Abstract
Objective
Human blood plasma is a complex that communicates with most parts of the body and reflects the changes in the state of an organism. Identifying age-related biomarkers can help predict and monitor age-related physiological decline and diseases and identify new treatments for diseases.
Methods and Participants
In this study, TMT-LC-MS/MS was utilized to screen differentially expressed plasma proteins in 118 healthy adults of different ages. Participants were divided into three groups: 21–30 years of age (Young), 41–50 years of age (Middle) and ≥60 years of age (Old).
Results
The number of differentially expressed proteins in the comparisons of Young vs Middle, Middle vs Old and Young vs Old were 82, 22 and 99, respectively. These proteins were involved in numerous physiological processes, such as “negative regulation of smooth muscle cell proliferation” and “blood coagulation”. Moreover, when Young was compared with Middle or Old, “complement and coagulation cascades” was the top enriched pathway by KEGG pathway enrichment analysis. Functional phenotyping of the proteome demonstrated that the plasma proteomic profiles of young adults were strikingly dissimilar to those of the middle-aged or older adults.
Conclusions
The results of this study mapped the variation in the expression of plasma proteins and provided information about possible biomarkers/treatments for different age-related functional disorders.
Key words: Aging, healthy adults, plasma, proteomics, TMT-LC-MS/MS
Introduction
Human aging is one of the most common phenomena, and yet it is often ignored. Aging is the process of functional decline in cells, tissues and organs (1), and the molecular mechanism underlying the aging process remains unclear. In recent decades, to reveal the mechanism of human aging, genome studies have been performed, and the results have indicated that gene polymorphisms had influences on human aging and longevity (2, 3, 4). However, the examination of genome profiles alone does not provide an integrated analysis, as proteins are the direct executors of biological functions, and the proteome may not be accurately predicted by gene profiles (5). Compared with genome studies, proteome studies may better reflect the changes in biological functions with age and provide molecular insights into the human aging process.
Human blood plasma is a complex that communicates with most parts of the body and reflects the changes in the state of an organism as a whole (6). Therefore, circulating protein levels are known to be an important readout for diagnosing disease and tracking disease progression (7). In recent years, proteomic technology has developed rapidly, and many proteomics studies have been performed to identify proteins in human plasma. However, these studies focused largely on specific disease settings to identify biomarker proteins or the effect of certain drugs on changes in plasma protein expression (7, 8). To our knowledge, only a few studies have focused on age-dependent changes in the plasma proteome, and these studies have different limitations. For example, two studies focused on identifying the age-specific variability in plasma proteins in neonates, children and adults (6, 9); however, the authors did not provide any information about the proteomic signature of elderly people. Two studies identified potential aging biomarkers in adults (10, 11); however, they did not validate or further investigate their findings in the studies. Another study investigated age-dependent changes in the proteome utilizing cerebrospinal fluid, which was not an optimal substrate compared with plasma (12).
As it has been shown that health outcomes during aging can be determined during early development (13), identifying age-related biomarkers can help predict and monitor age-related physiological decline and disease (14) and find new treatments for diseases (15). Therefore, studies on the age-dependent changes in the plasma proteome, especially for adults, are still needed. In addition, the validation and further investigation of proteomic results is also needed to provide valuable information for both basic research and clinical application.
In this study, TMT-LC-MS/MS was utilized to screen differentially expressed plasma proteins in 118 adults of different ages and free of major diseases. The dataset was searched using Maxquant (v1.5.2.8) in the UniProt Human database. The differentially expressed proteins were analyzed using the UniProt-GOA database, InterPro domain database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Moreover, one of the differentially expressed proteins, IGF-1, was further validated and studied. The aim of the study was to identify age-specific differentially expressed proteins and to systematically characterize protein variation to provide information about possible biomarkers/treatments for different kinds of age-related functional disorders.
Materials and Methods
Ethics statement
The present study was approved by the Ethics Committee of Xinqiao Hospital, Army Medical University. Written informed consent was obtained from all blood donors. The study was conducted according to the ethical principles for medical research involving human subjects detailed in the Declaration of Helsinki.
