Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to the high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent enrichment and depletion workflows using platelet-poor plasma (PPP), platelet-rich plasma (PRP), and serum (SER). Our results show that depletion workflows including Top14 depletion and acid precipitation allow quantification of very different proteomes than methods based on enrichments of extracellular vesicles such as bead-based enrichment or ultracentrifugation. Enrichment methods are superior in terms of proteome depth and quantitative performance but may be less robust in large cohorts. There is a very high correlation between PPP and PRP samples for all methods and less to SER samples, especially with enrichment workflows. The correlation of 10 protein measurements, performed by clinical routine processes on a Cobas system, showed heterogeneous results. Low-abundant proteins with biological dynamics within a healthy cohort, including C-reactive protein and lipoprotein(a), correlated very well to proteomics-based workflows, while others, including albumin and transferrin, correlated poorly. In conclusion, the workflow for plasma proteomics should be aligned with the aim of the analysis and setup of the sample collection.
This study aims to explore the anti-inflammatory mechanism of Anwulignan (AN) by integrating proteomics, molecular docking, and in vitro cell models. A lipopolysaccharide (LPS)-induced inflammatory model in RAW264.7 cells was established. The cells were divided into a control group, a model group (LPS treatment), and a drug treatment group (LPS + AN). Data-independent acquisition proteomics was employed to screen differentially expressed proteins using the thresholds of fold change greater than 1.2 or less than 0.8 and p < 0.05. Bioinformatics analysis and molecular docking were combined to identify core targets and regulatory pathways, which were subsequently validated by Western blot. A total of 129 potential anti-inflammatory targets and six core targets of AN were identified. KEGG enrichment analysis indicated that the anti-inflammatory effects of AN primarily involve protein processing in the endoplasmic reticulum, as well as the p53, FoxO, and RIG-I-like receptor signaling pathways. Molecular docking analysis revealed that AN exhibits strong binding affinities with these core targets. Western blot validation further confirmed that AN significantly downregulates the expression of pro-inflammatory proteins (CYCS, MAPK14, ATM, and EIF2AK2) and upregulates the expression of anti-inflammatory proteins (CDKN1A and RBX1) in RAW264.7 cells. AN exerts synergistic anti-inflammatory effects through a multi-target and multi-pathway manner, primarily by modulating core targets, along with their associated signaling pathways.
Pancreatic ductal adenocarcinoma (PDAC) is frequently preceded by new-onset diabetes mellitus (NODM), yet differentiating PDAC-associated DM from type 2 diabetes (T2D) remains clinically challenging. We investigated whether plasma proteomic profiling combined with machine learning could discriminate these conditions. Plasma samples from individuals with PDAC (with and without DM), long-standing T2D, and controls were analyzed by MALDI-TOF mass spectrometry. Spectral features were processed through a nested cross-validation framework to prevent data leakage, and model interpretability was explored using SHAP values. In parallel, low-molecular-weight proteins were characterized by GeLC-MS followed by LC-MS/MS and differential abundance analysis. Machine learning models distinguished PDAC-associated DM from T2D with a balanced accuracy of 85%. Proteomic analyses identified distinct signatures in PDAC- associated DM, including downregulation of erythrocyte-related proteins and PPBP, and upregulation of acute-phase reactants such as FGA, CP, and SERPINA3. Treatment-naïve cases displayed increased circulating epithelial and keratin-associated proteins, which were attenuated after therapy, suggesting dynamic tumor-related remodeling. These findings demonstrate that integrating MALDI-TOF profiling with machine learning can capture plasma signatures associated with PDAC-associated DM. Although exploratory, this approach supports further validation in prospective cohorts aimed at improving PDAC risk stratification among individuals with NODM. SIGNIFICANCE: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, primarily due to late-stage diagnosis. The frequent occurrence of new-onset diabetes mellitus (NODM) as a paraneoplastic syndrome offers a critical window for early detection. However, the clinical challenge of distinguishing PDAC-associated diabetes (PDAC-DM) from type 2 diabetes mellitus (T2D) has hindered the implementation of effective screening strategies. This study addresses this significant clinical problem by leveraging a multi-faceted proteomics approach. We demonstrate that the integration of MALDI-TOF mass spectrometry peptide profiling with machine learning algorithms can accurately discriminate PDAC-DM from T2D with 85% accuracy. Furthermore, we used LC-MS/MS to identify specific low molecular weight proteins that are differentially regulated between these conditions, providing a molecular basis for the observed discrimination. Our work is significant as it presents a novel, high-throughput pipeline for biomarker discovery that combines the scalability of MALDI-TOF with the analytical power of LC-MS/MS and machine learning. The identified plasma signatures hold strong translational potential to improve risk stratification in patients with new-onset diabetes, ultimately enabling earlier diagnosis of PDAC and improving patient survival prospects. This research directly contributes to the field of clinical proteomics by providing a robust methodological framework and candidate biomarkers for the early detection of one of oncology's most challenging diseases.
