Hierarchical time-series analysis of dynamic bioprocess systems.

BACKGROUND: Monoclonal antibodies (mAbs) are leading types of 'blockbuster' biotherapeutics worldwide; they have been successfully used to treat various cancers and chronic inflammatory and autoimmune diseases. Biotherapeutics process development and manufacturing are complicated due to lack of understanding the factors that impact cell productivity and product quality attributes. Understanding complex interactions between cells, media, and process parameters on the molecular level is essential to bring biomanufacturing to the next level. This can be achieved by analyzing cell culture metabolic levels connected to vital process parameters like viable cell density (VCD). However, VCD and metabolic profiles are dynamic parameters and inherently correlated with time, leading to a significant correlation without actual causality. Many time-series methods deal with such issues. However, with metabolic profiling, the number of measured variables vastly exceeds the number of experiments, making most of existing methods ill-suited and hard to interpret. METHODS AND MAJOR RESULTS: Here we propose an alternative workflow using hierarchical dimension reduction to visualize and interpret the relation between evolution of metabolic profiles and dynamic process parameters. The first step of proposed method is focused on finding predictive relation between metabolic profiles and process parameter at all time points using OPLS regression. For each time point, the p(corr) obtained from OPLS model is considered as a differential metabogram and is further assessed using principal components analysis (PCA). CONCLUSIONS: Compared to traditional batch modeling, applying proposed methodology on metabolic data from Chinese Hamster Ovary (CHO) antibody production characterized the dynamic relation between metabolic profiles and critical process parameters.

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction.

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network "EL-RMLocNet", predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Early intervention with Bifidobacterium lactis NCC2818 modulates the host-microbe interface independent of the sustained changes induced by the neonatal environment.

Inflammatory and metabolic diseases can originate during early-life and have been correlated with shifts in intestinal microbial ecology. Here we demonstrate that minor environmental fluctuations during the early neonatal period had sustained effects on the developing porcine microbiota and host-microbe interface. These inter-replicate effects appear to originate during the first day of life, and are likely to reflect very early microbiota acquisition from the environment. We statistically link early systemic inflammation with later local increases in inflammatory cytokine (IL-17) production, which could have important enteric health implications. Immunity, intestinal barrier function, host metabolism and host-microbiota co-metabolism were further modified by Bifidobacterium lactis NCC2818 supplementation, although composition of the in situ microbiota remained unchanged. Finally, our robust model identified novel, strong correlations between urinary metabolites (eg malonate, phenylacetylglycine, alanine) and mucosal immunoglobulin (IgM) and cytokine (IL-10, IL-4) production, thus providing the possibility of the development of urinary 'dipstick' tests to assess non-accessible mucosal immune development and identify early precursors (biomarkers) of disease. These results have important implications for infants exposed to neonatal factors including caesarean delivery, antibiotic therapy and delayed discharge from hospital environments, which may predispose to the development of inflammatory and metabolic diseases in later life.

Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping.

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Neonatal environment exerts a sustained influence on the development of the intestinal microbiota and metabolic phenotype.

The postnatal environment, including factors such as weaning and acquisition of the gut microbiota, has been causally linked to the development of later immunological diseases such as allergy and autoimmunity, and has also been associated with a predisposition to metabolic disorders. We show that the very early-life environment influences the development of both the gut microbiota and host metabolic phenotype in a porcine model of human infants. Farm piglets were nursed by their mothers for 1 day, before removal to highly controlled, individual isolators where they received formula milk until weaning at 21 days. The experiment was repeated, to create two batches, which differed only in minor environmental fluctuations during the first day. At day 1 after birth, metabolic profiling of serum by (1)H nuclear magnetic resonance spectroscopy demonstrated significant, systemic, inter-batch variation which persisted until weaning. However, the urinary metabolic profiles demonstrated that significant inter-batch effects on 3-hydroxyisovalerate, trimethylamine-N-oxide and mannitol persisted beyond weaning to at least 35 days. Batch effects were linked to significant differences in the composition of colonic microbiota at 35 days, determined by 16 S pyrosequencing. Different weaning diets modulated both the microbiota and metabolic phenotype independently of the persistent batch effects. We demonstrate that the environment during the first day of life influences development of the microbiota and metabolic phenotype and thus should be taken into account when interrogating experimental outcomes. In addition, we suggest that intervention at this early time could provide 'metabolic rescue' for at-risk infants who have undergone aberrant patterns of initial intestinal colonisation.

