Statistical methods for the analysis of high-throughput metabolomics data

A review of emerging technologies, nutritional practices, and management strategies to improve intramuscular fat composition in beef cattle

The flavour, tenderness and juiciness of the beef are all impacted by the composition of the intramuscular fat (IMF), which is a key determinant of beef quality. Thus, enhancing the IMF composition of beef cattle has become a major area of research. Consequently, the aim of this paper was to provide insight and synthesis into the emerging technologies, nutritional practices and management strategies to improve IMF composition in beef cattle. This review paper examined the current knowledge of management techniques and nutritional approaches relevant to cattle farming in the beef industry. It includes a thorough investigation of animal handling, weaning age, castration, breed selection, sex determination, environmental factors, grazing methods, slaughter weight and age. Additionally, it rigorously explored dietary energy levels and optimization of fatty acid profiles, as well as the use of feed additives and hormone implant techniques with their associated regulations. The paper also delved into emerging technologies that are shaping future beef production, such as genomic selection methods, genome editing techniques, epigenomic analyses, microbiome manipulation strategies, transcriptomic profiling approaches and metabolomics analyses. In conclusion, a holistic approach combining genomic, nutritional and management strategies is imperative for achieving targeted IMF content and ensuring high-quality beef production.

Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented.

Lipid metabolic profiling and diagnostic model development for hyperlipidemic acute pancreatitis

Introduction

Hyperlipidemic acute pancreatitis (HLAP) is a form of pancreatitis induced by hyperlipidemia, posing significant diagnostic challenges due to its complex lipid metabolism disturbances.

Methods

This study compared the serum lipid profiles of HLAP patients with those of a healthy cohort using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to identify distinct lipid metabolites. Logistic regression and LASSO regression were used to develop a diagnostic model based on the lipid molecules identified.

Results

A total of 393 distinct lipid metabolites were detected, impacting critical pathways such as fatty acid, sphingolipid, and glycerophospholipid metabolism. Five specific lipid molecules were selected to construct a diagnostic model, which achieved an area under the curve (AUC) of 1 in the receiver operating characteristic (ROC) analysis, indicating outstanding diagnostic accuracy.

Discussion

These findings highlight the importance of lipid metabolism disturbances in HLAP. The identified lipid molecules could serve as valuable biomarkers for HLAP diagnosis, offering potential for more accurate and early detection.

Wolbachia modify host cell metabolite profiles in response to short-term temperature stress

Wolbachia are common heritable endosymbionts that influence many aspects of ecology and evolution in various insects, yet Wolbachia-mediated intracellular metabolic responses to temperature stress have been largely overlooked. Here, we introduced the Wolbachia strain wLhui from the invasive Liriomyza huidobrensis (Blanchard) into a Drosophila Schneider 2 cell line (S2) and investigated the metabolite profile of wLhui-infected (S2_wLhui) and uninfected cell lines (S2_wu) under short-term exposure to either high (37°C), moderate (27°C), or low (7 and 17°C) temperatures. We find that Wolbachia infection, temperature stress, and their interactions significantly affect cellular metabolic profiles. Most significantly, when comparing the changes in metabolites between S2_wLhui and S2_wu, glycerophospholipids, amino acids, and fatty acids associated with metabolic pathways, microbial metabolism in diverse environments, and other pathways were significantly accumulated at either low or high temperatures. Our findings suggest Wolbachia-induced cellular physiological responses to short-term temperature stress, which may in turn affect the fitness and adaptive ability of its host as an invasive species.

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD.

STUDY DESIGN

Cross-sectional.

SETTING & PARTICIPANTS

1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis.

PREDICTORS

Measurement of 448 known plasma metabolites.

OUTCOMES

The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument.

ANALYTICAL APPROACH

Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant."

RESULTS

Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models.

LIMITATIONS

Lack of a second independent cohort for external validation of our findings.

CONCLUSIONS

Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed.

PLAIN-LANGUAGE SUMMARY

Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

Dynamic relationships among pathways producing hydrocarbons and fatty acids of maize silk cuticular waxes

The hydrophobic cuticle is the first line of defense between aerial portions of plants and the external environment. On maize (Zea mays L.) silks, the cuticular cutin matrix is infused with cuticular waxes, consisting of a homologous series of very long-chain fatty acids (VLCFAs), aldehydes, and hydrocarbons. Together with VLC fatty-acyl-CoAs (VLCFA-CoAs), these metabolites serve as precursors, intermediates, and end-products of the cuticular wax biosynthetic pathway. To deconvolute the potentially confounding impacts of the change in silk microenvironment and silk development on this pathway, we profiled cuticular waxes on the silks of the inbreds B73 and Mo17, and their reciprocal hybrids. Multivariate interrogation of these metabolite abundance data demonstrates that VLCFA-CoAs and total free VLCFAs are positively correlated with the cuticular wax metabolome, and this metabolome is primarily affected by changes in the silk microenvironment and plant genotype. Moreover, the genotype effect on the pathway explains the increased accumulation of cuticular hydrocarbons with a concomitant reduction in cuticular VLCFA accumulation on B73 silks, suggesting that the conversion of VLCFA-CoAs to hydrocarbons is more effective in B73 than Mo17. Statistical modeling of the ratios between cuticular hydrocarbons and cuticular VLCFAs reveals a significant role of precursor chain length in determining this ratio. This study establishes the complexity of the product-precursor relationships within the silk cuticular wax-producing network by dissecting both the impact of genotype and the allocation of VLCFA-CoA precursors to different biological processes and demonstrates that longer chain VLCFA-CoAs are preferentially utilized for hydrocarbon biosynthesis.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

