Metabolomics Samples Research Articles

A systematic evaluation of quenching, extraction and analysis procedures for metabolomics study of the mechanism of QYSLD intervention in A549 cells.

The preparation of cellular metabolomics samples and how to achieve comprehensive coverage of different polar metabolites in cell samples in the analysis pose a challenge for cellular metabolomics. In this study, we optimized a metabolomics protocol based on ultra-high-performance liquid chromatography high-resolution mass spectrometry (UPLC/HRMS) for theextraction and detection of metabolites in A549 cells and exploration of the intervention effect of Qi-Yu-San-Long decoction (QYSLD) on A549 cells. The results indicate that the lowest level of ATP leakage was observed when A549 cells were quenched under liquid nitrogen. MeOH/chloroform/H2O (1:2:1) extraction yielded more chromatographic peaks and excellent reproducibility, and the relative extraction efficiency of most target metabolites was also high. And we optimized thechromatographic separation conditions in both HILIC and RPLC modes, enabling comprehensive detection and analysis of metabolites with varying polarities. Then, we applied the optimized method to UPLC-Q-TOF/MS-based metabolomics of A549 cells to study the mechanism of QYSLD intervention in non-small cell lung cancer (NSCLC). The CCK-8, EdU staining, and cell cycle assay showed that QYSLD inhibited the proliferation of A549 cells by interfering with the cell cycle and blocking them in theG1 phase. A total of 36 differential metabolites associated with the antitumor effects of QYSLD on NSCLC were identified, mainly involving nicotinate and nicotinamide metabolism, sphingolipid metabolism, and glycerophospholipid metabolism. And western blotting confirmed that thechange in 1-methylnicotinamide levels after QYSLD intervention was associated with theinhibition of nicotinamide N-methyltransferase expression in A549 cells.

Abstract Tu041: Gut microbiome Bifidobacterium is associated with improvement in severity of heart failure with reduced ejection fraction

Background: Heart failure (HF) has been associated with dysregulated gut microbiome. However, less is understood about how gut microbiome alterations relate to changes in HF severity. We sought to understand how gut microbial shifts and associated host signatures related to changes in clinical status of patients with HF with reduced ejection fraction (HFrEF). Methods: Twenty-six adults with HFrEF were recruited and completed 2 study visits approximately 6 months apart. At each visit they provided stool sample for microbiome profiling (metagenomic sequencing), and fasting blood samples for untargeted metabolomics (liquid chromatography–mass spectrometry) and cytokine assay (Luminex antibody-conjugated bead capture). Subjects also completed dietary surveys. Clinical improvement was measured as between-visit change in the New York Heart Association (NYHA) class. Results: Between-visit NYHA class improved in 6 individuals; 20 patients remained stable or worsened. After accounting for demographics, body mass index, and diet, genus Bifidobacterium was significantly enriched in the improved group and increased over time (Figure 1). Bifidobacterium was directly associated with circulating malonic acid, which is implicated in cardio-protection and cardiomyocyte regeneration, and with indole-3-propionate (IPA), anti-inflammatory gut microbial metabolite of dietary tryptophan (Figure 2). There was also a positive correlation between Bifidobacterium and IL10, a major anti-inflammatory cytokine that plays a role in cardiac remodeling. Conclusion: Bifidobacterium was independently associated with longitudinal improvement in functional status in patients with HFrEF. Additionally, Bifidobacterium correlated with circulating IPA, malonic acid, and IL10. Our findings support (1) gut Bifidobacterium as a potential biomarker of clinical improvement in HFrEF and a possible new therapeutic target, and (2) potential downstream mediators through which Bifidobacterium may regulate HF pathophysiology.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.Graphical

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Randomised, Placebo-Controlled Investigation of the Impact of Probiotic Consumption on Gut Microbiota Diversity and the Faecal Metabolome in Seniors.

