Fruit of the bine: Exploring the Chemodiversity of cultivated hops (Humulus lupulus L.)

In recent years, the utilization of molecular genetic methods is the best tool for the evaluation of individual genotypes. Modern molecular methods allow us to check the changes caused by the combination of parents, evaluate the relationships of individual genotypes or variability (diversity) within the population. Recently, we have developed an efficient marker system to control the authenticity of Czech hop (Humulus lupulus L.) cultivars, which we introduced into the system for the identification and control of hop cultivar purity. In our presented study, we evaluated molecular genetic variability within 109 world hop cultivars by hierarchical clustering techniques and principal coordinates analysis (PCoA). For molecular analyses, we used 269 amplified marker of SSR, STS and EST-SSR loci in total. Hop cultivars were divided into two major groups with North American or European origin and breeding influence by cluster analysis, as common knowledge from previous published results. PCoA more distinguished two breeding cultivar groups of ‘Saaz’ and ‘Fuggle’ hops in Europe. The usage of some cultivars during breeding history (‘Brewers Gold’, ‘Northern brewer’, ‘Cascade’, ‘Columbus’) was also noticeable.

The authors of this paper conducted a comparative metabolomic analysis of Ophiocordyceps sinensis (OS), providing the metabolic profiles of the stroma (OSBSz) and sclerotia (OSBSh) of OS by widely targeted metabolomics and untargeted metabolomics. The results showed that 778 and 1449 metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. The metabolites in OSBSz and OSBSh are significantly differentiated; 71 and 96 differentially expressed metabolites were identified by the widely targeted metabolomics and untargeted metabolomics approaches, respectively. This suggests that these 71 metabolites (riboflavine, tripdiolide, bromocriptine, lumichrome, tetrahymanol, citrostadienol, etc.) and 96 metabolites (sancycline, vignatic acid B, pirbuterol, rubrophen, epalrestat, etc.) are potential biomarkers. 4-Hydroxybenzaldehyde, arginine, and lumichrome were common differentially expressed metabolites. Using the widely targeted metabolomics approach, the key pathways identified that are involved in creating the differentiation between OSBSz and OSBSh may be nicotinate and nicotinamide metabolism, thiamine metabolism, riboflavin metabolism, glycine, serine, and threonine metabolism, and arginine biosynthesis. The differentially expressed metabolites identified using the untargeted metabolomics approach were mainly involved in arginine biosynthesis, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, and cysteine and methionine metabolism. The purpose of this research was to provide support for the assessment of the differences between the stroma and sclerotia, to furnish a material basis for the evaluation of the physical effects of OS, and to provide a reference for the selection of detection methods for the metabolomics of OS.

High-resolution mass spectrometers, particularly when paired with liquid chromatography, are the instrument of choice for untargeted metabolomics approaches. Instruments, such as the Orbitrap, offer high sensitivity, selectivity, and exceptional mass accuracy, though they pose certain technical challenges, complicating absolute and comparative quantification. Consequently, method validation is crucial to ensure reliable results, as untargeted metabolomics approaches require the detection and quantification of a large number of metabolites in a broad dynamic range. Methods can be assessed using performance characteristics like accuracy and linearity to ensure analytical reliability. This study evaluates the suitability of untargeted metabolomics methods for discovery-based investigations. A stable isotope–assisted strategy was used with wheat extracts analyzed by a Q Exactive HF Orbitrap. Results showed that 70% of all detected 1327 metabolites displayed non-linear effects in at least one of the nine dilution levels employed. However, when considering fewer levels, 47% of all metabolites demonstrated linear behavior in at least four levels (i.e., a difference factor of 8). Moreover, the analysis further suggests that the observed abundances in less concentrated samples and those outside the linear range were mostly overestimated compared to expected abundances, but hardly ever underestimated. Consequently, during statistical analysis, which is an important step in prioritizing detected metabolites and correlating them with the biological hypothesis, the number of false-positives was not inflated, but the number of false-negatives might be increased. Generally, (non-)linear behavior did not correlate with specific compound classes or polarity, suggesting non-linearity is not easily predictable based on chemical structures.Graphical

Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation

The success of forensic investigations involving fatalities very often depends on the establishment of the correct timeline of events. Currently used methods for estimating the postmortem interval (PMI) are mostly dependent on the professional and tacit experience of the investigator, and often with poor reliability in the absence of robust biological markers. The aim of this study was to investigate the potential of metabolomic approaches to highlight molecular markers for PMI. Rat and human muscle tissues, collected at various times postmortem, were analyzed using an untargeted metabolomics approach. Levels of certain metabolites (skatole, xanthine, n-acetylneuraminate, 1-methylnicotinamide, choline phosphate, and uracil) as well as most proteinogenic amino acids increased steadily postmortem. Threonine, tyrosine, and lysine show the most predictable evolution over the postmortem period, and may thus have potential for possible PMI markers in the future. This study demonstrates how a biomarker discovery approach can be extended to forensic investigations using untargeted metabolomics.

