Proteomic Signatures Research Articles

Microbial and proteomic signatures of type 2 diabetes in an Arab population

BackgroundThe rising prevalence of Type 2 diabetes mellitus (T2D) in the Qatari population presents a significant public health challenge, highlighting the need for innovative approaches to early detection and management. While most efforts are centered on using blood samples for biomarker discovery, the use of saliva remains underexplored.MethodsUsing noninvasive saliva samples from 2974 Qatari subjects, we analyzed the microbial communities from diabetic, pre-diabetic, and non-diabetic participants based on their HbA1C levels. The salivary microbiota was assessed in all subjects by sequencing the V1–V3 regions of 16S rRNA gene. For the proteomics profiling, we randomly selected 50 gender and age-matched non-diabetic and diabetic subjects and compared their proteome with SOMAscan. Microbiota and proteome profiles were then integrated to reveal candidate biomarkers for T2D.ResultsOur results indicate that the salivary microbiota of pre-diabetic and diabetic individuals differs significantly from that of non-diabetic subjects. Specifically, a significant increase in the abundance of Campylobacter, Dorea, and Bacteroidales was observed in the diabetic subjects compared to their non-diabetic controls. Metabolic pathway prediction analysis for these bacteria revealed a significant overrepresentation of genes associated with fatty acid and lipid biosynthesis, as well as aromatic amino acid metabolism in the diabetic group. Additionally, we observed distinct differences in salivary proteomic profiles between diabetic and non-diabetic subjects. Notably, levels of Haptoglobin, Plexin-C1, and MCL-1 were elevated, while Osteopontin (SPP1), Histone1H3A (HIST3H2A), and Histone H1.2 were reduced in diabetic individuals. Furthermore, integrated correlation analysis of salivary proteome and microbiota data demonstrated a strong positive correlation between HIST1H3A and HIST3H2A with Porphyromonas sp., all of which were decreased in the diabetic group.ConclusionThis is the first study to assess the salivary microbiota in T2D patients from a large cohort of the Qatari population. We found significant differences in the salivary microbiota of pre-diabetic and diabetic individuals compared to non-diabetic controls. Our study is also the first to assess the salivary proteome using SOMAScan in diabetic and non-diabetic subjects. Integration of the microbiota and proteome profiles revealed a unique signature for T2D that can be used as potential T2D biomarkers.

APOE genotype and brain amyloid are associated with changes in the plasma proteome in elderly subjects without dementia.

Recent work has bolstered the possibility that peripheral changes may be relevant to Alzheimer's disease pathogenesis in the brain. While age-associated blood-borne proteins have been targeted to restore function to the aged brain, it remains unclear whether other dysfunctional systemic states can be exploited for similar benefits. Here, we investigate whether APOE allelic variation or presence of brain amyloid are associated with plasma proteomic changes and the molecular processes associated with these changes. Using the SOMAscan assay, we measured 1305 plasma proteins from 53 homozygous, APOE3 and APOE4 subjects without dementia. We investigated the relationship of either the APOE-ε4 allele or amyloid positivity with plasma proteome changes by linear mixed effects modeling and ontology-based pathway and module-trait correlation analyses. APOE4 is associated with plasma protein differences linked to atherosclerosis, tyrosine kinase activity, cholesterol transport, extracellular matrix, and synaptogenesis pathways. Independent of APOE4, we found that subjects likely harboring brain amyloid exhibit plasma proteome signatures associated with AD-linked pathways, including neurovascular dysfunction. Our results indicate that APOE4 status or presence of brain amyloid are associated with plasma proteomic shifts prior to the onset of symptoms, suggesting that systemic pathways in certain risk contexts may be plausible targets for disease modification.

