Degradation of polyethylene terephthalate (PET) plastics by wastewater bacteria engineered via conjugation.

Wastewater treatment plants are one of the major pathways for microplastics to enter the environment. In general, microplastics are contaminants of global concern that pose risks to ecosystems and human health. Here, we present a proof-of-concept for reduction of microplastic pollution emitted from wastewater treatment plants: delivery of recombinant DNA to bacteria in wastewater to enable degradation of polyethylene terephthalate (PET). Using a broad-host-range conjugative plasmid, we enabled various bacterial species from a municipal wastewater sample to express FAST-PETase, which was released into the extracellular environment. We found that FAST-PETase purified from some transconjugant isolates could degrade about 40% of a 0.25 mm thick commercial PET film within 4 days at 50°C. We then demonstrated partial degradation of a post-consumer PET product over 5-7 days by exposure to conditioned media from isolates. These results have broad implications for addressing the global plastic pollution problem by enabling environmental bacteria to degrade PET.

Calibrating spatiotemporal models of microbial communities to microscopy data: A review.

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.