Molecular diversity of Bacteroides spp. in human fecal microbiota as determined by group-specific 16S rRNA gene clone library analysis

Bacteroides spp. represent a prominent bacterial group in human intestinal microbiota with roles in symbiosis and pathogenicity; however, the detailed composition of this group in human feces has yet to be comprehensively characterized. In this study, the molecular diversity of Bacteroides spp. in human fecal microbiota was analyzed from a seven-member, four-generation Chinese family using Bacteroides spp. group-specific 16S rRNA gene clone library analysis. A total of 549 partial 16S rRNA sequences amplified by Bacteroides spp.-specific primers were classified into 52 operational taxonomic units (OTUs) with a 99% sequence identity cut-off. Twenty-three OTUs, representing 83% of all clones, were related to 11 validly described Bacteroides species, dominated by Bacteroides coprocola, B. uniformis, and B. vulgatus. Most of the OTUs did not correspond to known species and represented hitherto uncharacterized bacteria. Relative to 16S rRNA gene universal libraries, the diversity of Bacteroides spp. detected by the group-specific libraries was much higher than previously described. Remarkable inter-individual differences were also observed in the composition of Bacteroides spp. in this family cohort. The comprehensive observation of molecular diversity of Bacteroides spp. provides new insights into potential contributions of various species in this group to human health and disease.     

Fingerprint detection and process prediction by multivariate analysis of fed‐batch monoclonal antibody cell culture data

This work presents a sequential data analysis path, which was successfully applied to identify important patterns (fingerprints) in mammalian cell culture process data regarding process variables, time evolution and process response. The data set incorporates 116 fed‐batch cultivation experiments for the production of a Fc‐Fusion protein. Having precharacterized the evolutions of the investigated variables and manipulated parameters with univariate analysis, principal component analysis (PCA) and partial least squares regression (PLSR) are used for further investigation. The first major objective is to capture and understand the interaction structure and dynamic behavior of the process variables and the titer (process response) using different models. The second major objective is to evaluate those models regarding their capability to characterize and predict the titer production. Moreover, the effects of data unfolding, imputation of missing data, phase separation, and variable transformation on the performance of the models are evaluated. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1633–1644, 2015     

Phylogenetic analysis of the human gut microbiota using 16S rDNA clone libraries and strictly anaerobic culture-based methods

The human gut microbiota from three healthy subjects were compared by the use of a sequence analysis of 16S rDNA libraries and a culture-based method. Direct counts ranged from 1.9 X 10" to 4.0 X 10" cells/g (wet weight), and plate counts totaled 6.6 X 10(10) to 1.2 X 10(11) CFU/g (wet weight). Sixty to seventy percent of the bacteria in the human intestinal tract cannot be cultured with currently available methods. The 16S rDNA libraries from three subjects were generated from total community DNA in the intestinal tract with universal primer sets. Randomly selected clones were partially sequenced. All purified colonies detected from the surface of the agar plate were used for a partial sequencing of 16S rDNA. On the basis of sequence similarities, the clones and colonies were classified into several clusters corresponding to the major phylum of the domain Bacteria. Among a total of 744 clones obtained, approximately 25% of them belonged to 31 known species. About 75% of the remaining clones were novel "phylotypes" (at least 98% similarity of clone sequence). The predominant intestinal microbial community consisted of 130 species or phylotypes according to the sequence data in this study. The 16S rDNA libraries and colonies included the Bacteroides group, Streptococcus group, Bifidobacterium group, and Clostridium rRNA clusters IV, IX, XIVa, and XVIII. Moreover, several previously uncharacterized and uncultured microorganisms were recognized in clone libraries and colonies. Our results also showed marked individual differences in the composition of intestinal microbiota.     

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.     

A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.     

Pattern analysis techniques to process fermentation curves: application to discrimination of enological alcoholic fermentations

In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p(1)) gathers all the kinetic curve points. The second (p(2)) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p(2) was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p(1) was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools.     

Evolution of the diatoms: IX. Two datasets resolving monophyletic Classes of diatoms are used to explore the validity of adding short clone library sequences to the analysis

A previous study of available diatom sequences tested the value of multiple outgroups in analyses of the 18S ribosomal RNA gene aimed at recovering the three diatom Classes (Coscinodiscophyceae, Mediophyceae, Bacillariophyceae) as monophyletic groups. Of the 34 datasets tested in that study, three recovered these Classes and two of these are explored here in more detail. The major differences between these two datasets were the number of outgroups, the number of nucleotides selected for the analysis and the addition to the dataset of short clone library sequences, primarily from raphid pennate taxa. The addition of short sequences resulted in a smaller dataset in terms of total nucleotides analysed because of the filter selected in the ARB program to generate the final dataset for phylogenetic analysis. The 50% parsimony filter selects a base for the final dataset if that position is parsimony-informative in at least 50% of the sequences chosen for the analysis. Thus, with short sequences, fewer bases are available for selection in the final dataset. A final dataset with fewer nucleotides is produced from the original alignment than from a similar one with no filter applied. If no filter is applied, then a final dataset is produced with all full-length sequences. From these analyses, it was determined that the addition of short clone library sequences recovered sister relationships for certain raphid diatoms that go against conventional wisdom with regard to valve morphology. Thus, the smaller database caused some of the groupings to be unnatural. It was determined that the optimal resolution of the taxa in these datasets resulted from using only full length sequences and selected multiple outgroups. Results obtained from the reduced dataset would be a cause for concern with next generation sequencing in which short amplicons are used to identify taxa.     

