Analysis of biomass productivity and physiology of Nitrososphaera viennensis grown in continuous culture.

Microbial ammonia oxidation is the first and usually rate limiting step in nitrification and is therefore an important step in the global nitrogen cycle. Ammonia-oxidizing archaea (AOA) play an important role in nitrification. Here, we report a comprehensive analysis of biomass productivity and the physiological response of Nitrososphaera viennensis to different ammonium and carbon dioxide (CO2) concentrations aiming to understand the interplay between ammonia oxidation and CO2 fixation of N. viennensis. The experiments were performed in closed batch in serum bottles as well as in batch, fed-batch, and continuous culture in bioreactors. A reduced specific growth rate (µ) of N. viennensis was observed in batch systems in bioreactors. By increasing CO2 gassing µ could be increased to rates comparable to that of closed batch systems. Furthermore, at a high dilution rate (D) in continuous culture (≥ 0.7 of µmax) the biomass to ammonium yield (Y(X/NH3)) increased up to 81.7% compared to batch cultures. In continuous culture, biofilm formation at higher D prevented the determination of D crit. Due to changes in Y(X/NH3) and due to biofilm, nitrite concentration becomes an unreliable proxy for the cell number in continuous cultures at D towards µmax. Furthermore, the obscure nature of the archaeal ammonia oxidation prevents an interpretation in the context of Monod kinetics and thus the determination of K S. Our findings indicate that the physiological response of N. viennensis might be regulated with different enzymatic make-ups, according to the ammonium catalysis rate. We reveal novel insights into the physiology of N. viennensis that are important for biomass production and the biomass yield of AOA. Moreover, our study has implications to the field of archaea biology and microbial ecology by showing that bioprocess technology and quantitative analysis can be applied to decipher environmental factors affecting the physiology and productivity of AOA.

Unexpected complexity of the ammonia monooxygenase in archaea.

Ammonia oxidation, as the first step of nitrification, constitutes a critical process in the global nitrogen cycle. However, fundamental knowledge of its key enzyme, the copper-dependent ammonia monooxygenase, is lacking, in particular for the environmentally abundant ammonia-oxidizing archaea (AOA). Here the structure of the enzyme is investigated by blue-native gel electrophoresis and proteomics from native membrane complexes of two AOA. Besides the known AmoABC subunits and the earlier predicted AmoX, two new protein subunits, AmoY and AmoZ, were identified. They are unique to AOA, highly conserved and co-regulated, and their genes are linked to other AMO subunit genes in streamlined AOA genomes. Modeling and in-gel cross-link approaches support an overall protomer structure similar to the distantly related bacterial particulate methane monooxygenase but also reveals clear differences in extracellular domains of the enzyme. These data open avenues for further structure-function studies of this ecologically important nitrification complex.

Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real-time during chromatographic separation.

Technological developments require the transfer to their location of application to make use of them. We describe the transfer of a real-time monitoring system for lab-scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF-2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, and MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real-time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test run at the new sites were on average twice as high as at the training site (model-wise 0.9-5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1% and 33%.

Error propagation in constraint-based modeling of Chinese hamster ovary cells.

Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational system-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet, it is not standard practice to devote sufficient attention to the accurate determination of these rates. In this work, we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analyzed time points. We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.

Predicting favorable landing pads for targeted integrations in Chinese hamster ovary cell lines by learning stability characteristics from random transgene integrations.

Chinese Hamster Ovary (CHO) cell lines are considered to be the preferred platform for the production of biotherapeutics, but issues related to expression instability remain unresolved. In this study, we investigated potential causes for an unstable phenotype by comparing cell lines that express stably to such that undergo loss in titer across 10 passages. Factors related to transgene integrity and copy number as well as the genomic profile around the integration sites were analyzed. Horizon Discovery CHO-K1 (HD-BIOP3) derived production cell lines selected for phenotypes with low, medium or high copy number, each with stable and unstable transgene expression, were sequenced to capture changes at genomic and transcriptomic levels. The exact sites of the random integration events in each cell line were also identified, followed by profiling of the genomic, transcriptomic and epigenetic patterns around them. Based on the information deduced from these random integration events, genomic loci that potentially favor reliable and stable transgene expression were reported for use as targeted transgene integration sites. By comparing stable vs unstable phenotypes across these parameters, we could establish that expression stability may be controlled at three levels: 1) Good choice of integration site, 2) Ensuring integrity of transgene and observing concatemerization pattern after integration, and 3) Checking for potential stress related cellular processes. Genome wide favorable and unfavorable genomic loci for targeted transgene integration can be browsed at https://www.borthlabchoresources.boku.ac.at/.

