Journal of Proteomics

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.

Understanding allelic variability is crucial for elucidating intrinsic bacterial mechanisms and distinguishing phenotypic profiles. However, such variability poses a major challenge for the reliable identification of proteins in data-independent acquisition (DIA) proteomics. To address this, we developed an analytical workflow that integrates protein sequence variability to enhance proteome coverage. Fifteen Legionella pneumophila isolates were analyzed using DIA-NN, with spectral libraries generated either from a reference proteome or incorporating allelic variability. Our workflow includes protein clustering and subsequent protein inference from these clusters, allowing the accurate assignment of shared and variant-specific peptides. Integration of variability enabled the identification of a comparable number of proteins as the reference proteome while capturing between 28 and 77 % of variant-specific sequences in each isolate, all while maintaining a low false positive rate. These findings demonstrate that accounting for allelic variability substantially improves proteomic coverage and identification confidence, providing a more comprehensive view of the proteome. This approach facilitates a deeper understanding of biological mechanisms and enables precise bacterial proteotyping of Legionella pneumophila isolates.

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

BackgroundInflammatory bowel disease (IBD) is a chronic inflammatory disorder characterized by gut microbial dysbiosis and immune dysregulation. While compositional changes in the microbiome are well studied, the functional mechanisms through which microbes influence host signalling remain poorly understood. PurposeThis study aimed to investigate microbial-host molecular mimicry in IBD and to elucidate its role in modulating immune and neuronal pathways through a newly proposed Microbial Signal Recognition and Neuronal Mimicry (SRNM) axis. MethodsShotgun metagenomic datasets from IBD patients and healthy controls were analyzed using a custom Molecular Mimicry In Silico Pipeline (MMIP). Reads were assembled, annotated, and subjected to protein homology mapping, Gene Ontology enrichment, PFAM domain analysis, and taxonomic profiling to identify microbial proteins mimicking human functional pathways. ResultsIBD-associated microbiomes exhibited significantly higher functional complexity and enrichment of eukaryote-like proteins compared to healthy controls. Microbial proteins mimicking host pathways involved in neuron projection development, signal recognition particle (SRP)-mediated protein targeting, immune signaling, and stress responses were markedly enriched in IBD. Key human-like targets included TRPV1, CAMK2D, SNCA, MTCP1, TCL1B, and PEAK3. PFAM analysis revealed overrepresentation of kinase domains, zinc-finger motifs, ankyrin repeats, and ABC transporters. These signatures were predominantly contributed by IBD-enriched taxa such as Gammaproteobacteria, Fusobacteria, and Betaproteobacteria. ConclusionThis study identifies a previously unrecognized SRNM axis in IBD, revealing how microbial molecular mimicry may influence neuroimmune signaling and disease pathogenesis, and highlight potential targets for microbiome-based therapeutic intervention.

Background and Aims: Alterations in immunoglobulin G (IgG) N-glycosylation are implicated in inflammatory bowel disease (IBD); however, the robustness of IgG glycan signatures across IBD cohorts with diverse demographics and geographic origins remains underexplored. We aimed to determine whether compositional data analysis (CoDA) and machine learning (ML) can identify IBD-related IgG N-glycan signatures and whether these signatures capture disease-associated acceleration of biological aging. Methods: We analyzed the IgG glycome profiles of 1,367 plasma samples collected from healthy controls (HC), symptomatic controls (SC), and people with newly diagnosed Crohn's (CD), and ulcerative colitis (UC) across four cohorts (UK, Italy, United States, and Netherlands). IgG glycosylation was analyzed by ultra-high-performance liquid chromatography, yielding 24 total-area-normalized glycan peaks (GPs). Analyses were performed using cross-sectional data obtained at baseline. CoDA-powered association analyses were used to identify disease-related effects on GPs while controlling for demographic covariates. ML models were trained and evaluated to assess generalizability to unseen cohorts and demographic subgroups, with a focus on discrimination and reliability. Results: Across all cohorts, people with IBD demonstrated accelerated biological aging as quantified by the GlycanAge index. This was accompanied by consistent reductions in IgG galactosylation, with effects partially modulated by age. Classification models trained on glycomics and demographics achieved robust discrimination (AUROC~0.80) between non-IBD (HC+SC) and IBD across cohorts. Conclusion: These findings reveal accelerated biological aging in people with IBD and support the translational potential of IgG glycans as biomarkers and a novel route toward clinically interpretable personalized risk estimates.

