HMDB: a knowledgebase for the human metabolome

Virtual screening and in vitro validation of natural compound inhibitors against SARS-CoV-2 spike protein

The COVID-19 pandemic caused by the SARS-CoV-2 virus has led to a major public health burden and has resulted in millions of deaths worldwide. As effective treatments are limited, there is a significant requirement for high-throughput, low resource methods for the discovery of novel antivirals. The SARS-CoV-2 spike protein plays a key role in viral entry and has been identified as a therapeutic target. Using the available spike crystal structure, we performed a virtual screen with a library of 527 209 natural compounds against the receptor binding domain of this protein. Top hits from this screen were subjected to a second, more comprehensive molecular docking experiment and filtered for favourable ADMET properties. The in vitro activity of 10 highly ranked compounds was assessed using a virus neutralisation assay designed to facilitate viral entry in a physiologically relevant manner via the plasma membrane route. Subsequently, four compounds ZINC02111387, ZINC02122196, SN00074072 and ZINC04090608 were identified to possess antiviral activity in the µM range. These findings validate the virtual screening method as a tool for identifying novel antivirals and provide a basis for future drug development against SARS-CoV-2.

Serum Metabolic Profiles Based on Nuclear Magnetic Resonance Spectroscopy among Patients with Deep Vein Thrombosis and Healthy Controls

Deep venous thrombosis (DVT) is associated with significant morbidity and mortality. Studies on changes in the level of metabolites could have the potential to reveal biomarkers that can assist in the early detection, diagnosis, monitoring of DVT progression, response to treatment, or recurrence of DVT. In this scenario, the metabolomic analysis can provide a better understanding of the biochemical dysregulations of thrombosis. Using an untargeted metabolomic approach through magnetic resonance spectroscopy and multi- and univariate statistical analysis, we compared 40 patients with previous venous thrombosis and 40 healthy individuals, and we showed important serum differences between patients and controls, especially in the spectral regions that correspond to glucose, lipids, unsaturated lipids, and glycoprotein A. Considering the groups depending on risk factors and the local of the previous episode (lower limbs or cerebral system), we also noticed differences in metabolites linked to lipids and lactate. Comparative analyses pointed to altered ratios of glucose/lactate and branched-chain amino acids (BCAAs)/alanine, which might be associated with the fingerprints of thrombosis. Although samples for metabolomic analysis were collected months after the acute episode, these results highlighted that, alterations can still remain and may contribute to a better understanding of the complications of the disease.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Bacterial challenge-associated metabolic phenotypes in Hermetia illucens defining nutritional and functional benefits

Black soldier fly (BSF, Hermetia illucens) is popular for its applications in animal feed, waste management and antimicrobial peptide source. The major advantages of BSF larva include their robust immune system and high nutritional content that can be further developed into more potential agricultural and medical applications. Several strategies are now being developed to exploit their fullest capabilities and one of these is the immunity modulation using bacterial challenges. The mechanism underlying metabolic responses of BSF to different bacteria has, however, remained unclear. In the current study, entometabolomics was employed to investigate the metabolic phenoconversion in response to either Escherichia coli, Staphylococcus aureus, or combined challenges in BSF larva. We have, thus far, characterised 37 metabolites in BSF larva challenged with different bacteria with the major biochemical groups consisting of amino acids, organic acids, and sugars. The distinct defense mechanism-specific metabolic phenotypes were clearly observed. The combined challenge contributed to the most significant metabolic phenoconversion in BSF larva with the dominant metabolic phenotypes induced by S. aureus. Our study suggested that the accumulation of energy-related metabolites provided by amino acid catabolism is the principal metabolic pathway regulating the defense mechanism. Therefore, combined challenge is strongly recommended for raising BSF immunity as it remarkably triggered amino acid metabolisms including arginine and proline metabolism and alanine, aspartate and glutamate metabolism along with purine metabolism and pyruvate metabolism that potentially result in the production of various nutritional and functional metabolites.

Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.

Metabolomic biomarkers of low BMD: a systematic review

Due to the metabolic nature of osteoporosis, this study was conducted to identify metabolomic studies investigating the metabolic profile of low bone mineral density (BMD) and osteoporosis. A comprehensive systematic literature search was conducted through PubMed, Web of Science, Scopus, and Embase databases up to April 08, 2020, to identify observational studies with cross-sectional or case-control designs investigating the metabolic profile of low BMD in adults using biofluid specimen via metabolomic platform. The quality assessment panel specified for the "omics"-based diagnostic research (QUADOMICS) tool was used to estimate the methodologic quality of the included studies. Ten untargeted and one targeted approach metabolomic studies investigating biomarkers in different biofluids through mass spectrometry or nuclear magnetic resonance platforms were included in the systematic review. Some metabolite panels, rather than individual metabolites, showed promising results in differentiating low BMD from normal. Candidate metabolites were of different categories including amino acids, followed by lipids and carbohydrates. Besides, certain pathways were suggested by some of the studies to be involved. This systematic review suggested that metabolic profiling could improve the diagnosis of low BMD. Despite valuable findings attained from each of these studies, there was great heterogeneity regarding the ethnicity and age of participants, samples, and the metabolomic platform. Further longitudinal studies are needed to validate the results and confirm the predictive role of metabolic profile on low BMD and fracture. It is also mandatory to address and minimize the heterogeneity in future studies by using reliable quantitative methods. Summary: Due to the metabolic nature of osteoporosis, researchers have considered metabolomic studies recently. This systematic review showed that metabolic profiling including different categories of metabolites could improve the diagnosis of low BMD. However, great heterogeneity was observed and it is mandatory to address and minimize the heterogeneity in future studies.

Metabolite Identification Using Infrared Ion Spectroscopy─Novel Biomarkers for Pyridoxine-Dependent Epilepsy

Untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics strategies are being increasingly applied in metabolite screening for a wide variety of medical conditions. The long-standing "grand challenge" in the utilization of this approach is metabolite identification─confidently determining the chemical structures of m/z-detected unknowns. Here, we use a novel workflow based on the detection of molecular features of interest by high-throughput untargeted LC-MS analysis of patient body fluids combined with targeted molecular identification of those features using infrared ion spectroscopy (IRIS), effectively providing diagnostic IR fingerprints for mass-isolated targets. A significant advantage of this approach is that in silico-predicted IR spectra of candidate chemical structures can be used to suggest the molecular structure of unknown features, thus mitigating the need for the synthesis of a broad range of physical reference standards. Pyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine metabolism, resulting from a mutation in the ALDH7A1 gene that leads to an accumulation of toxic levels of α-aminoadipic semialdehyde (α-AASA), piperideine-6-carboxylate (P6C), and pipecolic acid in body fluids. While α-AASA and P6C are known biomarkers for PDE in urine, their instability makes them poor candidates for diagnostic analysis from blood, which would be required for application in newborn screening protocols. Here, we use combined untargeted metabolomics-IRIS to identify several new biomarkers for PDE-ALDH7A1 that can be used for diagnostic analysis in urine, plasma, and cerebrospinal fluids and that are compatible with analysis in dried blood spots for newborn screening. The identification of these novel metabolites has directly provided novel insights into the pathophysiology of PDE-ALDH7A1.

