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12 Publications visible to you, out of a total of 12
A protocol for recombinant protein quantification by densitometry

Abstract (Expand)

The protein purity is generally checked using SDS-PAGE, where densitometry could be used to quantify the protein bands. In literature, few studies have been reported using image analysis for the … quantification of protein in SDS-PAGE: that is, imaged with Stain-Free™ technology. This study presents a protocol of image analysis for electrophoresis gels that allows the quantification of unknown proteins using the molecular weight markers as protein standards. Escherichia coli WK6/pHEN6 encoding the bispecific nanobody CH10-12 engineered by the Pasteur Institute of Tunisia was cultured in a bioreactor and induced with isopropyl β-D-1-thiogalactopyranoside (IPTG) at 28°C for 12 hr. Periplasmic proteins extracted by osmotic shock were purified by immobilized metal affinity chromatography (IMAC). Images of the SDS-PAGE gels were analyzed using ImageJ, and the lane profiles were obtained in grayscale and uncalibrated optical density. Protein load and peak area were linearly correlated, and optimal image processing was then performed by background subtraction using the rolling ball algorithm with radius size 250 pixels. No brightness and contrast adjustment was applied. The production of the nanobody CH10-12 was obtained through a fed-batch strategy and quantified using the band of 50 kDa in the marker as reference for 750 ng of recombinant protein. The molecular weight marker was used as a sole protein standard for protein quantification in SDS-PAGE gel images.

Authors: Susana María Alonso Villela, Hazar Kraïem, Balkiss Bouhaouala‐Zahar, Carine Bideaux, César Arturo Aceves Lara, Luc Fillaudeau

Date Published: 1st Jun 2020

Publication Type: Journal

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

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Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually …y requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.

Authors: Machtelt Braaksma, Elena S Martens-Uzunova, Peter J Punt, Peter J Schaap

Date Published: 1st Dec 2010

Publication Type: Journal

Cell Biology Methods to Study Recombinant Proteins in Seeds

Abstract

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Authors: Machtelt Braaksma, Elsa Arcalís, Elena S Martens-Uzunova, Emanuela Pedrazzini, Peter J Punt, Ulrike Hörmann-Dietrich, Peter J Schaap, Alessandro Vitale, Eva Stoger

Date Published: 2022

Publication Type: Journal

Effect of temperature on the production of a recombinant antivenom in fed-batch mode

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In the pharmaceutical industry, nanobodies show promising properties for its application in serotherapy targeting the highly diffusible scorpion toxins. The production of recombinant nanobodies in … Escherichia coli has been widely studied in shake flask cultures in rich medium. However, there are no upstream bioprocess studies of nanobody production in defined minimal medium and the effect of the induction temperature on the production kinetics. In this work, the effect of the temperature during the expression of the chimeric bispecific nanobody CH10-12 form, showing high scorpion antivenom potential, was studied in bioreactor cultures of E. coli. High biomass concentrations (25 g cdw/L) were achieved in fed-batch mode, and the expression of the CH10-12 nanobody was induced at temperatures 28, 29, 30, 33, and 37°C with a constant glucose feed. For the bispecific form NbF12-10, the induction was performed at 29°C. Biomass and carbon dioxide yields were reported for each culture phase, and the maintenance coefficient was obtained for each strain. Nanobody production in the CH10-12 strain was higher at low temperatures (lower than 30°C) and declined with the increase of the temperature. At 29°C, the CH10-12, NbF12-10, and WK6 strains were compared. Strains CH10-12 and NbF12-10 had a productivity of 0.052 and 0.021 mg/L/h of nanobody, respectively, after 13 h of induction. The specific productivity of the nanobodies was modeled as a function of the induction temperature and the specific growth rates. Experimental results confirm that low temperatures increase the productivity of the nanobody. Key points • Nanobodies with scorpion antivenom activity produced using two recombinant strains. • Nanobodies production was achieved in fed-batch cultures at different induction temperatures. • Low induction temperatures result in high volumetric productivities of the nanobody CH10-12.

