Biology subjects

Hankemeier, T.

Publications and source records attributed to Hankemeier, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-omics characterization of breast cancer metabolism identifies new metabolic targets

Breast cancer cells undergo metabolic reprogramming to support proliferation and metastasis, but these alterations are heterogeneous. To systematically assess this heterogeneity under controlled conditions, we profiled 31 intracellular polar metabolites and 50 amines, together with exchange rates of 57 amines, in a panel of 51 breast cancer cell lines spanning diverse phenotypes. These data were integrated with lipidomics and transcriptomics. Multi-omics factor analysis identified metabolic signatures linked to proliferation and to differences between luminal, HER2-positive, basal A, and basal B cell lines, overlapping with an epithelial-to-mesenchymal transition phenotype. Fast-proliferating, aggressive cell lines showed increased uptake of essential amino acids and altered nucleotide levels, while heterogeneity in glutamine metabolism was mainly driven the subtype. This was partially associated with heterogeneous expression of metabolite transporters. To test the functional relevance, 67 metabolic genes were silenced in Hs578T using siRNA, and effects on proliferation and migration were measured by a sulforhodamine B assay, live-cell imaging, and a random cell migration assay. This resulted in 34 knockdowns that reduced proliferation and 20 reduced migration, with strong effects for the glycosyltransferases EXT1 and EXT2, nucleotide metabolism genes GART and HPRT1, and metabolite transporters SLC7A1, SLC7A11, and SLC16A3. These findings highlight phenotype-specific metabolic dependencies and identify candidate drug targets in aggressive breast cancer.

cancer biology↗

bioRxiv2025

Longitudinal Metabolomic Profiling of Biogenic Amines in Plasma and CSF, and Their Correlation, Reveals Sex-Specific and Age Changes in TgF344 Alzheimer's Disease Transgenic and Wildtype Rats

BackgroundAlterations in amine metabolism have been implicated Alzheimers disease (AD). Cerebrospinal fluid (CSF) and plasma are key biofluids in AD research. CSF is considered to better reflect brain metabolic alterations than plasma, while plasma can be obtained more easily. However, plasma-CSF relationships are unclear. AimTo investigate longitudinal changes of amines in plasma and CSF, and their correlation across the two, in male and female TgF344 AD transgenic versus wildtype (WT) rats. MethodLC-MS-based targeted metabolomics was used to analyze 60 and 55 amines in plasma and CSF, respectively, of male and female TgF344-AD and WT rats, at 12, 25, 50 and 85 weeks. Statistical analysis was performed using generalized logistic regressions, Pearson correlations, and differential correlations between groups and matrixes. ResultsCompared to WT controls, at 12 weeks, TgF344-AD rats showed an increase of 3-methylhistidine, anserine, cysteine, s-methylcysteine, while at 25 weeks, male TgF344-AD rats showed pronounced increases in CSF levels of alpha-aminobutyric acid, asparagine, glycylglycine, glycylproline, histidine, isoleucine, kynurenine, leucine, methionine, methionine sulfone, norepinephrine, phenylalanine, proline, tyrosine, and valine. At 50 weeks, female TgF344-AD rats exhibited reductions in CSF for DL-3-aminoisobutyric acid, gamma-aminobutyric acid, ornithine, and putrescine. Distinct plasma-CSF correlations were found for 1-methylhistidine, 2-aminoadipic acid, putrescine, kynurenine, N6,N6,N6-trimethyl-lysine, DL-3-aminoisobutyric acid, and taurine, particularly in TgF344-AD rats. ConclusionsBody fluid, age- and sex-dependent amine alterations in CSF and plasma of TgF344-AD rats compared to WT controls provide important insights into AD disease processes and may aid early diagnosis and therapeutic targeting.

systems biology↗

bioRxiv2025

Metabolic Signatures of Pulmonary Embolism in COVID-19: Insights from Longitudinal Intensive Care Unit Profiles