Probing the chemical 'reactome' with high-throughput experimentation data

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

Impacts of neonicotinoid insecticides on bumble bee energy metabolism are revealed under nectar starvation

Bumble bees are an important group of insects that provide essential pollination services as a consequence of their foraging behaviors. These pollination services are driven, in part, by energetic exchanges between flowering plants and individual bees. Thus, it is important to examine bumble bee energy metabolism and explore how it might be influenced by external stressors contributing to declines in global pollinator populations. Two stressors that are commonly encountered by bees are insecticides, such as the neonicotinoids, and nutritional stress, resulting from deficits in pollen and nectar availability. Our study uses a metabolomic approach to examine the effects of neonicotinoid insecticide exposure on bumble bee metabolism, both alone and in combination with nutritional stress. We hypothesized that exposure to imidacloprid disrupts bumble bee energy metabolism, leading to changes in key metabolites involved in central carbon metabolism. We tested this by exposing Bombus impatiens workers to imidacloprid according to one of three exposure paradigms designed to explore how chronic versus more acute (early or late) imidacloprid exposure influences energy metabolite levels, then also subjecting them to artificial nectar starvation. The strongest effects of imidacloprid were observed when bees also experienced nectar starvation, suggesting a combinatorial effect of neonicotinoids and nutritional stress on bumble bee energy metabolism. Overall, this study provides important insights into the mechanisms underlying the impact of neonicotinoid insecticides on pollinators, and underscores the need for further investigation into the complex interactions between environmental stressors and energy metabolism.

Characterization of Hair Metabolome in 5xFAD Mice and Patients with Alzheimer's Disease Using Mass Spectrometry-Based Metabolomics

Hair emerged as a biospecimen for long-term investigation of endogenous metabolic perturbations, reflecting the chemical composition circulating in the blood over the past months. Despite its potential, the use of human hair for metabolomics in Alzheimer's disease (AD) research remains limited. Here, we performed both untargeted and targeted metabolomic approaches to profile the key metabolic pathways in the hair of 5xFAD mice, a widely used AD mouse model. Furthermore, we applied the discovered metabolites to human subjects. Hair samples were collected from 6-month-old 5xFAD mice, a stage marked by widespread accumulation of amyloid plaques in the brain, followed by sample preparation and high-resolution mass spectrometry analysis. Forty-five discriminatory metabolites were discovered in the hair of 6-month-old 5xFAD mice compared to wild-type control mice. Enrichment analysis revealed three key metabolic pathways: arachidonic acid metabolism, sphingolipid metabolism, and alanine, aspartate, and glutamate metabolism. Among these pathways, six metabolites demonstrated significant differences in the hair of 2-month-old 5xFAD mice, a stage prior to the onset of amyloid plaque deposition. These findings suggest their potential involvement in the early stages of AD pathogenesis. When evaluating 45 discriminatory metabolites for distinguishing patients with AD from nondemented controls, a combination of l-valine and arachidonic acid significantly differentiated these two groups, achieving a 0.88 area under the curve. Taken together, these findings highlight the potential of hair metabolomics in identifying disease-specific metabolic alterations and developing biomarkers for improving disease detection and monitoring.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

Higher abundance of 2-dehydro-d-gluconate in the plasma of sub-fertile or infertile Bos taurus heifers

Infertility or subfertility impacts approximately 5% and 15% of dairy and beef heifers (Bos taurus), respectively. Heifers that do not produce a calf within an optimum window of time have a significant negative impact on the profitability and sustainability of the cattle industry. Selection of heifers based on their fertility potential remains a challenge yet to be resolved. Here, we tested the hypothesis that heifers of different fertility potential have differing metabolome signatures in their plasma. We obtained blood from Bos taurus heifers at their first artificial insemination and processed the samples to separate the plasma. The heifers were classified based on their reproductive outcome as fertile (pregnant and delivered a calf after their first artificial insemination (AI)) or sub-fertile (Angus heifers: no pregnancy after two AI and exposure to a bull; Holstein heifers: no pregnancy by the third AI). We tested the relative abundance of 140 metabolites obtained from 22 heifers (Angus fertile n = 5, Angus sub-fertile n = 7, Holstein fertile N = 5, Holstein sub-fertile N = 5). The metabolite 2-Dehydro-D-gluconate (C6H10O7) was significantly more abundant in the plasma of sub-fertile heifers in both breeds (1.4-fold, false discovery rate < 0.1). In the context that a small proportion of circulating metabolites in the plasma were quantified in this study, the results show that the metabolomic profile in the blood stream may be associated with heifer fertility potential.

Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data

We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform causal analysis and discover causal relationships among multiple traits; (2) account for the possible presence of some invalid IVs; (3) allow for data analysis using only genome-wide association studies (GWAS) summary statistics when individual-level data are not available; (4) consider the possibility of bidirectional relationships between traits. Our method employs a simple stepwise selection to identify invalid IVs, thus avoiding false positives while possibly increasing true discoveries based on two-stage least squares (2SLS). We use both real GWAS data and simulated data to demonstrate the superior performance of our method over the standard 2SLS/SEMs. For real data analysis, our proposed approach is applied to a human blood metabolite GWAS summary data set to uncover putative causal relationships among the metabolites; we also identify some metabolites (putative) causal to Alzheimer's disease (AD), which, along with the inferred causal metabolite network, suggest some possible pathways of metabolites involved in AD.

Multi-omics analysis of hospital-acquired diarrhoeal patients reveals biomarkers of enterococcal proliferation and Clostridioides difficile infection

Hospital-acquired diarrhoea (HAD) is common, and often associated with gut microbiota and metabolome dysbiosis following antibiotic administration. Clostridioides difficile is the most significant antibiotic-associated diarrhoeal (AAD) pathogen, but less is known about the microbiota and metabolome associated with AAD and C. difficile infection (CDI) with contrasting antibiotic treatment. We characterised faecal microbiota and metabolome for 169 HAD patients (33 with CDI and 133 non-CDI) to determine dysbiosis biomarkers and gain insights into metabolic strategies C. difficile might use for gut colonisation. The specimen microbial community was analysed using 16 S rRNA gene amplicon sequencing, coupled with untargeted metabolite profiling using gas chromatography-mass spectrometry (GC-MS), and short-chain fatty acid (SCFA) profiling using GC-MS. AAD and CDI patients were associated with a spectrum of dysbiosis reflecting non-antibiotic, short-term, and extended-antibiotic treatment. Notably, extended antibiotic treatment was associated with enterococcal proliferation (mostly vancomycin-resistant Enterococcus faecium) coupled with putative biomarkers of enterococcal tyrosine decarboxylation. We also uncovered unrecognised metabolome dynamics associated with concomitant enterococcal proliferation and CDI, including biomarkers of Stickland fermentation and amino acid competition that could distinguish CDI from non-CDI patients. Here we show, candidate metabolic biomarkers for diagnostic development with possible implications for CDI and vancomycin-resistant enterococci (VRE) treatment.

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.

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.

Pseudomonassin, a New Bioactive Ribosomally Synthesised and Post-Translationally Modified Peptide from Pseudomonas sp. SST3

Genome mining and metabolomics have become valuable tools in natural products research to evaluate and identify potential new chemistry from bacteria. In the search for new compounds from the deep-sea organism, Pseudomonas sp. SST3, from the South Shetland Trough, Antarctica, a co-cultivation with a second deep-sea Pseudomonas zhaodongensis SST2, was undertaken to isolate pseudomonassin, a ribosomally synthesised and post-translationally modified peptide (RiPP) that belongs to a class of RiPP called lasso peptides. Pseudomonassin was identified using a genome-mining approach and isolated by means of mass spectrometric guided isolation. Extensive metabolomics analysis of the co-cultivation of Pseudomonas sp. SST3 and P. zhaodongensis SST2, Pseudomonas sp. SST3 and Escherichia coli, and P. zhaodongensis SST2 and E. coli were performed using principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA), which revealed potential new metabolites in the outlier regions of the co-cultivation, with other metabolites identified previously from other species of Pseudomonas. The sequence of pseudomonassin was completely deduced using high collision dissociation tandem mass spectrometry (HCD-MS/MS). Preliminary studies on its activity against the pathogenic P. aeruginosa and its biofilm formation have been assessed and produced a minimum inhibitory concentration (MIC) of 63 μg/mL and 28 μg/mL, respectively.

Unlocking Potentially Therapeutic Phytochemicals in Capadulla (Doliocarpus dentatus) from Guyana Using Untargeted Mass Spectrometry-Based Metabolomics

Doliocarpus dentatus is thought to have a wide variety of therapeutic phytochemicals that allegedly improve libido and cure impotence. Although a few biomarkers have been identified with potential antinociceptive and cytotoxic properties, an untargeted mass spectrometry-based metabolomics approach has never been undertaken to identify therapeutic biofingerprints for conditions, such as erectile dysfunction, in men. This study executes a preliminary phytochemical screening of the woody vine of two ecotypes of D. dentatus with renowned differences in therapeutic potential for erectile dysfunction. Liquid chromatography-mass spectrometry-based metabolomics was used to screen for flavonoids, terpenoids, and other chemical classes found to contrast between red and white ecotypes. Among the metabolite chemodiversity found in the ecotype screens, using a combination of GNPS, MS-DIAL, and SIRIUS, approximately 847 compounds were annotated at levels 2 to 4, with the majority of compounds falling under lipid and lipid-like molecules, benzenoids and phenylpropanoids, and polyketides, indicative of the contributions of the flavonoid, shikimic acid, and terpenoid biosynthesis pathways. Despite the extensive annotation, we report on 138 tentative compound identifications of potentially therapeutic compounds, with 55 selected compounds at a level-2 annotation, and 22 statistically significant therapeutic biomarkers, the majority of which were polyphenols. Epicatechin methyl gallate, catechin gallate, and proanthocyanidin A2 had the greatest significant differences and were also relatively abundant among the red and white ecotypes. These putatively identified compounds reportedly act as antioxidants, neutralizing damaging free radicals, and lowering cell oxidative stress, thus aiding in potentially preventing cellular damage and promoting overall well-being, especially for treating erectile dysfunction (ED).

