Biology subjects

Nielsen, A. T.

Publications and source records attributed to Nielsen, A. T..

10 recordsLinked to original sources

bioRxiv2026

Design and development of online pressure sensing for microbial batch cultivation

Gas production and consumption is a direct consequence of microbial activity in environmental and industrial settings. In closed batch cultivations, headspace pressure changes therefore give valuable insights into the microbial metabolism. For laboratory scale anaerobic batch cultivations, manual manometer measurements are routinely applied, as a simple and robust method, but it is labour intensive, causes disturbances in the headspace gas and temperature, leading to suboptimal growth, inhibition and noisy data. We built and tested an automated online pressure sensor for closed batch cultivations. It is designed for microbial cultivation and integrates with sterile and anaerobic cultivation workflows. The system uses an absolute pressure sensor (0-30 bar) mounted on a custom designed PCB, with a gas-tight needle mount. An ESP32 microcontroller logs pressure and temperature locally and generates a Wi-Fi access point for real-time visualization and direct CSV download through a local homepage. We detail hardware and software design decisions, assembly, and validation including long-term stability. Case studies demonstrate the applicability for: a multiphasic biogas kinetics during anaerobic digestion, capturing gas uptake dynamics and metabolic shifts during syngas fermentations and co-feeding experiments, and long-term robustness in a multi-year monitoring of a compressed-air system. More than 130 individual sensors have been deployed over 3 years in laboratories, at various academic and industrial settings. The platform provides reproducible, high-resolution pressure measurements that enable calculation of gas formation/consumption rates and improve experimental throughput without disturbing cultures. Design files, firmware, and example analysis scripts are openly available to support adoption and further development.

bioengineering↗

bioRxiv2026

Measure Catabolism: Real-time shifts in microbial metabolism through online pressure measurements

Microbial activity is often inferred from cell density measurements; however, biomass formation is merely an indirect, cumulative result of metabolism, known as anabolism. Microbial activity is more accurately indicated by energy conservation or catabolism. This is especially true under low or no-growth conditions, where anabolism remains constant, and shifts in catabolic fluxes go unnoticed with biomass measurements alone. In anaerobic and gas-based metabolic processes, net gas exchange is linked to energy conservation, and catabolism can then be quantified through headspace measurements. We introduce a sealed-vial, non-invasive workflow that uses high-resolution headspace pressure measurements to estimate gas exchange rates and catabolic reactions, enabling real-time visualisation of metabolic shifts throughout an entire batch cultivation cycle. The method was applied to carbon monoxide (CO) fermentations of three Clostridium autoethanogenum strains (JA1-1, LAbrini, and LAbrini_mut) cultivated in serum bottles. Two of them were indistinguishable by OD-derived max. Pressure-derived gas uptake rates resolved multiple exponential phases of gas consumption and identified specific shifts in the metabolism, consistent with transitions from mixotrophic to autotrophic growth. Small but significant differences in terminal headspace pressure were detected, providing an experimentally accessible end-state parameter for phenotypic characterisation that would be obscured by routine intrusive headspace sampling. Finally, pressure-derived catabolic rates further enabled estimates of relative product formation during the main autotrophic phase. The strains were successfully characterised and distinguished by identifying several exponential phases of gas consumption and their rates, as well as differences in the final absolute pressure threshold. This provided phenotypic characterisation and insights not obtainable from OD measurements alone. The work establishes a practical framework for catabolism-resolved microbial characterisation in sealed batch vials through high-resolution online pressure (gas exchange). The assumption that pressure measurements correlate with CO2 and catabolic rates is sensitive to solubility/buffering and temperature/vapour effects, but these limitations are addressable through controls and complementary analytics.

microbiology↗

bioRxiv2026

Mapping metabolic phases through online pressure and backscatter rates

Microbial metabolism can be represented as an energy-conserving process (catabolism) and a biomass-forming reaction (anabolism). Anabolism is traditionally measured through the turbidity of the culture, while catabolism is often assessed by the substrates consumed or the products formed. Standard measurements of biomass and products are intrusive and disrupt cultivation and headspace composition, potentially masking important analytical parameters and interactions. Online pressure and backscatter were combined in small-scale closed batch vials to obtain undisturbed real-time measurements of catabolic and anabolic rates, enabling mapping of metabolic phases throughout an entire batch cultivation cycle. The method identified discrete metabolic phases in yeast cultivation and thermophilic syngas fermentation. In nutrient-rich yeast cultivation, five metabolic phases were characterized, covering growth-associated and non-growth-associated gas formation. In a mixed community syngas fermentation, estimates of catabolic and anabolic rates distinguished early biomass increase from minimal net pressure change from two later gas-driven phases. An initial phase with a higher growth rate, linked to carboxydotrophy, followed by a phase with slightly lower growth and increased gas consumption, corresponding to hydrogenotrophic acetogenesis. The study demonstrates that a simple, affordable experimental setup with online pressure and backscatter measurements can be used to visualize phase-plane mapping of microbial metabolism. An additional advantage is the ability to detect sequential metabolic cascades in mixed microbial communities, which is not possible with gas-sparging bioreactor studies. Using a single simple batch culture, growth and maintenance data can be obtained, even when growth is low or absent, thereby yielding parameters applicable to phenotypic characterization and dynamic metabolic modelling.

microbiology↗

bioRxiv2025

MillionFull enables massive, full-length enzyme sequence-fitness data collection at low cost for machine learning-guided enzyme engineering