Super-resolution proximity labeling (SR-PL) advances spatial proteomics beyond conventional protein-level enrichment, enabling residue-resolved analysis of subcellular organization in living cells. Conventional proximity labeling relies on streptavidin-based capture and on-bead digestion, producing protein-centric readouts with limited structural insight. In contrast, SR-PL directly recovers biotinylated peptides and identifies labeled amino acid residues by LC-MS/MS. These site-specific labels serve as direct evidence of proximity, allowing for the precise mapping of protein surfaces, solvent accessibility and interaction interfaces. By linking spatial proximity to specific structural features, SR-PL enables mechanistic interpretation of spatial proteomic data and reframes proximity labeling as a structure-informed analytical framework. Recent advances in affinity capture strategies-including engineered probes, reversible affinity matrices, and optimized antibody reagents-have improved selective enrichment and gentle peptide release while reducing background contamination. Together, these developments position SR-PL for broad applications such as membrane topology mapping, organelle contact site analysis, and ligand-dependent interactions.
Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset remain incompletely understood. We aimed to define the shared and disease-specific immunometabolic architecture of major AREDs and to examine how circulating molecular features relate to retinal phenotypes, pre-diagnostic patterns, and disease risk. We performed a large-scale prospective multi-omics study in the UK Biobank integrating baseline plasma proteomics, metabolomics, retinal imaging-derived phenotypes, and longitudinal follow-up across five major AREDs: age-related macular degeneration, cataract, diabetic retinopathy, glaucoma, and retinal vascular occlusion. Cox regression, functional enrichment, protein-metabolite correlation, mediation analysis, trajectory analysis, and machine-learning models were applied. Proteome-wide analyses identified both shared and disease-specific circulating signatures, mainly involving immune, extracellular matrix, vascular, and stress-response pathways. Reconstructed population-level molecular patterns diverged from controls up to 15 years before diagnosis, with marked heterogeneity across diseases. Integration with retinal imaging linked immune- and matrix-related proteins to retinal neurodegenerative and microvascular phenotypes. Metabolite clustering and mediation analyses highlighted recurrent lipoprotein-related pathways, particularly HDL-related structure and composition, as cross-layer features associated with systemic protein signals, metabolic states, and disease risk. Combined proteomic-metabolomic models improved prediction of incident disease compared with protein-only models. Major AREDs share a systemic immunometabolic aging architecture while retaining substantial disease-specific molecular features. Circulating molecular alterations are detectable years before clinical onset and may support future biological stratification and risk prediction.