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing. The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review. Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package. The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations. The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

The profile of flavonoids in hops (Humulus lupulus L.) showed a significant cultivar diversity applicable for distinguishing cultivars. The aims of this study were (1) to identify and describe flavonol di- and tri-glycosides in hops; (2) to analyze the profile of flavonol glycosides in various hop cultivars; and (3) to develop a new approach for identifying hop cultivars using a manual cultivar identification diagram that consisted of the ratios between specific flavonol di- and tri-glycosides. The total of 22 different flavonol O-glucosides were detected and tentatively identified using liquid chromatography (RP-HPLC) coupled with high-resolution mass spectrometry (HR-MS/MS). The identified O-flavonol glycosides, mainly containing aglycones, quercetin, kaempferol and myricetin, were categorized into five groups according to their sugar moieties. Furthermore, fourteen characteristic peaks with a low sample-to-sample variation and acceptable coefficient of variation (< 20%) throughout growing seasons were selected for chemometric analysis. Hierarchical cluster analysis (HCA) was applied to two data sets to characterize the similarity and differences among samples: one data set contained five hop cultivars common in the Czech Republic and the second one consisted of 17 hop cultivars from around the world. The results of the HCA of flavonol di- and tri-glycosides proved the genotypic effect on their profile and allowed to select compounds primarily responsible for clustering hop cultivars into groups. These compounds were used for developing an efficient cultivar identification strategy based on the manual cultivar identification diagram. The functionality of the identification diagram was successfully tested and allowed, based on the specific ratios between flavonol glucosides, reliable discrimination among the five most common hop cultivars planted in the Czech Republic. Compared to the genetic or chemotaxonomic methods, the newly developed approach for the hop cultivar identification provided reliable results even if the tested materials were hop pellets or beer.

Wheat stem sawfly (WSS) causes devastating yield loss in both common bread wheat (Triticum aestivum L.) and durum wheat (Triticum turgidum L. var durum) in the North American Great Plains. The early stem solidness phenotype confers solid stems early in plant development coinciding with the flight period of WSS and provides protection to plants during the critical oviposition period. With this phenotype, pith is lost as the plant develops, which may allow for enhanced biological control of surviving larvae by braconid parasitoids Bracon cephi (Gahan) and Bracon lissogaster Muesebeck, as well as having additional potential yield benefits from utilizing reabsorbed pith components. Here, we use an untargeted transcriptomics and metabolomics approach to explore the mechanisms related to the early stem solidness phenotype in three cultivars of spring wheat and two cultivars of durum wheat in addition to three near- isogenic pairs of spring wheat and two near- isogenic pairs of durum wheat. We identified effects of growth stage and allele on expression of metabolites and transcripts associated with stem solidness, development of cell walls and programmed cell death. A caffeic acid methylesterase and pectin methylesterase were upregulated in hollow stemmed Reeder and lines with the 3BLa allele, which likely influences lignin subunit proportions as well as the production of volatile semiochemicals that impact the behavior of adult WSS. TaVPE3cB, a gene associated with programmed cell death and thickening of cell walls, also had increased expression in hollow stemmed lines and is likely partially responsible for the hollow stemmed phenotype observed. Growth stage and allele also affected the expression of transcripts and metabolites involved in the phenylpropanoid pathway, carbohydrate and glycoside biosynthesis and lipid biosynthesis, implicating the involvement of these pathways in resistance and plant response to infestation by WSS.