Harbour porpoises (Phocoena phocoena) in the North and Baltic Seas are increasingly impacted by anthropogenic pressures, including underwater noise, fisheries and pollution. These pressures correlate with declining population health, particularly affecting the respiratory system. Growing pathological lesions, partly resulting from high prevalence of parasitic infestations and subsequent diseases, can impair tissue function and oxygen supply to distant end-organs. In this study, we applied an integrative MultiOmics approach (proteomics, metabolomics, lipidomics) to analyse the lungs and muscles of 12 wild harbour porpoises with compromised respiratory health. Our aim was to identify dysregulated biological pathways across omics layers to advance insights into adaptive physiological responses and to define disease-associated molecular signatures that could assist health assessments. Our analysis revealed pronounced immune system and antioxidative responses in the lungs and muscles, indicated by enhanced immunoglobulins, plasmalogens and glutathione-related proteins. In the lungs, high cardiolipin levels and reduced collagen suggest impaired tissue structure and function, while tissue maintenance processes were elevated in the muscle. Both tissues exhibited metabolic alterations suggestive of energetic imbalance, including increased purine metabolism in the lung and decreased lipid metabolism in the muscle. Several dysregulated molecules were shared across tissues, pointing to pathophysiological effects. The proposed disease-associated molecular signatures included the protein SLC25A4, the metabolite O-phosphoethanolamine and the lipid TG O-16:0_16:0_20:4 for the lung, and the protein SPEG, the metabolite pipecolic acid, and the lipid BMP 18:1_22:6 in the muscle. Our findings elucidate the complexity of molecular mechanisms linking anthropogenic and environmental stressors with vulnerability and resilience in a marine sentinel species. Furthermore, this study highlights the potential of integrative omics to define disease-related marker panels, thereby supporting ongoing and future health monitoring and conservation efforts.

Osteosarcoma (OS) is an aggressive bone cancer that most commonly affects children and young adults. OS exhibits a high degree of genomic complexity, as well as cellular plasticity, and dynamic transcriptional regulation is suggested to contribute to treatment resistance and metastasis. Cell lines are well characterized as models to advance our knowledge on OS biology. HOS and U2OS cells have increased invasiveness and higher migratory ability compared with MG63. In this study, we employed a tandem array of consensus transcription factor response elements (catTFREs) proteomic approach to characterize transcription factor (TF) regulatory networks related to OS aggressiveness. We mapped 7,594 proteins and enriched 352 transcription factors and coregulators. When we integrated proteomics with cell line specific gene expression and chromatin accessibility we classified the proteins into different OS cell line dependent sub-clusters and identified TFs and coregulators common for all cell lines and specific for individual cell lines. We demonstrate that RUNX2, MYBL2 and HMGA2 are specifically enriched in HOS and U2OS and may be linked to the cell aggressiveness. ETV5, JUNB, NFIX and ZEB1 were among TFs specific to MG63. Our analysis provides a more comprehensive understanding of the transcriptional drivers that shape OS regulatory landscapes and may have future therapeutic implications.

