Metabolomics Data Processing Using XCMS

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Metabolic Profiles of Encapsulated Chondrocytes Exposed to Short-Term Simulated Microgravity

The mechanism by which chondrocytes respond to reduced mechanical loading environments and the subsequent risk of developing osteoarthritis remains unclear. This is of particular concern for astronauts. In space the reduced joint loading forces during prolonged microgravity (10 -6 g ) exposure could lead to osteoarthritis (OA), compromising quality of life post-spaceflight. In this study, we encapsulated human chondrocytes in an agarose gel of similar stiffness to the pericellular matrix to mimic the cartilage microenvironment. We then exposed agarose-chondrocyte constructs to simulated microgravity (SM) using a rotating wall vessel (RWV) bioreactor to better assess the cartilage health risks associated with spaceflight. Global metabolomic profiling detected a total of 1205 metabolite features across all samples, with 497 significant metabolite features identified by ANOVA (FDR-corrected p-value < 0.05). Specific metabolic shifts detected in response to SM exposure resulted in clusters of co-regulated metabolites, as well as key metabolites identified by variable importance in projection scores. Microgravity-induced metabolic shifts in gel constructs and media were indicative of protein synthesis, energy metabolism, nucleotide metabolism, and oxidative catabolism. The microgravity associated-metabolic shifts were consistent with early osteoarthritic metabolomic profiles in human synovial fluid, which suggests that even short-term exposure to microgravity (or other reduced mechanical loading environments) may lead to the development of OA.

Active Molecular Network Discovery Links Lifestyle Variables to Breast Cancer in the Long Island Breast Cancer Study Project

A healthy lifestyle has been associated with decreased risk of developing breast cancer. Using untargeted metabolomics profiling, which provides unbiased information regarding lifestyle choices such as diet and exercise, we aim to identify the molecular mechanisms connecting lifestyle and breast cancer through network analysis. A total of 100 postmenopausal women, 50 with breast cancer and 50 cancer-free controls, were selected from the Long Island Breast Cancer Study Project (LIBCSP). We measured untargeted plasma metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Using the "enet" package, we retained highly correlated metabolites representing active molecular network (AMN) clusters for analysis. LASSO was used to examine associations between cancer status and AMN metabolites and covariates such as BMI, age, and reproductive factors. LASSO was then repeated to examine associations between AMN metabolites and 10 lifestyle-related variables including smoking, physical activity, alcohol consumption, meat consumption, fruit and vegetable consumption, and supplemental vitamin use. Results were displayed as a network to uncover biological pathways linking lifestyle factors to breast cancer status. After filtering, 851 "active" metabolites out of 1797 metabolomics were retained in 197 correlation AMN clusters. Using LASSO, breast cancer status was associated with 71 "active" metabolites. Several of these metabolites were associated with lifestyle variables including meat consumption, alcohol consumption, and supplemental β-carotene, B12, and folate use. Those metabolites could potentially serve as molecular-level biological intermediaries connecting healthy lifestyle factors to breast cancer, even though direct associations between breast cancer and the investigated lifestyles at the phenotype level are not evident. In particular, DiHODE, a metabolite linked with inflammation, was associated with breast cancer status and connected to β-carotene supplement usage through an AMN. We found several plasma metabolites associated with lifestyle factors and breast cancer status. Future studies investigating the mechanistic role of inflammation in linking supplement usage to breast cancer status are warranted.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Broiler Spaghetti Meat Abnormalities: Muscle Characteristics and Metabolomic Profiles

