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Susceptibility for leishmaniasis is largely dependent on genetic- and immune factors of the host. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors, little is known on the mechanisms that mediate these associations. To characterize the functionality underpinning these associations between HLA and disease, we predicted the epitope binding repertoires for all known leishmaniasis-associated HLA variants collected in a thorough literature review. We identified several amino acid polymorphisms in the HLA sequences that distinguished protective-from risk-associated HLA-DRB1 alleles. Proteome-wide and multi-species T cell epitope binding predictions were carried out across these alleles, enabling us to map the effects on the epitope binding repertoires. The protective-associated HLA-DRB1 alleles were characterized by common binding core motifs, which map to the identified amino acid polymorphisms. These results strongly suggest that polymorphism in the HLA region, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes, of which a set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, were prioritized for further epitope discovery in the search for novel subunit-based vaccines.

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein–protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus–host interactome data set to detect protein–protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

RationaleAdvanced algorithmic solutions are necessary to process the ever increasing amounts of mass spectrometry data that is being generated. Here we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra.Methodsfalcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters.ResultsSeveral state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome dataset consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing.Conclusionsfalcon is a highly efficient spectrum clustering tool. It is publicly available as open source under the permissive BSD license at https://github.com/bittremieux/falcon.

Background Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians. Methods We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting. Results We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%. Conclusion Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets with about 2 million count records in less than a second whilst removing unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd’s simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

Background The multifactorial nature of non-alcoholic fatty liver disease cannot be explained solely by genetic factors. Recent evidence revealed that DNA methylation changes take place at proximal promoters within susceptibility genes. This emphasizes the need for integrating multiple data types to provide a better understanding of the disease’s pathogenesis. One such candidate gene is paraoxonase-1 (PON1). Substantial interindividual differences in PON1 are apparent and could influence disease risk later in life. The aim of this study was therefore to determine the different regulatory aspects of PON1 variability and to examine them in relation to the predisposition to obesity-associated fatty liver disease. Results A targeted multi-omics approach was applied to investigate the interplay between PON1 genetic variants, promoter methylation, expression profile and enzymatic activity in an adult patient cohort with extensive metabolic and hepatic characterisation including liver biopsy. Alterations in PON1 status were shown to correlate with waist-to-hip ratio and relevant features of liver pathology. Particularly, the regulatory polymorphism rs705379:C > T was strongly associated with more severe liver disease. Multivariable data analysis furthermore indicated a significant association of combined genetic and epigenetic PON1 regulation. This identified relationship postulates a role for DNA methylation as a mediator between PON1 genetics and expression, which is believed to further influence liver disease progression via modifications in PON1 catalytic efficiency. Conclusions Our findings demonstrate that vertical data-integration of genetic and epigenetic regulatory mechanisms generated a more in-depth understanding of the molecular basis underlying the development of obesity-associated fatty liver disease. We gained novel insights into how NAFLD classification and outcome are orchestrated, which could not have been obtained by exclusively considering genetic variation.

Aim: The purpose of this manuscript is to study the potential characteristics of acquired nutlin-3 resistant non-small cell lung cancer cells (NSCLC). Nutlin-3 is an inhibitor of the murine-double minute 2 protein, the main negative regulator of wild type p53, of which several derivatives are currently in clinical development. Methods: A549 NSCLC cells were exposed to increasing concentrations of nutlin-3 for a period of 18 weeks. Monoclonal derivates were cultured, and the most resistance subclone was selected for whole transcriptome analysis. Gene set enrichment analysis was performed on differentially expressed genes between A549 nutlin-3 resistant cancer cells and the parental A549 p53 wild type cancer cells. Relevant findings were validated at the gene, protein and/or functional level. Results: All nutlin-3 resistant subclones acquired mutations in the TP53 gene, resulting in overexpression of the mutant p53 protein. The most resistant subclone was enriched for genes related to epithelial to mesenchymal transition (EMT), resulting in increased migratory and invasive potential. Furthermore, these cells were enriched in genes related to inflammation, tissue remodelling, and angiogenesis. Importantly, expression of several immune checkpoints, including PD-L1 and PD-L2, was significantly upregulated, and cisplatin-induced cell death was reduced. Conclusion: Transcriptome analysis of a highly nutlin-3 resistant A549 subclone shows the relevance of studying (1) resistance to standard of care chemotherapy; (2) secretion of immunomodulating chemo- and cytokines; (3) immune checkpoint expression; and (4) EMT and invasion in nutlin-3 resistant cancer cells in addition to acquired mutations in the TP53 gene.