Human plasma collection
The plasma samples were collected from the Health Examinations Center of Xinqiao Hospital (The Second Affiliated Hospital), Army Medical University. Samples were collected from 118 adults free of major diseases, such as hepatitis, heart disease, infection within six months and immune system-related disease. The participants were divided into three groups: 21–30 years of age (Young), 41–50 years of age (Middle) and ≥60 years of age (Old). Blood (5 ml) was drawn from the antecubital vein and collected in ethylenediaminetetraacetic acid (EDTA) tubes, promptly mixed, and centrifuged for 20 min at 1000 g at room temperature. Plasma samples were kept in cryotubes at -80 °C until use.
Depletion of the plasma and the determination of the protein concentration
Abundant serum proteins were removed from the samples using a Bio-Rad Proteo Miner Protein Enrichment Large-Capacity kit (Bio-Rad Laboratories, Hercules, CA, USA) according to the manufacturer's instructions. Then, the protein concentration was determined with a BCA kit (Beyotime, China) according to the manufacturer's instructions.
Trypsin Digestion
The protein solution was mixed with 5 mM dithiothreitol and kept at 56 °C for 30 min. Then, the protein solution was alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. The protein sample was then diluted by adding 100 mM TEAB to a urea concentration less than 2 M. Finally, trypsin was added at a 1:50 (trypsin:mprotein) mass ratio for digestion overnight and subsequently digested with a 1:100 (trypsin:protein) mass ratio for 4 h.
TMT Labeling
After trypsin digestion, the peptide was desalted with a Strata X C18 SPE column (Phenomenex) and vacuum-dried. Peptides were reconstituted in 0.5 M TEAB and labeled with a TMT kit according to the manufacturer's instructions.
HPLC Fractionation
The tryptic peptides were fractionated into fractions by high pH reverse-phase HPLC using an Agilent 300Extend C18 column (5 µm particles, 4.6 mm ID, 250 mm in length). Briefly, peptides were separated with a gradient of 8% to 32% acetonitrile (pH 9.0) over 60 min into 60 fractions. The peptides were then combined into 18 fractions and dried by vacuum centrifugation.
LC-MS/MS Analysis
The peptides were dissolved in solvent A (0.1% formic acid), separated by the EASY-nLC 1000 UPLC system on a reversed-phase analytical column (15 cm in length, 75 µm i.d.). The gradient setting was as follows: 0∼60 min, 6–20% solvent B (0.1% formic acid in 98% acetonitrile); 60∼82 min, 20–30% solvent B; 82∼86 min, 30–80% solvent B; and 86∼90 min, 80% solvent B, all at a constant flow rate of 350 nL/min.
The peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Orbitrap fusion lumos (Thermo Fisher). The electrospray voltage applied was 2.4 kV. The m/z scan range was 350 to 1550 for full scan, at a resolution of 60,000. The second scanning started with 100 m/z at a resolution of 15,000. A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans with 15.0 s dynamic exclusion was used. Automatic gain control (AGC) was set at 5E4.
Database Search
The MS/MS data were processed using the Maxquant search engine (v.1.5.2.8). Tandem mass spectra were searched in the UniProt human database concatenated with a reverse decoy database. Trypsin/P was specified as a cleavage enzyme, and up to 2 missing cleavages were allowed. The mass tolerance for precursor ions in the first search and the main search were set as 20 ppm and 5 ppm, respectively, and the mass tolerance for fragment ions was set as 0.02 Da. A carbamidomefhyl on Cys was set as a fixed modification, and the oxidation of Met was set as a variable modification. FDR was adjusted to < 1%.
Bioinformatics analysis
Annotation Methods
Gene Ontology (GO) Annotation
Gene Ontology annotation proteome was derived from the UniProt-GOA database (www. http://www.ebi.ac.uk/GOA/). Briefly, the identified protein ID was converted to UniProt ID and then mapped to GO IDs by protein ID. If identified proteins were not annotated by the UniProt-GOA database, InterProScan software was used to annotate the protein's GO function based on the protein sequence alignment method. Then, proteins were classified by Gene Ontology annotation based on three categories: biological process, cellular component and molecular function.
