PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data

Selection for early reproduction leads to accelerated aging and extensive metabolic remodeling in Drosophila melanogaster populations

Experimental evolution studies that feature selection on life-history characters are a proven approach for studying the evolution of aging and variation in rates of senescence. Recently, the incorporation of genomic and transcriptomic approaches into this framework has led to the identification of hundreds of genes associated with different aging patterns. However, our understanding of the specific molecular mechanisms underlying these aging patterns remains limited. Here, we incorporated extensive metabolomic profiling into this framework to generate mechanistic insights into aging patterns in Drosophila melanogaster . Specifically, we characterized metabolomic change over time associated with accelerated aging in populations of D. melanogaster under selection for early reproduction compared to their controls. Using this data we: i) evaluated the evolutionary repeatability across the metabolome; ii) evaluated the value of the metabolome as a predictor of "biological age" in this system; and iii) identified specific metabolic pathways associated with accelerated aging. Generally, our findings suggest that the metabolome is a reliable predictor of age and senescence in populations that share a recent evolutionary history. Metabolomic analysis revealed that generations of selection for early reproduction resulted in highly repeatable alterations to the metabolome. Specifically, changes in carbohydrate, amino acid, and TCA cycle-related metabolite abundances over time point to metabolic remodeling that favors rapid early reproduction with long-term consequences for carbohydrate and protein utilization.

Metabolic Profiling in Plasma and Brain Induced by 17β-Estradiol Supplementation in Ovariectomized Mice

17β-Estradiol is an ovarian hormone that regulates energy circulation and storage by acting on the central nervous system. However, the metabolic differences between the blood and brain when stimulated by 17β-estradiol are poorly understood. Moreover, research using menopause-induced models to investigate primary metabolites in the blood and brain is limited. Thus, this study aimed to identify metabolic changes in the plasma and brain resulting from 17β-estradiol supplementation in an estrogen-deficient mouse model. Three groups of mice were utilized: sham-operated mice (Sham), ovariectomized mice (OVX), and ovariectomized mice that received a weekly supplementation of 17β-estradiol (E2). Plasma and brain samples from these mice were subjected to metabolic analysis using gas chromatography-time-of-flight-mass spectrometry. Compared with the plasma samples from the Sham and OVX groups, the plasma samples from the E2 group contained higher contents of branched-chain amino acids (BCAAs), such as valine, isoleucine, and leucine. Meanwhile, the brain samples from the E2 group contained higher contents of most metabolites, including BCAAs, neurotransmitters, tricarboxylic acid cycle intermediates, and fatty acids, than those from the two other groups. This study is the first to reveal differences in energy metabolism induced by 17β-estradiol supplementation through brain metabolic profiling of ovariectomized mice, emphasizing the importance of brain metabolic profiling in menopausal hormone research.

Effects of polystyrene microplastics on the metabolic level of Pseudomonas aeruginosa

Given the widespread presence of Pseudomonas aeruginosa in water and its threat to human health, the metabolic changes in Pseudomonas aeruginosa when exposed to polystyrene microplastics (PS-MPs) exposure were studied, focusing on molecular level. Through non-targeted metabolomics, a total of 64 differential metabolites were screened out under positive ion mode and 44 under negative ion mode. The content of bacterial metabolites changed significantly, primarily involving lipids, nucleotides, amino acids, and organic acids. Heightened intracellular oxidative damage led to a decrease in lipid molecules and nucleotide-related metabolites. The down-regulation of amino acid metabolites, such as L-Glutamic and L-Proline, highlighted disruptions in cellular energy metabolism and the impaired ability to synthesize proteins as a defense against oxidation. The impact of PS-MPs on organic acid metabolism was evident in the inhibition of pyruvate and citrate, thereby disrupting the cells' normal participation in energy cycles. The integration of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that PS-MPs mainly caused changes in metabolic pathways, including ABC transporters, Aminoacyl-tRNA biosynthesis, Purine metabolism, Glycerophospholipid metabolism and TCA cycle in Pseudomonas aeruginosa. Most of the differential metabolites enriched in these pathways were down-regulated, demonstrating that PS-MPs hindered the expression of metabolic pathways, ultimately impairing the ability of cells to synthesize proteins, DNA, and RNA. This disruption affected cell proliferation and information transduction, thus hampering energy circulation and inhibiting cell growth. Findings of this study supplemented the toxic effects of microplastics and the defense mechanisms of microorganisms, in turn safeguarding drinking water safety and human health.

