Robust volcano plot: identification of differential metabolites in the presence of outliers

Differentiation of three commercial tuna species through GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics and chemometrics

Food fraud is common in the tuna industry because of the economic benefits involved. Ensuring the authenticity of tuna species is crucial for protecting both consumers and tuna stocks. In this study, GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry-based metabolomics were used to investigate the metabolite profiles of three commercial tuna species (skipjack tuna, bigeye tuna and yellowfin tuna). A total of 22 and 77 metabolites were identified with high confidence using GC-Q-TOF and UPLC-Q/Orbitrap mass spectrometry, respectively. Further screening via chemometrics revealed that 38 metabolites could potentially serve as potential biomarkers. Hierarchical cluster analysis showed that the screened metabolite biomarkers successfully distinguished the three tested tuna species. Furthermore, a total of 27 metabolic pathways were identified through enrichment analysis based on the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways.

Arsenic-enhanced plant growth in As-hyperaccumulator Pteris vittata: Metabolomic investigations and molecular mechanisms

The first-known As-hyperaccumulator Pteris vittata is efficient in As uptake and translocation, which can be used for phytoremediation of As-contaminated soils. However, the underlying mechanisms of As-enhanced plant growth are unknown. We used untargeted metabolomics to investigate the potential metabolites and associated metabolic pathways regulating As-enhanced plant growth in P. vittata. After 60 days of growth in an MS-agar medium containing 15 mg kg-1 As, P. vittata biomass was 33-34 % greater than the no-As control. Similarly, the As contents in P. vittata roots and fronds were 272 and 1300 mg kg-1, considerably greater than the no-As control. Univariate and multivariate analyses based on electrospray ionization indicate that As exposure changed the expression of 1604 and 1248 metabolites in positive and negative modes. By comparing with the no-As control, As exposure significantly changed the expression of 14 metabolites including abscisic acid, d-glucose, raffinose, stachyose, chitobiose, xylitol, gibberellic acids, castasterone, citric acid, riboflavin-5-phosphate, ubiquinone, ubiquinol, UDP-glucose, and GDP-glucose. These metabolites are involved in phytohormone synthesis, energy metabolism, and sugar metabolism and may all potentially contribute to regulating As-enhanced plant growth in P. vittata. Our data provide clues to understanding the metabolic regulations of As-enhanced plant growth in P. vittata, which helps to enhance its phytoremediation efficiency of As-contaminated soils.

Identification of bitter peptides in aged Cheddar cheese by crossflow filtration-based Fractionation, Peptidomics, statistical screening and sensory analysis

Despite bitterness being a common flavor attribute of aged cheese linked to casein-derived peptides, excessive bitterness is a sensory flaw that can lead to consumer rejection and economic loss for creameries. Our research employs a unique approach to identify bitter peptides in cheese samples using crossflow filtration-based fractionation, mass spectrometry-based peptidomics, statistics and sensory analysis. Applying peptidomics and statistical screening tools, rather than traditional chemical separation techniques, to identify bitter peptides allows for screening the whole peptide profile. Five peptides-YPFPGP (β-casein [60-65]), YPFPGPIPN (βA2-casein [60-68]), LSQSKVLPVPQKAVPYPQRDMPIQA (β-casein [165-189]), YPFPGPIHNS (βA1-casein [60-69]) and its serine phosphorylated version YPFPGPIHN[S] (βA1-casein [60-69])- demonstrated high levels of bitterness with mean bitterness intensity values above 7 on a 15-point scale. In the future, this data can be combined with the microbial and protease profile of the Cheddar samples to help understand how these factors contribute to bitter taste development.

Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Antifungal Effect of Bacillus velezensis ZN-S10 against Plant Pathogen Colletotrichum changpingense and Its Inhibition Mechanism

In order to optimize crop production and mitigate the adverse impacts associated with the utilization of chemical agents, it is necessary to explore new biocontrol agents. Bacillus velezensis has been widely studied as a biocontrol agent because of its efficient and ecofriendly plant disease control mechanisms. This study shows that the strain ZN-S10 effectively reduces the area of leaf spots caused by the pathogen Colletotrichum changpingense ZAFU0163-1, which affects conidia production and germination, inhibits mycelium growth, and induces mycelium deformation. In antifungal experiments with crude extracts, we observed a delay in the cell cycle of conidia, which may be responsible for the inhibition of conidial germination. Among the bioactive metabolites detected through integrated LC-MS- and GC-MS-based untargeted metabolomics, 7-O-Succinyl macrolactin A, telocinobufagin, and surfactin A may be the main antifungal metabolites of strain ZN-S10. The presence of 7-O-Succinyl macrolactin A could explain the cell damage in germ tubes. This is the first report of telocinobufagin detected in B. velezensis. These results are significant for understanding the inhibitory mechanisms employed by B. velezensis and should serve as a reference in the production of biocontrol agents.

Bioprospecting-based untargeted metabolomics identifies alkaloids as potential anti-inflammatory bioactive markers of Ocotea species (Lauraceae)

BACKGROUND

Species within the Ocotea genus (Lauraceae), have demonstrated an interesting profile of bioactivities. Renowned for their diverse morphology and intricate specialized metabolite composition, Ocotea species have re-emerged as compelling candidates for bioprospecting in drug discovery research. However, it is a genus insufficiently studied, particularly regarding anti-inflammatory activity.

PURPOSE

To investigate the anti-inflammatory activity of Ocotea spp. extracts and determine the major markers in this genus.

METHODS

Extracts of 60 different Ocotea spp. were analysed by an ex vivo anti-inflammatory assay in human whole blood. The experiment estimates the prostaglandin E2 levels, which is one of the main mediators of the inflammatory cascade, responsible for the classical symptoms of fever, pain, and other common effects of the inflammatory process. Untargeted metabolomics analysis through liquid chromatography coupled with high-resolution mass spectrometry was performed, along with statistical analysis, to investigate which Ocotea metabolites are correlated with their anti-inflammatory activity.

RESULTS

The anti-inflammatory screening indicated that 49 out of 60 Ocotea spp. extracts exhibited significant inhibition of PGE2 release compared to the vehicle (p < 0.05). Furthermore, 10 of these extracts showed statistical similarity to the reference drugs. The bioactive markers were accurately identified using multivariate statistics combined with a fold change (> 1.5) and adjusted false discovery rate analysis as unknown compounds and alkaloids, with a majority of aporphine and benzylisoquinolines. These alkaloids were annotated with an increased level of confidence since MSE spectra were compared with comprehensive databases.

CONCLUSION

This study represents the first bioprospecting report revealing the anti-inflammatory potential of several Ocotea spp. The determination of their anti-inflammatory markers could contribute to drug discovery and the chemical knowledge of the Ocotea genus.

Red Wine High-Molecular-Weight Polyphenolic Complex Ameliorates High-Fat Diet-Induced Metabolic Dysregulation and Perturbation in Gut Microbiota in Mice

Red wine polyphenolic complexes have attracted increasing attention as potential modulators of human metabolic disease risk. Our previous study discovered that red wine high-molecular-weight polymeric polyphenolic complexes (HPPCs) could inhibit key metabolic syndrome-associated enzymes and favorably modulate human gut microbiota (GM) in simulated colonic fermentation assay in vitro. In this work, the efficacy of HPPC supplementation (150 and 300 mg/kg/day, respectively) against high-fat diet (HFD)-induced metabolic disturbance in mice was investigated. HPPCs effectively attenuated HFD-induced obesity, insulin resistance, and lipid and glucose metabolic dysregulation and ameliorated inflammatory response and hepatic and colonic damage. It also improved the relative abundance of Bacteroidetes and Firmicutes, consistent with an anti-obesity phenotype. The favorable modulation of GM was further supported by improvement in the profile of fecal short-chain fatty acids. The higher dosage generally had a better performance in these effects than the low dosage. Moreover, serum metabolite profiling and pathway enrichment analysis revealed that HPPCs significantly modulated vitamin B metabolism-associated pathways and identified N-acetylneuraminic acid and 2-methylbutyroylcarnitine as potential biomarkers of the favorable effect on HFD-induced metabolic dysregulation. These findings highlight that dietary supplementation with red wine HPPCs is a promising strategy for the management of weight gain and metabolic dysregulation associated with HFD.