Recurrent aphthous stomatitis is a common inflammatory condition of the oral mucosa characterized by painful ulcers that heal spontaneously but recur unpredictably. Although lesions resolve clinically, the biological processes occurring during remission remain poorly understood. This exploratory study investigated whether salivary molecular profiles persist after clinical healing and may be associated with the recurrent nature of the disease. In this hypothesis-generating case-control study, saliva samples were collected from patients with recurrent aphthous stomatitis during ulcerative and remission stages and from healthy controls. The salivary pellet was analyzed using discovery-based mass spectrometry proteomics. Complement components and dipeptidyl peptidase-4 (DPP4) were evaluated using immunological assays. Microbial identification was performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantitative polymerase chain reaction. The functional interaction between bacteria and oral keratinocytes was explored in vitro using a high-dose infection model to probe epithelial responsiveness. Proteomic analysis identified stage-dependent molecular differences across the clinical course of recurrent aphthous stomatitis. Complement-related proteins and Mammaglobin-B (SCGB2A1) were found to be enriched during remission compared with healthy controls, suggesting persistent molecular signatures of epithelial and immune activity after clinical healing. Microbial profiling revealed enrichment of Streptococcus pneumoniae and Clostridium species in disease stages. In a controlled experimental setting, exposure of oral keratinocytes to Streptococcus pneumoniae was associated with increased DPP4 expression and activation of pathways linked to epithelial stress and immune signaling. Collectively, these exploratory findings suggest that remission is characterized by molecular features distinct from the healthy state. This exploratory proteomic study suggests that persistent molecular features associated with inflammation and epithelial stress can be detected in saliva after clinical healing in recurrent aphthous stomatitis. Complement-related protein enrichment, epithelial stress markers, and microbial associations were observed during remission, pointing to incomplete molecular recovery of the oral mucosa. While causality cannot be inferred, these hypothesis-generating findings offer a basis for future mechanistic studies and suggest that salivary molecular features warrant investigation as potential candidates for monitoring disease activity.
Chronic kidney disease (CKD) is strongly associated with atrial fibrillation. Understanding the biological pathways for this association and creating predictive models has been challenging. Left atrial enlargement is a substrate for atrial fibrillation but any overlap between biomarkers of atrial fibrillation and left atrial enlargement in individuals with CKD is unknown. We evaluated 4,590 plasma proteins with SomaScan in two cohorts of adults with CKD: the Chronic Renal Insufficiency Cohort (CRIC, n=2,654) and the Atherosclerosis Risk in Communities Cohort (ARIC, n=1,326). Using Mendelian randomization, we identified proteins along the causal pathway to atrial fibrillation. We also identified proteins and corresponding pathways associated with larger echocardiographic left atrial size, a recognized substrate for atrial fibrillation. Lastly, we developed and validated a multi-protein risk score for incident atrial fibrillation in the CKD population. Over five years, incident atrial fibrillation occurred among 150 individuals in CRIC and 140 in ARIC. We identified three proteins causally linked to incident atrial fibrillation: neural epidermal growth factor like (NEL)-like protein 1 (NELL1), cartilage intermediate layer protein 2 (CILP2), and matrix metallopeptidase 12 (MMP12). Pathway analysis revealed an overlap in 8 of the top 10 canonical pathways for incident atrial fibrillation and left atrial enlargement. A risk model for incident atrial fibrillation comprised of proteins had annualized AUCs over 5 years ranging from 0.65 to 0.76, a performance similar to the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE AF) clinical risk score. This study identified causal proteins and biological mechanisms underlying incident atrial fibrillation in CKD. A proteomic risk score for incident atrial fibrillation in CKD performed similarly to CHARGE-AF.
Diagnostic pathology has long relied on the morphological interpretation of hematoxylin and eosin (H&E)-stained tissues to guide diagnosis and assess prognostic features. While pathologists intuitively recognize spatial patterns and architectural organization, these assessments remain largely qualitative and difficult to quantify systematically. Immunohistochemistry and immunofluorescence have introduced molecular specificity but are limited in multiplexing capacity, whereas bulk genomic and transcriptomic assays provide high molecular depth but lose spatial context by averaging signals across heterogeneous cell populations. Recent advances in spatial proteomics-including mass spectrometry-based imaging and cyclic immunofluorescence-now enable multiplexed, single-cell protein analysis within intact tissue architecture. These technologies have revealed complex immune and stromal microenvironments, spatially organized biomarkers predictive of therapeutic response, and molecular gradients underlying disease progression. By integrating histological and molecular information, spatial proteomics bridges traditional microscopy with high-dimensional omics, allowing quantitative, spatially resolved insights into tissue organization and disease mechanisms. This review summarizes recent developments in multiplexed spatial proteomics from both scientific and pathological perspectives, highlighting how these technologies extend beyond morphology to quantify histologic patterns, refine biomarker discovery, and facilitate clinical translation. The review also examines translational challenges and barriers to clinical implementation, including costs, standardization requirements, and workflow integration.
