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Hemmerich, J.

Publications and source records attributed to Hemmerich, J..

4 recordsLinked to original sources

ALiCE: A versatile, high yielding and scalable eukaryotic cell-free protein synthesis (CFPS) system

Eukaryotic cell-free protein synthesis (CFPS) systems have the potential to simplify and speed up the expression and high-throughput analysis of complex proteins with functionally relevant post-translational modifications (PTMs). However, low yields and the inability to scale such systems have so far prevented their widespread adoption in protein research and manufacturing. Here, we present a detailed demonstration for the capabilities of a CFPS system derived from Nicotiana tabacum BY-2 cell culture (BY-2 lysate; BYL). BYL is able to express diverse, functional proteins at high yields in under 48 hours, complete with native disulfide bonds and N-glycosylation. An optimised version of the technology is commercialised as ALiCE(R), engineered for high yields of up to 3 mg/mL. Recent advances in the scaling of BYL production methodologies have allowed scaling of the CFPS reaction. We show simple, linear scale-up of batch mode reporter proten expression from a 100 L microtiter plate format to 10 mL and 100 mL volumes in standard Erlenmeyer flasks, culminating in preliminary data from 1 L reactions in a CELL-tainer(R) CT20 rocking motion bioreactor. As such, these works represent the first published example of a eukaryotic CFPS reaction scaled past the 10 mL level by several orders of magnitude. We show the ability of BYL to produce the simple reporter protein eYFP and large, multimeric virus-like particles directly in the cytosolic fraction. Complex proteins are processed using the native microsomes of BYL and functional expression of multiple classes of complex, difficult-to-express proteins is demonstrated, specifically: a dimeric, glycoprotein enzyme, glucose oxidase; the monoclonal antibody adalimumab; the SARS-Cov-2 receptor-binding domain; human epidermal growth factor; and a G protein-coupled receptor membrane protein, cannabinoid receptor type 2. Functional binding and activity are shown using a combination of surface plasmon resonance techniques, a serology-based ELISA method and a G protein activation assay. Finally, in-depth post-translational modification (PTM) characterisation of purified proteins through disulfide bond and N-glycan analysis is also revealed - previously difficult in the eukaryotic CFPS space due to limitations in reaction volumes and yields. Taken together, BYL provides a real opportunity for screening of complex proteins at the microscale with subsequent amplification to manufacturing-ready levels using off-the-shelf protocols. This end-to-end platform suggests the potential to significantly reduce cost and the time-to-market for high value proteins and biologics.

biochemistry↗

bletl - A Python package for integrating microbioreactors in the design-build-test-learn cycle

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rate [Formula] based on a novel method of unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, t-distributed Stochastic Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes. Practical ApplicationThe bletl package can be used to analyze microbioreactor datasets in both data analysis and autonomous experimentation workflows. Using the example of BioLector datasets, we show that loading such datasets into commonly used data structures with one line of Python code is a significant improvement over spreadsheet or hand-crafted scripting approaches. On top of established standard data structures, practitioners may continue with their favorite data analysis routines, or make use of the additional analysis functions that we specifically tailored to the analysis of microbioreactor time series. Particularly our function to fit cross-validated smoothing splines can be used for on-line signals from any microbioreactor system and has the potential to improve robustness and objectivity of many data analyses. Likewise, our random walk based [Formula] method for inferring growth rates under uncertainty, but also the time-series feature extraction may be applied to on-line data from other cultivation systems as well.

bioengineering↗

pyFOOMB: Python Framework for Object Oriented Modelling of Bioprocesses

Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modelling with non-linear regression for model parameter estimation. The presented pyFOOMB package enables a guided and flexible implementation of bioprocess models in the form of ordinary differential equation systems (ODEs). By building on Python as powerful and multi-purpose programming language, ODEs can be formulated in an object-oriented manner, which facilitates their modular design, reusability and extensibility. Once the model is implemented, seamless integration and analysis of the experimental data is supported by various Python packages that are already available. In particular, for the iterative workflow of experimental data generation and subsequent model parameter estimation we employed the concept of replicate model instances, which are linked by common sets of parameters with global or local properties. For the description of multi-stage processes, discontinuities in the right-hand sides of the differential equations are supported via event handling using the freely available assimulo package. Optimization problems can be solved by making use of a parallelized version of the generalized island approach provided by the pygmo package. Furthermore, pyFOOMB in combination with Jupyter notebooks also supports education in bioprocess engineering and the applied learning of Python as scientific programming language. Finally, the applicability and strengths of pyFOOMB will be demonstrated by a comprehensive collection of notebook examples.

bioengineering↗

A widespread role for SLC transmembrane transporters in resistance to cytotoxic drugs

The activity and potency of a drug is inherently affected by the metabolic state of its target cell. Solute Carriers (SLCs) represent the largest family of transmembrane transporters in humans and constitute major determinants of cellular metabolism. Several SLCs have been shown to be required for the uptake of individual chemical compounds into cellular systems, but systematic surveys of transporter-drug relationships in human cells are currently lacking. We performed a series of genetic screens in the haploid human cell line HAP1 using a set of 60 cytotoxic compounds representative of the chemical space populated by approved drugs. By using a SLC-focused CRISPR/Cas9 lentiviral library, we identified transporters whose absence induced resistance to the drugs tested. Among the hundreds of drug-SLC relationships identified, we confirmed the role of the folate transporter SLC19A1 on the activity of antifolates and of SLC29A1 on several nucleoside analogs. Among the newly discovered dependencies, we identified the transporters SLC11A2/SLC16A1 for artemisinin derivatives and SLC35A2/SLC38A5 for cisplatin. The functional dependence on SLCs observed for a significant proportion of the compounds screened suggested a widespread role for SLCs in the uptake and cellular activity of cytotoxic drugs and provided an experimentally validated set of SLC-drug associations for a number of clinically relevant compounds.

cell biology↗