Weaning diet induces sustained metabolic phenotype shift in the pig and influences host response to Bifidobacterium lactis NCC2818.

BACKGROUND: The process of weaning causes a major shift in intestinal microbiota and is a critical period for developing appropriate immune responses in young mammals. OBJECTIVE: To use a new systems approach to provide an overview of host metabolism and the developing immune system in response to nutritional intervention around the weaning period. DESIGN: Piglets (n=14) were weaned onto either an egg-based or soya-based diet at 3 weeks until 7 weeks, when all piglets were switched onto a fish-based diet. Half the animals on each weaning diet received Bifidobacterium lactis NCC2818 supplementation from weaning onwards. Immunoglobulin production from immunologically relevant intestinal sites was quantified and the urinary (1)H NMR metabolic profile was obtained from each animal at post mortem (11 weeks). RESULTS: Different weaning diets induced divergent and sustained shifts in the metabolic phenotype, which resulted in the alteration of urinary gut microbial co-metabolites, even after 4 weeks of dietary standardisation. B lactis NCC2818 supplementation affected the systemic metabolism of the different weaning diet groups over and above the effects of diet. Additionally, production of gut mucosa-associated IgA and IgM was found to depend upon the weaning diet and on B lactis NCC2818 supplementation. CONCLUSION: The correlation of urinary (1)H NMR metabolic profile with mucosal immunoglobulin production was demonstrated, thus confirming the value of this multi-platform approach in uncovering non-invasive biomarkers of immunity. This has clear potential for translation into human healthcare with the development of urine testing as a means of assessing mucosal immune status. This might lead to early diagnosis of intestinal dysbiosis and with subsequent intervention, arrest disease development. This system enhances our overall understanding of pathologies under supra-organismal control.

1H HR-MAS NMR spectroscopy of tumor-induced local metabolic "field-effects" enables colorectal cancer staging and prognostication.

Colorectal cancer (CRC) is a major cause of morbidity and mortality in developed countries. Despite operative advances and the widespread adoption of combined-modality treatment, the 5-year survival rarely exceeds 60%. Improving our understanding of the biological processes involved in CRC development and progression will help generate new diagnostic and prognostic approaches. Previous studies have identified altered metabolism as a common feature in carcinogenesis, and quantitative measurement of this altered activity (metabonomics/metabolomics) has the potential to generate novel metabolite-based biomarkers for CRC diagnosis, staging and prognostication. In the present study we applied high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy to analyze metabolites in intact tumor samples (n = 83) and samples of adjacent mucosa (n = 87) obtained from 26 patients undergoing surgical resection for CRC. Orthogonal partial least-squares discriminant analysis (OPLS-DA) of metabolic profiles identified marked biochemical differences between cancer tissue and adjacent mucosa (R(2) = 0.72; Q(2) = 0.45; AUC = 0.91). Taurine, isoglutamine, choline, lactate, phenylalanine, tyrosine (increased concentrations in tumor tissue) together with lipids and triglycerides (decreased concentrations in tumor tissue) were the most discriminant metabolites between the two groups in the model. In addition, tumor tissue metabolic profiles were able to distinguish between tumors of different T- and N-stages according to TNM classification. Moreover, we found that tumor-adjacent mucosa (10 cm from the tumor margin) harbors unique metabolic field changes that distinguish tumors according to T- and N-stage with higher predictive capability than tumor tissue itself and are accurately predictive of 5-year survival (AUC = 0.88), offering a highly novel means of tumor classification and prognostication in CRC.