Spaghetti meat (SM) is a newly identified muscle abnormality that significantly affects modern broiler chickens, consequently exerting a substantial economic impact on the poultry industry worldwide. However, investigations into the meat quality and the underlying causative factors of SM in broilers remain limited. Therefore, this study was undertaken to systematically evaluate meat quality and muscle fiber characteristics of SM-affected meat. To elucidate the disparities between SM-affected and normal (NO) muscles in broiler chickens reared under identical conditions, we selected 18 SM-affected breast tissues and 18 NO breast tissues from 200 broiler chickens raised according to commercial standards under the same conditions for our study. The results showed that compared with the NO group, the muscle surface of the SM group lost integrity, similar to strip and paste. The brightness and yellowness values were significantly higher than those of the NO group. On the contrary, the shear force and protein were significantly lower in the SM group. Microscopic examination revealed that the muscle fibers in the SM group were lysed, necrotic, and separated from each other, with a large number of neutrophils diffusely distributed on the sarcolemma and endometrium. Thirty-five significantly different metabolites were observed in the breast muscles between both groups. Among them, the top differential metabolites-14,15-DiHETrE, isotretinoin, L-malic acid, and acetylcysteine-were mainly enriched in lipid metabolism and inflammatory pathways, including linoleic acid, arachidonic acid, phenylalanine, and histidine metabolism. Overall, these findings not only offer new insights into the meat quality and fiber traits of SM but also contribute to the understanding of potential mechanisms and nutritional regulators for SM myopathy.

Multimodal Omics Approaches to Aging and Age-Related Diseases

Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

A pilot study of alternative substrates in the critically Ill subject using a ketogenic feed

Bioenergetic failure caused by impaired utilisation of glucose and fatty acids contributes to organ dysfunction across multiple tissues in critical illness. Ketone bodies may form an alternative substrate source, but the feasibility and safety of inducing a ketogenic state in physiologically unstable patients is not known. Twenty-nine mechanically ventilated adults with multi-organ failure managed on intensive care units were randomised (Ketogenic n = 14, Control n = 15) into a two-centre pilot open-label trial of ketogenic versus standard enteral feeding. The primary endpoints were assessment of feasibility and safety, recruitment and retention rates and achievement of ketosis and glucose control. Ketogenic feeding was feasible, safe, well tolerated and resulted in ketosis in all patients in the intervention group, with a refusal rate of 4.1% and 82.8% retention. Patients who received ketogenic feeding had fewer hypoglycaemic events (0.0% vs. 1.6%), required less exogenous international units of insulin (0 (Interquartile range 0-16) vs.78 (Interquartile range 0-412) but had slightly more daily episodes of diarrhoea (53.5% vs. 42.9%) over the trial period. Ketogenic feeding was feasible and may be an intervention for addressing bioenergetic failure in critically ill patients. Clinical Trials.gov registration: NCT04101071.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans' algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

Integrative analysis of microbiota and metabolomics in chromium-exposed silkworm (Bombyx mori) midguts based on 16S rDNA sequencing and LC/MS metabolomics

The gut microbiota, a complex ecosystem integral to host wellbeing, is modulated by environmental triggers, including exposure to heavy metals such as chromium. This study aims to comprehensively explore chromium-induced gut microbiota and metabolomic shifts in the quintessential lepidopteran model organism, the silkworm (Bombyx mori). The research deployed 16S rDNA sequence analysis and LC/MS metabolomics in its experimental design, encompassing a control group alongside low (12 g/kg) and high (24 g/kg) feeding chromium dosing regimens. Considerable heterogeneity in microbial diversity resulted between groups. Weissella emerged as potentially resilient to chromium stress, while elevated Propionibacterium was noted in the high chromium treatment group. Differential analysis tools LEfSe and random forest estimation identified key species like like Cupriavidus and unspecified Myxococcales, offering potential avenues for bioremediation. An examination of gut functionality revealed alterations in the KEGG pathways correlated with biosynthesis and degradation, suggesting an adaptive metabolic response to chromium-mediated stress. Further results indicated consequential fallout in the context of metabolomic alterations. These included an uptick in histidine and dihydropyrimidine levels under moderate-dose exposure and a surge of gentisic acid with high-dose chromium exposure. These are critical players in diverse biological processes ranging from energy metabolism and stress response to immune regulation and antioxidative mechanisms. Correlative analyses between bacterial abundance and metabolites mapped noteworthy relationships between marker bacterial species, such as Weissella and Pelomonas, and specific metabolites, emphasizing their roles in enzyme regulation, synaptic processes, and lipid metabolism. Probiotic bacteria showed robust correlations with metabolites implicated in stress response, lipid metabolism, and antioxidant processes. Our study reaffirms the intricate ties between gut microbiota and metabolite profiles and decodes some systemic adaptations under heavy-metal stress. It provides valuable insights into ecological and toxicological aspects of chromium exposure that can potentially influence silkworm resilience.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

