Proteomic Signatures Research Articles

Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models.

Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical-biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics.

A novel proteomic prognostic signature characterizes the immune landscape and predicts nasopharyngeal carcinoma prognosis

BackgroundNasopharyngeal carcinoma (NPC) is a highly diverse and aggressive cancer type, leading to varying prognoses and responses to immunotherapy. This study aims to develop a protein-based signature that provides new insights into assessing the prognosis and immunotherapeutic response in NPC patients. Methods and ResultsWe obtained transcriptomic and proteomic data for NPC from TCGA and CPTAC databases, respectively. Differentially expressed proteins with prognostic significance were identified using limma combined with uniCox analysis. A prognostic protein signature was created utilizing the LASSO algorithm. Receiver operating characteristic (ROC) curve analysis along with Kaplan-Meier survival analysis was conducted to assess the predictive accuracy of this signature. To evaluate immune infiltration levels among patients categorized by high or low risk scores (RPscores), we employed ssGSEA and ESTIMATE methods, while TIDE was used to forecast responses to immunotherapy. Our research pinpointed four critical prognostic proteins: CdSTA, AGR3, DUSP14, and LRRC17, allowing us to compute risk scores (RPscores). Kaplan-Meier curves demonstrated that individuals in the low-risk category exhibited better survival rates. Furthermore, RPscore effectively predicted overall survival across both training and testing cohorts. The ssGSEA results indicated that RPscore is linked with an immune-suppressive microenvironment correlating with diminished immune responses. Notably, DUSP14 showed significant upregulation in NPC cases; its role in promoting cell invasion and metastasis was confirmed through in vitro studies. ConclusionWe have established a robust protein-related signature capable of accurately forecasting prognosis as well as immunotherapy outcomes for NPC patients. Moreover, DUSP14 emerged as a potential therapeutic target due to its strong association with patient prognosis in nasopharyngeal carcinoma.

Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients.

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19-positive patients (n = 400 for proteomics, n = 483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001) were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing a correlation with hospitalisation status. Conclusions: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19.

Urinary proteomic signature of mineralocorticoid receptor antagonism by spironolactone: evidence from the HOMAGE trial

ObjectiveHeart failure (HF) is characterised by collagen deposition. Urinary proteomic profiling (UPP) followed by peptide sequencing identifies parental proteins, for over 70% derived from collagens. This study aimed to refine...

Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort.

The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits. Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO2max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R2) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM. Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R2 of truncal fat and VO2max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM. Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

Abstract B090: Evaluating the molecular determinants of PDAC racial health disparities

Abstract Pancreatic cancer is the 3rd leading cause of cancer-related deaths, with a 5-year survival rate of just 13%. Pancreatic ductal adenocarcinoma (PDAC), accounting for over 90% of pancreatic malignancies, has the highest incidence rate, mortality rate, and shortest survival times in non- Hispanic Black (defined here as African and/or African American ancestry) patients compared to other races. To identify molecular features that may contribute to racial health disparities in PDAC, we used quantitative mass spectrometry to determine the proteomic signatures unique to tumor racial origin in a cohort of 30 Caucasian and 12 African American patients. This analysis of the bulk tumor proteome revealed 183 proteins were differentially expressed between these groups. The protein with the highest expression in tumors from Black patients relative to Whites was Ring Finger Protein 2 (RNF2) that acts as an epigenetic transcriptional repressor in developmental processes pertaining to cellular differentiation. To determine the molecular pathways in PDAC regulated by RNF2, chromatin immunoprecipitation and sequencing (ChIP-seq) was performed on MIA-PaCa2 and a primary PDAC cell line derived from a Black patient to identify binding sites for RNF2 in PDAC cells. From the 1,142 replicated peaks identified, RNF2 peaks were localized over the transcription start site of 644 unique transcripts, including GATA6, which is an important determinant in PDAC subtype delineation with higher GATA6 expression associated with the less aggressive and more chemosensitive Classical subtype. Modulating RNF2 levels/activity by exogenous expression, short hairpin RNA (shRNA) knockdown, and small molecule inhibition confirms that GATA6 is a target of RNF2 repression in PDAC. Multi-omic (transcriptomic, proteomic, and phosphoproteomic) characterization was performed on MIA-PaCa2 cells with CRISPR-Cas9 knockout (KO) of RNF2. Differential gene expression (DE) analysis revealed changes in platinum drug resistance, EMT, and innate immune response, which are characteristic of the aggressive Inflammatory PDAC subtype we previously reported using proteomics. Total and phospho-proteomics corroborated these results identifying changes in DNA repair, inflammation response, TNFα mediated signaling, and apoptotic signaling. These results reflect the enrichment of Inflammatory subtype markers in tumors originating in Black patients. In agreement with these results, RNF2 KO shows a significant reduction in migration, as well as an increase in sensitivity to oxaliplatin. Our work identifies differences in the underlying proteome of PDAC tumors associated with tumor racial origin and describes how elevated expression of RNF2 in PDACs from Black patients contribute to inflammation and subtype delineation. This study delineates the associations between tumor racial origin, subtype specification, and response to therapy that could mitigate PDAC health disparities and be broadly applied across all PDAC patients. Citation Format: Anthony E Johansen-Sallee, Alexa M Barber, Henry C.-H. Law, Vignesh Vudatha, Jose Trevino, Nicholas T Woods. Evaluating the molecular determinants of PDAC racial health disparities [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B090.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort.

