The European Union (EU) enforces strict regulations on the traceability and labeling of genetically modified organisms (GMOs), including genome-edited (GE) lines produced through new genomic techniques (NGTs). Identifying GE organisms created by single nucleotide variations (SNVs) is however challenging, as a single SNV alone cannot unambiguously define a GE line. Recently, we introduced the concept of generating a genetic fingerprint to distinguish a specific GE rice line. This proof-of-concept approach integrated whole-genome sequencing (WGS)-based characterization with the Illumina technology, the public 3 K Rice Genomes (3KRG) database, and statistical feature-selection tools, to select and combine key genetic elements, including GE on-target site(s) and cultivar-specific 2-SNV barcodes, into a unique genetic fingerprint. In the present study, we expand this concept into a generalized data-driven framework allowing identification of multiple rice lines. Supported by newly developed bioinformatics and statistical feature-selection-based pipelines, this optimized strategy enables the generation of genetic fingerprints irrespective of a rice cultivar's inclusion in publicly available databases like 3KRG. In addition, this refined strategy can leverage WGS data generated from both Illumina and Oxford Nanopore Technologies (ONT) platforms for fingerprint generation and GE line identification. Using two distinct in-house GE rice lines from different cultivars, along with various publicly available WGS datasets, we demonstrated the robustness, scalability, and specificity of this approach for reliable GE rice line identification. Our findings provide a methodological foundation for data-driven traceability of GE rice lines, reinforcing regulatory compliance, supporting intellectual property (IP) protection, and contributing to the responsible implementation of EU GMO/NGT legislation.

Understanding the molecular mechanisms underlying T-cell acute lymphoblastic leukemia (T-ALL) is essential for developing more effective therapeutic strategies. Despite therapeutic advances, the role of RNA-binding proteins in the pathogenesis of T-ALL remains poorly understood. Here, we investigate the RNA-binding Quaking protein (QKI), identifying it as a key regulator of splicing with tumor-suppressive properties in T-ALL. Through the analysis of two independent pediatric T-ALL cohorts, we demonstrate that QKI expression is frequently reduced in T-ALL, particularly within the HOXA subtype, and this reduction correlates with poor overall and event-free survival. Using T-ALL cell lines, we show that QKI depletion induces widespread splicing alterations, with numerous events corroborated in patient samples. Transcriptome profiling indicates that QKI downregulation leads to broad changes in gene expression, notably affecting pathways related to cell cycle progression, cholesterol homeostasis, and epithelial-mesenchymal transition. Functional assays demonstrate that QKI overexpression in T-ALL cells significantly reduces cell proliferation, induces G0/G1 cell cycle arrest, and limits leukemia progression and dissemination, ultimately improving survival in xenograft models. Together, these findings provide compelling evidence that QKI functions as a regulator of RNA splicing with tumor-suppressive activity in T-ALL.

BackgroundVitiligo is a chronic inflammatory skin disease characterized by clinical and molecular heterogeneity across lesional, perilesional and non-lesional skin within the same individual. Understanding these region-specific differences is essential for identifying early disease processes and developing targeted therapeutic strategies. Skin biopsies represent a key approach to explore these differences, yet conventional 3–5 mm biopsies are invasive, requiring sutures, extended healing time and leaving visible scarring.ObjectivesThe objective of this study was to characterize region-specific transcriptomic alterations across lesional, perilesional, and non-lesional skin in vitiligo, using minimally invasive 1 mm skin punch biopsies.MethodsIn this study, bulk RNA sequencing was performed on 105 skin biopsies obtained from perilesional and (non-)lesional skin of non-segmental vitiligo patients, as well as healthy control skin. Differential gene expression was followed by pathway-level analyses, and Connectivity Map–based perturbational profiling was performed to predict candidate therapeutic compounds capable of reversing the lesional transcriptional signature.ResultsTranscriptomic profiling of 1 mm biopsies revealed disease-associated changes across lesional, perilesional, and non-lesional vitiligo skin, with distinct region-specific gene signatures reflecting immune activation and metabolic reprogramming. Pathway analysis further identified dysregulation of both canonical and underexplored pathways, including NOD-like receptor signaling and neutrophil extracellular trap formation, alongside altered cellular clearance mechanisms potentially implicated in lesion persistence. Connectivity Map analysis nominated compounds predicted to reverse the lesional transcriptional signature, spanning epigenetic regulators, tyrosine kinase inhibitors, and metabolic modulators.ConclusionThis approach uncovers potentially pathogenic pathways contributing to vitiligo pathogenesis and provides a translational framework for therapeutic hypothesis generation and future clinical studies.