RATIONALE Advanced algorithmic solutions are necessary to process the ever-increasing amounts of mass spectrometry data that are being generated. In this study, we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra. METHODS falcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters. RESULTS Several state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome data set consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing. CONCLUSIONS falcon is a highly efficient spectrum clustering tool, which is publicly available as an open source under the permissive BSD license at https://github.com/bittremieux/falcon.

Auranofin (AF) is an FDA-approved antirheumatic drug with anticancer properties that acts as a thioredoxin reductase 1 (TrxR) inhibitor. The exact mechanisms through which AF targets cancer cells remain elusive. To shed light on the mode of action, this study provides an in-depth analysis on the molecular mechanisms and immunogenicity of AF-mediated cytotoxicity in the non-small cell lung cancer (NSCLC) cell line NCI–H1299 (p53 Null) and its two isogenic derivates with mutant p53 R175H or R273H accumulation. TrxR is highly expressed in a panel of 72 NSCLC patients, making it a valid druggable target in NSCLC for AF. The presence of mutant p53 overexpression was identified as an important sensitizer for AF in (isogenic) NSCLC cells as it was correlated with reduced thioredoxin (Trx) levels in vitro. Transcriptome analysis revealed dysregulation of genes involved in oxidative stress response, DNA damage, granzyme A (GZMA) signaling and ferroptosis. Although functionally AF appeared a potent inhibitor of GPX4 in all NCI–H1299 cell lines, the induction of lipid peroxidation and consequently ferroptosis was limited to the p53 R273H expressing cells. In the p53 R175H cells, AF mainly induced large-scale DNA damage and replication stress, leading to the induction of apoptotic cell death rather than ferroptosis. Importantly, all cell death types were immunogenic since the release of danger signals (ecto-calreticulin, ATP and HMGB1) and dendritic cell maturation occurred irrespective of (mutant) p53 expression. Finally, we show that AF sensitized cancer cells to caspase-independent natural killer cell-mediated killing by downregulation of several key targets of GZMA. Our data provides novel insights on AF as a potent, clinically available, off-patent cancer drug by targeting mutant p53 cancer cells through distinct cell death mechanisms (apoptosis and ferroptosis). In addition, AF improves the innate immune response at both cytostatic (natural killer cell-mediated killing) and cytotoxic concentrations (dendritic cell maturation)., Graphical abstract Image 1

Disease relapse and therapy resistance remain key challenges in treating multiple myeloma. Underlying (epi-)mutational events can promote myelomagenesis and contribute to multi-drug and apoptosis resistance. Therefore, compounds inducing ferroptosis, a form of iron and lipid peroxidation-regulated cell death, are appealing alternative treatment strategies for multiple myeloma and other malignancies. Both ferroptosis and the epigenetic machinery are heavily influenced by oxidative stress and iron metabolism changes. Yet, only a limited number of epigenetic enzymes and modifications have been identified as ferroptosis regulators. In this study, we found that MM1 multiple myeloma cells are sensitive to ferroptosis induction and epigenetic reprogramming by RSL3, irrespective of their glucocorticoid-sensitivity status. LC-MS/MS analysis revealed the formation of non-heme iron-histone complexes and altered expression of histone modifications associated with DNA repair and cellular senescence. In line with this observation, EPIC BeadChip measurements of significant DNA methylation changes in ferroptotic myeloma cells demonstrated an enrichment of CpG probes located in genes associated with cell cycle progression and senescence, such as Nuclear Receptor Subfamily 4 Group A member 2 (NR4A2). Overall, our data show that ferroptotic cell death is associated with an epigenomic stress response that might advance the therapeutic applicability of ferroptotic compounds. ispartof: INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES vol:22 issue:22 ispartof: location:Switzerland status: published

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

The natural course of chronic hepatitis B virus (HBV) infections follows distinct clinical disease phases, characterized by fluctuating levels of serum HBV DNA and ALT. The immune cells and their features that govern these clinical disease transitions remain unknown. In the current study, we performed RNA sequencing on purified B cells from blood (n = 42) and liver (n = 10) of healthy controls and chronic HBV patients. We found distinct gene expression profiles between healthy controls and chronic HBV patients, as evidenced by 190 differentially expressed genes (DEG), but also between the clinical phenotypes of a chronic HBV infection (17–110 DEG between each phase). Numerous immune pathways, including the B cell receptor pathway were upregulated in liver B cells when compared to peripheral B cells. Further investigation of the detected DEG suggested an activation of B cells during HBeAg seroconversion and an active regulation of B cell signalling in the liver.