Glycan Biosynthesis Ability of Gut Microbiota Increased in Primary Hypertension Patients Taking Antihypertension Medications and Potentially Promoted by Macrophage-Adenosine Monophosphate-Activated Protein Kinase

Increasing evidences suggest that the gut microbiota have their contributions to the hypertension, but the metagenomic characteristics and potential regulating mechanisms in primary hypertension patients taking antihypertension drugs are not clear yet. We carried out a metagenomic analysis in 30 primary hypertension patients taking antihypertension medications and eight healthy adults without any medication. We found that bacterial strains from species, such as Bacteroides fragilis, Bacteroides vulgatus, Escherichia coli, Klebsiella pneumoniae, and Streptococcus vestibularis, were highly increased in patients; and these strains were reported to generate glycan, short-chain fatty acid (SCFA) and trimethylamine (TMA) or be opportunistic pathogens. Meanwhile, Dorea longicatena, Eubacterium hallii, Clostridium leptum, Faecalibacterium prausnitzii, and some other strains were greatly decreased in the patient group. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that ortholog groups and pathways related to glycan biosynthesis and multidrug resistance were significantly increased in the patient group, and some of the hub genes related to N-glycan biosynthesis were increased in the patient group, while those related to TMA precursor metabolism and amino acid metabolism both increased and decreased in the patient group. Metabolites tested by untargeted liquid chromatography–mass spectrometry (LC-MS) proved the decrease of acetic acid, choline, betaine, and several amino acids in patients’ fecal samples. Moreover, meta-analysis of recent studies found that almost all patients were taking at least one kind of drugs that were reported to regulate adenosine monophosphate-activated protein kinase (AMPK) pathway, so we further investigated if AMPK regulated the metagenomic changes by using angiotensin II-induced mouse hypertensive model on wild-type and macrophage-specific AMPK-knockout mice. We found that the changes in E. coli and Dorea and glycan biosynthesis-related orthologs and pathways were similar in our cohort and hypertensive wild-type mice but reversed after AMPK knockout. These results suggest that the gut microbiota-derived glycan, SCFA, TMA, and some other metabolites change in medication-taking primary hypertension patients and that medications might promote gut microbiota glycan biosynthesis through activating macrophage-AMPK.

Glutamine restores mitochondrial respiration in bleomycin-injured epithelial cells

Whether from known or unknown causes, loss of epithelial repair plays a central role in the pathogenesis of pulmonary fibrosis. Recently, diminished mitochondrial function has been implicated as a factor contributing to the loss of epithelial repair but the mechanisms mediating these changes have not been defined. Here, we investigated the factors contributing to mitochondrial respiratory dysfunction after bleomycin, a widely accepted agent for modeling pulmonary fibrosis in mice and in vitro systems. In agreement with previous reports, we found that mitochondrial respiration was decreased in lung epithelial cells exposed to bleomycin, but also observed that responses differed depending on the type of metabolic fuel available to cells. For example, we found that mitochondrial respiration was dramatically reduced when glucose served as the primary fuel. Moreover, this associated with a marked decrease in glucose uptake, expression of glucose uptake transport 1 and capacity to augment glycolysis to either glucose or oligomycin. Conversely, mitochondrial respiration was largely preserved if glutamine was present in culture medium. The addition of glutamine also led to increased intracellular metabolite levels, including multiple TCA cycle intermediates and the glycolytic intermediate lactate, as well as reduced DNA damage and cell death to bleomycin. Taken together, these findings indicate that glutamine, rather than glucose, supports mitochondrial respiration and metabolite production in injured lung epithelial cells, and suggest that this shift away from glucose utilization serves to protect the lung epithelium from injury.

A molecular spectroscopy approach for the investigation of early phase ochronotic pigment development in Alkaptonuria

Alkaptonuria (AKU), a rare genetic disorder, is characterized by the accumulation of homogentisic acid (HGA) in organs due to a deficiency in functional levels of the enzyme homogentisate 1,2-dioxygenase (HGD), required for the breakdown of HGA, because of mutations in the HGD gene. Over time, HGA accumulation causes the formation of the ochronotic pigment, a dark deposit that leads to tissue degeneration and organ malfunction. Such behaviour can be observed also in vitro for HGA solutions or HGA-containing biofluids (e.g. urine from AKU patients) upon alkalinisation, although a comparison at the molecular level between the laboratory and the physiological conditions is lacking. Indeed, independently from the conditions, such process is usually explained with the formation of 1,4-benzoquinone acetic acid (BQA) as the product of HGA chemical oxidation, mostly based on structural similarity between HGA and hydroquinone that is known to be oxidized to the corresponding para-benzoquinone. To test such correlation, a comprehensive, comparative investigation on HGA and BQA chemical behaviours was carried out by a combined approach of spectroscopic techniques (UV spectrometry, Nuclear Magnetic Resonance, Electron Paramagnetic Resonance, Dynamic Light Scattering) under acid/base titration both in solution and in biofluids. New insights on the process leading from HGA to ochronotic pigment have been obtained, spotting out the central role of radical species as intermediates not reported so far. Such evidence opens the way for molecular investigation of HGA fate in cells and tissue aiming to find new targets for Alkaptonuria therapy.

The Investigation of Metabonomic Pathways of Serum of Iranian Women with Recurrent Miscarriage Using 1H NMR

Purpose

Recurrent miscarriage applies to pregnancy loss expulsion of the fetus within the first 24 weeks of pregnancy. This study is aimed at comparatively investigating the sera of women with RM with those who have no record of miscarriages to identify if there were any metabolite and metabolic pathway differences using ¹H NMR spectroscopy.