The European Union regulates genetically modified organisms (GMOs) in the food chain (Regulations (EC) N° 1829/2003 and N° 1830/2003) to ensure safety, traceability, and freedom of choice. Since 2018, genome-edited (GE) organisms fall under this legislation. However, their detection and unambiguous identification are more challenging, sometimes differing from their wild-type by only one or a few single nucleotide variations (SNVs). High-throughput sequencing with SNV-based genetic fingerprint detection helps overcome these limitations but has so far only been applied to pure samples. Sequencing complex food mixtures renders reliable SNV detection costly and technically challenging. This study, for the first time, explored using high-throughput sequencing with adaptive sampling (AS) to selectively enrich a target species in food mixtures, reducing matrix complexity and enabling the detection and identification of GE lines. As a proof-of-concept, mixtures of soybean -and trace levels of GE or wild-type rice were analyzed under three sequencing modes: standard, AS enriching rice, and AS depleting soybean. Sequencing data were analyzed to determine whether the rice line, GE or wild-type, was successfully enriched and identified using its respective genetic fingerprint. This promising proof-of-concept represents a first step toward facilitating the detection and identification of GE organisms in the food chain.

ABSTRACT The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

Red meat consumption has been associated with less favorable health outcomes, whereas fish intake is often considered beneficial. These differences may partly relate to variations in fatty acid composition and heme iron content, which can influence oxidative processes during digestion and thereby affect intestinal and systemic responses. This study investigated the effects of pork- and salmon-based diets, differing primarily in fatty acid profile and heme iron content but matched for macronutrient composition, on oxidative stress, gut microbiota, fermentation metabolites, and inflammation in rats. The pork-based diet supplied 2.3-fold more SFA and 14-fold more heme iron than the salmon-based diet, which in turn provided 14-fold more n-3 PUFA. Consumption of pork significantly increased propanal (+81%), hexanal (17-fold), and 4-hydroxy-2-nonenal (27-fold) in stomach contents, and elevated thiobarbituric acid reactive substances (TBARS, +45%) in plasma. In contrast, salmon consumption raised TBARS in duodenal mucosa (+19%) and C-reactive protein (+30%) in plasma. Only subtle diet-related changes were observed in gut microbiota composition and fermentation metabolites, with no difference in fecal calprotectin. A small number of discriminant taxa were identified, including Clostridioides and Muribaculum in salmon-fed rats and Eubacterium fissicatena, Sellimonas, and Dielma in pork-fed rats, while valerate levels were higher in pork-fed rats. Transcriptomic analysis revealed that no individual genes remained significant after correction for multiple testing. Overall, pork and salmon diets differentially modulated lipid oxidation along the gastrointestinal tract, whereas their effects on gut microbiota were limited.

The COVID-19 pandemic has accelerated interest in immuno-multiple reaction monitoring (immuno-MRM) for peptide quantification, with early efforts focusing on SARS-CoV-2 biomarker detection in clinical nasopharyngeal swabs. However, the emergence of mRNA vaccines has created a new and pressing need for robust methods to quantify antigen expression. Here, we present an optimized immuno-MRM method targeting the SARS-CoV-2 spike fusion peptide SFIEDLLFNK, designed and validated to quantify antigen expression following mRNA or plasmid transfection. This method offers high sensitivity, precision, and linearity across a broad dynamic range, enabling an accurate assessment of protein translation in vitro. In addition to measuring antigen levels, analysis of the flow-through provides insight into host cell proteomic responses, supporting the comprehensive characterization of mRNA vaccine efficacy and safety. This dual-function workflow serves as a powerful tool for vaccine development, quality control, and regulatory evaluation of RNA-based therapeutics.

Reliable large-scale testing for respiratory viruses, including influenza viruses, coronaviruses, such as SARS-CoV-2, and respiratory syncytial virus (RSV) is essential for both endemic surveillance and pandemic response. While RT-qPCR remains the current gold standard, the COVID-19 pandemic highlighted the need for alternative, scalable detection platforms. Although alternative approaches like liquid chromatography coupled with mass spectrometry (LC-MS) are well-suited for viral multiplexing, they have yet to undergo clinical validation. Here, we present a quantitative immuno-multiple reaction monitoring (iMRM) assay, called Winterplex, for the simultaneous detection of influenza A, influenza B, RSV, and SARS-CoV-2, each targeted via two proteotypic peptides per virus. The method was validated according to ISO standards for in vitro diagnostics and benchmarked against RT-qPCR. To enhance future pandemic preparedness, urgent investment is needed to translate and expand multiplexed MS-based diagnostic methodologies, which offer the flexibility to incorporate a wide range of targets, making them ideally suited for rapid and adaptable responses to emerging viral threats.

