Orthogonal Partial Least Research Articles

Effect of Ex-Vivo Lung Perfusion (EVLP) on Metabolomic Profile of Human Lungs

Purpose 1H Nuclear Magnetic Resonance Spectroscopy (NMR-spec) can identify all major metabolites in an organ, characterizing the metabolic phenotype. We used NMR-spec to determine changes in the metabolome of human lung tissue before and after normothermic EVLP with Steen solution, and in the EVLP perfusate hourly. Methods and Materials Lung biopsies were obtained before and after normothermic EVLP (6 hrs n=2; 3 hrs n=1) and extracted with 40% acetonitrile. NMR spectra were analyzed with Principal Component Analysis and Orthogonal Partial Least Squares Discriminant Analysis. EVLP fluid was analyzed directly with addition of 10% deuterated phosphate buffer. Results The primary 5 metabolites identified in the perfusate are glucose, choline glycine, lactate, & acetate. Lactate increased the most during EVLP. In the polar (water soluble) extract, there was a clear separation between pre and post EVLP biopsies, with significant increases in lactate & alanine (glucose metabolites), ATP & ADP, and acetate (indicating β-oxidation of free fatty acids). The ketone bodies, β-hydroxybutyrate, acetoacetate, and acetone, were not detected. Conclusions This pilot study shows that NMR-spec can characterize the metabolomic phenotype of lung tissue. During EVLP energy stores are replenished. Lactate is the major metabolite secreted into the perfusate during EVLP, presumably metabolized from glucose contained in Steen solution. Accumulation of acetate suggests that the TCA cycle was at capacity and AcetylCoA accumulation led to hydrolysis forming acetate. Lack of ketone bodies in the presence of large amounts of actetate suggests the lung cannot create ketone bodies, a function reserved primarily for the liver. By determining the lung metabolome, NMR-spec may identify biomarkers associated with poor or irretrievable lung function, or identify therapeutic targets that might be measured with point-of-care devices for therapy during EVLP to improve yield or quality of lungs for transplant.

Modeling CMM Dynamic Measuring Error Based on the 3B-Spline Orthogonal Projection Partial Least Squares

Abstract It’s difficult to predict the dynamic error for coordinate measurement machine (CMM) by analyzing the error sources, because they are very complicated and have unknown interaction. In this paper an innovative modeling method is proposed by integrating 3B spline (3BS) transformation and orthogonal projection partial least squares (OPPLS). Three dimensional coordinates and direct computer control (DCC) parameters including positioning velocity, approaching distance and contacting velocity are used as the original independent variables of the model. The nonlinear relationship between the original independent variables and the CMM dynamic measurement errors is worked out by 3B spline transform. Then the orthogonal projection is used to build a new explanatory matrix by eliminating the components which are irrelative to the dependent variables. Finally, the operation of partial least-squares regression can be used to reduce the dimension and estimate the model parameters. With the proposed modeling method the nonlinear relationship between the independent variables and the dynamic measurement errors can be worked out without analyzing the error sources and their interactions. Besides, the problem of multi-collinearity caused by too many explanatory variables can also be overcome. The experimental results show that the mean square error of 3BS-OPPLS model is smaller than the 3B spline-partial least squares (3BS-PLS) model without the orthogonal projection, and the prediction accuracy of the model is notably improved.

Is NMR metabolic profiling of spent embryo culture media useful to assist in vitro human embryo selection?

The prediction of embryo viability by usual morphological analysis is currently unsatisfactory. New non-invasive techniques such as high-resolution nuclear magnetic resonance ((1)H-NMR) spectroscopy that allows assessment of metabolic profiling in spent culture media might help embryologists to predict embryo development. Individual microdrops of culture media were analysed after 24 h of embryo culture (from day 3 to day 4) by spectroscopy using a 1 mm microliter probe allowing analysis without sample dilution. Embryos were divided into two groups on day 5: non-arrested embryos (n = 19) and arrested embryos unable to reach the blastocyst stage (n = 20). Multivariate analysis techniques such as Principal Component Analysis (PCA) and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were performed to compare extracellular metabolite balance. (1)H-NMR used in combination with a 1 mm probe suggested that in vitro cultured human embryos that have a high developmental potential modify their environment slightly compared to embryos that cease to develop. However, differences between the two groups did not reach statistical significance and multivariate statistical analysis did not allow clustering of the two groups. This study indicated that this technique would not be sufficiently powerful alone to provide information that might help to assess the developmental potential of individual embryos for in vitro fertilisation (IVF).