Nitrogen Isotope Fractionation During Archaeal Ammonia Oxidation: Coupled Estimates From Measurements of Residual Ammonium and Accumulated Nitrite.

The naturally occurring nitrogen (N) isotopes, 15N and 14N, exhibit different reaction rates during many microbial N transformation processes, which results in N isotope fractionation. Such isotope effects are critical parameters for interpreting natural stable isotope abundances as proxies for biological process rates in the environment across scales. The kinetic isotope effect of ammonia oxidation (AO) to nitrite (NO2 -), performed by ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB), is generally ascribed to the enzyme ammonia monooxygenase (AMO), which catalyzes the first step in this process. However, the kinetic isotope effect of AMO, or ε A M O , has been typically determined based on isotope kinetics during product formation (cumulative product, NO2 -) alone, which may have overestimated ε A M O due to possible accumulation of chemical intermediates and alternative sinks of ammonia/ammonium (NH3/NH4 +). Here, we analyzed 15N isotope fractionation during archaeal ammonia oxidation based on both isotopic changes in residual substrate (RS, NH4 +) and cumulative product (CP, NO2 -) pools in pure cultures of the soil strain Nitrososphaera viennensis EN76 and in highly enriched cultures of the marine strain Nitrosopumilus adriaticus NF5, under non-limiting substrate conditions. We obtained ε A M O values of 31.9-33.1‰ for both strains based on RS (δ15NH4 +) and showed that estimates based on CP (δ15NO2 -) give larger isotope fractionation factors by 6-8‰. Complementary analyses showed that, at the end of the growth period, microbial biomass was 15N-enriched (10.1‰), whereas nitrous oxide (N2O) was highly 15N depleted (-38.1‰) relative to the initial substrate. Although we did not determine the isotope effect of NH4 + assimilation (biomass formation) and N2O production by AOA, our results nevertheless show that the discrepancy between ε A M O estimates based on RS and CP might have derived from the incorporation of 15N-enriched residual NH4 + after AMO reaction into microbial biomass and that N2O production did not affect isotope fractionation estimates significantly.

Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass in Escherichia coli cultivations.

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on-line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real-time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D-fluorescence data and process data, was developed for a comprehensive study with a 20-L experimental design, for Escherichia coli fed-batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D-fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D-fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression-model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on-line, enabling Quality by Design.

A cross-species whole genome siRNA screen in suspension-cultured Chinese hamster ovary cells identifies novel engineering targets.

High-throughput siRNA screens were only recently applied to cell factories to identify novel engineering targets which are able to boost cells towards desired phenotypes. While siRNA libraries exist for model organisms such as mice, no CHO-specific library is publicly available, hindering the application of this technique to CHO cells. The optimization of these cells is of special interest, as they are the main host for the production of therapeutic proteins. Here, we performed a cross-species approach by applying a mouse whole-genome siRNA library to CHO cells, optimized the protocol for suspension cultured cells, as this is the industrial practice for CHO cells, and developed an in silico method to identify functioning siRNAs, which also revealed the limitations of using cross-species libraries. With this method, we were able to identify several genes that, upon knockdown, enhanced the total productivity in the primary screen. A second screen validated two of these genes, Rad21 and Chd4, whose knockdown was tested in additional CHO cell lines, confirming the induced high productivity phenotype, but also demonstrating the cell line/clone specificity of engineering effects.

Real-time monitoring and model-based prediction of purity and quantity during a chromatographic capture of fibroblast growth factor 2.

Process analytical technology combines understanding and control of the process with real-time monitoring of critical quality and performance attributes. The goal is to ensure the quality of the final product. Currently, chromatographic processes in biopharmaceutical production are predominantly monitored with UV/Vis absorbance and a direct correlation with purity and quantity is limited. In this study, a chromatographic workstation was equipped with additional online sensors, such as multi-angle light scattering, refractive index, attenuated total reflection Fourier-transform infrared, and fluorescence spectroscopy. Models to predict quantity, host cell proteins (HCP), and double-stranded DNA (dsDNA) content simultaneously were developed and exemplified by a cation exchange capture step for fibroblast growth factor 2 expressed in Escherichia coliOnline data and corresponding offline data for product quantity and co-eluting impurities, such as dsDNA and HCP, were analyzed using boosted structured additive regression. Different sensor combinations were used to achieve the best prediction performance for each quality attribute. Quantity can be adequately predicted by applying a small predictor set of the typical chromatographic workstation sensor signals with a test error of 0.85 mg/ml (range in training data: 0.1-28 mg/ml). For HCP and dsDNA additional fluorescence and/or attenuated total reflection Fourier-transform infrared spectral information was important to achieve prediction errors of 200 (2-6579 ppm) and 340 ppm (8-3773 ppm), respectively.

