which may undermine other values associated with living one's own life. Another answer, popular among philosophers, is moderate: We are subject to a requirement to provide aid, but this requirement is limited so as to be compatible one's own values in living a life. A third answer, which I sketch in this paper, is minimalist: There are no non-voluntary obligations to contribute to aid. This paper argues against the probative value of moral intuitions on the matter, and on this basis favors either the demanding or minimalist answers. Second, the paper suggests the minimalist answer merits greater attention, and is compatible with at least some prominent descriptions of effective altruism.

BackgroundDue to their high energy density and compatible physical properties, several monoterpenes have been investigated as potential renewable transportation fuels, either as blendstocks with petroleum or as drop-in replacements for use in vehicles (both heavy and light-weight) or in aviation. Sustainable microbial production of these biofuels requires the ability to utilize cheap and readily available feedstocks such as lignocellulosic biomass, which can be depolymerized into fermentable carbon sources such as glucose and xylose. However, common microbial production platforms such as the yeast Saccharomyces cerevisiae are not naturally capable of utilizing xylose, hence requiring extensive strain engineering and optimization to efficiently utilize lignocellulosic feedstocks. In contrast, the oleaginous red yeast Rhodosporidium toruloides is capable of efficiently metabolizing both xylose and glucose, suggesting that it may be a suitable host for the production of lignocellulosic bioproducts. In addition, R. toruloides naturally produces several carotenoids (C40 terpenoids), indicating that it may have a naturally high carbon flux through its mevalonate (MVA) pathway, providing pools of intermediates for the production of a wide range of heterologous terpene-based biofuels and bioproducts from lignocellulose.ResultsSixteen terpene synthases (TS) originating from plants, bacteria and fungi were evaluated for their ability to produce a total of nine different monoterpenes in R. toruloides. Eight of these TS were functional and produced several different monoterpenes, either as individual compounds or as mixtures, with 1,8-cineole, sabinene, ocimene, pinene, limonene, and carene being produced at the highest levels. The 1,8-cineole synthase HYP3 from Hypoxylon sp. E74060B produced the highest titer of 14.94 ± 1.84 mg/L 1,8-cineole in YPD medium and was selected for further optimization and fuel properties study. Production of 1,8-cineole from lignocellulose was also demonstrated in a 2L batch fermentation, and cineole production titers reached 34.6 mg/L in DMR-EH (Deacetylated, Mechanically Refined, Enzymatically Hydorlized) hydrolysate. Finally, the fuel properties of 1,8-cineole were examined, and indicate that it may be a suitable petroleum blend stock or drop-in replacement fuel for spark ignition engines.ConclusionOur results demonstrate that Rhodosporidium toruloides is a suitable microbial platform for the production of non-native monoterpenes with biofuel applications from lignocellulosic biomass.

Microbial production of fuels and commodity chemicals has been performed primarily using natural or slightly modified enzymes, which inherently limits the types of molecules that can be produced. Type I modular polyketide synthases (PKSs) are multi-domain enzymes that can produce unique and diverse molecular structures by combining particular types of catalytic domains in a specific order. This catalytic mechanism offers a wealth of engineering opportunities. Here we report engineered microbes that produce various short-chain (C5-C7) ketones using hybrid PKSs. Introduction of the genes into the chromosome of Streptomyces albus enables it to produce >1 g · l-1 of C6 and C7 ethyl ketones and several hundred mg · l-1 of C5 and C6 methyl ketones from plant biomass hydrolysates. Engine tests indicate these short-chain ketones can be added to gasoline as oxygenates to increase the octane of gasoline. Together, it demonstrates the efficient and renewable microbial production of biogasolines by hybrid enzymes.

Screening a large number of biologically derived molecules for potential fuel compounds without recourse to experimental testing is important in identifying understudied yet valuable molecules. Experimental testing, although a valuable standard for measuring fuel properties, has several major limitations, including the requirement of testably high quantities, considerable expense, and a large amount of time. This paper discusses the development of a general-purpose fuel property tool, using machine learning, whose outcome is to screen molecules for desirable fuel properties. BioCompoundML adopts a general methodology, requiring as input only a list of training compounds (with identifiers and measured values) and a list of testing compounds (with identifiers). For the training data, BioCompoundML collects open data from the National Center for Biotechnology Information, incorporates user-provided features, imputes missing values, performs feature reduction, builds a classifier, and clusters compounds. BioCompoundML then collects data for the testing compounds, predicts class membership, and determines whether compounds are found in the range of variability of the training data set. This tool is demonstrated using three different fuel properties: research octane number (RON), threshold soot index (TSI), and melting point (MP). We provide measures of its success with these properties using randomized train/test measurements: average accuracy is 88% in RON, 85% in TSI, and 94% in MP; average precision is 88% in RON, 88% in TSI, and 95% in MP; and average recall is 88% in RON, 82% in TSI, and 97% in MP. The receiver operator characteristics (area under the curve) were estimated at 0.88 in RON, 0.86 in TSI, and 0.87 in MP. We also measured the success of BioCompoundML by sending 16 compounds for direct RON determination. Finally, we provide a screen of 1977 hydrocarbons/oxygenates within the 8696 compounds in MetaCyc, identifying compounds with high predictive strength for high or low RON.