Pair-wise multicomparison and OPLS analyses of cold-acclimation phases in Siberian spruce

Analysis of metabolomics data often goes beyond the task of discovering biomarkers and can be aimed at recovering other important characteristics of observed metabolomic changes. In this paper we explore different methods to detect the presence of distinctive phases in seasonal-responsive changes of metabolomic patterns of Siberian spruce (Picea obovata) during cold acclimation occurred in the period from mid-August to January. Multivariate analysis, specifically orthogonal projection to latent structures discriminant analysis (OPLS-DA), identified time points where the metabolomic patterns underwent substantial modifications as a whole, revealing four distinctive phases during acclimation. This conclusion was re-examined by a univariate analysis consisting of multiple pair-wise comparisons to identify homogeneity intervals for each metabolite. These tests complemented OPLS-DA, clarifying biological interpretation of the classification: about 60% of metabolites found responsive to the cold stress indeed changed at one or more of the time points predicted by OPLS-DA. However, the univariate approach did not support the proposed division of the acclimation period into four phases: less than 10% of metabolites altered during the acclimation had homogeneous levels predicted by OPLS-DA. This demonstrates that coupling the classification found by OPLS-DA and the analysis of dynamics of individual metabolites obtained by pair-wise multicomparisons reveals a more correct characterization of biochemical processes in freezing tolerant trees and leads to interpretations that cannot be deduced by either method alone. The combined analysis can be used in other ‘omics’-studies, where response factors have a causal dependence (like the time in the present work) and pair-wise multicomparisons are not conservative.     

Lipidomic changes during different growth stages of Nitzschia closterium f. minutissima

Ultra Performance Liquid Chromatography-Electrospray ionization-Quadrupole-Time of Flight Mass Spectrometry (UPLC-ESI-Q-TOF–MS) is a powerful lipidomic tool. In this study, we developed a UPLC/Q-TOF–MS based method to investigate the lipid metabolomic changes in different growth phases of Nitzschia closterium f. minutissima. The data classification and biomarker selection were carried out by using multivariate statistical analysis, including principal components analysis (PCA), projection to latent structures with discriminant analysis (PLS-DA), and orthogonal projection to latent structures with discriminant analysis (OPLS-DA). We discovered that the intercellular lipid metabolites were significantly different among exponential, early stationary and late stationary phases. Thirty-one lipid molecules were selected and identified as putative biomarkers, including free fatty acid, Harderoporphyrin, phosphatidylglycerol, 1,2-diacyglycerl-3-O-4′-(N,N-trimethy)-homoserine, triacylglycerol, cholesterol, sulfoquinovosyldiacylglycerol, lyso-sulfoquinovosyldiacylglycerol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol and lyso-digalactosyldiacylglycerol. These lipids have been shown previously to function in energy storage, membrane stability and photosynthesis efficiency during the growth of diatoms. Further analysis on the putative biomarkers demonstrated that nitrate starvation played critical role in the transition from exponential phase to stationary phase in N. closterium. This study is the first one to explore the lipidomic changes of microalgae in different growth phases, which promotes better understanding of their physiology and ecology.     

Comparative metabolomics of Japanese Black cattle beef and other meats using gas chromatography–mass spectrometry

Progress in metabolomic analysis now allows the evaluation of food quality. This study aims to identify the metabolites in meat from livestock using a metabolomic approach. Using gas chromatography–mass spectrometry (GC/MS), many metabolites were reproducibly detected in meats, and distinct differences between livestock species (cattle, pigs, and chickens) were indicated. A comparison of metabolites between tissues types (muscle, intramuscular fat, and intermuscular fat) in marbled beef of Japanese Black cattle revealed that most metabolites are abundant in the muscle tissue. Several metabolites (medium-chain fatty acids, etc.) involved in triacylglycerol synthesis were uniquely detected in fat tissue. Additionally, the results of multivariate analysis suggest that GC/MS analysis of metabolites can distinguish between cattle breeds. These results provide useful information for the analysis of meat quality using GC/MS-based metabolomic analysis.