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Study of the Stability of Wine Samples for 1H-NMR Metabolomic Profile Analysis through Chemometrics Methods

Wine is a temperature, light, and oxygen-sensitive product, so its physicochemical characteristics can be modified by variations in temperature and time when samples are either sampled, transported, and/or analyzed. These changes can alter its metabolomic fingerprinting, impacting further classification tasks and quality/quantitative analyses. For these reasons, the aim of this work is to compare and analyze the information obtained by different chemometric methods used in a complementary form (PCA, ASCA, and PARAFAC) to study 1H-NMR spectra variations of four red wine samples kept at different temperatures and time lapses. In conjunction, distinctive changes in the spectra are satisfactorily tracked with each chemometric method. The chemometric analyses reveal variations related to the wine sample, temperature, and time, as well as the interactions among these factors. Moreover, the magnitude and statistical significance of the effects are satisfactorily accounted for by ASCA, while the time-related effects variations are encountered by PARAFAC modeling. Acetaldehyde, formic acid, polyphenols, carbohydrates, lactic acid, ethyl lactate, methanol, choline, succinic acid, proline, acetoin, acetic acid, 1,3-propanediol, isopentanol, and some amino acids are identified as some of the metabolites which present the most important variations.

Metabolic pathways altered by air pollutant exposure in association with lipid profiles in young adults

Mounting evidence suggests that air pollution influences lipid metabolism and dyslipidemia. However, the metabolic mechanisms linking air pollutant exposure and altered lipid metabolism is not established. In year 2014-2018, we conducted a cross-sectional study on 136 young adults in southern California, and assessed lipid profiles (triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, very-low-density lipoprotein (VLDL)-cholesterol), and untargeted serum metabolomics using liquid chromatography-high-resolution mass spectrometry, and one-month and one-year averaged exposures to NO2, O3, PM2.5 and PM10 air pollutants at residential addresses. A metabolome-wide association analysis was conducted to identify metabolomic features associated with each air pollutant. Mummichog pathway enrichment analysis was used to assess altered metabolic pathways. Principal component analysis (PCA) was further conducted to summarize 35 metabolites with confirmed chemical identity. Lastly, linear regression models were used to analyze the associations of metabolomic PC scores with each air pollutant exposure and lipid profile outcome. In total, 9309 metabolomic features were extracted, with 3275 features significantly associated with exposure to one-month or one-year averaged NO2, O3, PM2.5 and PM10 (p < 0.05). Metabolic pathways associated with air pollutants included fatty acid, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism. PCA of 35 metabolites identified three main PCs which together explained 44.4% of the variance, representing free fatty acids and oxidative byproducts, amino acids and organic acids. Linear regression indicated that the free fatty acids and oxidative byproducts-related PC score was associated with air pollutant exposure and outcomes of total cholesterol and LDL-cholesterol (p < 0.05). This study suggests that exposure to NO2, O3, PM2.5 and PM10 contributes to increased level of circulating free fatty acids, likely through increased adipose lipolysis, stress hormone and response to oxidative stress pathways. These alterations were associated with dysregulation of lipid profiles and potentially could contribute to dyslipidemia and other cardiometabolic disorders.

Emodin Exerts its Therapeutic Effects Through Metabolic Remodeling in Severe Acute Pancreatitis-Related Intestinal Injury

Background

Intestinal injury caused by severe acute pancreatitis (SAP) can induce peripancreatic and systemic infection, and aggravate systemic inflammation. Emodin has demonstrated efficacy in mitigating SAP-associated intestinal injury. Although metabolites in tissues cause histopathophysiological changes, data on the mechanisms of emodin on metabolic processes remain scant.

Methods

The SAP-related intestinal injury rat model was induced by injection of 3.5% sodium taurocholate solution through the biliopancreatic duct. The protective effect of emodin on intestinal injury was evaluated by histologic analyses. On the other hand, we assessed the effect of emodin on metabolic remodeling in intestinal tissues using untargeted metabolomics.

Results

Out of the analyzed 1187 metabolites, untargeted metabolomics identified 99 differential metabolites in the intestinal tissues. Emodin significantly alleviated the inflammatory injury in the pancreas and intestines. Emodin treatment led to significant changes in bile acid metabolism, amino acid metabolism, intestinal microbiota related metabolism, and glycerol phospholipid metabolism in the intestinal tissues. In addition, using the weighted gene co-expression network analysis, we constructed emodin related metabolite–metabolite interaction network and showed that intestinal microbiota related metabolites and glycerol phospholipid metabolism were associated with emodin treatment. Glycine, LPC (0:0/22:6), Spermidine, 11β-hydroxyprogesterone, and N1-methyl-2-Pyridone-5-carboxamide may be efficient molecules after emodin treatment.

Conclusion

Taken together, our data demonstrated that intestinal injury caused by SAP induces an obvious metabolic disorder. Emodin exerts its therapeutic effects through metabolic remodeling.

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis

PURPOSE

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men in Western and Asian countries. Serum prostate-specific antigen (PSA) test has been the routine diagnostic method despite the tremendous research in diagnostic markers for early detection of PCa. A shift towards a promising and potential biomarker for PCa detection is through metabolomic profiling of biofluids, particularly the blood and urine samples. Finding reliable, routinely usable circulating metabolite biomarkers may not be a distant reality.