ABSTRACTO_ST_ABSBackgroundC_ST_ABSPulmonary embolism is a severe complication of COVID-19 infection, associated with a hypercoagulable state and heightened risk of blood clots. As SARS-CoV-2 has become endemic, understanding pulmonary embolisms metabolic effects in COVID-19 patients is warranted. This study investigated the longitudinal metabolic profiles of 66 Intensive Care Unit-admitted COVID-19 patients at Erasmus Medical Center to identify metabolites and mechanisms associated with pulmonary embolism. MethodA total of 1209 metabolic species were measured, including amines and lipids. Metabolic changes were analysed across four timeframes: i) general analysis of pulmonary embolism, ii) 72 hours prior to pulmonary embolism, iii) 48 hours prior and the day of pulmonary embolism, and iv) the day of and 48 hours post-pulmonary embolism. ResultsThe general analysis revealed significant upregulation of amines, triglycerides, phosphatidylethanolamines, ether-linked phosphatidylethanolamines, and eicosanoids in patients who developed a pulmonary embolism. Phosphatidylethanolamines containing the 20:3 fatty acid side chain were notably elevated. Minimal metabolic dysregulation was observed 72 hours before pulmonary embolism, with subtle increases in lysophosphatidylcholines and lysophosphatidylethanolamines. In contrast, there was a strong metabolic response during and post-pulmonary embolism, phosphatidylethanolamines (47%), ether-linked phosphatidylethanolamines(96%) and sphingosines(40%). ConclusionThese findings underscore the critical role of lipid metabolism in pulmonary embolism, particularly triglycerides and specific lipid species. The limited metabolic perturbations before pulmonary embolism suggest early prediction challenges, emphasising the need for further research into temporal metabolic changes and their clinical applications.

molecular biology↗

bioRxiv2025

Reduced triacylglycerols and lipid droplets are associated with resilience to Alzheimers disease

IntroductionWhile it has become clear that alterations in lipid metabolism are associated with AD, it is unclear how they contribute to both cognitive decline and the pathophysiology of AD. MethodsWe performed lipidomics and activity-based protein profiling in the frontal cortex of control, AD and resilient donors, i.e. individuals with AD pathology without cognitive decline. Subsequently we integrated these data using multi-omics factor analysis and correlated the multi-omics profiles to disease and clinical parameters. ResultsThe most pronounced alterations in lipids were in the {omega}6-derived oxylipins, which were particularly increased in the AD patients. Both triacylglycerols (TAGs) and lipid droplets were more abundant in the AD donors compared to the resilient donors. Enzyme activities showed a similar direction in the AD and resilient donors, including decreased activity of ABHD6. Multi-omics factor analysis showed that increased oxylipins, loss of inhibitory cells, synaptic genes and genes related to the inflammatory response were associated with A{beta} plaque load in both AD and resilient donors. ConclusionOur multi-omics data show a response associated with A{beta} load shared among AD and resilient donors and, for the first time, reduced lipid droplets in resilient donors.

neuroscience↗

bioRxiv2025

Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer's Disease - Finding from Alzheimer's Disease Neuroimaging Initiative.

Mounting evidence implicates inflammation as a key factor in Alzheimers disease (AD) development. We previously identified pro-inflammatory soluble epoxide hydrolase (sEH) metabolites to be elevated in plasma and CSF of AD patients and to be associated with lower cognition in non-AD subjects. Soluble epoxide hydrolase is a key enzyme converting anti-inflammatory epoxy fatty acids to pro-inflammatory diols, reported to be elevated in multiple cardiometabolic disorders. Here we analyzed over 700 fasting plasma samples from the baseline of Alzheimers Disease Neuroimaging Initiative (ADNI) 2/GO study. We applied targeted mass spectrometry method to provide absolute quantifications of over 150 metabolites from oxylipin and endocannabinoids pathway, interrogating the role for inflammation/immune dysregulation and the key enzyme soluble epoxide hydrolase in AD. We provide further insights into the regulation of this pathway in different disease stages, APOE genotypes and between sexes. Additionally, we investigated in mild cognitive impaired (MCI) patients, metabolic signatures that inform about resilience to progression and conversion to AD. Key findings include I) confirmed disruption in this key central pathway of inflammation and pointed to dysregulation of sEH in AD with sex and disease stage differences; II) identified markers of disease progression and cognitive resilience using sex and ApoE genotype stratified analysis highlighting an important role for bile acids, lipid peroxidation and stress response hormone cortisol. In conclusion, we provide molecular insights into a central pathway of inflammation and links to cognitive dysfunction, suggesting novel therapeutic approaches that are based on targeting inflammation tailored for subgroups of individuals based on their sex, APOE genotype and their metabolic profile.