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography-mass spectrometry-based untargeted metabolomics

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography-mass spectrometry from 30 Heterotrigona itama (H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares-discriminant analysis model from the discriminant analysis were validated using cross-validation (R2 : 0.93; Q2 : 0.83; accuracy: 0.97) and the permutation test (p < 0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP > 1.0) and the Kruskal-Wallis test (p < 0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

Metabolomics of Personalized Body Elements in Thai Traditional Medicine Response to Herbal Medicine for Body Elements Balancing in Healthy Volunteers

Background

In Thai traditional medicine (TTM), the dominant body element called "Dhat Chao Ruean" (DCR) is an integral part in the diagnostic process of Thai traditional medicine. TTM practitioners usually use Thai herbal Benjakul formula (BKF) for adjusting and balancing the body elements. However, the effects of BKF on metabolism and individual response to it have not been studied yet.

Methods

This study proposed to investigate the metabolic profiling in 24 volunteers categorized by their types of birth month DCR (bDCR) after the administration of BKF (450 mg, three tablets three times a day before meals) for seven days. Differences in metabolic profiling between bDCR groups were investigated by using liquid chromatography coupled with mass spectrometry for untargeted analysis, and in addition, the safety was assessed by testing the plasma biochemical level.

Results

This study identified 57 biomarkers in positive ESI and 12 in negative ESI. Piperine was found in varying amount among the participants but it was the highest in the earth group. In addition, this study found that elemicin, phenylpropionic acid, ricinoleic acid, and β-sitosterol are important substances in a single herb of BKF. Regarding biochemical tests, the results indicated that BKF can decrease the lipid profile and it has no toxic effects on liver and kidney functions.

Conclusion

The findings indicated that it is safe to use BKF which can help to improve health in chronic diseases by adjusting abnormality of the elements of the body. In addition, the information gathered from this study is valuable for further study in the field of Thai traditional medicine.

Untargeted metabonomic analysis of a cerebral stroke model in rats: a study based on UPLC-MS/MS

Introduction

Brain tissue damage caused by ischemic stroke can trigger changes in the body's metabolic response, and understanding the changes in the metabolic response of the gut after stroke can contribute to research on poststroke brain function recovery. Despite the increase in international research on poststroke metabolic mechanisms and the availability of powerful research tools in recent years, there is still an urgent need for poststroke metabolic studies. Metabolomic examination of feces from a cerebral ischemia-reperfusion rat model can provide new insights into poststroke metabolism and identify key metabolic pathways, which will help reveal diagnostic and therapeutic targets as well as inspire pathophysiological studies after stroke.

Methods

We randomly divided 16 healthy adult pathogen-free male Sprague-Dawley (SD) rats into the normal group and the study group, which received middle cerebral artery occlusion/reperfusion (MCAO/R). Ultra-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS) was used to determine the identities and concentrations of metabolites across all groups, and filtered high-quality data were analyzed for differential screening and differential metabolite functional analysis.

Results

After 1 and 14 days of modeling, compared to the normal group, rats in the study group showed significant neurological deficits (p < 0.001) and significantly increased infarct volume (day 1: p < 0.001; day 14: p = 0.001). Mass spectra identified 1,044 and 635 differential metabolites in rat feces in positive and negative ion modes, respectively, which differed significantly between the normal and study groups. The metabolites with increased levels identified in the study group were involved in tryptophan metabolism (p = 0.036678, p < 0.05), arachidonic acid metabolism (p = 0.15695), cysteine and methionine metabolism (p = 0.24705), and pyrimidine metabolism (p = 0.3413), whereas the metabolites with decreased levels were involved in arginine and proline metabolism (p = 0.15695) and starch and sucrose metabolism (p = 0.52256).