While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD. Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis. The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD. A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

The proteomic signature of circulating extracellular vesicles following intracerebral hemorrhage: Novel insights into mechanisms underlying recovery

Circulating extracellular vesicles (EVs) can participate in innate repair processes triggered after intracerebral hemorrhage (ICH). We aimed to describe changes in the proteomic profile of circulating EVs between the acute and subacute phases of ICH and to compare the findings depending on outcomes, as an approach to unraveling such repair mechanisms.This was a prospective observational study including patients with non-traumatic supratentorial ICH. Exclusion criteria were previous disability, signs of herniation on baseline computed tomography, or limited life expectancy. EVs were isolated from blood samples at 24 h and 7 days after symptom onset. After 6-months' follow-up, patients were dichotomized into poor and good outcomes, defining good as an improvement of >10 points or > 50 % on the National Institutes of Health Stroke Scale and a modified Rankin Scale of 0–2. The protein cargo was analyzed by quantitative mass spectrometry and compared according to outcomes.Forty-four patients completed follow-up, 16 (35.5 %) having good outcomes. We identified 1321 proteins in EVs, 37 with differential abundance. In patients with good outcomes, proteins related to stress response (DERA, VNN2, TOMM34) and angiogenesis (RHG01) had increased abundance at 7 days. EVs from patients with poor outcomes showed higher levels of acute-phase reactants (CRP, SAA2) at 7 days compared with 24 h.In conclusion, the protein content of circulating EVs in patients with ICH changes over time, the changes varying depending on the clinical outcome, with greater abundance of proteins potentially involved in the repair processes of patients with good outcomes.

Hypoxia and Foxn1 alter the proteomic signature of dermal fibroblasts to redirect scarless wound healing to scar-forming skin wound healing in Foxn1−/− mice