Datasets for the effects of RUNX2 silencing on transcriptomic and metabolomic profiles in SJSA-1 osteosarcoma cells

Osteosarcoma is the most common primary malignant bone tumor with a high risk of metastasis and recurrence. Metabolic reprogramming is a hallmark of osteosarcoma and other cancers and is associated with genetic and epigenetic alterations. RUNX2 is an important transcription factor for osteoblastic differentiation, and aberrant expression of the gene contributes to the development and progression of osteosarcoma. To identify the effects of RUNX2 silencing on transcriptomic and metabolomic profiles in osteosarcomas, we generated SJSA-1 osteosarcoma cells stably expressing RUNX2 shRNA and SJSA-1 cells stably expressing scramble shRNA and analyzed transcriptome and metabolome profiles in the two cell types using Illumina NovaSeq 6000 and ultrahigh-performance liquid chromatography coupled with time-of-flight mass spectrometry, respectively. The datasets can be used by researchers to identify novel targets of RUNX2 and elucidate the role and underlying mechanism of RUNX2 in osteosarcoma pathogenesis and metabolic reprogramming.

A de novo evolved gene contributes to rice grain shape difference between indica and japonica

The role of de novo evolved genes from non-coding sequences in regulating morphological differentiation between species/subspecies remains largely unknown. Here, we show that a rice de novo gene GSE9 contributes to grain shape difference between indica/xian and japonica/geng varieties. GSE9 evolves from a previous non-coding region of wild rice Oryza rufipogon through the acquisition of start codon. This gene is inherited by most japonica varieties, while the original sequence (absence of start codon, gse9) is present in majority of indica varieties. Knockout of GSE9 in japonica varieties leads to slender grains, whereas introgression to indica background results in round grains. Population evolutionary analyses reveal that gse9 and GSE9 are derived from wild rice Or-I and Or-III groups, respectively. Our findings uncover that the de novo GSE9 gene contributes to the genetic and morphological divergence between indica and japonica subspecies, and provide a target for precise manipulation of rice grain shape.

MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis

Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).

Age-related alterations in metabolome and microbiome provide insights in dietary transition in giant pandas

We conducted UPLC-MS-based metabolomics, 16S rRNA, and metagenome sequencing on the fecal samples of 44 captive giant pandas (Ailuropoda melanoleuca) from four age groups (i.e., Cub, Young, Adult, and Old) to comprehensively understand age-related changes in the metabolism and gut microbiota of giant pandas. We characterized the metabolite profiles of giant pandas based on 1,376 identified metabolites, with 152 significantly differential metabolites (SDMs) found across the age groups. We found that the metabolites and the composition/function of the gut microbiota changed in response to the transition from a milk-dominant diet in panda cubs to a bamboo-specific diet in young and adult pandas. Lipid metabolites such as choline and hippuric acid were enriched in the Cub group, and many plant secondary metabolites were significantly higher in the Young and Adult groups, while oxidative stress and inflammatory related metabolites were only found in the Old group. However, there was a decrease in the α-diversity of gut microbiota in adult and old pandas, who exclusively consume bamboo. The abundance of bacteria related to the digestion of cellulose-rich food, such as Firmicutes, Streptococcus, and Clostridium, significantly increased from the Cub to the Adult group, while the abundance of beneficial bacteria such as Faecalibacterium, Sarcina, and Blautia significantly decreased. Notably, several potential pathogenic bacteria had relatively high abundances, especially in the Young group. Metagenomic analysis identified 277 CAZyme genes including cellulose degrading genes, and seven of the CAZymes had abundances that significantly differed between age groups. We also identified 237 antibiotic resistance genes (ARGs) whose number and diversity increased with age. We also found a significant positive correlation between the abundance of bile acids and gut bacteria, especially Lactobacillus and Bifidobacterium. Our results from metabolome, 16S rRNA, and metagenome data highlight the important role of the gut microbiota-bile acid axis in the regulation of age-related metabolism and provide new insights into the lipid metabolism of giant pandas. IMPORTANCE The giant panda is a member of the order Carnivora but is entirely herbivorous. The giant panda's specialized diet and related metabolic mechanisms have not been fully understood. It is therefore crucial to investigate the dynamic changes in metabolites as giant pandas grow and physiologically adapt to their herbivorous diet. This study conducted UPLC-MS-based metabolomics 16S rRNA, and metagenome sequencing on the fecal samples of captive giant pandas from four age groups. We found that metabolites and the composition/function of gut microbiota changed in response to the transition from a milk-dominant diet in cubs to a bamboo-specific diet in young and adult pandas. The metabolome, 16S rRNA, and metagenome results highlight that the gut microbiota-bile acid axis has an important role in the regulation of age-related metabolism, and our study provides new insights into the lipid metabolism of giant pandas.