Abstract Recombinase polymerase amplification (RPA) enables rapid nucleic acid testing in low-resource environments, but poorly characterized byproducts can compromise assay specificity and cause false-positive results. Here, we amplified the thirteen original CODIS core loci and Amelogenin to characterize recurrent RPA artefacts and establish conditions that reduce their formation. First, RPA products were analyzed for two reference samples by Oxford Nanopore Technologies sequencing. This revealed two distinct classes of multimeric products: primer multimers and amplicon multimers, consisting of repeated primer or amplicon sequences, respectively. Individual artefacts contained up to 281 primer copies or 22 amplicon copies, demonstrating the extensive range of these products. Next, we performed an optimization study to evaluate the effects of reaction temperature and reagent concentrations at two representative loci, D3S1358 and D5S818. Among the conditions tested, temperature had the most pronounced effect. Reducing the temperature from 42°C to 34°C increased the relative target amplicon fraction from 15% to 83% for D3S1358 and from 84% to 98% for D5S818, while maintaining or increasing absolute target concentration. Lower primer concentrations and higher T4 UvsX concentrations also reduced multimer formation, although lower primer concentrations reduced target yield and caused allelic dropout. Finally, amplification at 34°C was evaluated across all fourteen loci by sequencing. Relative to 42°C, the target read fraction increased by more than 5 percentage points for 7/14 loci in one reference sample and 9/14 loci in the other, with the largest improvements at multimer-prone loci. These findings identify multimers as an important class of RPA artefacts and establish reaction temperature and T4 UvsX concentration as promising conditions to improve RPA specificity.

In human athletes, training-induced improvements in insulin sensitivity typically coincide with increased GLUT4 expression and a glucose-centric fuel strategy. In horses, recent studies show the opposite: training is associated with reduced total GLUT4/GLUT12 abundance, challenging the assumption that enhanced glucose transport underpins improved insulin sensitivity. This study examined how insulin-dependent glucose transporters relate to insulin sensitivity in trained horses. After eight weeks of standardized aerobic harness training, horses displayed a more efficient insulin economy during an oral glucose tolerance test, with reduced peak insulin, lower insulin AUC, and a delayed time to peak. Intravenous glucose tolerance indices did not show corresponding improvements. Total GLUT4 and GLUT12 decreased with training, most clearly in acute post-exercise samples. Acute GLUT12 correlated positively with peak and total insulin responses, and its training-induced decline correlated with increased insulin time-to-peak. These data indicate that horses improve insulin sensitivity without upregulating or even downregulating GLUT4/GLUT12. This supports the emerging concept that equine training shifts insulin’s primary role toward non-glucose substrates (e.g., amino-acid–supported anaplerosis, lipid oxidation, microbiome-derived fuels). OGTT-type tests may be more sensitive to training adaptations than intravenous tests, and nutrition should support this shifted insulin profile with adequate amino acids, forage-first feeding, and moderated starch.