Methods

Serum samples were collected from women with RM (n = 30) and those who had no records of RM (n = 30) to obtain metabolomics information. ¹H NMR spectroscopy was carried out on the samples using Carr Purcell Meiboom Gill spin echo; also, Partial Least Squares Discriminant Analysis was performed in MATLAB software using the ProMetab program to obtain the classifying chemical shifts; the metabolites were identified by using the Human Metabolome Database (HMDB) in both the experimental and control groups. The pathway analysis option of the Metaboanalyst.ca website was used to identify the changed metabolic pathways.

Results

The results of the study revealed that 14 metabolites were different in the patients with RM. Moreover, the pathway analysis showed that taurine and hypotaurine metabolism along with phenylalanine, tyrosine, and tryptophan biosynthesis was significantly different in patients with RM.

Conclusion

The present study proposes that any alteration in the above metabolic pathways might lead to metabolic dysfunctions which may result in a higher probability of RM.

Integration of Choline Chloride-Based Natural Deep Eutectic Solvents and Macroporous Resin for Green Production of Enriched Oil Palm Flavonoids as Natural Wound Healing Agents

Huge quantities of oil palm (Elaeis guineensis Jacq.) leaves (OPL) are generated as agricultural biomass from oil palm plantations. OPL are known to contain significant amounts of flavonoids. For maximal exploitation of these valuable antioxidant compounds, an innovative and sustainable extraction method employing natural deep eutectic solvents (NaDES) combined with ultrasonic assisted extraction was developed. Various NaDES composed of choline chloride as the hydrogen bond donor (HBD) and 1,2 propanediol (PD), 1,4 butanediol (BD), glycerol (GLY), glucose (GLU), maltose (MAL), and lactic acid (LA) as the hydrogen bond acceptor (HBA) were synthesized. The influence of these compositions, the methods of their synthesis, molar ratios, and water contents on their capacity to extract flavonoids from OPL was evaluated. Based on the results, it was found that methods which incorporate a heating step produced NaDES with the best capacity to extract OPL flavonoids. These thermal methods combined with molar ratios of 1:3 or 1:4 and water contents of 17 to 50% were found to be the optimal conditions for preparing NaDES, specifically when applied to the PD, BD, and GLY NaDES. Subsequently, UHPLC-UV/PDA-MS/MS analysis revealed NaDES extracts recovered by macroporous adsorption resin XAD7HP were able to optimally extract at least twelve luteolin and apigenin derivatives in OPL NaDES extracts prepared from glycerol and 1,4-butanediol demonstrated better and comparable efficiency as aqueous methanol in extracting flavonoids from OPL. The in vitro studies of antioxidant and wound healing properties supported these findings by exhibiting good free radical scavenging, cell proliferation, and migration activities. Additionally, the NaDES extracts also showed non-cytotoxicity effects at 1000 µg/mL and below on 3T3 fibroblast cells. Results of the study showed that NaDES could be a promising eco-friendly green solvent to extract bioactive OPL flavonoids that have great potential for applications as wound healing agents.

Biofluid Metabolomics of Mice Exposed to External Low-Dose Rate Radiation in a Novel Irradiation System, the Variable Dose-Rate External 137Cs Irradiator

An important component of ionizing radiation (IR) exposure after a radiological incident may include low-dose rate (LDR) exposures either externally or internally, such as from ¹³⁷Cs deposition. In this study, a novel irradiation system, VAriable Dose-rate External ¹³⁷Cs irradiatoR (VADER), was used to expose male and female mice to a variable LDR irradiation over a 30 d time span to simulate fall-out-type exposures in addition to biofluid collection from a reference dose rate (0.8 Gy/min). Radiation markers were identified by untargeted metabolomics and random forests. Mice exposed to LDR exposures were successfully identified from control groups based on their urine and serum metabolite profiles. In addition to metabolites commonly perturbed after IR exposure, we identified and validated a novel metabolite (hexosamine-valine-isoleucine-OH) that increased up to 150-fold after LDR and 80-fold after conventional exposures in urine. A multiplex panel consisting of hexosamine-valine-isoleucine-OH with other urinary metabolites (N6,N6,N6-trimethyllysine, carnitine, 1-methylnicotinamide, and α-ketoglutaric acid) achieved robust classification performance using receiver operating characteristic curve analysis, irrespective of the dose rate or sex. These results show that in terms of biodosimetry, dysregulated energy metabolism is associated with IR exposure for both LDR and conventional IR exposures. These mass spectrometry data have been deposited to the NIH data repository via Metabolomics Workbench with study IDs ST001790, ST001791, ST001792, ST001793, and ST001806.

Isolation and Identification of Non-Conjugated Linoleic Acid from Processed Panax ginseng Using LC-MS/MS and 1H-NMR

Black ginseng exhibits numerous pharmacological activities due to higher and more diverse ginsenosides than unprocessed white ginseng. The ginsenoside derivatives have been investigated in order to determine their chemical structures and pharmacological activities. We found a peak which was increased 10-fold but unidentified in the methanol extracts of a black ginseng product. The unknown peak was tracked and identified as linoleic acid rather than a ginsenoside derivative using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR analysis confirmed no presence of conjugated linoleic acids. Ginsenoside profiles and linoleic acid contents in black ginseng products were quantified using LC-MS/MS. Linoleic acid content was more directly proportional to the number of applied thermal cycles in the manufacturing process than any ginsenosides.

Precise pathological classification of non-small cell lung adenocarcinoma and squamous carcinoma based on an integrated platform of targeted metabolome and lipidome

BACKGROUND

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most common subtypes of NSCLC. Despite genetic differences between LUAD and LUSC have been clarified in depth, the metabolic differences of these two subtypes are still unclear.

METHODS

Totally, 128 plasma samples of NSCLC patients were collected before initial treatments, followed by determination of LC-ESI-Q TRAP-MS/MS. Differentially expressed metabolites were screened based on a strict standard.

RESULTS

Based on the integrated platform of targeted metabolome and lipidome, a total of 1141 endogenous metabolites (including 809 lipids) were finally detected in the plasma of NSCLC patients, including 16 increased and 3 decreased endogenous compounds in LUAD group when compared with LUSC group. Thereafter, a logistic regression model integrating four differential metabolites [2-(Methylthio) ethanol, Cortisol, D-Glyceric Acid, and N-Acetylhistamine] was established and could accurately differentiate LUAD and LUSC with an area under the ROC curve of 0.946 (95% CI 0.886-1.000). The cut-off value showed a satisfactory efficacy with 92.0% sensitivity and 92.9% specificity. KEGG functional enrichment analysis showed these differentially expressed metabolites could be further enriched in riboflavin metabolism, steroid hormone biosynthesis, prostate cancer, etc. The endogenous metabolites identified in this study have the potential to be used as novel biomarkers to distinguish LUAD from LUSC.

