Journal of Proteomics

Insect herbivory triggers cytosolic proteome reprogramming by activating defense pathways and modulating key metabolic processes. We found that simulated herbivory in pigeon pea (Cajanus cajan) induced reactive oxygen species (ROS) production and molecular alterations within 12 hours (h) of post treatment. We compared the leaf proteome profiles of two cultivated genotypes, ICPL 332 (moderately resistant) and ICPL 87 (susceptible), using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) coupled with mass spectrometry (MS). More than 220 protein spots were detected in ICPL 332 and over 200 in ICPL 87. Comparative analysis revealed 75 differentially accumulated proteins (DAPs), of which 40 were consistently reproducible across biological replicates. These included 11 unique to ICPL 87, 9 unique to ICPL 332, and 10 common to both genotypes. Among the shared DAPs, ICPL 332 showed five upregulated and five downregulated, whereas ICPL 87 exhibited only two upregulated and eight downregulated. Functional categorization grouped DAPs into primary metabolism, stress response, and growth and development. Proteins related to primary metabolism were largely downregulated in both genotypes, while stress-associated proteins exhibited substantial downregulation in ICPL 87 compared to ICPL 332. Overall, the results demonstrate proteomic adjustments underlying defense responses in pigeon pea genotypes.

Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to [~]11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time. Significance of the StudyAs DIA mass spectrometry proteome depth increases, the quality of the associated protein quantifications must be considered alongside identification breadth, particularly in complex matrices such as plasma, which presents additional technical challenges. The spectral library used for protein identification and quantification is a critical determinant of DIA performance, and its composition requires considerable consideration. This work illustrates an initial step toward improving protein quantification starting at the spectral library level by filtering precursors which are poor quantitative representatives of their parent proteins. In doing so, the resulting data is more reliable for downstream and biological interpretation, with fewer false differential abundance assignments and reduced quantitative noise. As such, this work represents a broader shift away from the habitual focus of MS workflows on maximising the number of protein and differential abundance identifications and instead prioritises the quality of quantification over quantity. These initial findings lay the groundwork for further development of spectral library refinement strategies, with the potential to continue improving the accuracy and precision of protein quantification in DIA-based proteomics.

Maintaining honey bee health in crop production systems is increasingly difficult because worker bees encounter multiple chemical and biological pressures from pesticides and pathogens. How these field-realistic pressures affect molecular physiology across functionally distinct tissues remains poorly understood. Here, we tested whether tissue-resolved proteomics could separate stable tissue-specific patterns from crop-associated molecular changes. To do this, we profiled abdomen, gut, and head proteomes from honey bees collected across four Canadian crop ecosystems over two consecutive years, and integrated these data with pesticide-residue and pathogen-load measurements. Proteomic variation was structured by both tissue identity and crop environment. Tissue-specific proteomic profiles were characterized across samples, whereas crop-associated effects were detected in both years and were stronger in 2021, the second year of the study. Tissue-specific enrichment and network analyses linked the abdomen to lipid catabolism and ubiquitin-proteasome proteostasis, the gut to central carbon metabolism, membrane transport, vesicle trafficking, and cytoskeletal organization, and the head to neurosensory and mitochondrial functions, together with amino-sugar metabolism and vesicle-associated quality-control modules. Among the measured pesticide residues, boscalid was the most reproducible chemical correlate of proteomic variation, with the strongest signal in the gut. Cross-year validation associated boscalid exposure with reduced abundance of gut proteins involved in mitochondrial metabolism, protein quality control, vesicle trafficking, nutrient transport, and biosynthetic pathways. Additionally, integrated proteome-transcriptome-microbiome factor analysis further identified gut-centered components associated with measured stressor variables and linked protein-level variation to coordinated transcriptomic and microbial shifts. Independent-year validation showed that compact crop-associated protein signatures detected in 2020 were also present in 2021. Together, these results show that honey bee tissues maintain stable proteomic identities while showing tissue- and year-specific responses to pesticide and pathogen pressures encountered in crop ecosystems. The gut proteome may specifically provide a sensitive molecular indicator of pesticide-associated perturbation under field conditions.

