Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups—including naphthalene derivatives, phenols, and organosulphurs—augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

A Study of the Lipidomic Profiles of the CAL-27 and HOK Cell Lines Using EMS Spectra

ObjectiveThe aim of this study was to explore the lipidomic profiles of the CAL-27 human tongue cancer cell line and the human oral keratinocyte (HOK) cell line.MethodsThe lipidomic differences between the CAL-27 and the HOK cell lines were investigated using non-targeted high-performance liquid chromatography–mass spectrometry lipidomic analysis. The resulting data were then further mined via bioinformatics analysis technology and metabolic pathway analysis was conducted in order to map the most affected metabolites and pathways in the two cell lines.ResultsA total of 711 lipids were identified, including 403 glycerophospholipids (GPs), 147 glycerolipids, and 161 sphingolipids. Comparison of the enhanced MS (EMS) spectra of the two cell lines in positive and negative ionization modes showed the lipid compositions of HOK and CAL-27 cells to be similar. The expressions of most GP species in CAL-27 cells showed an increasing trend as compared with HOK, whereas a significant increase in phosphatidylcholine was observed (p < 0.05). Significant differences in the lipid composition between CAL-27 and HOK cells were shown as a heatmap. Through principal component analysis and orthogonal partial least squares discriminant analysis, noticeably clear separation trends and satisfactory clustering trends between groups of HOK and CAL-27 cells were identified. The numbers of specific lipid metabolites that could distinguish CAL-27 from HOK in positive and negative modes were 100 and 248, respectively. GP metabolism was the most significantly altered lipid metabolic pathway, with 4 metabolites differentially expressed in 39 hit products.ConclusionThis study demonstrated the potential of using untargeted mass spectra and bioinformatics analysis to describe the lipid profiles of HOK and CAL-27 cells.

Assessing Causal Relationship Between Human Blood Metabolites and Five Neurodegenerative Diseases With GWAS Summary Statistics

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

Carbon Ion Radiotherapy Evokes a Metabolic Reprogramming and Individualized Response in Prostate Cancer

Introduction: Carbon ion radiotherapy (CIRT) is a novel treatment for prostate cancer (PCa). However, the underlying mechanism for the individualized response to CIRT is still not clear. Metabolic reprogramming is essential for tumor growth and proliferation. Although changes in metabolite profiles have been detected in patients with cancer treated with photon radiotherapy, there is limited data regarding CIRT-induced metabolic changes in PCa. Therefore, the study aimed to investigate the impact of metabolic reprogramming on individualized response to CIRT in patients with PCa.Materials and Methods: Urine samples were collected from pathologically confirmed patients with PCa before and after CIRT. A UPLC-MS/MS system was used for metabolite detection. XCMS online, MetDNA, and MS-DIAL were used for peak detection and identification of metabolites. Statistical analysis and metabolic pathway analysis were performed on MetaboAnalyst.Results: A total of 1,701 metabolites were monitored in this research. Principal component analysis (PCA) revealed a change in the patient's urine metabolite profiles following CIRT. Thirty-five metabolites were significantly altered, with the majority of them being amino acids. The arginine biosynthesis and histidine metabolism pathways were the most significantly altered pathways. Hierarchical cluster analysis (HCA) showed that after CIRT, the patients could be clustered into two groups according to their metabolite profiles. The arginine biosynthesis and phenylalanine, tyrosine, and tryptophan biosynthesis pathways are the most significantly discriminated pathways.Conclusion: Our preliminary findings indicate that metabolic reprogramming and inhibition are important mechanisms involved in response to CIRT in patients with PCa. Therefore, changes in urine metabolites could be used to timely assess the individualized response to CIRT.

Plasma concentrations of branched-chain amino acids differ with Holstein genetic strain in pasture-based dairy systems

In pasture-based systems, there are nutritional and climatic challenges exacerbated across lactation; thus, dairy cows require an enhanced adaptive capacity compared with cows in confined systems. We aimed to evaluate the effect of lactation stage (21 vs. 180 days in milk, DIM) and Holstein genetic strain (North American Holstein, NAH, n = 8; New Zealand Holstein, NZH, n = 8) on metabolic adaptations of grazing dairy cows through plasma metabolomic profiling and its association with classical metabolites. Although 67 metabolites were affected (FDR < 0.05) by DIM, no metabolite was observed to differ between genetic strains while only alanine was affected (FDR = 0.02) by the interaction between genetic strain and DIM. However, complementary tools for time-series analysis (ASCA analysis, MEBA ranking) indicated that alanine and the branched-chain amino acids (BCAA) differed between genetic strains in a lactation-stage dependent manner. Indeed, NZH cows had lower (P-Tukey < 0.05) plasma concentrations of leucine, isoleucine and valine than NAH cows at 21 DIM, probably signaling for greater insulin sensitivity. Metabolic pathway analysis also revealed that, independently of genetic strains, AA metabolism might be structurally involved in homeorhetic changes as 40% (19/46) of metabolic pathways differentially expressed (FDR < 0.05) between 21 and 180 DIM belonged to AA metabolism.