BackgroundFoxn1−/− deficient mice are a rare model of regenerative skin wound healing among mammals. In wounded skin, the transcription factor Foxn1 interacting with hypoxia-regulated factors affects re-epithelialization, epithelial-mesenchymal transition (EMT) and dermal white adipose tissue (dWAT) reestablishment and is thus a factor regulating scar-forming/reparative healing. Here, we hypothesized that transcriptional crosstalk between Foxn1 and Hif-1α controls the switch from scarless (regenerative) to scar-present (reparative) skin wound healing. To verify this hypothesis, we examined (i) the effect of hypoxia/normoxia and Foxn1 signalling on the proteomic signature of Foxn1−/− (regenerative) dermal fibroblasts (DFs) and then (ii) explored the effect of Hif-1α or Foxn1/Hif-1α introduced by a lentiviral (LV) delivery vector to injured skin of regenerative Foxn1−/− mice with particular attention to the remodelling phase of healing.ResultsWe showed that hypoxic conditions and Foxn1 stimulation modified the proteome of Foxn1−/− DFs. Hypoxic conditions upregulated DF protein profiles, particularly those related to extracellular matrix (ECM) composition: plasminogen activator inhibitor-1 (Pai-1), Sdc4, Plod2, Plod1, Lox, Loxl2, Itga2, Vldlr, Ftl1, Vegfa, Hmox1, Fth1, and F3. We found that Pai-1 was stimulated by hypoxic conditions in regenerative Foxn1−/− DFs but was released by DFs to the culture media exclusively upon hypoxia and Foxn1 stimulation. We also found higher levels of Pai-1 protein in DFs isolated from Foxn1+/+ mice (reparative/scar-forming) than in DFs isolated from Foxn1−/− (regenerative/scarless) mice and triggered by injury increase in Foxn1 and Pai-1 protein in the skin of mice with active Foxn1 (Foxn1+/+ mice). Then, we demonstrated that the introduction of Foxn1 and Hif-1α via lentiviral injection into the wounded skin of regenerative Foxn1−/− mice activates reparative/scar-forming healing by increasing the wounded skin area and decreasing hyaluronic acid deposition and the collagen type III to I ratio. We also identified a stimulatory effect of LV-Foxn1 + LV-Hif-1α injection in the wounded skin of Foxn1−/− mice on Pai-1 protein levels.ConclusionsThe present data highlight the effect of hypoxia and Foxn1 on the protein profile and functionality of regenerative Foxn1−/− DFs and demonstrate that the introduction of Foxn1 and Hif-1α into the wounded skin of regenerative Foxn1−/− mice activates reparative/scar-forming healing.

Integrating DIA Single-Cell Proteomics Data with the DiagnoMass Proteomic Hub for Biological Insights.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease's pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

Proteomic Risk Score of Increased Respiratory Susceptibility: A Multi-Cohort Study.

Accelerated decline in lung function is associated with incident COPD, hospitalizations and death. However, identifying this trajectory with longitudinal spirometry measurements is challenging in clinical practice. To determine whether a proteomic risk score trained on accelerated decline in lung function can assess risk of future respiratory disease and mortality. In CARDIA, a population-based cohort starting in young adulthood, longitudinal measurements of FEV1 percent predicted (up to six timepoints over 30 years) were used to identify accelerated and normal decline trajectories. Protein aptamers associated with an accelerated decline trajectory were identified with multivariable logistic regression followed by LASSO regression. The proteomic respiratory susceptibility score was derived based on these circulating proteins and applied to the UK Biobank and COPDGene studies to examine associations with future respiratory morbidity and mortality. Higher susceptibility score was independently associated with all-cause mortality (UKBB: HR 1.56, 95%CI 1.50-1.61; COPDGene: HR 1.75, 95%CI 1.63-1.88), respiratory mortality (UKBB: HR 2.39, 95% CI 2.16-2.64; COPDGene: HR 1.83, 95%CI 1.33-2.51), incident COPD (UKBB: HR 1.84, 95%CI 1.71-1.98), incident respiratory exacerbation (COPDGene: OR 1.11, 95%CI 1.03-1.20), and incident exacerbation requiring hospitalization (COPDGene: OR 1.18, 95%CI 1.08-1.28). A proteomic signature of increased respiratory susceptibility identifies people at risk of respiratory death, incident COPD, and respiratory exacerbations. This susceptibility score is comprised of proteins with well-known and novel associations with lung health and holds promise for the early detection of lung disease without requiring years of spirometry measurements.

Connecting genomic and proteomic signatures of amyloid burden in the brain.

