Mixed-culture biotechnologies producing storage compounds as biofuels and biomaterials: engineering waste treatment communities

提高废水处理系统的可持续性部分通过回收增值产品和能源，如生物塑料（例如，聚羟基链烷酸酯，PHA）和生物柴油（来自脂质）。活性污泥处理系统中的微生物群落由于其环境条件的快速变化而自然地产生储存化合物。由于碳储存化合物包括PHA、三酰甘油脂质和糖原，因此废水处理系统是生产工业相关生物材料的潜在动力来源。以PHA为例，目前纯培养生产成本比化学合成塑料成本高4-9倍。很明显，从废物流中生产，不需要底物和灭菌，有可能大大降低这一成本。 缺乏对微生物选择导致给定储存化合物积累的基本理解，以维持这些过程的合理开发方法。当葡萄糖或乙酸盐作为单一碳源加入混合活性污泥培养时，为什么糖原或PHA会积累？我们如何选择具有积累不同种类PHA（短链vs长链）能力的生物体。提出了一个（Meta）基因组学的方法来解决这些问题，首先使用的基因组规模的代谢模型的红球菌Jostii RHA 1，我们构建和确定的目标函数，最相关的活性污泥的情况下生长。第二，这些发现将通过向选择的异养生物的基因组规模模型提出类似的问题来扩展，以预测活性污泥中微生物竞争的结果。最后，基因和蛋白质的表达谱将在具有不同的存储容量的人口在全规模的活性污泥系统中确定特定的生物体的生态功能，他们的存储化合物的积累。这些数据将为优化活性污泥处理的合理设计提供指导，以生产特定的储存化合物。

Improving the sustainability of wastewater treatment systems passes in part by the recovery of value added products and energy such as bioplastics (e.g., polyhydroxyalkanoates, PHA) and biodiesel (from lipids). Microbial communities in activated sludge treatment systems naturally produce storage compounds because of the rapidly changing conditions of their environment. Since carbon storage compounds include PHA, triacyglycerol lipids, and glycogen, wastewater treatment systems are potential powerhouses for the production of industrially relevant biomaterials. In the case of PHA, the current pure culture production costs are 4-9 times higher than the cost of chemically synthesized plastics. It is clear that production from a waste stream where substrate and sterilization would not be necessary has the potential to reduce tremendously this cost. A fundamental understanding of microbial selection leading to the accumulation of given storage compounds is missing to sustain a rational development approach to these processes. Why glycogen or PHA are accumulated when glucose or acetate are fed to the mixed activated sludge culture as single carbon sources? How can we select for organisms with capacities to accumulate different kinds of PHA (short-chains vs long-chains). A (meta)genomics approach to these problems is proposed by first using the genome-scale metabolic model of Rhodococcus jostii RHA1 that we constructed and determining the objective functions that are most relevant for growth in activated sludge situations. Second, these finding will be expanded by asking similar questions to a selection of genome-scale models of heterotrophs to predict the outcome of microbial competitions in activated sludge. Finally, gene and protein expression profiles will be determined in populations with different storage capacities in full-scale activated sludge systems to link the ecological function of specific organism to their accumulation of storage compounds. These data will provide directions for the rational design of activated sludge treatments optimized for the production of specific storage compounds.