CONCLUSIONS

Our research might provide more evidence for exploring the pathogenesis and differentiation of NSCLC. This research could promote a deeper understanding and precise treatment of lung cancer.

Serum metabolomics of end-stage renal disease patients with depression: potential biomarkers for diagnosis

Background

End-stage renal disease (ESRD) is the final stage during the development of renal failure. Depression is the most common psychiatric disorder in patients with ESRD, which in turn aggravates the progression of renal failure, however, its underlying mechanism remains unclear. This study aimed to reveal the pathogenesis and to discover novel peripheral biomarkers for ESRD patients with depression through metabolomic analysis.

Methods

Ultra-high-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) was used to explore changes of serum metabolites among healthy controls, ESRD patients with or without depression. The differential metabolites between groups were subjected to clustering analysis, pathway analysis, receiver operating characteristic (ROC) curve analysis.

Results

A total of 57 significant serum differential metabolites were identified between ESRD patients with or without depression, which were involved in 19 metabolic pathways, such as energy metabolism, glycerolipid metabolism, and glutamate-centered metabolism. Moreover, the area under the ROC curve of gentisic acid, uric acid, 5-hydroxytryptamine, 2-phosphoglyceric acid, leucyl-phenylalanine, propenyl carnitine, naloxone, pregnenolone, 6-thioxanthene 5'-monophosphate, hydroxyl ansoprazole, zileuton O-glucuronide, cabergoline, PA(34:2), PG(36:1), probucol and their combination was greater than 0.90.

Conclusions

Inflammation, oxidative stress and energy metabolism abnormalities, glycerolipid metabolism, and glutamate-centered metabolism are associated with the pathogenesis of ESRD with depression, which may be promising targets for therapy. Furthermore, the identified differential metabolites may serve as biomarkers for the diagnosis of ESRD patients with depression.

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Metabolic Profiling and Metabolites Fingerprints in Human Hypertension: Discovery and Potential

Early detection of pathogenesis through biomarkers holds the key to controlling hypertension and preventing cardiovascular complications. Metabolomics profiling acts as a potent and high throughput tool offering new insights on disease pathogenesis and potential in the early diagnosis of clinical hypertension with a tremendous translational promise. This review summarizes the latest progress of metabolomics and metabolites fingerprints and mainly discusses the current trends in the application in clinical hypertension. We also discussed the associated mechanisms and pathways involved in hypertension's pathogenesis and explored related research challenges and future perspectives. The information will improve our understanding of the development of hypertension and inspire the clinical application of metabolomics in hypertension and its associated cardiovascular complications.

Development and application of sequential window acquisition of all theoretical mass spectra data acquisition modes on ultra-high-performance liquid chromatography triple-quadrupole/time-of-flight mass spectrometry for metabolic profiling of amino acids in human plasma

Accumulating evidence suggests that amino acids are important indicators of nutritional and metabolic status. A high-resolution mass spectrometry method based on sequential window acquisition of all theoretical mass spectra acquisition was developed for the simultaneous determination of 16 amino acids in human plasma. Sample preparation by protein precipitation using a mixture of acetonitrile and formic acid was followed by a BEH Amide column. The superiority of this method was investigated by comparing it to time-of-flight scan and multiple reaction monitoring modes. The limit of detection in sequential window acquisition of all theoretical mass spectra mode for threonine, methionine, histidine, citrulline, and tryptophan is 0.1 ng on the column; for lysine and asparagine is 0.2 ng; for alanine, pyroglutamic acid, leucine, ornithine, and aspartate is 0.5 ng, for arginine is 1.0 ng; for glutamate and serine is 2.0 ng; for glutamine is 10.0 ng. This method was linear in the range 0.8-40 μg/mL for arginine, citrulline, glutamate, histidine, leucine, methionine, pyroglutamic acid, threonine, tryptophan; 2-100 μg/mL for asparagine, aspartate, lysine, ornithine, serine; and 4-200 μg/mL for alanine, glutamine with good accuracy and precision. Significantly different levels in 11 amino acids were observed between childhood and adulthood, representing the growth and development of individuals relating to the level of amino acids.

Human serum metabolomic analysis reveals progression for high blood pressure in type 2 diabetes mellitus

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is one of the most ordinary metabolic disorders and manifests as a high blood sugar level; 80%-90% of patients with T2DM will develop high blood pressure (HBP), which exacerbates irreversible organ damage. Understanding the metabolic basis of HBP is essential to facilitating early diagnosis and prompt treatments of diabetic complications.

RESEARCH DESIGN AND METHODS

34 patients who originally had T2DM and then developed HBP within 1 year were selected from physical examination participants. Using ultrahigh-performance liquid chromatography quadrupole time-of-flight metabolomic analysis, we compared the metabolomic profile of patients with 30 healthy controls. The results showed a clear discrimination in metabolomic profiles between T2DM and T2DM+HBP when employing orthogonal projection to latent structure with discriminant analysis with electrospray ionization modes.

RESULTS

Eight differential metabolites changed significantly during disease progression, among which L-isoleucine, L-glutamic acid, pyroglutamic acid and linoleic acid decreased, while sphinganine, Cer(d18:0/16:0), Cer(d18:0/18:0), and citric acid increased. These metabolites are associated with the γ-glutamyl cycle, tricarboxylic acid cycle, and ceramide metabolism.

CONCLUSIONS

These novel serum biomarkers may improve the management of T2DM and HBP complications, thus reducing the use of incorrect medical care.

European oak chemical diversity - from ecotypes to herbivore resistance

Climate change is increasing insect pressure and forcing plants to adapt. Although chemotypic differentiation and phenotypic plasticity in spatially separated tree populations are known for decades, understanding their importance in herbivory resistance across forests remains challenging. We studied four oak forest stands in Germany using nontarget metabolomics, elemental analysis, and chemometrics and mapped the leaf metabolome of herbivore-resistant (T-) and herbivore-susceptible (S-) European oaks (Quercus robur) to Tortrix viridana, an oak pest that causes severe forest defoliation. Among the detected metabolites, we identified reliable metabolic biomarkers to distinguish S- and T-oak trees. Chemotypic differentiation resulted in metabolic shifts of primary and secondary leaf metabolism. Across forests, T-oaks allocate resources towards constitutive chemical defense enriched of polyphenolic compounds, e.g. the flavonoids kaempferol, kaempferol and quercetin glucosides, while S-oaks towards growth-promoting substances such as carbohydrates and amino-acid derivatives. This extensive work across natural forests shows that oaks' resistance and susceptibility to herbivory are linked to growth-defense trade-offs of leaf metabolism. The discovery of biomarkers and the developed predictive model pave the way to understand Quercus robur's susceptibility to herbivore attack and to support forest management, contributing to the preservation of oak forests in Europe.