Giant axonal neuropathy (GAN): cross-sectional data on phenotypes, genotypes, and proteomic signature from a German cohort

Giant axonal neuropathy (GAN) is a progressive neurodegenerative disease affecting the peripheral and central nervous system and is caused by bi-allelic variants in the GAN gene, leading to loss of functional gigaxonin protein. A treatment does not exist, but a first clinical trial using a gene therapy approach has recently been completed. Here, we conducted the first systematic study of GAN patients treated by German-speaking child neurologists. We collected clinical, genetic, and epidemiological data from a total of 15 patients representing one of the largest cohorts described thus far. Average age of patients was 11.7 years at inclusion. The most frequently reported symptoms (HPO coded) were gait disturbance and muscle weakness, abnormality of muscle size, and abnormal reflexes. In line with the frequency of homozygous variants, in five families, parents reported being at least distantly related. In 14 patients, diagnosis was confirmed by molecular genetic testing, revealing eight different GAN variants, four being reported as pathogenic in the literature. Proteomics of white blood cells derived from four patients was conducted to obtain unbiased insights into the underlying pathophysiology and revealed dysregulation of 111 proteins implicated in diverse biological processes. Of note, diverse of these proteins is known to be crucial for proper synaptic function and transmission and affection of intermediate filament organisation and proteolysis, which is in line with the known functions of gigaxonin.

The cellular and extracellular proteomic signature of human dopaminergic neurons carrying the LRRK2 G2019S mutation.

Extracellular vesicles are easily accessible in various biofluids and allow the assessment of disease-related changes in the proteome. This has made them a promising target for biomarker studies, especially in the field of neurodegeneration where access to diseased tissue is very limited. Genetic variants in the LRRK2 gene have been linked to both familial and sporadic forms of Parkinson's disease. With LRRK2 inhibitors entering clinical trials, there is an unmet need for biomarkers that reflect LRRK2-specific pathology and target engagement. In this study, we used induced pluripotent stem cells derived from a patient with Parkinson's disease carrying the LRRK2 G2019S mutation and an isogenic gene-corrected control to generate human dopaminergic neurons. We isolated extracellular vesicles and neuronal cell lysates and characterized their proteomic signature using data-independent acquisition proteomics. Then, we performed differential expression analysis to identify dysregulated proteins in the mutated line. We used Metascape and gene ontology enrichment analysis on the dysregulated proteomes to identify changes in associated functional networks. We identified 595 significantly differentially regulated proteins in extracellular vesicles and 3,205 in cell lysates. We visualized functionally relevant protein-protein interaction networks and identified key regulators within the dysregulated proteomes. Using gene ontology, we found a close association with biological processes relevant to neurodegeneration and Parkinson's disease. Finally, we focused on proteins that were dysregulated in both the extracellular and cellular proteomes. We provide a list of ten biomarker candidates that are functionally relevant to neurodegeneration and linked to LRRK2-associated pathology, for example, the sonic hedgehog signaling molecule, a protein that has tightly been linked to LRRK2-related disruption of cilia function. In conclusion, we characterized the cellular and extracellular proteome of dopaminergic neurons carrying the LRRK2 G2019S mutation and proposed an experimentally based list of biomarker candidates for future studies.

Proteome-Wide Analysis of Antibody Responses in Asymptomatic Omicron BA.2-Infected Individuals at the Amino Acid Resolution.

Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.

Differential proteomic profiles between cognitive frail and robust older adults from the MELoR cohort.

The present study explored for the first time the blood-based proteomic signature that could potentially distinguish older adults with and without cognitive frailty (CF). The participants were recruited under the Malaysian Elders Longitudinal Research (MELoR) study. Cognition and physical frailty were determined using the Montreal Cognitive Assessment (MoCA) and Fried's criteria, respectively. The differential protein expression in the blood samples (38 CF vs 40 robust) were then determined using the Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH) analysis. A total of 294 proteins were found to be differentially expressed in the CF group as opposed to the robust group. Considering proteins with fold change (FC) ≥ ± 2 and p-values < 0.05, 13 proteins were significantly upregulated and nine proteins significantly downregulated in the CF group when compared to the robust group. Subsequent correlation analysis identified nine dysregulated proteins, namely APOA1, APOA2, APOA4, APOC1, APOE, GPX3, RBP4, SERPINC1 and TTR, to exhibit significantly and moderately strong correlations with parameters of cognitive and/or frailty assessments. These proteins could potentially serve as useful proteomic signature of CF given their sensitivity > 78%, specificity > 75%, accuracy > 80% and area under the curve (AUC) > 0.8. The major biological pathways that could be potentially dysregulated by the nine proteins were associated with lipid metabolism and the retinoid system. The present findings warrant further validation in future studies that involve a larger cohort.