Discussion

We determined that UPLC-MS/MS could be employed for untargeted metabolomics research. Moreover, tryptophan metabolic pathways may have been disordered in the study group. Alterations in the tryptophan metabolome may provide additional theoretical and data support for elucidating stroke pathogenesis and selecting pathways for intervention.

Jujube Fruit Metabolomic Profiles Reveal Cultivar Differences and Function as Cultivar Fingerprints

Jujube is a nutritious fruit, and is high in vitamin C, fiber, phenolics, flavonoids, nucleotides, and organic acids. It is both an important food and a source of traditional medicine. Metabolomics can reveal metabolic differences between Ziziphus jujuba fruits from different jujube cultivars and growth sites. In the fall of 2022, mature fresh fruit of eleven cultivars from replicated trials at three sites in New Mexico-Leyendecker, Los Lunas, and Alcalde-were sampled from September to October for an untargeted metabolomics study. The 11 cultivars were Alcalde 1, Dongzao, Jinsi (JS), Jinkuiwang (JKW), Jixin, Kongfucui (KFC), Lang, Li, Maya, Shanxi Li, and Zaocuiwang (ZCW). Based on the LC-MS/MS analysis, there were 1315 compounds detected with amino acids and derivatives (20.15%) and flavonoids (15.44%) as dominant categories. The results reveal that the cultivar was the dominant factor in metabolite profiles, while the location was secondary. A pairwise comparison of cultivar metabolomes revealed that two pairs had fewer differential metabolites (i.e., Li/Shanxi Li and JS/JKW) than all the other pairs, highlighting that pairwise metabolic comparison can be applied for cultivar fingerprinting. Differential metabolite analysis also showed that half of drying cultivars have up-regulated lipid metabolites compared to fresh or multi-purpose fruit cultivars and that specialized metabolites vary significantly between cultivars from 35.3% (Dongzao/ZCW) to 56.7% (Jixin/KFC). An exemplary analyte matching sedative cyclopeptide alkaloid sanjoinine A was only detected in the Jinsi and Jinkuiwang cultivars. Overall, our metabolic analysis of the jujube cultivar's mature fruits provides the largest resource of jujube fruit metabolomes to date and will inform cultivar selection for nutritional and medicinal research and for fruit metabolic breeding.

Decoding Metabolic Reprogramming in Plants under Pathogen Attacks, a Comprehensive Review of Emerging Metabolomics Technologies to Maximize Their Applications

In their environment, plants interact with a multitude of living organisms and have to cope with a large variety of aggressions of biotic or abiotic origin. What has been known for several decades is that the extraordinary variety of chemical compounds the plants are capable of synthesizing may be estimated in the range of hundreds of thousands, but only a fraction has been fully characterized to be implicated in defense responses. Despite the vast importance of these metabolites for plants and also for human health, our knowledge about their biosynthetic pathways and functions is still fragmentary. Recent progress has been made particularly for the phenylpropanoids and oxylipids metabolism, which is more emphasized in this review. With an increasing interest in monitoring plant metabolic reprogramming, the development of advanced analysis methods should now follow. This review capitalizes on the advanced technologies used in metabolome mapping in planta, including different metabolomics approaches, imaging, flux analysis, and interpretation using bioinformatics tools. Advantages and limitations with regards to the application of each technique towards monitoring which metabolite class or type are highlighted, with special emphasis on the necessary future developments to better mirror such intricate metabolic interactions in planta.

Widely Targeted Metabolomics Reveals Metabolite Diversity in Jalapeño and Serrano Chile Peppers (Capsicum annuum L.)