GC-MS Analysis and PLS-DA Validation of the Trimethyl Silyl-Derivatization Techniques

Problem statement: Comparison and validation analysis of the conventional and power-adjusted Microwave-Assisted (MA) techniques of TMS derivatization using Gas Chromatography-Mass Spectrometry (GC-MS) and Multivariate Analysis (MVA) of Partial Least Squares Analysis (PLS-DA). Further improvement of the conventional technique using vigorous shaking was tested and analyzed. Approach: Cross-validation and response permutation test of PLS-DA and S-plot of Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was applied to the extracellular data of Lactococcus lactis, which was analyzed by GC-MS. The analyzed samples were firstly derivatized using Methoximination (MeOX) and N-methyl-N-trimethylsilyl fluoroacetamide (MSTFA) followed by conventional and power-adjusted Microwave-Assisted (MA) heating treatment. Results: The supervised PLS-DA applied to extracellular data failed to show the same clustering results between conventional and power-adjusted Microwave-Assisted (MA) techniques. It was suggested that the type of heating used in the derivatization techniques had affected the detection of groups of metabolites. Furthermore using the UV-scaling method, S-plot and Variable Importance for Projection (VIP), about 40 metabolites that were responsible towards the clustering and separation showed in PLS-DA score plots were successfully indicated. Conventional technique with vigorous shaking showed clear clustering according to groups compared to MA technique. Conclusion: Type of heating applied to the TMS derivatization showed effects towards the detection of metabolites where conventional technique indicated strong clustering compared to MA technique.

Chemical images of marine bio-active compounds by surface enhanced Raman spectroscopy and transposed orthogonal partial least squares (T-OPLS)

Surface enhanced Raman spectroscopy combined with transposed Orthogonal Partial Least Squares (T-OPLS) was shown to produce chemical images of the natural antibacterial surface-active compound 1,1,3,3-tetrabromo-2-heptanone (TBH) on Bonnemaisonia hamifera. The use of gold colloids functionalised with the internal standard 4-mercapto-benzonitrile (MBN) made it possible to create images of the relative concentration of TBH over the surfaces. A gradient of TBH could be mapped over and in the close vicinity of the B. hamifera algal vesicles at the attomol/pixel level.T-OPLS produced a measure of the spectral correlation for each pixel of the hyperspectral images whilst not including spectral variation that was linearly independent of the target spectrum. In this paper we show the possibility to retrieve specific spectral information with a low magnitude in a complex matrix.

Metabolite target analysis of human urine combined with pattern recognition techniques for the study of symptomatic gout

Recurrent attacks and irregularity are two important characteristics of gout disease. Uric acid as a single evaluation indicator for clinical diagnosis is insufficient considering the versatile properties of gout. The aim of this work is to identify several endogenous metabolites from urine samples for the elucidation and prediction of gout disease. Metabolite target analysis was established for human urine by high performance liquid chromatography-diode array detection (HPLC-DAD). The targeted metabolites selected included hippuric acid, uracil, phenylalanine, tryptophan, uric acid and creatinine as well as nine purine compounds. Useful information was extracted from multivariate data through Fisher Linear Discriminant Analysis (FDA) and Orthogonal Signal Correction Partial Least Squares Discriminant Analysis (OSC-PLS-DA). Uric acid, hypoxanthine, xanthosine, guanosine, inosine and tryptophan were identified as important metabolites among the acute and chronic gout and controls. Based on OSC-PLS-DA models, the regression equations obtained could discriminate gout from the controls as well as the acute from chronic. The recognition and prediction ability is respectively 100% and 85.0% for the gout, 100% and 83.3% for the acute, and 90.91% and 89.9% for the chronic. Metabolic dysfunction of tryptophan and excessive metabolism of xanthosine and hypoxanthine to xanthine were confirmed for gout disease. Metabolic dysfunction of tryptophan was also proven to be induced by allopurinol in case of Kunming mice with hyperuricemia. Potential biomarkers can be used not only to distinguish gout patients from healthy people, but also to evaluate the disease state.

Differences in Volatile Profiles of Turnip Plants Subjected to Single and Dual Herbivory Above- and Belowground

Plants attacked by herbivorous insects emit volatile organic compounds that are used by natural enemies to locate their host or prey. The composition of the blend is often complex and specific. It may vary qualitatively and quantitatively according to plant and herbivore species, thus providing specific information for carnivorous arthropods. Most studies have focused on simple interactions that involve one species per trophic level, and typically have investigated the aboveground parts of plants. These investigations need to be extended to more complex networks that involve multiple herbivory above- and belowground. A previous study examined whether the presence of the leaf herbivore Pieris brassicae on turnip plants (Brassica rapa subsp. rapa) influences the response of Trybliographa rapae, a specialist parasitoid of the root feeder Delia radicum. It showed that the parasitoid was not attracted by volatiles emitted by plants under simultaneous attack. Here, we analyzed differences in the herbivore induced plant volatile (HIPV) mixtures that emanate from such infested plants by using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). This multivariate model focuses on the differences between odor blends, and highlights the relative importance of each compound in an HIPV blend. Dual infestation resulted in several HIPVs that were present in both isolated infestation types. However, HIPVs collected from simultaneously infested plants were not the simple combination of volatiles from isolated forms of above- and belowground herbivory. Only a few specific compounds characterized the odor blend of each type of damaged plant. Indeed, some compounds were specifically induced by root herbivory (4-methyltridecane and salicylaldehyde) or shoot herbivory (methylsalicylate), whereas hexylacetate, a green leaf volatile, was specifically induced after dual herbivory. It remains to be determined whether or not these minor quantitative variations, within the background of more commonly induced odors, are involved in the reduced attraction of the root feeder’s parasitoid. The mechanisms involved in the specific modification of the odor blends emitted by dual infested turnip plants are discussed in the light of interferences between biosynthetic pathways linked to plant responses to shoot or root herbivory.