The metabolic enzyme lactate dehydrogenase C4 (LDHC4) is aberrantly expressed in cancers and linked to poor prognosis. However, its role in lung adenocarcinoma (LUAD) and the molecular mechanisms beyond glycolysis remain unclear. This study investigates whether LDHC4 promotes LUAD by modulating protein lactylation, a lactate-derived post-translational modification, focusing on the tumor suppressor retinoblastoma protein (RB1). LDHC4 expression and its correlation with clinicopathological features and survival were analyzed using public databases (UALCAN, Kaplan-Meier Plotter, LOGpc) and validated in a cohort of 90 paired LUAD tissues via immunohistochemistry. The functional impact of LDHC4 on proliferation, migration, and invasion was assessed in A549 and PC-9 cells using gain- and loss-of-function models. The global lactylation profile was analyzed using DIA-based lactylation proteomics on the Astral platform. The interaction between RB1 and E2F1 (E2F transcription factor 1) was examined through molecular dynamics simulations, co-immunoprecipitation (Co-IP), and immunofluorescence. The functional consequences of site-specific RB1 lactylation at lysine 900 (RB1-K900lac) were determined using RB1-K900R mutant constructs and cell cycle analysis. LDHC4 was significantly overexpressed in LUAD tissues, correlating with poor patient survival, and was an independent prognostic risk factor. In vitro, LDHC4 promoted LUAD cell proliferation, migration, and invasion, and its tumor-promoting role was corroborated in an LUAD xenograft model, in which derived tumors exhibited increased volume and weight compared with mock-transfected controls. Mechanistically, LDHC4 overexpression elevated global protein lactylation levels and specifically increased lactylation of RB1. Bioinformatics and molecular dynamics simulations identified K900 as a key conserved residue for RB1-E2F1 binding; its lactylation destabilized the complex by increasing structural fluctuation and weakening intermolecular interactions. Cellular experiments confirmed that the lactylation-resistant RB1-K900R mutant bound E2F1 more strongly than wild-type RB1. Functionally, cells expressing RB1-K900R exhibited suppressed malignant phenotypes and G1/S cell cycle arrest, accompanied by downregulation of CDKs/cyclins and upregulation of P21. This study uncovers a novel LDHC4-driven oncogenic axis in LUAD. LDHC4 facilitates RB1 lactylation at the K900 residue, which disrupts the RB1-E2F1 tumor-suppressive complex, leading to cell cycle dysregulation and tumor progression. These findings may position the "LDHC4-RB1 lactylation" axis as a promising therapeutic target for LUAD.
While single-omics analyses of Parkinson's Disease (PD) have demonstrated their ability in revealing the underlying molecular mechanisms, they often fail to provide a comprehensive view of the complete disease mechanisms. In this study, we leveraged multi-omics data from 64 heterogeneous, well-phenotyped PD patients, generated plasma metabolomics data and Olink proteomics data together with the gut and saliva metagenomics data, and investigated the altered molecular mechanisms and their interactions in association with the severity of motor function disorders in PD patients. Based on our multi-omics approach, we identified a panel of 58 biomarkers comprising one clinical variable, 10 proteins, and 17 metabolites from plasma, 26 gut species, and 4 saliva species for PD severity. These biomarkers exhibited superior predictive performance for assessing PD severity compared to those derived from single-omics datasets. The predictive power of our machine learning models based on these biomarkers was validated using additional multi-omics data from the same group of PD patients after a 3-month follow-up. The contribution of each omics dataset was evaluated by both supervised and unsupervised machine learning approaches, highlighting the importance of plasma metabolomics in disease stratification. Our study unveiled disease-related molecular alterations across multiple omics datasets, offering potential diagnostic and therapeutic insights for PD. Moreover, it underpinned the significance of employing multi-omics analyses when studying complex diseases like PD.