Chile peppers (Capsicum annuum L.) are good sources of vitamins and minerals that can be included in the diet to mitigate nutritional deficiencies. Metabolomics examines the metabolites involved in biological pathways to understand the genes related to complex phenotypes such as the nutritional quality traits. The current study surveys the different metabolites present in jalapeño ('NuMex Pumpkin Spice') and serrano ('NuMex LotaLutein') type chile peppers grown in New Mexico using a widely targeted metabolomics approach, with the 'NuMex LotaLutein' as control. A total of 1088 different metabolites were detected, where 345 metabolites were differentially expressed; 203 (59%) were downregulated and 142 (41%) were upregulated (i.e., relative metabolite content is higher in 'NuMex Pumpkin Spice'). The upregulated metabolites comprised mostly of phenolic acids (42), flavonoids (22), and organic acids (13). Analyses of principal component (PC) and orthogonal partial least squares demonstrated clustering based on cultivars, where at least 60% of variation was attributed to the first two PCs. Pathway annotation identified 89 metabolites which are involved in metabolic pathways and the biosynthesis of secondary metabolites. Altogether, metabolomics provided insights into the different metabolites present which can be targeted for breeding and selection towards the improvement of nutritional quality traits in Capsicum.

Characterizing natural variability of lignin abundance and composition in fine roots across temperate trees: a comparison of analytical methods

Lignin is an important root chemical component that is widely used in biogeochemical models to predict root decomposition. Across ecological studies, lignin abundance has been characterized using both proximate and lignin-specific methods, without much understanding of their comparability. This uncertainty in estimating lignin limits our ability to comprehend the mechanisms regulating root decomposition and to integrate lignin data for large-scale syntheses. We compared five methods of estimating lignin abundance and composition in fine roots across 34 phylogenetically diverse tree species. We also assessed the feasibility of high-throughput techniques for fast-screening of root lignin. Although acid-insoluble fraction (AIF) has been used to infer root lignin and decomposition, AIF-defined lignin content was disconnected from the lignin abundance estimated by techniques that specifically measure lignin-derived monomers. While lignin-specific techniques indicated lignin contents of 2-10% (w/w) in roots, AIF-defined lignin contents were c. 5-10-fold higher, and their interspecific variation was found to be largely unrelated to that determined using lignin-specific techniques. High-throughput pyrolysis-gas chromatography-mass spectrometry, when combined with quantitative modeling, accurately predicted lignin abundance and composition, highlighting its feasibility for quicker assessment of lignin in roots. We demonstrate that AIF should be interpreted separately from lignin in fine roots as its abundance is unrelated to that of lignin polymers. This study provides the basis for informed decision-making with respect to lignin methodology in ecology.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

A Framework for Automating Psychiatric Distress Screening in Ophthalmology Clinics Using an EHR-Derived AI Algorithm

Purpose

In patients with ophthalmic disorders, psychosocial risk factors play an important role in morbidity and mortality. Proper and early psychiatric screening can result in prompt intervention and mitigate its impact. Because screening is resource intensive, we developed a framework for automating screening using an electronic health record (EHR)-derived artificial intelligence (AI) algorithm.

Methods

Subjects came from the Duke Ophthalmic Registry, a retrospective EHR database for the Duke Eye Center. Inclusion criteria included at least two encounters and a minimum of 1 year of follow-up. Presence of distress was defined at the encounter level using a computable phenotype. Risk factors included available EHR history. At each encounter, risk factors were used to discriminate psychiatric status. Model performance was evaluated using area under the receiver operating characteristic (ROC) curve and area under the precision-recall curve (PR AUC). Variable importance was presented using odds ratios (ORs).

Results

Our cohort included 358,135 encounters from 40,326 patients with an average of nine encounters per patient over 4 years. The ROC and PR AUC were 0.91 and 0.55, respectively. Of the top 25 predictors, the majority were related to existing distress, but some indicated stressful conditions, including chemotherapy (OR = 1.36), esophageal disorders (OR = 1.31), central pain syndrome (OR = 1.25), and headaches (OR = 1.24).

Conclusions

Psychiatric distress in ophthalmology patients can be monitored passively using an AI algorithm trained on existing EHR data.

Translational Relevance

When paired with an effective referral and treatment program, such algorithms may improve health outcomes in ophthalmology.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

Metabolomic profiling in ankylosing spondylitis using time-of-flight mass spectrometry

BACKGROUND & AIMS

Ankylosing spondylitis (AS) is an inflammatory disease associated with destructive changes in the skeleton and joints. The exact molecular mechanism of the disease has not been fully elucidated. This study aimed to determine metabolic differences between active AS patients and healthy controls to understand the molecular mechanism of AS.