Metaproteomics enables the functional characterization of microbiomes and host-microbe interactions by detecting and quantifying thousands of proteins. In data-dependent acquisition metaproteomics, protein quantification is commonly performed using either MS1-based area under the curve (AUC) or MS2-based peptide spectral counts (SpC). In AUC quantification, match between runs (MBR) is frequently employed to minimize data sparsity, yet its impact on metaproteomic data remains unclear. Understanding MBRs impact on metaproteomics data is especially important due to the high peak density in the MS1 mass spectra and the potential presence of not only proteins, but even entire organisms, in one sample and their absence in the other, which would complicate accurate feature mapping and transfer. While accurate quantification is essential for deriving meaningful biological inferences from metaproteomic analyses, systematic evaluations of AUC and SpC quantification in metaproteomics remain scarce. In this study, we used defined complex metaproteomic samples to perform a ground truth-based evaluation of AUC and SpC quantification and to determine the impact of MBR on AUC quantification. We found that MBR led to a substantial number of falsely identified proteins in complex samples. Protein identifications from an organism not present in the sample were wrongly transferred from other samples when MBR was used. We found that MBR-free AUC data had a wider dynamic range, higher quantitative accuracy, and more sensitive detection of abundance differences. Significance of the StudyAlthough metaproteomics is increasingly used to advance microbiome research, quantification strategies in metaproteomics are mostly selected based on convention rather than evidence, due to a lack of ground truth-based evaluation of quantification strategies in metaproteomics. Accurate protein quantification is key to deriving meaningful biological inferences from metaproteomic samples, yet it remains challenging due to their high complexity and uneven protein abundances. Here, we used defined metaproteomic samples to evaluate widely used quantification strategies in metaproteomics and to determine the effects of match between runs (MBR) on quantitative accuracy. Based on our findings, MBR adds falsely identified proteins to metaproteomic data. While MBR-free AUC offers a broader dynamic range and higher quantitative accuracy, SpC offers better proteome coverage. With this study, we provide an evidence-based framework for the informed selection of quantification strategies in metaproteomics, and highlight the strengths and limitations of these approaches with respect to proteome coverage, dynamic range, quantitative accuracy, and error propagation. Our findings also have important implications for the biological interpretation of data derived from these strategies and lay the groundwork for future studies validating quantitative approaches in data-independent acquisition workflows.

The rise of MDR Klebsiella pneumoniae and its resistance to the last-resort antibiotic colistin poses a significant threat to global healthcare. While genomic studies have identified several resistance mutations, the transient proteomic shifts that occur during the initial exposure of sensitive strains to lethal antibiotic doses remain poorly characterised. In this study, we employed a label-free quantitative proteomics approach to investigate the protein expression profile of K. pneumoniae strain ATCC 13883 treated with colistin at its MIC. Membrane proteins were extracted at critical growth stages, and differentially abundant proteins (DAPs) were analysed using Gene Ontology and KEGG pathway enrichment analysis. Our proteomic analysis identified 718 DAPs (339 upregulated and 379 downregulated). The cellular response was characterised primarily by outer membrane remodelling and a significant upregulation of the capsule-associated kinase Wzc and the ArnBCADTEF operon, which facilitates lipid A modification with L-Ara4N moiety. Paradoxically, while RND-family efflux pumps (AcrAB) were significantly induced, the global activator RamA and major porins (OmpA, OmpX, LamB) were downregulated, possibly to minimise antibiotic entry. KEGG pathway enrichment analysis further revealed a synchronised metabolic shift, characterised by an intensified TCA cycle flux to fuel high-energy resistance processes despite a general slowdown in carbohydrate metabolism. Our findings demonstrate that K. pneumoniae responds to colistin stress through a rapid, multifaceted proteomic reorganisation involving charge neutralisation, structural reinforcement of the cell envelope, and metabolic re-routing. These results provide a molecular blueprint of the early adaptive response, identifying several proteins as potential therapeutic targets.