METHODS

We performed a PubMed-based literature search of metabolite biomarkers in blood and urine for the early detection of prostate cancer. The timeline of these searches was limited between 2007 and 2022 and the following keywords were used: 'metabolomics', 'liquid biopsy', 'circulating metabolites', 'serum metabolite', 'plasma metabolite', and 'urine metabolite' with respect to 'prostate cancer'. We focussed only on diagnosis-based studies with only the subject-relevant articles published in the English language and excluded all of the other irrelevant publications that included prostate tissue biomarkers and cell line biomarkers.

RESULTS

We have consolidated all the blood and urine-based potential metabolite candidates in individual as well as panels, including lipid classes, fatty acids, amino acids, and volatile organic compounds which may become useful for PCa diagnosis.

CONCLUSION

All these metabolome findings unveil the impact of different dimensions of PCa development, giving a promising strategy to diagnose the disease since suspected individuals can be subjected to repeated and largescale blood and urine testing.

Metabolomics and NMR

The purpose of this manuscript will be to convince the reader to dive deeper into NMR spectroscopy and prevent the technique from being just another "black-box" in the lab. We will try to concisely highlight interesting topics and supply additional references for further exploration at each stage. The advantages of delving into the technique will be shown. The secondary objective, i.e., avoiding common problems before starting, will hopefully then become clear. Lastly, we will emphasize the spectrometer information needed for manuscript reporting to allow reproduction of results and confirm findings.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Ecological and metabolomic responses of plants to deer exclosure in a suburban forest

Trees and shrubs in suburban forests can be subject to chronic herbivory from abundant white-tailed deer, influencing survival, growth, secondary metabolites, and ecological success in the community. We investigated how deer affect the size, cover, and metabolomes of four species in the understory of a suburban forest in central New Jersey, USA: the woody shrubs Euonymus alatus and Lindera benzoin, the tree Nyssa sylvatica, and the semi-woody shrub Rosa multiflora. For each species, we compared plants in 38 16 m2 plots with or without deer exclosure, measuring proportion cover and mean height after 6.5 years of fencing. We scored each species in all plots for deer browsing over 8 years and assessed selection by deer among the species. We did untargeted metabolomics by sampling leaves from three plants of each species in an equal number of fenced and unfenced plots, conducting chloroform-methanol extractions followed by LC-MS/MS, and conducting statistical analysis on MetaboAnalyst. The proportion of a species browsed ranged from 0.24 to 0.35. Nyssa sylvatica appeared most selected by and susceptible to deer; in unfenced plots, both its cover and mean height were significantly lower. Only cover or height was lower for E. alatus and L. benzoin in unfenced plots, while R. multiflora height was greater. The metabolomic analysis identified 2333 metabolites, which clustered by species but not fencing treatment. However, targeted analysis of the top metabolites grouped by fencing for all samples and for each species alone and was especially clear in N. sylvatica, which also grouped by fencing using all metabolites. The most significant metabolites that were upregulated in fenced plants include some involved in defense-related metabolic pathways, e.g., monoterpenoid biosynthesis. In overbrowsed suburban forests, variation of deer impact on species' ecological success, potentially mediated by metabolome-wide chemical responses to deer, may contribute to changes in community structure.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI^- and ESI⁺ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Nanoformulation of Seaweed Eisenia bicyclis in Albumin Nanoparticles Targeting Cardiovascular Diseases: In Vitro and In Vivo Evaluation

Natural products, especially those derived from seaweeds, are starting to be seen as effective against various diseases, such as cardiovascular diseases (CVDs). This study aimed to design a novel oral formulation of bovine albumin serum nanoparticles (BSA NPs) loaded with an extract of Eisenia bicyclis and to validate its beneficial health effects, particularly targeting hypercholesterolemia and CVD prevention. Small and well-defined BSA NPs loaded with Eisenia bicyclis extract were successfully prepared exhibiting high encapsulation efficiency. Antioxidant activity and cholesterol biosynthesis enzyme 3-hydroxy-3 methylutaryl coenzyme A reductase (HMGR) inhibition, as well as reduction of cholesterol permeation in intestinal lining model cells, were assessed for the extract both in free and nanoformulated forms. The nanoformulation was more efficient than the free extract, particularly in terms of HMGR inhibition and cholesterol permeation reduction. In vitro cytotoxicity and in vivo assays in Wistar rats were performed to evaluate its safety and overall effects on metabolism. The results demonstrated that the Eisenia bicyclis extract and BSA NPs were not cytotoxic against human intestinal Caco-2 and liver HepG2 cells and were also safe after oral administration in the rat model. In addition, an innovative approach was adopted to compare the metabolomic profile of the serum from the animals involved in the in vivo assay, which showed the extract and nanoformulation's impact on CVD-associated key metabolites. Altogether, these preliminary results revealed that the seaweed extract and the nanoformulation may constitute an alternative natural dosage form which is safe and simple to produce, capable of reducing cholesterol levels, and consequently helpful in preventing hypercholesterolemia, the main risk factor of CVDs.