Ellagitannin Punicalagin Disrupts the Pathways Related to Bacterial Growth and Affects Multiple Pattern Recognition Receptor Signaling by Acting as a Selective Histone Deacetylase Inhibitor

Punicalagin (PA) is a key ellagitannin abundant in pomegranate with wide-ranging biological activities. In this study, we examined the biological processes by which PA regulates bacterial growth and inflammation in human cells using multiomics and molecular docking approaches. PA promoted macrophage-mediated bacterial killing and inhibited the growth of Citrobacter rodentium by inducing a distinct metabolome pattern. PA acted as a selective regulator of histone deacetylases (HDACs) and affected 37 pathways in macrophages, including signaling mediated by pattern recognition receptors, such as Toll-like and NOD-like receptors. In silico simulation showed that PA can bind with high affinity to HDAC7. PA downregulated HDAC7 at both mRNA and protein levels and resulted in a decrease in the level of histone 3 lysine 27 acetylation. Our findings provide evidence that PA exerts its biological effects via multiple pathways, which can be exploited in the development of this bioactive food ingredient for disease management.

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.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Beta-Glucan Alters Gut Microbiota and Plasma Metabolites in Pre-Weaning Dairy Calves

The present study aims to evaluate the alterations in gut microbiome and plasma metabolites of dairy calves with β-glucan (BG) supplementation. Fourteen healthy newborn dairy calves with similar body weight were randomly divided into control (n = 7) and BG (n = 7) groups. All the calves were fed on the basal diet, while calves in the BG group were supplemented with oat BG on d 8 for 14 days. Serum markers, fecal microbiome, and plasma metabolites at d 21 were analyzed. The calves were weaned on d 60 and weighed. The mean weaning weight of the BG group was 4.29 kg heavier than that of the control group. Compared with the control group, the levels of serum globulin, albumin, and superoxide dismutase were increased in the BG group. Oat BG intake increased the gut microbiota richness and decreased the Firmicutes-to-Bacteroidetes ratio. Changes in serum markers were found to be correlated with the plasma metabolites, including sphingosine, trehalose, and 3-methoxy-4-hydroxyphenylglycol sulfate, and gut microbiota such as Ruminococcaceae_NK4A214, Alistipes, and Bacteroides. Overall, these results suggest that the BG promotes growth and health of pre-weaning dairy calves by affecting the interaction between the host and gut microbiota.

mTORC1 and mTORC2 Complexes Regulate the Untargeted Metabolomics and Amino Acid Metabolites Profile through Mitochondrial Bioenergetic Functions in Pancreatic Beta Cells