Despite the general agreement on the importance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain, while this knowledge may indicate how to adjust vaccines and maintain robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to epitopes unique to SARS-CoV-2 (SC-unique) or shared with other coronaviruses (CoV-common), we trained a large number of TCR-epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. Applying those models to longitudinal COVID-19 TCR repertoires of critical and non-critical COVID-19 patients, we discovered that notwithstanding comparable CD8+ T-cell depletion and the sizes of putative CoV-common CD8+ TCR repertoires in all symptomatic patients at the initial stage of the disease, the temporal dynamics of putative SC2-unique TCRs differed depending on the disease severity. Only non-critical patients had developed large and diverse SC2-unique CD8+ T-cell response by the second week of the disease. Additionally, only this patient group demonstrated redundancy in CD8+ TCRs putatively recognizing unique and common SARS-CoV-2 epitopes. Our findings thus emphasize the role of thede novoCD8+ T-cell response and support the argument against the clinical benefit of pre-existing cross-reactive CD8+ T cells. Now, the analytical framework of this study can not only be employed to track specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire but also be generalized to more epitopes and be employed for adaptive immune response assessment and monitoring to inform public health decisions.

Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a model can be trained to learn patterns in time series data, allowing the detection of adverse outcomes before they become clinically apparent. In this review we provide an overview of the different machine learning techniques that have been used to develop models in hemodynamic care for newborn infants. We focus on their potential benefits, research pitfalls, and challenges related to their implementation in clinical care.

Herniaria hirsuta L. (Caryophyllaceae) is used for treatment of urinary stones and as a diuretic. Little is known about the active compounds and the mechanism of action. The phytochemical composition of H. hirsuta was comprehensively characterized using UHPLC-UV-HRMS (Ultrahigh-Performance Liquid Chromatography-Ultraviolet-High Resolution Mass Spectrometry) data. An in vitro gastrointestinal model was used to simulate biotransformation, which allowed the monitoring of the relative abundances of individual compounds over time. To analyze the longitudinal multiclass LC&ndash, MS data, XCMS, a platform that enables online metabolomics data processing and interpretation, and EDGE, a statistical method for time series data, were used to extract significant differential profiles from the raw data. An interactive Shiny app in R was used to rate the quality of the resulting features. These ratings were used to train a random forest model. The most abundant aglycone after gastrointestinal biotransformation was subjected to hepatic biotransformation using human S9 fractions. A diversity of compounds was detected, mainly saponins and flavonoids. Besides the known saponins, 15 new saponins were tentatively identified as glycosides of medicagenic acid, acetylated medicagenic acid and zanhic acid. It is suggested that metabolites of phytochemicals present in H. hirsuta, most likely saponins, are responsible for the pharmaceutical effects. It was observed that the relative abundance of saponin aglycones increased, indicating loss of sugar moieties during colonic biotransformation, with medicagenic acid as the most abundant aglycone. Hepatic biotransformation of this aglycone resulted in different metabolites formed by phase I and II reactions.

Recognition of cancer epitopes by T cells is fundamental for the activation of targeted antitumor responses. As such, the identification and study of epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) directly from TCR repertoire data and perform epitope-specificity enrichment analyses. This chapter details the use of the TCRex web tool.

There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit-for-purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide-spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in-house data set of our benchmark organism, Caenorhabditis elegans, and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability to identify unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.Author SummaryMass spectrometry is one of most used techniques to detect and identify metabolites. However, learning metabolite structures directly from mass spectrometry data has always been a challenging task. Thousands of mass spectra from various biological systems still remain unanalyzed simply because no current bioinformatic tools are able to generate structural hypotheses. By manually studying mass spectra of standard compounds, chemists discovered that metabolites that share common substructures can also share spectral features. As data scientists, we believe that such relationships can be unraveled from massive structure and spectra data by machine learning. In this study, we adapted “association rule mining”, traditionally used in market basket analysis, to structural and spectral data, allowing us to investigate all spectral features - metabolite substructures relationships. We further collected all statistically sound relationships into a database and used them to assign substructral hypotheses to unexplored spectra. We named our approach MESSAR, MEtabolite SubStructure Auto-Recommender, available to the metabolomics and mass spectrometry community as a free and open web service.

Recognition of cancer epitopes by T cells is fundamental for the activation of targeted antitumor responses. As such, the identification and study of epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) directly from TCR repertoire data and perform epitope-specificity enrichment analyses. This chapter details the use of the TCRex web tool.