The natural variation that distinguishes living organisms within a single species is currently being studied intensively, primarily at the genetic level. Unfortunately, studies of natural variants at the level of protein gene products are not very common, mainly due to the lack of appropriate databases and bioinformatics tools. The main research technique used to study proteomes/peptidomes is mass spectrometry (MS). A classic method for interpreting raw mass spectrometry data in proteomic/peptidomic studies involves the use of databases containing representative (canonical) sequences that define the proteome of the organism under study. In this paper, we present the AliceDB database, which contains information on over 7 million natural variants of protein sequences described in the scientific literature for Homo sapiens. The data contained in the AliceDB database can be utilized using widely available and commonly used software for interpreting proteomic data. Test results regarding the use of the AliceDB database for the interpretation of proteomic data indicate that accounting for the presence of natural variants increases both the number and quality of identified proteins. Furthermore, it is easy to identify protein sequence variants that may, for example, be of significance in medicine.

Adipogenesis is the process of adipose-derived stem cells (ADSCs) responding to extracellular signals from the stem cell niche to differentiate into adipocytes (fat cells) and may be studied in vitro using a cocktail of chemicals that promote adipogenic differentiation to produce differentiated ADSCs (dADSCs). The global membrane N- and O-glycosylation changes of this process have been previously analysed and compared to native adipocytes as a benchmark for a true adipocyte profile, and revealed that bisecting GlcNAc type N-glycans are characteristic of adipogenesis. As stem cell differentiation has been widely reported to result in cellular protein changes, the same cells (ADSCs, dADSCs and mature adipocytes) were characterised for their membrane proteome here using label-free quantitative shotgun proteomics analysis. The membrane proteome displayed more differences in protein numbers between the cell types compared to the previously reported N-glycome which had shown high identical glycomes between stem cells and in vitro dADSCs, suggesting that the proteome is more dynamic during in vitro adipogenesis. Following the global shotgun proteomics analysis, a more targeted approach of carrying out proteomic analysis of de-N-glycosylated peptides of gel-separated proteins unearthed new glycoproteins not detected in the shotgun proteomic analysis. This approach identified the adipogenic marker, CD36, to be under-represented in the shotgun proteome analysis, but as the dominant (glyco)protein in the adipocyte membrane proteome that was also up-regulated at the mRNA transcript level in both the in vitro differentiated ADSCs (7.1-fold increase) and mature adipocytes (102.9-fold increase). A comparison of CD36 sequence coverage in the global shotgun analysis with the de-N-glycosylated CD36 revealed a 41% increase when N-glycans were removed prior to trypsin digestion, explaining its observed increased abundance and highlights the crucial need for de-N-glycosylation of proteins in proteomics experiments for increased identification of glycoproteins. The systems glycobiology approach by the integration of previously reported glycomics data and the proteomics and transcriptomics analyses in this work extended the investigation of membrane protein glycosylation changes in adipose-derived stem cell differentiation. The work provides a framework for future glycoproteomics-based investigations into the differentiation of stem cells into adipocytes, and will allow their related pathologies and potential therapeutic applications to be discovered. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/722121v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@189a786org.highwire.dtl.DTLVardef@5563b8org.highwire.dtl.DTLVardef@5cb5borg.highwire.dtl.DTLVardef@69e11f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Parasitic flatworms, including cestodes and trematodes, are covered by a specialized syncytial tegument that mediates nutrient uptake and host-parasite interactions. While the tegument of trematodes has been extensively characterized, its molecular composition in cestodes remains largely unknown. In this work, we performed a comparative proteomic analysis of the tegument of three cestode species, including larval and adult stages: Hymenolepis microstoma, Mesocestoides corti (syn. M. vogae) and Echinococcus multilocularis. Using stringent enrichment criteria relative to whole-worm extracts, we identified hundreds of tegument-enriched proteins in each species. Comparative analyses revealed a conserved core of tegumental proteins shared among all three species, including members of the Tegument Allergen-Like (TAL) family, vesicular trafficking components and calcium-sensing proteins, and identified candidates for nutrient uptake activities such as glucose and nucleoside transporters. Further comparative analyses revealed a set of shared tegumental proteins with the trematode Schistosoma mansoni, including conserved proteins that are specific to parasitic flatworms, supporting the existence of a conserved ancestral tegumental proteome. Finally, we confirmed tegumental expression of several candidate genes in H. microstoma and E. multilocularis, and demonstrated regionally restricted gene expression among tegumental cytons, suggesting functional specialization within the syncytial tegument. Altogether, these results reveal an evolutionarily conserved composition of the tegument of parasitic flatworms, providing a foundation for future work targeting this critical host-parasite interface.