ABBREVIATIONS: GC/MS: gas chromatography-mass spectrometry; NMR: nuclear magnetic resonance; MS: mass spectrometry; IS: 2-isopropylmalic acid; MSTFA: N-Methyl-N-trimethylsilyltrifluoroacetamide; CV: coefficient of variation; TBS: Tris-buffered saline; MHC: myosin fast type; PCA: principal component analysis; OPLS-DA: orthogonal partial least-squares discriminant analysis; O2PLS: two-way orthogonal partial least-squares     

Lipidomics focusing on serum polar lipids reveals species dependent stress resistance of fish under tropical storm

The serum polar lipid metabolic changes for two common cage-cultured fishes, yellow coraker Pseudosciaena crocea and Japanese seabass Lateolabrax japonicus, after tropical storm attack have been studied by ultra-performance liquid chromatography—quadrupole-time of flight mass spectrometry (UPLC-qTOF-MS). The full scan mass spectrometry combined with principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) indicated that yellow croaker underwent significant chemico-physiological changes during the recovery process, whereas Japanese seabass did not show such noticeable time-dependent consistent metabolites change patterns. Further identification of the metabolite biomarkers showed the increase of phosphatidylcholine with high unsaturated fatty acid and lysophospholipids, and the decrease of phosphatidylcholine with saturated fatty acids and plasmologens, which indicated the need of energy supplement and successive stressful inflammation. The increase of taurocholic acid and decrease of cortol could be regarded as the physiological alleviation measure during the recovery period. This is the first metabolomic study to tackle the fish physiological response for the complex environmental changes, and demonstrated that lipidomics is an effective analytical tool for predicting the stress resistance of fish to ultra uncontrolled environmental stress.     

Change in membrane fatty acid compositions and cold-induced responses in chickpea

Plant cells often increase cold tolerance by reprogramming their genes expression which results in adjusted metabolic alternations, a process enhanced under cold acclimation (CA) phase. In present study, we assessed the changes of membrane fatty acid compositions and defense machine (like antioxidative enzymes) along with damage indexes like electrolyte leakage index (ELI) and malondialdehyde (MDA) during CA, cold stress (CS) and recovery (R) phases in chickpea (Cicer arietinum L.). Results showed an increase in unsaturated fatty acids ratio compare to saturated ones which is a sign of cold tolerance especially after CA phase. Antioxidant enzymes had an important role during CA and R phases while CS affected their activity which can be a sign for associating other metabolites or enzymes activities to create cold tolerance in plants. To investigation of enzymes assay under experimental treatments, the expression pattern of some enzymes including superoxide dismutase (sod), catalase (cat) and lipoxygenase (lox) was studied using quantitative real time PCR. LOX activity has shown a bilateral behavior: a positive relation with membrane damage index in CA and an interesting link with double bond index (DBI) in CS indicating probably its role in secondary metabolites like jasmonic acid signaling pathway. It was suggested that increased DBI and low LOX activity under CS could be a reason for plant cold tolerance.     

Habitat temperature and the temporal scaling of cold hardening in the high Arctic collembolan, Hypogastrura tullbergi (Schäffer)

Abstract.  1. Cold tolerance is a fundamental adaptation of insects to high latitudes. Flexibility in the cold hardening process, in turn, provides a useful indicator of the extent to which polar insects can respond to spatial and temporal variability in habitat temperature.
2. A scaling approach was adopted to investigate flexibility in the cold tolerance of the high Arctic collembolan, Hypogastrura tullbergi , over different time-scales. The cold hardiness of animals was compared from diurnal warming and cooling phases in the field, and controlled acclimation and cooling treatments in the laboratory. Plasticity in acclimation responses was examined using three parameters: low temperature survival, cold shock survival, and supercooling points (SCPs).
3. Over time-scales of 24–48 h, both field animals from warm diurnal phases and laboratory cultures from a 'warm' acclimation regime (18 °C) consistently showed greater or equivalent cold hardiness to animals from cool diurnal phases and acclimation regimes (3 °C).
4. No significant evidence was found of low temperature acclimation after either hours or days of low temperature exposure. The cold hardiness of H. tullbergi remained 'seasonal' in character and mortality throughout was indicative of the summer state of acclimatization.
5. These data suggest that H. tullbergi employs an 'all or nothing' cryoprotective strategy, cold hardening at seasonal but not diel-temporal scales.
6. It is hypothesised that rapid cold hardening offers little advantage to these high Arctic arthropods because sub-zero habitat temperatures during the summer on West Spitsbergen are rare and behavioural migration into soil profiles offers sufficient buffering against low summer temperatures.     

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples.

     

Warm temperature accelerates short photoperiod-induced growth cessation and dormancy induction in hybrid poplar (Populus × spp.)