Untargeted metabolomics analysis assisted by signal selection for comprehensively identifying metabolites of new psychoactive substances: 4-MeO-α-PVP as an example

New psychoactive substances (NPS) have been rapidly emerged as legal alternatives to controlled drugs, which raised severe public health issue. The detection and monitoring of its intake by complete metabolic profiling is an urgent and vital task. Untargeted metabolomics approach has been applied for several NPS metabolites studies. Although the number of such works is relatively limited but with a rapidly growing need. The present study aimed to propose a procedure that includes liquid chromatography high-resolution mass spectrometry (LC-HRMS) analysis and a signal selection software, MetaboFinder, programed as a web tool. The comprehensive metabolites profile of one kind of NPS, 4-methoxy-α-pyrrolidinovalerophenone (4-MeO-α-PVP), was studied by using this workflow. In this study, two different concentrations of 4-MeO-α-PVP along with as blank sample were incubated with human liver S9 fraction for the conversion to their metabolites and followed by LC-MS analysis. After retention time alignment and feature identification, 4640 features were obtained and submitted to statistical analysis for signal selection by using MetaboFinder. A total of 50 features were considered as 4-MeO-α-PVP metabolite candidates showing significant changes (p < 0.00001 and fold change >2) between the two investigated groups. Targeted LC-MS/MS analysis was conducted focusing on these significantly expressed features. Assisted by chemical formula determination according to high mass accuracy and in silico MS2 fragmentation prediction, 19 chemical structure identifications were achieved. Among which, 8 metabolites have been reported derived from 4-MeO-α-PVP in a previous literature while 11 novel 4-MeO-α-PVP metabolites were identified by using our strategy. Further in vivo animal experiment confirmed that 18 compounds were 4-MeO-α-PVP metabolites, which demonstrated the feasibility of our strategy for screening the 4-MeO-α-PVP metabolites. We anticipate that this procedure may support and facilitate traditional metabolism studies and potentially being applied for routine NPS metabolites screening.

Chemical-Biology and Metabolomics Studies in Phage-Host Interactions

For many years, several studies have explored the molecular mechanisms involved in the infection of bacteria by their specific phages to understand the main infection strategies and the host defense strategies. The modulation of the mechanisms involved in the infection, as well as the expression of key substances in the development of the different life cycles of phages, function as a natural source of strategies capable of promoting the control of different pathogens that are harmful to human and animal health. Therefore, this chapter aims to provide an overview of the mechanisms involved in virus-bacteria interaction to explore the main compounds produced or altered as a chemical survival strategy and the metabolism modulation when occurring a host-phage interaction. In this context, emphasis will be given to the chemistry of peptides/proteins and enzymes encoded by bacteriophages in the control of pathogenic bacteria and the use of secondary metabolites recently reported as active participants in the mechanisms of phage-bacteria interaction. Finally, metabolomics strategies developed to gain new insights into the metabolism involved in the phage-host interaction and the metabolomics workflow in host-phage interaction will be presented.

Deep phenotyping of oxidative stress in emergency room patients reveals homoarginine as a novel predictor of sepsis severity, length of hospital stay, and length of intensive care unit stay

Background

We aimed to determine primary markers of oxidative stress (OS) in ED patients which predict hospital length of stay (LoS), intensive care unit (ICU) LoS, and sepsis severity.