Metabolomics and Inflammatory Mediator Profiling for the Differentiation of Life-Threatening and Non-Severe Appendicitis in the Pediatric Population

While children with appendicitis often have excellent clinical outcomes, some develop life-threatening complications including sepsis and organ dysfunction requiring pediatric intensive care unit (PICU) support. Our study applied a metabolomics and inflammatory protein mediator (IPM) profiling approach to determine the bio-profiles of children who developed severe appendicitis compared with those that did not. We performed a prospective case-control study of children aged 0-17 years with a diagnosis of appendicitis. Cases had severe disease resulting in PICU admission. Primary controls had moderate appendicitis (perforation without PICU); secondary controls had mild appendicitis (non-perforated). Serum samples were analyzed using Proton Nuclear Magnetic Resonance (¹H NMR) Spectroscopy and Gas Chromatography-Mass Spectrometry (GC-MS); IPM analysis was performed using plasma bead-based multiplex profiling. Comparisons were made using multivariate data statistical analysis. Fifty-three children were included (15 severe, 38 non-severe). Separation between severe and moderate appendicitis demonstrated excellent sensitivity and specificity (100%, 88%; 14 compounds), separation between severe and mild appendicitis also showed excellent sensitivity and specificity (91%, 90%; 16 compounds). Biomarker patterns derived from metabolomics and IPM profiling are capable of distinguishing children with severe appendicitis from those with less severe disease. These findings provide an important first step towards developing non-invasive diagnostic tools for clinicians in early identification of children who are at a high risk of developing severe appendicitis.

Extraction Processes Affect the Composition and Bioavailability of Flavones from Lamiaceae Plants: A Comprehensive Review

Lamiaceae plants are a widespread family of herbaceous plants with around 245 plant genera and nearly 22,576 species distributed in the world. Some of the most representative and widely studied Lamiaceae plants belong to the Ocimum, Origanum, Salvia, and Thymus genera. These plants are a rich source of bioactive molecules such as terpenes, flavonoids, and phenolic acids. In this sense, there is a subgroup of flavonoids classified as flavones. Flavones have antioxidant, anti-inflammatory, anti-cancer, and anti-diabetic potential; thus, efficient extraction techniques from their original plant matrixes have been developed. Currently, conventional extraction methods involving organic solvents are no longer recommended due to their environmental consequences, and new environmentally friendly techniques have been developed. Moreover, once extracted, the bioactivity of flavones is highly linked to their bioavailability, which is often neglected. This review aims to comprehensively gather recent information (2011–2021) regarding extraction techniques and their important relationship with the bioavailability of flavones from Lamiaceae plants including Salvia, Ocimum, Thymus, and Origanum.

Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.

Unusual Aggregates Formed by the Self-Assembly of Proline, Hydroxyproline, and Lysine

There is a plethora of significant research that illustrates toxic self-assemblies formed by the aggregation of single amino acids, such as phenylalanine, tyrosine, tryptophan, cysteine, and methionine, and their implication on the etiology of inborn errors of metabolisms (IEMs), such as phenylketonuria, tyrosinemia, hypertryptophanemia, cystinuria, and hypermethioninemia, respectively. Hence, studying the aggregation behavior of single amino acids is very crucial from the chemical neuroscience perspective to understanding the common etiology between single amino acid metabolite disorders and amyloid diseases like Alzheimer's and Parkinson's. Herein we report the aggregation properties of nonaromatic single amino acids l-proline (Pro), l-hydroxyproline (Hyp), and l-lysine hydrochloride (Lys). The morphologies of the self-assembled structures formed by Pro, Hyp, and Lys were extensively studied by various microscopic techniques, and controlled morphological transitions were observed under varied concentrations and aging times. The mechanism of structure formation was deciphered by concentration-dependent ¹H NMR analysis, which revealed the crucial role of hydrogen bonding and hydrophobic interactions in the structure formation of Pro, Hyp, and Lys. MTT assays on neural (SHSY5Y) cell lines revealed that aggregates formed by Pro, Hyp, and Lys reduced cell viability in a dose-dependent manner. These results may have important implications in the understanding of the patho-physiology of disorders such as hyperprolinemia, hyperhydroxyprolinemia, and hyperlysinemia since all these IEMs are associated with severe neurodegenerative symptoms, including intellectual disability, seizures, and psychiatric problems. Our future studies will endeavor to study these biomolecular assemblies in greater detail by immuno-histochemical analysis and advanced biophysical assays.

Food Composition Databases: Does It Matter to Human Health?

Food provides humans with more than just energy and nutrients, addressing both vital needs and pleasure. Food habits are determined by a wide range of factors, from sensorial stimuli to beliefs and, once commanded by local and seasonal availability, are nowadays driven by marketing campaigns promoting unhealthy and non-sustainable foodstuffs. Top-down and bottom-up changes are transforming food systems, driven by policies on SDGs and by consumer's concerns about environmental and health impacts. Food quality, in terms of taste, safety, and nutritional value, is determined by its composition, described in food composition databases (FDBs). FDBs are then useful resources to agronomists, food and mechanical engineers, nutritionists, marketers, and others in their efforts to address at maximum human nutrient needs. In this work, we analyse some relevant food composition databases (viz., purpose, type of data, ease of access, regularity of updates), inspecting information on the health and environmental nexus, such as food origin, production mode as well as nutritional quality. The usefulness and limitations of food databases are discussed regarding what concerns sustainable diets, the food 'matrix effect', missing compounds, safe processing, and in guiding innovation in foods, as well as in shaping consumers' perceptions and food choices.

