Joint BioEnergy Institute, Emeryville, CA, United States of America.
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
PLoS One. 2024 Oct 8;19(10):e0305336. doi: 10.1371/journal.pone.0305336. eCollection 2024.
Sustainably grown biomass is a promising alternative to produce fuels and chemicals and reduce the dependency on fossil energy sources. However, the efficient conversion of lignocellulosic biomass into biofuels and bioproducts often requires extensive testing of components and reaction conditions used in the pretreatment, saccharification, and bioconversion steps. This restriction can result in a significant and unwieldy number of combinations of biomass types, solvents, microbial strains, and operational parameters that need to be characterized, turning these efforts into a daunting and time-consuming task. Here we developed a high-throughput feedstocks-to-fuels screening platform to address these challenges. The result is a miniaturized semi-automated platform that leverages the capabilities of a solid handling robot, a liquid handling robot, analytical instruments, and a centralized data repository, adapted to operate as an ionic-liquid-based biomass conversion pipeline. The pipeline was tested by using sorghum as feedstock, the biocompatible ionic liquid cholinium phosphate as pretreatment solvent, a "one-pot" process configuration that does not require ionic liquid removal after pretreatment, and an engineered strain of the yeast Rhodosporidium toruloides that produces the jet-fuel precursor bisabolene as a conversion microbe. By the simultaneous processing of 48 samples, we show that this configuration and reaction conditions result in sugar yields (70%) and bisabolene titers (1500 mg/L) that are comparable to the efficiencies observed at larger scales but require only a fraction of the time. We expect that this Feedstocks-to-Fuels pipeline will become an effective tool to screen thousands of bioenergy crop and feedstock samples and assist process optimization efforts and the development of predictive deconstruction approaches.
可持续生长的生物质是生产燃料和化学品、减少对化石能源依赖的有前途的替代物。然而，将木质纤维素生物质高效转化为生物燃料和生物制品通常需要对预处理、糖化和生物转化步骤中使用的成分和反应条件进行广泛测试。这种限制可能导致需要对生物质类型、溶剂、微生物菌株和操作参数进行大量组合测试，从而使这些工作变得艰巨且耗时。在这里，我们开发了一种高通量原料到燃料筛选平台来应对这些挑战。其结果是一个小型化的半自动化平台，利用固体处理机器人、液体处理机器人、分析仪器和集中式数据存储库的功能，适应基于离子液体的生物质转化管道的操作。该管道使用高粱作为原料、生物相容性离子液体磷酸胆碱作为预处理溶剂、无需在预处理后去除离子液体的“一锅法”工艺配置以及产喷气燃料前体双环醇的酵母 Rhodosporidium toruloides 工程菌株进行了测试。通过同时处理 48 个样本，我们表明这种配置和反应条件导致的糖收率（70%）和双环醇浓度（1500mg/L）与在更大规模上观察到的效率相当，但所需时间仅为其一小部分。我们预计，这种原料到燃料管道将成为筛选数千种生物能源作物和原料样本的有效工具，并有助于优化工艺和开发预测性解构方法。