Rationale: The current methods for identifying peptides in mass spectral product ion data still struggle to do so for the majority of spectra. Based on the experimental setup and other assumptions, such methods restrict the search space to speed up computations, but at the cost of creating blind spots. The proteomics community would greatly benefit from a method that is capable of covering the entire search space without using any restrictions, thus establishing a baseline for identification. Methods: We conceived the "mass pattern paradigm" (MPP) that enables the creation of such an identification method, and we implemented it into a prototype database search engine "PRiSM" (PRotein-Spectrum Matching). We then assessed its operational characteristics by applying it to publicly available high-precision mass spectra of low and high identification difficulty. We used those characteristics to gain theoretical insights into trade-offs between sensitivity and speed when trying to establish a baseline for identification. Results: Of 100 low difficulty spectra, PRiSM and SEQUEST agree on 84 identifications (of which 75 are statistically significant). Of 15 of 100 spectra not identified in a previous study (using SEQUEST), 13 are considered reliable after visual inspection and represent 3 proteins (out of 9 in total) not detected previously. Conclusions: Despite leaving noise intact, the simple PRiSM prototype can make statistically reliable identifications, while controlling the false discovery rate by fitting a null distribution. It also identifies some spectra previously unidentifiable in an "extremely open" SEQUEST search, paving the way to establishing a baseline for identification in proteomics. Department of Economy, Science and Innovation, Flanders; Vlaamse Instelling voor Technologisch Onderzoek, Grant/Award Number: UH_PhD_1701

Interesting pattern discovery is an important topic in data mining research. Many different definitions have been proposed to describe whether a pattern is interesting. Among these many definitions, unexpectedness has shown to be a highly promising measure. Mining unexpected patterns allows one to identify a failing in prior knowledge and may suggest an aspect of the data that deserves further investigation. Unexpected patterns are typically mined using belief-driven methods, but these require an established belief system. Prior studies have manually built their own partial belief systems to apply their method, but these remain laborious to create. In this study, we propose a novel approach that is able to automatically detect beliefs from data, which can in turn be used to reveal unexpected patterns. Central to this approach is a clustering-based method in which clusters represent beliefs and outliers are potential unexpected patterns. We also propose a pattern representation that captures the semantic relation between patterns rather than the lexical difference. An experimental evaluation on different datasets and a comparison to some other methods demonstrate the effectiveness of the proposed method, as well as the relevance of the discovered patterns.

Thanks to the recommendation of a combined Measles/Mumps/Rubella (MMR) vaccine, like Priorix®, these childhood diseases are less common now. This is beneficial to limit the spread of these diseases and work towards their elimination. However, the measles, mumps and rubella antibody titers show a large variability in short- and long-term immunity. The recent outbreaks worldwide of measles and mumps and previous studies, which mostly focused on only one of the three virus responses, illustrate that there is a clear need for better understanding the immune responses after vaccination. Our healthy cohort was already primed with the MMR antigens in their childhood. In this study, the adult volunteers received one Priorix® vaccine dose at day 0. First, we defined 4 different groups of responders, based on their antibody titers’ evolution over 4 time points (Day 0, 21, 150 and 365). This showed a high variability within and between individuals. Second, we determined transcriptome profiles using 3′mRNA sequencing at day 0, 3 and 7. Using two analytical approaches, “one response group per time point” and “a time comparison per response group”, we correlated the short-term gene expression profiles to the different response groups. In general, the list of differentially expressed genes is limited, however, most of them are clearly immune-related and upregulated at day 3 and 7, compared to the baseline day 0. Depending on the specific response group there are overlapping signatures for two of the three viruses. Antibody titers and transcriptomics data showed that an additional Priorix vaccination does not facilitate an equal immune response against the 3 viruses or among different vaccine recipients.

Omics technologies, and in particular metabolomics, have received an increasing attention during the assessment of hepatotoxicity in vitro. However, at present, a consensus on good metabolomics practices has yet to be reached. Therefore, in this review, a range of experimental approaches, applied methodologies, and data processing workflows are compared and critically evaluated. Experimental designs among the studies are similar, reporting the use of primary hepatocytes or hepatic cell lines as the most frequently used cell sources. Experiments are usually conducted in short time-frames (

Tris (2-butoxyethyl) phosphate (TBOEP) is a compound produced at high volume that is used as both a flame retardant and a plasticizer. It is persistent and bioaccumulative, yet little is known of its toxicological modes of action. Such insight may aid risk assessment in a weight-of-evidence approach supplementing current testing strategies. We used an RNA sequencing approach as an unbiased and sensitive tool to explore potential negative health effects of sub-cytotoxic concentrations of TBOEP on the transcriptome of the human liver hepatocellular carcinoma cell line, HepG2, with the lowest concentration used potentially holding relevance to human physiological levels. Over-representation and gene set enrichment analysis corresponded well and revealed that TBOEP treatments resulted in an upregulation of genes involved in protein and energy metabolism, along with DNA replication. Such increases in cell and macromolecule metabolism could explain the increase in mitochondrial activity at lower TBOEP concentrations. In addition, TBOEP affected a wide variety of biological processes, the most notable one being the general stress response, wound healing. Finally, TBOEP showed effects on steroid hormone biosynthesis and activation, regulation, and potentiation of immune responses, in agreement with other studies. As such, this study is the first study investigating genome-wide changes in gene transcription in response to TBOEP in human cells.