Spatially Resolved Molecular Characterization of Noninvasive Follicular Thyroid Neoplasms with Papillary-like Nuclear Features (NIFTPs) Identifies a Distinct Proteomic Signature Associated with RAS-Mutant Lesions.

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

Personalized Antifibrotic Therapy in CKD Progression.

Chronic kidney disease (CKD) is a chronic disorder characterized by kidney fibrosis and extracellular matrix accumulation that can lead to end-stage kidney disease. Epithelial-to-mesenchymal transition, inflammatory cytokines, the TGF-β pathway, Wnt/β-catenin signaling, the Notch pathway, and the NF-κB pathway all play crucial roles in the progression of fibrosis. Current medications, such as renin-angiotensin-aldosterone system inhibitors, try to delay disease development but do not stop or reverse fibrosis. This review emphasizes the growing need for tailored antifibrotic medications for CKD treatment. Precision medicine, which combines proteomic, metabolomic, and genetic data, provides a practical way to personalize treatment regimens. Proteomic signatures, such as CKD273, and genetic markers, such as APOL1 and COL4A5, help in patient stratification and focused therapy development. Two recently developed antifibrotic medications, nintedanib and pirfenidone, have been proven to diminish fibrosis in preclinical animals. Additionally, research is being conducted on the efficacy of investigational drugs targeting CTGF and galectin-3 in the treatment of kidney fibrosis.

Proteomic Signature of BMI and Risk of Cardiovascular Disease.

Obesity, defined by body mass index (BMI) alone, is a metabolically heterogeneous disorder with distinct cardiovascular manifestations across individuals. This study aimed to investigate the associations of a proteomic signature of BMI with risk of major subtypes of cardiovascular disease (CVD). A total of 40 089 participants from UK Biobank, free of CVD at baseline, had complete data on proteomic data measured by the Olink assay. A BMI-proteomic score (pro-BMI score) was calculated from 67 pre-identified plasma proteins associated with BMI. A higher pro-BMI score was significantly associated with higher risks of ischemic heart disease (IHD) and heart failure (HF), but not with risk of stroke. Comparing the highest with the lowest quartiles, the adjusted hazard ratio (HR) for IHD was 1.49 (95% CI, 1.32-1.67) (P-trend < 0.001), and the adjusted HR for HF was 1.52 (95% CI, 1.25-1.85) (P-trend < 0.001). Further analyses showed that the association of pro-BMI score with HF risk was largely driven by the actual BMI, whereas the association of the pro-BMI score with IHD risk was independent of actual BMI and waist-to-hip ratio (WHR). The association between pro-BMI score and IHD risk appeared to be stronger in the normal BMI group than other BMI groups (P-interaction = 0.004) and stronger in the normal WHR group than the high WHR group (P-interaction = 0.049). Higher pro-BMI score is significantly associated with higher IHD risk, independent of actual BMI levels. Our findings suggest that plasma proteins hold promise as complementary markers for diagnosing obesity and may facilitate personalized interventions.

Plasma proteomic signatures of adiposity are associated with cardiovascular risk factors and type 2 diabetes risk in a multi-ethnic Asian population.

The biomarkers connecting obesity and cardiometabolic diseases are not fully understood. We aimed to (i) evaluate the associations between body mass index (BMI), waist circumference (WC), and ∼5,000 plasma proteins (SomaScan v4), (ii) identify protein signatures of BMI and WC, and (iii) evaluate the associations between the protein signatures and cardiometabolic health including metabolically unhealthy obesity and type 2 diabetes incidence in the Singapore Multi-Ethnic Cohort (MEC1). Among 410 BMI-associated and 385 WC-associated proteins, we identified protein signatures of BMI and WC and validated them in an independent dataset across two timepoints and externally in the Atherosclerosis Risk in Communities (ARIC) study. The BMI- and WC-protein signatures were highly correlated with total and visceral body fat, respectively. Furthermore, the protein signatures were significantly associated with cardiometabolic risk factors and metabolically unhealthy obesity. In prospective analyses, the protein signatures were strongly associated with type 2 diabetes risk in MEC1 (odds ratio per SD increment in WC-protein signature = 2.84, 95% CI 2.47 to 3.25) and ARIC (hazard ratio = 1.98, 95% CI 1.88 to 2.08). Our protein signatures have potential uses for the monitoring of metabolically unhealthy obesity.