Development of a multivariate statistical model to predict peroxisome proliferation in the rat,based on urinary 1H-NMR spectral patterns

A previous report of this work (Ringeissen et al. 2003) described the use of nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical data analysis (MVDA) to identify novel biomarkers of peroxisome proliferation (PP) in Wistar Han rats. Two potential biomarkers of peroxisome proliferation in the rat were described, N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY). The inference from these results was that the tryptophan-nicotinamide adenine dinucleotide (NAD⁺) pathway was altered in correlation with peroxisome proliferation, a hypothesis subsequently confirmed by TaqMan® analysis of the relevant genes encoding two key enzymes in the pathway, aminocarboxymuconate-semialdehyde decarboxylase (EC 4.1.1.45) and quinolinate phosphoribosyltransferase (EC 2.4.2.19). The objective of the present study was to investigate these data further and identify other metabolites in the NMR spectrum correlating equally with PP. MVDA Partial Least Squares (PLS) models were constructed that provided a better prediction of PP in Wistar Han rats than levels of 4PY and NMN alone. The resulting Wistar Han rat predictive models were then used to predict PP in a test group of Sprague Dawley rats following administration of fenofibrate. The models predicted the presence or absence of PP (above on arbitrary threshold of >2-fold mean control) in all Sprague Dawley rats in the test group.     

Bioassay responses to sediment elutriates and multivariate data analysis for hazard assessment of sediment-bound chemicals

A sediment study, involving both chemical and biological analyses, was carried out in the Hamburg harbour area. A total of 71 stations were sampled during 1988 and the sediments extracted using a 1:4 sediment:water ratio either with or without an addition of a water-soluble detergent to solubilize organic compounds. The resulting extracts were applied in algal and bacterial assays to measure toxicity. A principal components analysis showed that no single bioassay explained all the variation in toxicity among the locations studied. Hierarchical cluster analysis was used to rank sediments into four groups based on their toxicity. The relationship of toxic responses to the chemistry of the sediments was determined using varimax factor analysis. One factor was loaded with algal responses and mercury contents of sediments, another with bacterial responses and Lindane contents of sediments.     

Segmented linear regression models applied to the analysis of data from a cross-sectional growth experiment

Several different methods of analysis are applied to data consisting of weight measurements, taken at specified post-treatment times, of harvested thyroids from rats given one of four treatments. Previous studies of this type of data indicated that the growth is initially rapid, and that a second phase of less rapid growth is followed by a final phase in which little additional growth occurs. The data are further characterized by increasing variance through time. The primary purpose of the analysis is to study the effect of the treatments at the end of the study period. One-way analysis of variance tests among groups are performed on each day, but the results are not particularly helpful. However, results from two-way analyses of variance (over subsets of days and groups) are consistent with the three phase model and accordingly indicate significant group differences during each. Finally, maximum likelihood methods are used to fit a three part segmented linear regression model.     

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.     

Multiway analysis applied to time‐resolved chemiluminescence for simultaneous determination of paracetamol and codeine in pharmaceuticals

This method is based on the enhancing effect of codeine (COD) and paracetamol (PAR) on the chemiluminescence (CL) reaction of Ru(phen)₃²⁺ with Ce(IV). In the batch mode, COD gives a relatively sharp peak with the highest CL intensity at 4.0 s, whereas the maximum CL intensity of the PAR appears at ~60 s after injection of Ce(IV) solution. Whole CL time profiles allowed use of the time‐resolved CL data in combination with multiway calibration techniques, as multiway partial least squares (N‐PLS), for the quantitative determination of both COD and PAR in binary mixtures. In this work, we found that the impact of Ce(IV) concentration on the CL intensity was different for COD and PAR. Therefore, a Ce(IV) concentration mode was added to the time and sample modes to obtain 3D data. The percent relative standard deviation (%RSD) values for 10 determinations of 1.0 × 10^?5 mol/L of COD and 1.0 × 10^?4 mol/L of PAR were 6.1% and 8.7%, respectively. The limit of detection (LOD) values (S/N = 3) were 0.9 × 10^?8 mol/L and 1.0 × 10^?6 mol/L for COD and PAR, respectively. The proposed method was successfully applied to the determination of PAR and COD in commercial pharmaceutical formulations. Acceptable recoveries (90–110%) were obtained for the quantification of these drugs in the real samples. Copyright © 2016 John Wiley & Sons, Ltd.