Parabiotics (also termed paraprobiotics) are defined as non-viable microbial cells or their components, including peptidoglycans, teichoic acids, surface proteins, that confer health benefits without requiring viability which distinguishes them from traditional probiotics. Their non-viable nature eliminates risks such as microbial translocation, bacteremia, and sepsis, making them suitable for vulnerable populations including immunocompromised, critically ill, paediatric and elderly individuals. In addition, parabiotic exhibit improved thermal stability, extended shelf life, and easier incorporation into functional foods, nutraceuticals, and pharmaceutical formulations without cold-chain requirements. Mechanistically, parabiotics retain immunomodulatory, anti-inflammatory and have barrier-enhancing activities through interactions with host pattern recognition receptors, including Toll-like receptors, modulation of cytokine responses, and reinforcement of gut epithelial integrity. Preclinical and clinical studies support their therapeutic potential such as in case of heat-killed Lactobacillus acidophilus LB (L. acidophilus) has shown efficiency in managing acute paediatric diarrhoea, while heat-inactivated Lacticaseibacillus paracasei PS23 (Lcb. paracasei) has demonstrated improvements in muscle strength and inflammatory markers, including reduced C-reactive protein and interleukin-6 and increased interlukin-10 in elderly individuals. Similarly, inactivated Lactiplantibacillus plantarum (Lpb. plantarum) and Bifidobacterium strains have been associated with benefits in irritable bowel syndrome, atopic dermatitis, respiratory infections, visceral fat reduction, and antibiotic-associated dysbiosis. Synergistic combinations with prebiotics, postbiotics and related bioactives further enhance therapeutic outcomes in inflammatory, metabolic and infectious conditions. Advances in metagenomics, next-generation sequencing, proteomics, metabolomics, CRISPR-Cas systems, and synthetic biology are accelerating strain characterization, functional evaluation, and scalable production. Despite ongoing challenges in standardization and regulated harmonization, parabiotics represent a safe and effective approach for microbiome-targeted interventions. This review synthesizes current evidence on their therapeutic applications, technological advancements, and translational potential, highlighting their role in precision health and next-generation functional nutrition.
Alzheimer's disease (AD) is associated with dysregulation of membrane proteins controlling amyloid processing, synaptic signaling, and neuronal communication, yet most proteomic studies focus on soluble fractions, limiting insight into membrane-centered pathology. Here, we apply a membrane-mimetic, data-independent acquisition workflow to define disease- and drug-induced remodeling of the cortical membrane proteome in an APP mouse model of AD. Female wildtype B6C3F1/J and APP/PS1 mice were aged to 9 months, treated ± the M1 positive allosteric modulator VU0486846, and validated by enrichment of APP in cortical membranes of APP/PS1 mice. This confirmed pathological context enabled direct interrogation of membrane remodeling, revealing pronounced, genotype-specific changes characterized by selective enrichment of AD-linked membrane proteins including RyR2, PLD3, ITM2C, and CNTNAP2, alongside broader shifts in pathways related to calcium signaling, synaptic organization, and membrane trafficking. In contrast, wildtype membranes were enriched in proteins associated with axon guidance and synaptic structure, such as EPHA5 and ROBO2. M1 activation produced minimal changes in wildtype mice but selectively enhanced proteins linked to neuronal trafficking and synaptic plasticity in APP/PS1 mice, including SORCS2, PLXND1, and CADM1, indicating preferential engagement of disease-altered pathways. These findings demonstrate that AD-associated remodeling is concentrated at the membrane level and highlight Peptidisc-enabled membrane proteomics as a powerful approach to resolve disease mechanisms and therapeutic target engagement.