neuroscience↗

bioRxiv2025

Lipidomic Fingerprints Reveal Sex-, Age-, and Disease-Dependent Differences in the TgF344-AD Transgenic Rats

BackgroundGathering information on Alzheimers disease (AD) progression in human poses significant challenges due to the lengthy timelines and ethical considerations involved. Animal AD models provide a valuable alternative for conducting mechanistic studies and testing potential therapeutic strategies. Disturbed lipid homeostasis is among the earliest neuropathological features of AD. AimTo identify longitudinal plasma lipidomic changes associated with age, sex, and AD in male and female TgF344-AD and wild-type rats. MethodsA total of 751 lipids in 141 rats (n=73 TgF344-AD; n= 68 WT) were quantified at 12, 25, 50, and 85 weeks). Differential abundances of lipids were assessed using generalized logical regression models, correcting for i) age and sex, for ii) individual age groups, and iii) sex-specific differences. Predictive lipid signature models for AD were developed using stepwise feature selection for the full age range, as well as for midlife. ResultsSex differences were identified among all ages in sphingomyelin (SM), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) lipid classes. AD and age-related differences were found in the SM class in mid-life (25-50 weeks). Other AD and age-related differences were found in the ratios of linoleic acid and 5 of its products. Moreover, similarities in lipidomic profile changes were observed for humans and rats. The full age range and mid-life predictive lipid signatures for AD resulted in an AUC of 0.75 and 0.68, respectively. ConclusionsOur findings highlight the value of lipidomic in identifying early AD-related lipid alterations, offering a promising avenue for understanding disease mechanisms and advancing biomarker discovery.

systems biology↗

bioRxiv2025

mzQuality: A tool for quality monitoring and reporting of targeted mass spectrometry measurements

Analyzing metabolites using mass spectrometry can offer valuable insight into an individuals health or disease status. However, various sources of experimental variation can affect the data, making robust quality control essential. In this context, we introduce mzQuality, a user-friendly software tool designed to evaluate and correct technical variations in mass spectrometry-based metabolomics data. MzQuality offers key quality control features, such as batch correction, outlier identification, and analysis of signal-to-noise ratios. It supports any peak-integrated processed data independent of vendor software and does not require the user to have any programming skills. We demonstrate the functionality of mzQuality with a data set of 419 samples measured across six batches, in which mzQuality effectively minimized experimental variation, ensuring the datas readiness for statistical analysis and biological interpretation. With customizable settings, mzQuality can be seamlessly integrated into research workflows to produce more accurate and reproducible metabolomics data.

bioinformatics↗

bioRxiv2024

fluxTrAM: Integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis

Quantitative inference of intracellular reaction rates is essential for characterising metabolic phenotypes. The classical experimental method for measuring metabolic fluxes makes use of stable-isotope tracing of metabolites through the metabolic network, followed by mass spectrometry analysis. The most common 13C-based metabolic flux analysis requires multidisciplinary knowledge in analytical chemistry, cell biology, and mathematical modelling, as well as the use of multiple independent tools for handling mass spectrometry data. Besides, flux analysis is usually carried out within a small network to validate a specific biological hypothesis. To overcome interdisciplinary barriers and extend flux interpretation towards a genome-scale level, we developed fluxTrAM, a semi-automated pipeline for processing tracer- based metabolomics data and integrating it with atomically resolved genome-scale metabolic networks to enable flux predictions at genome-scale. fluxTrAM integrates different software packages inside and outside of the COBRA Toolbox v3.4 for the generation of metabolite structure and reaction databases for a genome-scale model, labelled mass spectrometry data processing into standardised mass isotopologue distribution data (MID), and metabolic flux analysis. To demonstrate the utility of this pipeline, we generated 13C-labeled metabolomics data on an in vitro human induced pluripotent stem cell (iPSC)-derived dopaminergic neuronal culture and processed 13C-labeled MID datasets. In parallel, we generated a cheminformatic database of standardised and context-specific metabolite structures, and atom-mapped reactions for a genome-scale dopaminergic neuronal metabolic model. MID data could be exported into established flux inference software for conventional flux inference on a core model scale. It could also be integrated into the atomically resolved metabolic model for flux inference at genome-scale using moiety fluxomics method. The core model flux solution and moiety flux solution were then compared to two additional flux solutions predicted via flux balance analysis and entropic flux balance analysis. The extensive computational flux analysis and comparison helped to better evaluate the obtained flux feasibility of the neuron-specific genome-scale model and suggested new tracer-based metabolomics experiments with novel labeling configurations, such as labelling a moiety within the thymidine metabolite. Overall, fluxTrAM enables the automation of labelled liquid chromatography (LC)-mass spectrometry (MS) data processing into MID datasets and atom mapping for any given genome-scale metabolic model. It contributes to the standardisation and high throughput of metabolic flux analysis at genome- scale.