Alzheimer's disease (AD) has a high heritable component characteristic of complex diseases, yet many of the genetic risk factors remain unknown. We combined genome-wide association studies (GWAS) on amyloid endophenotypes measured in cerebrospinal fluid (CSF) and positron emission tomography (PET) as surrogates of amyloid pathology, which may be helpful to understand the underlying biology of the disease. We performed a meta-analysis of GWAS of CSF Aβ42 and PET measures combining six independent cohorts (n=2,076). Due to the opposite effect direction of Aβ phenotypes in CSF and PET measures, only genetic signals in the opposite direction were considered for analysis (n=376,599). Polygenic risk scores (PRS) were calculated and evaluated for AD status and amyloid endophenotypes. We then searched the CSF proteome signature of brain amyloidosis using SOMAscan proteomic data (Ace cohort, n=1,008) and connected it with GWAS results of loci modulating amyloidosis. Finally, we compared our results with a large meta-analysis using publicly available datasets in CSF (n=13,409) and PET (n=13,116). This combined approach enabled the identification of overlapping genes and proteins associated with amyloid burden and the assessment of their biological significance using enrichment analyses. After filtering the meta-GWAS, we observed genome-wide significance in the rs429358-APOE locus and nine suggestive hits were annotated. We replicated the APOE loci using the large CSF-PET meta-GWAS and identified multiple AD-associated genes as well as the novel GADL1 locus. Additionally, we found a significant association between the AD PRS and amyloid levels, whereas no significant association was found between any Aβ PRS with AD risk. CSF SOMAscan analysis identified 1,387 FDR-significant proteins associated with CSF Aβ42 levels. The overlap among GWAS loci and proteins associated with amyloid burden was very poor (n=35). The enrichment analysis of overlapping hits strongly suggested several signalling pathways connecting amyloidosis with the anchored component of the plasma membrane, synapse physiology and mental disorders that were replicated in the large CSF-PET meta-analysis. The strategy of combining CSF and PET amyloid endophenotypes GWAS with CSF proteome analyses might be effective for identifying signals associated with the AD pathological process and elucidate causative molecular mechanisms behind the amyloid mobilization in AD.

Proteomic Correlates and Prognostic Significance of Kidney Injury in Heart Failure With Preserved Ejection Fraction.

Kidney disease is common in heart failure with preserved ejection fraction (HFpEF). However, the biologic correlates and prognostic significance of kidney injury (KI), in HFpEF, beyond the estimated glomerular filtration rate (eGFR), are unclear. Using baseline plasma samples from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial, we measured the following KI biomarkers: cystatin-C, fatty acid-binding protein-3, Beta-2 microglobulin, neutrophil gelatinase-associated lipocalin, and kidney-injury molecule-1. Factor analysis was used to extract the common variability underlying these biomarkers. We assessed the relationship between the KI-factor score and the risk of death or HF-related hospital admission in models adjusted for the Meta-Analysis Global Group in Chronic Heart Failure risk score and eGFR. We also assessed the relationship between the KI factor score and ~5000 plasma proteins, followed by pathway analysis. We validated our findings among HFpEF participants in the Penn Heart Failure Study. KI was associated with the risk of death or HF-related hospital admission independent of the Meta-Analysis Global Group in Chronic Heart Failure risk score and eGFR. Both the risk score and eGFR were no longer associated with death or HF-related hospital admission after adjusting for the KI factor score. KI was predominantly associated with proteins and biologic pathways related to complement activation, inflammation, fibrosis, and cholesterol homeostasis. KI was associated with 140 proteins, which reproduced across cohorts. Findings regarding biologic associations and the prognostic significance of KI were also reproduced in the validation cohort. KI is associated with adverse outcomes in HFpEF independent of baseline eGFR. Patients with HFpEF and KI exhibit a plasma proteomic signature indicative of complement activation, inflammation, fibrosis, and impaired cholesterol homeostasis.

Unveiling novel serum biomarkers in intrahepatic cholangiocarcinoma: a pilot proteomic exploration.

Recent advancements in proteomics have shown promise in identifying biomarkers for various cancers. Our study is the first to compare the serum proteomes of intrahepatic cholangiocarcinoma (iCCA) with cirrhosis (CIR), primary sclerosing cholangitis (PSC), and hepatocellular carcinoma (HCC), aiming to identify a proteomic signature that can effectively distinguish among these conditions. Utilizing high-throughput mass spectrometry on serum samples, we identified 845 proteins, of which 646 were suitable for further analysis. Unique clustering patterns were observed among the five groups, with significant proteomic differences. Our key findings include: S100 calcium-binding protein A9 (S100A9) and haptoglobin (HP) were more abundant in iCCA, while intercellular adhesion molecule 2 (ICAM2) was higher in HCC. Serum amyloid A1 (SAA1) and A4 (SAA4) emerged as potential biomarkers, with SAA1 significantly different in the iCCA vs healthy controls (HC) comparison, and SAA4 in the HCC vs HC comparison. Elevated levels of vascular cell adhesion molecule 1 (VCAM-1) in HCC suggested its potential as a differentiation and diagnostic marker. Angiopoietin-1 receptor (TEK) also showed discriminatory and diagnostic potential in HCC. ELISA validation corroborated mass spectrometry findings. Our study underscores the potential of proteomic profiling in distinguishing iCCA from other liver conditions and highlights the need for further validation to establish robust diagnostic biomarkers.