Post-translational modifications (PTMs) are crucial regulatory mechanisms that modulate the structure, function, and stability of proteins, playing an essential role in the regulation of cellular processes. Dysregulated PTMs are associated with various aspects of cancer development, including uncontrolled cell growth, evasion of apoptosis, metastasis, and drug resistance. This review offers a detailed examination of several major PTMs, including phosphorylation, acetylation, ubiquitination, SUMOylation, and methylation, discussing their distinct roles in cancer biology. It also provides an in-depth analysis of the latest advancements in the study of PTMs in cancer biology, focusing on the mechanisms by which these modifications contribute to tumorigenesis and their potential as therapeutic targets. It highlights the significant progress made in the identification of PTMs across different cancer types, emphasizing the role of PTMs in shaping cancer progression and immune modulation. Additionally, the paper discusses cutting-edge technologies, particularly mass spectrometry and computational proteomics, that have revolutionized the detection and characterization of PTMs. These advancements have enabled the identification of novel cancer biomarkers and therapeutic targets, offering new avenues for early detection, prognostic monitoring, and the development of targeted therapies in cancer treatment.
Multi-omics integration combines data from transcriptomics, proteomics, and metabolomics to provide insights into biological systems. Here, we present a protocol for integrating and interpreting multi-omics data using unsupervised multi-omics factor analysis (MOFA), supervised projection-based integration (data integration analysis for biomarker discovery using latent components, DIABLO), along with general single-omics analysis techniques and visualizations. We describe steps for data preparation, model construction, and biological interpretation of multi-omics datasets. These approaches identify coordinated molecular changes across biological layers and reveal regulatory mechanisms that drive biological processes. For complete details on the use and execution of this protocol, please refer to Anagho-Mattanovich et al.1.
The electrospray regime is a key determinant of ion formation during electrospray ionization (ESI), but it is often unreported and rarely verified in separation-coupled mass spectrometry (MS) workflows. In capillary electrophoresis (CE)-mass spectrometry (CE-ESI-MS), sheath-flow microflow ESI interfaces are widely used for metabolite analysis, whereas nanoflow ESI interfaces dominate peptide and protein measurements. Here, we benchmarked CE-ESI-MS performance in the cone-jet (CJ) and pulsating (P) regimes for trace-level metabolites, peptides, and proteome digests across microflow (μESI) and nanoflow (nanoESI) operation. In CE-μESI on a time-of-flight mass spectrometer, CJ increased metabolite sensitivity by up to ∼2-fold, improved signal stability, and shifted peptide ion generation to higher charge states relative to P. In CE-nanoESI proteomics on a timsTOF platform, CJ increased HeLa peptide and protein identifications by ∼50% and extended coverage to lower-abundance proteins while improving quantification completeness across technical replicates (i.e., a higher fraction of proteins quantified in all runs). For Xenopus laevis embryonic samples at single-cell-scale input, CJ significantly increased the number of detected metabolite features and improved recovery of functionally annotated protein groups. These results establish ESI regime as a controllable driver of sensitivity and quantitative performance in CE-ESI-MS and provide practical guidance for selecting ionization conditions for trace-limited analyses.
The efficacy of inhaled drugs is significantly influenced by airway epithelial transporters that mediate their transport and distribution. Because inhaled drugs are used across diverse populations with inflammatory airway diseases, understanding factors that modulate transporter expression is crucial. Therefore, we investigated how inflammatory states, demographic factors, and airway anatomic location impact drug transporter abundance. Using quantitative targeted absolute proteomics, we measured transporter abundance in primary human bronchial epithelia (HBE) from 42 demographically diverse donors. To model distinct inflammatory environments, HBE were exposed to interleukin (IL)-1β (T-helper type [TH]1/neutrophilic inflammation) or IL-13 (TH2/eosinophilic inflammation) for 4 days, alongside baseline controls. We also measured transporter protein concentrations in small airway HBE from 5 donors to assess regional influence. In the primary set of HBE, multidrug resistance protein (MRP)1 (3.96 ± 1.64 pmol/mg protein) and peptide transporter 2 (3.12 ± 0.95 pmol/mg protein) were the most abundant transporters, and their concentrations were responsive to inflammatory signals. Specifically, IL-1β significantly reduced MRP1 (0.80-fold) and peptide transporter 2 (0.65-fold), whereas IL-13 modestly increased MRP1 (1.1-fold) but reduced peptide transporter 2 (0.73-fold) and novel organic cation transporter (OCTN)1 (0.44-fold). Several transporters present at lower abundance, including MRP4, MRP5, MRP6, and OCTN1, were also impacted by inflammation. Demographic factors also played a role, with MRP4 higher in females, and age positively correlated with MRP6 but negatively with OCTN1. Analysis of small airway-derived HBE did not show a significant impact of anatomic location on transporter abundance. Our findings demonstrate that airway inflammation and donor demographics significantly impact the protein expression of key drug transporters, highlighting dynamic factors crucial for optimizing inhaled drug delivery and efficacy. SIGNIFICANCE STATEMENT: Human airway epithelia express multiple transporters, with multidrug resistance protein 1 and peptide transporter 2 present at the highest levels. Transporter abundance is influenced by inflammation, age, and biological sex, but not by airway anatomic location. This information can guide the modeling of inhaled drug pharmacokinetics.