Robust data processing and normalization strategy for MALDI mass spectrometric imaging.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides localized information about the molecular content of a tissue sample. To derive reliable conclusions from MSI data, it is necessary to implement appropriate processing steps in order to compare peak intensities across the different pixels comprising the image. Here, we review commonly used normalization methods, and propose a rational data processing strategy, for robust evaluation and modeling of MSI data. The approach includes newly developed heuristic methods for selecting biologically relevant peaks and pixels to reduce the size of a data set and remove the influence of the applied MALDI matrix. The methods are demonstrated on a MALDI MSI data set of a sagittal section of rat brain (4750 bins, m/z = 50-1000, 111 × 185 pixels) and the proposed preferred normalization method uses the median intensity of selected peaks, which were determined to be independent of the MALDI matrix. This was found to effectively compensate for a range of known limitations associated with the MALDI process and irregularities in MS image sampling routines. This new approach is relevant for processing of all MALDI MSI data sets, and thus likely to have impact in biomarker profiling, preclinical drug distribution studies, and studies addressing underlying molecular mechanisms of tissue pathology.

Colonization-induced host-gut microbial metabolic interaction.

UNLABELLED: The gut microbiota enhances the host's metabolic capacity for processing nutrients and drugs and modulate the activities of multiple pathways in a variety of organ systems. We have probed the systemic metabolic adaptation to gut colonization for 20 days following exposure of axenic mice (n = 35) to a typical environmental microbial background using high-resolution (1)H nuclear magnetic resonance (NMR) spectroscopy to analyze urine, plasma, liver, kidney, and colon (5 time points) metabolic profiles. Acquisition of the gut microbiota was associated with rapid increase in body weight (4%) over the first 5 days of colonization with parallel changes in multiple pathways in all compartments analyzed. The colonization process stimulated glycogenesis in the liver prior to triggering increases in hepatic triglyceride synthesis. These changes were associated with modifications of hepatic Cyp8b1 expression and the subsequent alteration of bile acid metabolites, including taurocholate and tauromuricholate, which are essential regulators of lipid absorption. Expression and activity of major drug-metabolizing enzymes (Cyp3a11 and Cyp2c29) were also significantly stimulated. Remarkably, statistical modeling of the interactions between hepatic metabolic profiles and microbial composition analyzed by 16S rRNA gene pyrosequencing revealed strong associations of the Coriobacteriaceae family with both the hepatic triglyceride, glucose, and glycogen levels and the metabolism of xenobiotics. These data demonstrate the importance of microbial activity in metabolic phenotype development, indicating that microbiota manipulation is a useful tool for beneficially modulating xenobiotic metabolism and pharmacokinetics in personalized health care. IMPORTANCE: Gut bacteria have been associated with various essential biological functions in humans such as energy harvest and regulation of blood pressure. Furthermore, gut microbial colonization occurs after birth in parallel with other critical processes such as immune and cognitive development. Thus, it is essential to understand the bidirectional interaction between the host metabolism and its symbionts. Here, we describe the first evidence of an in vivo association between a family of bacteria and hepatic lipid metabolism. These results provide new insights into the fundamental mechanisms that regulate host-gut microbiota interactions and are thus of wide interest to microbiological, nutrition, metabolic, systems biology, and pharmaceutical research communities. This work will also contribute to developing novel strategies in the alteration of host-gut microbiota relationships which can in turn beneficially modulate the host metabolism.

Metabonomic studies on human hepatocyte in primary culture.

Mechanisms involved in induction processes have been investigated using fresh human hepatocytes in culture as a cellular model and using mass spectrometry-based metabonomics as a global investigation tool. Sample preparation to data analysis have been detailed in an approach enabling to separate drug-induced (endogenous metabolites) and drug-related (drug metabolites) biomarkers for reference inducers. Rifampicin, a nuclear pregnane X receptor (PXR) ligand; CITCO, a nuclear constitutive androstane receptor (CAR) ligand; and phenobarbital, which activates both CAR and PXR, have been used. Specific intra-cellular metabolites have been isolated for rifampicin and CITCO as potential endogenous biomarkers of their respective induction mechanism. A mixture of these two types of biomarkers modified in the same way after treatment with either rifampicin or CITCO on the one hand and with phenobarbital on the other hand has been found.