Background: Mucopolysaccharidosis type IIIA (MPS IIIA; Sanfilippo syndrome) is a fatal neurodegenerative lysosomal storage disorder caused by impaired degradation of heparan sulfate (HS). Despite rapid advances in gene and enzyme therapies, there remains a critical need for an analytically validated, quantitative biomarker that accurately reflects central nervous system (CNS) substrate burden. Such biomarker would be a valuable tool in assessing disease progression and monitoring therapeutic efficacy. Objective: This study describes the method development, fit for purpose validation, and preliminary clinical application of a quantitative liquid chromatography-mass spectrometry (LC-MS/MS) assay for the HS-derived disaccharide N-sulfoglucosamine-glucuronic acid (GlcNS-GlcUA) in human cerebrospinal fluid (CSF), a critical biomarker for diagnosis, disease monitoring, and regulatory evaluation of emerging MPS IIIA therapies. Methods: A structurally defined GlcNS-GlcUA reference standard and its [13C6]-labeled internal standard were used in a derivatization and detection workflow employing 1-phenyl-3-methyl-5-pyrazolone labeling, and LC-MS/MS. Results: The method exhibited acceptable linearity across 0.005-0.500 nmol/mL (r[≥]0.9976), with intra- and inter-assay imprecision [≤]3.5%CV and accuracy within 95%-110% of nominal concentrations. No matrix or hemolysis interference or carryover was observed, and the analyte remained stable during freeze-thaw storage conditions. Application of the method to 12 CSF samples from patients with MPS IIIA demonstrated quantifiable GlcNS-GlcUA levels ranging from 0.0054 to 0.106 nmol/mL, confirming suitability for clinical and regulatory use. Comparison of the MPS IIIA sample results between the development laboratory and the contract research organization laboratory support robust inter-lab assay transfer. Conclusions: This validated LC-MS/MS method establishes a regulatory-grade quantitative assay for measurement of CSF HS in MPS IIIA. Its high analytical sensitivity and reproducibility enable reliable assessment of CNS substrate reduction and pharmacodynamic response, supporting biomarker-driven therapeutic development and accelerated approval pathways for neuronopathic mucopolysaccharidoses.

Anemia is one of the most debilitating and frequent complications of inflammatory bowel diseases (IBD) and is often treated with iron supplementation, which has limited efficacy. Damaged intestinal barrier function is a hallmark of IBD and causes the translocation of endotoxins from gut bacteria into the bloodstream. In a previous study in mice, we reported that endotoxin suppresses erythropoiesis by reprogramming erythroblastic island macrophages (EBI M{varphi}). Here, we show that IBD patients and mice with acute colitis developed endotoxemia associated with anemia. Endotoxemia in IBD patients was negatively correlated with blood erythrocyte counts. In line with this, mice with acute colitis caused by drinking water containing dextrin sodium sulphate (DSS) had endotoxemia together with anemia characterized by reduced red blood cell counts, hemoglobin content and hematocrit., and reduced medullary erythropoiesis which was in part compensated by increased extramedullary erythropoiesis. As the endotoxin receptor TLR4 is expressed by CD169+ gut-resident macrophages and erythroid island macrophages in the bone marrow, we tested the hypothesis that TLR4 expressed by these CD169+ macrophages mediate both inflammatory colitis and anemia. Indeed, mice with conditional deletion of the Tlr4 gene specifically in CD169+ tissue-resident macrophages were protected from DSS-induced anemia and colitis. In addition, treatment of DSS mice with the TLR4 inhibitor C34 abated inflammation and anemia. These results suggest that endotoxins leaking from the inflamed gut may play a crucial role in IBD and associated anemia and that drugs targeting TLR4 may protect against IBD-associated anemia. Key pointsO_LIPatients with IBD and mice with acute colitis are anemic with increased endotoxemia and inflammation. C_LIO_LIEndotoxemia is inversely correlated with blood erythrocyte counts in IBD patients. C_LIO_LIConditional deletion of endotoxin receptor gene Tlr4 specifically in CD169+ tissue-resident macrophages or administration of synthetic TLR4 inhibitor significantly reduced colitis-induced anemia in mice. C_LI