Prediction of the Quantity and Purity of an Antibody Capture Process in Real Time.

Regulatory recommendations for quality by design instead of quality by testing raise increasing interest in new sensor technologies. An online monitoring system for downstream processes is developed, which is based on an array of online detectors. Besides standard detectors (UV, pH, and conductivity), our chromatographic workstation is equipped with a fluorescence and a mid-infrared spectrometer, a light scattering, and a refractive index detector. The combination of these sensors enables the prediction of specific protein concentration and various purity attributes, such as high molecular weight impurities, DNA and host cell protein content during the elution phase of a chromatographic antibody capture process. Prediction models solely based on online signals are set up providing real-time predictions. No mechanistic models or information about the chromatographic runs is used. These predictions allow online pooling decisions replacing time- and labor-intensive laboratory measurements. Different process variations, such as changes in the column load or elution buffer, are introduced to test the predictive power of the models. Extrapolation of the models worked well when the column load is changed, whereas model adjustment is necessary when the elution conditions are changed considerably.

Transient manipulation of the expression level of selected growth rate correlating microRNAs does not increase growth rate in CHO-K1 cells.

Engineering of Chinese Hamster Ovary cells by manipulating microRNA (miRNA) expression levels has been shown to induce advantageous, desired phenotypes. Most of these studies so far were concerned with increasing productivity or reducing growth rate (with the implied intention of thus freeing cellular resources to also increase productivity). Here we evaluated the ability of growth correlating miRNAs to increase the growth rate of CHO-K1 cells by transient overexpression or knock down, respectively. Candidates were selected based on the correlation between growth rate and miRNA expression levels as observed in previous studies. These candidates were then up- or downregulated initially by transfection of mimics or inhibitors and subsequently by transfection of plasmids bearing the corresponding miRNAs or sponges. None of the 40 selected candidates was able to induce a better growth phenotype under these conditions. Overlap between miRNAs identified to correlate to growth in published miRNA expression studies and those identified to actively increase growth rate in a functional screen is minimal, indicating that the here selected approach of traditional overexpression/knock down engineering of miRNAs may not be a suitable strategy for the purpose of increasing growth rate.

A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset.

One of the main aims of accident data analysis is to derive the determining factors associated with road traffic accident occurrence. While current studies mainly use variants of count data regression to achieve this aim, the problem can also be considered as a binary classification task, with the dichotomous target variable indicating events (accidents) and non-events (no accidents). The effects of 45 variables - describing road condition and geometry, traffic volume and regulations, weather, and accident time - are analyzed using a dataset in high temporal (1 h) and spatial (250 m) resolution, covering the whole highway network of Austria over the period of four consecutive years. A combination of synthetic minority oversampling and maximum dissimilarity undersampling is used to balance the training dataset. We employ and compare a series of statistical learning techniques with respect to their predictive performance and discuss the importance of determining factors of accident occurrence from the ensemble of models. Findings substantiate that a trade-off between accuracy and sensitivity is inherent to imbalanced classification problems. Results show satisfying performance of tree-based methods which exhibit accuracies between 75% and 90% while exhibiting sensitivities between 30% and 50%. Overall, this analysis emphasizes the merits of using high-resolution data in the context of accident analysis.

Candidatus Nitrosocaldus cavascurensis, an Ammonia Oxidizing, Extremely Thermophilic Archaeon with a Highly Mobile Genome.