Qualitative Characterization of the Rat Liver Mitochondrial Lipidome Using All Ion Fragmentation on an Exactive Benchtop Orbitrap MS

Untargeted lipidomics profiling by liquid chromatography -mass spectrometry (LC-MS) allows researchers to observe the occurrences of lipids in a biological sample without showing intentional bias to any specific class of lipids and allows retrospective reanalysis of data collected. Typically, and in the specific method described, a general extraction method followed by LC separation is used to achieve nonspecific class coverage of the lipidome prior to high resolution accurate mass (HRAM) MS detection . Here we describe a workflow including the isolation of mitochondria from liver tissue, followed by mitochondrial lipid extraction and the LC-MS conditions used for data acquisition. We also highlight how, in this method, all ion fragmentation can be used to identify species of lower abundances, often missed by data dependent fragmentation techniques. Here we describe the isolation of mitochondria from liver tissue, followed by mitochondrial lipid extraction and the LC-MS conditions used for data acquisition.

NFnetFu: A novel workflow for microbiome data fusion

Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions

Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the laborious, costly, and time-consuming traditional wet-lab methods. Most proposed methods focus on separated drug and target descriptors, calculated, respectively, from chemical structures and protein sequences, and fail to introduce and extract features where the interaction information is embedded. In this paper, we propose a new three-step method based on matrix factorisation and generative adversarial network (GAN) for drug-target interaction prediction. Firstly, the matrix factorisation technique is used to capture and extract the joint interaction feature, for both drugs and targets, from the drug-target interaction matrix. Then, a GAN is introduced for data augmentation. It generates a fake positive sample similar to the real positive sample (known interactions) in order to balance the samples, allow the exploitation of the entire negative sample, and increase the data size for an accurate prediction. Finally, a fully connected four-layer neural network is built for classification. Experimental results illustrate a higher prediction performance of the proposed method compared to shallow classifiers and to state-of-the-art methods with an accuracy higher than 97%. Moreover, the data generation effect is confirmed by evaluating the proposed method with and without the generation step. These results demonstrated the efficiency of the latent interaction features and data generation on predicting new drugs or repurposing existing drugs. Overview of the WGANMF-DTI workflow for the Drug-Target Interaction Prediction task.

Seminal plasma metabolomics profiles following long (4-7 days) and short (2 h) sexual abstinence periods

OBJECTIVE

Metabolomic profiling of seminal plasma has been suggested as a possible approach for a fast and non-invasive male infertility evaluation diagnosis. However, metabolomics profiles in normozoospermic men have not been thoroughly investigated, and the influence of ejaculation-abstinence has not been described. To provide interim reference values and find associations between the metabolomics profiles of human seminal plasma and length of ejaculation-abstinence period in normozoospermic men.

STUDY DESIGN

Semen samples collected after long (4-7 days) and short abstinence (2 h) from 31 normozoospermic males were assessed for routine quality parameters before the seminal plasma was separated by centrifugation. Metabolomics profiles of the seminal plasma were then determined using untargeted Nuclear Magnetic Resonance Spectroscopy.

RESULTS

In total, 30 metabolites were identified. Pyruvate showed a higher concentration, while fructose, acetate, choline, methanol, N-acetylglucosamine, O-acetylcarnitine, uridine, and sn-glycero-3-phosphocoline showed lower concentrations in samples collected after short abstinence (vs. long). All metabolites showed lower absolute amounts (volume × concentration) following shorter abstinence. However, the lower sperm concentration in samples collected after short abstinence resulted in higher absolute amounts of pyruvate and taurine per spermatozoa: pyruvate 1.92 (1.12-3.87) vs. 1.29 (0.83-2.62) (P < 0.001) and taurine 0.58 (0.36-0.92) vs. 0.43 (0.28-0.95) (P < 0.05) ng/10⁶ spermatozoa. Simultaneously, there was a higher percentage of progressively motile spermatozoa in samples collected after the short abstinence.

CONCLUSION

The generally lower concentrations of seminal metabolites after short abstinence periods may be related to the shorter time available for secretion and collection of these metabolites by the accessory glands and the epididymides. The concomitant lower number of spermatozoa in the second ejaculate resulted in increased absolute amounts of pyruvate and taurine per spermatozoa, accompanied by increased spermatozoa motility in these samples. The simultaneous increase in percentages of motile spermatozoa and absolute amounts of pyruvate and taurine per spermatozoa after shorter abstinence might indicate that these two metabolites play a more critical role in sperm motility, which should be further investigated in future studies.

Impact of Weaning and Maternal Immune Activation on the Metabolism of Pigs

Weaning wields environmental, social, and nutritional stresses that are detectable in the blood metabolite levels of the offspring. Prenatal stress in the form of maternal immune activation (MIA) in response to infection, which is associated with health and behavior disorders, also elicits prolonged changes in blood and brain cytokine and metabolite levels of the offspring. The goal of this study was to investigate the effects of weaning and MIA on the offspring’s liver function to advance the understanding of the impact of stressors on peripheral and central nervous systems, physiology, and health. Gas chromatography–mass spectrometry analysis was used to compare the level of hepatic metabolites from 22-day-old pigs (n = 48) evenly distributed among weaning (nursed or weaned), viral MIA exposure (yes or no), and sexes. Weaning effects were detected on 38 metabolites at p-value < 0.05 (28 metabolites at FDR p-value < 0.05), and sex-dependent MIA effects were detected on 11 metabolites. Multiple intermediate and final products of the enriched (FDR p-value < 0.05) glycolysis and gluconeogenesis and pentose phosphate pathways were over-abundant in nursed relative to weaned pigs. The enriched pathways confirm the impact of weaning on hepatic metabolic shift, oxidative stress, and inflammation. Higher levels of the glucogenic amino acid histidine are observed in pigs exposed to MIA relative to controls, suggesting that the role of this metabolite in modulating inflammation may supersede the role of this amino acid as an energy source. The lower levels of cholesterol detected in MIA pigs are consistent with hypocholesterolemia profiles detected in individuals with MIA-related behavior disorders. Our findings underline the impact of weaning and MIA stressors on hepatic metabolites that can influence peripheral and central nervous system metabolic products associated with health and behavior disorders.