Neuroinflammatory Stress Exerts Distinct Proteomic Effects on Soma, Synapses and Mitochondria in Excitatory Neurons

AbstractBackgroundNeuroinflammation plays a critical role in Alzheimer’s disease pathogenesis. Neurons are anatomically divided in subcellular compartments (axons, soma, and synapses), which may be distinctly impacted by neuroinflammation. This study aims to examine cellular compartment‐specific proteomic signatures in excitatory neurons following a systemic neuroinflammatory stress.MethodWe used our innovative CIBOP (cell type‐specific in vivo biotinylation of proteins) approach to selectively label Camk2a excitatory neuron proteomes in vivo. Neuron‐CIBOP transgenic mice and their littermate controls were treated with lipopolysaccharide (LPS, [500 µg/kg, i.p.]) during 4 consecutive days, which induces robust microglial activation and sickness behavior. After euthanasia, brains were quickly removed, and crude synaptosomal fractions (P2 fractions) were prepared by differential centrifugation. Neuron‐specific biotinylated proteins were then enriched and analyzed by label‐free quantitative mass spectrometry (MS) to identify differentially‐enriched proteins (DEPs) and biological pathways (by gene set variation analysis/GSVA). Neuron‐derived biotinylated key cellular signaling pathways (MAPK and Akt/mTOR) were directly measured by Luminex in homogenates and P2 fractions (Fig. 1A).ResultElectron micrographs validated the subcellular composition of the P2 fractions showing synaptosomes containing synaptic vesicles and mitochondria (Fig. 1B). MS studies confirmed that neuronal homogenates were enriched in microtubule and cytoskeleton‐related proteins, while P2 fractions were enriched in mitochondria and synapse‐related proteins (Fig. 1C). Interestingly, LPS induced unique compartment‐specific proteomic effects, P2 fraction has 52 unique DEPs, while homogenate has 57 DEPs, and only 2 DEPs overlapped (Fig. 1D). Neuronal homogenate proteomes showed upregulation of detoxification and oxidoreductase activity, while a reduced neuron‐synapse, somatodendritic compartment, and cytoskeleton organization. In P2 fraction proteomes, LPS upregulated mitochondrial envelope formation and metabolic activity, including purine containing compound metabolic process, but downregulated nucleoside triphosphate regulator activity. Increased aerobic respiration and mitochondria response overlapped among compartments (Fig. 1E). We also observed LPS‐induced decrease in MAPK signaling specifically in the P2 fraction, not evident at the level of whole neurons, nor at the bulk brain tissue level (Fig. 1F).ConclusionOur neuron and synaptosome‐enriched proteomics approach revealed unique molecular and signaling effects of neuroinflammation that may preferentially impact the synapses of excitatory neurons.

Tear proteomic signature associated with mild cognitive impairment and dementia: an exploratory analysis