Aging has been associated with a changed composition and function of the gut microbiota (GM). Here, we investigate the effects of the multi-strain probiotic HOWARU® Restore on GM composition and function in seniors. Ninety-eight healthy adult volunteers aged ≥75 years were enrolled in a randomised, double-blinded intervention (NCT02207140), where they received HOWARU Restore (1010 CFU) or the placebo daily for 24 weeks, with 45 volunteers from each group completing the intervention. Questionnaires monitoring the effects on gastro-intestinal discomfort and bowel movements were collected. Faecal samples for GM characterisation (qPCR, 16S rRNA gene amplicon sequencing) and metabolomics (GC-FID, 1H NMR) were collected at the baseline and after 24 weeks. In the probiotic group, self-reported gastro-intestinal discomfort in the form of flatulence was significantly decreased during the intervention. At the baseline, 151 'core species' (present in ≥95% of samples) were identified. Most core species belonged to the Lachnospiraceae and Ruminococcaceae families. Neither alpha diversity nor beta diversity or faecal metabolites was affected by probiotic intake. On the contrary, we observed high intra-individual GM stability, with 'individual' accounting for 72-75% of variation. In conclusion, 24 weeks of HOWARU Restore intake reduced gastro-intestinal discomfort in the form of flatulence in healthy seniors without significantly influencing GM composition or activity.

Abstract LB290: Whole exome analysis of oral squamous cell carcinoma in local patients at tertiary care hospitals in Peshawar, Pakistan

Abstract The Precision Medicine Lab (PML) has been mandated by the Federal Government in 2018 to pilot precision medicine in Pakistan using genomics in oral cancer. With the Indian subcontinent as its capital, oral squamous cell carcinoma (OSCC), also dubbed as ‘poor man’s cancer', constitutes 90% of all head and neck cancers and is the highest incidence cancer found in Pakistani males. Backed by a bio-repository of clinical samples as well as a catalogue of primary cell clines, PML takes a multi-omic approach to discovering new, actionable biomarkers for diagnostic, prognostic and therapeutic applications. With all necessary ethical approvals obtained from IRBs at each site along with written informed consent from each patient, treatment-naive biopsy-confirmed patients of OSCC were recruited in two main tertiary care hospitals in Peshawar, Pakistan. Our cohort was predominantly older with 37.84% between 41-60 years, late-stage with 78.46% presenting in stage IV and self-reportedly non-alcohol consuming (96.9%). About 44.6% reported the use of smokeless tobacco while 30.8% reported the use of both smoked and smokeless tobacco. Majority of the tumours were located in the buccal mucosa (29.2%), alveolar region (27.7%) or the vestibular region (24.6%). We obtained blood and tumour samples for matched-pair whole exome sequencing, transcriptomics and metabolomics as well as generation of primary cell lines. We also obtained saliva samples for oral microbiome analysis. Combined with the patient’s clinical history and digital histopathology images, this helps us create a multi-omic stack that we intend to make available for integrated machine learning experiments. With our whole exome sequencing now complete, we undertook a preliminary analysis for this late breaking abstract. Raw reads were aligned to the GRCh38.p14 reference genome and were called for germline and somatic SNPs and INDELs using GATK (v4.5). Mutational analysis was conducted using the maftools (v2.18.0) package in R (v4.3) by comparing against the COSMIC SBS Signature database (v3.2). We report MUC12 (100%), ZNF717 (100%), FOLR3 (86%), EPPK1 (79%) and SPATA31A6 (79%) as the top mutated genes in the subset of exomes analysed thus far for our cohort. Genes reported previously including TP53, NOTCH1 and PIK3CA appear to rank lower on the list. In terms of cancer-related pathways, RTK-RAS (92.8%), Hippo (92.8%) and Notch (78.5%) were affected in most of the samples. To our knowledge, we describe for the first time the mutational landscape in an OSCC cohort from the Pakistani population. We also go beyond whole exomes as our multi-omic approach offers the potential on machine learning experiments to identify putative mutational signatures and biomarkers for OSCC that can be of diagnostic, prognostic and therapeutic value to the local (Pakistani and Afghan) patient population. With the availability of primary cell lines for every patient, we also intend to undertake experimental validation of high priority targets using primary patient-derived cell lines in the lab. Citation Format: Hina Zamir, Khudeja Salim, Madina Sherdil, Aqsa Hussain, Yusra Ilyas, Arsalan Riaz, Momal Agha, Zainab Jahan, Khadim Shah, Yasir Rehman Khattak, Fahim Uddin, Zahid Qayyum Shah, Zubair Durrani, Muhammad Mushtaq Khattak, Johar Ali, Faisal F. Khan. Whole exome analysis of oral squamous cell carcinoma in local patients at tertiary care hospitals in Peshawar, Pakistan [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB290.