There is increasing evidence that temperature, in addition to photoperiod, may be an important factor regulating bud dormancy. The impact of temperature during growth cessation, dormancy development, and subsequent cold acclimation was examined in four hybrid poplar clones with contrasting acclimation patterns: ‘Okanese’—EARLY, ‘Walker’—INT1, ‘Katepwa’—INT2, and ‘Prairie Sky’—LATE. Four day–night temperature treatments (13.5/8.5, 18.5/13.5, 23.5/8.5, and 18.5/3.5°C) were applied during a 60-day induction period to reflect current and predicted future annual variation in autumn temperature for Saskatoon, SK. Warm night temperature (18.5/13.5°C) strongly accelerated growth cessation, dormancy development, and cold acclimation in all four clones. Day temperature had the opposite effect of night temperature. Day and night temperatures appeared to act antagonistically against each other during growth cessation and subsequent dormancy development and cold acclimation. Growth cessation, dormancy development, and cold acclimation in EARLY and LATE were less affected by induction temperature than INT1 and INT2 suggesting that genotypic variations exist in response to temperature. Separating specific phenological stages and the impact by temperature on each clone revealed the complexity of fall phenological changes and their interaction with temperature. Most importantly, future changes in temperature may affect time to growth cessation, subsequently altering the depth of dormancy and cold hardiness in hybrid poplar.     

Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model

Joint analysis of microbiome and metabolomic data represents an imperative objective as the field moves beyond basic microbiome association studies and turns towards mechanistic and translational investigations. We present a censored Gaussian graphical model framework, where the metabolomic data are treated as continuous and the microbiome data as censored at zero, to identify direct interactions (defined as conditional dependence relationships) between microbial species and metabolites. Simulated examples show that our method metaMint performs favorably compared to the existing ones. metaMint also provides interpretable microbe-metabolite interactions when applied to a bacterial vaginosis data set. R implementation of metaMint is available on GitHub.

     

Urinary metabolomics as a potentially novel diagnostic and stratification tool for knee osteoarthritis

Metabolomics has been used as a tool in disease diagnosis and phenotype prediction. A urinary metabolomic study based on GC–MS in combination with multivariate statistics was used here to classify between knee osteoarthritis (OA) and healthy controls. OPLS-DA of the spectral data showed distinct metabolic profile variations between OA patients and healthy controls and between two OA phenotypes. Differential metabolites reveal up-regulated TCA cycle associated with OA and histamine metabolism disorders accompanied with knee effusion symptoms. This metabolomic method is potentially applicable as a novel strategy for OA diagnosis and patient stratification.     

Physiological and environmental parameters associated with mass spectrometry-based salivary metabolomic profiles

Mass spectrometry (MS)-based metabolomic methods enable simultaneous profiling of hundreds of salivary metabolites, and may be useful to diagnose a wide range of diseases using saliva. However, few studies have evaluated the effects of physiological or environmental factors on salivary metabolomic profiles. Therefore, we used capillary electrophoresis-MS to analyze saliva metabolite profiles in 155 subjects with reasonable oral hygiene, and examined the effects of physiological and environmental factors on the metabolite profiles. Overall, 257 metabolites were identified and quantified. The global profiles and individual metabolites were evaluated by principle component analysis and univariate tests, respectively. Collection method, collection time, sex, body mass index, and smoking affected the global metabolite profiles. However, age also might contribute to the bias in sex and collection time. The profiles were relatively unaffected by other parameters, such as alcohol consumption and smoking, tooth brushing, or the use of medications or nutritional supplements. Temporomandibular joint disorders had relatively greater effects on salivary metabolites than other dental abnormalities (e.g., stomatitis, tooth alignment, and dental caries). These findings provide further insight into the diversity and stability of salivary metabolomic profiles, as well as the generalizability of disease-specific biomarkers.     

Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

Potential metabolites from the metabolic pathways could be therapeutic targets and useful for the discovery of broad spectrum drugs. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA and Heatmap were integrated to examine the global metabolic signature of insomnia and intervention effects of Jujuboside A (JuA). Six unique pathways of the insomnia were identified using Ingenuity Pathway Analysis (IPA) software. The VIP-value threshold cutoff of the metabolites was set to 10, above this threshold, were filtered out as potential target biomarkers. Sixteen distinct metabolites were identified from these pathways, and 6 of them can be considered for rational drug design. It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis. Potential metabolite network of the insomnia was preliminarily predicted JuA-target interaction networks, and could be further explored for in silico docking studies with suitable drugs. Thus, our method is an efficient procedure for drug target identification through metabolic analysis. It can guide testable predictions, provide insights into drug action mechanisms and enable us to increase research productivity toward metabolomic drug discovery.