Recent advances in liquid chromatography mass spectrometry (LCMS) have accelerated the adoption of high-throughput workflows that deliver deep proteome coverage using minimal sample amounts. This trend is largely driven by clinical and single-cell proteomics, where sensitivity and reproducibility are essential. Here, we extend our previous benchmark dataset (PXD028735) using next-generation LC-MS platforms optimized for rapid proteome analysis. We generated an extensive DDA/DIA dataset using a human-yeast-E. coli hybrid proteome. The proteome sample was distributed across multiple laboratories together with standardized analytical protocols specifying two short LC gradients (5 and 15 min) and low sample input amounts. This dataset includes data acquired on four different platforms, and features new scanning quadrupole-based implementations, extending coverage across different instruments and acquisition strategies. Our comprehensive evaluation highlights how technological advances and reduced LC gradients may affect proteome depth, quantitative precision, and cross-instrument consistency. The release of this benchmark dataset via ProteomeXchange (PXD070049 and PXD071205), allows for the acceleration of cross-platform algorithm development, enhance data mining strategies, and supports standardization of short-gradient, high-throughput LC-MS-based proteomics. ### Competing Interest Statement Frederic Fontaine is employed by Thermo Fisher Scientific. Ihor Batruch, Patrick Pribil and Jean-Baptiste Vincendet are employed by SCIEX. Bart Van Puyvelde joined SCIEX after the completion of this work. Research Foundation - Flanders, https://ror.org/03qtxy027, 1278023N, 1SH9O24N, G010023N, 12A6L24N Ghent University Special Research Fund, BOF/PDO/2025/049, BOF21/GOA/033 Horizon Europe, 101080544, 101191739 CHIST-ERA, G0GDV23N European Molecular Biology Laboratory, 208391/Z/17/Z, 223745/Z/21/Z BBSRC, BB/X001911/1 Agence Nationale de la Recherche - French Proteomic Infrastructure (ProFI), ProFI UAR2048, ANR-10-INBS08-03, ANR-24-INBS-0015 Region Grand-Est, SC-Proteomics project ITMO Cancer of Aviesan the Interdisciplinary Thematic Institute IMS IdEx Unistra, ANR-10-IDEX-0002 SFRI-STRAT’US, ANR-20-SFRI-0012

Predictive toxicology increasingly emphasizes methods that combine scalable chemical screening with biologically interpretable mechanistic information. Existing computational approaches, however, rely largely on chemical structure alone and often fail to capture the cellular programs underlying compound-induced cellular responses. Herein, we describe a multimodal modeling framework that integrates chemical fingerprints with high-throughput transcriptomic (HTTr) dose–response profiles to predict activity for 41 curated Tox21 assay endpoints. HTTr data were obtained from TempO-Seq screens in MCF-7, U-2 OS, and HepaRG cells following exposure to ToxCast compounds across an eight-point concentration series ranging from 0.03 to 100 µM. Using gradient-boosted decision trees and nested compound-aware cross-validation, 13 assays achieved robust performance (mean area under the precision–recall curve (AUPRC) > 0.75), spanning nuclear receptor signaling, stress-response pathways, and xenobiotic metabolism. SHapley Additive exPlanations (SHAP)-based feature attribution analysis showed that predictions depend on both structural motifs and transcriptional programs, in a manner consistent with established mechanistic relationships between chemical structure, nuclear receptor biology, and adaptive cellular responses. These findings illustrate how structure and high-throughput transcriptomic dose–response signature integration enables models that are accurate and mechanistically grounded, shifting computational toxicology toward transparent and biologically informed mechanistic bioactivity prediction. We introduce a scalable multimodal framework for mechanistically interpretable Tox21 bioactivity prediction that jointly leverages chemical structure and multi-dose HTTr response profiles across three human cell lines to model 41 curated assay endpoints under compound-aware validation. Unlike prior work that is typically structure-only or limited to single-dose and/or single-cell-line transcriptomic designs, our approach integrates dose–response–derived biological summaries with interpretable structural keys and demonstrates robust generalization. We further differentiate the framework by providing model interpretability via SHAP, explicitly linking predictive performance to endpoint-relevant structural motifs and transcriptional programs.

Understanding gene expression within its spatial context is essential for unravelling biological processes. Laser capture microdissection (LCM) enables targeted isolation of cells or regions from tissue sections while preserving spatial context. LCM-based RNA sequencing is predominantly applied to fresh-frozen tissue, and for formalin-fixed paraffin-embedded (FFPE) material, most workflows rely on commercial kits requiring high input quantities, limiting their use for rare cells. Here, we introduce LCM-FFPEseq, combining LCM with a modified Smart-seq3xpress protocol for spatial transcriptomics of FFPE sections. LCM-FFPEseq detects over 14,000 protein-coding genes per sample from as few as 30 cells, with no substantial gains at higher inputs. Single-cell profiling is feasible, although more variable, yielding on average 7,353 and 6,490 protein-coding genes for K562 and Sertoli cells, respectively. To demonstrate clinical utility, we applied LCM-FFPEseq to archived testicular FFPE tissue from transgender females receiving gender-affirming hormone therapy. Transcriptomic profiling of isolated seminiferous tubules revealed tubular hyalinization to be associated with upregulation of extracellular matrix remodelling and inflammatory pathways, alongside downregulation of spermatogenesis-associated pathways, suggesting testicular fibrosis and/or tubular hyalinization may contribute to germ cell loss. By enabling high-sensitivity transcriptomics in archived FFPE samples, LCM-FFPEseq unlocks new possibilities for investigating rare cells, spatial heterogeneity, and tissue remodelling.