Key role of hormone signal transduction and lipid metabolism in the development of Nitraria sibirica leaves: An integrated metabolomic and transcriptomic analysis

Plant growth is a complex process controlled by a variety of genetic and environmental variables. Nitraria sibirica Pall. is a multi-purpose euhalophyte with economic and medicinal value. However, the molecular mechanisms underlying the changes at different growth stages in N. sibirica remain unreported. In this study, the morphology and physiological characteristics of N. sibirica were compared at the vegetative, full flowering, and fruiting stages. Metabolomics and transcriptomics were used to describe metabolite accumulation and transcriptome expression at different stages, and the relationship between gene expression patterns and metabolite accumulation was studied based on a joint analysis of the two approaches. The results indicated significant variations in the morphological and physiological characteristics of N. sibirica at different growth stages (P < 0.05). Transcriptome and metabolome samples corresponding to the three stages were clearly distinguished, and the metabolite and gene module showed high consistency. The amount of DEMs and DEGs decreased as leaf growth progressed. Differentially expressed genes (DEGs) predominantly belonged to the MYB transcription family, whereas differentially expressed metabolites (DEMs) were dominated by lipids and lipid-like molecules. DEGs were mainly enriched in plant hormone signal transduction, cutin, suberine, and wax biosynthesis pathways, whereas DEMs were mainly enriched in linoleic acid, alpha-linolenic acid metabolism, and phenylpropane biosynthesis pathways. Combined transcriptomic and metabolomic analyses revealed that plant hormone signal transduction, linoleic acid, alpha-linolenic acid, cutin, suberine, and wax biosynthesis were the most significant key pathways for the co-enrichment of DEMs and DEGs. Pearson correlation analysis showed that physiological traits were strongly correlated with DEGs, whereas morphological traits were strongly correlated with DEMs. Moreover, a strong correlation was also observed between DEMs and DEGs. In conclusion, this research provides insights into the metabolic and transcriptional regulatory networks involved in plant development.

Evaluating protocols for reproducible targeted metabolomics by NMR.

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Pubertal Girls With Overweight/Obesity Have Higher Androgen Levels-Can Metabolomics Tell us Why?

Pubertal girls with higher total body fat (TBF) demonstrate higher androgen levels. The cause of this association is unknown but is hypothesized to relate to insulin resistance. This work aimed to investigate the association between higher TBF and higher androgens in pubertal girls using untargeted metabolomics. Serum androgens were determined using a quantitative mass spectrometry (MS)-based assay. Metabolomic samples were analyzed using liquid chromatography high-resolution MS. Associations between TBF or body mass index (BMI) z score (exposure) and metabolomic features (outcome) and between metabolomic features (exposure) and serum hormones (outcome) were examined using gaussian generalized estimating equation models with the outcome lagged by one study visit. Benjamini-Hochberg false discovery rate (FDR) adjusted P values were calculated to account for multiple testing. RaMP-DB (relational database of metabolomic pathways) was used to conduct enriched pathway analyses among features nominally associated with body composition or hormones. Sixty-six pubertal, premenarchal girls (aged 10.9 ± 1.39 SD years; 60% White, 24% Black, 16% other; 63% normal weight, 37% overweight/obese) contributed an average of 2.29 blood samples. BMI and TBF were negatively associated with most features including raffinose (a plant trisaccharide) and several bile acids. For BMI, RaMP-DB identified many enriched pathways related to bile acids. Androstenedione also showed strong negative associations with raffinose and bile acids. Metabolomic analyses of samples from pubertal girls did not identify an insulin resistance signature to explain the association between higher TBF and androgens. Instead, we identified potential novel signaling pathways that may involve raffinose or bile acid action at the adrenal gland.

Abstract 14367: Multimodal Artificial Intelligence Algorithm to Distinguish Paroxysmal versus Persistent Atrial Fibrillation and Predict Outcomes After Ablation

Introduction: We aimed to develop effective multimodal machine learning (ML) techniques to predict: 1) duration of AF; 2) arrhythmia recurrence after ablation. Methods: Clinical data, intracardiac EGMs and metabolomic samples were obtained from patients undergoing AF ablation. Patients were prospectively followed to assess for recurrence after a 90-day blanking period with monitors every three months and if symptomatic. A prototype model was implemented via projection onto latent structures (PLS) logistic regression (LR), which accounts for collinearity in the predictors by estimating a small number of latent vectors that are aligned with the binomial response. PLS LR was used to estimate models for distinguishing paroxysmal from persistent AF and predicting recurrence. The latent structures identified by PLS LR combine strength from measurements of multiple correlated features. We generated a reference distribution to assess whether the model is better than chance by permuting the responses while keeping the inputs fixed. Models were compared based on the Akaike Information Criterion. Results: Three models were compared: EGM data (n=39), metabolomic data (n=29), multimodal approach on the combined two datasets (n=23). The multimodal model consistently produced a better fit despite smaller sample size. Only the model based on EGMs was significantly better than chance at distinguishing PAF from PeAF (p=0.03). The models based on EGMs and the combined data were significantly better than chance at predicting recurrence (p=0.02, p=0.04, respectively). Predictor EGM and metabolomics variables are shown in tables. Conclusion: Multimodal ML algorithm is promising to effectively combine multidimensional data for clinical use. The combined model consistently produced a better fit compared to the others, indicating that multimodal approaches are promising. According to AIC, the fit was better when the datasets were combined.