The trajectory of vesicular proteomic signatures from HBV-HCC by chitosan-magnetic bead-based separation and DIA-proteomic analysis.

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer often associated with chronic hepatitis B virus infection (CHB) and liver cirrhosis (LC), underscoring the critical need for biomarker discovery to improve patient outcomes. Emerging as a promising avenue for biomarker development, proteomic technology leveraging liquid biopsy from small extracellular vesicles (sEV) offers new insights. Here, we evaluated various methods for sEV isolation and identified polysaccharide chitosan (CS) as an optimal approach. Subsequently, we employed optimized CS-based magnetic beads (Mag-CS) for sEV separation from serum samples of healthy controls, CHB, LC, and HBV-HCC patients. Leveraging data-independent acquisition mass spectrometry coupled with machine learning, we uncovered potential vesicular protein biomarker signatures (KNG1, F11, KLKB1, CAPNS1, CDH1, CPN2, NME2) capable of distinguishing HBV-HCC from CHB, LC, and non-HCC conditions. Collectively, our findings highlight the utility of Mag-CS-based sEV isolation for identifying early detection biomarkers in HBV-HCC.

Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential interventions. Leveraging data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline, we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident depression. Linear regression was then used to assess associations between depression-related proteins and brain structures, genetic factors, and stress-related events. Our analysis identified 157 proteins associated with incident depression (P <1.71 × 10−5), including novel associations with proteins such as GAST, PLAUR, LRRN1, BCAN, and ITGA11. Notably, higher expression levels of GDF15 (P = 6.18 × 10−26) and PLAUR (P = 2.88 × 10−14) were linked to an increased risk of depression, whereas higher levels of LRRN1 (P = 4.28 × 10−11) and ITGA11 (P = 3.68 × 10−9) were associated with a decreased risk. Dysregulation of the 157 proteins is correlated with brain regions implicated in depression, including the hippocampus and middle temporal gyrus. Additionally, these protein alterations were strongly correlated with stress-related events, including self-harm events, adult, and childhood trauma. Biological pathway enrichment analysis highlighted the critical roles of the immune response. EGFR and TNF emerged as key proteins in the protein–protein interaction network. BTN3A2, newly linked to incident depression (P = 4.35 × 10−10), was confirmed as a causal factor through Mendelian randomization analysis. In summary, our research identified the proteomic signatures associated with the onset of depression, highlighting its potential for early intervention and tailored therapeutic avenues.

Reaffirmation of Mechanistic Proteomic Signatures Accompanying SGLT2 Inhibition in Patients With Heart Failure: A Validation Cohort of the EMPEROR Program