Integrated comparison of drug-related and drug-induced ultra performance liquid chromatography/mass spectrometry metabonomic profiles using human hepatocyte cultures.

The biochemical variations induced in human primary hepatocyte cultures by reference activators of xenoreceptor CAR (NR1I3) and PXR (NR1I2), i.e., rifampicin, phenobarbital, and 6-(4-chlorophenyl)imidazo[2,1-b] [1,3]thiazole-5-carbaldehyde O-3,4-dichlorobenzyl) oxime (CITCO), were investigated using a global metabonomics approach. Cultured human hepatocytes were treated with the three drugs before analysis of intracellular and extracellular media by ultra performance liquid chromatography/time-of-flight-mass spectrometry (UPLC/TOF-MS) technique, in order to list endogenous compounds potentially related to a PXR or CAR induction mechanism and to identify drug metabolites related to each treatment. The emphasis was put on the quality of the analytical data (dilution/filtration strategy before data processing) and on the appropriate pattern recognition techniques. In cellular media, the most significant variations seen in the data are not related to the treatments but to the source of hepatocytes, illustrating the importance of the genetic and/or environmental background in human liver experiments. However when applying classical multivariate statistical approaches (principal component analysis (PCA) and orthogonal partial least squares (O-PLS)), the statistical weight due to drug metabolites, present only in the treated groups, hinders the interpretation because of their predominance compared to most of the changes seen in endogenous metabolites. A new statistical approach, called shared and unique structure (SUS) plot, enabling the comparison of different treatments having the same control has been applied, allowing separation of clearly exogenous variables (drug metabolites) from endogenous biomarkers. Endogenous variables (either up- or down-regulated) have been attributed specifically to the impact of rifampicin (PXR ligand), CITCO (CAR ligand), and phenobarbital (CAR and PXR activator) on the biological regulation pathways of the hepatocytes. This global approach coupled to a statistical pretreatment of the data, enabling the separate capture of both drug related and drug induced biomarkers, represents a powerful technique for future mechanistic studies using cellular tools.

Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies.

The application of a (1)H nuclear magnetic resonance (NMR) spectroscopy-based screening method for determining the use of two widely available analgesics (acetaminophen and ibuprofen) in epidemiologic studies has been investigated. We used samples and data from the cross-sectional INTERMAP Study involving participants from Japan (n = 1145), China (n = 839), U.K. (n = 501), and the U.S. (n = 2195). An orthogonal projection to latent structures discriminant analysis (OPLS-DA) algorithm with an incorporated Monte Carlo resampling function was applied to the NMR data set to determine which spectra contained analgesic metabolites. OPLS-DA preprocessing parameters (normalization, bin width, scaling, and input parameters) were assessed systematically to identify an optimal acetaminophen prediction model. Subsets of INTERMAP spectra were examined to verify and validate the presence/absence of acetaminophen/ibuprofen based on known chemical shift and coupling patterns. The optimized and validated acetaminophen model correctly predicted 98.2%, and the ibuprofen model correctly predicted 99.0% of the urine specimens containing these drug metabolites. The acetaminophen and ibuprofen models were subsequently used to predict the presence/absence of these drug metabolites for the remaining INTERMAP specimens. The acetaminophen model identified 415 out of 8436 spectra as containing acetaminophen metabolite signals while the ibuprofen model identified 245 out of 8604 spectra as containing ibuprofen metabolite signals from the global data set after excluding samples used to construct the prediction models. The NMR-based metabolic screening strategy provides a new objective approach for evaluation of self-reported medication data and is extendable to other aspects of population xenometabolome profiling.

Xenobiotic-metabolizing enzymes and transporters in the normal human brain: regional and cellular mapping as a basis for putative roles in cerebral function.

Cytochrome P450 (P450) enzymes and ATP-binding cassette (ABC) transporters modulate the transport and metabolism of both endogenous and exogenous substrates and could play crucial roles in the human brain. In this study, we report the transcript expression profile of seven ABC transporters (ABCB1, ABCC1-C5, and ABCG2), 24 P450s (CYP1, CYP2, and CYP3 families and CYP46A1), and 14 related transcription factors [aryl hydrocarbon receptor, nuclear receptor (NR)1I2/pregnane X receptor, NR1I3/constitutive androstane receptor and NR1C/peroxisome proliferator-activated receptor, NR1H/liver X receptor, NR2B/retinoid X receptor, and NR3A/estrogen receptor subfamilies] in the whole brain, the dura mater, and 17 different encephalic areas. In addition, Western blotting and immunohistochemistry analysis were used to characterize the distribution of the P450s at the cellular and subcellular levels in some brain regions. Our results show the presence of a large variety of xenobiotic transporters and metabolizing enzymes in human brain and show for the first time their apparent selective distribution in different cerebral regions. The most abundant transporters were ABCC5 and ABCG2, which, interestingly, had a higher mRNA expression in the brain compared with that found in the liver. CYP46A1, CYP2J2, CYP2U1, CYP1B1, CYP2E1, and CYP2D6 represented more than 90% of the total P450 and showed selective distribution in different brain regions. Their presence in both microsomal and mitochondrial fractions was shown both in neuronal and glial cells in several brain areas. Thus, our study shows key enzymes of cholesterol and fatty acid metabolism to be present in the human brain and provides novel information of importance for elucidation of enzymes responsible for normal and pathological processes in the human brain.

Dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1H NMR kinetic statistical total correlation spectroscopy.

Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz (1)H NMR spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-acetylglucosamine, and uridine are associated with higher peptidase activity. The novel approach adopted here has the potential to capture dynamic information in any complex mixture of reacting chemicals including other biofluids or cell extracts.

First example of hepatocyte transplantation to alleviate ornithine transcarbamylase deficiency, monitored by NMR-based metabonomics.

We demonstrate the effective use of NMR spectroscopic profiles of urine and plasma from the first successful use of hepatocyte transplantation as a bridge to auxiliary partial orthotopic liver transplantation in a child antenatally diagnosed with severe ornithine transcarbamylase deficiency. In this single-patient study, NMR profiles indicated that the disrupted urea cycle could be normalized by hepatocyte cell infusion and this was confirmed using orthogonal partial least-squares-based chemometrics. However, despite dietary manipulations and adminstration of ammonia scavengers, the desired reduction in plasma ammonia was not consistently achieved between sessions of hepatocyte transplantation due to episodes of sepsis. A subsequent liver transplant corrected the metabolic abnormalities. The use of metabolic profiling has been shown to be a promising method for evaluating the efficacy of cell infusions and has demonstrated the capability for the early detection of response to therapy in real time, an approach that may be of use in wider clinical settings.

Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes.

To characterize the impact of gut microbiota on host metabolism, we investigated the multicompartmental metabolic profiles of a conventional mouse strain (C3H/HeJ) (n=5) and its germ-free (GF) equivalent (n=5). We confirm that the microbiome strongly impacts on the metabolism of bile acids through the enterohepatic cycle and gut metabolism (higher levels of phosphocholine and glycine in GF liver and marked higher levels of bile acids in three gut compartments). Furthermore we demonstrate that (1) well-defined metabolic differences exist in all examined compartments between the metabotypes of GF and conventional mice: bacterial co-metabolic products such as hippurate (urine) and 5-aminovalerate (colon epithelium) were found at reduced concentrations, whereas raffinose was only detected in GF colonic profiles. (2) The microbiome also influences kidney homeostasis with elevated levels of key cell volume regulators (betaine, choline, myo-inositol and so on) observed in GF kidneys. (3) Gut microbiota modulate metabotype expression at both local (gut) and global (biofluids, kidney, liver) system levels and hence influence the responses to a variety of dietary modulation and drug exposures relevant to personalized health-care investigations.

Piecewise multivariate modelling of sequential metabolic profiling data.

BACKGROUND: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. RESULTS: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. CONCLUSION: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Phylometabonomic patterns of adaptation to high fat diet feeding in inbred mice.

Insulin resistance plays a central role in type 2 diabetes and obesity, which develop as a consequence of genetic and environmental factors. Dietary changes including high fat diet (HFD) feeding promotes insulin resistance in rodent models which present useful systems for studying interactions between genetic background and environmental influences contributing to disease susceptibility and progression. We applied a combination of classical physiological, biochemical and hormonal studies and plasma (1)H NMR spectroscopy-based metabonomics to characterize the phenotypic and metabotypic consequences of HFD (40%) feeding in inbred mouse strains (C57BL/6, 129S6, BALB/c, DBA/2, C3H) frequently used in genetic studies. We showed the wide range of phenotypic and metabonomic adaptations to HFD across the five strains and the increased nutrigenomic predisposition of 129S6 and C57BL/6 to insulin resistance and obesity relative to the other strains. In contrast mice of the BALB/c and DBA/2 strains showed relative resistance to HFD-induced glucose intolerance and obesity. Hierarchical metabonomic clustering derived from (1)H NMR spectral data of the strains provided a phylometabonomic classification of strain-specific metabolic features and differential responses to HFD which closely match SNP-based phylogenetic relationships between strains. Our results support the concept of genomic clustering of functionally related genes and provide important information for defining biological markers predicting spontaneous susceptibility to insulin resistance and pathological adaptations to fat feeding.

Magic angle spinning NMR and 1H-31P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies.

Previously we have demonstrated the use of 1H magic angle spinning (MAS) NMR spectroscopy for the topographical variations in functional metabolic signatures of intact human intestinal biopsy samples. Here we have analyzed a series of MAS 1H NMR spectra (spin-echo, one-dimensional, and diffusion-edited) and 31P-{1H} spectra and focused on analyzing the enhancement of information recovery by use of the statistical total correlation spectroscopy (STOCSY) method. We have applied a heterospectroscopic cross-examination performed on the same samples and between 1H and 31P-{1H} spectra (heteronuclear STOCSY) to recover latent metabolic information. We show that heterospectroscopic correlation can give new information on the molecular compartmentation of metabolites in intact tissues, including the statistical "isolation" of a phospholipid/triglyceride vesicle pool in intact tissue. The application of 31P-1H HET-STOCSY allowed the cross-assignment of major 31P signals to their equivalent 1H NMR spectra, e.g., for phosphorylcholine and phosphorylethanolamine. We also show pathway correlations, e.g., the ascorbate-glutathione pathway, in the STOCSY analysis of intact tissue spectra. These 31P-1H HET-STOCSY spectra also showed different topographical regions, particular for minor signals in different tissue microenvironments. This approach could be extended to allow the detection of altered distributions within metabolic subcompartments as well as conventional metabonomics concentration-based diagnostics.

Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis.

The investigation presented here describes a protocol designed to perform high-throughput metabolic profiling analysis on human blood plasma by ultra-performance liquid chromatography/mass spectrometry (UPLC/MS). To address whether a previous extraction protocol for gas chromatography (GC)/MS-based metabolic profiling of plasma could be used for UPLC/MS-based analysis, the original protocol was compared with similar methods for extraction of low-molecular-weight compounds from plasma via protein precipitation. Differences between extraction methods could be observed, but the previously published extraction method was considered the best. UPLC columns with three different stationary phases (C8, C18, and phenyl) were used in identical experimental runs consisting of a total of 60 injections of extracted male and female plasma samples. The C8 column was determined to be the best for metabolic profiling analysis on plasma. The acquired UPLC/MS data of extracted male and female plasma samples was subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Furthermore, a strategy for compound identification was applied here, demonstrating the strength of high-mass-accuracy time-of-flight (TOF)/MS analysis in metabolic profiling.