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC–TOF MS metabolome samples

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as “rodents”, “digestive tract”, or “amino acids”. The ontologies are particularly relevant for BinDiscover’s ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover’s extensive applicability in three showcases across biological domains.

Large-scale omics dataset of polymer degradation provides robust interpretation for microbial niche and succession on different plastisphere

While biodegradable polymers have received increased attention due to the recent marine plastic problem, few studies have compared microbiomes and their degradation processes among biodegradable polymers. In this study, we set up prompt evaluation systems for polymer degradation, allowing us to collect 418 microbiome and 125 metabolome samples to clarify the microbiome and metabolome differences according to degradation progress and polymer material (polycaprolactone [PCL], polybutylene succinate-co-adipate [PBSA], polybutylene succinate [PBS], polybutylene adipate-co-terephthalate [PBAT], and poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) [PHBH]). The microbial community compositions were converged to each polymer material, and the largest differences were observed between PHBH and other polymers. Such gaps were probably formed primarily by the presence of specific hydrolase genes (i.e., 3HB depolymerase, lipase, and cutinase) in the microorganisms. Time-series sampling suggested several steps for microbial succession: (1) initial microbes decrease abruptly after incubation starts; (2) microbes, including polymer degraders, increase soon after the start of incubation and show an intermediate peak; (3) microbes, including biofilm constructers, increase their abundance gradually. Metagenome prediction showed functional changes, where free-swimming microbes with flagella adhered stochastically onto the polymer, and certain microbes started to construct a biofilm. Our large-dataset-based results provide robust interpretations for biodegradable polymer degradation.

Point-of-care applicable metabotyping using biofluid-specific electrospun MetaSAMPs directly amenable to ambient LA-REIMS.

In recent years, ambient ionization mass spectrometry (AIMS) including laser ablation rapid evaporation IMS, has enabled direct biofluid metabolome analysis. AIMS procedures are, however, still hampered by both analytical, i.e., matrix effects, and practical, i.e., sample transport stability, drawbacks that impede metabolome coverage. In this study, we aimed at developing biofluid-specific metabolome sampling membranes (MetaSAMPs) that offer a directly applicable and stabilizing substrate for AIMS. Customized rectal, salivary, and urinary MetaSAMPs consisting of electrospun (nano)fibrous membranes of blended hydrophilic (polyvinylpyrrolidone and polyacrylonitrile) and lipophilic (polystyrene) polymers supported metabolite absorption, adsorption, and desorption. Moreover, MetaSAMP demonstrated superior metabolome coverage and transport stability compared to crude biofluid analysis and was successfully validated in two pediatric cohorts (MetaBEAse, n = 234 and OPERA, n = 101). By integrating anthropometric and (patho)physiological with MetaSAMP-AIMS metabolome data, we obtained substantial weight-driven predictions and clinical correlations. In conclusion, MetaSAMP holds great clinical application potential for on-the-spot metabolic health stratification.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation

IntroductionMetabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification.ObjectivesDevelop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community.MethodsMetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference.ResultsMetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples.ConclusionWe offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Plasma metabolomics reveals major changes in carbohydrate, lipid, and protein metabolism of abruptly weaned beef calves