Exploring the tropical acclimation of European and American hop cultivars (Humulus lupulus L.): Focus on physiology, productivity, and chemical composition

The infection rate of syphilis continues to rise globally, and the difficulty in diagnosis of neurosyphilis promptly needs to be resolved. More specific and sensitive diagnostic markers for latent syphilis and neurosyphilis should be found. Here the metabolic profiles of 88 cerebrospinal fluid samples from syphilis patients and controls were analyzed by LC/MS-based untargeted metabolomics. In total, 272 metabolites based on 3937 features obtained in ESI- mode and 252 metabolites based on 3799 features in ESI+ mode were identified. The experimental process was evaluated by principal component analysis, partial least squares discriminant analysis, and hierarchical cluster analysis. A clear separation between latent syphilis and neurosyphilis was found. Levels of lipid and linoleic acid metabolites, such as 9-oxo-octadecadienoic acid and 9,10,13-trihydroxyoctadecenoic acid, were increased in syphilis patients. In patients with neurosyphilis, significant changes in levels of 5-hydroxy-L-tryptophan (5-HTP) and acetyl-N-formyl-5-methoxykynurenamine (AFMK) in the tryptophan-kynurenine pathway were also detected. Only one metabolite, theophylline, differed significantly between symptomatic and asymptomatic neurosyphilis patients. Additionally, KEGG analysis revealed significant enrichment of tryptophan metabolism pathways, indicating a high correlation between tryptophan metabolism and syphilis symptoms. Levels of linoleic acid metabolites, 5-HTP, AFMK and theophylline were significantly altered in different patients. The role of these differential metabolites in the development of syphilis is worthy of further exploration. Our results may promote the development of biomarkers for diagnosis of latent syphilis from neurosyphilis, and for that of asymptomatic neurosyphilis from symptomatic neurosyphilis in the future.

Untargeted metabolomics as a tool to monitor biocontrol product residues' fate on field-treated Prunus persica

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 52 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation.

Severe acute malnutrition (SAM) is a major cause of child mortality worldwide, however the pathogenesis of SAM remains poorly understood. Recent studies have uncovered an altered gut microbiota composition in children with SAM, suggesting a role for microbes in the pathogenesis of malnutrition. To elucidate the metabolic consequences of SAM and whether these changes are associated with changes in gut microbiota composition. We applied an untargeted multi-platform metabolomics approach [gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS)] to stool and plasma samples from 47 Nigerian children with SAM and 11 control children. The composition of the stool microbiota was assessed by 16S rRNA gene sequencing. The plasma metabolome discriminated children with SAM from controls, while no significant differences were observed in the microbial or small molecule composition of stool. The abundance of 585 features in plasma were significantly altered in malnourished children (Wilcoxon test, FDR corrected P < 0.1), representing approximately 15% of the metabolome. Consistent with previous studies, children with SAM exhibited a marked reduction in amino acids/dipeptides and phospholipids, and an increase in acylcarnitines. We also identified numerous metabolic perturbations which have not been reported previously, including increased disaccharides, truncated fibrinopeptides, angiotensin I, dihydroxybutyrate, lactate, and heme, and decreased bioactive lipids belonging to the eicosanoid and docosanoid family. Our findings provide a deeper understanding of the metabolic consequences of malnutrition. Further research is required to determine if specific metabolites may guide improved management, and/or act as novel biomarkers for assessing response to treatment.

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs. Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique. Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible. When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Metabolomics has become an important tool for clinical research, especially for analyzing inherited metabolic disorders (IMDs). The purpose of this study was to explore the performance of metabolomics in diagnosing IMDs using an untargeted metabolomic approach. A total of 40 urine samples were collected: 20 samples from healthy children and 20 from pediatric patients, of whom 13 had confirmed IMDs and seven had suspected IMDs. Samples were analyzed by Orbitrap mass spectrometry in positive and negative mode alternately, coupled with ultra-high liquid chromatography. Raw data were processed using Compound Discovery 2.0 ™ and then exported for partial least squares discriminant analysis (PLS-DA) by SIMCA-P 14.1. After comparing with m/zCloud and chemSpider libraries, compounds with similarity above 80% were selected and normalized for subsequent relative quantification analysis. The uncommon compounds discovered were analyzed based on the Kyoto Encyclopedia of Genes and Genomes to explore their possible metabolic pathways. All IMDs patients were successfully distinguished from controls in the PLS-DA. Untargeted metabolomics revealed a broader metabolic spectrum in patients than what is observed using routine chromatographic methods for detecting IMDs. Higher levels of certain compounds were found in all 13 confirmed IMD patients and 5 of 7 suspected IMD patients. Several potential novel markers emerged after relative quantification. Untargeted metabolomics may be able to diagnose IMDs from urine and may deepen insights into the disease by revealing changes in various compounds such as amino acids, acylcarnitines, organic acids, and nucleosides. Such analyses may identify biomarkers to improve the study and treatment of IMDs.