AbstractBackgroundTear samples were low‐invasive to access and may reflect changes in the brain. We performed an exploratory proteomic analysis using tear samples to identify proteomic signature and potential pathways that may be associated with mild cognitive impairment (MCI) and dementia.MethodWe performed a matched case‐control study using tear samples collected from community‐dwelling older adults, comprising 13 dementia, as well as 34 MCI and 34 age‐, sex‐, educational‐matched normal cognition (NC) controls (age: 73.3 ± 7.1 years; female: 68.4%). The disease staging has been distinguished by clinical dementia rating (CDR). PulseDIA‐based (gas phase fractionation‐assisted data‐independent acquisition mass spectrometry) proteomics analysis was performed. Logistic regression models were used to identify differential proteins when comparing dementia versus NC and conditional logistic regression models were used when comparing MCI versus NC. Identified differential proteins were used to perform enrichment analysis. Lasso regression models were utilized to select protein biomarkers for dementia prediction.ResultA total of 70 tear proteins were found to be significantly associated with dementia and demonstrated same directions in relation to MCI. In particular, 37 proteins were positively related to dementia, with OR ranging from 3.79 (KLHDC4) to 71.91 (GLO1); 33 proteins were inversely related to dementia, with OR ranging from 0.27 (ABCC2) to 0.03 (COPS7B). The 3 strongest pathways identified by the protein‐protein interaction enrichment analysis were central nervous system neuron development, Eukaryotic Translation Termination, and amide biosynthetic process. In addition, incorporating 13 protein biomarkers to the traditional risk factors‐based model significantly improved the predictive ability of dementia (AUC from 0.813 to 0.971, P = 0.032).ConclusionDifferential tear proteins between dementia and normal cognition were identified in this exploratory analysis, which may contribute to discovering novel and low‐invasive biomarkers of dementia.

Proteomic Signature of The Cerebrospinal Fluid (CSF) in Preclinical and prodromal AD: A key role for adhesion proteins

AbstractBackgroundAlzheimer's Disease (AD) proteomic studies have focused on understanding the pathophysiology of AD during the late stages of AD. However, recent studies have suggested that the preclinical stage of AD represents a golden window for intervention. Yet, little is known about the influence of the cerebrospinal fluid (CSF) molecular environment on the mechanisms underlying the pathogenesis of AD during the preclinical stage.MethodWe performed targeted proteomics using Olink® platform that measured 276 proteins related to neuronal injury, endothelial function, and cardiometabolic pathways in samples from 354 participants recruited for the BSHARP study. Advanced bioinformatic pipeline for data reduction, protein‐protein interaction networks, and module development were used. Resultant Hub proteins that are associated with various clinical phenotypes and biological traits such as levels of CSF AD biomarkers (Aβ42, Tau, and pTau) and the Aβ42/Tau Ratio (TAR) were identified. We then ran the list of Hub proteins through the STRING database to identify critical proteins and performed a pathway enrichment analysis to understand their role in AD.ResultWe compared CSF and plasma proteomic profiles between participants who have normal cognitive function and those who have based on TAR status. As a result, we identified critical proteins that are associated with AD clinical phenotypes and levels of CSF AD biomarkers such as Aβ42, Tau, and pTau (all p‐values &lt;0.05). We also found a significant difference in levels of 6 of the critical genes based on TAR status (HGF, ICAM1, VCAM1, NRP1, NRP2, SCARB2, CCL3). Pathway enrichment of the critical proteins using Gene Ontology (GO) Biological Process identified a series of pathways related to cell adhesion, inflammation, and endothelial function as well as response to Amyloid (all p‐value &lt;0.05) pathways which are known to be related to AD.ConclusionOur findings reveal that adhesion and endothelial‐related pathways are highly associated with AD phenotypes, especially in the preclinical stage. This suggests a possible proteomic signature in the CSF of individuals with preclinical AD that is driven by adhesion molecules and contributes to the pathogenesis of AD. Future studies investigating this contribution might provide insight into the molecular mechanisms underlying AD.

In vivo TurboID labeling for paired proteomic and transcriptomic profiling of neurons and astrocytes