systems biology↗

bioRxiv2024

Conserved moiety fluxomics

Fluxomics seeks to infer reaction fluxes at genome-scale by introducing isotopically labelled metabolites into a living system and measuring the enrichment of isotopic tracers in downstream metabolites. This approach has the potential to quantify fluxes at genome-scale but current approaches to infer steady-state metabolic reaction fluxes are either not tractable or not efficient at genome-scale as the corresponding computational models are high-dimensional, involve nonlinear constraints and lack mathematical guarantees of convergence to a global optimum. Herein, we introduce conserved moiety fluxomics, which is a novel, efficient, mathematically transparent, and computationally efficient method to infer metabolic reaction flux at genome-scale. We demonstrate how identification of the set of conserved moieties for a given metabolic network leads to a novel moiety graph decomposition of a stoichiometric matrix that can be used to linearly relate metabolic reaction fluxes to the rate at which conserved moieties transition between metabolites. This linear formulation avoids combinatorial explosion because the number of conserved moieties is bounded, most reactions correspond to a small number of conserved moiety transitions, it is sufficient to represent the flow of moieties that have the potential to be labelled, and it is sufficient to represent constraints from those isotopologues that are actually measured. A non-linear yet strictly convex objective is used to ensure that inferred fluxes satisfy energy conservation and the second law of thermodynamics. Numerical results demonstrate the computational tractability of conserved moiety fluxomics at genome-scale, given liquid chromatography-mass spectrometry derived stationary mass isotopologue distribution data from an in vitro dopaminergic neuronal culture fed 13C labelled glucose. Conserved moiety fluxomics unites the strengths of constraint-based modelling and metabolic flux analysis and has the potential to be further enhanced to enable inference of fluxes in whole-body metabolic models, including from non-stationary isotopic labelling data.

systems biology↗

bioRxiv2024

Foamy microglia link oxylipins to disease progression in multiple sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disease characterized by expanding demyelinating lesions, leading to severe and irreversible disability. The mechanisms driving lesion expansion, however, remain poorly understood. Here, using a multi-omics approach, we identified foamy microglia as primary contributors to the molecular profile of lesions and disease progression in secondary progressive MS. Lesions with foamy microglia are marked by the accumulation of cholesterol esters, bismonoacylglycerolphosphates (BMP), and oxylipins, along with high B-cell infiltration, increased levels of immunoglobulin G1, and elevated expression of Fc{gamma}- and complement receptors. Lesions with foamy GPNMB+-microglia display markers of enhanced phagocytosis, lipid metabolism, lysosomal dysfunction, and antigen presentation, but lack classical pro-inflammatory markers. Our data suggest that sustained phagocytosis of myelin overwhelms microglial endo-lysosomal capacity, leading to lipid droplet and oxylipin formation. This microglial phenotype may induce further recruitment of adaptive immune cells, axonal damage, drive lesion expansion and prevent remyelination. Monoacylglycerol lipase, involved in producing oxylipin precursors, was identified as a potential therapeutic target to disrupt this cycle and prevent chronic lesion expansion.