Alterations in urine, serum and brain metabolomic profiles exhibit sexual dimorphism during malaria disease progression

BackgroundMetabolic changes in the host in response to Plasmodium infection play a crucial role in the pathogenesis of malaria. Alterations in metabolism of male and female mice infected with Plasmodium berghei ANKA are reported here.Methods1H NMR spectra of urine, sera and brain extracts of these mice were analysed over disease progression using Principle Component Analysis and Orthogonal Partial Least Square Discriminant Analysis.ResultsAnalyses of overall changes in urinary profiles during disease progression demonstrate that females show a significant early post-infection shift in metabolism as compared to males. In contrast, serum profiles of female mice remain unaltered in the early infection stages; whereas that of the male mice changed. Brain metabolite profiles do not show global changes in the early stages of infection in either sex. By the late stages urine, serum and brain profiles of both sexes are severely affected. Analyses of individual metabolites show significant increase in lactate, alanine and lysine, kynurenic acid and quinolinic acid in sera of both males and females at this stage. Early changes in female urine are marked by an increase of ureidopropionate, lowering of carnitine and transient enhancement of asparagine and dimethylglycine. Several metabolites when analysed individually in sera and brain reveal significant changes in their levels in the early phase of infection mainly in female mice. Asparagine and dimethylglycine levels decrease and quinolinic acid increases early in sera of infected females. In brain extracts of females, an early rise in levels is also observed for lactate, alanine and glycerol, kynurenic acid, ureidopropionate and 2-hydroxy-2-methylbutyrate.ConclusionsThese results suggest that P. berghei infection leads to impairment of glycolysis, lipid metabolism, metabolism of tryptophan and degradation of uracil. Characterization of early changes along these pathways may be crucial for prognosis and better disease management. Additionally, the distinct sexual dimorphism exhibited in these responses has a bearing on the understanding of the pathophysiology of malaria.

ChemGPS‐NP Mapping of Chemical Compounds for Prediction of Anticancer Mode of Action

AbstractA combined graph describing the growth inhibition values from a number of human cancer cell lines represents an activity profile for a compound. The fact that compounds with similar activity profiles often show similar Mode of Action (MOA) has frequently been used in prediction of MOA. Obtaining the profiles is demanding with respect to both time and resources. Therefore, as a work and time efficient alternative, we explore the central premise of medicinal chemistry that structurally similar molecules often have similar biological activity. In this study we investigate correlations between chemical structure and MOA, and subsequently use this as a complementing basis for prediction. The correlations between MOA and activity profile on one hand and between MOA and chemical structure on the other were analyzed for anticancer agents, classified with regard to MOA, using Principal Component Analysis (PCA), chemographic mapping with ChemGPS‐NP, and Orthogonal Partial Least Squares Discriminant Analysis (OPLS‐DA). The compounds clustered according to MOA both based on chemical structures and activity profiles. The subsequent validation with external test sets showed that initial PCA scores prediction or chemographic mapping followed by OPLS‐DA could be used for the prediction of MOA as well as identification of novel MOAs in a highly accurate way. An efficient and straight forward procedure for the prediction of MOA of anticancer agents is suggested. With today's resistance problems in cancer therapy, there is a need for new anticancer agents and mechanisms. We believe that the fast initial virtual guidance this procedure implies, especially the novel step using ChemGPS‐NP, could be of general use in early stages of cancer research.

Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares

Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood's anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands "process understanding" in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.

Preprocessing peptide sequences for multivariate sequence-property analysis

The increasing number of peptide sequences with different lengths, available from synthesised peptide libraries and sequenced proteins are potentially valuable for evaluating structure–activity relationships. However, in order to apply multivariate classification or Quantitative Structure–Activity Relationship (QSAR) analyses on such sequences, it is necessary to have a preprocessing method that translates them into a uniform set of variables. By describing each amino acid by principal properties ( z-scales) and then calculating auto cross covariances (ACCs) for each sequence, a new uniform matrix is generated, i.e., each sequence is described by a vector with equal length. The ACC approach has been used before for classification of peptides, but here, a QSAR analysis based on 20 peptide sequences of different lengths is presented. The results show that it is possible to obtain a predictive multivariate QSAR model ( R 2 Y cum=86.2%, Q cum 2=60.3%) based on the ACC preprocessing method, together with Orthogonal Signal Correction (OSC) and Partial Least Squares (PLS). The model generated was further validated by permutation tests and found to be valid. The new variables generated by ACCs can also be interpreted, i.e., used to identify important features in the original sequences.