Parkinsons disease is a progressive neurodegenerative disorder associated with mitochondrial dysfunction, oxidative stress, impaired autophagy, and dysregulated cellular signaling pathways. Although Glycyrrhiza glabra has been reported to exhibit neuroprotective properties, the early phosphorylation-mediated signaling mechanisms underlying its protective effects remain poorly understood. In this study, we employed a Tandem Mass Tag (TMT)-based temporal quantitative phosphoproteomic approach to investigate early signaling events associated with Glycyrrhiza glabra-mediated neuroprotection in a rotenone-induced in vitro PD model. Differentiated IMR-32 neuronal cells were treated with rotenone alone or in combination with Glycyrrhiza glabra extract, and phosphoproteomic alterations were analyzed at 2, 5, 15, and 30 minutes using liquid chromatography coupled with tandem mass spectrometer. Temporal phosphoproteomic analysis identified 6,424 phosphopeptides corresponding to 2,368 phosphoproteins and 5,468 phosphorylation sites. Comparative analysis revealed extensive phosphorylation rewiring induced by rotenone and restoration of several dysregulated phosphorylation events following Glycyrrhiza glabra co-treatment. More than 130 phosphoproteins and multiple kinase-associated signaling pathways were dynamically regulated across the temporal conditions. Kinase enrichment analysis identified restoration of several critical kinases, including AKT1, MTOR, MAPK1/3, PRKACA, PRKCD, and GSK3A/B, which are associated with neuronal survival, stress adaptation, and autophagy. Integrated pathway and kinase-substrate interaction analyses further revealed enrichment of AMPK signaling, FOXO signaling, receptor tyrosine kinase signaling, RNA processing, and cell-cycle regulatory pathways. Notably, several spliceosome-associated phosphoproteins demonstrated dynamic phosphorylation changes during the early neuroprotective response. Collectively, this study provides a detailed temporal phosphoproteomic landscape of early signaling events associated with Glycyrrhiza glabra-mediated neuroprotection and highlights kinase-driven signaling pathways that may represent potential therapeutic targets in Parkinsons disease.