neuroscience↗

bioRxiv2024

Normalization strategies for lipidome data in cell line panels

Sample collection can significantly affect measurements of relative lipid concentrations in cell line panels, hiding intrinsic biological properties of interest between cell lines. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass. We studied how commonly used data normalization and transformation steps during analysis influenced the resulting lipid data distributions. We compared normalization by biological properties such as cell count or total protein concentration, to statistical and data-based approaches, such as median, mean, or probabilistic quotient-based normalization and used intraclass correlation to estimate how similarity between replicates changed after normalization. Normalizing lipidomic data by cell count improved similarity between replicates, but only for a study with cell lines with similar morphological phenotypes. For cell line panels with multiple morphologies collected over a longer time, neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between replicates of the same cell line. Data-based normalizations increased these similarities, but also created artifacts in the data caused by a bias towards the large and variable lipid class of triglycerides. This artifact was reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics.

bioinformatics↗

bioRxiv2023

Tracer-based metabolomics for profiling nitric oxide metabolites in a 3D microvessel-on-a-chip model

Endothelial dysfunction is a common denominator in cardiovascular diseases (CVDs) associated with diabetes, hypertension, obesity, renal failure or hypercholesterolemia. In these disease states, circulating adverse metabolic or hemostatic risk factors drive the progression of inflammation, thrombosis, platelet activation and atherosclerosis. A hallmark of endothelial dysfunction is the reduced bioavailability of nitric oxide (NO), a signaling molecule essential for vascular homeostasis. Numerous studies have focused on NO synthesis by endothelial cells (ECs) using in vitro cultures to understand the pathophysiology of endothelial dysfunction. A limitation of these studies is that the expression of the NO-generating enzyme, endothelial nitric oxide synthase (eNOS), in physiological conditions is modulated by the exposure of the ECs to laminar shear stress, a stimulus that is clearly lacking in most two-dimensional (2D) cultures. Here we developed a tracer-based metabolomics approach to measure NO-specific metabolites with mass spectrometry (MS) and show the impact of unidirectional fluid flow on metabolic parameters associated with NO synthesis using 2D and three-dimensional (3D) platforms. Specifically, we tracked the conversion of stable-isotope labeled NO substrate L-Arginine to L-Citrulline and L-Ornithine to determine eNOS activity. We demonstrated that when human coronary artery endothelial cells (HCAECs) cultured in media containing 13C6,15N4-L-Arginine treated with eNOS stimulator - vascular endothelial growth factor (VEGF), eNOS inhibitor - L-NAME and arginase inhibitor - S-(2- boronoethyl)-L-cysteine (BEC), their downstream metabolites - 13C6,15N3 L-Citrulline and 13C5,15N2 L- Ornithine showed clear responses as measured using Ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). In this study, we also assessed the NO metabolic status of a static 2D culture, a 3D microvessel model with bidirectional flow, and our 3D model with unidirectional fluid flow generated by a microfluidic pump. Compared to 2D culture, our 3D model showed significant effects in the control and microvessels exposed to VEGF when Citrulline/Ornithine ratio was analyzed. The obtained result indicates that the 2D static culture mimics more endothelial dysfunction status. Our detection method and 3D model with a unidirectional fluid flow provides a more representative physiological environment that exhibits perfect model to study endothelial dysfunction.

cell biology↗

bioRxiv2023

Comparative metabolomics and microbiome analysis of Ethanol vs. OMNImet/gene GUT fecal stabilization

Metabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide stability of the fecal metabolome and microbiome at room temperature (RT). For this purpose, we investigated the stability of various metabolites and the microbiome (16S ribosomal RNA) in feces collected in 95% ethanol (EtOH) or OMNImet(R)*GUT/ OMNIgene(R)*GUT. To simulate in field-collection scenarios, the samples were stored at different temperatures at varying durations (24h +4{degrees}C, 24h RT, 36h RT, 48h RT, and 7 days RT), and compared to aliquots immediately frozen at -80{degrees}C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar untargeted metabolites, endocannabinoids, short chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the non-stabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet(R)*GUT. When comparing the metabolite levels between fecal aliquots stored at room temperature and at +4{degrees}C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing fecal samples at room temperature and stabilizing them in 95% EtOH yielded metabolomic results comparable to flash freezing. We also found that overall composition of the gut microbiome did not vary significantly between different storage types. However, there were notable differences observed in alpha diversity. Taken together, the stability of the metabolome and microbiome in 95 % EtOH provided similar results as the validated commercial collection kits OMNImet(R)*GUT and OMNIgene(R)*GUT, respectively. IMPORTANCEThe analysis of the gut metabolome and microbiome requires the separate collection of fecal specimens using conventional methods or commercial kits. However, these approaches can potentially introduce sampling errors and biases. In addition, the logistical requirements of studying large human cohorts have driven the need for home collection and transport of human fecal specimens at room temperature. By adopting a unified sampling approach at room temperature, we can enhance sampling convenience and practicality, leading to a more precise and comprehensive understanding of gut microbial function. However, the development and applications of such unified sampling systems still face limitations. The results presented in this study aim to address this knowledge gap by investigating the stability of metabolites and the microbiome (16S ribosomal RNA) from fecal samples collected using 95% EtOH, in comparison to well-established commercial collection kits for fecal metabolome (OMNImet(R)*GUT) and microbiome (OMNIgene(R) *GUT) profiling. Additionally, we perform a comparative analysis of various platforms and metabolomic coverage using matrices containing ethanol, evaluating aspects of sensitivity, robustness, and throughput.

biochemistry↗

bioRxiv2023

How to prevent chick culling in the poultry industry? Discovery of a new biomarker for in ovo gender screening

Chicken eggs are one of the most consumed foods worldwide. However, the practice of chicken culling in the poultry industry involves unnecessary animal suffering and finding a way to put an end to this has become a societal priority. One approach that has been propagated as acceptable is based on the selection of female eggs early in the incubation process and the devitalization of the male eggs. It is with this objective in mind that we searched for a biomarker for early gender screening in eggs. Applying an untargeted mass spectrometry approach, we profiled allantoic fluid of different day-old eggs and identified the feature 3-[(2-aminoethyl)sulfanyl]butanoic acid (ASBA) as a strong biomarker for in-ovo gender prediction for day-9 old embryos. After validation using LC-APCI-MRM with an internal standard, we found ASBA can predict the female gender with a sensitivity and specificity well above 95% in our experiments. HighlightsO_LIDiscovery of a biomarker of chicken embryo gender in allantoic fluid from eggs C_LIO_LIDay 9 after laying was determined as optimum for sex prediction and animal welfare C_LIO_LI3-[(2-aminoethyl)sulfanyl]butanoic acid was identified using mass spectrometry C_LIO_LIThe biomarker was validated on large cohorts of different chicken species C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/551526v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@c1330borg.highwire.dtl.DTLVardef@1fe2f28org.highwire.dtl.DTLVardef@c9f245org.highwire.dtl.DTLVardef@40a5f5_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2022

Heritability of Urinary Amines, Organic Acids, and Steroid Hormones in Children

Variation in metabolite levels reflects individual differences in genetic and environmental factors. Here, we investigated the role of these factors in urinary metabolomics data in children. We examined the effects of sex and age on 86 metabolites, as measured on three metabolomics platforms that target amines, organic acids, and steroid hormones. Next, we estimated their heritability in a twin cohort of 1300 twins (age range: 5.7 - 12.9 years). We observed associations between age and 50 metabolites and between sex and 21 metabolites. The mean monozygotic (MZ) and dizygotic (DZ) correlations for urinary metabolites were 0.51 (range: 0.25-0.75) and 0.16 (range: 0.01-0.46) for the amines, 0.52 (range: 0.33-0.64) and 0.23 (range: 0.07-0.35) for the organic acids, and 0.61 (range: 0.43-0.81) and 0.25 (range: 0.11-0.44) for the steroids. Broad-sense heritability was 0.49 (range: 0.25-0.64), 0.50 (range: 0.33-0.62), and 0.64 (range: 0.43-0.81) for 50 amines, 13 organic acids, and 6 steroids, and narrow-sense heritability was 0.50 (range: 0.37-0.68), 0.50 (0.23-0.61), and 0.47 (range: 0.32-0.70) for 6 amines, 7 organic acids, and 4 steroids. We conclude that urinary metabolites in children have substantial heritability, with similar estimates for amines and organic acids, and higher estimates for steroid hormones.

genomics↗

bioRxiv2021

XomicsToModel: Multiomics data integration and generation of thermodynamically consistent metabolic models

