Biology subjects

Vanhaecke, L.

Publications and source records attributed to Vanhaecke, L..

5 recordsLinked to original sources

bioRxiv2026

Lemonite: identification of regulatory metabolites through data-driven, interpretable integration of transcriptomics and metabolomics data

Current transcriptomics-metabolomics integration approaches are either limited by poor interpretability or constrained by incomplete prior knowledge, preventing the systematic identification of regulatory metabolites. Here, we present Lemonite, a data-driven and interpretable framework for integrating bulk transcriptomics and metabolomics data to uncover regulatory metabolites acting on gene modules. Lemonite extends module network inference to jointly associate transcription factors and metabolites with gene programs, without requiring prior differential analysis or complete metabolome annotation. To contextualize predictions, we constructed a comprehensive gene/protein-metabolite knowledge graph integrating over 370 000 metabolite-gene/protein and 2.1 million protein-protein interactions. Applied to glioblastoma (n=99) and inflammatory bowel disease (n=75) cohorts, Lemonite identified over 50 functionally coherent gene modules per disease, revealing established and previously uncharacterized metabolite-gene regulatory relationships. In glioblastoma, myo-inositol and phosphatidylcholines, together with IRF6, regulate mesenchymal-like immune programs, which upon integration with single-cell transcriptomics are primarily expressed in tumor-associated macrophages and monocytes. In inflammatory bowel disease, regulatory metabolites were prioritized that change the expression of their predicted target genes in colonic epithelial cells in vitro. Overall, Lemonite provides a principled framework to explore the genome-wide regulatory potential of the metabolome and to generate biologically interpretable, experimentally testable hypotheses from multi-omics data.

bioinformatics↗

bioRxiv2026

Ion Mobility-Enhanced LA-REIMS Improves Molecular Resolution in Ambient Biofluid Metabolomics

Ambient metabolomics techniques such as laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) enable fast, preparation-free fingerprinting of biological samples but are inherently limited by spectral congestion in the absence of chromatographic separation. While ion mobility spectrometry provides additional gas-phase separation, maintaining ion transmission under the transient signals characteristic of laser desorption, remains analytically challenging. Here, we define operating conditions for cyclic traveling-wave ion mobility spectrometry (cIMS) that preserve transmission under LA-REIMS duty-cycle constraints and systematically evaluate how cIMS integration reshapes biofluid fingerprints and enhances chemical specificity in chromatography-free metabolomics analysis. Under optimized single-pass conditions, cIMS separation reorganized LA-REIMS spectra into structured mass/mobility feature domains, enabling selective mobility-based filtering of matrix-derived salt cluster ions. This reduced non-biological background contributions by up to 35% of total spectral intensity while preserving over 90% of detected untargeted features. Although cIMS operation introduced a sensitivity penalty relative to time-of-flight-only acquisition, approximately 80% of the total ion current was recovered under optimized conditions. Mobility-resolved data revealed coherent homologous series and class-specific structural trends, particularly for lipids, supporting class-level annotation. Analysis of 101 metabolite and lipid standards covering a broad physicochemical range (logP -5.30 to 19.40) demonstrated comprehensive molecular coverage, high mass accuracy (mean 2.4 ppm), and good agreement with reference CCS values (mean deviation 4.0%), with isomer separation observed for biologically important secondary bile acids in extended separation cycles. Collectively, these results establish LA-REIMS-cIMS as a practical analytical strategy for enhancing chemical specificity and spectral interpretability in support of high-throughput large-scale metabolic fingerprinting. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/709786v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@18a2dfdorg.highwire.dtl.DTLVardef@d165d6org.highwire.dtl.DTLVardef@1750291org.highwire.dtl.DTLVardef@fbbce9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Ion mobility spectrometry adds an orthogonal gas-phase separation to LA-REIMS, reorganizing complex biofluid spectra into distinct mass-mobility feature bands and improving molecular resolution in rapid ambient ionization metabolomics. C_FIG

biochemistry↗

bioRxiv2025

Robust metabolomics data normalization across scales and experimental designs

Metabolomics studies employing liquid chromatography-mass spectrometry are affected by signal drift and batch effects, introducing technical variance that impedes biological knowledge discovery. Quality control (QC) sample-based normalization strategies are widely implemented but remain vulnerable to outliers, thereby reducing normalization performance. We introduce rLOESS, rGAM and tGAM, three robust normalization methods that improve resistance to outliers by downweighting or accommodating them. Leveraging additive models, the rGAM and tGAM methods allow flexible non-linear modeling, differential sample weighting, and data-driven QC representativeness evaluation. Implementations of these methods are gathered in the Metanorm R package, integrating robust normalization with visualization for performance verification, while supporting efficient parallel processing. In in silico and/or experimental datasets, the robust methods, relative to several popular existing strategies, improved replicate concordance, and reduced drift and batch effects. The robust methods, with improved recovery of the underlying signal demonstrated in simulation, produced distinct differential abundance results, highlighting the impact of normalization on downstream statistical inference. Overall, tGAM-based normalization suggested the best performance across scenarios and is proposed as default choice. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups.