Metabolomics and Chemoinformatics in Agricultural Biotechnology Research: Complementary Probes in Unravelling New Metabolites for Crop Improvement

The United Nations (UN) estimate that the global population will reach 10 billion people by 2050. These projections have placed the agroeconomic industry under immense pressure to meet the growing demand for food and maintain global food security. However, factors associated with climate variability and the emergence of virulent plant pathogens and pests pose a considerable threat to meeting these demands. Advanced crop improvement strategies are required to circumvent the deleterious effects of biotic and abiotic stress and improve yields. Metabolomics is an emerging field in the omics pipeline and systems biology concerned with the quantitative and qualitative analysis of metabolites from a biological specimen under specified conditions. In the past few decades, metabolomics techniques have been extensively used to decipher and describe the metabolic networks associated with plant growth and development and the response and adaptation to biotic and abiotic stress. In recent years, metabolomics technologies, particularly plant metabolomics, have expanded to screening metabolic biomarkers for enhanced performance in yield and stress tolerance for metabolomics-assisted breeding. This review explores the recent advances in the application of metabolomics in agricultural biotechnology for biomarker discovery and the identification of new metabolites for crop improvement. We describe the basic plant metabolomics workflow, the essential analytical techniques, and the power of these combined analytical techniques with chemometrics and chemoinformatics tools. Furthermore, there are mentions of integrated omics systems for metabolomics-assisted breeding and of current applications.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

Evaluation of Biological Activity of Natural Compounds: Current Trends and Methods

Natural compounds have diverse structures and are present in different forms of life. Metabolites such as tannins, anthocyanins, and alkaloids, among others, serve as a defense mechanism in live organisms and are undoubtedly compounds of interest for the food, cosmetic, and pharmaceutical industries. Plants, bacteria, and insects represent sources of biomolecules with diverse activities, which are in many cases poorly studied. To use these molecules for different applications, it is essential to know their structure, concentrations, and biological activity potential. In vitro techniques that evaluate the biological activity of the molecules of interest have been developed since the 1950s. Currently, different methodologies have emerged to overcome some of the limitations of these traditional techniques, mainly via reductions in time and costs. These emerging technologies continue to appear due to the urgent need to expand the analysis capacity of a growing number of reported biomolecules. This review presents an updated summary of the conventional and relevant methods to evaluate the natural compounds' biological activity in vitro.

Evaluation of Novel Quorum Sensing Inhibitors Targeting Auto-Inducer 2 (AI-2) for the Control of Avian Pathogenic Escherichia coli Infections in Chickens

Avian pathogenic Escherichia coli (APEC) associated with colibacillosis results in high morbidity and mortality, and severe economic losses to the poultry industry. APEC is a zoonotic pathogen and can infect humans through contaminated poultry products. Vaccination and antibiotic treatment are currently used to control APEC infections; however, the limited effect of vaccines and the emergence of antibiotic-resistant strains have necessitated the development of novel therapeutics. Here, we evaluated seven quorum sensing inhibitors (QSI) identified in our previous study, in APEC-infected chickens. QSIs were administered orally (~92 to 120 μg/bird) and chickens were challenged subcutaneously with APEC. Among them, QSI-5 conferred the best protection (100% reduction in mortality, 82% to 93% reduction in lesions [airsacculitis, perihepatitis, lung congestion, pericarditis] severity, and 5.2 to 6.1 logs reduction in APEC load). QSI-5 was further tested in chickens raised on built-up floor litter using an optimized dose (1 mg/L) in drinking water. QSI-5 reduced the mortality (88.4%), lesion severity (72.2%), and APEC load (2.8 logs) in chickens, which was better than the reduction observed with currently used antibiotic sulfadimethoxine (SDM; mortality 35.9%; lesion severity up to 36.9%; and APEC load up to 2.4 logs). QSI-5 was detected in chicken's blood after 0.5 h with no residues in muscle, liver, and kidney. QSI-5 increased the body weight gain with no effect on the feed conversion ratio and cecal microbiota of the chickens. Metabolomic studies revealed reduced levels of 5'-methylthioadenosine in QSI-5-treated chicken serum. In conclusion, QSI-5 displayed promising effects in chickens and thus, represents a novel anti-APEC therapeutic. IMPORTANCE Avian pathogenic Escherichia coli (APEC), a subgroup of ExPEC, is a zoonotic pathogen with public health importance. Quorum sensing is a mechanism that regulates virulence, biofilm formation, and pathogenesis in bacteria. Here, we identified a novel quorum sensing autoinducer-2 inhibitor, QSI-5, which showed higher anti-APEC efficacy in chickens compared to the currently used antibiotic, sulfadimethoxine at a much lower dose (up to 4,500 times). QSI-5 is readily absorbed with no residues in the tissues. QSI-5 also increased the chicken's body weight gain and did not impact the cecal microbiota composition. Overall, QSI-5 represents a promising lead compound for developing novel anti-virulence therapies with significant implications for treating APEC infections in chickens as well as other ExPEC associated infections in humans. Further identification of its target(s) and understanding the mechanism of action of QSI-5 in APEC will add to the future novel drug development efforts that can overcome the antimicrobial resistance problem.

