Chenomx Inc.

Edmonton, Canada

Chenomx Inc.

Edmonton, Canada

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Mercier P.,Chenomx Inc. | Lewis M.J.,Chenomx Inc. | Chang D.,Chenomx Inc. | Baker D.,Pfizer | Wishart D.S.,University of Alberta
Journal of Biomolecular NMR | Year: 2011

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values. © 2011 Springer Science+Business Media B.V.


Sokolenko S.,University of Waterloo | McKay R.,National High Field Nuclear Magnetic Resonance Center | McKay R.,University of Alberta | Blondeel E.J.M.,University of Waterloo | And 4 more authors.
Metabolomics | Year: 2013

The growing use of 'targeted profiling' approaches for the deconvolution of 1D-1H-NMR spectra by comparison to a pure compound library has created a need for an in-depth characterization of quantification variability that is beyond what is currently available in the literature. In this study, we explore the underlying source of quantification variability (tube insertion, spectra acquisition, and profiling) as well as a number of other factors, such as temporal consistency of repeated NMR scans, human consistency in repeated profiles, and human versus machine sampling. We also look at the effect of different pulse sequences on the differences between acquired spectra and the peak reference library. Two sample types were considered for this work-a synthetic five compound mixture as well as human urine. The result is a comprehensive examination of 1D-1H-NMR quantification error. Our investigation into variability sources revealed that apart from profiling, sample insertion and/or shimming can play a significant role in final quantification, a finding that is equally applicable to all integration-based methods of quantification. Both sources of error were also found to have temporal relationships, with bias identified as a function of both scan and profiling order, reinforcing the need for randomization in scanning and profiling. As well as presenting a practical estimate of variability in human urine samples, we have uncovered a considerable amount of complexity in underlying NMR variability that will hopefully serve as impetus for future exploration in this area. © 2013 Springer Science+Business Media New York.


Karunasena E.,Virginia Polytechnic Institute and State University | McMahon K.W.,Virginia Polytechnic Institute and State University | Chang D.,Chenomx Inc. | Brashears M.M.,Texas Tech University
Applied and Environmental Microbiology | Year: 2014

Differences between microbial pathogenesis in male and female hosts are well characterized in disease conditions connected to sexual transmission. However, limited biological insight is available on variances attributed to sex specificity in host-microbe interactions, and it is most often a minimized variable outside these transmission events. In this work, we studied two gut microbes- a pathogen, Mycobacterium avium subsp. paratuberculosis, and a probiotic, Lactobacillus animalis NP-51-and the interaction between each agent and the male and female gastrointestinal systems. This trial was conducted in BALB/c mice (n = 5 per experimental group and per sex at a given time point), with analysis at four time points over 180 days. Host responses to M. avium subsp. paratuberculosis and L. animalis were sensitive to sex. Cytokines that were significantly different (P≤0.05) between the sexes included interleukin-1α/β (IL-1α/β), IL-17, IL-6, IL-10, IL-12, and gamma interferon (IFN-γ) and were dependent on experimental conditions. However, granulocyte-macrophage colony-stimulating factor (GM-CSF), vascular endothelial growth factor (VEGF), and IL-13/23 showed no sex specificity. A metabolomics study indicated a 0.5- to 2.0-fold (log2 scale) increase in short-chain fatty acids (butyrate and acetate) in males and greater increases in o-phosphocholine or histidine from female colon tissues; variances distinct to each sex were observed with age or long-term probiotic consumption. Two genera, Staphylococcus and Roseburia, were consistently overrepresented in females compared to males; other species were specific to one sex but fluctuated depending on experimental conditions. The differences observed suggest that male and female gut tissues and microbiota respond to newly introduced microorganisms differently and that gut-associated microorganisms with host immune system responses and metabolic activity are supported by biology distinct to the host sex. © 2014, American Society for Microbiology.


