Membrane proteins perform critical cellular functions in all living organisms and constitute major targets for drug discovery. Escherichia coli has been the most popular overexpression host for membrane protein biochemical/structural studies. Bacterial production of recombinant membrane proteins, however, is typically hampered by poor cellular accumulation and severe toxicity for the host, which leads to low final biomass and minute volumetric yields. In this work, we aimed to rewire the E. coli protein-producing machinery to withstand the toxicity caused by membrane protein overexpression in order to generate engineered bacterial strains with the ability to achieve high-level membrane protein production. To achieve this, we searched for bacterial genes whose coexpression can suppress membrane protein-induced toxicity and identified two highly potent effectors: the membrane-bound DnaK cochaperone DjlA, and the inhibitor of the mRNA-degrading activity of the E. coli RNase E, RraA. E. coli strains coexpressing either djlA or rraA, termed SuptoxD and SuptoxR, respectively, accumulated markedly higher levels of final biomass and produced dramatically enhanced yields for a variety of prokaryotic and eukaryotic recombinant membrane proteins. In all tested cases, either SuptoxD, or SuptoxR, or both, outperformed the capabilities of commercial strains frequently utilized for recombinant membrane protein production purposes.

SummaryThe bacterium Escherichia coli is the most widely used expression host for overexpression trials of membrane proteins. Usually, different strains, culture conditions and expression regimes are screened for to identify the optimal overexpression strategy. However, yields are often not satisfactory, especially for eukaryotic membrane proteins. This has initiated a revolution of membrane protein overexpression in bacteria. Recent studies have shown that it is feasible to (i) engineer or select for E. coli strains with strongly improved membrane protein overexpression characteristics, (ii) use bacteria other than E. coli for the expression of membrane proteins, (iii) engineer or select for membrane protein variants that retain functionality but express better than the wild‐type protein, and (iv) express membrane proteins using E. coli‐based cell‐free systems.

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Neurodegenerative disorders are a highly prevalent class of diseases, whose pathological mechanisms start before the appearance of any clear symptoms. This fact has prompted scientists to search for biomarkers that could aid early treatment. These currently incurable pathologies share the presence of aberrant aggregates called amyloids in the nervous system, which are composed of specific proteins. In this review, we discuss how these proteins, their conformations and modifications could be exploited as biomarkers for diagnostic purposes. We focus on proteins that are associated with the most prevalent neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases, amyotrophic lateral sclerosis, and frontotemporal dementia. We also describe current challenges in detection, the most recent techniques with diagnostic potentials and possible future developments in diagnosis.

Individual cells exhibit specific proliferative responses to changes in microenvironmental conditions. Whether such potential is constrained by the cell density throughout the growth process is however unclear. Here, we identify a theoretical framework that captures how the information encoded in the initial density of cancer cell populations impacts their growth profile. By following the growth of hundreds of populations of cancer cells, we found that the time they need to adapt to the environment decreases as the initial cell density increases. Moreover, the population growth rate shows a maximum at intermediate initial densities. With the support of a mathematical model, we show that the observed interdependence of adaptation time and growth rate is significantly at odds both with standard logistic growth models and with the Monod-like function that governs the dependence of the growth rate on nutrient levels. Our results (i) uncover and quantify a previously unnoticed heterogeneity in the growth dynamics of cancer cell populations; (ii) unveil how population growth may be affected by single-cell adaptation times; (iii) contribute to our understanding of the clinically-observed dependence of the primary and metastatic tumor take rates on the initial density of implanted cancer cells.

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