Domain Annotation
Identified protein domain functional descriptions were annotated by InterProScan (a sequence analysis application) based on the protein sequence alignment method, and the InterPro domain database (http://www.ebi.ac.uk/interpro/) was used.
KEGG Pathway Annotation
The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate protein pathways. First, the KEGG tool KAAS was used to annotate the KEGG database descriptions. Then, the annotation result was mapped to the KEGG pathway database using the KEGG tool KEGG mapper.
Subcellular Localization
We used WoLF PSORT, subcellular localization predication software, to predict the subcellular localization. WoLF PSORT is an updated version of PSORT/PSORT II for the prediction of eukaryotic sequences.
Functional Enrichment
Enrichment of Gene Ontology analysis
Proteins were classified by GO annotation into three categories: biological process, cellular compartment and molecular function. For each category, a two-tailed Fisher’s exact test was employed to test the enrichment of a differentially expressed protein against all identified proteins. GO terms with a corrected p-value < 0.05 were considered significant.
Enrichment of pathway analysis
The KEGG database was used to identify enriched pathways by a two-tailed Fisher’s exact test to test the enrichment of a differentially expressed protein against all identified proteins. The pathway with a corrected p-value < 0.05 was considered significant. These pathways were classified into hierarchical categories according to the KEGG website.
Enrichment of protein domain analysis
For each category of proteins, the InterPro database was searched, and a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed proteins against all identified proteins. Protein domains with a p-value < 0.05 were considered significant.
Proteomic phenotyping (Enrichment-based Clustering)
Further hierarchical clustering based on different protein functional enrichment was performed as previously reported (16, 17). We first collated all the categories obtained after enrichment along with their P values and then filtered for those categories that were enriched in at least one of the clusters with a P value < 0.05. This filtered P value matrix was transformed by the function x = −log10 (P value). Finally, these x values were z-transformed for each functional category. These z scores were then clustered by one-way hierarchical clustering (Euclidean distance, average linkage clustering) in Genesis. Cluster membership was visualized by a heat map using the “heatmap.2” function from the “gplots” R-package.
Statistical analysis
The basic characteristics of each cohort are presented as the mean ± SD. For continuous variables (age, triglyceride, blood glucose, hemoglobin, leukocyte, lymphocyte percentage and neutrophil percentage), one-way ANOVA was used to evaluate statistical significance. For categorical variables (sex), chisquare analysis was used to evaluate statistical significance, p values less than 0.05 were considered significant. Analyses were performed with SPSS 13.0 software.
Results
Subject characteristics
TMT-LC-MS/MS was performed on 118 healthy individual samples. The participants were divided into three groups: 21–30 years of age (Young), 41–50 years of age (Middle) and ≥60 years of age (Old). The basic characteristics of each cohort are summarized in Table SI. Although the blood glucose, leukocyte, and lymphocyte percentages were significantly different among the groups, all the data were in the normal reference range. No significant differences in sex, triglyceride, hemoglobin or neutrophil percentage were found among the different groups.
Differentially expressed proteins revealed by quantitative proteomic analysis
The fractions obtained after the depletion of high abundance proteins were detected using TMT-LC-MS/MS. A total of 1069 proteins were identified using MaxQuant (v 1.5.2.8), among which 845 proteins were quantified (95% CI, FDR < 1%). For comparison among the Young, Middle and Old groups, a protein featuring a fold change > 1.3 and a p value < 0.05 was regarded as differentially expressed. The differentially expressed proteins are presented in Figure 1A and 1B. The number of differentially expressed proteins in the comparisons of Young vs Middle, Middle vs Old and Young vs Old were 82, 22 and 99, respectively (Figure 1A and 1B). In detail, when Young was compared with Middle, 33 proteins were upregulated, while 49 proteins were downregulated; when Middle was compared with Old, 13 proteins were upregulated, while 9 proteins were downregulated; when Young was compared with Old, a total of 37 proteins were upregulated, while 62 proteins were downregulated. Three proteins were differentially expressed in all 3 groups (Young, Middle and Old, Figure 1B): cystatin-A (CSTA), apolipoprotein C-1 (APOC1) and insulin-like growth factor 1 (IGF-1). Differentially expressed proteins are summarized in Table S2-S4.**
Figure 1.
