A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

脂肪酶产生菌Aspergillus sp. FS132产酶条件的初步优化及其18S rRNA基因序列的比较分析

从新疆火焰山口土样中分离的一株脂肪酶产生菌FS132,菌落形态表明为霉菌。对其产酶条件的初步优化表明,产酶的最适培养时间为45h,培养基最适初始pH 7.0,最适培养温度36℃,最适摇瓶空气量25mL/250mL锥形瓶。为进一步鉴定该菌株,克隆测定了该菌18S rRNA基因序列。并对其进行系统进化树分析,结果表明该菌与已报道的Aspergillus tamarii具有最紧密亲缘关系。     

脂肪酶产生菌Aspergillus sp.FS132产酶条件的初步优化及其18S rRNA基因序列的比较分析

从新疆火焰山口土样中分离的一株脂肪酶产生菌FS132,菌落形态表明为霉菌。对其产酶条件的初步优化表明,产酶的最适培养时间为45h,培养基最适初始pH7．0,最适培养温度36℃,最适摇瓶空气量25mL／250mL锥形瓶。为进一步鉴定该菌株,克隆测定了该菌18SrRNA基因序列。并对其进行系统进化树分析,结果表明该菌与已报道的Aspergillus tamarii具有最紧密亲缘关系。     

Combining mechanistic and data‐driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed‐batch CHO process

A growing body of knowledge is available on the cellular regulation of overflow metabolism in mammalian hosts of recombinant protein production. However, to develop strategies to control the regulation of overflow metabolism in cell culture processes, the effect of process parameters on metabolism has to be well understood. In this study, we investigated the effect of pH and temperature shift timing on lactate metabolism in a fed‐batch Chinese hamster ovary (CHO) process by using a Design of Experiments (DoE) approach. The metabolic switch to lactate consumption was controlled in a broad range by the proper timing of pH and temperature shifts. To extract process knowledge from the large experimental dataset, we proposed a novel methodological concept and demonstrated its usefulness with the analysis of lactate metabolism. Time‐resolved metabolic flux analysis and PLS‐R VIP were combined to assess the correlation of lactate metabolism and the activity of the major intracellular pathways. Whereas the switch to lactate uptake was mainly triggered by the decrease in the glycolytic flux, lactate uptake was correlated to TCA activity in the last days of the cultivation. These metabolic interactions were visualized on simple mechanistic plots to facilitate the interpretation of the results. Taken together, the combination of knowledge‐based mechanistic modeling and data‐driven multivariate analysis delivered valuable insights into the metabolic control of lactate production and has proven to be a powerful tool for the analysis of large metabolic datasets. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1657–1668, 2015     

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real‐time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration‐related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1586–1599, 2015     

Application of multivariate analysis and mass transfer principles for refinement of a 3‐L bioreactor scale‐down model—when shake flasks mimic 15,000‐L bioreactors better

Characterization of manufacturing processes is key to understanding the effects of process parameters on process performance and product quality. These studies are generally conducted using small‐scale model systems. Because of the importance of the results derived from these studies, the small‐scale model should be predictive of large scale. Typically, small‐scale bioreactors, which are considered superior to shake flasks in simulating large‐scale bioreactors, are used as the scale‐down models for characterizing mammalian cell culture processes. In this article, we describe a case study where a cell culture unit operation in bioreactors using one‐sided pH control and their satellites (small‐scale runs conducted using the same post‐inoculation cultures and nutrient feeds) in 3‐L bioreactors and shake flasks indicated that shake flasks mimicked the large‐scale performance better than 3‐L bioreactors. We detail here how multivariate analysis was used to make the pertinent assessment and to generate the hypothesis for refining the existing 3‐L scale‐down model. Relevant statistical techniques such as principal component analysis, partial least square, orthogonal partial least square, and discriminant analysis were used to identify the outliers and to determine the discriminatory variables responsible for performance differences at different scales. The resulting analysis, in combination with mass transfer principles, led to the hypothesis that observed similarities between 15,000‐L and shake flask runs, and differences between 15,000‐L and 3‐L runs, were due to pCO₂ and pH values. This hypothesis was confirmed by changing the aeration strategy at 3‐L scale. By reducing the initial sparge rate in 3‐L bioreactor, process performance and product quality data moved closer to that of large scale. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1370–1380, 2015     

Immunoproteomic analysis of the antibody response obtained in mouse following vaccination with a T-cell vaccine

T-cell vaccination (TCV), the application of irradiated activated T cells, has been shown to prevent effectively and treat experimental autoimmune diseases. It has been reported that anti-lymphocytic antibodies induced by TCV were capable of strongly inhibiting T-cell proliferation and of ameliorating experimental autoimmune disease. The present study was undertaken to characterize the antigen specificity of these Abs. We used activated mouse ovalbumin (OVA)-specific T cells (OVA-T) as vaccine immunized mice. By combination of 2-DE, 2-D Western blot and Q-TOF mass spectrometry we have identified 11 antigens in activated T cells that were recognized by the anti-T-cell Abs. The resulting antigenic molecules included calreticulin (CRT), ERp57, Vimentin, HSP70-4, tubulin β5 chain, coronin-1A, pyruvate kinase, ATP synthase β chain and transketolase most of which belong to so-called damage-associated molecular pattern molecules (DAMPs). CRT, ERp57 and vementin were further examined by Western blot and cellular ELISA to identify molecular targets which may be involved in the TCV immunotherapy. On the basis of our results, γ-radiation induced the activated T cells "immunogenic apoptosis" and exposed/secreted DAMPs (CRT, ERp57 and Vementin) played an important role in TCV therapy.     