1H NMR-based metabolomics was used to study the effect of abrupt weaning on the blood metabolome of beef calves. Twenty Angus calves (258 ± 5 kg BW; 5 to 6 months old) were randomly assigned to a non-weaned (NW) group that remained grazing with their dam or a weaned (W) group that underwent abrupt separation from their dam to a separate paddock on d 0 of the study. Body weight, behaviour, and blood samples for cortisol and metabolomics were measured at d 0, 1, 2, 7, and 14 of the study. On d 1 and 2, W calves spent less time grazing and ruminating, and more time vocalising and walking, had a greater concentration of cortisol, NEFA, 3-hydroxybutyrate, betaine, creatine, and phenylalanine, and lesser abundance of tyrosine (P < 0.05) compared to NW calves. Compared to NW calves at d 14, W calves had greater (P < 0.01) relative abundance of acetate, glucose, allantoin, creatinine, creatine, creatine phosphate, glutamate, 3-hydroxybutyrate, 3-hydroxyisobutyrate, and seven AA (alanine, glutamate, leucine, lysine, phenylalanine, threonine and valine) but lesser (P < 0.05) relative abundance of low density and very low-density lipids, and unsaturated lipids. Both PCA and OPLS-DA showed no clustering or discrimination between groups at d 0 and increasing divergence to d 14. Blood metabolomics is a useful tool to quantify the acute effects of stress in calves during the first 2 days after abrupt weaning, and longer-term changes in carbohydrate, lipid and protein metabolism due to nutritional changes from cessation of milk intake and greater reliance on forage intake.

The time elapsed between assessments of blood metabolome and live weight affects associations between the abundance of metabolites and growth rate in beef cattle

IntroductionThis study aimed to assess the associations between the relative abundance (RA) of blood metabolites and growth rate (i.e., live weight change, LWC) calculated using different intervals of time between live weight (LW) measurements from the metabolome assessment.MethodsGrazing beef cattle were raised for 56 days and blood samples from each animal were taken on day 57. Live weight was continuously measured using an automatic in-paddock weighing scale. The RA of plasma metabolites were determined using proton nuclear magnetic resonance (NMR). Live weight data were filtered for outliers and one LW record was selected every 1, 7, 14, 21, 28, 35, 42, 49 and 56 days before the metabolome assessment (LWC1 to LWC56, respectively). Live weight change was then re-calculated for each interval between LW data selected.ResultsAssociations between LWC calculations and the RA of metabolites were greatly affected by the interval of time between LW data selected. Thus, the number of significant associations decreased from 9 for LWC1 to 5 for LWC35 whereas no significant associations were found for LWC56 (P > 0.05). There were 7 metabolites negatively associated with LWC1 including leucine, 2-hydroxybutyrate, valine, creatinine, creatine, phenylalanine and methylhistidine; however, correlations were positive for 2 lipids. The strength of the correlation coefficients decreased as the length of the interval between LW measures increased although this reduction was greater for some metabolites such as leucine compared to others such as lipids. Our findings suggest that the time frame in which a particular response variable, such as LWC, is measured and metabolomic samples are taken could largely impact associations and thus conclusions drawn.ConclusionsDepending on the variable to be explored, rapid changes in cattle metabolome may not be reflected in correlations if they are not assessed close in time. Our findings suggest that LWC should be measured for a period shorter than 28 days before the metabolome assessment as the number of significant associations decreases when LWC is measured for longer periods.

Optimization of Carob Products Preparation for Targeted LC-MS/MS Metabolomics Analysis

Carob (Ceratonia siliqua) is an exceptional source of significant bioactive compounds with great economic importance in the Mediterranean region, where it is widely cultivated. Carob fruit is used for the production of a variety of products and commodities such as powder, syrup, coffee, flour, cakes, and beverages. There is growing evidence of the beneficial effects of carob and the products made from it on a range of health problems. Therefore, metabolomics could be used to explore the nutrient-rich compounds of carob. Sample preparation is a crucial step in metabolomics-based analysis and has a great impact on the quality of the data obtained. Herein, sample preparation of carob syrup and powder was optimized, to enable highly efficient metabolomics-based HILIC-MS/MS analysis. Pooled powder and syrup samples were extracted under different conditions by adjusting pH, solvent type, and sample weight to solvent volume ratio (Wc/Vs). The metabolomics profiles obtained were evaluated using the established criteria of total area and number of maxima. It was observed that the Wc/Vs ratio of 1:2 resulted in the highest number of metabolites, regardless of solvent type or pH. Aqueous acetonitrile with a Wc/Vs ratio of 1:2 satisfied all established criteria for both carob syrup and powder samples. However, when the pH was adjusted, basic aqueous propanol 1:2 Wc/Vs and acidic aqueous acetonitrile 1:2 Wc/Vs provided the best results for syrup and powder, respectively. We strongly believe that the current study could support the standardization of the metabolomics sample preparation process to enable more efficient LC-MS/MS carob analysis.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics.

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

Multiplatform untargeted metabolomics.

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.