Skeletal muscle regeneration is often impaired after acute muscle damage induced by viperid snake venoms, such as that of Bothrops asper, a medically-relevant species in Latin America. It has been shown that traces of venom that remain in the damaged muscle affect myogenic cells in culture, raising the possibility of inhibition of these toxins during the regenerative process as a way to improve regeneration. Using a mouse model of myonecrosis and regeneration, we evaluated the effects of Varespladib (a phospholipase A2 inhibitor) or Marimastat (a metalloproteinase inhibitor) on muscle regeneration when administered intravenously 24 h after the onset of myonecrosis, i.e., after muscle damage has occurred. The regenerative process was evaluated 14 and 28 days after venom injection. Results show that Marimastat, or a combination of both inhibitors, improved the extent of skeletal muscle regeneration and reduced the extent of tissue fibrosis when compared to tissue from mice receiving venom and no inhibitors, as judged by qualitative and quantitative histological assessment. Results underscore the deleterious role of traces of venom components in the damaged muscle during muscle regeneration and suggest that the administration of metalloproteinase inhibitors, or a combination of metalloproteinase and phospholipase A2 inhibitors, even when muscle damage has developed, may be a therapeutic alternative for improving the extent of muscle regeneration.

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

Cardiac transthyretin amyloidosis (ATTR-CA) is caused by myocardial deposition of misfolded transthyretin, leading to progressive heart failure. Disease pathology, however, extends beyond passive amyloid deposition and also involves active processes such as extracellular matrix (ECM) remodeling and immune activation. Mass spectrometry (MS) is the gold standard for amyloid typing in diagnostics. Here, we applied quantitative MS-driven proteomics on formalin-fixed paraffin-embedded whole cardiac tissue sections from six ATTR-CA cases, ten unaffected controls and four AL-CA controls to investigate protein expression changes. In addition to transthyretin, over 500 proteins were upregulated in ATTR-CA biopsies, including complement and coagulation factors as well as extracellular matrix (ECM) remodeling proteins. Among these, members of the A Disintegrin and Metalloproteinase with Thrombospondin Motifs (ADAMTS) family, metalloproteinases (MMPs), and Tissue Inhibitor of Metalloproteinases (TIMP3) showed significant upregulation. These proteins are key regulators of ECM turnover and structural integrity. Immunohistochemistry confirmed ADAMTS4 enrichment in amyloid deposits, while TIMP3 showed strong expression in cardiomyocytes and weaker staining within amyloid deposits. Together, these findings indicate that ECM remodeling, alongside complement and coagulation activation, represents a reproducible feature of cardiac ATTR amyloidosis. Whole-tissue proteomics provides biological insights that extend beyond amyloid typing, with potential implications for biomarker discovery and therapeutic targeting in ATTR-CA.

Tyrosine Kinase inhibitors (TKIs) are widely used as effective chemotherapeutic agents for treating patients with EGFR-mutated NSCLC. Unfortunately, after treatment, patients eventually develop resistance to TKI therapy. The most common resistance mechanism for the TKI Osimertinib is the overexpression of the MET Proto-Oncogene, Receptor Tyrosine Kinase (MET). We previously demonstrated that miR-411-5p A-to-I edited at position 5 (miR-411ed) can directly target MET in A549 and H1299 cells. MiR-411ed in combination with Osimertinib reduced cell proliferation in two TKI resistant EGFR-mutated cell lines: HCC827R and PC9R. MiR-411ed did not downregulate MET expression in HCC827R, suggesting an alternative mechanism for TKI response. In this study, we aim to identify the mechanism of miR-411ed TKI response using a multi-omics approach of RNAseq and protein mass spectrometry. In our cellular model, we identified miR-411ed affected genes independent of MET activity, resulting in 211 genes (RNAseq) and 36 proteins (proteomics). Pathway analysis identified an increase in interferon signaling for RNAseq and combined omics, and a decrease in ERK/MAPK signaling in proteomics. Using the IsoTar target prediction tool, we identified STAT3 as a key regulator and confirmed STAT3 protein downregulation upon transfection with miR-411ed. We further investigated the effect of miR-411ed in vivo, observing a reduction in tumor size with miR-411ed in combination with Osimertinib but not with miR-411ed or Osimertinib treatment alone, confirming the effectiveness of miR-411ed in TKI response.