PATIENTS AND METHODS

The study included 38 subjects, comprising 18 patients with active AS and 20 healthy controls. Metabolic profiling of the plasma was performed using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC Q-TOF/MS). Data acquisition, classification, and identification were achieved with the METLIN (https://metlin.scripps.edu/) database and XCMS (https://xcmsonline.scripps.edu).

RESULTS

Significant alterations were identified in the unsaturated fatty acids (FA), linoleic acid, alpha-linolenic acid, FA degradation, and FA biosynthesis pathways. Down -regulations were observed in phosphatidylcholine (PC) (16:0/0:0), beta-d-Fructose, stearic acid, trimipramine N-Oxide and muconic acid, and up-regulation were detected in PC (18:2/0:0), 3-Methylindole, palmitic acid (PA), alpha-Tocotrienol, and beta-d-glucopyranoside in active AS patients compared to the healthy control subjects.

CONCLUSION

Pathway analysis revealed that dysregulation in FA metabolism is associated with AS, and therefore, modulation of diet according to PA and PC may be potential therapeutic targets.

Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection

Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.

There is detectable variation in the lipidomic profile between stable and progressive patients with idiopathic pulmonary fibrosis (IPF)

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by fibrosis and progressive loss of lung function. The pathophysiological pathways involved in IPF are not well understood. Abnormal lipid metabolism has been described in various other chronic lung diseases including asthma and chronic obstructive pulmonary disease (COPD). However, its potential role in IPF pathogenesis remains unclear.

Methods

In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize lipid changes in plasma derived from IPF patients with stable and progressive disease. We further applied a data-independent acquisition (DIA) technique called SONAR, to improve the specificity of lipid identification.

Results

Statistical modelling showed variable discrimination between the stable and progressive subjects, revealing differences in the detection of triglycerides (TG) and phosphatidylcholines (PC) between progressors and stable IPF groups, which was further confirmed by mass spectrometry imaging (MSI) in IPF tissue.

Conclusion

This is the first study to characterise lipid metabolism between stable and progressive IPF, with results suggesting disparities in the circulating lipidome with disease progression.

Effects of time and temperature during melanging on the volatile profile of dark chocolate

Chocolate made from small-batch production is known for distinct sensory properties that differentiate its products from large-scale production. Specifically, small-batch processing includes a melanging step, a chocolate refining (a process involving time and temperature to refine texture and flavor) process that occurs in a stone wet-grinder. Chocolatiers understand that melanging is essential to flavor and overall quality, however the influence of melanging on the aroma chemistry of the finished chocolate is anecdotal and largely uncharacterized. Here, we evaluated the effects of time and temperature of melanging on the volatile chemistry of the finished chocolate. Specifically, chocolate aroma was profiled using HS/SPME-GC-MS for three different time and temperature combinations. A total of 88 compounds were annotated by mass spectrometry and included a diverse set of chemical classes such as pyrazines, aldehydes, terpenes, alcohols, esters, and ketones. Analysis of variance (ANOVA), principal component analysis (PCA), and partial least squares analysis (PLS) revealed that the overall aroma profile was influenced by the type of melanging, and time had a greater effect than temperature. Example compounds affected by time include 2-methylpropanal, dimethyl sulfide, and benzaldehyde. Particle size was also measured for each sample. Majority particle size was found to be below 25 microns generally at all time points beyond 8 h. Analysis showed significant p-values for the temperature variable for several compounds, but significant p-values for the time variable were apparent for a greater number of compounds. For compounds which showed dependency on both time and temperature, the p-value for the time variable was much smaller in most cases. Both PCA and OPLS analyses suggested the same trends. These data support that time is a critical factor in determining the aroma chemistry of chocolate and affects a diverse set of known flavor active compounds.