Background: Pancreatic beta cells regulate bioenergetics efficiency and secret insulin in response to glucose and nutrient availability. The mechanistic Target of Rapamycin (mTOR) network orchestrates pancreatic progenitor cell growth and metabolism by nucleating two complexes, mTORC1 and mTORC2. Objective: To determine the impact of mTORC1/mTORC2 inhibition on amino acid metabolism in mouse pancreatic beta cells (Beta-TC-6 cells, ATCC-CRL-11506) using high-resolution metabolomics (HRM) and live-mitochondrial functions. Methods: Pancreatic beta TC-6 cells were incubated for 24 h with either: RapaLink-1 (RL); Torin-2 (T); rapamycin (R); metformin (M); a combination of RapaLink-1 and metformin (RLM); Torin-2 and metformin (TM); compared to the control. We applied high-resolution mass spectrometry (HRMS) LC-MS/MS untargeted metabolomics to compare the twenty natural amino acid profiles to the control. In addition, we quantified the bioenergetics dynamics and cellular metabolism by live-cell imaging and the MitoStress Test XF24 (Agilent, Seahorse). The real-time, live-cell approach simultaneously measures the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) to determine cellular respiration and metabolism. Statistical significance was assessed using ANOVA on Ranks and post-hoc Welch t-Tests. Results: RapaLink-1, Torin-2, and rapamycin decreased L-aspartate levels compared to the control (p = 0.006). Metformin alone did not affect L-aspartate levels. However, L-asparagine levels decreased with all treatment groups compared to the control (p = 0.03). On the contrary, L-glutamate and glycine levels were reduced only by mTORC1/mTORC2 inhibitors RapaLink-1 and Torin-2, but not by rapamycin or metformin. The metabolic activity network model predicted that L-aspartate and AMP interact within the same activity network. Live-cell bioenergetics revealed that ATP production was significantly reduced in RapaLink-1 (122.23 ± 33.19), Torin-2 (72.37 ± 17.33) treated cells, compared to rapamycin (250.45 ± 9.41) and the vehicle control (274.23 ± 38.17), p < 0.01. However, non-mitochondrial oxygen consumption was not statistically different between RapaLink-1 (67.17 ± 3.52), Torin-2 (55.93 ± 8.76), or rapamycin (80.01 ± 4.36, p = 0.006). Conclusions: Dual mTORC1/mTORC2 inhibition by RapaLink-1 and Torin-2 differentially altered the amino acid profile and decreased mitochondrial respiration compared to rapamycin treatment which only blocks the FRB domain on mTOR. Third-generation mTOR inhibitors may alter the mitochondrial dynamics and reveal a bioenergetics profile that could be targeted to reduce mitochondrial stress.

C. elegans as a model for inter-individual variation in metabolism

Individuals can exhibit differences in metabolism that are caused by the interplay of genetic background, nutritional input, microbiota and other environmental factors1-4. It is difficult to connect differences in metabolism to genomic variation and derive underlying molecular mechanisms in humans, owing to differences in diet and lifestyle, among others. Here we use the nematode Caenorhabditis elegans as a model to study inter-individual variation in metabolism. By comparing three wild strains and the commonly used N2 laboratory strain, we find differences in the abundances of both known metabolites and those that have not to our knowledge been previously described. The latter metabolites include conjugates between 3-hydroxypropionate (3HP) and several amino acids (3HP-AAs), which are much higher in abundance in one of the wild strains. 3HP is an intermediate in the propionate shunt pathway, which is activated when flux through the canonical, vitamin-B12-dependent propionate breakdown pathway is perturbed5. We show that increased accumulation of 3HP-AAs is caused by genetic variation in HPHD-1, for which 3HP is a substrate. Our results suggest that the production of 3HP-AAs represents a 'shunt-within-a-shunt' pathway to accommodate a reduction-of-function allele in hphd-1. This study provides a step towards the development of metabolic network models that capture individual-specific differences of metabolism and more closely represent the diversity that is found in entire species.

Untargeted metabolomic profiling identifies disease-specific and outcome-related signatures in chronic rhinosinusitis

BACKGROUND

Although metabolomics provides novel insights into disease mechanisms and biomarkers, the metabolic alterations in local tissues affected by chronic rhinosinusitis (CRS) are unknown.

OBJECTIVE

This study aims to determine the metabolomic profiles of sinonasal tissues associated with different types of CRS and their treatment outcomes.