Nucleobase-containing peptides (nucleopeptides) represent a unique class of biohybrid molecules that combine the structural versatility of peptides with the functional properties of nucleobases, enabling programmable self-assembly and selective molecular interactions. These features position nucleopeptides as promising tools for biomedical and supramolecular applications. Here, we report the synthesis and characterization of a novel nucleopeptide derived from a tryptophan dipeptide (WW) functionalized with a thymine (T) base, termed WWT. Circular Dichroism and UV-Vis spectroscopy revealed distinctive spectral features and supramolecular organization in solution, confirmed by Dynamic Light Scattering. Binding studies showed no detectable interaction with DNA or RNA, whereas measurable spectral perturbations indicated affinity for bovine serum albumin. Metal-binding experiments with Ni(II) and Cu(II) further highlighted the influence of these ions on the modulation of WWT's optical properties, while computational modelling using HDOCK complemented the experimental data by predicting aggregation modes and protein-binding interfaces. To explore biological relevance, we integrated controlled exposure of IMR-90 human fibroblasts and Jurkat T lymphocytes with proteomics and metabolomics, identifying time-dependent modulation of pathways linked to metabolism, RNA/DNA processing, and T-cell signaling. Functionally, WWT enhanced fibroblast migration without altering lysosomal, mitochondrial, or cytoskeletal organization. Overall, this study provides the first combined synthetic, spectroscopic, computational, and multi-omics evaluation of WWT properties and effects, revealing its organized supramolecular behaviour, selective biomolecular interactions, and potential pro-regenerative properties.
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are neurodegenerative diseases with overlapping pathology. Mutations in CCNF, encoding the E3 ubiquitin ligase, Cyclin F, can cause ALS, FTD, or both, even within the same family. Most prior studies of CCNFS621G have relied on overexpression systems, potentially confounding outcomes through disruption of endogenous Cyclin F. Here, we generated the first knock-in mouse model of endogenous CcnfS621G using CRISPR/Cas9. Heterozygous and homozygous CcnfS621G mice showed no motor decline or neuronal loss after 18 months, however immunohistochemistry revealed increased hippocampal astrocyte ramification, with sex-, age, and subfield-dependent effects. These data indicate that endogenous CcnfS621G may prime early astrocyte alterations in the absence of overt neurodegeneration. Similar astrocyte morphological changes were observed in canonically affected regions of sporadic ALS and FTD-ALS patients post mortem, as well as in CCNFS621G iPSC-derived astrocytes following inflammatory stimulation. Proteomics on Ccnf mice identified early dysregulation of pathways related to translation, mitochondrial function, cytoskeletal remodelling, synaptic transmission and neuroinflammation. Correspondingly, CCNFS621G iPSC-derived astrocytes displayed impaired mitochondrial membrane potential and altered network morphology under both basal and inflammatory stimuli. As altered neuronal excitability is a hallmark of ALS, we examined astrocyte-driven changes to neuronal excitability. CCNFS621G iPSC-derived motor neurons cultured alone were hyperexcitable, firing more action potentials than isogenic controls. Remarkably, co-culture with CCNFS621G astrocytes, but not isogenic control astrocytes, abolished repetitive firing, increased the proportion of neurons unable to generate action potentials, and reduced voltage-gated sodium currents in CCNFS621G and isogenic control neurons. Together, these findings identify astrocyte alterations as an early feature of CCNFS621G-mediated disease, in the absence of neuronal loss. Moreover, the combination of astrocytic mitochondrial dysfunction and the ability of CCNFS621G astrocytes to suppress repetitive neuronal firing suggests a critical astrocyte-driven non-cell autonomous mechanism that may contribute to an oligogenic role for CCNF in ALS/FTD pathogenesis.