AbstractBackgroundOur group has developed the innovative proximity labeling cell‐type specific in vivo biotinylation of proteins (CIBOP) approach to quantify cell‐specific in vivo proteomic and transcriptomic signatures that may lead to identify novel therapeutic targets for Alzheimer’s disease (AD) pathogenesis. CIBOP uses TurboID, a biotin ligase, selectively expressed in the cell type of interest using a conditional Cre/lox genetic strategy to label the cytosolic proteome. Using mass spectrometry (MS)‐based proteomics, we have found that TurboID biotinylates many RNA‐binding and ribosomal proteins. We extended the CIBOP approach to obtain representative cell type‐specific transcriptomes and proteomes.MethodWe crossed cell‐specific Cre lines (astrocytic: Aldh1l1‐Cre‐ert2 and neuronal: Camk2a‐Cre‐ert2) and Rosa26TurboID/wt floxed mice for cell‐specific proteomic labeling (astrocyte‐CIBOP and neuron‐CIBOP). CIBOP and control (Cre‐only) mice received tamoxifen, followed by biotin‐containing water. While maintaining RNA‐protein interactions, cortical tissue was lysed, biotinylated proteins were enriched via streptavidin beads, and RNA and proteins were eluted. Immunofluorescent microscopy (IF), biochemical assays, MS‐based proteomics, and RNA‐sequencing were completed to confirm cell‐specific molecular profiling. Concordance analysis of paired proteomes and transcriptomes from CIBOP mouse brains was conducted.ResultWestern blot analysis of the cortex confirmed biotinylation of the cellular proteome of CIBOP mice when compared to controls. Cell‐type specificity was further validated by IF images showing that biotin‐labeled proteins colocalized with corresponding astrocytic (e.g., GFAP and NDRG2) or neuronal markers (e.g., MAP2 and beta‐tubulin‐3). RNA gel electrophoresis displayed high levels of RNA from CIBOP brain streptavidin pulldowns and low RNA yield from control pulldowns. MS‐based proteomics and RNA‐sequencing analysis showed upregulation of astrocytic proteins (e.g., Hepacam, Glu, Aqp4, Plpp3) and genes (e.g., Sox9, Aqp4, Gfap, Apoe) from astrocyte‐CIBOP brain. In contrast, upregulation of neuronal proteins (e.g., Map2, Ncam1, Mapt) and genes (e.g., Pdyn, Tmem130, Ptpn7) was observed in neuron‐CIBOP brain samples, confirming specificity.ConclusionTogether, these results validate the CIBOP approach to capturing the cortical Aldh1l1‐positive astrocytic and Camk2a‐positive neuronal proteome and transcriptome. Our innovative in vivo cell type‐specific and native‐state dual‐omics approach provides complementary transcriptomic and proteomic information that can extend to AD models to investigate disease mechanisms, discover new biomarkers, and identify therapeutic targets.

Distinct CSF proteomic signatures are associated with cognitive decline in A+T+ and A+T‐ individuals with MCI due to Alzheimer’s Disease

Distinct CSF proteomic signatures are associated with cognitive decline in A+T+ and A+T‐ individuals with MCI due to Alzheimer’s Disease

New proteomic signatures in Alzheimer Disease identification and Mild Cognitive Impairment Classification

AbstractBackgroundEmerging technologies and novel biomarker tools are transforming the field of Alzheimer’s disease, allowing for a more in‐depth exploration of biological mechanisms underpinning the disease aetio‐pathogenesis. In this context, there is growing recognition of the potential of plasma proteomics for AD risk assessment and disease characterization. On the other hand, differences between proteomics platforms introduce uncertainties regarding cross‐platform applicability.MethodElastic net analysis was applied to 190 analytes measured by the Luminex xMAP platform in plasma from 566 participants [58 cognitively normal (CN), 396 with mild cognitive impairment (MCI) and 58 with dementia due to AD], from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Validation was conducted on 1303 participants (113 CN, 795 MCI, 395 AD) from the Spanish ACE study, which was analysed by the SOMAscan 7k proteomic platform. MCI participants were classified into MCI stable or decliners according to their follow‐up diagnosis (ADNI: 158 vs. 217, Ace: 449 vs. 346). Signatures derived from our elastic net analysis and other methods applied in prior ADNI proteomic signature studies were compared, with our approach aimed at reducing dimensionality of signatures of AD vs. CN or MCI subgroup classification.ResultWe identified a signature including 11 plasma analytes and sex, age, and APOEε4 gene status, for distinguishing AD from CN, with 94% accuracy on ADNI (sensitivity 88%, specificity 94%) and 95% on Ace (specificity 96%, sensitivity 93%). Another 19‐analyte signature with the above confounders was used for classifying MCI stable and decliners, with a 77% accuracy on ADNI (sensitivity 85%, specificity 40%) and 51% on Ace (sensitivity 68%, specificity 36%). These two signatures share one common analyte, Vitronectin, a protein found to appear in degenerative diseases related abnormal deposits, such as amyloid.ConclusionCompared with prior proteomic signature studies, all based on ADNI, our findings attained higher specificity and sensitivity, while utilizing a smaller panel for AD classification. They also confirm the reliability and consistency of a group of plasma proteomic biomarkers across two different platforms.

