Correction to: Proteomic profiling identifies novel proteins for genetic risk of severe COVID-19: the Atherosclerosis Risk in Communities Study.
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PROVIDER: S-EPMC9840203 | biostudies-literature | 2023 Jan
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Correction to: Proteomic profiling identifies novel proteins for genetic risk of severe COVID-19: the Atherosclerosis Risk in Communities Study.
Human molecular genetics 20230101 2
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Project description:BackgroundGenome-wide association studies have identified six genetic variants associated with severe COVID-19, yet the mechanisms through which they may affect disease remains unclear. We investigated proteomic signatures related to COVID-19 risk variants rs657152 (ABO), rs10735079 (OAS1/OAS2/OAS3), rs2109069 (DPP9), rs74956615 (TYK2), rs2236757 (IFNAR2) and rs11385942 (SLC6A20/LZTFL1/CCR9/FYCO1/CXCR6/XCR1) as well as their corresponding downstream pathways that may promote severe COVID-19 in risk allele carriers and their potential relevancies to other infection outcomes.MethodsA DNA aptamer-based array measured 4870 plasma proteins among 11 471 participants. Linear regression estimated associations between the COVID-19 risk variants and proteins with correction for multiple comparisons, and canonical pathway analysis was conducted. Cox regression assessed associations between proteins identified in the main analysis and risk of incident hospitalized respiratory infections (2570 events) over a 20.7-year follow-up.ResultsThe ABO variant rs657152 was associated with 84 proteins in 7241 white participants with 24 replicated in 1671 Black participants. The TYK2 variant rs74956615 was associated with ICAM-1 and -5 in white participants with ICAM-5 replicated in Black participants. Of the 84 proteins identified in the main analysis, seven were significantly associated with incident hospitalized respiratory infections including Ephrin type-A receptor 4 (hazard ratio (HR): 0.87; P = 2.3 × 10-11) and von Willebrand factor type A (HR: 1.17; P = 1.6x10-13).ConclusionsNovel proteomics signatures and pathways for COVID-19-related risk variants TYK2 and ABO were identified. A subset of these proteins predicted greater risk of incident hospitalized pneumonia and respiratory infections. Further studies to examine these proteins in COVID-19 patients are warranted.
| S-EPMC9307314 | biostudies-literature
Project description:Despite recent advances in mass spectrometry, proteomic characterization of transport vesicles remains challenging. Here, we describe a multivariate proteomics approach to analyzing clathrin-coated vesicles (CCVs) from HeLa cells. siRNA knockdown of coat components and different fractionation protocols were used to obtain modified coated vesicle-enriched fractions, which were compared by stable isotope labeling of amino acids in cell culture (SILAC)-based quantitative mass spectrometry. 10 datasets were combined through principal component analysis into a "profiling" cluster analysis. Overall, 136 CCV-associated proteins were predicted, including 36 new proteins. The method identified >93% of established CCV coat proteins and assigned >91% correctly to intracellular or endocytic CCVs. Furthermore, the profiling analysis extends to less well characterized types of coated vesicles, and we identify and characterize the first AP-4 accessory protein, which we have named tepsin. Finally, our data explain how sequestration of TACC3 in cytosolic clathrin cages causes the severe mitotic defects observed in auxilin-depleted cells. The profiling approach can be adapted to address related cell and systems biological questions.
| S-EPMC3317806 | biostudies-other
Project description:Aims/hypothesisGenetic predisposition to type 2 diabetes is well-established, and genetic risk scores (GRS) have been developed that capture heritable liabilities for type 2 diabetes phenotypes. However, the proteins through which these genetic variants influence risk have not been thoroughly investigated. This study aimed to identify proteins and pathways through which type 2 diabetes risk variants may influence pathophysiology.MethodsUsing a proteomics data-driven approach in a discovery sample of 7241 White participants in the Atherosclerosis Risk in Communities Study (ARIC) cohort and a replication sample of 1674 Black ARIC participants, we interrogated plasma levels of 4870 proteins and four GRS of specific type 2 diabetes phenotypes related to beta cell function, insulin resistance, lipodystrophy, BMI/blood lipid abnormalities and a composite score of all variants combined.ResultsTwenty-two plasma proteins were identified in White participants after Bonferroni correction. Of the 22 protein-GRS associations that were statistically significant, 10 were replicated in Black participants and all but one were directionally consistent. In a secondary analysis, 18 of the 22 proteins were found to be associated with prevalent type 2 diabetes and ten proteins were associated with incident type 2 diabetes. Two-sample Mendelian randomisation indicated that complement C2 may be causally related to greater type 2 diabetes risk (inverse variance weighted estimate: OR 1.65 per SD; p=7.0 × 10-3), while neuropilin-2 was inversely associated (OR 0.44 per SD; p=8.0 × 10-3).Conclusions/interpretationIdentified proteins may represent viable intervention or pharmacological targets to prevent, reverse or slow type 2 diabetes progression, and further research is needed to pursue these targets.
| S-EPMC9742300 | biostudies-literature
Project description:Cyclic AMP acts as a secondary messenger involving different cellular functions in eukaryotes. Here, proteomic and transcriptomic profiling has been combined to identify novel early developmentally regulated proteins in eukaryote cells. These proteomic and transcriptomic experiments were performed in Dictyostelium discoideum given the unique advantages that this organism offers as a eukaryotic model for cell motility and as a nonmammalian model of human disease. By comparing whole-cell proteome analysis of developed (cAMP-pulsed) wild-type AX2 cells and an independent transcriptomic analysis of developed wild-type AX4 cells, our results show that up to 70% of the identified proteins overlap in the two independent studies. Among them, we have found 26 proteins previously related to cAMP signaling and identified 110 novel proteins involved in calcium signaling, adhesion, actin cytoskeleton, the ubiquitin-proteasome pathway, metabolism, and proteins that previously lacked any annotation. Our study validates previous findings, mostly for the canonical cAMP-pathway, and also generates further insight into the complexity of the transcriptomic changes during early development. This article also compares proteomic data between parental and cells lacking glkA, a GSK-3 kinase implicated in substrate adhesion and chemotaxis in Dictyostelium. This analysis reveals a set of proteins that show differences in expression in the two strains as well as overlapping protein level changes independent of GlkA.
| S-EPMC6830349 | biostudies-literature
Project description:BackgroundThe plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays.MethodsWe assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011-2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status.ResultsThere were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: -0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status.ConclusionsThere is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.
| S-EPMC9812856 | biostudies-literature
Project description:BackgroundThe inflammatory response to cerebral ischemia is complex; however, most clinical studies of stroke outcome focus on a few selected proteins. We, therefore, aimed to profile a broad range of inflammation-related proteins to: identify proteins associated with ischemic stroke outcome that are independent of established clinical predictors; identify proteins subsets for outcome prediction; and perform sex and etiological subtype stratified analyses.MethodsAcute-phase plasma levels of 65 inflammation-related proteins were measured in 534 ischemic stroke cases. Logistic regression was used to estimate associations to unfavorable 3-month functional outcome (modified Rankin Scale score > 2) and LASSO regressions to identify proteins with independent effects.ResultsTwenty proteins were associated with outcome in univariable models after correction for multiple testing (FDR < 0.05), and for 5 the association was independent of clinical variables, including stroke severity (TNFSF14 [LIGHT], OSM, SIRT2, STAMBP, and 4E-BP1). LASSO identified 9 proteins that could best separate favorable and unfavorable outcome with a predicted diagnostic accuracy (AUC) of 0.81; three associated with favorable (CCL25, TRAIL [TNFSF10], and Flt3L) and 6 with unfavorable outcome (CSF-1, EN-RAGE [S100A12], HGF, IL-6, OSM, and TNFSF14). Finally, we identified sex- and etiologic subtype-specific associations with the best discriminative ability achieved for cardioembolic, followed by cryptogenic stroke.ConclusionsWe identified candidate blood-based protein biomarkers for post-stroke functional outcome involved in, e.g., NLRP3 inflammasome regulation and signaling pathways, such as TNF, JAK/STAT, MAPK, and NF-κB. These proteins warrant further study for stroke outcome prediction as well as investigations into the putative causal role for stroke outcome.
| S-EPMC10548608 | biostudies-literature
Project description:Formalin-fixed paraffin embedded (FFPE) clinical tissues represent an abundant and unique resource for translational proteomic studies. In the US, melanoma is the 5th and 6th most common cancer in men and women, respectively, affecting over 230,000 people annually and metastasising in 5-15% of cases. Median survival time for distant metastatic melanoma is 6-9 months with a 5-year-survival of < 15%. In this study, 24 primary FFPE tumours which have metastasised (P-M) and 24 primary FFPE tumours which did not metastasise (P-NM) were subjected to proteomic profiling. In total, 2750 proteins were identified, of which 16 were significantly differentially expressed. Analysis of TCGA data demonstrated that expression of the genes encoding for 6 of these 16 proteins had a significant effect on survival in cutaneous melanoma. Pathway analysis of the proteomics data revealed mechanisms likely involved in the process of melanoma metastasis, including cytoskeleton rearrangement, extracellular changes and immune system alterations. A machine learning prediction model scoring an AUC of 0.922, based on these 16 differentially expressed proteins was able to accurately classify samples into P-M and P-NM. This study has identified potential biomarkers and key processes relating to melanoma metastasis using archived clinical samples, providing a basis for future studies in larger cohorts.
| S-EPMC7663670 | biostudies-literature
Project description:BackgroundWithin healthy dietary patterns, manipulation of the proportion of macronutrient can reduce CVD risk. However, the biological pathways underlying healthy diet-disease associations are poorly understood. Using an untargeted, large-scale proteomic profiling, we aimed to (1) identify proteins mediating the association between healthy dietary patterns varying in the proportion of macronutrient and lipoproteins, and (2) validate the associations between diet-related proteins and lipoproteins in the Atherosclerosis Risk in Communities (ARIC) Study.MethodsIn 140 adults from the OmniHeart trial, a randomized, cross-over, controlled feeding study with 3 intervention periods (carbohydrate-rich; protein-rich; unsaturated fat-rich dietary patterns), 4,958 proteins were quantified at the end of each diet intervention period using an aptamer assay (SomaLogic). We assessed differences in log2-transformed proteins in 3 between-diet comparisons using paired t-tests, examined the associations between diet-related proteins and lipoproteins using linear regression, and identified proteins mediating these associations using a causal mediation analysis. Levels of diet-related proteins and lipoprotein associations were validated in the ARIC study (n = 11,201) using multivariable linear regression models, adjusting for important confounders.ResultsThree between-diet comparisons identified 497 significantly different proteins (protein-rich vs. carbohydrate-rich = 18; unsaturated fat-rich vs. carbohydrate-rich = 335; protein-rich vs. unsaturated fat-rich dietary patterns = 398). Of these, 9 proteins [apolipoprotein M, afamin, collagen alpha-3(VI) chain, chitinase-3-like protein 1, inhibin beta A chain, palmitoleoyl-protein carboxylesterase NOTUM, cathelicidin antimicrobial peptide, guanylate-binding protein 2, COP9 signalosome complex subunit 7b] were positively associated with lipoproteins [high-density lipoprotein (HDL)-cholesterol (C) = 2; triglyceride = 5; non-HDL-C = 3; total cholesterol to HDL-C ratio = 1]. Another protein, sodium-coupled monocarboxylate transporter 1, was inversely associated with HDL-C and positively associated with total cholesterol to HDL-C ratio. The proportion of the association between diet and lipoproteins mediated by these 10 proteins ranged from 21 to 98%. All of the associations between diet-related proteins and lipoproteins were significant in the ARIC study, except for afamin.ConclusionsWe identified proteins that mediate the association between healthy dietary patterns varying in macronutrients and lipoproteins in a randomized feeding study and an observational study.Trial registrationNCT00051350 at clinicaltrials.gov.
| S-EPMC10316599 | biostudies-literature
Project description:ObjectiveElevated fasting glucose level is associated with increased carotid intima-media thickness (IMT), a measure of subclinical atherosclerosis. It is unclear if this association is causal. Using the principle of Mendelian randomization, we sought to explore the causal association between circulating glucose and IMT by examining the association of a genetic risk score with IMT.Research design and methodsThe sample was drawn from the Atherosclerosis Risk in Communities (ARIC) study and included 7,260 nondiabetic Caucasian individuals with IMT measurements and relevant genotyping. Components of the fasting glucose genetic risk score (FGGRS) were selected from a fasting glucose genome-wide association study in ARIC. The score was created by combining five single nucleotide polymorphisms (SNPs) (rs780094 [GCKR], rs560887 [G6PC2], rs4607517 [GCK], rs13266634 [SLC30A8], and rs10830963 [MTNR1B]) and weighting each SNP by its strength of association with fasting glucose. IMT was measured through bilateral carotid ultrasound. Mean IMT was regressed on the FGGRS and on the component SNPs, individually.ResultsThe FGGRS was significantly associated (P = 0.009) with mean IMT. The difference in IMT predicted by a 1 SD increment in the FGGRS (0.0048 mm) was not clinically relevant but was larger than would have been predicted based on observed associations between the FFGRS, fasting glucose, and IMT. Additional adjustment for baseline measured glucose in regression models attenuated the association by about one third.ConclusionsThe significant association of the FGGRS with IMT suggests a possible causal association of elevated fasting glucose with atherosclerosis, although it may be that these loci influence IMT through nonglucose pathways.
| S-EPMC3012190 | biostudies-literature
Project description:Atherosclerosis Risk in Communities - Imaging
| PRJNA1230280 | ENA