Lipidomic Profiling of the Epidermis in a Mouse Model of Dermatitis Reveals Sexual Dimorphism and Changes in Lipid Composition before the Onset of Clinical Disease

Atopic dermatitis (AD) is a multifactorial disease associated with alterations in lipid composition and organization in the epidermis. Multiple variants of AD exist with different outcomes in response to therapies. The evaluation of disease progression and response to treatment are observational assessments with poor inter-observer agreement highlighting the need for molecular markers. SHARPIN-deficient mice (Sharpin^cpdm) spontaneously develop chronic proliferative dermatitis with features similar to AD in humans. To study the changes in the epidermal lipid-content during disease progression, we tested 72 epidermis samples from three groups (5-, 7-, and 10-weeks old) of cpdm mice and their WT littermates. An agnostic mass-spectrometry strategy for biomarker discovery termed multiple-reaction monitoring (MRM)-profiling was used to detect and monitor 1,030 lipid ions present in the epidermis samples. In order to select the most relevant ions, we utilized a two-tiered filter/wrapper feature-selection strategy. Lipid categories were compressed, and an elastic-net classifier was used to rank and identify the most predictive lipid categories for sex, phenotype, and disease stages of cpdm mice. The model accurately classified the samples based on phospholipids, cholesteryl esters, acylcarnitines, and sphingolipids, demonstrating that disease progression cannot be defined by one single lipid or lipid category.

Label-free plasma proteomics identifies haptoglobin-related protein as candidate marker of idiopathic pulmonary fibrosis and dysregulation of complement and oxidative pathways

Idiopathic pulmonary fibrosis (IPF) is a lung parenchymal disease of unknown cause usually occurring in older adults. It is a chronic and progressive condition with poor prognosis and diagnosis is largely clinical. Currently, there exist few biomarkers that can predict patient outcome or response to therapies. Together with lack of markers, the need for novel markers for the detection and monitoring of IPF, is paramount. We have performed label-free plasma proteomics of thirty six individuals, 17 of which had confirmed IPF. Proteomics data was analyzed by volcano plot, hierarchical clustering, Partial-least square discriminant analysis (PLS-DA) and Ingenuity pathway analysis. Univariate and multivariate statistical analysis overlap identified haptoglobin-related protein as a possible marker of IPF when compared to control samples (Area under the curve 0.851, ROC-analysis). LXR/RXR activation and complement activation pathways were enriched in t-test significant proteins and oxidative regulators, complement proteins and protease inhibitors were enriched in PLS-DA significant proteins. Our pilot study points towards aberrations in complement activation and oxidative damage in IPF patients and provides haptoglobin-related protein as a new candidate biomarker of IPF.

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data

Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen-protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high-throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full-range reflectance spectra (visible (VIS)-near-infrared (NIR)-short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R² in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ-aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri-food industry.

Metabolome Profiling Supports the Key Role of the Spike in Wheat Yield Performance

Although the relevance of spike bracts in stress acclimation and contribution to wheat yield was recently revealed, the metabolome of this organ and its response to water stress is still unknown. The metabolite profiles of flag leaves, glumes and lemmas were characterized under contrasting field water regimes in five durum wheat cultivars. Water conditions during growth were characterized through spectral vegetation indices, canopy temperature and isotope composition. Spike bracts exhibited better coordination of carbon and nitrogen metabolisms than the flag leaves in terms of photorespiration, nitrogen assimilation and respiration paths. This coordination facilitated an accumulation of organic and amino acids in spike bracts, especially under water stress. The metabolomic response to water stress also involved an accumulation of antioxidant and drought tolerance related sugars, particularly in the spikes. Furthermore, certain cell wall, respiratory and protective metabolites were associated with genotypic outperformance and yield stability. In addition, grain yield was strongly predicted by leaf and spike bracts metabolomes independently. This study supports the role of the spike as a key organ during wheat grain filling, particularly under stress conditions and provides relevant information to explore new ways to improve wheat productivity including potential biomarkers for yield prediction.