Asparaginase is essential for curing acute lymphoblastic leukemia (ALL), but its use is limited by asparaginase-associated pancreatitis (AAP), a severe and unpredictable toxicity lacking validated prospective biomarkers. We sought to define early systemic molecular features of susceptibility to AAP. We performed longitudinal lipidomic and proteomic profiling in two independent pediatric ALL cohorts (n = 161; 79 AAP cases, 82 controls) using paired blood samples collected before asparaginase exposure and at the end of induction therapy (including a single dose of asparaginase), thereby capturing pre-injury biology rather than consequences of pancreatitis. We applied differential abundance and network-based analyses, and integrated lipid-cytokine associations using proteomics. Across cohorts, we identified a reproducible lysophosphatidylcholine (LPC)-centered signature characterized by attenuated induction therapy-associated LPC responses and disruption of LPC co-regulation at the network level. Proteomic profiling revealed enrichment of cytokine signaling pathways, and integrative analyses demonstrated altered lipid-cytokine coupling, including a flip in association direction for LPC species and interleukin-18 (IL-18) between cases and controls. Although IL-18/LPC ratios do not differ globally, elevated post-induction IL-18/LPC ratios identify AAP risk within a protocol-defined very high-risk ALL subgroup (AUC = 0.81). These findings support a systems-level model in which failure of coordinated lipid-immune responses under therapeutic stress confers vulnerability to AAP, providing a framework for validation and mitigation strategies. NCT00400946; NCT01574274; NCT03020030 (parent trials). Servier Pharmaceuticals (IIT-95014-027-USA); SDRC (P30DK116074); Stanford SPARK; Fonds de Recherche du Québec - Santé; Fondation Charles-Bruneau; The Leukemia & Lymphoma Society of Canada.
Isolates of the E. coli O101 serogroup collected from bacteremia patients were demonstrated to harbor different glycoforms of the O-antigen polysaccharide. Investigation by high-resolution mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy of the O-antigen structure of the reference E. coli O101 strain H510a and of one naturally occurring variant (Onovel32) confirmed the previously reported simultaneous presence of O-polysaccharide repeat units made up of disaccharides PS1 and PS2. A newly discovered O101 glycoform was shown to be composed of the same backbones modified with a previously unrecognized structure having an additional terminal 4-O-methyl N-acetyl-galactosamine (4-O-Me-GalNAc) residue on the non-reducing end of the PS2 backbone. Another naturally occurring variant (Onovel32MT-) does not contain any PS2 repeat units, nor any 4-O-Me-GalNAc capping. Analyses indicated that the polysaccharide glycoforms from these three O101 strains varied in terms of their ratios of PS1 to PS2 repeat units and the amount of 4-O-Me-GalNAc terminal residue. From the Smith degradation data of the reference O101 strain and the Onovel32MT- variant, it is hypothesized that the PS1 and PS2 repeat units join into a PS1-PS2 block copolymer, which is formed by the addition of the PS2 backbone on the already existing PS1 core backbone. Finally, 4-O-Me-GalNAc acts as a terminal capping residue on the PS2 backbone. Such block copolymer structures have hitherto been reported in galactans of Klebsiella and in Brucella. To our knowledge, this is the first reported occurrence of such a block copolymer O-antigen in E. coli.