ConSIG: consistent discovery of molecular signature from OMIC data

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Effects of chlorpyrifos on the metabolic profiling of Bacillus megaterium strain RRB

Many microorganisms have been reported to degrade organic pollutants in the environment and plants, however, the specific information about the effect of organic pollutants on the metabolism of microorganisms is poorly investigated. In the present study, the effect of the pesticide chlorpyrifos on the metabolic profiling of Bacillus megaterium strain RRB was investigated using metabolomics. Our data show that chlorpyrifos acting as an energy source was readily concentrated in the strain RRB from the culture medium. During early cultivation, the shift in energy sources from tryptic soy broth to chlorpyrifos may temporarily cause the strain RRB to enter the starvation stage, where some synthesis-related amino acids and intermediates in the pathways of TCA cycle and pyridoxine metabolism were decreased. The increase of nucleotides and lysine may help the strain RRB cope with the starvation stage. During later cultivation, many metabolites including organic acids, nucleosides and sugar phosphates were gradually accumulated, which indicates that chlorpyrifos could be utilized by the stain RRB to generate metabolites bacteria needed. In addition, arginine acting as a nitrogen-storage amino acid was gradually decreased with later cultivation, suggesting that chlorpyrifos could not provide enough nitrogen for bacteria.

UPLC-G2Si-HDMS Untargeted Metabolomics for Identification of Yunnan Baiyao's Metabolic Target in Promoting Blood Circulation and Removing Blood Stasis

Yunnan Baiyao is a famous Chinese patent medicine in Yunnan Province. However, its mechanism for promoting blood circulation and removing blood stasis is not fully explained. Our study used metabonomics technology to reveal the regulatory effect of Yunnan Baiyao on small molecular metabolites in promoting blood circulation and removing blood stasis, and exploring the related urine biomarkers. The coagulation function, blood rheology, and pathological results demonstrated that after Yunnan Baiyao treatment, the pathological indexes in rats with epinephrine hydrochloride-induced blood stasis syndrome improved and returned to normal levels. This is the basis for the effectiveness of Yunnan Baiyao. UPLC-G2Si-HDMS was used in combination with multivariate statistical analysis to conduct metabonomic analysis of urine samples. Finally, using mass spectrometry technology, 28 urine biomarkers were identified, clarifying the relevant metabolic pathways that play a vital role in the Yunnan Baiyao treatment. These were used as the target for Yunnan Baiyao to promote blood circulation and remove blood stasis. This study showed that metabolomics strategies provide opportunities and conditions for a deep and systematic understanding of the mechanism of action of prescriptions.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Serum metabolomic analysis reveals several novel metabolites in association with excessive alcohol use - an exploratory study

Appropriate screening tool for excessive alcohol use (EAU) is clinically important as it may help providers encourage early intervention and prevent adverse outcomes. We hypothesized that patients with excessive alcohol use will have distinct serum metabolites when compared to healthy controls. Serum metabolic profiling of 22 healthy controls and 147 patients with a history of EAU was performed. We employed seemingly unrelated regression to identify the unique metabolites and found 67 metabolites (out of 556), which were differentially expressed in patients with EAU. Sixteen metabolites belong to the sphingolipid metabolism, 13 belong to phospholipid metabolism, and the remaining 38 were metabolites of 25 different pathways. We also found 93 serum metabolites that were significantly associated with the total quantity of alcohol consumption in the last 30 days. A total of 15 metabolites belong to the sphingolipid metabolism, 11 belong to phospholipid metabolism, and 7 metabolites belong to lysolipid. Using a Venn diagram approach, we found the top 10 metabolites with differentially expressed in EAU and significantly associated with the quantity of alcohol consumption, sphingomyelin (d18:2/18:1), sphingomyelin (d18:2/21:0,d16:2/23:0), guanosine, S-methylmethionine, 10-undecenoate (11:1n1), sphingomyelin (d18:1/20:1, d18:2/20:0), sphingomyelin (d18:1/17:0, d17:1/18:0, d19:1/16:0), N-acetylasparagine, sphingomyelin (d18:1/19:0, d19:1/18:0), and 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1). The diagnostic performance of the top 10 metabolites, using the area under the ROC curve, was significantly higher than that of commonly used markers. We have identified a unique metaboloic signature among patients with EAU. Future studies to validate and determine the kinetics of these markers as a function of alcohol consumption are needed.

Cocoa bean fingerprinting via correlation networks

Cocoa products have a remarkable chemical and sensory complexity. However, in contrast to other fermentation processes in the food industry, cocoa bean fermentation is left essentially uncontrolled and is devoid of standardization. Questions of food authenticity and food quality are hence particularly challenging for cocoa. Here we provide an illustration how network science can support food fingerprinting and food authenticity research. Using a large dataset of 140 cocoa samples comprising three cocoa fermentation/processing stages and eight countries, we obtain correlation networks between the cocoa samples by computing measures of pairwise correlation from their liquid chromatography-mass spectrometry (LC-MS) profiles. We find that the topology of correlation networks derived from untargeted LC-MS profiles is indicative of the fermentation and processing stage as well as the origin country of cocoa samples. Progressively increasing the correlation threshold firstly reveals network clusters based on processing stage and later country-based clusters. We present both, qualitative and quantitative evidence through network visualization, network statistics and concepts from machine learning. In our view, this network-based approach for classifying mass spectrometry data has broad applicability beyond cocoa.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Metabolomics study identified bile acids as potential biomarkers for gastric cancer: A case control study