Mycetoma is a neglected tropical disease caused by various bacterial and fungal pathogens that has a significant health impact across a broad geographically defined "mycetoma belt" spanning South America, Africa and Asia. Histologically, mycetoma is characterised by invasive and destructive granuloma development in the skin, deep tissues and bone, leading to tissue destruction, deformities and high morbidity. The presence of macroscopic, highly compacted pathogen microcolonies, or "grains," is a key diagnostic feature, and the formation of grains supports pathogen persistence and disease chronicity. However, there is a paucity of information on immune responses in mycetoma patients and on the relative importance of phylogeny and/or grains in establishing the local immune landscape. Here, we used spatial proteomics to examine the distribution of 43 immune-related proteins in surgical biopsies from 11 patients with mycetoma of bacterial (Actinomycetoma; Actinomadura pelletierii and Streptomyces somaliensis; n=6) and fungal (Eumycetoma; Madurella mycetomatis; n=5) origin. Using mixed-effects modelling, an exploratory analysis across species and pathogen classes revealed few significant differences in immune marker expression. In contrast, and independently of pathogen class, the cellular infiltrate closest to grain boundaries had higher per-cell expression of CD66b+, ARG1, and VISTA. The preferential accumulation of CD66b+ARG1+VISTA+ cells at grain boundaries was confirmed by quantitative immunofluorescence analysis. Hence, the local tissue microenvironment surrounding the mycetoma grain represents a specialised immunosuppressive niche, with parallels to the tumour microenvironment.

Spotty Liver Disease (SLD) is an acute bacterial infection of layer chickens in production, caused by Campylobacter hepaticus, and occurs most frequently in barn-housed and free-range systems. The disease is characterized by a sharp decline in egg production and increased mortality. The hallmark pathological feature is 1-2 mm white to grey necrotic foci distributed across the liver surface. Despite its growing economic impact on commercial poultry, the molecular mechanisms underlying host responses to C. hepaticus infection remain poorly understood. To address this gap, we performed a comprehensive transcriptome analysis of liver tissue from chickens naturally infected with SLD compared to uninfected controls. High-throughput transcriptome sequencing, yielding 9,277 differentially expressed genes (DEG), of which 3,063 were upregulated and 6,214 were downregulated. Functional pathway enrichment analysis revealed significant alterations in immune and metabolic processes associated with SLD pathophysiology. Infected chickens exhibited significant activation of immune response pathways, particularly cytokine-cytokine receptor interactions involving interleukins IL-22, IL-21, and IL-6, along with enhanced cell signaling, and cell adhesion. Among the individual genes, C1QTNF1 and the adhesion molecule gene ADGRD1 were notably overexpressed, indicating enhanced inflammatory activity. In contrast, core hepatic metabolic functions were profoundly reduced, as evidenced by downregulation of oxidative phosphorylation, fatty acid metabolism, iron ion binding, and heme binding pathways. A marked increase in serum amyloid A gene (SAA) expression further confirmed robust acute-phase responses and compromised liver function during infection. Together, these findings demonstrate a complex interplay between inflammatory activation and metabolic dysregulation during SLD. The strong upregulation of acute-phase proteins and pro-inflammatory cytokines demonstrates the hosts vigorous attempt to combat bacterial infection, whereas the concurrent suppression of essential metabolic pathways reflects the pathological consequences of SLD. This study provides a transcriptomic characterization of host responses to C. hepaticus infection, offering insights into SLD pathogenesis and potential avenues for targeted intervention.