Prepregnant Obesity of Mothers in a Multiethnic Cohort Is Associated with Cord Blood Metabolomic Changes in Offspring

Maternal obesity has become a growing global health concern that may predispose the offspring to medical conditions later in life. However, the metabolic link between maternal prepregnant obesity and healthy offspring has not yet been fully elucidated. In this study, we conducted a case-control study using a coupled untargeted and targeted metabolomic approach from the newborn cord blood metabolomes associated with a matched maternal prepregnant obesity cohort of 28 cases and 29 controls. The subjects were recruited from multiethnic populations in Hawaii, including rarely reported Native Hawaiian and other Pacific Islanders (NHPI). We found that maternal obesity was the most important factor contributing to differences in cord blood metabolomics. Using an elastic net regularization-based logistic regression model, we identified 29 metabolites as potential early-life biomarkers manifesting intrauterine effect of maternal obesity, with accuracy as high as 0.947 after adjusting for clinical confounding (maternal and paternal age, ethnicity, parity, and gravidity). We validated the model results in a subsequent set of samples (N = 30) with an accuracy of 0.822. Among the metabolites, six metabolites (galactonic acid, butenylcarnitine, 2-hydroxy-3-methylbutyric acid, phosphatidylcholine diacyl C40:3, 1,5-anhydrosorbitol, and phosphatidylcholine acyl-alkyl 40:3) were individually and significantly different between the maternal obese and normal-weight groups. Interestingly, hydroxy-3-methylbutyric acid showed significantly higher levels in cord blood from the NHPI group compared to that from Asian and Caucasian groups. In summary, significant associations were observed between maternal prepregnant obesity and offspring metabolomic alternation at birth, revealing the intergenerational impact of maternal obesity.

Unravelling the Metabolic Reconfiguration of the Post-Challenge Primed State in Sorghum bicolor Responding to Colletotrichum sublineolum Infection

Priming is a natural phenomenon that pre-conditions plants for enhanced defence against a wide range of pathogens. It represents a complementary strategy, or sustainable alternative that can provide protection against disease. However, a comprehensive functional and mechanistic understanding of the various layers of priming events is still limited. A non-targeted metabolomics approach was used to investigate metabolic changes in plant growth-promoting rhizobacteria (PGPR)-primed Sorghum bicolor seedlings infected with the anthracnose-causing fungal pathogen, Colletotrichum sublineolum, with a focus on the post-challenge primed state phase. At the 4-leaf growth stage, the plants were treated with a strain of Paenibacillus alvei at 10⁸ cfu mL⁻¹. Following a 24 h PGPR application, the plants were inoculated with a C. sublineolum spore suspension (10⁶ spores mL⁻¹), and the infection monitored over time: 1, 3, 5, 7 and 9 days post-inoculation. Non-infected plants served as negative controls. Intracellular metabolites from both inoculated and non-inoculated plants were extracted with 80% methanol-water. The extracts were chromatographically and spectrometrically analysed on an ultra-high performance liquid chromatography (UHPLC) system coupled to high-definition mass spectrometry. The acquired multidimensional data were processed to create data matrices for chemometric modelling. The computed models indicated time-related metabolic perturbations that reflect primed responses to the fungal infection. Evaluation of orthogonal projection to latent structure-discriminant analysis (OPLS-DA) loading shared and unique structures (SUS)-plots uncovered the differential stronger defence responses against the fungal infection observed in primed plants. These involved enhanced levels of amino acids (tyrosine, tryptophan), phytohormones (jasmonic acid and salicylic acid conjugates, and zeatin), and defence-related components of the lipidome. Furthermore, other defence responses in both naïve and primed plants were characterised by a complex mobilisation of phenolic compounds and de novo biosynthesis of the flavones, apigenin and luteolin and the 3-deoxyanthocyanidin phytoalexins, apigeninidin and luteolinidin, as well as some related conjugates.