Suhre K.,Helmholtz Center for Environmental Research | Suhre K.,Ludwig Maximilians University of Munich | Suhre K.,Cornell College | Wallaschofski H.,University of Greifswald | And 24 more authors.
Nature Genetics | Year: 2011

We present a genome-wide association study of metabolic traits in human urine, designed to investigate the detoxification capacity of the human body. Using NMR spectroscopy, we tested for associations between 59 metabolites in urine from 862 male participants in the population-based SHIP study. We replicated the results using 1,039 additional samples of the same study, including a 5-year follow-up, and 992 samples from the independent KORA study. We report five loci with joint P values of association from 3.2 × 10- 19 to 2.1 × 10-182. Variants at three of these loci have previously been linked with important clinical outcomes: SLC7A9 is a risk locus for chronic kidney disease, NAT2 for coronary artery disease and genotype-dependent response to drug toxicity, and SLC6A20 for iminoglycinuria. Moreover, we identify rs37369 in AGXT2 as the genetic basis of hyper- 2-aminoisobutyric aciduria. © 2011 Nature America, Inc. All rights reserved.


Sokolenko S.,University of Waterloo | Blondeel E.J.M.,University of Waterloo | Azlah N.,University of Waterloo | George B.,University of Waterloo | And 4 more authors.
Analytical Chemistry | Year: 2014

Single-dimension hydrogen, or proton, nuclear magnetic resonance spectroscopy (1D-1H NMR) has become an attractive option for characterizing the full range of components in complex mixtures of small molecular weight compounds due to its relative simplicity, speed, spectral reproducibility, and noninvasive sample preparation protocols compared to alternative methods. One challenge associated with this method is the overlap of NMR resonances leading to "convoluted" spectra. While this can be mitigated through "targeted profiling", there is still the possibility of increased quantification error. This work presents the application of a Plackett-Burman experimental design for the robust estimation of precision and accuracy of 1D-1H NMR compound quantification in synthetic mixtures, with application to mammalian cell culture supernatant. A single, 20 sample experiment was able to provide a sufficient estimate of bias and variability at different metabolite concentrations. Two major sources of bias were identified: incorrect interpretation of singlet resonances and the quantification of resonances from protons in close proximity to labile protons. Furthermore, decreases in measurement accuracy and precision could be observed with decreasing concentration for a small fraction of the components as a result of their particular convolution patterns. Finally, the importance of a priori concentration estimates is demonstrated through the example of interpreting acetate metabolite trends from a bioreactor cultivation of Chinese hamster ovary cells expressing a recombinant antibody. © 2014 American Chemical Society.


Lacy P.,University of Alberta | Finkel M.,University of Michigan | Karnovsky A.,University of Michigan | Woehler S.,University of Michigan | And 3 more authors.
PLoS ONE | Year: 2014

We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-1H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data. © 2014 Lacy et al.


Athersuch T.J.,Imperial College London | Malik S.,Chenomx Inc. | Weljie A.,University of Calgary | Weljie A.,University of Pennsylvania | And 2 more authors.
Analytical Chemistry | Year: 2013

A strategy for evaluating the performance of quantitative spectral analysis tools in conditions that better approximate background variation in a metabonomics experiment is presented. Three different urine samples were mixed in known proportions according to a {3, 3} simplex lattice experimental design and analyzed in triplicate by 1D 1H NMR spectroscopy. Fifty-four urinary metabolites were subsequently quantified from the sample spectra using two methods common in metabolic profiling studies: (1) targeted spectral fitting and (2) targeted spectral integration. Multivariate analysis using partial least-squares (PLS) regression showed the latent structure of the spectral set recapitulated the experimental mixture design. The goodness-of-prediction statistic (Q2) of each metabolite variable in a PLS model was calculated as a metric for the reliability of measurement, across the sample compositional space. Several metabolites were observed to have low Q2 values, largely as a consequence of their spectral resonances having low s/n or strong overlap with other sample components. This strategy has the potential to allow evaluation of spectral features obtained from metabolic profiling platforms in the context of the compositional background found in real biological sample sets, which may be subject to considerable variation. We suggest that it be incorporated into metabolic profiling studies to improve the estimation of matrix effects that confound accurate metabolite measurement. This novel method provides a rational basis for exploiting information from several samples in an efficient manner and avoids the use of multiple spike-in authentic standards, which may be difficult to obtain. © 2013 American Chemical Society.