Proteomic signatures of ambient air pollution and risk of non-alcoholic fatty liver disease: A prospective cohort study in the UK Biobank.

Air pollution has been linked with non-alcoholic fatty liver disease (NAFLD), but the underlying mechanisms characterized by perturbations in the circulating proteome profile are largely unknown. Therefore, we included 51,357 participants from the UK Biobank with 2941 plasma proteins measured in blood samples collected between 2006 and 2010, measurements of annual fine particular matter <2.5μm in diameter (PM2.5) and nitrogen dioxide (NO2), and follow-up data on NAFLD (743 incident cases occurred over a median follow-up of 13.6years). Multiple linear regression was used to identify proteins associated with PM2.5 and NO2. Cox proportional hazards models were applied to assess associations of PM2.5 and NO2 and identified proteins with incident NAFLD. Mediation analyses were conducted to explore the mediation role of proteins in the associations between air pollution and incident NAFLD. After adjusting for selected covariates, PM2.5 (hazard ratio [HR]=2.57, 95%CI:1.27, 5.21, per ln increase) and NO2 (HR=1.43, 95%CI: 1.10, 1.84, per ln increase) were positively associated with incident NAFLD. We identified 138 proteins associated with PM2.5 (92 positively, 46 inversely, FDR <0.05) and 143 with NO2 (100 positively, 43 inversely). Of the proteins that were significantly associated with both PM2.5 and NO2, 93 (79 positively, 14 inversely) and 79 (69 positively, 10 inversely) were significantly associated with incident NAFLD. Furthermore, 84PM2.5-associated proteins and 66 NO2-associated proteins significantly mediated the corresponding association between air pollutants and incident NAFLD, with the proportion of mediation effects ranging from 3.2% to 27.3% for PM2.5 and 2.6% to 20.8% for NO2, respectively. Of note, the majority of significant mediating proteins were enriched in pathways of cytokine-cytokine receptor interaction, viral protein interaction with cytokine and cytokine receptor. Our findings suggested that long-term exposure to PM2.5 and NO2 was associated with an increased risk of NAFLD partially by perturbating circulating proteins involved in pathways of inflammation and immunity responses.

Similar brain proteomic signatures in Alzheimer’s disease and epilepsy

AbstractBackgroundPrevalence of epilepsy is increased among Alzheimer’s Disease (AD) patients and cognitive impairment is common among people with epilepsy, with seizures occurring in 10‐22% of AD patients, subclinical epileptiform abnormalities in 22‐54% of AD patients, and cognitive deficits in up to 80% of epilepsy patients. These shared pathophysiological changes remain poorly defined.MethodsWe aimed to identify protein differences associated with epilepsy and AD using published proteomics datasets, including our previous analysis in epilepsy hippocampus and from our searchable database of 38 AD proteomics studies (NeuroPro). Additionally, laser capture microdissection was performed in dual diagnosed (AD+epilepsy) cases, epilepsy, AD, and control cases in hippocampal CA1‐3 (n = 7/group), followed by label‐free quantitative mass spectrometry to identify protein differences.ResultsWe observed a significant overlap in protein differences in epilepsy and AD: 89% (689/777) of proteins altered in the hippocampus of epilepsy patients were significantly altered in advanced AD. Of the proteins altered in both epilepsy and AD, 340 were altered in the same direction, while 216 proteins were altered in the opposite direction. Synapse and mitochondrial proteins were markedly decreased in epilepsy and AD, suggesting common disease mechanisms. In contrast, ribosome proteins were increased in epilepsy but decreased in AD. Notably, many of the proteins altered in epilepsy interact with tau or are regulated by tau. This suggests tau likely mediates common protein changes in epilepsy and AD. Immunohistochemistry for amyloid‐beta and multiple phosphorylated tau species (pTau396/404, pTau217, pTau231) showed a trend for increased intraneuronal pTau217 and pTau231 but no phosphorylated tau aggregates or amyloid plaques in epilepsy hippocampal sections. To better understand mechanisms of epilepsy in AD, we performed localized proteomics in the hippocampal CA1‐3 of dual diagnosed cases. We observed protein changes compared to controls (275 proteins), with fewer differences than either AD (67) or epilepsy (87) alone cases. Altered proteins in Dual vs. AD included those associated with mTOR signalling related pathways, regulation of eIF4 and p70S6K (p = 1.62 × 10‐4) and mTOR signalling (p = 2.19 × 10‐2).ConclusionOur results provide insights into common mechanisms in epilepsy and AD and highlight the potential role of tau and mTOR signalling in both disease groups.