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M − H]−) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

Rationale Using mass spectrometry, the analysis of known metabolite structures has become feasible in a systematic high-throughput fashion. Nevertheless, the identification of previously unknown structures remains challenging, partially because many unidentified variants originate from known molecules that underwent unexpected modifications. Here, we present a method for the discovery of unknown metabolite modifications and conjugate metabolite isoforms in a high-throughput fashion. Methods The method is based on user-controlled in-source fragmentation which is used to induce loss of weakly bound modifications. This is followed by the comparison of product ions from in-source fragmentation and collision-induced dissociation (CID). Diagonal MS2 -MS3 matching allows the detection of unknown metabolite modifications, as well as substructure similarities. As the method relies heavily on the advantages of in-source fragmentation and its ability to 'magically' elucidate unknown modification, we have named it inSourcerer as a portmanteau of in-source and sorcerer. Results The method was evaluated using a set of 15 different cytokinin standards. Product ions from in-source fragmentation and CID were compared. Hierarchical clustering revealed that good matches are due to the presence of common substructures. Plant leaf extract, spiked with a mix of all 15 standards, was used to demonstrate the method's ability to detect these standards in a complex mixture, as well as confidently identify compounds already present in the plant material. Conclusions Here we present a method that incorporates a classic liquid chromatography/mass spectrometry (LC/MS) workflow with fragmentation models and computational algorithms. The assumptions upon which the concept of the method was built were shown to be valid and the method showed that in-source fragmentation can be used to pinpoint structural similarities and indicate the occurrence of a modification.

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both known and novel epitopes. In addition, we present a novel feature representation approach which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are particular to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets, and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to novel epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

Metabolites represent the most downstream information of the cellular organisation. Hence, metabolomics experiments are extremely valuable to unravel the endogenous pathways involved in a toxicological mode of action. However, every external stimulus can introduce alterations in the cell homeostasis, thereby obscuring the involved endogenous pathways, biasing the interpretation of the results. Here we report on sodium saccharin, which is considered to be not hepatotoxic and therefore can serve as a reference compound to detect metabolic alterations that are not related to liver toxicity. Exposure of HepaRG cells to high levels of sodium saccharin (&gt, 10 mM) induced cell death, probably due to an increase in the osmotic pressure. Yet, a low number (n = 15) of significantly altered metabolites were also observed in the lipidome, including a slight decrease in phospholipids and an increase in triacylglycerols, upon daily exposure to 5 mM sodium saccharin for 72 h. The observation that a non-hepatotoxic compound can affect the metabolome underpins the importance of correct experimental design and data interpretation when investigating toxicological modes of action via metabolomics.

International audience; Whole genome sequencing (WGS) of Mycobacterium tuberculosis has rapidly progressed from a research tool to a clinical application for the diagnosis and management of tuberculosis and in public health surveillance. This development has been facilitated by drastic drops in cost, advances in technology and concerted efforts to translate sequencing data into actionable information. There is, however, a risk that, in the absence of a consensus and international standards, the widespread use of WGS technology may result in data and processes that lack harmonization, comparability and validation. In this Review, we outline the current landscape of WGS pipelines and applications, and set out best practices for M. tuberculosis WGS, including standards for bioinformatics pipelines, curated repositories of resistance-causing variants, phylogenetic analyses, quality control and standardized reporting.

Background After transplantation, cell-free deoxyribonucleic acid (DNA) derived from the donor organ (ddcfDNA) can be detected in the recipient’s circulation. We aimed to investigate the role of plasma ddcfDNA as biomarker for acute kidney rejection. Methods From 107 kidney transplant recipients, plasma samples were collected longitudinally after transplantation (Day 1 to 3 months) within a multicentre set-up. Cell-free DNA from the donor was quantified in plasma as a fraction of the total cell-free DNA by next generation sequencing using a targeted, multiplex polymerase chain reaction-based method for the analysis of single nucleotide polymorphisms. Results Increases of the ddcfDNA% above a threshold value of 0.88% were significantly associated with the occurrence of episodes of acute rejection (P = 0.017), acute tubular necrosis (P = 0.011) and acute pyelonephritis (P = 0.032). A receiver operating characteristic curve analysis revealed an equal area under the curve of the ddcfDNA% and serum creatinine of 0.64 for the diagnosis of acute rejection. Conclusions Although increases in plasma ddcfDNA% are associated with graft injury, plasma ddcfDNA does not outperform the diagnostic capacity of the serum creatinine in the diagnosis of acute rejection.

