Back

Degradation and bioconversion of complex municipal solid waste streams into human biotherapeutics and biopolymers

Gonzalez, G. A.; Chacon, M.; Fisher, K.; Berepiki, A.; dixon, N.

2023-02-13 synthetic biology Community evaluation
10.1101/2023.02.13.528311 bioRxiv
Show abstract

The use of biomass and organic waste as a feedstock for the production of fuels, chemicals and materials offers great potential to support the transition to net-zero and circular economic models. However, such renewable feedstocks are often complex, highly heterogeneous, and subject to geographical and seasonal variability, creating supply-chain inconsistency that impedes adoption. Towards addressing these challenges, the development of engineered microorganisms equipped with the ability to flexibly utilise complex, heterogenous substrate compositions for growth and bio-production would be greatly enabling. Here we show through careful strain selection and metabolic engineering, that Pseudomonas putida can be employed to permit efficient co-utilisation of highly heterogeneous substrate compositions derived from hydrolysed mixed municipal-like waste fractions, with remarkable resilience to compositional variability. To further illustrate this, one pot enzymatic pre-treatments of the five most abundant, hydrolytically labile, mixed waste feedstocks was performed - including food, plastic, organic, paper and cardboard, and textiles - for growth and synthesis of exemplar bio-products by engineered P. putida. Finally, prospective life cycle assessment and life cycle costing illustrated the climate change and economic advantage, respectively, of using the waste-derived feedstock for biomanufacturing compared to conventional waste treatment options. This work demonstrates the potential for expanding the treatment strategies for mixed municipal waste to include engineered microbial bio-production platforms that can accommodate variability in feedstock inputs to synthesise a range of chemical and material outputs.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.