Forensic DNA profiling commonly relies on polymerase chain reaction (PCR) amplification followed by capillary electrophoresis (CE) or massively parallel sequencing (MPS), which requires expensive, laboratory-based equipment that depends on a stable power supply and is unsuitable for field applications. Here, we present a proof-of-concept assay that uses recombinase polymerase amplification (RPA) combined with exo probe detection for rapid, isothermal genotyping of insertion–deletion (InDel) markers. To the best of our knowledge, this study represents the first demonstration of forensic DNA typing using RPA coupled with exo probes. The reaction proceeds at 39 °C and combines amplification and detection in a single 20 min step. Thirteen DNA samples were genotyped in triplicate across eight InDel loci using allele-specific fluorescent probes. Genotypes were derived from differential endpoint fluorescence between matched and mismatched probes. Compared with benchmark genotyping, 97.07% of genotypes (n = 307) were correct at 1 ng DNA input. Accurate profiles were reliably obtained for DNA inputs as low as 250 pg, and partial profiles were still detectable at 31 pg. The results demonstrate that RPA-based InDel genotyping is fast, sensitive, and reproducible. With further optimization, such as refined probe design and selection of robust loci, the assay has clear potential to achieve complete accuracy and to be integrated into portable lab-on-a-chip platforms for rapid, field-deployable forensic identification.

Epigenetic modifications are dynamic and reversible, making them attractive targets for therapeutic intervention in cancer. Although several drugs targeting epigenetic modifications (epidrugs) have been clinically approved, their application in T-cell acute lymphoblastic leukemia (T-ALL) remains limited, and predictive biomarkers of response are lacking. Here, we present a mass spectrometry (MS)-based pharmacoepigenetic approach to profile histone post-translational modifications (hPTMs) to identify signatures associated with drug sensitivity in T-ALL . Baseline hPTM landscapes were previously established by our group for 21 T-ALL cell lines using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Here, we treated these cell lines with a panel of nine drugs including histone deacetylase inhibitors and DNA methyltransferase inhibitors (epidrugs), alongside anthracyclines, which were included due to their known chromatin-related effects. Correlation of cell viability data with hPTM levels revealed distinct hPTM signatures linked to sensitivity for each drug class. These signatures were subsequently evaluated in T-ALL patient-derived xenograft (PDX) models. However, our analysis revealed substantial discrepancies in hPTM sensitivity signatures compared to those observed in vitro. Co-variation network analysis highlighted divergence in hPTM-hPTM correlation between the two models, underscoring limitations of cell lines for modeling dynamic epigenetic regulation in vivo. Our findings establish a framework for MS-based hPTM profiling in T-ALL and emphasize the importance of model selection in developing predictive epigenetic biomarkers.

Mast cell tumors (MCTs) are one of the most common skin neoplasms in dogs, yet their biological behaviour and the underlying molecular mechanisms remain poorly understood. In this study, neoplastic mast cells (MCs) were isolated from formalin-fixed paraffin-embedded (FFPE) canine MCT biopsies using laser capture microdissection (LCM), followed by RNA sequencing of these cells. In this way, MC-specific mechanisms were investigated in high- versus low-grade canine MCTs. To this end, 32 canine cutaneous MCTs were analysed, of which 18 were graded as low- and 14 as high-grade tumors based on histopathology. A total of 74 differentially expressed genes were identified including MOB3B, EXOC6B, BAIAP2, and TRMO, which have established associations with several human cancers. Gene Set Enrichment Analysis revealed significant enrichment of tumorigenic pathways, including KIT signalling, Ras signalling, ubiquitin-mediated proteolysis, and neurotrophin signalling in high-grade MCTs. Additionally, immune-related pathways, including IL-2 signalling, natural killer cell-mediated cytotoxicity, and leukocyte transendothelial migration, underscored the role of immune interactions in high-grade MCTs. This is the first study to focus on gene expression patterns derived from LCM-isolated neoplastic MCs and their role in the molecular mechanisms driving MC malignancies.