Open modification searching (OMS) is a powerful search strategy to identify peptides with any type of modification. OMS works by using a very wide precursor mass window to allow modified spectra to match against their unmodified variants, after which the modification types can be inferred from the corresponding precursor mass differences. A disadvantage of this strategy, however, is the large computational cost, because each query spectrum has to be compared against a multitude of candidate peptides. We have previously introduced the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. Here we demonstrate how this candidate selection procedure can be further optimized using graphics processing units. Additionally, we introduce a feature hashing scheme to convert high-resolution spectra to low-dimensional vectors. On the basis of these algorithmic advances, along with low-level code optimizations, the new version of ANN-SoLo is up to an order of magnitude faster than its initial version. This makes it possible to efficiently perform open searches on a large scale to gain a deeper understanding about the protein modification landscape. We demonstrate the computational efficiency and identification performance of ANN-SoLo based on a large data set of the draft human proteome. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo. This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in the Journal of Proteome Research, copyright © American Chemical Society after peer review. To access the final edited and published work see https://pubs.acs.org/articlesonrequest/AOR-TCA25r6IZJVZDTDN337H.

STUDY QUESTION Does oocyte maturation under lipolytic conditions have detrimental carry-over effects on post-hatching embryo development of good-quality blastocysts after transfer? SUMMARY ANSWER Surviving, morphologically normal blastocysts derived from bovine oocytes that matured under lipotoxic conditions exhibit long-lasting cellular dysfunction at the transcriptomic and metabolic levels, which coincides with retarded post-hatching embryo development. WHAT IS KNOWN ALREADY There is increasing evidence showing that following maturation in pathophysiologically relevant lipotoxic conditions (as in obesity or metabolic syndrome), surviving blastocysts of good (transferable) morphological quality have persistent transcriptomic and epigenetic alteration even when in vitro embryo culture takes place under standard conditions. However, very little is known about subsequent development in the uterus after transfer. STUDY DESIGN, SIZE, DURATION Bovine oocytes were matured in vitro in the presence of pathophysiologically relevant, high non-esterified fatty acid (NEFA) concentrations (HIGH PA), or in basal NEFA concentrations (BASAL) as a physiological control. Eight healthy multiparous non-lactating Holstein cows were used for embryo transfers. Good-quality blastocysts (pools of eight) were transferred per cow, and cows were crossed over for treatments in the next replicate. Embryos were recovered 7 days later and assessed for post-hatching development, phenotypic features and gene expression profile. Blastocysts from solvent-free and NEFA-free maturation (CONTROL) were also tested for comparison. PARTICIPANTS/MATERIALS, SETTING, METHODS Recovered Day 14 embryos were morphologically assessed and dissected into embryonic disk (ED) and extraembryonic tissue (EXT). Samples of EXT were cultured for 24 h to assess cellular metabolic activity (glucose and pyruvate consumption and lactate production) and embryos’ ability to signal for maternal recognition of pregnancy (interferon-τ secretion; IFN-τ). ED and EXT samples were subjected to RNA sequencing to evaluate the genome-wide transcriptome patterns. MAIN RESULTS AND THE ROLE OF CHANCE The embryo recovery rate at Day 14 p.i. was not significantly different among treatment groups (P > 0.1). However, higher proportions of HIGH PA embryos were retarded in growth (in spherical stage) compared to the more elongated tubular stage embryos in the BASAL group (P LARGE-SCALE DATA The datasets of RNA sequencing analysis are available in the NCBI’s Gene Expression Omnibus (GEO) repository, series accession number GSE127889 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127889). Datasets of differentially expressed genes and their gene ontology functions are available in the Mendeley datasets at http://dx.doi.org/10.17632/my2z7dvk9j.2. LIMITATIONS, REASONS FOR CAUTION The bovine model was used here to allow non-invasive embryo transfer and post-hatching recovery on Day 14. There are physiological differences in some characteristics of post-hatching embryo development between human and cows, such as embryo elongation and trophoblastic invasion. However, the main carry-over effects of oocyte maturation under lipolytic conditions described here are evident at the cellular level and therefore may also occur during post-hatching development in other species including humans. In addition, post-hatching development was studied here under a healthy uterine environment to focus on carry-over effects originating from the oocyte, whereas additional detrimental effects may be induced by maternal metabolic disorders due to adverse changes in the uterine microenvironment. RNA sequencing results were not verified by qPCR, and no solvent control was included. WIDER IMPLICATIONS OF THE FINDINGS Our observations may increase the awareness of the importance of maternal metabolic stress at the level of the preovulatory oocyte in relation to carry-over effects that may persist in the transferrable embryos. It should further stimulate new research about preventive and protective strategies to optimize maternal metabolic health around conception to maximize embryo viability and thus fertility outcome. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the Flemish Research Fund (FWO grant 11L8716N and FWO project 42/FAO10300/6541). The authors declare there are no conflicts of interest.