Bone is a highly durable biological tissue widely used in forensic, archaeological, and anthropological investigations; however, efficient protein recovery and understanding of protein stability over time remain major challenges in skeletal proteomics. Here, we systematically evaluated three bone protein extraction workflows and integrated them with data-independent acquisition (DIA) mass spectrometry to assess proteome coverage, reproducibility, and temporal protein dynamics under environmentally exposed conditions. Comparative analysis demonstrated that extraction strategy is a primary determinant of detectable proteome composition. EDTA-based demineralization followed by SDS extraction provided the deepest proteome coverage and highest reproducibility, whereas guanidine hydrochloride extraction preferentially enriched collagen and extracellular matrix proteins. In contrast, acid-based extraction yielded limited protein recovery. Temporal profiling of bone samples collected at 10 and 45 days post-exposure revealed two distinct protein classes. A temporally stable module, enriched in collagens and extracellular matrix proteins including COL1A2, COL5A2, BGN, SPARCL1, and NID2, exhibited minimal abundance change, indicating resistance to environmental degradation. In contrast, temporally dynamic proteins, enriched in mitochondrial, metabolic, and intracellular pathways such as ACO2, OGDH, PDHA1, ATP5PO, and PFKM, showed marked decline over time. These findings support a two-compartment model of bone protein preservation in which matrix-embedded proteins are preferentially retained while exposed intracellular proteins undergo progressive degradation. Collectively, this study establishes an integrated framework linking extraction methodology with temporal proteome stability and identifies candidate markers for skeletal preservation assessment and temporal biomarker development in forensic and archaeological applications.

We compared traditional data-dependent acquisition mass spectrometry (DDA-MS) with the increasingly adopted data-independent acquisition (DIA-MS) to evaluate their relative utility for large-scale quantitative biofluid proteomics of lung compartments, specifically paired bronchoalveolar lavage (BAL) cells and bronchoalveolar lavage fluid (BALF). Using beryllium-related granulomatous lung disease as a focused model, we analyzed BALF and BAL cells from beryllium-sensitized (BeS) individuals using both acquisition strategies to assess proteome depth, quantitative completeness, and analytical robustness. In BAL cells, 5,640 proteins were identified by DDA-MS and 5,227 by DIA-MS; however, DIA-MS yielded markedly improved quantitative completeness, with 5,178 proteins ([~]99%) quantified across all samples compared with 3,539 ([~]63%) quantified by DDA-MS. While 3,397 proteins were quantified by both methods, DIA-MS uniquely quantified 1,781 lower-abundance proteins. Proteins identified by both DIA and DDA-MS approaches revealed pathways associated with granulomatous inflammation, including Toll-like receptor, clathrin-mediated endocytosis, sirtuin, and C-type lectin receptor signaling, whereas DIA-MS resolved additional pathways, such as the complement cascade, coagulation system, and JAK/IL-6-type cytokine signaling. In BALF, although more proteins were identified by DDA-MS than by DIA-MS (2,069 vs 1,742), DIA-MS achieved greater quantitative completeness, with 1,695 proteins quantified across all samples compared with 1,050 using DDA-MS, underscoring its suitability for biomarker-oriented analyses in lung fluid compartments. Together, these results support DIA-MS as a robust and sensitive platform for quantitative lung proteomics and discovery of disease-relevant protein signatures.

Sonication is a fundamental technique in proteome sample preparation, primarily used for protein solubilization and shearing of genomic DNA. Although the mechanical shearing of DNA is well-characterized, its unintended impact on protein structural integrity remains a significant "blind spot" in high-throughput analytical workflows. In this study, we systematically investigated sonication-induced protein fragmentation by combining gel-based fractionation (PEPPI-MS) with sequence-level compositional analysis and bioinformatic mapping. Our results demonstrate that sonication does not significantly alter overall proteome identification or the recovery of membrane proteins; however, it induces extensive and non-random protein fragmentation. Sonication caused an approximately three-fold increase in the abundance of >45 kDa protein-derived fragments migrating into the <40 kDa fraction, and 1,620 high-molecular-weight (MW) proteins were uniquely detected in the lower-MW fraction upon sonication, an eight-fold increase over non-sonicated controls. Peptide-level amino acid composition analysis revealed subtle but directional shifts in the sonication-derived fragments. This residue-level signature is reinforced by two orthogonal structural analyses (MobiDB peptide-level mapping and protein-level profiling using metapredict V3 software), which show that sonication-susceptible proteins harbor more than twice the disordered content of length-matched controls (median 40% vs. 18%). This study identifies a previously unrecognized "structural bias" whereby intrinsically disordered region (IDR)-rich proteins are selectively compromised during sample preparation. Because these fragments are indistinguishable from enzymatic digestion products in conventional bottom-up proteomics, the underlying structural damage is effectively masked in global quantitative datasets, potentially distorting biological interpretations related to protein size, isoforms, and stability, particularly for IDR-rich classes, such as transcription factors and signaling molecules. We propose that optimizing and standardizing sonication parameters is essential for ensuring the accuracy and reproducibility of quantitative proteomic analyses.