bioinformatics↗

bioRxiv2022

Evolutionary survival strategies of the female giant panda: optimizing energy resources and expenditure prior to pregnancy by postponing corpus luteum reactivation

The giant panda reproductive physiology shows important similarities with at least six of the eight existing bear species: the occurrence of diapause and/or pseudopregnancy is commonly described in bears. Nevertheless, significant differences including the earlier breeding season with - in general - a single estrus, a shorter delay of implantation and a more variable birth season, indicate an evolutionary adaptation from the seasonal reproductive traits described in hibernating bear species. In this study we aimed to determine true pregnancy length for giant pandas and to open the discussion on the peculiarities of giant panda reproductive biology, more specifically focusing on the rationale behind their short-seasoned reproductive cycle compared to the other bear species. For this purpose, metabolic (body weight and fecal output) profiles were matched with endocrine changes, mainly urinary progesterone metabolites, in 5 pregnant, 8 non-birth and 6 pseudopregnant cycles of 6 female giant pandas. Pregnancy in giant pandas lasts only 42 days from early reactivation of the corpora lutea (CLs) until birth. In addition, our findings urged the need to redefine the generally accepted biphasic progesterone profile into a triphasic primary progesterone rise (corpus luteum dormancy (CLD) I, II and III) prior to entering the active luteal phase (= secondary progesterone rise). Two episodes of progesterone increase (CLDII: 81.20 {+/-} 5.85 days versus CLDIII: 60.80 {+/-} 3.83 days prior to birth for pregnant cycles) were identified, respectively corresponding to CL reactivation (74-88 days prior to birth) and implantation ({+/-} 60 days prior to birth) in other bear species. The progesterone concentration during CLDIII was higher in pregnant cycles, indicating a potential communication between maternal tissues and blastocyst(s) enhancing progesterone concentrations and thus allowing optimal priming of uterine tissues to better prepare for blastocyst reactivation/development. Compared with other bear species, giant pandas seem to shorten the active luteal phase, and thus pregnancy, by approximately 30 days by actively postponing CL reactivation. Potential mechanisms in play overruling and suppressing the evolutionary conserved photoperiodical triggers of CL reactivation are discussed while a parallel study will further elaborate on the CL dynamics during giant panda gestation.

evolutionary biology↗

bioRxiv2021

Triangulation of microbial fingerprinting in anaerobic digestion reveals consistent fingerprinting profiles

The anaerobic digestion microbiome has been puzzling us since the dawn of molecular methods for mixed microbial community analysis. Monitoring of the anaerobic digestion microbiome can either take place via a non-targeted holistic evaluation of the microbial community through fingerprinting or by targeted monitoring of selected taxa. Here, we compared four different microbial community fingerprinting methods, i.e., amplicon sequencing, metaproteomics, metabolomics and cytomics, in their ability to characterise the full-scale anaerobic digestion microbiome. Cytometric fingerprinting through cytomics reflects a, for anaerobic digestion, novel, single cell-based approach of direct microbial community fingerprinting by flow cytometry. Three different digester types, i.e., sludge digesters, digesters treating agro-industrial waste and dry anaerobic digesters, each reflected different operational parameters. The -diversity analysis yielded inconsistent results, especially for richness, across the different methods. In contrast, {beta}-diversity analysis resulted in comparable profiles, even when translated into phyla or functions, with clear separation of the three digester types. In-depth analysis of each methods features i.e., operational taxonomic units, metaproteins, metabolites, and cytometric traits, yielded certain similar features, yet, also some clear differences between the different methods, which was related to the complexity of the anaerobic digestion process. In conclusion, cytometric fingerprinting through flow cytometry is a reliable, fast method for holistic monitoring of the anaerobic digestion microbiome, and the complementary identification of key features through other methods could give rise to a direct interpretation of anaerobic digestion process performance.

bioengineering↗