Authors: Susana María Alonso Villela, Hazar Ghezal-Kraïem, Balkiss Bouhaouala-Zahar, Carine Bideaux, César Arturo Aceves Lara, Luc Fillaudeau

Date Published: 1st Feb 2021

Publication Type: Journal

From legacy systems to data pipelines modernization in fermentation process

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Developments in biotechnology using high throughput systems are increasingly and consequently the creation and consumption of data continue to grow rapidly. Data migration is an essential part of legacy … system modernization in bioprocess. Migration process involves transferring data from outdated platforms or unknown data schemas to more advanced and secure systems. Data migration can be represented through data pipelines including data extraction, transformation and loading (ETL). The data pipelines are implemented in order to increase the overall efficiency of data-flow from the source (raw data) to the knowledge generation (Mohanty et al., 2013). Legacy systems in fermentation generally occur in bioreactor components as sensors, protocols, software or databases. These issues can limit the integration with modern tools and systems as Process Analytical Technology (PAT) instruments (Gerzon et al., 2022), avoiding real-time data on process parameters and thereby fail to assist operators in maintain optimal conditions for cell growth and production. The aim of this research is to present a guided process for designing data pipelines in bioreactors legacy systems. We present as use case a set of 24 mini-bioreactors of 50 mL. We conducted unit testing for components of the ETL process in order to ensure the integration and migration process of the legacy DB.

Editor:

Date Published: 2024

Publication Type: Journal

Galaxy-SynBioCAD: Synthetic Biology Design Automation tools in Galaxy workflows

Abstract

Not specified

Authors: Melchior du Lac, Thomas Duigou, Joan Hérisson, Pablo Carbonell, Neil Swainston, Valentin Zulkower, Forum Shah, Léon Faure, Mostafa Mahdy, Paul Soudier, Jean-Loup Faulon

Date Published: 15th Jun 2020

Publication Type: Journal

GC/MS-based 13C metabolic flux analysis resolves the parallel and cyclic photomixotrophic metabolism of Synechocystis sp. PCC 6803 and selected deletion mutants including the Entner-Doudoroff and phosphoketolase pathways

Abstract (Expand)

Abstract Background Cyanobacteria receive huge interest as green catalysts. While exploiting energy from sunlight, they co-utilize sugar and CO 2 . This photomixotrophic mode enables fast growth and …ze sugar and CO 2 . This photomixotrophic mode enables fast growth and high cell densities, opening perspectives for sustainable biomanufacturing. The model cyanobacterium Synechocystis sp. PCC 6803 possesses a complex architecture of glycolytic routes for glucose breakdown that are intertwined with the CO 2 -fixing Calvin-Benson-Bassham (CBB) cycle. To date, the contribution of these pathways to photomixotrophic metabolism has remained unclear. Results Here, we developed a comprehensive approach for 13 C metabolic flux analysis of Synechocystis sp. PCC 6803 during steady state photomixotrophic growth. Under these conditions, the Entner-Doudoroff (ED) and phosphoketolase (PK) pathways were found inactive but the microbe used the phosphoglucoisomerase (PGI) (63.1%) and the oxidative pentose phosphate pathway (OPP) shunts (9.3%) to fuel the CBB cycle. Mutants that lacked the ED pathway, the PK pathway, or phosphofructokinases were not affected in growth under metabolic steady-state. An ED pathway-deficient mutant ( Δeda ) exhibited an enhanced CBB cycle flux and increased glycogen formation, while the OPP shunt was almost inactive (1.3%). Under fluctuating light, ∆eda showed a growth defect, different to wild type and the other deletion strains. Conclusions The developed approach, based on parallel 13 C tracer studies with GC–MS analysis of amino acids, sugars, and sugar derivatives, optionally adding NMR data from amino acids, is valuable to study fluxes in photomixotrophic microbes to detail. In photomixotrophic cells, PGI and OPP form glycolytic shunts that merge at switch points and result in synergistic fueling of the CBB cycle for maximized CO 2 fixation. However, redirected fluxes in an ED shunt-deficient mutant and the impossibility to delete this shunt in a GAPDH2 knockout mutant, indicate that either minor fluxes (below the resolution limit of 13 C flux analysis) might exist that could provide catalytic amounts of regulatory intermediates or alternatively, that EDA possesses additional so far unknown functions. These ideas require further experiments.

Authors: Dennis Schulze, Michael Kohlstedt, Judith Becker, Edern Cahoreau, Lindsay Peyriga, Alexander Makowka, Sarah Hildebrandt, Kirstin Gutekunst, Jean-Charles Portais, Christoph Wittmann

Date Published: 1st Dec 2022

Publication Type: Journal

High-level production of industrially relevant oxidases by a two-stage fed-batch approach: overcoming catabolite repression in arabinose-inducible Escherichia coli systems

Abstract

Not specified

Authors: Ramón Román, Nikola Lončar, Antoni Casablancas, Marco W. Fraaije, Glòria Gonzalez

Date Published: 1st Jun 2020

Publication Type: Journal

RetroPath2.0: A retrosynthesis workflow for metabolic engineers.