Data-independent acquisition (DIA) mass spectrometry enables rapid proteomic quantification, yet the reliability of statistical inference in DIA-based protein quantification remains incompletely understood. Here, we systematically evaluated missingness, false discovery rate (FDR), and statistical power, defined as true positive rate (i.e. sensitivity or recall), using technical replicates and a spike-in benchmark with known ground truth. Analysis of 18 HeLa replicates revealed persistent, abundance-dependent missingness. In the spike-in experiment with five replicates, human peptides were titrated against a stable yeast background, allowing fold changes (FCs) to be compared with expected values. Across comparisons with log2FCs ranging from 0.2 to 2.5, the nominal BH-FDR substantially underestimated the true FDR. For example, at a BH-FDR threshold of 0.05, the true FDR was [~]0.2. Statistical power was [~]40% for a log2FC of 0.2 and increased to nearly 100% for a log2FC of 2.5. Additional incorporation of FC thresholds improved the true FDR for large-FC comparisons, with slight loss of power, but markedly reduced sensitivity for small-FC comparisons. Together, these results indicate that nominal FDR does not necessarily reflect actual error rates in DIA proteomics and that DIA performance is influenced by protein abundance and expected fold changes. This study provides a framework for experimental design and data interpretation in DIA-based proteomic studies.

Recombinant human lactopontin (rhLPN), an equivalent of human milk lactopontin, is of increasing interest for human nutrition applications due to its roles in mineral binding, gastrointestinal function and immune modulation. These properties depend strongly on post-translational modifications, particularly phosphorylation and glycosylation. Here, we report the production of rhLPN in Kluyveromyces lactis at laboratory and pilot scale and present a comprehensive molecular comparison with native human lactopontin (nhLPN) isolated from human milk. Mass spectrometry-based peptide mapping confirmed the primary structure and identified extensive phosphorylation, consistent with the native protein. Middle-up analyses demonstrated closely matched phosphoform distributions between rhLPN and nhLPN, while glycosylation profiling revealed a defined population of low-complexity O-glycoforms localized to the N-terminus. Functional assessment demonstrated substantially greater iron binding by phosphorylated rhLPN compared with dephosphorylated and non-phosphorylated forms. Similar phosphorylation-dependent behaviour was observed for bovine lactopontin, supporting a conserved role for phosphorylation in mineral interaction. Across five 750 L pilot scale batches, both phosphorylation and glycoform distributions were highly consistent, indicating robust process reproducibility. Together, these results demonstrate that rhLPN produced in K. lactis recapitulates key structural and functional attributes of nhLPN, supporting its suitability as a scalable ingredient for nutrition applications.