BackgroundSodium-glucose cotransporter 2 (SGLT2) inhibitors exert a distinctive pattern of direct biological effects on the heart and kidney under experimental conditions, but the meaningfulness of these signatures for patients with heart failure has not been fully defined. ObjectivesWe performed the first mechanistic validation study of large-scale proteomics in a double-blind randomized trial of any treatment in patients with heart failure. MethodsIn a discovery cohort from the EMPEROR (Empagliflozin Outcome Trial in Patients With Chronic Heart Failure and Reduced Ejection Fraction) program, we studied the effect of randomized treatment with placebo or empagliflozin on 1,283 circulating proteins in 1,134 patients with heart failure with a reduced or preserved ejection fraction. In a validation cohort, we expanded the number to 2,155 assessed proteins, which were measured in 1,120 EMPEROR participants who had not been studied previously. ResultsIn the validation cohort, 25 proteins were the most differentially enriched by empagliflozin (ie, ≥15% between-group difference and false discovery rate <1% at 12 weeks with known effects on the heart or kidney): 1) 13 proteins promote autophagy and other cellular quality-control functions (IGFBP1, OTUB1, DNAJB1, DNAJC9, RBP2, IST1, HSPA8, H-FABP, FABP6, ATPIFI, TfR1, EPO, IGBP1); 2) 12 proteins enhance mitochondrial health and ATP production (UMtCK, TBCA, L-FABP, H-FABP, FABP5, FABP6, RBP2, IST1, HSPA8, ATPIFI, TfR1, EPO); 3) 7 proteins augment cellular iron mobilization or erythropoiesis (TfR1, EPO, IGBP1, ERMAP, UROD, ATPIF1, SNCA); 4) 3 proteins influence renal tubular sodium handling; and 5) 9 proteins have restorative effects in the heart or kidneys, with many proteins exerting effects in >1 domain. These biological signatures replicated those observed in our discovery cohort. When the threshold for a meaningful between-group difference was lowered to ≥10%, there were 58 additional differentially enriched proteins with actions on the heart and kidney, but the biological signatures remained the same. ConclusionsThe replication of mechanistic signatures across discovery and validation cohorts closely aligns with the experimental effects of SGLT2 inhibitors. Thus, the actions of SGLT2 inhibitors—to promote autophagy, restore mitochondrial health and production of ATP, promote iron mobilization and erythropoiesis, influence renal tubular ion reabsorption, and normalize cardiac and renal structure and function—are likely to be relevant to patients with heart failure. (EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction [EMPEROR-Preserved], NCT03057951; EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Reduced Ejection Fraction [EMPEROR-Reduced], NCT03057977)

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Common molecular and pathophysiological underpinnings of delirium and Alzheimer’s disease: molecular signatures and therapeutic indications

BackgroundDelirium and Alzheimer's disease (AD) are common causes of cognitive dysfunction among older adults. These neurodegenerative diseases share a common and complex relationship, and can occur individually or concurrently, increasing the chance of permanent mental dysfunction. However, the common molecular pathophysiology, key proteomic biomarkers, and functional pathways are largely unknown, whereby delirium is superimposed on AD and dementia.MethodsWe employed an integrated bioinformatics and system biology analysis approach to decipher such common key proteomic signatures, pathophysiological links between delirium and AD by analyzing the gene expression data of AD-affected human brain samples and comparing them with delirium-associated proteins. The present study identified the common drug target hub-proteins examining the protein–protein interaction (PPI) and gene regulatory network analysis. The functional enrichment and pathway analysis was conducted to reveal the common pathophysiological relationship. Finally, the molecular docking and dynamic simulation was used to computationally identify and validate the potential drug target and repurposable drugs for delirium and AD.ResultsWe detected 99 shared differentially expressed genes (sDEGs) associated with AD and delirium. The sDEGs-set enrichment analysis detected the transmission across chemical synapses, neurodegeneration pathways, neuroinflammation and glutamatergic signaling pathway, oxidative stress, and BDNF signaling pathway as the most significant signaling pathways shared by delirium and AD. The disease-sDEGs interaction analysis highlighted the other disease risk factors with delirium and AD development and progression. Among the sDEGs of delirium and AD, the top 10 hub-proteins including ALB, APP, BDNF, CREB1, DLG4, GAD1, GAD2, GFAP, GRIN2B and GRIN2A were found by the PPI network analysis. Based on the maximum molecular docking binding affinities and molecular dynamic simulation (100 ns) results, the ALB and GAD2 were found as prominent drug target proteins when tacrine and donepezil were identified as potential drug candidates for delirium and AD.ConclusionThe study outlined the common key biomolecules and biological pathways shared by delirium and AD. The computationally reported potential drug molecules need a deeper investigation including clinical trials to validate their effectiveness. The outcomes from this study will help to understand the typical pathophysiological relationship between delirium and AD and flag future therapeutic development research for delirium.

Adipose tissue–derived adipsin marks human aging in non-type 2 diabetes population

IntroductionAdipsin or complement factor D is an adipokine that augments insulin secretion, is altered in various degrees of obesity, and is involved in alternative complement pathway. However, whether adipsin has...