Metabolomics in the study of retinal health and disease

Metabolomics is the qualitative and quantitative assessment of the metabolites (small molecules < 1.5 kDa) in body fluids. The metabolites are the downstream of the genetic transcription and translation processes and also downstream of the interactions with environmental exposures; thus, they are thought to closely relate to the phenotype, especially for multifactorial diseases. In the last decade, metabolomics has been increasingly used to identify biomarkers in disease, and it is currently recognized as a very powerful tool with great potential for clinical translation. The metabolome and the associated pathways also help improve our understanding of the pathophysiology and mechanisms of disease. While there has been increasing interest and research in metabolomics of the eye, the application of metabolomics to retinal diseases has been limited, even though these are leading causes of blindness. In this manuscript, we perform a comprehensive summary of the tools and knowledge required to perform a metabolomics study, and we highlight essential statistical methods for rigorous study design and data analysis. We review available protocols, summarize the best approaches, and address the current unmet need for information on collection and processing of tissues and biofluids that can be used for metabolomics of retinal diseases. Additionally, we critically analyze recent work in this field, both in animal models and in human clinical disease, including diabetic retinopathy and age-related macular degeneration. Finally, we identify opportunities for future research applying metabolomics to improve our current assessment and understanding of mechanisms of vitreoretinal diseases, and to hence improve patient assessment and care.

Variable selection in omics data: A practical evaluation of small sample sizes

In omics experiments, variable selection involves a large number of metabolites/ genes and a small number of samples (the n < p problem). The ultimate goal is often the identification of one, or a few features that are different among conditions- a biomarker. Complicating biomarker identification, the p variables often contain a correlation structure due to the biology of the experiment making identifying causal compounds from correlated compounds difficult. Additionally, there may be elements in the experimental design (blocks, batches) that introduce structure in the data. While this problem has been discussed in the literature and various strategies proposed, the over fitting problems concomitant with such approaches are rarely acknowledged. Instead of viewing a single omics experiment as a definitive test for a biomarker, an unrealistic analytical goal, we propose to view such studies as screening studies where the goal of the study is to reduce the number of features present in the second round of testing, and to limit the Type II error. Using this perspective, the performance of LASSO, ridge regression and Elastic Net was compared with the performance of an ANOVA via a simulation study and two real data comparisons. Interestingly, a dramatic increase in the number of features had no effect on Type I error for the ANOVA approach. ANOVA, even without multiple test correction, has a low false positive rates in the scenarios tested. The Elastic Net has an inflated Type I error (from 10 to 50%) for small numbers of features which increases with sample size. The Type II error rate for the ANOVA is comparable or lower than that for the Elastic Net leading us to conclude that an ANOVA is an effective analytical tool for the initial screening of features in omics experiments.

Problematic internet use as an age-related multifaceted problem: Evidence from a two-site survey

BACKGROUND AND AIMS

Problematic internet use (PIU; otherwise known as Internet Addiction) is a growing problem in modern societies. There is scarce knowledge of the demographic variables and specific internet activities associated with PIU and a limited understanding of how PIU should be conceptualized. Our aim was to identify specific internet activities associated with PIU and explore the moderating role of age and gender in those associations.

METHODS

We recruited 1749 participants aged 18 and above via media advertisements in an Internet-based survey at two sites, one in the US, and one in South Africa; we utilized Lasso regression for the analysis.

RESULTS

Specific internet activities were associated with higher problematic internet use scores, including general surfing (lasso β: 2.1), internet gaming (β: 0.6), online shopping (β: 1.4), use of online auction websites (β: 0.027), social networking (β: 0.46) and use of online pornography (β: 1.0). Age moderated the relationship between PIU and role-playing-games (β: 0.33), online gambling (β: 0.15), use of auction websites (β: 0.35) and streaming media (β: 0.35), with older age associated with higher levels of PIU. There was inconclusive evidence for gender and gender × internet activities being associated with problematic internet use scores. Attention-deficit hyperactivity disorder (ADHD) and social anxiety disorder were associated with high PIU scores in young participants (age ≤ 25, β: 0.35 and 0.65 respectively), whereas generalized anxiety disorder (GAD) and obsessive-compulsive disorder (OCD) were associated with high PIU scores in the older participants (age > 55, β: 6.4 and 4.3 respectively).

CONCLUSIONS

Many types of online behavior (e.g. shopping, pornography, general surfing) bear a stronger relationship with maladaptive use of the internet than gaming supporting the diagnostic classification of problematic internet use as a multifaceted disorder. Furthermore, internet activities and psychiatric diagnoses associated with problematic internet use vary with age, with public health implications.

Elastic net regularized regression for time-series analysis of plasma metabolome stability under sub-optimal freezing condition

In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R² = 0.6378 [positive mode], R² = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.