Abstract (Expand)

Synthetic biology applied to industrial biotechnology is transforming the way we produce chemicals. However, despite advances in the scale and scope of metabolic engineering, the research and development … process still remains costly. In order to expand the chemical repertoire for the production of next generation compounds, a major engineering biology effort is required in the development of novel design tools that target chemical diversity through rapid and predictable protocols. Addressing that goal involves retrosynthesis approaches that explore the chemical biosynthetic space. However, the complexity associated with the large combinatorial retrosynthesis design space has often been recognized as the main challenge hindering the approach. Here, we provide RetroPath2.0, an automated open source workflow for retrosynthesis based on generalized reaction rules that perform the retrosynthesis search from chassis to target through an efficient and well-controlled protocol. Its easiness of use and the versatility of its applications make this tool a valuable addition to the biological engineer bench desk. We show through several examples the application of the workflow to biotechnological relevant problems, including the identification of alternative biosynthetic routes through enzyme promiscuity or the development of biosensors. We demonstrate in that way the ability of the workflow to streamline retrosynthesis pathway design and its major role in reshaping the design, build, test and learn pipeline by driving the process toward the objective of optimizing bioproduction. The RetroPath2.0 workflow is built using tools developed by the bioinformatics and cheminformatics community, because it is open source we anticipate community contributions will likely expand further the features of the workflow.

Authors: B. Delepine, T. Duigou, P. Carbonell, J. L. Faulon

Date Published: No date defined

Publication Type: Not specified

RetroRules: a database of reaction rules for engineering biology.

Abstract (Expand)

RetroRules is a database of reaction rules for metabolic engineering (https://retrorules.org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows … in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the community-standard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center). Such numerous representations of reactions expand natural chemical diversity by predicting de novo reactions of promiscuous enzymes.

Authors: T. Duigou, M. du Lac, P. Carbonell, J. L. Faulon

Date Published: No date defined

Publication Type: Not specified

Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations

Abstract (Expand)

Monitoring bioprocesses is a challenging task where most of the variables of interest can only be measured offline. Soft sensors have emerged as a solution to provide online estimations. This work … compares interpretable learners such as CART, M5, CUBIST, and Random Forest as soft sensors for industrial-scale fed-batch fermentation of penicillin production. A structured model of industrial-scale penicillin fermentation is implemented to generate the dataset and train the interpretable learners. Variables such as substrate feed rate, agitation, temperature, pH, dissolved oxygen, vessel volume, CO2, and O2 percent in off-gas are considered as independent (predictors). The CUBIST model has achieved the best results with values of 0.908, 9.916, 3.149, and 1.920 for the coefficient of determination, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, respectively. These results demonstrate the feasibility of developing soft sensors based on interpretable models to predict penicillin concentration at an industrial scale.

Authors: Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, David Griol, Fayza Daboussi, Cesar Arturo Aceves-Lara, David Camilo Corrales

Date Published: 1st Aug 2024

Publication Type: Journal

Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentation

Abstract (Expand)

Real-time predictions in fermentation processes are crucial because they enable continuous monitoring and control of bioprocessing. However, the availability of online measurements is limited by the … availability and feasibility of sensing technology. Soft sensors - or software sensors that convert available measurements into measurements of interest (product yield, quality, etc.) - have the potential to improve efficiency and product quality. Machine learning (ML) based soft sensors have gained increased popularity over the years since they can incorporate variables that are measured in real-time, and exploit the intricate patterns embedded in such voluminous datasets. However, ML-based soft sensor requires more than just a classical ML learner with an unseen test set to evaluate the quality prediction of the model. When a ML model is deployed in production, its performance can deteriorate rapidly leading to an unanticipated decline in the quality of the output and predictions. Here a proof concept of Machine Learning Operations (MLOps) to automate the end-to-end soft sensor lifecycle in industrial scale fed-batch fermentation, from development and deployment to maintenance and monitoring is proposed. Using the industrial-scale penicillin fermentation (IndPenSim) dataset that includes 100 fermentation batches, to build a soft sensor based on Long Short Term Memory (LSTM) for penicillin concentration prediction. The batches containing deviations in the processes (91–100) were used to assess concept drift of the LSTM soft sensor. The evaluation of concept drift is evidenced by the soft sensor performance falling below the set threshold based on the Population Stability Index (PSI), which automatically triggers an alert to run the retraining pipeline.

Authors: Brett Metcalfe, Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, George K. Georgakilas, Theodore Dalamagas, Cesar Arturo Aceves-Lara, Fayza Daboussi, Jasper J Koehorst, David Camilo Corrales

Date Published: 1st Mar 2025

Publication Type: Journal

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