The increasing demand for rare earth elements (REEs) has spurred interest in the development of recovery methods from aqueous waste streams. Acidophilic microalgae have gained attention for REE biosorption as they can withstand high concentrations of transition metals and do not require added organic carbon to grow, potentially allowing simultaneous sorption and self-replication of the sorbent. Here, we assessed the potential of Galdieria sulphuraria for REE biosorption under acidic, nutrient-replete conditions from solutions containing ≤ 15 ppm REEs. Sorption at pH 1.5–2.5 (the growth optimum of G. sulphuraria) was poor but improved up to 24-fold at pH 5.0 in phosphate-free conditions. Metabolic activity had a negative impact on REE sorption, additionally challenging the feasibility of REE biosorption under ideal growth conditions for acidophiles. We further examined the possibility of REE biosorption in the presence of phosphate for biomass growth at elevated pH (pH ≥ 2.5) by assessing aqueous La concentrations in various culture media. Three days after adding La into the media, dissolved La concentrations were up to three orders of magnitude higher than solubility predictions due to supersaturation, though LaPO₄ precipitation occurred under all conditions when seed was added. We concluded that biosorption should occur separately from biomass growth to avoid REE phosphate precipitation. Furthermore, we demonstrated the importance of proper control experiments in biosorption studies to assess potential interactions between REEs and matrix ions such as phosphates. Key points: • REE biosorption with G. sulphuraria increases significantly when raising pH to 5 • Phosphate for biosorbent growth has to be supplied separately from biosorption • Biosorption studies have to assess potential matrix effects on REE behavior

BACKGROUND: Downstream processing of soluble recombinant proteins from Escherichia coli is complicated by the need to access the intracellular product by cell disruption and to separate the target protein from impurities, particularly host cell protein, DNA, endotoxins and lipids. We previously demonstrated the ability of the E. coli X‐press strain to leak high amounts of product to the culture medium without sacrificing viability. In this case study, we assessed the economic and ecological benefit of this strain for downstream processing in direct comparison to the industrial standard E. coli BL21(DE3). Staphylococcal Protein A was used as a model protein. We performed recombinant protein production, primary recovery and capture by anion exchange chromatography at laboratory scale, and used the data obtained for estimating costs and resource consumption by economic modeling.RESULTS: After primary recovery, the X‐press process resulted in a 1.5‐fold higher product purity, a 150‐fold lower DNA, 3.5‐fold lower endotoxin and 3.4‐fold lower lipid load compared to BL21(DE3). Consequently, anion exchanger binding capacity was increased 2.7‐fold and purity and concentration of the eluate also was increased. Extracellular protein production with X‐press resulted in a 25% reduction of costs and a 36% reduction of both water usage and water‐related CO₂ emissions compared to intracellular production with BL21(DE3).CONCLUSIONS: This case study performed with stapyhlococcal Protein A demonstrated the potential of E. coli X‐press to reduce costs for downstream processing and improve the environmental footprint by simplified primary recovery, lower impurity load and consequently higher chromatographic efficiency. © 2021 The Authors. Journal of Chemical Technology & Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.