Background Meningitis can be caused by several viruses and bacteria. Identifying the causative pathogen as quickly as possible is crucial to initiate the most optimal therapy, as acute bacterial meningitis is associated with a significant morbidity and mortality. Bacterial meningitis requires antibiotics, as opposed to enteroviral meningitis, which only requires supportive therapy. Clinical presentation is usually not sufficient to differentiate between viral and bacterial meningitis, thereby necessitating cerebrospinal fluid (CSF) analysis by PCR and/or time-consuming bacterial cultures. However, collecting CSF in children is not always feasible and a rather invasive procedure. Methods In 12 Belgian hospitals, we obtained acute blood samples from children with signs of meningitis (49 viral and 7 bacterial cases) (aged between 3 months and 16 years). After pathogen confirmation on CSF, the patient was asked to give a convalescent sample after recovery. 3 ' mRNA sequencing was performed to determine differentially expressed genes (DEGs) to create a host transcriptomic profile. Results Enteroviral meningitis cases displayed the largest upregulated fold change enrichment in type I interferon production, response and signaling pathways. Patients with bacterial meningitis showed a significant upregulation of genes related to macrophage and neutrophil activation. We found several significantly DEGs between enteroviral and bacterial meningitis. Random forest classification showed that we were able to differentiate enteroviral from bacterial meningitis with an AUC of 0.982 on held-out samples. Conclusions Enteroviral meningitis has an innate immunity signature with type 1 interferons as key players. Our classifier, based on blood host transcriptomic profiles of different meningitis cases, is a possible strong alternative for diagnosing enteroviral meningitis. Al Thrasher early career research Grant, the University of Antwerp; Hercules Foundation-Belgium; Research Foundation Flanders (FWO)FWO [151316, 1861219N]

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on, f.i., mixed models can take a lot of (computing) time. MS-based proteomics labs are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at https://qcquan.net/) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow, and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

Cholestasis is a liver disease associated with retention of bile in the liver, which leads to local hepatic inflammation and severe liver damage. In order to investigate the mode of action of drug-induced cholestasis, in vitro models have shown to be able to recapitulate important elements of this disease. In this study, we applied untargeted metabolomics to investigate the metabolic perturbances in HepaRG® cells exposed for 24 h and 72 h to bosentan, a cholestatic reference toxicant. Intracellular profiles were extracted and analysed with liquid chromatography and accurate-mass spectrometry. Metabolites of interest were selected using partial least-squares discriminant analysis and random forest classifier models. The observed metabolic patterns associated with cholestasis in vitro were complex. Acute (24 h) exposure revealed metabolites related to apoptosis, such as ceramide and triglyceride accumulation, in combination with phosphatidylethanolamine, choline and carnitine depletion. Metabolomic alterations during exposure to lower dosages and a prolonged exposure (72 h) included carnitine upregulation and changes in the polyamine metabolism. These metabolites were linked to changes in phospholipid metabolism, mitochondrial pathways and energy homeostasis. The metabolic changes confirmed the mitotoxic effects of bosentan and revealed the potential involvement of phospholipid metabolism as part of the mode of action of drug-induced cholestasis.

Motivation The T-cell receptor (TCR) is responsible for recognizing epitopes presented on cell surfaces. Linking TCR sequences to their ability to target specific epitopes is currently an unsolved problem, yet one of great interest. Indeed, it is currently unknown how dissimilar TCR sequences can be before they no longer bind the same epitope. This question is confounded by the fact that there are many ways to define the similarity between two TCR sequences. Here we investigate both issues in the context of TCR sequence unsupervised clustering. Results We provide an overview of the performance of various distance metrics on two large independent datasets with 412 and 2835 TCR sequences respectively. Our results confirm the presence of structural distinct TCR groups that target identical epitopes. In addition, we put forward several recommendations to perform unsupervised T-cell receptor sequence clustering. Availability and implementation Source code implemented in Python 3 available at https://github.com/pmeysman/TCRclusteringPaper. Supplementary information Supplementary data are available at Bioinformatics online.

Pathogens of past and current infections have been identified directly by means of PCR or indirectly by measuring a specific immune response (e.g., antibody titration). Using a novel approach, Emerson and colleagues showed that the cytomegalovirus serostatus can also be accurately determined by using a T cell receptor repertoire data mining approach. In this study, we have sequenced the CD4+ memory T cell receptor repertoire of a Belgian cohort with known cytomegalovirus serostatus. A random forest classifier was trained on the CMV specific T cell receptor repertoire signature and used to classify individuals in the Belgian cohort. This study shows that the novel approach can be reliably replicated with an equivalent performance as that reported by Emerson and colleagues. Additionally, it provides evidence that the T cell receptor repertoire signature is to a large extent present in the CD4+ memory repertoire.