Ammonia oxidizing archaea (AOA) of the phylum Thaumarchaeota are widespread in moderate environments but their occurrence and activity has also been demonstrated in hot springs. Here we present the first enrichment of a thermophilic representative with a sequenced genome, which facilitates the search for adaptive strategies and for traits that shape the evolution of Thaumarchaeota. Candidatus Nitrosocaldus cavascurensis has been enriched from a hot spring in Ischia, Italy. It grows optimally at 68°C under chemolithoautotrophic conditions on ammonia or urea converting ammonia stoichiometrically into nitrite with a generation time of approximately 23 h. Phylogenetic analyses based on ribosomal proteins place the organism as a sister group to all known mesophilic AOA. The 1.58 Mb genome of Ca. N. cavascurensis harbors an amoAXCB gene cluster encoding ammonia monooxygenase and genes for a 3-hydroxypropionate/4-hydroxybutyrate pathway for autotrophic carbon fixation, but also genes that indicate potential alternative energy metabolisms. Although a bona fide gene for nitrite reductase is missing, the organism is sensitive to NO-scavenging, underlining the potential importance of this compound for AOA metabolism. Ca. N. cavascurensis is distinct from all other AOA in its gene repertoire for replication, cell division and repair. Its genome has an impressive array of mobile genetic elements and other recently acquired gene sets, including conjugative systems, a provirus, transposons and cell appendages. Some of these elements indicate recent exchange with the environment, whereas others seem to have been domesticated and might convey crucial metabolic traits.

Changes in Chromosome Counts and Patterns in CHO Cell Lines upon Generation of Recombinant Cell Lines and Subcloning.

Chinese hamster ovary (CHO) cells are the number one production system for therapeutic proteins. A pre-requirement for their use in industrial production of biopharmaceuticals is to be clonal, thus originating from a single cell in order to be phenotypically and genomically identical. In the present study it was evaluated whether standard procedures, such as the generation of a recombinant cell line in combination with selection for a specific and stable phenotype (expression of the recombinant product) or subcloning have any impact on karyotype stability or homogeneity in CHO cells. Analyses used were the distribution of chromosome counts per cell as well as chromosome painting to identify specific karyotype patterns within a population. Results indicate that subclones both of the host and the recombinant cell line are of comparable heterogeneity and (in)stability as the original pool. In contrast, the rigorous selection for a stably expressing phenotype generated cell lines with fewer variation and more stable karyotypes, both at the level of the sorted pool and derivative subclones. We conclude that the process of subcloning itself does not contribute to an improved karyotypic homogeneity of a population, while the selection for a specific cell property inherently can provide evolutionary pressure that may lead to improved chromosomal stability as well as to a more homogenous population.

Karyotype variation of CHO host cell lines over time in culture characterized by chromosome counting and chromosome painting.

Genomic rearrangements are a common phenomenon in rapidly growing cell lines such as Chinese hamster ovary (CHO) cells, a feature that in the context of production of biologics may lead to cell line and product instability. Few methods exist to assess such genome wide instability. Here, we use the population distribution of chromosome numbers per cell as well as chromosome painting to quantify the karyotypic variation in several CHO host cell lines. CHO-S, CHO-K1 8 mM glutamine, and CHO-K1 cells adapted to grow in media containing no glutamine were analyzed over up to 6 months in culture. All three cell lines were clearly distinguishable by their chromosome number distribution and by the specific chromosome rearrangements that were present in each population. Chromosome Painting revealed a predominant karyotype for each cell line at the start of the experiment, completed by a large number of variants present in each population. Over time in culture, the predominant karyotype changed for CHO-S and CHO-K1, with the diversity increasing and new variants appearing, while CHO-K1 0 mM Gln preferred chromosome pattern increased in percent of the population over time. As control, Chinese hamster lung fibroblasts were shown to also contain an increasing number of variants over time in culture.

Boosted structured additive regression for Escherichia coli fed-batch fermentation modeling.

The quality of biopharmaceuticals and patients' safety are of highest priority and there are tremendous efforts to replace empirical production process designs by knowledge-based approaches. Main challenge in this context is that real-time access to process variables related to product quality and quantity is severely limited. To date comprehensive on- and offline monitoring platforms are used to generate process data sets that allow for development of mechanistic and/or data driven models for real-time prediction of these important quantities. Ultimate goal is to implement model based feed-back control loops that facilitate online control of product quality. In this contribution, we explore structured additive regression (STAR) models in combination with boosting as a variable selection tool for modeling the cell dry mass, product concentration, and optical density on the basis of online available process variables and two-dimensional fluorescence spectroscopic data. STAR models are powerful extensions of linear models allowing for inclusion of smooth effects or interactions between predictors. Boosting constructs the final model in a stepwise manner and provides a variable importance measure via predictor selection frequencies. Our results show that the cell dry mass can be modeled with a relative error of about ±3%, the optical density with ±6%, the soluble protein with ±16%, and the insoluble product with an accuracy of ±12%. Biotechnol. Bioeng. 2017;114: 321-334. © 2016 Wiley Periodicals, Inc.

Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers.