Materials and methods

This prospective, single center observational study was conducted in adult patients recruited from the ED who were diagnosed with either sepsis, infection without sepsis, or non-infectious, age-matched controls. 290 patients were admitted to the hospital and 24 patients had direct admission to the ICU. A panel of 269 OS and related metabolic markers were profiled for each cohort. Clinical outcomes were direct ICU admission, hospital LoS, ICU LoS, and post-hoc, adjudicated sepsis severity scoring. Bonferroni correction was used for pairwise comparisons. Principal component regression was used for dimensionality reduction and selection of plasma metabolites associated with sepsis. Multivariable negative binomial regression was applied to predict admission, hospital, and ICU LoS.

Results

Homoarginine (hArg) was the top discriminator of sepsis severity [sepsis vs. control: ROC-AUC = 0.86 (95% CI 0.81-0.91)], [sepsis vs. infection: ROC-AUC = 0.73 (95% CI 0.68-0.78)]. The 25th percentile of hArg [odds ratio (OR) = 8.57 (95% CI 1.05-70.06)] was associated with hospital LoS [IRR = 2.54 (95% CI 1.83-3.52)] and ICU LOS [IRR = 18.73 (95% CI 4.32-81.27)]. In prediction of outcomes, hArg had superior performance compared to arginine (Arg) [hArg ROC-AUC = 0.77 (95% CI 0.67-0.88) vs. Arg ROC-AUC = 0.66 (95% CI 0.55-0.78)], and dimethylarginines [SDMA ROC-AUC 0.68 (95% CI 0.55-0.79) and ADMA ROC-AUC = 0.68 (95% CI 0.56-0.79)]. Ratio of hArg and Arg/NO metabolic markers and creatinine clearance provided modest improvements in clinical prediction.

Conclusion

Homoarginine is associated with sepsis severity and predicts hospital and ICU LoS, making it a useful biomarker in guiding treatment decisions for ED patients.

Non-targeted analysis for organic components of microplastic leachates

Organic components of microplastic leachates were investigated in an integrated non-targeted analysis study that included statistical analysis on leachates generated under different leaching scenarios. Leaching experiments were undertaken with simulated gastric fluid (SGF), river water, and seawater with common polymer types, including polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polyester fabrics comprising both raw and recycled materials. Totals of 111.0 ± 26.7, 98.5 ± 20.3, and 53.5 ± 4.7 different features were tentatively identified as compounds in SGF, freshwater, and seawater leachates, respectively, of which 5 compounds were confirmed by reference standards. The leaching capacities of the media were compared, and the clusters of structurally related features leached in the same medium were studied. For leachates generated from raw and recycled plastics, volcano plots and Pearson's Chi-squared tests were used to identify characteristic features. More characteristic features (3-20) had an average intensity across all recycled plastics that were significantly higher (p < 0.05) than that (1-3) of raw plastics under different conditions. The results indicate that gastric solution is more likely to leach components from microplastics, and there exists the difference of leachate's organic composition between raw and recycled materials, providing new insights into understanding microplastic environmental effects.

Investigation of the Variability of Alkaloids in Buxus sempervirens L. Using Multivariate Data Analysis of LC/MS Profiles

Buxus sempervirens L. is a common ornamental plant in southern and central Europe, and has been used ethopharmacologically against a wide variety of diseases due to it containing nor-triterpene alkaloids of the nor-cycloartane type. Recently, we demonstrated the interesting antiprotozoal potential of some of these compounds. To characterize the temporal variability in the alkaloid profile of two different varieties and their leaves and twigs, 30 different extracts of B. sempervirens were evaluated by Ultra High Performance Liquid Chromatography/positive Mode-Electrospray Ionization Quadrupole Time-of-Flight-Tandem Mass Spectrometry (UHPLC/+ESI-QqTOF-MS/MS). The analytical profiles were thoroughly investigated by various methods of multivariate data analysis (MVDA). A principal component analysis (PCA) model elucidates the seasonal variation in the phytochemical composition of B. sempervirens var. arborescens and suffruticosa along with differences between the varieties. Analysis of a volcano plot illustrated the differences between the two organs, the leaf and twig. Eighteen compounds were highlighted by the models as constituents of the plant characteristic for a season, variety or organ. These compounds were dereplicated based on their chromatographic and +ESI-QqTOF-MS and –MS/MS data. In addition, mass spectral fragmentation pathways for already known alkaloids as well as new natural products could be postulated for the first time. In conclusion, the MVDA models give detailed information on the temporal variability in the alkaloid profile of two different varieties and their organs (leaf vs. twig) of B. sempervirens. Thus, the results of this study allow, e.g., the identification of characteristic compounds for the different varieties, plant organs, seasons, and the optimal harvesting time for the isolation of particular Buxus-alkaloids of interest for subsequent studies.

Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.

Potential Antihypertensive Mechanisms of the Egg White-Derived Peptide QIGLF in Spontaneously Hypertensive Rats Revealed Using Untargeted Serum Metabolomics

The angiotensin-converting enzyme (ACE) inhibitory peptide QIGLF derived from egg white was shown to have significant in vivo antihypertensive effects in our previous study, but the intervention mechanisms at the metabolic level are still unclear. The UPLC-QTOF/MS-based untargeted metabolomics approach was used to clarify the potential antihypertensive mechanisms of QIGLF in the serum of spontaneously hypertensive rats (SHRs). Multivariate statistical analysis showed a clear difference in the metabolite profiles between the QIGLF and model groups. The results suggested that eight potential biomarkers were identified, that is, adrenic acid, ursodeoxycholic acid, glycocholic acid, taurocholic acid, tryptophan, acetylindoxyl, tyrosine, and 2-phenylethanol, which were mainly involved in aromatic amino acid biosynthesis and metabolism, biosynthesis of bile acid, and biosynthesis of unsaturated fatty acids. QIGLF might exert antihypertensive effects by improving endothelial dysfunction. This study provides a theoretical basis for future research and application of ACE inhibitory peptides in the prevention and improvement of hypertension.

A LCMS Metabolomic Workflow to Investigate Metabolic Patterns in Human Intestinal Cells Exposed to Hydrolyzed Crab Waste Materials

We have developed a LCMS metabolomic workflow to investigate metabolic patterns from human intestinal cells treated with simulated gastrointestinal-digested hydrolyzed crab waste materials. This workflow facilitates smart and reproducible comparisons of cell cultures exposed to different treatments. In this case the variable was the hydrolysis methods, also accounting for the GI digestion giving an output of direct correlation between cellular metabolic patterns caused by the treatments. In addition, we used the output from this workflow to select treatments for further evaluation of the Caco-2 cell response in terms of tentative anti-inflammatory activity in the hopes to find value in the crab waste materials to be used for food products. As hypothesized, the treatment identified to change the cellular metabolomic pattern most readily, was also found to cause the greatest effect in the cells, although the response was pro-inflammatory rather than anti-inflammatory, it proves that changes in cellular metabolic patterns are useful predictors of bioactivity. We conclude that the developed workflow allows for cost effective, rapid sample preparation as well as accurate and repeatable LCMS analysis and introduces a data pipeline specifically for probe the novel metabolite patterns created as a means to assess the performing treatments.

An Educational Bioinformatics Project to Improve Genome Annotation

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student's investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor's, and experiential learning projects.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Urinary Volatile Terpenes Analyzed by Gas Chromatography-Mass Spectrometry to Monitor Breast Cancer Treatment Efficacy in Mice

Urinary volatile terpene (VT) levels are significantly altered with induced models of breast cancer in mice. The question arises whether VTs can detect the efficacy of antitumor treatments. BALB/c mice were injected with 4T1.2 murine tumor cells in the mammary pad or iliac artery to model localized breast cancer and induced bone metastasis. The effect of two dopaminergic antitumor agents was tested by conventional histology and altered VT levels. The headspace of urine specimens was analyzed by gas chromatography-mass spectrometry. In the localized model, the statistical significance (p < 0.05) was identified for 26% of VTs, and in the metastasis model, 19% of VTs. The authors discovered separate VT panels classifying localized/control [area under the curve (AUC) = 1.0] and metastasis/control (AUC = 0.98). Treatment samples were tested using these panels, which showed that mice treated with either agent were statistically significantly different from cancer samples, which is consistent with conventional analysis.