Gastric cancer (GC) is a common lethal malignancy worldwide. Gastroscopy is an effective screening technique for decreasing mortality. However, there are still limited useful non-invasive markers for early detection of GC. Bile acids are important molecules for the modulation of energy metabolism. With an in-depth targeted method for accurate quantitation of 80 bile acids (BAs), we aimed to find potential biomarkers for the early screening of GC. A cohort with 280 participants was enrolled, including 113 GC, 22 benign gastric lesions (BGL) and 145 healthy controls. Potential markers were identified using a random forest machine algorithm in the discovery cohort (n=180), then validated in an internal validation cohort (n=78) and a group with 22 BGL. The results represented significant alterations in the circulating BA pool between GC and the controls. BAs also exhibited significant correlations with various clinical traits. Then, we developed a diagnostic panel that comprised six BAs or ratios for GC detection. The panel showed high accuracy for the diagnosis of GC with AUC of 1 (95%CI: 1.00-1.00) and 0.98 (95%CI: 0.93-1.00) in the discovery and validation cohort, respectively. This 6-BAs panel was also able to identify early GC with AUC of 1 (95%CI: 0.999-1.00) and 0.94 (95%CI: 0.83-1.00) in the discovery and validation cohort, respectively. Meanwhile, this panel achieved a good differential diagnosis between GC and BGL and the AUC was 0.873 (95%CI: 0.812-0.934). The alternations of serum bile acids are characteristic metabolic features of GC. Bile acids could be promising biomarkers for the early diagnosis of GC.

Metabolomic profiling of stool of two-year old children from the INSIGHT study reveals links between butyrate and child weight outcomes

BACKGROUND

Metabolomic analysis is commonly used to understand the biological underpinning of diseases such as obesity. However, our knowledge of gut metabolites related to weight outcomes in young children is currently limited.

OBJECTIVES

To (1) explore the relationships between metabolites and child weight outcomes, (2) determine the potential effect of covariates (e.g., child's diet, maternal health/habits during pregnancy, etc.) in the relationship between metabolites and child weight outcomes, and (3) explore the relationship between selected gut metabolites and gut microbiota abundance.

METHODS

Using ¹ H-NMR, we quantified 30 metabolites from stool samples of 170 two-year-old children. To identify metabolites and covariates associated with children's weight outcomes (BMI [weight/height² ], BMI z-score [BMI adjusted for age and sex], and growth index [weight/height]), we analysed the ¹ H-NMR data, along with 20 covariates recorded on children and mothers, using LASSO and best subset selection regression techniques. Previously characterized microbiota community information from the same stool samples was used to determine associations between selected gut metabolites and gut microbiota.

RESULTS

At age 2 years, stool butyrate concentration had a significant positive association with child BMI (p-value = 3.58 × 10^-4 ), BMI z-score (p-value = 3.47 × 10^-4 ), and growth index (p-value = 7.73 × 10^-4 ). Covariates such as maternal smoking during pregnancy are important to consider. Butyrate concentration was positively associated with the abundance of the bacterial genus Faecalibacterium (p-value = 9.61 × 10^-3 ).

CONCLUSIONS

Stool butyrate concentration is positively associated with increased child weight outcomes and should be investigated further as a factor affecting childhood obesity.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies

Background

Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH.

Results

PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate.

Conclusion

PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04478-w.

LpNOL-knockdown suppression of heat-induced leaf senescence in perennial ryegrass involving regulation of amino acid and organic acid metabolism

The nonyellow COLORING 1-like gene (NOL) is known for its roles in accelerating leaf senescence, but the underlying metabolic mechanisms for heat-induced leaf senescence remain unclear. The objectives of this study were to identify metabolites and associated metabolic pathways regulated by knockdown of NOL in perennial ryegrass (Lolium perenne) and to determine the metabolic mechanisms of NOL controlling heat-induced leaf senescence. Wild-type (WT; cv. "Pinnacle") and two lines (Noli-1 and Noli-2) of perennial ryegrass with LpNOL knockdown were exposed to heat stress at 35/33°C (day/night) or nonstress control temperatures at 25/22°C (day/night) for 30 days in growth chambers. Leaf electrolyte leakage, chlorophyll (Chl) content, photochemical efficiency (F_v /F_m ), and net photosynthetic rate (Pn) were measured as physiological indicators of leaf senescence, while gas chromatography-mass spectrometry was performed to identify metabolites regulated by LpNOL. Knockdown of LpNOL suppressed heat-induced leaf senescence and produced a stay-green phenotype in perennial ryegrass, as manifested by increased Chl content, photochemical efficiency, net photosynthetic rate, and cell membrane stability in Noli-1 and Noli-2. Five metabolites (valine, malic acid, threonic acid, shikimic acid, chlorogenic acid) were uniquely upregulated in LpNOL plants exposed to heat stress, and six metabolites (aspartic acid, glutamic acid, 5-oxoproline, phenylalanine, proline, tartaric acid) exhibited more pronounced increases in their content in LpNOL plants than the WT. LpNOL could regulate heat-induced leaf senescence in perennial ryegrass through metabolic reprogramming in the pathways of respiration, secondary metabolism, antioxidant metabolism, and protein synthesis.