Although some herbal remedies have been used for decades, little is known about the active compounds and the mechanism of action. Many natural products, such as glycosides, can be considered as prodrugs, which become active after biotransformation. To optimize the workflow of in vitro biotransformation followed by automated data analysis, hederacoside C was used as a model compound for saponins. Hederacoside C was subjected to gastrointestinal enzymes and fecal microflora. Samples were analyzed with UHPLC-PDA-HRMS before, during and after in vitro biotransformation, which allowed the monitoring of the relative abundances of the compound and its metabolites. The data-analysis workflow was optimized to render as much information as possible from the longitudinal LCMS data. XCMS was used to convert the raw data into features via peak-picking, followed by grouping, and EDGE was used for the extraction of significant differential profiles. To evaluate if the workflow was suitable for dynamic multiclass metabolomics data, an interactive Shiny web app was developed in R to rate the quality of the resulting features. These ratings were used to train a random forest model for predicting experts response. A performance analysis revealed that the random forest model was capable of correctly predicting the reviewers response in most cases (AUC 0.926 with 10 fold cross validation). The automated data analysis workflow was used for unbiased screening for metabolites and revealed the biotransformation of hederacoside C. As expected, a decrease in relative abundance of hederacoside C was observed over time. Additionally, the relative abundance of metabolites increased, illustrating the biotransformation of hederacoside C, especially in the colon phase, where microbial fermentation takes place. Stepwise progressive elimination of sugar moieties was the major metabolic pathway.

Susceptibility for leishmaniasis is largely dependent on host genetic and immune factors. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors for leishmaniasis, little is known regarding the mechanisms that underpin these associations. To better understand this underlying functionality, we first collected all known leishmaniasis-associated HLA variants in a thorough literature review. Next, we aligned and compared the protection- and risk-associated HLA-DRB1 allele sequences. This identified several amino acid polymorphisms that distinguish protection- from risk-associated HLA-DRB1 alleles. Subsequently, T cell epitope binding predictions were carried out across these alleles to map the impact of these polymorphisms on the epitope binding repertoires. For these predictions, we used epitopes derived from entire proteomes of multiple Leishmania species. Epitopes binding to protection-associated HLA-DRB1 alleles shared common binding core motifs, mapping to the identified HLA-DRB1 amino acid polymorphisms. These results strongly suggest that HLA polymorphism, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes. A set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, was prioritized for further epitope discovery in the search for novel subunit-based vaccines.

Cytosine C5 methylation is an important epigenetic control mechanism in a wide array of eukaryotic organisms and generally carried out by proteins of the C-5 DNA methyltransferase family (DNMTs). In several protozoans, the status of this mechanism remains elusive, such as in Leishmania, the causative agent of the disease leishmaniasis in humans and a wide array of vertebrate animals. In this work, we showed that the Leishmania donovani genome contains a C-5 DNA methyltransferase (DNMT) from the DNMT6 subfamily, whose function is still unclear, and verified its expression at the RNA level. We created viable overexpressor and knock-out lines of this enzyme and characterized their genome-wide methylation patterns using whole-genome bisulfite sequencing, together with promastigote and amastigote control lines. Interestingly, despite the DNMT6 presence, we found that methylation levels were equal to or lower than 0.0003% at CpG sites, 0.0005% at CHG sites, and 0.0126% at CHH sites at the genomic scale. As none of the methylated sites were retained after manual verification, we conclude that there is no evidence for DNA methylation in this species. We demonstrated that this difference in DNA methylation between the parasite (no detectable DNA methylation) and the vertebrate host (DNA methylation) allowed enrichment of parasite vs. host DNA using methyl-CpG-binding domain columns, readily available in commercial kits. As such, we depleted methylated DNA from mixes of Leishmania promastigote and amastigote DNA with human DNA, resulting in average Leishmania:human enrichments from 62&times, up to 263&times, These results open a promising avenue for unmethylated DNA enrichment as a pre-enrichment step before sequencing Leishmania clinical samples.

Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. However, as datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficient. In addition, as the ground truth for metabolomics experiments is intrinsically unknown, there is no way to critically evaluate the performance of tools. Here, we investigate the problem of dynamic multi-class metabolomics experiments using a simulated dataset with a known ground truth and evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning, and compare it to EDGE, a statistical method for sequence data. This paper presents three novel outcomes. First we present a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, we show that the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs control metabolomics data. Last, we introduce the tinderesting method to analyse more complex dynamic metabolomics experiments that performs on par with statistical methods. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.