Ammonia-oxidizing archaea (AOA) are among the most abundant microorganisms and key players in the global nitrogen and carbon cycles. They share a common energy metabolism but represent a heterogeneous group with respect to their environmental distribution and adaptions, growth requirements, and genome contents. We report here the genome and proteome of Nitrososphaera viennensis EN76, the type species of the archaeal class Nitrososphaeria of the phylum Thaumarchaeota encompassing all known AOA. N. viennensis is a soil organism with a 2.52-Mb genome and 3,123 predicted protein-coding genes. Proteomic analysis revealed that nearly 50% of the predicted genes were translated under standard laboratory growth conditions. Comparison with genomes of closely related species of the predominantly terrestrial Nitrososphaerales as well as the more streamlined marine Nitrosopumilales [Candidatus (Ca.) order] and the acidophile "Ca. Nitrosotalea devanaterra" revealed a core genome of AOA comprising 860 genes, which allowed for the reconstruction of central metabolic pathways common to all known AOA and expressed in the N. viennensis and "Ca Nitrosopelagicus brevis" proteomes. Concomitantly, we were able to identify candidate proteins for as yet unidentified crucial steps in central metabolisms. In addition to unraveling aspects of core AOA metabolism, we identified specific metabolic innovations associated with the Nitrososphaerales mediating growth and survival in the soil milieu, including the capacity for biofilm formation, cell surface modifications and cell adhesion, and carbohydrate conversions as well as detoxification of aromatic compounds and drugs.

The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations.

Product quality assurance strategies in production of biopharmaceuticals currently undergo a transformation from empirical "quality by testing" to rational, knowledge-based "quality by design" approaches. The major challenges in this context are the fragmentary understanding of bioprocesses and the severely limited real-time access to process variables related to product quality and quantity. Data driven modeling of process variables in combination with model predictive process control concepts represent a potential solution to these problems. The selection of statistical techniques best qualified for bioprocess data analysis and modeling is a key criterion. In this work a series of recombinant Escherichia coli fed-batch production processes with varying cultivation conditions employing a comprehensive on- and offline process monitoring platform was conducted. The applicability of two machine learning methods, random forest and neural networks, for the prediction of cell dry mass and recombinant protein based on online available process parameters and two-dimensional multi-wavelength fluorescence spectroscopy is investigated. Models solely based on routinely measured process variables give a satisfying prediction accuracy of about ± 4% for the cell dry mass, while additional spectroscopic information allows for an estimation of the protein concentration within ± 12%. The results clearly argue for a combined approach: neural networks as modeling technique and random forest as variable selection tool.

Nitrososphaera viennensis gen. nov., sp. nov., an aerobic and mesophilic, ammonia-oxidizing archaeon from soil and a member of the archaeal phylum Thaumarchaeota.

A mesophilic, neutrophilic and aerobic, ammonia-oxidizing archaeon, strain EN76(T), was isolated from garden soil in Vienna (Austria). Cells were irregular cocci with a diameter of 0.6-0.9 µm and possessed archaella and archaeal pili as cell appendages. Electron microscopy also indicated clearly discernible areas of high and low electron density, as well as tubule-like structures. Strain EN76(T) had an S-layer with p3 symmetry, so far only reported for members of the Sulfolobales. Crenarchaeol was the major core lipid. The organism gained energy by oxidizing ammonia to nitrite aerobically, thereby fixing CO2, but growth depended on the addition of small amounts of organic acids. The optimal growth temperature was 42 °C and the optimal pH was 7.5, with ammonium and pyruvate concentrations of 2.6 and 1 mM, respectively. The genome of strain EN76(T) had a DNA G+C content of 52.7 mol%. Phylogenetic analyses of 16S rRNA genes showed that strain EN76(T) is affiliated with the recently proposed phylum Thaumarchaeota, sharing 85% 16S rRNA gene sequence identity with the closest cultivated relative 'Candidatus Nitrosopumilus maritimus' SCM1, a marine ammonia-oxidizing archaeon, and a maximum of 81% 16S rRNA gene sequence identity with members of the phyla Crenarchaeota and Euryarchaeota and any of the other recently proposed phyla (e.g. 'Korarchaeota' and 'Aigarchaeota'). We propose the name Nitrososphaera viennensis gen. nov., sp. nov. to accommodate strain EN76(T). The type strain of Nitrososphaera viennensis is strain EN76(T) ( = DSM 26422(T) = JMC 19564(T)). Additionally, we propose the family Nitrososphaeraceae fam. nov., the order Nitrososphaerales ord. nov. and the class Nitrososphaeria classis nov.