Introduction of a new method for two-dimensional NMR quantitative analysis in metabolomics studies

NMR is one of the most important platforms for metabolomic studies. Though 2D NMR has been applied in metabolomics, most applications have mainly focused on metabolite identification whilst limitations causing a bottle-neck for applying high-throughput 2D NMR data for quantity related statistical analysis lies on the data interpretation methods. In this study, instead of using the traditional methods of calculating the 2D NMR data to search for the important features, a new procedure, which applies the high-resolution 1D NMR metabolites chemical shift range to filter the 2D NMR data, was developed. This new method was demonstrated using both a mixture of standard metabolites and a case study on plant extracts using 2D non-uniform sampling (NUS) total correlation spectroscopy (TOCSY) data. As a result, our method successfully filtered out the important features with a high success rate, and the extracted peaks showed high linearity between the calculated intensities and the concentrations of metabolites from a range of 0.05 mM-2 mM. The method was successfully applied to a metabolomics case study which included 18 Begonia samples that showed excellent peak extractions. In summary, our study has provided a practical new 2D NMR data extraction method for use in future metabolomics studies.

Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data

Clinical metabolomics aims at finding statistically significant differences in metabolic statuses of patient and control groups with the intention of understanding pathobiochemical processes and identification of clinically useful biomarkers of particular diseases. After the raw measurements are integrated and pre-processed as intensities of chromatographic peaks, the differences between controls and patients are evaluated by both univariate and multivariate statistical methods. The traditional univariate approach relies on t-tests (or their nonparametric alternatives) and the results from multiple testing are misleadingly compared merely by p-values using the so-called volcano plot. This paper proposes a Bayesian counterpart to the widespread univariate analysis, taking into account the compositional character of a metabolome. Since each metabolome is a collection of some small-molecule metabolites in a biological material, the relative structure of metabolomic data, which is inherently contained in ratios between metabolites, is of the main interest. Therefore, a proper choice of logratio coordinates is an essential step for any statistical analysis of such data. In addition, a concept of b-values is introduced together with a Bayesian version of the volcano plot incorporating distance levels of the posterior highest density intervals from zero. The theoretical background of the contribution is illustrated using two data sets containing samples of patients suffering from 3-hydroxy-3-methylglutaryl-CoA lyase deficiency and medium-chain acyl-CoA dehydrogenase deficiency. To evaluate the stability of the proposed method as well as the benefits of the compositional approach, two simulations designed to mimic a loss of samples and a systematical measurement error, respectively, are added.

Identification of DNA motifs that regulate DNA methylation

DNA methylation is an important epigenetic mark but how its locus-specificity is decided in relation to DNA sequence is not fully understood. Here, we have analyzed 34 diverse whole-genome bisulfite sequencing datasets in human and identified 313 motifs, including 92 and 221 associated with methylation (methylation motifs, MMs) and unmethylation (unmethylation motifs, UMs), respectively. The functionality of these motifs is supported by multiple lines of evidence. First, the methylation levels at the MM and UM motifs are respectively higher and lower than the genomic background. Second, these motifs are enriched at the binding sites of methylation modifying enzymes including DNMT3A and TET1, indicating their possible roles of recruiting these enzymes. Third, these motifs significantly overlap with "somatic QTLs" (quantitative trait loci) of methylation and expression. Fourth, disruption of these motifs by mutation is associated with significantly altered methylation level of the CpGs in the neighbor regions. Furthermore, these motifs together with somatic mutations are predictive of cancer subtypes and patient survival. We revealed some of these motifs were also associated with histone modifications, suggesting a possible interplay between the two types of epigenetic modifications. We also found some motifs form feed forward loops to contribute to DNA methylation dynamics.