NMR Spectroscopy for Protein Higher Order Structure Similarity Assessment in Formulated Drug Products

Peptide and protein drug molecules fold into higher order structures (HOS) in formulation and these folded structures are often critical for drug efficacy and safety. Generic or biosimilar drug products (DPs) need to show similar HOS to the reference product. The solution NMR spectroscopy is a non-invasive, chemically and structurally specific analytical method that is ideal for characterizing protein therapeutics in formulation. However, only limited NMR studies have been performed directly on marketed DPs and questions remain on how to quantitively define similarity. Here, NMR spectra were collected on marketed peptide and protein DPs, including calcitonin-salmon, liraglutide, teriparatide, exenatide, insulin glargine and rituximab. The 1D ¹H spectral pattern readily revealed protein HOS heterogeneity, exchange and oligomerization in the different formulations. Principal component analysis (PCA) applied to two rituximab DPs showed consistent results with the previously demonstrated similarity metrics of Mahalanobis distance (D_M) of 3.3. The 2D ¹H-¹³C HSQC spectral comparison of insulin glargine DPs provided similarity metrics for chemical shift difference (Δδ) and methyl peak profile, i.e., 4 ppb for ¹H, 15 ppb for ¹³C and 98% peaks with equivalent peak height. Finally, 2D ¹H-¹⁵N sofast HMQC was demonstrated as a sensitive method for comparison of small protein HOS. The application of NMR procedures and chemometric analysis on therapeutic proteins offer quantitative similarity assessments of DPs with practically achievable similarity metrics.

From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics

Mass spectrometry (MS) is a powerful tool in chemical research and substance identification. For the computational modeling of electron ionization MS, we have developed the quantum-chemical electron ionization mass spectra (QCEIMS) program. Here, we present an extension of QCEIMS to calculate collision-induced dissociation (CID) spectra. The more general applicability is accounted for by the new name QCxMS, where "x" refers to EI or CID. To this end, fragmentation and rearrangement reactions are computed "on-the-fly" in Born-Oppenheimer molecular dynamics (MD) simulations with the semiempirical GFN2-xTB Hamiltonian, which provides an efficient quantum mechanical description of all elements up to Z = 86 (Rn). Through the explicit modeling of multicollision processes between precursor ions and neutral gas atoms as well as temperature-induced decomposition reactions, QCxMS provides detailed insight into the collision kinetics and fragmentation pathways. In combination with the CREST program to determine the preferential protonation sites, QCxMS becomes the first standalone MD-based program that can predict mass spectra based solely on molecular structures as input. We demonstrate this for six organic molecules with masses ranging from 159 to 296 Da, for which QCxMS yields CID spectra in reasonable agreement with experiments.

Application of Metabolomics to Identify Hepatic Biomarkers of Foie Gras Qualities in Duck

Foie gras is a traditional dish in France that contains 50 to 60% of lipids. The high-fat content of the liver improves the organoleptic qualities of foie gras and reduces its technological yield at cooking (TY). As the valorization of the liver as foie gras products is strongly influenced by the TY, classifying the foie gras in their potential technological quality before cooking them is the main challenge for producers. Therefore, the current study aimed to identify hepatic biomarkers of foie gras qualities like liver weight (LW) and TY. A group of 120 male mule ducks was reared and overfed for 6–12 days, and their livers were sampled and analyzed by proton nuclear magnetic resonance (¹H-NMR). Eighteen biomarkers of foie gras qualities were identified, nine for LW and TY, five specific to LW, and four specific to TY. All biomarkers were strongly negatively correlated to the liver weights and positively correlated to the technological yield, except for the lactate and the threonine, and also for the creatine that was negatively correlated to foie gras technological quality. As a result, in heavy livers, the liver metabolism was oriented through a reduction of carbohydrate and amino acid metabolisms, and the plasma membrane could be damaged, which may explain the low technological yield of these livers. The detected biomarkers have been strongly discussed with the metabolism of the liver in nonalcoholic steatohepatitis.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

Modulation of Gastrointestinal Mucus Properties with Ionic Liquids for Drug Delivery

The mucus barrier lining the gastrointestinal tract poses a significant barrier to the oral delivery of macromolecular drugs. Successful approaches to overcoming this barrier have primarily focused on reducing drug and carrier interactions with mucus or disrupting the mucus layer directly. Choline-based ionic liquids (ILs) such as choline geranate and choline glycolate (CGLY) have recently been shown to be effective in enhancing the intestinal absorption of macromolecules such as insulin and immunoglobulin (IgG), respectively. Herein, the use of choline-based ILs as mucus-modulating agents for safely improving drug penetration through mucus is described. Choline-based ILs significantly increase the diffusion rates of cationic dextrans through mucin solution. Choline-maleic acid (CMLC 2:1) enhances the diffusion of 4 kDa cationic dextran in mucin solution by more than fourfold when compared to phosphate-buffered saline control. Choline-based ILs also reduce mucus viscosity without significantly impacting the native mucus gel structure. In vitro studies in a mucus-secreting coculture model with Caco-2 and HT29MTX-E12 cells further demonstrate the effectiveness of ILs in improving transport of cationic molecules in the presence of secreted mucus. This work demonstrates the potential for choline-based ionic liquids to be used as nondestructive mucus-modulating agents for enabling enhanced oral delivery of macromolecular drugs.

MetaboKit: a comprehensive data extraction tool for untargeted metabolomics

We have developed MetaboKit, a comprehensive software package for compound identification and relative quantification in mass spectrometry-based untargeted metabolomics analysis. In data dependent acquisition (DDA) analysis, MetaboKit constructs a customized spectral library with compound identities from reference spectral libraries, adducts, dimers, in-source fragments (ISF), MS/MS fragmentation spectra, and more importantly the retention time information unique to the chromatography system used in the experiment. Using the customized library, the software performs targeted peak integration for precursor ions in DDA analysis and for precursor and product ions in data independent acquisition (DIA) analysis. With its stringent identification algorithm requiring matches by both MS and MS/MS data, MetaboKit provides identification results with significantly greater specificity than the competing software packages without loss in sensitivity. The proposed MS/MS-based screening of ISFs also reduces the chance of unverifiable identification of ISFs considerably. MetaboKit's quantification module produced peak area values highly correlated with known concentrations in a DIA analysis of the metabolite standards at both MS1 and MS2 levels. Moreover, the analysis of Cdk1Liv-/- mouse livers showed that MetaboKit can identify a wide range of lipid species and their ISFs, and quantitatively reconstitute the well-characterized fatty liver phenotype in these mice. In DIA data, the MS1-level and MS2-level peak area data produced similar fold change estimates in the differential abundance analysis, and the MS2-level peak area data allowed for quantitative comparisons in compounds whose precursor ion chromatogram was too noisy for peak integration.

