Search Results (106)

This paper summarizes the results of multi-year studies performed by our research team, focusing on an analysis of protein recoding mediated by messenger RNA editing by ADAR adenosine deaminases. Searching for ADAR-mediated protein recoding was performed in the central nervous system of the model organisms, fruit fly and mouse, as well as in the human proteomic datasets. The proteogenomic approach has made it possible to identify dozens of editing events in the proteome, thus validating the results of transcriptomic studies. The observed recoding events in animals, ranging from insects to mammals, mainly affect the cytoskeletal components and proteins involved in synaptic transmission. In humans, recoding changes are most often observed in the central nervous system or tumor tissues. Over 15 million editing sites have been identified in humans; only a few thousand of those can potentially yield amino acid substitutions. Using a proteogenomic approach, dozens of protein recoding sites are identified, demonstrating their origin in ADAR RNA editing. Moreover, this revealed that the level of recoding at specific sites is not directly related to the abundance of ADAR enzymes per se or their target proteins. The recoding processes probably have differential regulation of interactions at the mRNA level that is yet to be clarified. Full article

Objectives: Mesothelin (MSLN) is overexpressed in pancreatic ductal adenocarcinoma (PDAC), promoting cell proliferation, migration, and inhibiting apoptosis. While its oncogenic properties have been documented, the role of MSLN in regulating cellular senescence—a tumor-suppressive mechanism—has remained unexplored. This study is the first to identify and characterize a novel mesothelin-associated anti-senescence (MAAS) effect in PDAC. Methods: A proteogenomic analysis of PDAC tissue samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) was performed to evaluate MSLN-associated senescence pathways using WebGestalt. Human and murine PDAC cell lines with modified MSLN expression were analyzed for senescence phenotypes via SA-β-gal staining, Western blotting of key regulators (P53, P21^waf1, and P16^ink4a), γH2AX immunoblotting, and IL-8 quantification using ELISA. Results: The CPTAC analysis revealed an inverse correlation between MSLN expression and DNA damage/repair pathways. MSLN-deficient cells exhibited classic senescence features—growth arrest, an enlarged morphology, and elevated SA-β-gal activity. The expression of P53, P21^waf1, and P16^ink4a was upregulated, alongside increased γH2AX levels, indicating the activation of the DNA damage response. IL-8 secretion was significantly higher in the MSLN knockdown cells and reduced in the MSLN-overexpressing cells, consistent with the modulation of the SASP. Notably, MSLN deficiency impaired cell viability without inducing overt cytotoxicity, supporting a shift toward senescence. Conclusions: Our findings uncover a previously unrecognized mechanism through which MSLN promotes tumor progression by suppressing senescence via P53-associated pathways. Targeting the MAAS pathway may offer a novel therapeutic strategy to restore tumor-suppressive senescence and enhance treatment efficacy in PDAC. Full article

Background: Melanoma is a highly heterogeneous disease, and a deeper molecular classification is essential for improving patient stratification and treatment approaches. Here, we describe the histopathology-driven proteogenomic landscape of 142 treatment-naïve metastatic melanoma samples to uncover molecular subtypes and clinically relevant biomarkers. Methods: We performed an integrative proteogenomic analysis to identify proteomic subtypes, assess the impact of BRAF V600 mutations, and study the molecular profiles and cellular composition of the tumor microenvironment. Clinical and histopathological data were used to support findings related to tissue morphology, disease progression, and patient outcomes. Results: Our analysis revealed five distinct proteomic subtypes that integrate immune and stromal microenvironment components and correlate with clinical and histopathological parameters. We demonstrated that BRAF V600-mutated melanomas exhibit biological heterogeneity, where an oncogene-induced senescence-like phenotype is associated with improved survival. This led to a proposed mortality risk-based stratification that may contribute to more personalized treatment strategies. Furthermore, tumor microenvironment composition strongly correlated with disease progression and patient outcomes, highlighting a histopathological connective tissue-to-tumor ratio assessment as a potential decision-making tool. We identified a melanoma-associated SAAV signature linked to extracellular matrix remodeling and SAAV-derived neoantigens as potential targets for anti-tumor immune responses. Conclusions: This study provides a comprehensive stratification of metastatic melanoma, integrating proteogenomic insights with histopathological features. The findings may aid in the development of tailored diagnostic and therapeutic strategies, improving patient management and outcomes. Full article

Objectives: Annexin-A1 is a 37 kDa phospholipid-binding protein which is concentrated in a truncated 34 kDa form (AnnA1) in caveolae on the tumor vascular endothelial cell surface with expression in many tumor types. PRISM developed the monoclonal mouse antibody mAnnA1 against AnnA1 for evaluation of AnnA1 as a potential target for imaging and therapy in oncology. mAnnA1 was humanized to make hAnnA1 for translation to clinical studies. Both PRISM-produced mAnnA1 and cGMP contractor-produced hAnnA1 were investigated using noninvasive PET/CT imaging, and dosimetry was evaluated to enable clinical translation of this strategy and to investigate in vivo behavior of hAnnA1. Methods: Antibodies mAnnA1 and hAnnA1 (PRISM “hAnnA1-P” or contractor generated “hAnnA1-C”) were conjugated with the chelator deferoxamine and evaluated for immunoreactivity with ELISA. Conjugated antibodies were radiolabeled with zirconium-89. Naïve mice, rats, and non-human primates (NHP) were injected with [⁸⁹Zr]mAnnA1 or [⁸⁹Zr]hAnnA1 and imaged with PET/CT up to 10 days post injection. After imaging, mice and rats were euthanized and organs were collected, weighed, and radioactivity was quantified using a gamma counter. Dosimetry in mice and NHPs were calculated using OLINDA. Results: [⁸⁹Zr]mAnnA1 showed similar biodistribution to other antibodies with slow clearance through the liver. Transition to [⁸⁹Zr]hAnnA1-C during the dosimetry studies revealed substantial uptake in the spleen (130 ± 48% ID/g at day 5 post injection in female BALB/c), which was not observed with [⁸⁹Zr]mAnnA1 (5.6 ± 1.7% ID/g at day 7 PI). Further studies in multiple strains of mice showed variable elevated splenic uptake of [⁸⁹Zr]hAnnA1-C across mouse strains, with the highest uptake observed in female BALB/c mice (118.4 ± 23.1% ID/g) and the lowest uptake observed in male CD1 mice (34.7 ± 10.2% ID/g). Additionally, splenic uptake of hAnnA1-C was observed in Fischer rats (2.8 ± 0.6% ID/organ) and NHPs (1.6 ± 0.6% ID/organ), although at lower levels than what was observed in BALB/c mice (8.8 ± 1.8% ID/organ). Dosimetry results showed similar values between estimates based on mouse and NHP data, with the largest difference seen in the spleen (5.2 vs. 2.6 mSv/MBq in females respectively). Sequencing of hAnnA1-C revealed a frameshift mutation in the antibody sequence introduced during cGMP manufacture. Restoration of the antibody sequence by PRISM returned the antibody distribution into alignment with mAnnA1. Conclusions: An aberration introduced during cGMP production of hAnnA1-C resulted in increased splenic uptake and alteration of the biodistribution in mice. PET imaging enabled quantitative detection of the immunogenic behavior of hAnnA1, which led to detection of the sequence error. Restoration of the sequence resulted in an antibody which was non-immunogenic to mice. Full article

Background/Objectives: A mouse antibody directed against truncated Annexin A1 showed high tumor retention in pre-clinical cancer models and was approved by the National Cancer Institute Experimental Therapeutics (NExT) program for humanization and large batch cGMP production for toxicology and clinical trials. In this process, a contractor for Leidos accidentally produced a mutated version of humanized AnnA1 (hAnnA1-mut) with a single nucleotide deletion in the terminal Fc coding region that increased the translated size by eight amino acids with random alterations in the final twenty-four amino acids. We investigated the tissue distribution of hAnnA1-mut, hAnnA1, mAnnA1, and isotope-matched human IgG1 under various injection and conjugation conditions with C57BL/6, FVB, and BALB/c nude mice strains. Methods: Biodistribution studies were performed 24 h after injection of Tc-99m-HYNIC radiolabeled antibodies (purity > 98%). Non-reducing gel electrophoresis studies were conducted with IR680 labeled antibodies incubated with various mouse sera. Results: Our results showed that Tc-99m-HYNIC-hAnnA1 had low spleen and liver retention not statistically different from Tc-99m-HYNIC-IgG1 and Tc-99m-HYNIC-mAnnA1, with corresponding higher blood levels; however, Tc-99m-HYNIC-hAnnA1-mut had high levels in the spleen and liver with differences identified among the mouse strains, radiolabeling conditions, and injection routes. Histopathology showed no morphological change in the liver or spleen from any conditions. Gel electrophoresis showed an upward shift of hAnnA1-mut, consistent with the binding of blood serum protein. Conclusions: The changes in the Fc region of hAnnA1-mut led to higher liver and spleen uptake, suggesting the antibody’s recognition by the innate immune system (likely complement protein binding) and subsequent clearance. Future clinical translation using hAnnA1 and other antibodies needs to limit protein modifications that could drastically reduce blood clearance. Full article

Nelumbo nucifera is an aquatic plant with a high ornamental value due to its flower. Despite the release of several versions of the lotus genome, its annotation remains inefficient, which makes it difficult to obtain a more comprehensive knowledge when –omic studies are applied to understand the different biological processes. Focusing on the petaloid of the lotus flower, we conducted a comparative proteomic analysis among five major floral organs. The proteogenomic strategy was applied to analyze the mass spectrometry data in order to dig out novel proteoforms that are involved in the petaloids of the lotus flower. The results revealed that a total of 4863 proteins corresponding to novel genes were identified, with 227 containing single amino acid variants (SAAVs), and 72 originating from alternative splicing (AS) genes. In addition, a range of post-translational modifications (PTMs) events were also identified in lotus. Through functional annotation and homology analysis with 24 closely related plant species, we identified five candidate proteins associated with floral organ development, which were not identified by ordinary proteomic analysis. This study not only provides new insights into understanding the mechanism of petaloids in lotus but is also helpful in identifying new proteoforms to improve the annotation of the lotus genome. Full article

Background/Objectives: Exosomes, nano-sized extracellular vesicles released by all cells, play a key role in intercellular communication and carry tumorigenic properties that impact surrounding or distant cells. The complexity of the exosomal molecular interactome and its effects on recipient cells still remain unclear. This study aims to decipher the molecular profile and interactome of lung adenocarcinoma A549 cell-derived exosomes using multi-omics and bioinformatics approaches. Methods: We performed comprehensive morphological and physicochemical characterization of exosomes isolated from cell culture supernatant of A549 cells in vitro, using DLS, cryo-TEM, Western blot, and flow cytometry. Proteomic and miRNA high-throughput profiling, coupled with bioinformatics network analysis, were applied to elucidate the exosome molecular cargo. A comparative miRNA analysis was also conducted with exosomes derived from normal lung fibroblast MRC-5 cells. Results: Exosomes exhibited an average size of ~40 nm and disk-shaped lipid bilayer structures, with tetraspanins CD9 and CD63 validated as exosomal markers. Proteomic analysis identified 68 proteins, primarily linked to the extracellular matrix organization and metabolic processes. miRNA sequencing revealed 72 miRNAs, notably hsa-miR-619-5p, hsa-miR-122-5p, hsa-miR-9901, hsa-miR-7704, and hsa-miR-151a-3p, which are involved in regulating metabolic processes, gene expression, and tumorigenic pathways. Th integration of proteomic and miRNA data through a proteogenomics approach identified dually affected genes including ERBB2, CD44, and APOE, impacted by both exosomal miRNA targeting and protein interactions through synergistic or antagonistic interactions. Differential analysis revealed a distinct miRNA profile in A549 exosomes, associated with cancer-related biological processes, compared to MRC-5 exosomes; notably, hsa-miR-619-5p emerged as a promising candidate for future clinical biomarker studies. The network analysis also revealed genes targeted by multiple upregulated tumor-associated miRNAs in potential exosome-recipient cells. Conclusions: This integrative study provides insights into the molecular interactome of lung adenocarcinoma A549 cell-derived exosomes, providing a foundation for future research on exosomal cargo and its role in tumor cell communication, growth, and progression. Full article

Follicular-patterned thyroid neoplasms comprise a diverse group of lesions that pose significant challenges in terms of differential diagnosis based solely on morphologic and genetic features. Thus, the identification of easily testable biomarkers complementing microscopic and genetic analyses is a highly anticipated advancement that could improve diagnostic accuracy, particularly for noninvasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTPs). These tumors exhibit considerable morphological and molecular heterogeneity, which may complicate their distinction from structurally similar neoplasms, especially when genetic analyses reveal shared genomic alterations (e.g., RAS mutations). Here, we integrated next-generation sequencing (NGS) with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to perform a proteogenomic analysis on 85 NIFTPs (n = 30 RAS-mutant [RAS-mut] and n = 55 RAS-wild type [RAS-wt]), with the aim to detect putative biomarkers of RAS-mut lesions. Through this combined approach, we identified four proteins that were significantly underexpressed in RAS-mut as compared to RAS-wt NIFTPs. These proteins could serve as readily accessible markers in morphologically borderline cases showing RAS mutations. Additionally, our findings may provide insights into the distinct pathogenic pathways through which RAS-mut and RAS-wt NIFTPs arise, highlighting the pivotal role of constitutive RAS–mitogen-activated protein kinase (MAPK) pathway activation in the development and progression of RAS-mut tumors. Full article

The mouse bleomycin model is useful in pre-clinical IPF research to understand pathophysiological mechanisms and pharmacological interventions. In the present study, we systematically investigated the effects of bleomycin at a 60-fold dose range on experimental features of lung fibrosis in the mouse bleomycin model. We analyzed the effect of intratracheal (i.t.) dosing of 0.05–3 U/kg bleomycin on disease phenotypes, including weight loss, morbidity and mortality, pulmonary inflammation, lung collagen content, various BALF biomarkers, and histology in a 14-day mouse model when the animals are in the active phase of fibrosis. In mice, challenge with 1–2 U/kg bleomycin doses induced significant and saturated responses on fibrotic endpoints, confirmed by collagen content, BALF biomarker levels, and marked weight loss compared to the normal control (NC). We observed 100% mortality in 3 U/kg of bleomycin-treated mice. In contrast, 0.05–0.5 U/kg bleomycin doses induced a dose-dependent fibrotic phenotype. The mice challenged with doses of 0.25–0.5 U/kg bleomycin showed optimum body weight loss, a significant increase in pulmonary inflammation, and the fibrotic phenotype compared to NC. Furthermore, we showed 0.25–0.5 U/kg bleomycin increases expression levels of (pro-) fibrotic cytokines, which are the mediators involved in the activation of myofibroblast during fibrogenesis (TGF-β1, IL-13, IL-6, WISP-1, VEGF), angiogenesis (VEGF), matrix remodeling (TIMP-1), and non-invasive lung function biomarker (CRP) compared to NC. A modified Ashcroft scale quantified that the fibrotic changes in the lungs were significantly higher in the lung of mice dosed at 0.25–0.5 U/kg > 0.1 U/kg bleomycin and non-significant in mice lung dosed at 0.05 U/kg bleomycin compared to NC. We demonstrated that the changes due to 0.25–0.5 U/kg i.t. bleomycin on protein biomarkers are enough to drive robust and detectable fibrotic pathology without mortality. The 0.1 U/kg has a moderate phenotype, and 0.05 U/kg had no detectable phenotype. The Goodness of Fit (r²) and Pearson correlation coefficient (r) analyses revealed a positive linear association between change evaluated in all experimental features of fibrosis and bleomycin concentrations (0.05–0.5 U/kg). Here, we provide an examination of a highly calibrated relationship between 60-fold bleomycin concentrations and a set of in vivo readouts that covers various facets of experimental fibrosis. Our study shows that there is a dose-dependent effect of bleomycin on the features of experimental fibrosis at <1 U/kg, whereas saturated responses are achieved at >1 U/kg. Our careful experimental observations, accuracy, and comprehensive data set provided meaningful insights into the effect of bleomycin dose(s) on the fibrotic phenotype, which is valuable in preclinical drug development and lung fibrosis research. In addition, we have presented a set of reproducible frameworks of endpoints that can be used for reliable assessment of the fibrotic phenotype, and in vivo therapeutic intervention(s) with improved accuracy. Full article

Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets. Full article

The complex biology of the microbiome was elucidated once the genomics era began. The proteogenomic approach analyzes and integrates genetic makeup (genomics) and microbial communities′ expressed proteins (proteomics). Therefore, researchers gained insights into gene expression, protein functions, and metabolic pathways, understanding microbial dynamics and behavior, interactions with host cells, and responses to environmental stimuli. In this context, our work aims to bring together data regarding the application of genomics, proteomics, and bioinformatics in microbiome research and to provide new perspectives for applying microbiota modulation in clinical practice with maximum efficiency. This review also synthesizes data from the literature, shedding light on the potential biomarkers and therapeutic targets for various diseases influenced by the microbiome. Full article

Furin is an important proteolytic enzyme, converting several proteins from inactive precursors to their active forms. Recently, proteo-genomic analyses in European and East Asian populations suggested a causal association of furin with ischaemic heart disease, and there is growing interest in its role in cardiovascular disease (CVD) aetiology. In this narrative review, we present a critical appraisal of evidence from population studies to assess furin’s role in CVD risk and potential as a drug target for CVD. Whilst most observational studies report positive associations between furin expression and CVD risk, some studies report opposing effects, which may reflect the complex biological roles of furin and its substrates. Genetic variation in FURIN is also associated with CVD and its risk factors. We found no evidence of current clinical development of furin as a drug target for CVD, although several phase 1 and 2 clinical trials of furin inhibitors as a type of cancer immunotherapy have been completed. The growing field of proteo-genomics in large-scale population studies may inform the future development of furin and other potential drug targets to improve the treatment and prevention of CVD. Full article

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization. Full article

Triticum aestivum is an important crop whose reference genome (International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.1) offers a valuable resource for understanding wheat genetic structure, improving agronomic traits, and developing new cultivars. A key aspect of gene model annotation is protein-level evidence of gene expression obtained from proteomics studies, followed up by proteogenomics to physically map proteins to the genome. In this research, we have retrieved the largest recent wheat proteomics datasets publicly available and applied the Basic Local Alignment Search Tool (tBLASTn) algorithm to map the 861,759 identified unique peptides against IWGSC RefSeq v2.1. Of the 92,719 hits, 83,015 unique peptides aligned along 33,612 High Confidence (HC) genes, thus validating 31.4% of all wheat HC gene models. Furthermore, 6685 unique peptides were mapped against 3702 Low Confidence (LC) gene models, and we argue that these gene models should be considered for HC status. The remaining 2934 orphan peptides can be used for novel gene discovery, as exemplified here on chromosome 4D. We demonstrated that tBLASTn could not map peptides exhibiting mid-sequence frame shift. We supply all our proteogenomics results, Galaxy workflow and Python code, as well as Browser Extensible Data (BED) files as a resource for the wheat community via the Apollo Jbrowse, and GitHub repositories. Our workflow could be applied to other proteomics datasets to expand this resource with proteins and peptides from biotically and abiotically stressed samples. This would help tease out wheat gene expression under various environmental conditions, both spatially and temporally. Full article

The identification of important proteins is critical for the medical diagnosis and prognosis of common diseases. Diverse sets of computational tools have been developed for omics data reduction and protein selection. However, standard statistical models with single-feature selection involve the multi-testing burden of low power with limited available samples. Furthermore, high correlations among proteins with high redundancy and moderate effects often lead to unstable selections and cause reproducibility issues. Ensemble feature selection in machine learning (ML) may identify a stable set of disease biomarkers that could improve the prediction performance of subsequent classification models and thereby simplify their interpretability. In this study, we developed a three-stage homogeneous ensemble feature selection (HEFS) approach for both identifying proteins and improving prediction accuracy. This approach was implemented and applied to ovarian cancer proteogenomics datasets comprising (1) binary putative homologous recombination deficiency (HRD)- positive or -negative samples; (2) multiple mRNA classes (differentiated, proliferative, immunoreactive, mesenchymal, and unknown samples). We conducted and compared various ML methods with HEFS including random forest (RF), support vector machine (SVM), and neural network (NN) for predicting both binary and multiple-class outcomes. The results indicated that the prediction accuracies varied for both binary and multiple-class classifications using various ML approaches with the proposed HEFS method. RF and NN provided better prediction accuracies than simple Naive Bayes or logistic models. For binary outcomes, with a sample size of 122 and nine selected prediction proteins using our proposed three-stage HEFS approach, the best ensemble ML (Treebag) achieved 83% accuracy, 85% sensitivity, and 81% specificity. For multiple (five)-class outcomes, the proposed HEFS-selected proteins combined with Principal Component Analysis (PCA) in NN resulted in prediction accuracies for multiple-class classifications ranging from 75% to 96% for each of the five classes. Despite the different prediction accuracies of the various models, HEFS identified consistent sets of proteins linked to the binary and multiple-class outcomes. Full article