Abstract Species identification in palaeoproteomics relies on genome-derived protein sequences which are often poor-quality, and lacks tools to cope with multi-species samples. Here, we address both challenges through the analysis of ‘physical and genetic mixtures’. Species that are absent from our database are considered a ‘genetic mixture’, i.e. a patchwork of peptides from closely related species. Inversely, various overlapping peptide stretches allow us to resolve complex ‘physical mixtures’. This is benchmarked by analysing physical mixtures of modern bone fragments, including genetic mixtures. We illustrate the impact of our approach via a rapid and high-throughput analysis of >2500 bone fragments, revealing the Eemian-era faunal environment around Scladina Cave, including the first Palaeoloxodon antiquus identified at this site. Abstract Figure

Clinical pharmacogenomics (PGx) testing strategies are mainly based on targeted PCR, microarrays, or short-read sequencing. These methods perform well for detecting known single-nucleotide variants (SNVs), small insertions/deletions (indels), and certain copy number variants (CNVs), but they fall short in resolving complex structural variants (SVs), particularly in complex pharmacogenes such as CYP2D6. Therefore, we previously developed a targeted PGx test based on long-read Oxford Nanopore Technologies (ONT) sequencing. Harnessing adaptive sampling (AS) for in silico enrichment of a panel of PGx genes, we illustrated superior performance in star-allele calling compared to the Genetic Testing Reference Materials Program (GeT-RM) truth set. However, accurate diplotyping of CYP2D6 remained challenging. In this work, we adopted the latest basecalling, variant calling, phasing, and star-allele calling tools on our pre-existing data from the HG001, HG01190, NA19785, HG002, and HG005 reference samples. Additionally, we benchmarked the results to public data obtained using the long-read compatible Twist Alliance PGx panel. The re-analyzed ONT-AS data demonstrated correct CYP2D6 star-alleles compared to the GeT-RM truth set. Upon benchmarking to the Twist Alliance PGx panel, perfect star-allele matching was obtained between our panel and the Twist PGx panel for all included Clinical Pharmacogenomics Implementation Consortium (CPIC) Level A genes. However, our ONT-AS panel demonstrated superior variant phasing, resulting in three times more variants per phasing block. These findings confirm the robustness of ONT-AS for targeted long-read PGx applications and highlight its potential to support more accurate pharmacogenomic testing, particularly for structurally complex genes like CYP2D6.

Lithic tools are the most abundant cultural artefacts found at prehistoric archaeological sites. Through their study, we can understand essential activities such as hunting, processing of animal carcasses and plant materials, and working of resources such as ochre and bone, as well as broader technological and cultural practices. For the first time, proteomics was applied alongside use-wear and optical microscopy residue analyses, enabling the identification of bovine, plant, and human proteins on faceted tools from the Mesolithic-Neolithic site of Bazel-Sluis, Belgium. This retrieval of identifiable protein from an area with poor organic preservation demonstrates wide-reaching potential for archaeological research, opening a new avenue of investigation into prehistoric lifeways and adding to the growing corpus of evidence accessible through palaeoproteomics. A workflow for future analyses is suggested, based on our integrated sampling strategy requiring no additional tool manipulations. ### Competing Interest Statement The authors have declared no competing interest. Ghent University, https://ror.org/00cv9y106, BOF.GOA.2022.0002.03, BOF.GOA.2022.0002.02 European Research Council, 639286, HIDDEN FOODS

It is unknown how human health is affected by the current increased consumption of ultra-processed plant-based meat analogues (PBMA). In the present study, rats were fed an experimental diet based on pork or a commercial PBMA, matched for protein, fat, and carbohydrate content for three weeks. Rats on the PBMA diet exhibited metabolic changes indicative of lower protein digestibility and/or dietary amino acid imbalance, alongside increased mesenteric (+38%) and retroperitoneal (+20%) fat depositions despite lower food and energy intake. In contrast, rats on the pork diet demonstrated signs of a disturbed gut-liver axis with increased liver weight (+15%) and blood low-density lipoprotein (+86%), which may have been facilitated by gut microbial changes. The colon of rats on the PBMA diet was characterized by an outgrowth of bacterial groups including Muribaculaceae, Roseburia and various Eubacterium spp. known to improve cholesterol metabolism, whereas a remarkable outgrowth of Akkermansia, Oscillospiraceae and Desulfovibrionaceae in rats on the pork diet may be conducive to colon mucin degradation. Effects on oxidative stress parameters were equivocal, with increased lipid oxidation (+27%) in the colon mucosa of PBMA-fed rats, whereas lower blood levels of the endogenous antioxidant glutathione (-30%) were found in pork-fed. Overall, the present rat study reveals major differences in the physiological and microbiota-related responses to diets containing either conventional pork or PBMA, which could have implications for human health.