Metabolome modulation of the host adaptive immunity in human malaria

Host responses to infection with the malaria parasite Plasmodium falciparum vary among individuals for reasons that are poorly understood. Here we reveal metabolic perturbations as a consequence of malaria infection in children and identify an immunosuppressive role of endogenous steroid production in the context of P. falciparum infection. We perform metabolomics on matched samples from children from two ethnic groups in West Africa, before and after infection with seasonal malaria. Analysing 306 global metabolomes, we identify 92 parasitaemia-associated metabolites with impact on the host adaptive immune response. Integrative metabolomic and transcriptomic analyses, and causal mediation and moderation analyses, reveal an infection-driven immunosuppressive role of parasitaemia-associated pregnenolone steroids on lymphocyte function and the expression of key immunoregulatory lymphocyte genes in the Gouin ethnic group. In children from the less malaria-susceptible Fulani ethnic group, we observe opposing responses following infection, consistent with the immunosuppressive role of endogenous steroids in malaria. These findings advance our understanding of P. falciparum pathogenesis in humans and identify potential new targets for antimalarial therapeutic interventions.

Urine analysis using FTIR spectroscopy: A study on healthy adults and children

Urine spectra from 108 healthy volunteers are studied by attenuated total refraction-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral features are correlated with observable urine components. The variation of spectra within a healthy population is quantified and a library of reference spectra is constructed. Using the band assignments, these spectra are compared with both age-wise and gender-wise. Children show the least intensity variations compared to both adult groups. Young adults show the highest variation, particularly in the 1650 to 1400 cm^-1 and 1200 to 900 cm^-1 regions. These results indicate the importance of the size of the control group in comparative studies utilizing FTIR. Age-wise comparisons reveal that phosphate and sulfate excretion decreases with age, and that the variance of phosphate among individuals is higher with adults. As for gender-wise comparisons, females show a slightly higher citrate content at 1390 cm^-1 regardless of the age and they show a higher variance in the 1200 to 1000 cm^-1 region when compared to men.

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC

ETHNOPHARMACOLOGICAL RELEVANCE

Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied.

AIM OF STUDY

A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC.

MATERIALS AND METHODS

Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats.

RESULTS

Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro.

CONCLUSION

The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

Characterization of Chinese hamster ovary cell culture feed media precipitate

Process intensification of monoclonal antibody production is leading to more concentrated feed media causing issues with precipitation of solids from the media solution. This results in processing problems since components in the precipitate are no longer in solution, changing the media composition and leading to variability in cell culture performance. The goal of this work is to characterize the feed media precipitate, and in particular to identify the precipitated components so that mitigation strategies can be developed. From the conducted analysis, the precipitate was predominately found to be organic and was analyzed with liquid chromatography-mass spectrometry and inductively coupled plasma-optical emission spectroscopy (ICP-OES) to identify the constituent components. Up to ten amino acids were identified with tyrosine (approximately 77 wt.%) and phenylalanine (approximately 4 wt.%) being the most prevalent amino acids. Elemental analysis with ICP-OES revealed that inorganic components were accounted for less than one weight percentage of the solid precipitate with metal sulfates being the predominant inorganic components.

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

Decreased MEF2A Expression Regulated by Its Enhancer Methylation Inhibits Autophagy and May Play an Important Role in the Progression of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by amyloid plaques and neurofibrillary tangles which significantly affects people's life quality. Recently, AD has been found to be closely related to autophagy. The aim of this study was to identify autophagy-related genes associated with the pathogenesis of AD from multiple types of microarray and sequencing datasets using bioinformatics methods and to investigate their role in the pathogenesis of AD in order to identify novel strategies to prevent and treat AD. Our results showed that the autophagy-related genes were significantly downregulated in AD and correlated with the pathological progression. Furthermore, enrichment analysis showed that these autophagy-related genes were regulated by the transcription factor myocyte enhancer factor 2A (MEF2A), which had been confirmed using si-MEF2A. Moreover, the single-cell sequencing data suggested that MEF2A was highly expressed in microglia. Methylation microarray analysis showed that the methylation level of the enhancer region of MEF2A in AD was significantly increased. In conclusion, our results suggest that AD related to the increased methylation level of MEF2A enhancer reduces the expression of MEF2A and downregulates the expression of autophagy-related genes which are closely associated with AD pathogenesis, thereby inhibiting autophagy.

Plasma metabolomic profiles as affected by diet and stress in Spanish goats

The effects of high-condensed tannin (CT) diet combined with preslaughter stress have not been studied at the metabolome level in goats. This study was conducted to determine the effects of feeding sericea lespedeza (SL; Lespedeza cuneata), a high-CT legume, and transportation stress on plasma metabolome in goats. Uncastrated male Spanish goats (age = 8 months; BW = 26.0 ± 0.48 kg) were either fed ground 'Serala' SL hay (SER), bermudagrass (Cynodon dactylon) hay (BG), or bermudagrass hay-dewormed goats (BG-DW; Control) at 75% of intake, with a corn-based supplementation (25%) for 8 weeks (n = 12/Diet). At the end of the trial, goats were subjected to one of two stress treatments (ST): transported for 90 min to impose stress (TS) or held in pens (NTS) before slaughtering, in two replicates. Live and carcass weights, and blood samples were collected at 0, 30, 60 and 90 min of transportation or holding time (Time). The data were analyzed using MIXED procedures in SAS and metabolomics data were analyzed using the R software. When measured after ST, SER group had the lowest body weight (P < 0.05) among the three diet groups. Carcass weights were high in the BG-DW, low in SER, and intermediate in BG group. Plasma creatine concentrations decreased over Time (P < 0.01) in the TS goats in all diet groups. Meat crude protein percentages were higher (P < 0.05) in SER (22.5 ± 0.22) and BG-DW (22.3 ± 0.22) groups compared to the BG group (21.6 ± 0.22). At the metabolome level, SER group had the lowest (P < 0.05) glycine, alanine, threonine, taurine, trans-hydroxyproline, methionine, and histidine concentrations and highest (P < 0.01) lysine and citrulline concentrations among the Diet groups. Butyric acid, concentration was higher (P < 0.05) in the SER group compared to BG group. Eight medium- and long-chained acylcarnitines were higher (P < 0.05) in the BG-DW group than SER or BG groups. In general, amino acid levels decreased and acylcarnitine increased with Time (P < 0.05) in all groups. Sericea diet can be beneficial in enhancing stress coping abilities in goats due to elevated butyrate, lysine, and citrulline levels; however, SER resulted in lower energy level in goats compared to BG or BG-DW groups. Fatty acid metabolism is the main energy pathway in all groups during prolonged stress. Inclusion of certain varieties of SL in the diet must be carefully controlled to prevent possible negative effect.