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody–peptide fusion

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody–peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high‐throughput (HT) micro‐bioreactor system (Ambr^TM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on‐line and off‐line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale‐up. Biotechnol. Bioeng. 2017;114: 2222–2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.     

Day-to-day variation in nitrogenase activity of Alnus incana explained by weather variables: a multivariate time series analysis

A modelling system is described that indicates the extent to which day-to-day variations in nitrogenase activity in young Alnus incana (L.) Moench, grown in defined conditions in the field, may be affected by weather conditions both during and prior to the day of measurement. Nitrogenase activity (acetylene reduction activity, ARA) was measured weekly on intact field-grown grey alder (A. incana) plants, 0.15–0.42 m tall at planting, nodulated with Frankia. The measurements were done at noon on two groups of plants in 1987 and on two other groups in 1988. Each group was made up of five or six plants. Seven weather variables: daily sunshine hours, daily mean, maximum and minimum air temperature, daily mean and 1300 h relative humidity, and daily rainfall were used. The relation between log(ARA/leaf area) and the weather variables were analysed using a PLS model (partial least squares projection to latent structures). The advantage of PLS is that it can handle x-variables that are correlated. Data from 1987 were chosen as a training set. Multivariate PLS time series analysis was made by adding, in a stepwise manner, the weather data up to 5 d before the day of measurement. This procedure gave six models with n * 7 x-variables (n= 1–6). With the models from the time series analysis of 1987 data, true predictions of ARA per leaf area were made from weather data 1988 (test set 1) and from ‘early-season’ weather data from 1987 and 1988 (test set 2). The variation in ARA/leaf area could be predicted from the weather conditions. The predictions of the two test sets improved when the weather conditions one and two days before the day of measurements were added to the model. The further addition of weather data from 3 to 5 d before the day of measurement did not improve the model. The good predictions of ARA/leaf area show that the alders responded to the variable weather conditions in the same way in 1988 as in 1987, despite the ten-fold difference in size (leaf area) at the end of the growing season. Among the weather variables, air temperature and the daily sunshine hours were positively correlated to ARA, while relative air humidity and rainfall were negatively correlated to ARA. The daily minimum temperature and rainfall appeared to have least impact on ARA. By use of PLS, we could extract information out of a data set containing highly correlated x-variables, information that is non-accessible with conventional statistical tools such as multiple regression. When making measurements of nitrogenase activities under field conditions, we propose that attention should be paid to the weather conditions on the days preceding the day of measurement. The day-to-day variation in nitrogenase activity is discussed with reference to known effects of stress factors under controlled conditions.     

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co‐aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF‐SIMS at 10 μm and 1 μm lateral resolution. Intense M^+● and M^?● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long‐chain aliphatic saturated alcohols (C₂₁–C₃₀), hydrocarbons (C₂₅–C₃₃) and wax esters (WEs; C₄₄–C₄₈) were clearly observed. These data correlated with the ⁷Li‐chelation matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF‐SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five‐factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed ‘crystals’, were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C₂₃ or C₂₉ alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra‐high‐resolution TOF‐SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface.     

利用木质纤维素类生物质产微生物絮凝剂Pseudomonas boreopolis GO2的全基因组测序及比较基因组学分析

【目的】Pseudomonas boreopolis GO2可以利用木质纤维素类生物质为唯一碳源发酵产微生物絮凝剂。解析菌株GO2的全基因组特征可为利用木质纤维素类生物质定向合成多糖型微生物絮凝剂提供分子基础。【方法】利用Illumina NovaSeq测序平台对菌株GO2进行测序,用SMRT等软件进行基因组组装、系统发育分析、基因预测和功能注释,并与4株近缘模式株进行了比较基因组分析。【结果】菌株GO2基因组大小为4 498 896 bp,GC含量为69.5%,共编码3 906个基因。菌株GO2与Pseudomonas boreopolis JCM 13306的16S r RNA基因相似性、平均核苷酸一致性(average nucleotide identity, ANI)、DNA-DNA杂交(DNA-DNA hybridization, DDH)值最高,分别为99.93%、98.36%和88.00%,将菌株GO2命名为Pseudomonas boreopolis GO2。比较基因组分析发现,GO2与4个近缘模式菌株共有2 348个直系同源核心基因,主要参与碳水化合物代谢、氨基酸代谢...     

The use of differential measurements with a glucose biosensor for interference compensation during glucose determinations by flow injection analysis

A novel detection system for the determination of glucose in the presence of clinically important interferents, based on the use of dual sensors and flow-injection analysis (FIA), is described. The normalisation methodology involves measurement of the interference signal at a reference sensor; this signal can then be subtracted from the glucose sensor signal (post-run) to give a corrected measurement of the glucose concentration. The detection system consists of a thin layer cell with dual glassy carbon working electrodes. One electrode was surface modified to act asglucose biosensor by immobilisation of glucose oxidase (GOx) (from Aspergillus niger) with 1% glutaraldehyde and bovine serum albumin. The second electrode (glucose oxidase omitted) was utilised to measure the interference signal responding only to electroactive species present in the injected sample. A computer controlled multichannel potentiostat was used for potential application and current monitoring duties. The sensor responses were saved in ASCII format to facilitate post-run analysis in Microsoft Excel. Cyclic voltammetry (CV) was utilised to investigate the manner in which the interference signal contributed to the total signal obtained at the biosensor in the presence of glucose. The kinetic parameters I_max and the apparent Michaelis-Menten constant, K′_m, were calculated for the sensor operating under flow-injection conditions.     

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45(o)N and polewards) for the period 1900-2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.