Machine learning holds great promise for accelerating enzyme optimization, but its power is fundamentally constrained by the limited availability of sequence-fitness data. Here, we introduce MillionFull, a low-cost method that enables high-throughput full-length sequence- fitness mapping for enzymes of arbitrary length. Each run yields on the order of 10-10 data points, capturing sequence-function relationships at unprecedented scale. By overcoming the data bottleneck, MillionFull provides a foundation for dramatically advancing AI-driven enzyme engineering.

synthetic biology↗

bioRxiv2023

Probing efficient microbial CO2 utilizationthrough metabolic and process modeling

Microbial gas fermentation is proving to be a promising technology to upcycle carbon-rich waste gasses into value-added biochemicals, though production yields of varied products are currently limited. Through the holistic pairing of process modeling with host agnostic black box metabolic modeling, here we investigate an efficient thermophilic CO2 upcycling process, based on acetogenic carbon utilization. From a process engineering perspective, higher temperatures were found to favor overall gas transfer rates, even with lower gas solubility, particularly for the more expensive and often limiting H2 gas. Metabolically, for growth coupled products, thermophilic production favors higher product yields as a result of a higher maintenance energy input. A process simulation for acetate production in a large-scale bubble column reactor predicts an optimal feed gas composition of approximately 9:1 mol H2 to mol CO2 and a process with higher production yields and rates at higher temperatures. To assess the expansion of the product portfolio beyond acetate, both a product volatility analysis and a metabolic pathway model were implemented. In-situ recovery of volatile products is shown to be within range for acetone but challenging due to the extensive evaporation of water, while the production of more valuable compounds is energetically unfavorable compared to acetate. We discuss alternative approaches to overcome these challenges to utilize acetogenic CO2 fixation for the production of a wider range of carbon negative chemicals.

bioengineering↗

bioRxiv2022

From empirical to data-driven host selection: a broad-host-range expression platform to facilitate chassis screening

Nature has provided a vast landscape of organisms through evolution, each with unique phenotypic traits adapted to varying environments. Nevertheless, host selection in biotechnological research is exceedingly dominated by empirical preference, where the endogenous physiology of the selected host is often not suited to the desired application. Considering that large parts of cellular regulation and metabolism remain obscure, empirical selection of a preferred model organism may lead to undue caveats in further engineering attempts, arising from intrinsic metabolism. One reason for the empirical host selection is the lack of engineering tools for screening novel organisms. In this study, we provide a modular, single vector-based expression platform, compatible with a wide range of prokaryotes. It centers around a tight and titratable promoter system, inducible by anhydrotetracyclin within an 84-fold dynamic range. It enables easy screening of recombinant proteins and pathways in both mesophilic and thermophilic Gram-negative and Gram-positive hosts. Overall, this platform enables simple screening of heterologous expression and production in a variety of hosts, including the exploration of previously unconsidered hosts thereby aiding the transition from empirical to data-driven host selection.

synthetic biology↗

bioRxiv2020

Synergistic stabilization of a double mutant in CI2 from an in-cell library screen

Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ/mol more stable than the wild-type. In one case the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ/mol more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.

biophysics↗

bioRxiv2020

A dual-reporter system for investigating and optimizing protein translation and folding in E. coli

Strategies for investigating and optimising the expression and folding of proteins for biotechnological and pharmaceutical purposes are in high demand. Here, we describe a dual-reporter biosensor system that simultaneously assesses in vivo protein translation and protein folding, thereby enabling rapid screening of mutant libraries. We have validated the dual-reporter system on five different proteins and find an excellent correlation between reporter intensity signals and the levels of protein expression and solubility of the proteins. We further demonstrate the applicability of the dual-reporter system as a screening assay for deep mutational scanning experiments. The system enables high throughput selection of protein variants with high expression levels and altered protein stability. Next generation sequencing analysis of the resulting libraries of protein variants show a good correlation between computationally predicted and experimentally determined protein stabilities. We furthermore show that the mutational experimental data obtained using this system may be useful for protein structure calculations.

biochemistry↗

bioRxiv2019

A metabolic reconstruction of Lactobacillus reuteri JCM 1112 and analysis of its potential as a cell factory

BackgroundLactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data.\n\nResultsA genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden-Meyerhof-Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden-Meyerhof-Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies.\n\nConclusionWe have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory.

systems biology↗

bioRxiv2019

Adaptive laboratory evolution reveals general and specific chemical tolerance mechanisms and enhances biochemical production

Tolerance to high product concentrations is a major barrier to achieving economically viable processes for bio-based chemical production. Chemical tolerance mechanisms are often unknown, thus their rational design is not achievable. To reveal unknown tolerance mechanisms we used an automated platform to evolve Escherichia coli to grow in previously toxic concentrations of 11 chemicals that have applications as polymer precursors, chemical intermediates, or biofuels. Re-sequencing of isolates from 88 independently evolved populations, reconstruction of mutations, and cross-compound tolerance profiling was employed to uncover general and specific tolerance mechanisms. We found that: 1) the broad tolerance of strains towards chemicals varied significantly depending on the chemical stress condition under which the strain was evolved; 2) the strains that acquired high levels of NaCl tolerance also became broadly tolerant to most chemicals; 3) genetic tolerance mechanisms included alterations in regulatory, cell wall, transcriptional and translational functions, as well as more chemical-specific mechanisms related to transport and metabolism; 4) using pre-tolerized starting strains can significantly enhance subsequent production of chemicals when a production pathway is inserted; and 5) only a subset of the evolved isolates showed improved production indicating that this approach is especially useful when a large number of independently evolved isolates are screened for production. We provide a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 224 chemical tolerant strains.

microbiology↗