Constraint-based modelling can mechanistically simulate the behaviour of a biochemical system, permitting hypotheses generation, experimental design and interpretation of experimental data, with numerous applications, especially modelling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in each context. However, existing model extraction algorithms are unable to ensure that a context-specific model is thermodynamically flux consistent. This protocol introduces XomicsToModel, a semiautomated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. One of the key advantages of the XomicsToModel pipeline is its ability to seamlessly incorporate omics data into metabolic reconstructions, ensuring not only mechanistic accuracy but also physicochemical consistency. This functionality allows for more accurate metabolic simulations and predictions across different biological contexts, enhancing its utility in diverse research fields, including systems biology, drug development, and personalised medicine. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model; it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. It can be implemented by anyone who has basic MATLAB programming skills and the fundamentals of constraint-based modelling. Key pointsXomicsToModel is a semi-automated pipeline that integrates bibliomic, transcriptomic, proteomic, and metabolomic data with a generic genome-scale metabolic reconstruction or model. It enables the seamless incorporation of multi-omics datasets into metabolic reconstructions, ensuring mechanistic accuracy and physicochemical consistency.

systems biology↗

bioRxiv2021

New from old: discovery of the novel antibiotic actinomycin L in Streptomyces sp. MBT27

Streptomycetes are major producers of bioactive natural products, including the majority of the antibiotics. While much if the low-hanging fruit has been discovered, it is predited that less than 5% of the chemical space has been mined. Here, we describe the novel actinomycins L1 and L2, which are produced by Streptomyces sp. MBT27. The molecules were discovered via metabolic analysis combined with molecular networking of cultures grown with different combinations of carbon sources. Actinomycins L1 and L2 are diastereoisomers, and the structure of actinomycin L2 was resolved using NMR and single crystal X-ray crystallography. Actinomycin L is formed via a unique spirolinkage of anthranilamide to the 4-oxoproline moiety of actinomycin X2, prior to the condensation of the actinomycin halves. Feeding anthranilamide to cultures of Streptomyces antibioticus, which has the same biosynthetic gene cluster as Streptomyces sp. MBT27 but only produces actinomycin X2, resulted in the production of actinomycin L. This shows that actinomycin L results from joining two distinct metabolic pathways, namely those for actinomycin X2 and for anthranilamide. Actinomycins L1 and L2 showed significant antimicrobial activity against Gram- positive bacteria. Our work shows how new molecules can still be identified even in the oldest of natural product families. IMPORTANCEActinomycin was the first antibiotic discovered in an actinobacterium by Selman Waksman and colleagues, as early as 1940. This period essentially marks the start of the golden era of antibiotic discovery. Over time, emerging antimicrobial resistance (AMR) and the declining success rate of antibiotic discovery resulted in the current antibiotic crisis. We surprisingly discovered that under some growth conditions, Streptomyces sp. MBT27 can produce actinomycins that are significantly different from those that have been published so far. The impact of this work is not only that we have discovered a novel molecule with very interesting chemical modifications in one of the oldest antibiotics ever described, but also that this requires the combined action of primary and secondary metabolic pathways, namely the biosynthesis of anthranilamide and of actinomycin X2, respectively. The implication of the discovery is that even the most well-studied families of natural products may still have surprises in store for us.

microbiology↗

bioRxiv2021

Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism

Starting with a comprehensive generic reconstruction of human metabolism, we generated high-quality, constraint-based, genome-scale, cell-type and condition specific models of metabolism in human dopaminergic neurons, the cell type most vulnerable to degeneration in Parkinsons disease. They are a synthesis of extensive manual curation of the biochemical literature on neuronal metabolism, together with novel, quantitative, transcriptomic and targeted exometabolomic data from human stem cell-derived, midbrainspecific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling enabled qualitatively accurate and moderately quantitatively accurate prediction of dopaminergic neuronal metabolite exchange fluxes, including predicting the consequences of metabolic perturbations in a manner also consistent with literature on monogenic mitochondrial Parkinsons disease. These dopaminergic neurons models provide a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinsons disease. Moreover, the plethora of novel mathematical and computational approaches required to develop them are generalisable to study any other disease associated with metabolic dysfunction.

systems biology↗