METHODS

Untargeted metabolomic profiling was performed on sinonasal tissues obtained from patients with eosinophilic CRS with nasal polyps (CRSwNP), noneosinophilic CRSwNP or CRS without nasal polyps (CRSsNP), and controls. The mRNA levels of inflammatory cytokines in nasal tissues were detected by quantitative RT-PCR. Nasal polyp tissues were cultured ex vivo and treated with glutathione.

RESULTS

Distinct metabolomic profiles were observed for the CRS subtypes. Eosinophilic CRSwNP had profoundly enhanced unsaturated fatty acid oxidization, which correlated with mucosal eosinophil numbers and IL-5 mRNA levels. Noneosinophilic CRSwNP was characterized by uric acid accumulation. Increased uric acid levels were positively correlated with mucosal neutrophil numbers and IFN-γ, IL-17A, IL-1β, and IL-8 mRNA levels. Disrupted purine metabolism was specifically detected in CRSsNP. Reduced levels of amino acid metabolites were found in eosinophilic CRSwNP and CRSsNP, and were inversely associated with mucosal total inflammatory cell numbers and inflammatory cytokines. Compared to non-difficult-to-treat CRS, difficult-to-treat CRS had higher glutathione disulfide levels, which were positively correlated with IL-8 mRNA levels. Glutathione treatment reduced IL-8 mRNA expression in cultured nasal polyp tissues.

CONCLUSIONS

Specific metabolic signatures are associated with different types of CRS, inflammatory patterns and disease outcomes, which may provide novel insights into pathophysiological mechanisms, subtype-specific biomarkers, and treatment targets of CRS.

Porcine gut microbiota in mediating host metabolic adaptation to cold stress

The gut microbiota plays a key role in host metabolic thermogenesis by activating UCP1 and increasing the browning process of white adipose tissue (WAT), especially in cold environments. However, the crosstalk between the gut microbiota and the host, which lacks functional UCP1, making them susceptible to cold stress, has rarely been illustrated. We used male piglets as a model to evaluate the host response to cold stress via the gut microbiota (four groups: room temperature group, n = 5; cold stress group, n = 5; cold stress group with antibiotics, n = 5; room temperature group with antibiotics, n = 3). We found that host thermogenesis and insulin resistance increased the levels of serum metabolites such as glycocholic acid (GCA) and glycochenodeoxycholate acid (GCDCA) and altered the compositions and functions of the cecal microbiota under cold stress. The gut microbiota was characterized by increased levels of Ruminococcaceae, Prevotellaceae, and Muribaculaceae under cold stress. We found that piglets subjected to cold stress had increased expression of genes related to bile acid and short-chain fatty acid (SCFA) metabolism in their liver and fat lipolysis genes in their fat. In addition, the fat lipolysis genes CLPS, PNLIPRP1, CPT1B, and UCP3 were significantly increased in the fat of piglets under cold stress. However, the use of antibiotics showed a weakened or strengthened cold tolerance phenotype, indicating that the gut microbiota plays important role in host thermogenesis. Our results demonstrate that the gut microbiota-blood-liver and fat axis may regulate thermogenesis during cold acclimation in piglets.

Chemical Constituents and Molecular Mechanism of the Yellow Phenotype of Yellow Mushroom (Floccularia luteovirens)

(1) Background: Yellow mushroom (Floccularia luteovirens) is a natural resource that is highly nutritional, has a high economic value, and is found in Northwest China. Despite its value, the chemical and molecular mechanisms of yellow phenotype formation are still unclear. (2) Methods: This study uses the combined analysis of transcriptome and metabolome to explain the molecular mechanism of the formation of yellow mushroom. Subcellular localization and transgene overexpression techniques were used to verify the function of the candidate gene. (3) Results: 112 compounds had a higher expression in yellow mushroom; riboflavin was the ninth most-expressed compound. HPLC showed that a key target peak at 23.128 min under visible light at 444 nm was Vb2. All proteins exhibited the closest relationship with Agaricus bisporus var. bisporus H97. One riboflavin transporter, CL911.Contig3_All (FlMCH5), was highly expressed in yellow mushrooms with a different value (log₂ fold change) of −12.98, whereas it was not detected in white mushrooms. FlMCH5 was homologous to the riboflavin transporter MCH5 or MFS transporter in other strains, and the FlMCH5-GFP fusion protein was mainly located in the cell membrane. Overexpression of FlMCH5 in tobacco increased the content of riboflavin in three transgenic plants to 26 μg/g, 26.52 μg/g, and 36.94 μg/g, respectively. (4) Conclusions: In this study, it is clear that riboflavin is the main coloring compound of yellow mushrooms, and FlMCH5 is the key transport regulatory gene that produces the yellow phenotype.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Transcriptome and metabolome analyses of response of Synechocystis sp. PCC 6803 to methyl viologen