Many proteomics protocols rely on enzymatic digestion of complex protein mixtures to generate peptides with predictable cleavage patterns for the mass spectrometry analysis. One of the most utilized enzymes, trypsin, is classically defined as a serine endopeptidase with high specificity for cleaving peptide bonds on the C-terminal side of internal lysine and arginine residues. Accordingly, trypsin is not expected to remove the N-terminal arginine, which may arise through posttranslational modification such as arginylation or by proteolysis exposing internal residues as the new N-termini. N-terminal arginine plays important biological roles, including functioning as an N-degron and modulating protein interactions/signaling through its positive charge. Curiously, prior mass spectrometry-based studies utilizing trypsin to identify proteins bearing N-terminal arginine have frequently reported low and inconsistent yields, suggesting potential systematic bias in current proteomic approaches. Here, we explored whether trypsin would affect the integrity of the N-terminal arginine. By using antibodies specifically recognizing N-terminal arginine of different peptides, and by using mass spectrometry peptide analysis, we show that trypsin can remove N-terminal arginine residues in an exopeptidase-like manner. This effect occurs across a range of digestion conditions consistent with standard proteomic workflows, on peptides or whole proteins, and depends on trypsin concentration, incubation time, and catalytic activity. In addition, we show that the alternative arginine-cleavage enzyme Arg-C can also affect N-terminal arginine in a sequence-dependent context. In contrast, Lys-C and LysargiNase do not exhibit such effects, providing suitable alternative digestion strategies. Together, these findings reveal an unappreciated enzymatic behavior of arginine-cleaving proteases and suggest that their widespread use may systematically compromise the detection of N-terminal arginine in proteomic studies.

Data-dependent acquisition mass spectrometry (DDA-MS) and data-independent acquisition mass spectrometry (DIA-MS) have historically offered complementary strengths in bottom-up proteomics, with DDA providing high-selectivity spectra for post-translational modification (PTM) analysis and DIA enabling more systematic peptide sampling. Here, we asked if this is still the case for the Orbitrap Astral platform that offers high-speed DDA and (ultra-) narrow-window DIA (nDIA) capabilities across proteome and phosphoproteome applications. When DDA and DIA measurements were parameter-matched (to the extent possible), the differences in analytical performance diminished markedly. Across extensive replicate analyses, both methods continued to identify new peptides and proteins without reaching saturation, indicating that the molecular complexity of biological samples still overwhelms even the fastest liquid chromatography-MS (LC-MS) methods. Incomplete sampling also contributed to substantial peptide-level non-overlap between DDA and nDIA and data completeness was only modestly better for nDIA than DDA across many replicates. Quantitatively, DDA and nDIA showed broadly similar precision and accuracy, with nDIA offering slightly higher precision and DDA slightly better accuracy in controlled mixture experiments. MS1-based quantification outperformed MS2-based quantification, particularly for short gradients, supporting MS1 quantification as a robust and general strategy for high-throughput proteomics. In phosphoproteomic samples, DDA and nDIA identified similar numbers of phosphopeptides, but DDA retained a small edge for phosphorylation site localisation. Together, the results show that advances in acquisition speed and sensitivity are narrowing the historical gap between DDA and DIA, while also revealing that current LC-MS workflows remain far from providing comprehensive proteome coverage. Going forward, further gains in dynamic range, scan speed, sensitivity, and transparent software tools will be required to reach systematic, comprehensive and reliable measurements of complex proteomes in a single shot.

Peptide spectrum annotation is critical for the assignment of peptides and the localisation of modifications. While many existing tools provide spectrum annotation capacities, they often lack the flexibility required to allow bespoke spectral annotation of peptides containing multiple labile modifications or the accurate assignment of peptides in which fragmentation deviates from canonical patterns. In these cases, user-guided annotation is widely used to improve assignment completeness, however it typically does not integrate peptide scoring, making it challenging to assess the empirical improvement of the associated annotation and its impact on downstream false-discovery rate estimations. Here, we introduce an interactive annotation environment, the 'MassSpectrum Analyzer', which aims to streamline the exploration and analysis of modified peptides by enabling user-defined customisation with peptide scoring. Using (2-Aminoethyl)trimethylammonium carboxyl-derivatised peptides and glycopeptides as case studies we demonstrate the capacity of the MassSpectrum Analyzer to rapidly explore and allow the assessment of modified peptide datasets. By enabling direct assessment of the impact of user-guided choices on peptide scoring, we show how the detection of highly modified peptides can be improved through post-search integration of modification fragmentation information in a statistically robust manner. Similarly, by permitting comparisons of peptide ion intensities across spectra, we show that global fragmentation patterns can be quantified allowing the interrogation of trends that only become clear when spectra are assessed en masse. Combined, the MassSpectrum Analyzer streamlines the generation of publication-ready spectra and provides a means to assess how the inclusion of annotated features influences assignment scores.

Glioblastoma is a very aggressive brain tumor with few therapeutics options. Type I and II Interferons (IFNs) co-formulation HeberFERON has been used in cancer treatment, with promising results in high grade brain tumors. High throughput techniques in easy-to-handle models have been important to interrogate biomolecules changes, describe mechanisms and find pharmacodynamic biomarkers. This study aims to elucidate the effect of HeberFERON over the cell proteome in comparison to its individual IFNs components. Proteomic changes with HeberFERON in the glioblastoma-derived cell line U-87MG, in comparison with individual IFN-2b and IFN-{gamma}, were studied using a nanoLC instrument EasyLC coupled to Velos Pro mass spectrometer; Maxquant and Perseus were also used. Several enrichment tools, networking analysis and canSAR for drug targets were employed. Translation, RNA processing, mitotic cell cycle, cytoskeleton and chromosome organization, apoptosis, autophagy, DNA repair are enriched to limit cellular growing together with changes in immune response components, supporting HeberFERON as a multitarget treatment. This co-formulation is distinguished at modulating RNA splicing with SMN complex, cytoskeleton organization and microtubule-based movement, nuclear envelope breakdown, DNA conformational changes, and oxidative phosphorylation, with a better drawing of effects over a variety of systems inside the tumoral cell. Together with previous microarray experiment, informative genes and proteins as pharmacodynamic biomarkers for antiproliferative effects showed up (ex. STAT1/2, CENPE, ATRIP, MAP1B, LIMA1, VCP, several ribosomal, spliceosome and proteasomal complexes proteins). This study complements transcriptomic and phosphoproteomic previous experiments in this model and underscore HeberFERON as a glioblastoma therapeutic.

To successfully infect their myriad hosts, generalist plant pathogens must tolerate a vast arsenal of plant specialized defense metabolites. To understand how host-specific metabolites influence plant-generalist pathogen interactions, we conducted a co-proteomic analysis of both Arabidopsis thaliana and Botrytis cinerea proteomes from the same samples during early infection. The Arabidopsis proteomic responses to Botrytis center around induction and suppression of defense metabolite pathways, particularly camalexin and glucosinolates. Several Botrytis proteins involved in key virulence pathways were induced within 32-48 hours, including potential defense metabolite detoxification proteins. Co-proteomic analysis using a panel of Arabidopsis genotypes with differing glucosinolate profiles revealed that disruptions to the glucosinolate pathway had broad changes on the Arabidopsis proteome, and that Botrytis induces specific proteins in response to presence/absence of Arabidopsis defense metabolites. Among the proteins that were induced quickly on infection and linked to the presence of glucosinolates, we validated a novel isothiocyanate hydrolase in Botrytis, BcSaxA, that catabolizes isothiocyanates in vitro. Gene expression data further indicated BcSaxA is expressed only in dicot hosts containing isothiocyanates. Our study describes a highly dynamic host proteome during infection with Botrytis and elucidates metabolite-specific infection strategies for a generalist pathogen.

Manchester Proteome Profiler (MPP) is an open-source R Shiny application that streamlines downstream analysis of quantitative proteomic data. Compatible with grouped protein intensities tables from MaxQuant, FragPipe, Proteome Discoverer and other custom layouts, MPP provides an integrated platform for filtering, normalisation, imputation, differential expression analysis and cluster analysis across user-chosen experimental conditions. MPP supports both single- and dual-dataset comparisons, incorporates SAINTexpress for affinity purification and proximity labelling experiments, and downstream analysis of the significant protein list clusters to functional enrichment and interaction networks via Gene Ontology, BioGRID and STRING. Benchmarking with a KRAS proximity biotinylation dataset demonstrated the ability of MPP to identify reproducible clusters of differentially expressed proteins and reveal biologically meaningful patterns, including enrichment of solute carrier transporters and adhesion molecules. With interactive visualisations, customisable reports, and support for complex experimental designs, MPP offers a novel, versatile and user-friendly environment for proteomic data exploration and hypothesis generation.

BackgroundThe commercialization of Nigerias single-line pod borer-resistant (PBR) cowpea (IT97KT), the first transgenic cowpea variety in the world expressing Cry1Ab gene, has raised questions about potential unintended molecular changes and their ecological implications. This study employed integrated proteomic and metabolomic profiling to compare the transgenic line with its non-transgenic isoline (IT97KN) and assess molecular indicators associated with ecosystem resilience. MethodsProteomic analyses were conducted using LC-MS/MS following filter-assisted sample preparation, while metabolomic profiling employed GC-MS and UHPLC-MS/MS platforms. Differential protein and metabolite abundance were assessed using label-free quantification, volcano plot analysis, principal component analysis (PCA), hierarchical clustering, and Gene Ontology (GO) enrichment analyses. ResultsProteomic profiling revealed substantial overlap between IT97KT and IT97KN, with only a limited subset of proteins exhibiting significant differential abundance. Upregulated proteins in IT97KT were primarily associated with seed storage, redox regulation, oxidative stress mitigation, and defense-related functions, including Late Embryogenesis Abundant Protein 1 (LEA1), vicilins, thioredoxin, and iron superoxide dismutase. Among 37 proteins linked to ecological adaptation, only LEA1, CPRD22, and Bg7S showed significant differences. Similarly, only carbonic anhydrase II displayed differential abundance among proteins associated with potential ecological risk. PCA and clustering analyses demonstrated high proteomic similarity between genotypes. Metabolomic analyses identified sixteen major metabolites, predominantly fatty acids, with no statistically significant differences in abundance or composition between transgenic and non-transgenic lines ConclusionsThe transgenic PBR cowpea exhibited minimal unintended proteomic and metabolomic alterations relative to its non-transgenic isoline. These findings indicate that Cry1Ab insertion did not substantially disrupt molecular pathways associated with ecological adaptation, environmental risk, or metabolic homeostasis, providing molecular evidence supporting the environmental and biosafety equivalence of PBR cowpea.

Respiratory virus infections affect both humans and livestock, causing considerable mortality and morbidity. While respiratory pathogens such as swine influenza A virus (pH1N1) and porcine respiratory coronavirus (PRCV) often present with overlapping clinical symptoms, their pathological trajectories and outcomes differ. Given the propensity for pathogen spillover and the use of pigs as a physiologically relevant large-animal translational model, we aimed to characterise host serum protein signatures that detect and differentiate pH1N1 from PRCV, enabling improved disease monitoring and control. Using high-resolution mass spectrometry- based proteomics, we identified 162 serum proteins that were significantly dysregulated across 3 infection timepoints (1, 5, and 12 days post-infection (DPI)), with signatures correlating with viral shedding and lung pathology as early as 1 DPI. Notably, multiplexed targeted analysis of a subset of proteins in an independent cohort from a different breed and geographic location demonstrated detection, femtomole-level targeted quantitation, and validation of SRGN as a diagnostic marker for pH1N1 and PRCV (AUC=0.85). Further, SOD1 was validated as an early marker for PRCV, increasing as early as 1 DPI (AUC= 0.9). Finally, a multi-peptide signature composed of SRGN, SOD1, and RAN demonstrated reasonable predictive power for pH1N1 (AUC=0.75) and PRCV (AUC=0.65) at 1 DPI. Our data validate the proteomic screening, provide insights into the role of early protein markers in distinguishing respiratory viral infections, and pave the way for the development of point-of-care diagnostics and targeted prevention strategies, enhancing preparedness against emerging zoonotic threats.