Blondeel E.J.M.,University of Waterloo | Braasch K.,University of Manitoba | McGill T.,University of Waterloo | Chang D.,Chenomx Inc. | And 4 more authors.
Journal of Biotechnology | Year: 2015

Glycosylation is a critical quality attribute of many therapeutic proteins, particularly monoclonal antibodies (MAbs). Nucleotide-sugar precursors supplemented to growth medium to affect the substrate supply chain of glycosylation has yielded promising but varied results for affecting glycosylation. Glucosamine (GlcN), a precursor for N-acetylglucosamine (GlcNAc), is a major component of mammalian glycans. The supplementation of GlcN to CHO cells stably-expressing a chimeric heavy-chain monoclonal antibody, EG2-hFc, reduces the complexity of glycans to favour G0 glycoforms, while also negatively impacting cell growth. Although several researchers have examined the supplementation of glucosamine, no clear explanation of its impact on cell growth has been forthcoming. In this work, the glucosamine metabolism is examined. We identified the acetylation of GlcN to produce GlcNAc to be the most likely cause for the negative impact on growth due to the depletion of intracellular acetyl-CoA pools in the cytosol. By supplementing GlcNAc in lieu of GlcN to CHO cells producing EG2-hFc, we achieve the same shift in glycan complexity with marginal impacts on the cell growth and protein production. © 2015 Elsevier B.V.


PubMed | University of Manitoba, Chenomx Inc. and University of Waterloo
Type: | Journal: Journal of biotechnology | Year: 2015

Glycosylation is a critical quality attribute of many therapeutic proteins, particularly monoclonal antibodies (MAbs). Nucleotide-sugar precursors supplemented to growth medium to affect the substrate supply chain of glycosylation has yielded promising but varied results for affecting glycosylation. Glucosamine (GlcN), a precursor for N-acetylglucosamine (GlcNAc), is a major component of mammalian glycans. The supplementation of GlcN to CHO cells stably-expressing a chimeric heavy-chain monoclonal antibody, EG2-hFc, reduces the complexity of glycans to favour G0 glycoforms, while also negatively impacting cell growth. Although several researchers have examined the supplementation of glucosamine, no clear explanation of its impact on cell growth has been forthcoming. In this work, the glucosamine metabolism is examined. We identified the acetylation of GlcN to produce GlcNAc to be the most likely cause for the negative impact on growth due to the depletion of intracellular acetyl-CoA pools in the cytosol. By supplementing GlcNAc in lieu of GlcN to CHO cells producing EG2-hFc, we achieve the same shift in glycan complexity with marginal impacts on the cell growth and protein production.


PubMed | Biberach University of Applied Sciences, Chenomx Inc., NRC Biotechnology Research Institute and University of Waterloo
Type: | Journal: Journal of biotechnology | Year: 2016

Expression of recombinant proteins exerts stress on cell culture systems, affecting the expression of endogenous proteins, and contributing to the depletion of nutrients and accumulation of waste metabolites. In this work, 2D-DIGE proteomics was employed to analyze differential expression of proteins following stable transfection of a Chinese Hamster Ovary (CHO) cell line to constitutively express a heavy-chain monoclonal antibody. Thirty-four proteins of significant differential expression were identified and cross-referenced with cellular functions and metabolic pathways to identify points of cell stress. Subsequently, 1D-(1)H NMR metabolomics experiments analyzed cultures to observe nutrient depletion and waste metabolite accumulations to further examine these cell stresses and pathways. From among fifty metabolites tracked in time-course, eight were observed to be completely depleted from the production media, including: glucose, glutamine, proline, serine, cystine, asparagine, choline, and hypoxanthine, while twenty-three excreted metabolites were also observed to accumulate. The differentially expressed proteins, as well as the nutrient depletion and accumulation of these metabolites corresponded with upregulated pathways and cell systems related to anaplerotic TCA-replenishment, NADH/NADPH replenishment, tetrahydrofolate cycle C1 cofactor conversions, limitations to lipid synthesis, and redox modulation. A nutrient cocktail was assembled to improve the growth medium and alleviate these cell stresses to achieve a 75% improvement to peak cell densities.

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