The toxicity of methyl viologen (MV) to organisms is mainly due to the oxidative stress caused by reactive oxygen species produced from cell response. This study mainly investigated the response of Synechocystis sp. PCC 6803 to MV by combining transcriptomic and metabolomic analyses. Through transcriptome sequencing, we found many genes responding to MV stress, and analyzed them by weighted gene co-expression network analysis (WGCNA). Meanwhile, many metabolites were also found by metabolomic analysis to be regulated post MV treatment. Based on the analysis results of Kyoto encyclopedia of genes and genomes (KEGG) of the differentially expressed genes (DEGs) in the transcriptome and the differential metabolites in the metabolome, the dynamic changes of genes and metabolites involved in ten metabolic pathways in response to MV were analyzed. The results indicated that although the oxidative stress caused by MV was the strongest at 6 h, the proportion of the upregulated genes and metabolites involved in these ten metabolic pathways was the highest. Photosynthesis positively regulated the response to MV-induced oxidative stress, and the regulation of environmental information processing was inhibited by MV. Other metabolic pathways played different roles at different times and interacted with each other to respond to MV. This study comprehensively analyzed the response of Synechocystis sp. PCC 6803 to oxidative stress caused by MV from a multi-omics perspective, with providing key data and important information for in-depth analysis of the response of organisms to MV, especially photosynthetic organisms. KEY POINTS: • Methyl viologen (MV) treatment caused regulatory changes in genes and metabolites. • Proportion of upregulated genes and metabolites was the highest at 6-h MV treatment. • Photosynthesis and environmental information processing involved in MV response.

Metabolic Analysis of Potential Key Genes Associated with Systemic Lupus Erythematosus Using Liquid Chromatography-Mass Spectrometry

The biological mechanism underlying the pathogenesis of systemic lupus erythematosus (SLE) remains unclear. In this study, we found 21 proteins upregulated and 38 proteins downregulated by SLE relative to normal protein metabolism in our samples using liquid chromatography-mass spectrometry. By PPI network analysis, we identified 9 key proteins of SLE, including AHSG, VWF, IGF1, ORM2, ORM1, SERPINA1, IGF2, IGFBP3, and LEP. In addition, we identified 4569 differentially expressed metabolites in SLE sera, including 1145 reduced metabolites and 3424 induced metabolites. Bioinformatics analysis showed that protein alterations in SLE were associated with modulation of multiple immune pathways, TP53 signaling, and AMPK signaling. In addition, we found altered metabolites associated with valine, leucine, and isoleucine biosynthesis; one carbon pool by folate; tyrosine metabolism; arginine and proline metabolism; glycine, serine, and threonine metabolism; limonene and pinene degradation; tryptophan metabolism; caffeine metabolism; vitamin B6 metabolism. We also constructed differently expressed protein-metabolite network to reveal the interaction among differently expressed proteins and metabolites in SLE. A total of 481 proteins and 327 metabolites were included in this network. Although the role of altered metabolites and proteins in the diagnosis and therapy of SLE needs to be further investigated, the present study may provide new insights into the role of metabolites in SLE.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Age-Related Metabolic Pathways Changes in Dental Follicles: A Pilot Study