Photosynthetic electron transport is mediated by several protein supercomplexes that are spatially arranged in the thylakoid membranes of chloroplasts. The chloroplast NADH dehydrogenase-like (NDH) complex is part of the photosynthetic alternative electron transport (AET) chain, which reduces the plastoquinone (PQ) pool using reduced ferredoxin as a substrate. This NDH complex is associated with photosystem I (PSI) and mediates a portion of AET in stroma lamellae, whereas photosystem II (PSII) is concentrated in grana stacks. This study presents the findings regarding post-illumination chlorophyll fluorescence increase (PIFI), a protein crucial for regulating AET via the NDH pathway. A marked increase in NDH activity and a reduction in the PQ pool in the dark were observed in PIFI-deficient mutant strains (g-pifi) generated by genome editing. Blue native PAGE analysis indicated that PIFI was associated with the NDH-PSI supercomplex in the wild type, and the NDH complex was dissociated from PSI in the g-pifi mutants. Additionally, the g-pifi mutants exhibited a decrease in the maximum quantum yield of PSII (Fv/Fm). Notably, Fv/Fm was restored in a double mutant harboring both g-pifi and NDH-deficient pnsl1 mutations, demonstrating that deregulated NDH activity in g-pifi causes downregulation of PSII efficiency. However, the lower Fv/Fm was not observed in a mutant lacking thioredoxin m4 (trxm4), which showed deregulated NDH activity but maintained the NDH-PSI supercomplex. These data suggest that PIFI stabilizes the NDH-PSI supercomplex and maintains the spatial localization of PQ reduction via AET in thylakoid membranes, which is essential for the proper functioning of PSII.

Altered iron homeostasis has long been implicated in Parkinson's Disease (PD), although the mechanisms have not been clear. Given the critical role of PD-related activating mutations in LRRK2 (leucine-rich repeat protein kinase 2) within membrane trafficking pathways we examined the impact of a homozygous mutant LRRK2G2019S on iron homeostasis within the RAW macrophage cell line with high iron capacity. Proteomics analysis revealed a dysregulation of iron-related proteins in steady state with highly elevated levels of ferritin light chain and a reduction of ferritin heavy chain. LRRK2G2019S mutant cells showed efficient ferritinophagy upon iron chelation, but upon iron overload there was a near complete block in the degradation of the ferritinophagy adaptor NCOA4. These conditions lead to an accumulation of phosphorylated Rab8 at the plasma membrane, which is selectively inhibited by LRRK type II kinase inhibitors. Iron overload then leads to increased oxidative stress and ferroptotic cell death. These data implicate LRRK2 as a key regulator of iron homeostasis and point to the need for an increased focus on the mechanisms of iron dysregulation in PD.

The unfolded protein response (UPR) helps reinstate cellular proteostasis upon an accumulation of misfolded proteins in the endoplasmic reticulum (ER), in part through ER-associated degradation (ERAD). Ube2j2 is an ER-localized E2 ubiquitin-conjugating enzyme that participates in ERAD. We used mass spectrometry analysis of cultured U2OS cells to investigate how the loss of Ube2j2 affects the cellular proteome in response to tunicamycin-induced ER stress. We constructed a network of twelve statistically distinct modules of protein abundance profiles across conditions. We describe the Gene Ontology annotations for each module along with the "hub gene" proteins whose abundance levels most closely adhere to each modules protein abundance profile. Our analysis identifies known Ube2j2-associated pathways (e.g., the UPR and ERAD) and cellular functions that were previously unassociated with Ube2j2 (e.g., RNA metabolism, ER-Golgi transport, and cell-cycle progression). These data are available via ProteomeXchange with identifier PXD076153 and provide avenues for further investigation into the cellular functions of Ube2j2 under basal and ER-stressed conditions.