Large‐Scale Analysis of the Plasma Proteome and Cognitive Correlates in Traumatic Encephalopathy Syndrome and Chronic Traumatic Encephalopathy

AbstractBackgroundChronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy specific to individuals with repetitive head trauma. Traumatic encephalopathy syndrome (TES) is the proposed clinical syndrome resulting from CTE with or without other contributing neuropathologies. Pathophysiological mechanisms driving CTE and underlying TES symptoms are not understood. Using network‐based bioinformatics, we conducted the first large‐scale analysis of the plasma proteome in TES and CTE.MethodsWe leveraged a proximity extension assay (Olink Explore) to quantify 2,778 plasma proteins in 34 TES, 39 biomarker‐confirmed Alzheimer’s disease (AD), and 44 controls (HC; Table 1). TES was subdivided on Aβ‐PET status (23 TESAβ‐, 11 TESAβ+). Secondary analyses focused on antemortem plasma ofrom brain donors with autopsy‐confirmed CTE (N=8) and without (N=12). Differential abundance and network analyses of proteomic signatures were annotated via gene ontology and cell type enrichment. Protein co‐expression modules were age‐ and sex‐adjusted, and compared between groups using analysis of variance (Tukey’s post‐hoc with Cohen’s d). Within TESAβ‐, differential Spearman’s correlations examined individual protein associations with cognitive composites (memory, executive function, language, visuospatial) and co‐expression modules were tested for overrepresentation of cognitive‐associated proteins using Fisher’s exact tests with false discovery rate (FDR) correction.ResultsDifferential abundance analysis revealed a bias towards increased plasma protein abundance in TESAβ‐, with enrichment for mitochondrial proteins (e.g., MECR, TOMM20). Network analysis identified 9 protein co‐expression modules (M1‐M9), of which 5 were elevated in TESAβ‐, but not TESAβ+ or AD, compared to HC (M1‐cell division/mitochondrion, d=1.4; M2‐TNF signaling/cell adhesion, d=0.72; M4‐cell metabolism, d=0.90; M5‐chemokine, d=0.80; M8‐muscle, d=0.88; Figure 1). Metabolic and immune‐linked signals in TESAβ‐ were replicated when comparing autopsy‐confirmed CTE to plasma of brain donors without CTE or those living with AD and HC. Within TESAβ‐, M2‐TNF signaling was highly enriched for proteins negatively associated with memory (FDR‐p=8.5e‐08) and executive function (FDR‐p=2.3e‐6).ConclusionsNetwork‐based proteomics identified elevated peripheral molecular signatures that differentiated TES and autopsy‐confirmed CTE from healthy older adults and biomarker‐confirmed AD. Mitochondrial and inflammatory pathway dysregulation may be uniquely upregulated in CTE and underly cognitive changes in TES. Future protein‐specific assessment (Figure 2) will inform biomarker and therapeutic targets in older adults at greatest risk for CTE.

The Analysis of Plasma Proteomics for Luminal A Breast Cancer.

Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype. In this study, we aimed to search for a non-invasive, specific, blood-based biomarker for the early detection of luminal A breast cancer through proteomic studies. To explore new potential plasma biomarkers, we applied data-independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer-associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls. The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non-breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3-10, and IGHV3-73 through multi-model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91). The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer.