Proteomic Landscape Research Articles

Large-scale deep proteomic analysis in Alzheimer's disease brain regions across race and ethnicity.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within non-Hispanic White (NHW) populations. Here we provide an extensive survey of the proteomic landscape of AD across diverse racial/ethnic groups. Two cortical regions, from multiple centers, were harmonized by uniform neuropathological diagnosis. Among 998 unique donors, 273 donors self-identified as African American, 229 as Latino American, and 434 as NHW. While amyloid precursor protein and the microtubule-associated protein tau demonstrated higher abundance in AD brains, no significant race-related differences were observed. Further proteome-wide and focused analyses (specific amyloid beta [Aβ] species and the tau domains) supported the absence of racial differences in these AD pathologies within the brain proteome. Our findings indicate that the racial differences in AD risk and clinical presentation are not underpinned by dramatically divergent patterns in the brain proteome, suggesting that other determinants account for these clinical disparities. We present a large-scale proteome (∼10,000 proteins) of DLPFC (998) and STG (244) across AD cases. About 50% of samples were from racially and ethnically diverse brain donors. Key AD proteins (amyloid and tau) correlated with CERAD and Braak stages. No significant race-related differences in amyloid and tau protein levels were observed in AD brains. AD-associated protein changes showed a strong correlation between the brain proteomes of African American and White individuals. This dataset advances understanding of ethnoracial-specific AD pathways and potential therapies.

EPCO-10. PROTEIN GENERATOR ENABLES PROTEOMICS-BASED SUBTYPING OF IDH MUTANT ASTROCYTOMA

Abstract The understanding of the proteomic landscape of IDH mutant astrocytoma has been limited in previous studies. To address this knowledge gap, we developed an innovative approach using artificial intelligence (AI) to generate proteomic profiles of tumors based on their transcriptome data. Particularly, we employed a combination of Variational Auto-Encoder and Contrastive Language Image Pretraining techniques to develop an AI model. The model was trained on publicly available databases containing matched RNA and protein data. Leveraging this extensive dataset, we applied the trained model to infer proteomic profiles for over one thousand adult IDH mutant astrocytoma specimens using their transcriptome sequencing data. This novel approach allowed us to explore and identify previously undiscovered subtypes based on the predicted proteomic profiles. As a result, we identified seven distinct clusters: IDHmut-noncodel-Proliferative, IDHmut-noncodel-Mesenchymal, IDHmut-noncodel-Mitochondrial, IDHmut-NFkB-signal, IDHmut-codel-Proliferative, IDHmut-Metabolism, and IDHmut-Neuronal. Of particular clinical significance, the IDHmut-noncodel-Proliferative and IDHmut-noncodel-Immune-Mesenchymal subtypes displayed the worst prognoses, while the IDHmut-Neuronal, IDHmut-NFkB-signal, and IDHmut-Metabolism subtypes exhibited relatively better survival outcomes. Remarkably, within the IDHmut-noncodel-Proliferative cluster, we observed a high prevalence of CDKN2A homozygous deletion, providing a potential explanation for its poor survival prognosis. Conversely, the IDHmut-noncodel-Mesenchymal cluster did not demonstrate any significant genetic events but exhibited elevated expression of mesenchymal marker genes. Furthermore, this cluster displayed notable characteristics, including the presence of gemistocytic differentiated tumor cells and infiltration of activated CD8 T lymphocytes. Moreover, we encompassed an evolutionary analysis of 189 initial-recurrent IDH mutant astrocytoma pairs, and discovered that gemistocytic differentiation represents an early event in tumor progression. In summary, we introduced a novel method utilizing an AI model to generate proteomic profiles, thus enabling the discovery of previously unknown subtypes of IDH mutant glioma with clinical relevance. These findings contribute to a deeper understanding of the molecular landscape of IDH mutant astrocytoma, potentially leading to improved prognostication and personalized treatment strategies.

Mass Spectrometry Advances in Analysis of Glioblastoma.

Some cancers such as glioblastoma (GBM), show minimal response to medical interventions, often only capable of mitigating tumor growth or alleviating symptoms. High metabolic activity in the tumor microenvironment marked by immune responses and hypoxia, is a crucial factor driving tumor progression. The many developments in mass spectrometry (MS) over the last decades have provided a pivotal tool for studying proteins, along with their posttranslational modifications. It is known that the proteomic landscape of GBM comprises a wide range of proteins involved in cell proliferation, survival, migration, and immune evasion. Combination of MS imaging and microscopy has potential to reveal the spatial and molecular characteristics of pathological tissue sections. Moreover, integration of MS in the surgical process in form of techniques such as DESI-MS or rapid evaporative ionization MS has been shown as an effective tool for rapid measurement of metabolite profiles, providing detailed information within seconds. In immunotherapy-related research, MS plays an indispensable role in detection and targeting of cancer antigens which serve as a base for antigen-specific therapies. In this review, we aim to provide detailed information on molecular profile in GBM and to discuss recent MS advances and their clinical benefits for targeting this aggressive disease.

The proteomic landscape of extracellular vesicles derived from human intervertebral disc cells.

Extracellular vesicles (EVs) function as biomarkers and are crucial in cell communication and regulation, with therapeutic potential for intervertebral disc (IVD)-related low back pain (LBP). EV cargo is often affected by tissue health, which may affect the therapeutic potential. There is currently limited knowledge of how the cargo of IVD cell-derived EVs varies with tissue health and how differences in proteomic profile affect the predicted biological functions. Our study purified EVs from human IVD cell conditioned media by size-exclusion chromatography. Nanoparticle tracking analysis was conducted to measure EV size and concentration. Transmission electron microscopy and Western blot were performed to examine EV structure and markers. Tandem mass tag-mass spectrometry was conducted to determine protein cargo. Most EVs were exosomes and intermediate microvesicles with an increasing amount linked to disease progression. Of the proteins detected, 88.6% were shared across the non-degenerate, mildly-degenerate, and degenerate samples. GO and KEGG analyses revealed that cargo from the mildly-degenerate samples was the most distinct, with the proteins in high abundance strongly associated with extracellular matrix (ECM) organization and structure. Shared proteins, highly expressed in the non-degenerate and degenerate samples, showed strong associations with cell adhesion, ECM-receptor interaction, and vesicle-mediated transport, respectively. Our findings indicate that EVs from IVD cells from tissue with different degrees of degeneration share a majority of the cargo proteins. However, the level of expression differs with degeneration grade. Cargo from the mildly-degenerate samples exhibits the most differences. A better understanding of changes in EV cargo in the degenerative process may provide novel information related to molecular mechanisms underlying IVD degeneration and suggest new potential treatment modalities for IVD-related LBP.

Fecal microbiota transplantation alters the proteomic landscape of inflammation in HIV: identifying bacterial drivers

BackgroundDespite effective antiretroviral therapy, people with HIV (PWH) experience persistent systemic inflammation and increased morbidity and mortality. Modulating the gut microbiome through fecal microbiota transplantation (FMT) represents a novel therapeutic strategy. We aimed to evaluate proteomic changes in inflammatory pathways following repeated, low-dose FMT versus placebo.MethodsThis double-masked, placebo-controlled pilot study assessed the proteomic impacts of weekly FMT versus placebo treatment over 8 weeks on systemic inflammation in 29 PWH receiving stable antiretroviral therapy (ART). Three stool donors with high Faecalibacterium and butyrate profiles were selected, and their individual stools were used for FMT capsule preparation. Proteomic changes in 345 inflammatory proteins in plasma were quantified using the proximity extension assay, with samples collected at baseline and at weeks 1, 8, and 24. Concurrently, we characterized shifts in the gut microbiota composition and annotated functions through shotgun metagenomics. We fitted generalized additive models to evaluate the dynamics of protein expression. We selected the most relevant proteins to explore their correlations with microbiome composition and functionality over time using linear mixed models.ResultsFMT significantly reduced the plasma levels of 45 inflammatory proteins, including established mortality predictors such as IL6 and TNF-α. We found notable reductions persisting up to 16 weeks after the final FMT procedure, including in the expression of proteins such as CCL20 and CD22. We identified changes in 46 proteins, including decreases in FT3LG, IL6, IL10RB, IL12B, and IL17A, which correlated with multiple bacterial species. We found that specific bacterial species within the Ruminococcaceae, Succinivibrionaceae, Prevotellaceae families, and the Clostridium genus, in addition to their associated genes and functions, were significantly correlated with changes in inflammatory markers.ConclusionsTargeting the gut microbiome through FMT effectively decreased inflammatory proteins in PWH, with sustained effects. These findings suggest the potential of the microbiome as a therapeutic target to mitigate inflammation-related complications in this population, encouraging further research and development of microbiome-based interventions.3bGeWVUV_8zBqrmtjgQWmWVideo

Mannose-6-Phosphate Isomerase Functional Status Shapes a Rearrangement in the Proteome and Degradome of Mannose-Treated Melanoma Cells.

Metabolic reprogramming is a ubiquitous feature of transformed cells, comprising one of the hallmarks of cancer and enabling neoplastic cells to adapt to new environments. Accumulated evidence reports on the failure of some neoplastic cells to convert mannose-6-phosphate into fructose-6-phosphate, thereby impairing tumor growth in cells displaying low levels of mannose-6-phosphate isomerase (MPI). Thus, we performed functional analyses and profiled the proteome landscape and the repertoire of substrates of proteases (degradome) of melanoma cell lines with distinct mutational backgrounds submitted to treatment with mannose. Our results suggest a significant rearrangement in the proteome and degradome of melanoma cell lines upon mannose treatment including the activation of catabolic pathways (such as protein turnover) and differences in protein N-terminal acetylation. Even though MPI protein abundance and gene expression status are not prognostic markers, perturbation in the network caused by an exogenous monosaccharide source (i.e., mannose) significantly affected the downstream interconnected biological circuitry. Therefore, as reported in this study, the proteomic/degradomic mapping of mannose downstream effects due to the metabolic rewiring caused by the functional status of the MPI enzyme could lead to the identification of specific molecular players from affected signaling circuits in melanoma.

The Altered Proteomic Landscape in Renal Tubular Epithelial Cells under High Oxalate Stimulation.

Our study aimed to apply a proteomic approach to investigate the molecular mechanisms underlying the effects of oxalate on rat renal tubular epithelial cells. NRK-52E cells were treated with or without oxalate and subjected to quantitative proteomics to identify key proteins and key pathological changes under high oxalate stimulation. A total of 268 differentially expressed proteins (DEPs) between oxalate-treated and control groups were identified, with 132 up-regulated and 136 down-regulated proteins. Functional enrichment analysis revealed that DEPs are associated with oxidative stress, apoptosis, ferroptosis, pro-inflammatory cytokines, vitamin D, and biomineralization. SPP1, MFGE8, ANKS1A, and NAP1L1 were up-regulated in the oxalate-treated cells and the hyperoxaluric stone-forming rats, while SUB1, RNPS1, and DGLUCY were down-regulated in both cases. This altered proteomic landscape sheds light on the pathological processes involved in oxalate-induced renal damage and identifies potential biomarkers and therapeutic targets to mitigate the effects of hyperoxaluria and reduce the risk of CaOx stone formation.

DiDBiT-TMT: A Novel Method to Quantify Changes in the Proteomic Landscape Induced by Neural Plasticity.

Direct detection of biotinylated proteins (DiDBiT) is a proteomic method that can enrich and detect newly synthesized proteins (NSPs) labeled with bio-orthogonal amino acids with 20-fold improved detectability compared to conventional methods. However, DiDBiT has currently been used to compare only two conditions per experiment. Here, we present DiDBiT-TMT, a method that can be used to quantify NSPs across many conditions and replicates in the same experiment by combining isobaric tandem mass tagging (TMT) with DiDBiT. We applied DiDBiT-TMT to brain slices to determine changes in the de novo proteome that occur after inducing chemical long-term potentiation (cLTP) or treatment with the neuromodulator norepinephrine. We successfully demonstrated DiDBiT-TMT's capacity to quantitatively compare up to 9 samples in parallel. We showed that there is a minimal overlap among NSPs that are differentially expressed in cLTP-treated organotypic brain slices, norepinephrine-treated organotypic brain slices, and organotypic slices undergoing combinatorial treatment with norepinephrine and cLTP. Our results point to the possible divergence of the molecular mechanisms underlying these treatments and showcase the applicability of DiDBiT-TMT for studying neurobiology.

Mapping the proteomic landscape and anti-inflammatory role of Streptococcus parauberis extracellular vesicles

Bacterial extracellular vesicles (BEVs) are nanoscale membrane-bound structures involved in intercellular communication and transport of bioactive molecules. In this study, we described the proteomic insight and anti-inflammatory activity of Streptococcus parauberis BEVs (SpEVs). Proteomics analysis of SpEVs identified 6209 distinct peptides and 1039 proteins. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated enrichment in pathways related to the biosynthesis of aminoacyl tRNA, amino acids, and secondary metabolites. Based on the predicted protein-protein interactions, we discovered key immunological proteins such as IL12A, IL12B, IL8, CD28, and NF-κB between SpEVs and human proteins. Functionally, SpEVs exhibit strong anti-inflammatory activity in LPS-stimulated Raw 264.7 cells by reducing the production of key inflammatory mediators. These include nitric oxide (NO), reactive oxygen species (ROS), inflammatory cytokines such as TNFα and IL6, as well as inflammation-related proteins like inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2). qRT-PCR and immunoblotting results clearly indicate that SpEVs modulate the NF-κB and MAPK pathways to induce anti-inflammatory activity. Furthermore, in vivo experiments with zebrafish larvae demonstrated that SpEVs treatment reduced the NO and ROS production with minimal cell mortality. Finally, we validated the anti-inflammatory activity of SpEVs in vivo by systematically assessing the inhibition of NO production, reduction in ROS generation, prevention of cell death, and modulation of NF-κB and MAPK signaling pathways. In conclusion, SpEVs contain rich in unique proteins that play crucial roles in mediating anti-inflammatory effects.

Proteomic basis for pancreatic acinar cell carcinoma and pancreatoblastoma as similar yet distinct entities

Acinar cell carcinoma (ACC) and pancreatoblastoma (PBL) are rare pancreatic malignancies with acinar differentiation. Proteogenomic profiling of ACC and PBL revealed distinct protein expression patterns compared to pancreatic ductal adenocarcinoma (PDAC) and benign pancreas. ACC and PBL exhibited similarities, with enrichment in proteins related to RNA processing, chromosome organization, and the mitoribosome, while PDACs overexpressed proteins associated with actin-based processes, extracellular matrix, and immune-active stroma. Pathway activity differences in metabolic adaptation, epithelial-to-mesenchymal transition, and DNA repair were characterized between these diseases. PBL showed upregulation of Wnt-CTNNB1 and IGF2 pathways. Seventeen ACC-specific proteins suggested connections to metabolic diseases with mitochondrial dysfunction, while 34 PBL-specific proteins marked this pediatric cancer with an embryonic stem cell phenotype and alterations in chromosomal proteins and the cell cycle. This study provides novel insights into the proteomic landscapes of ACC and PBL, offering potential targets for diagnostic and therapeutic development.

Tumor-on-chip platforms for breast cancer continuum concept modeling.

Our previous article entitled "Proteomics and its applications in breast cancer", proposed a Breast Cancer Continuum Concept (BCCC), including a Breast Cancer Cell Continuum Concept as well as a Breast Cancer Proteomic Continuum Concept. Breast cancer-on-chip (BCoC), breast cancer liquid biopsy-on-chip (BCLBoC), and breast cancer metastasis-on-chip (BCMoC) models successfully recapitulate and reproduce in vitro the principal mechanisms and events involved in BCCC. Thus, BCoC, BCLBoC, and BCMoC platforms allow for multiple cell lines co-cultivation to reproduce BC hallmark features, recapitulating cell proliferation, cell-to-cell communication, BC cell-stromal crosstalk and stromal activation, effects of local microenvironmental conditions on BC progression, invasion/epithelial-mesenchymal transition (EMT)/migration, intravasation, dissemination through blood and lymphatic circulation, extravasation, distant tissues colonization, and immune escape of cancer cells. Moreover, tumor-on-chip platforms are used for studying the efficacy and toxicity of chemotherapeutic drugs/nano-drugs or nutraceuticals. Therefore, the aim of this review is to summarize and analyse the main bio-medical roles of on-chip platforms that can be used as powerful tools to study the metastatic cascade in BC. As future direction, integration of tumor-on-chip platforms and proteomics-based specific approaches can offer important cues about molecular profile of the metastatic cascade, alowing for novel biomarker discovery. Novel microfluidics-based platforms integrating specific proteomic landscape of human milk, urine, and saliva could be useful for early and non-invasive BC detection. Also, risk-on-chip models may improve BC risk assessment and prevention based on the identification of biomarkers of risk. Moreover, multi-organ-on-chip systems integrating patient-derived BC cells and patient-derived scaffolds have a great potential to study BC at integrative level, due to the systemic nature of BC, for personalized and precision medicine. We also emphasized the strengths and weaknesses of BCoC and BCMoC platforms.

Comprehensive Proteomic Analysis of Dysferlinopathy Unveiling Molecular Mechanisms and Biomarkers Linked to Pathological Progression.

Previous proteomics studies in dysferlinopathy muscle have been limited in scope, often utilizing 2D-electrophoresis and yielding only a small number of differential expression calls. To address this gap, this study aimed to employ high-resolution proteomics to explore the proteomic landscapes of dysferlinopathy and analyze the correlation between muscle pathological changes and alterations in protein expression in muscle biopsies. We conducted a comprehensive approach to investigate the proteomic profile and disease-associated changes in the muscle tissue proteome from 15 patients with dysferlinopathy, exhibiting varying degrees of dystrophic pathology, alongside age-matched controls. Our methodology encompasses tandem mass tag (TMT)-labeled liquid chromatography-mass spectrometry (LC-MS/MS)-based proteomics, protein-protein interaction (PPI) network analysis, weighted gene co-expression network analysis, and differential expression analysis. Subsequently, we examined the correlation between the expression of key proteins and the clinical characteristics of the patients to identify pathogenic targets associated with DYSF mutations in dysferlinopathy. A total of 1600 differentially expressed proteins were identified, with 1321 showing high expression levels and 279 expressed at lower levels. Our investigation yields a molecular profile delineating the altered protein networks in dysferlinopathy-afflicted skeletal muscle, uncovering dysregulation across numerous cellular pathways and molecular processes, including mRNA metabolic processes, regulated exocytosis, immune response, muscle system processes, energy metabolic processes, and calcium transmembrane transport. Moreover, we observe significant associations between the protein expression of ANXA1, ANXA2, ANXA4, ANXA5, LMNA, PYGM, and the extent of histopathologic changes in muscle biopsies from patients with dysferlinopathy, validated through immunoblotting and immunofluorescence assays. Through the aggregation of expression data from dysferlinopathy-impacted muscles exhibiting a range of pathological alterations, we identified multiple key proteins associated with the dystrophic pathology of patients with dysferlinopathy. These findings provide novel insights into the pathogenesis of dysferlinopathy and propose promising targets for future therapeutic endeavors.

Profiling Cullin4-E3 Ligases Interactomes and Their Rewiring in Influenza a Virus Infection

Understanding the integrated regulation of cellular processes during viral infection is crucial for developing host-targeted approaches. We have previously reported that an optimal in vitro infection by influenza A (IAV) requires three components of Cullin 4-RING E3 ubiquitin ligases (CRL4) complexes, namely the DDB1 adaptor and two Substrate Recognition Factors (SRF), DCAF11 and DCAF12L1, which mediate non-degradative poly-ubiquitination of the PB2 subunit of the viral polymerase. However, the impact of IAV infection on the CRL4 interactome remains elusive. Here, using Affinity Purification coupled with Mass Spectrometry (AP-MS) approaches, we identified cellular proteins interacting with these CRL4 components in IAV-infected and non-infected contexts. IAV infection induces significant modulations in protein interactions, resulting in a global loss of DDB1 and DCAF11 interactions, and an increase in DCAF12L1-associated proteins. The distinct rewiring of CRL4's associations upon infection impacted cellular proteins involved in protein folding, ubiquitination, translation, splicing, and stress responses. Using a split-nanoluciferase-based assay, we identified direct partners of CRL4 components and via siRNA-mediated silencing validated their role in IAV infection, representing potential substrates or regulators of CRL4 complexes. Our findings unravel the dynamic remodeling of the proteomic landscape of CRL4's E3 ubiquitin ligases during IAV infection, likely involved in shaping a cellular environment conducive to viral replication and offer potential for the exploration of future host-targeted antiviral therapeutic strategies.

Advanced Techniques In Oral Oncology: AComprehensiveReview

The most common type of oral cancer, oral squamous cell carcinoma (OSCC), is still amajor global health concern. Due to delayed diagnoses, which are frequently caused by low public awareness and fewscreeningalternatives, the five-year survival rate is still low. Modernmethods in oral oncology have arisen to enhance patient outcomes and early identification, including as molecular and imaging diagnostics. This review showcases several cutting-edge diagnostic techniques, including flow cytometry, next-generation sequencing(NGS), chemiluminescence, toluidine blue, immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR), and nano-diagnostics. By providing insights into tumor behaviourand enabling customized treatment approaches, these techniques advance our knowledge of the genetic and proteomic landscape of oral cancer. Combining these cutting-edge approaches with conventional diagnosticsmight greatly improve the accuracy of OSCC detection and risk assessment, despite drawbacks like false positives and expensive procedures. Improving patient care greatly depends on the creation of quick, noninvasive procedures and the investigation of genetic biomarkers. The present review highlightsthe need for continued research and training in oral oncology to maximize early diagnosis and intervention strategies

Transcriptomic and proteomic spatial profiling of pediatric and adult diffuse midline glioma H3 K27-Altered.

Diffuse midline glioma, H3 K27-altered (DMG) are highly aggressive malignancies of the central nervous system (CNS) that primarily affect the pediatric population. Large scale spatial transcriptomic studies have implicated that tumor microenvironmental landscape plays an important role in determining the phenotypic differences in tumor presentation and clinical course, however, data connecting overall transcriptomic changes to the protein level is lacking. The NanoString GeoMx™Digital Spatial Profiler platform was used to determine the spatial transcriptomic and proteomic landscape in a cohort of both pediatric and adult H3 K27-altered DMG biopsy samples. Three fluorescently labeled antibodies targeting immune cells (CD45), epithelial cells (PanCK), tumor cells (H3 K27M) and a nucleic acid stain (SYTO-13) were used to establish regions of interest (ROI) for genomic and proteomic analysis. We found genetic alterations within the tumor which can be delineated across patient age and spatial location. We show that the H3 K27M mutation itself has a profound impact on tumor cells transcriptomics and interestingly we found limited fidelity between overall transcriptome and proteome. Our data also validate a previously described genomic signature at the proteomic level and reveal a special shift in the signature based on the local TME composition.

A Proteomic Analysis of Nasopharyngeal Carcinoma in a Moroccan Subpopulation.

Nasopharyngeal carcinoma (NPC) is a distinct cancer of the head and neck that is highly prevalent in Southeast Asia and North Africa. Though an extensive analysis of environmental and genetic contributors has been performed, very little is known about the proteome of this disease. A proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissues can provide valuable information on protein expression and molecular patterns for both increasing our understanding of the disease and for biomarker discovery. To date, very few NPC proteomic studies have been performed, and none focused on patients from Morocco and North Africa. Label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used to perform a proteomic analysis of FFPE tissue samples from a cohort of 41 NPC tumor samples of Morocco and North Africa origins. The LC-MS/MS data from this cohort were analyzed alongside 21 healthy controls using MaxQuant 2.4.2.0. A differential expression analysis was performed using the MSstats package in R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotations were carried out using the DAVID bioinformatic tool. 3341 proteins were identified across our NPC cases, revealing three main clusters and five DEPs with prognostic significance. The sex disparity of NPC was investigated from a proteomic perspective in which 59 DEPs were found between males and females, with significantly enriched terms associated with the immune response and gene expression. Furthermore, 26 DEPs were observed between patients with early and advanced stages of NPC with a significant cluster related to the immune response, implicating up-regulated DEPs such as IGHA, IGKC, and VAT1. Across both datasets, 6532 proteins were quantified between NPC patients and healthy controls. Among them, 1507 differentially expressed proteins (DEPs) were observed. GO and KEGG pathway analyses showed enriched terms of DEPs related to increased cellular activity, cell proliferation, and survival. PI3K and MAPK proteins as well as RAC1 BCL2 and PPIA were found to be overexpressed between cancer tissues and healthy controls. EBV infection was also one of the enriched pathways implicating its latent genes like LMP1 and LMP2 that activate several proteins and signaling pathways including NF-Kappa B, MAPK, and JAK-STAT pathways. Our findings unveil the proteomic landscape of NPC for the first time in the Moroccan population. These studies additionally may provide a foundation for identifying potential biomarkers. Further research is still needed to help develop tools for the early diagnosis and treatment of NPC in Moroccan and North African populations.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

An interpretable deep learning framework identifies proteomic drivers of Alzheimer's disease.

Alzheimer's disease (AD) is the leading neurodegenerative pathology in aged individuals, but many questions remain on its pathogenesis, and a cure is still not available. Recent research efforts have generated measurements of multiple omics in individuals that were healthy or diagnosed with AD. Although machine learning approaches are well-suited to handle the complexity of omics data, the models typically lack interpretability. Additionally, while the genetic landscape of AD is somewhat more established, the proteomic landscape of the diseased brain is less well-understood. Here, we establish a deep learning method that takes advantage of an ensemble of autoencoders (AEs) - EnsembleOmicsAE-to reduce the complexity of proteomics data into a reduced space containing a small number of latent features. We combine brain proteomic data from 559 individuals across three AD cohorts and demonstrate that the ensemble autoencoder models generate stable latent features which are well-suited for downstream biological interpretation. We present an algorithm to calculate feature importance scores based on the iterative scrambling of individual input features (i.e., proteins) and show that the algorithm identifies signaling modules (AE signaling modules) that are significantly enriched in protein-protein interactions. The molecular drivers of AD identified within the AE signaling modules derived with EnsembleOmicsAE were missed by linear methods, including integrin signaling and cell adhesion. Finally, we characterize the relationship between the AE signaling modules and the age of death of the patients and identify a differential regulation of vimentin and MAPK signaling in younger compared with older AD patients.

Unraveling the proteomic landscape of fibrosis in lupus nephritis through CI-based analysis.

The underlying mechanism by which lupus nephritis (LN) progresses to chronic kidney disease remains elusive. Fibrosis is a hallmark feature of chronic kidney disease, including LN. The chronicity index (CI) score, which incorporates glomerular sclerosis, fibrous crescents, tubular atrophy, and interstitial fibrosis, summarizes the extent of kidney tissue fibrosis. In this study, we employed label-free quantitative proteomics based on mass spectrometry to generate kidney protein profiles with varying CI scores. A total of 98 proteins exhibiting linear correlation with CI scores were initially screened out by linear model (CI linearly related proteins), and subsequently, 12 key proteins were derived based on the CI linearly related proteins using Cytohubba. LN patients were stratified into two subtypes based on CI scores and epithelial-mesenchymal transition (EMT) characteristics. These subtypes exhibited significant disparities in immune infiltration and molecular pathways. The high EMT group exhibited heightened activation of immune cells, such as memory B cells, gamma delta T cells, and resting mast cells. Gene Set Enrichment Analysis (GSEA) uncovered substantial dysregulation in critical biological processes and signaling pathways, including NF-κB, JNK, PI3K/AKT/mTOR signaling pathway, lipoprotein biosynthetic process, and endocytosis, in both subgroups. In conclusion, this study establishes molecular subgroups based on the CI score, providing novel insights into the molecular mechanisms governing chronicity in the kidneys of diverse LN patients. Key Points • Fibrosis is a fundamental and characteristic pathological process underlying the NIH-CI in LN. • Different EMT status presented variant clinical characteristics, immune features in LN.

Proteomic landscape of tunneling nanotubes reveals CD9 and CD81 tetraspanins as key regulators.

Tunneling nanotubes (TNTs) are open actin- and membrane-based channels, connecting remote cells and allowing direct transfer of cellular material (e.g. vesicles, mRNAs, protein aggregates) from the cytoplasm to the cytoplasm. Although they are important especially, in pathological conditions (e.g. cancers, neurodegenerative diseases), their precise composition and their regulation were still poorly described. Here, using a biochemical approach allowing to separate TNTs from cell bodies and from extracellular vesicles and particles (EVPs), we obtained the full composition of TNTs compared to EVPs. We then focused on two major components of our proteomic data, the CD9 and CD81 tetraspanins, and further investigated their specific roles in TNT formation and function. We show that these two tetraspanins have distinct non-redundant functions: CD9 participates in stabilizing TNTs, whereas CD81 expression is required to allow the functional transfer of vesicles in the newly formed TNTs, possibly by regulating docking to or fusion with the opposing cell.