Aging is not a matter of choice; it is our fate. The “time-dependent functional decline that affects most living organisms” is coupled with several alterations in cellular processes, such as cell senescence, epigenetic alterations, genomic instability, stem cell exhaustion, among others. Age-related morphological changes in dental follicles have been investigated for decades, mainly motivated by the fact that cysts and tumors may arise in association with unerupted and/or impacted teeth. The more we understand the physiology of dental follicles, the more we are able to contextualize biological events that can be associated with the occurrence of odontogenic lesions, whose incidence increases with age. Thus, our objective was to assess age-related changes in metabolic pathways of dental follicles associated with unerupted/impacted mandibular third molars from young and adult individuals. For this purpose, a convenience sample of formalin-fixed paraffin-embedded (FFPE) dental follicles from young (<16 y.o., n = 13) and adult (>26 y.o., n = 7) individuals was selected. Samples were analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS)-based untargeted metabolomics. Multivariate and univariate analyses were conducted, and the prediction of altered pathways was performed by mummichog and Gene Set Enrichment Analysis (GSEA) approaches. Dental follicles from young and older individuals showed differences in pathways related to C21-steroid hormone biosynthesis, bile acid biosynthesis, galactose metabolism, androgen and estrogen biosynthesis, starch and sucrose metabolism, and lipoate metabolism. We conclude that metabolic pathways differences related to aging were observed between dental follicles from young and adult individuals. Our findings support that similar to other human tissues, dental follicles associated with unerupted tooth show alterations at a metabolic level with aging, which can pave the way for further studies on oral pathology, oral biology, and physiology.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Unveiling metabolic changes in marsupialized odontogenic keratocyst: A pilot study

OBJECTIVE

We aimed to assess which metabolic pathways would be implicated in the phenotypic changes of the epithelial lining of odontogenic keratocyst after marsupialization, comparing pre- and post-marsupialized lesions with adjacent oral mucosa.

MATERIALS AND METHODS

Eighteen formalin-fixed and paraffin-embedded tissues from six subjects were divided into three paired groups: odontogenic keratocyst pre- (n = 6) and post-marsupialization (n = 6), and adjacent oral mucosa (n = 6). The metabolic pathways found in these groups were obtained by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics performed.

RESULTS

Through putative metabolite annotation followed by pathway enrichment and predictive analysis with automated algorithms (Mummichog and Gene Set Enrichment Analysis), we found differences in many cellular processes that may be involved in inflammation, oxidative stress response, keratinocyte-basal membrane attachment, differentiation, and proliferation functions, all relevant to odontogenic keratocyst pathobiology and the phenotype acquired after marsupialization.

CONCLUSION

Our study was able to identify several metabolic pathways potentially involved in the metaplastic changes induced by marsupialization of odontogenic keratocysts. An improved comprehension of this process could pave the way for the development of targeted therapies.

Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources

The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS.

A Novel LC-MS Based Targeted Metabolomic Approach to Study the Biomarkers of Food Intake

SCOPE

In this work, an integrated strategy is developed for rapid discovery, precise identification, and automated quantification for the biomarkers of food intake (BFIs) for specific food exposure using an ultra-high-pressure liquid chromatography-high-resolution mass spectrometry (MS) based targeted metabolomics approach.

METHODS AND RESULTS

Using whole grain (WG) wheat intake as an example, the combination of paired mass distance networking and parallel reaction monitoring analysis is applied to selectively extract and identify WG metabolites in human urine samples. As a result, a total of 76 wheat phytochemical-derived metabolites, including 17 alkylresorcinol metabolites, 20 benzoxazinoid derivatives, and 39 phenolic acid metabolites are identified. Subsequently, a MS spectral database consisting of the identified metabolites is created by mzVault. The characteristics of identified metabolites from the database are incorporated into the TraceFinder software to establish a quantification platform. Using a standardized urine sample, the authors are able to simultaneously quantify both free and conjugated (sulfate and glucuronide) WG wheat metabolites in real samples without further enzymatic hydrolysis, which is validated by using authentic standards to quantify these metabolites.

CONCLUSION

This novel strategy opens the window to study the biomarkers of specific food intake and make it feasible to validate the BFIs in large-scale human studies.

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.