Biology subjects

Harms, A. C.

Publications and source records attributed to Harms, A. C..

15 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

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

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

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↗

bioRxiv2020

Tuberculosis causes highly conserved metabolic changes in human patients, mycobacteria-infected mice and zebrafish larvae

Tuberculosis is a highly infectious and potentially fatal disease accompanied by wasting symptoms, which cause severe metabolic changes in infected people. In this study we have compared the effect of mycobacteria infection on the level of metabolites in blood of humans and mice and whole zebrafish larvae using one highly standardized mass spectrometry pipeline, ensuring technical comparability of the results. Quantification of a range of circulating small amines showed that the levels of the majority of these compounds were significantly decreased in all three groups of infected organisms. Ten of these metabolites were common between the three different organisms comprising: methionine, asparagine, cysteine, threonine, serine, tryptophan, leucine, citrulline, ethanolamine and phenylalanine. The metabolomic changes of zebrafish larvae after infection were confirmed by nuclear magnetic resonance spectroscopy. Our study identified common biomarkers for tuberculosis disease in humans, mice and zebrafish, showing across species conservation of metabolic reprogramming processes as a result of disease. Apparently, the mechanisms underlying these processes are independent of environmental, developmental and vertebrate evolutionary factors. The zebrafish larval model is highly suited to further investigate the mechanism of metabolic reprogramming and the connection with wasting syndrome due to infection by mycobacteria.

molecular biology↗

bioRxiv2019

The genomic architecture of blood metabolites based on a decade of genome-wide analyses

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes and lipid species. We performed a review of all genetic association studies, and identified > 800 class-specific metabolite loci that influence metabolite levels. In a twin-family cohort (N = 5,117), these metabolite loci were leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study revealed significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation had higher h2Metabolite-hits estimates than phosphatidylcholines with a low degree of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes and lipid species.

genetics↗

bioRxiv2019

Genome-wide association study of plasma lipids

There is continuous interest in the genetic determinants of plasma triglycerides (TGs) and phospholipids and their role in the etiology of cardiovascular disease (CVD). Here, we report the results of a Dutch genome wide association study (GWAS) of an in-house developed lipidomics platform, focusing on 90 plasma lipids. Lipids were assessed by liquid chromatography mass spectrometry in participants from the Leiden Longevity Study, the Netherlands Twin Register and the Erasmus Rucphen Family (ERF) study and meta-analysed, resulting in a sample size of 5537 participants. In addition, we performed genetic correlation analyses between the 90 plasma lipids and markers of metabolic health, as well as vascular pathology and CVD combining our GWAS results with publicly available GWAS outputs. We replicated previously known associations between 34 lipids and 10 lipid quantitative trait loci (lipQTL) (GCKR, APOA1, FADS1, SGPP1,TMEM229B, LIPC, PDXDC1, CETP, CERS4 and SPTLC3) with metabolome-wide (P < 1.61 x 10-9) significance. Moreover, we report 6 novel phospholipid-related and 5 triglyceride (TG)-related loci: SGGP1 (SM21:0), SPTLC3 (SM21:0 and SM25:1), FADS1 (LPCO16:1, PC38:2, PEO36:5, PEO38:5, TG56:5, TG56:6, and TG56:7), TMEM229 (LPCO16:1), GCKR (TG50:2), and APOA1 (TG54:4). In addition, we report suggestively significant (P < 5 x 10-8) associations mapping to eleven novel lipid quantitative trait loci (lipQTLs), three of which are supported by mining previous GWAS data: MAU (PC34:4), LDLR (SM16:0), and MLXIPL (TG48:1 and TG50:1)). Genetic correlation analysis indicates that one specific specific sphingomyelin, SM22:0, shares common genetic background with CVD. Levels of SM22:0 also positively associate with carotid artery intima-media thickness in the ERF study, and this observation is independent of LDL-C level. Our findings yield higher resolution of plasma lipid species and new insights in the biology of circulating phosholipids and their relation to CVD risk.

epidemiology↗