Metabolomic diferences between COVID-19 and H1N1 influenza induced ARDS

Background Acute respiratory distress syndrome (ARDS) is a type of respiratory failure characterized by lung inflammation and pulmonary edema. Coronavirus disease 2019 (COVID-19) is associated with ARDS in the more severe cases. This study aimed to compare the specificity of the metabolic alterations induced by COVID-19 or Influenza A pneumonia (IAP) in ARDS. Methods Eighteen patients with ARDS due to COVID-19 and twenty patients with ARDS due to IAP, admitted to the intensive care unit. ARDS was defined as in the American-European Consensus Conference. As compared with patients with COVID-19, patients with IAP were younger and received more often noradrenaline to maintain a mean arterial pressure > 65 mm Hg. Serum samples were analyzed by Nuclear Magnetic Resonance Spectroscopy. Multivariate Statistical Analyses were used to identify metabolic differences between groups. Metabolic pathway analysis was performed to identify the most relevant pathways involved in ARDS development. Results ARDS due to COVID-19 or to IAP induces a different regulation of amino acids metabolism, lipid metabolism, glycolysis, and anaplerotic metabolism. COVID‐19 causes a significant energy supply deficit that induces supplementary energy-generating pathways. In contrast, IAP patients suffer more marked inflammatory and oxidative stress responses. The classificatory model discriminated against the cause of pneumonia with a success rate of 100%. Conclusions Our findings support the concept that ARDS is associated with a characteristic metabolomic profile that may discriminate patients with ARDS of different etiologies, being a potential biomarker for the diagnosis, prognosis, and management of this condition. Graphical Abstract

Hybrid de novo Genome Assembly of Erwinia sp. E602 and Bioinformatic Analysis Characterized a New Plasmid-Borne lac Operon Under Positive Selection

Our previous study identified a new β-galactosidase in Erwinia sp. E602. To further understand the lactose metabolism in this strain, de novo genome assembly was conducted by using a strategy combining Illumina and PacBio sequencing technology. The whole genome of Erwinia sp. E602 includes a 4.8 Mb chromosome and a 326 kb large plasmid. A total of 4,739 genes, including 4,543 protein-coding genes, 25 rRNAs, 82 tRNAs and 7 other ncRNAs genes were annotated. The plasmid was the largest one characterized in genus Erwinia by far, and it contained a number of genes and pathways responsible for lactose metabolism and regulation. Moreover, a new plasmid-borne lac operon that lacked a typical β-galactoside transacetylase (lacA) gene was identified in the strain. Phylogenetic analysis showed that the genes lacY and lacZ in the operon were under positive selection, indicating the adaptation of lactose metabolism to the environment in Erwinia sp. E602. Our current study demonstrated that the hybrid de novo genome assembly using Illumina and PacBio sequencing technologies, as well as the metabolic pathway analysis, provided a useful strategy for better understanding of the evolution of undiscovered microbial species or strains.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

Metabolic Pathway Analysis in the Presence of Biological Constraints

Metabolic pathway analysis is a key method to study a metabolism in its steady state, and the concept of elementary fluxes (EFs) plays a major role in the analysis of a network in terms of non-decomposable pathways. The supports of the EFs contain in particular those of the elementary flux modes (EFMs), which are the support-minimal pathways, and EFs coincide with EFMs when the only flux constraints are given by the irreversibility of certain reactions. Practical use of both EFMs and EFs has been hampered by the combinatorial explosion of their number in large, genome-scale systems. The EFs give the possible pathways in a steady state but the real pathways are limited by biological constraints, such as thermodynamic or, more generally, kinetic constraints and regulatory constraints from the genetic network. We provide results on the mathematical structure and geometrical characterization of the solution space in the presence of such biological constraints (which is no longer a convex polyhedral cone or a convex polyhedron) and revisit the concept of EFMs and EFs in this framework. We show that most of the results depend only on very general properties of compatibility of constraints with vector signs: either sign-invariance, satisfied by regulatory constraints, or sign-monotonicity (a stronger property), satisfied by thermodynamic and kinetic constraints. We show in particular that the solution space for sign-monotone constraints is a union of particular faces of the original polyhedral cone or polyhedron and that EFs still coincide with EFMs and are just those of the original EFs that satisfy the constraint, and we show how to integrate their computation efficiently in the double description method, the most widely used method in the tools dedicated to EFs computation. We show that, for sign-invariant constraints, the situation is more complex: the solution space is a disjoint union of particular semi-open faces (i.e., without some of their own faces of lesser dimension) of the original polyhedral cone or polyhedron and, if EFs are still those of the original EFs that satisfy the constraint, their computation cannot be incrementally integrated into the double description method, and the result is not true for EFMs, that are in general strictly more numerous than those of the original EFMs that satisfy the constraint.

Widely Targeted Metabolomics Analysis to Reveal Transformation Mechanism of Cistanche Deserticola Active Compounds During Steaming and Drying Processes

Cistanche deserticola is one of the most precious plants, traditionally as Chinese medicine, and has recently been used in pharmaceutical and healthy food industries. Steaming and drying are two important steps in the processing of Cistanche deserticola. Unfortunately, a comprehensive understanding of the chemical composition changes of Cistanche deserticola during thermal processing is limited. In this study, ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based widely targeted metabolomics analysis was used to investigate the transformation mechanism of Cistanche deserticola active compounds during steaming and drying processes. A total of 776 metabolites were identified in Cistanche deserticola during thermal processing, among which, 77 metabolites were differentially regulated (p &lt; 0.05) wherein 39 were upregulated (UR) and 38 were downregulated (DR). Forty-seven (17 UR, 30 DR) and 30 (22 UR, 8 DR) differential metabolites were identified during steaming and drying, respectively. The most variation of the chemicals was observed during the process of steaming. Metabolic pathway analysis indicated that phenylpropanoid, flavonoid biosynthesis, and alanine metabolism were observed during steaming, while glycine, serine, and threonine metabolism, thiamine metabolism, and unsaturated fatty acid biosynthesis were observed during drying. The possible mechanisms of the chemical alterations during thermal processing were also provided by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Furthermore, the blackening of the appearance of Cistanche deserticola mainly occurred in the steaming stage rather than the drying stage, which is associated with the metabolism of the amino acids. All results indicated that the formation of active compounds during the processing of Cistanche deserticola mainly occurred in the steaming stage.