Summary of differentially expressed proteins. (A) Differentially expressed protein statistics of different comparable groups; (B) Venn diagram of the identified proteins in different comparable groups; (C) functional classification of the differentially expressed proteins in the comparison of Young vs Old
Functional classification of differentially expressed proteins
For an overview of the differentially expressed proteins, GO classification was performed to study the biological function of the identified proteins. The identified proteins were classified into “biological process”, “cellular component” and “molecular function”. As shown in Figure 1C, when Young vs Old was compared, proteins were involved in 15 biological processes, including “cellular process”, “single-organism process”, “biological regulation”, “metabolic process” and “response to stimulus”. For cellular components, Figure 1C illustrates that the proteins were involved in “extracellular region”, “organelle”, “cell” and “membrane”, etc. The molecular function category suggested that the functions of the differentially expressed proteins were “binding” and “catalytic activity”. Moreover, subcellular analysis revealed that the identified proteins were involved in 9 subcellular locations, and the represented location was “extracellular” (Figure 1C). The comparisons of Young vs Middle and Middle vs Old are shown in Figure S1.
Functional enrichment of differentially expressed proteins
GO enrichment
In the comparison of Young vs Old (Figure 2C), 8 molecular functions were enriched among the upregulated proteins and 8 molecular functions were enriched among the downregulated proteins. The most enriched molecular functions among the upregulated proteins and downregulated proteins were “endonuclease activity” and “binding, bridging”, respectively. The number of enriched biological processes for upregulated proteins and downregulated proteins were 14 and 14, respectively. The enriched biological processes among the upregulated proteins included “negative regulation of smooth muscle cell proliferation”, “RNA phosphodiester bond hydrolysis”, and “cellular protein complex assembly”, while the enriched biological processes among the downregulated proteins included “regulation of body fluid levels”, “blood coagulation”, and “coagulation”. Eight cellular components were enriched among the downregulated proteins, including “fibrinogen complex”, “Golgi lumen”, and “platelet alpha granule lumen”. The GO enrichment results for Young vs Middle and Middle vs Old are shown in Figure 2A and 2B.
Figure 2.
GO enrichment of the differentially expressed proteins. (A) Young vs Middle; (B) Middle vs Old; (C) Young vs Old. The identified proteins in all three groups were stratified into three categories: cellular component, molecular function and biological process. The value of −log10 (Fisher's exact test p value) represents the degree of enrichment
KEGG pathway enrichment
In the comparison of Young vs Old, 1 pathway enriched among the upregulated proteins and 4 pathways enriched among the downregulated proteins were identified. Moreover, 4 enriched pathways among the downregulated proteins were identified when Young was compared with Middle. No significantly enriched pathway was found when Middle was compared with Old (Figure 3A). When Young was compared with Middle or Old, the top enriched pathway among the differentially expressed proteins was “hsa04610 complement and coagulation cascades”. Images of this pathway are shown in Figure 3B, while other enriched pathway images are shown in Figure S2. The data of other pathway images are shown in Figure S3.
Figure 3.
KEGG pathway enrichment of the differentially expressed proteins. (A) Enrichment pathways for Young/Middle, Middle/Old and Young/Old. The value of -loglO (Fisher's exact test p value) represents the degree of enrichment. (B) Represented enrichment pathway image: coagulation and complement cascades
Protein domain enrichment
In our study, protein domain enrichment was also analyzed, and the results are shown in Figure 4. It was found that for downregulated proteins, “serine proteases, trypsin domain” and “peptidase S1, PA clan” were two of the top enriched protein domains when Young was compared with Middle or Old. Moreover, “fibrinogen, alpha/beta/gamma chain, coiled coil domain” and “coagulation factor, subgroup, Gla domain” were also representative of enriched domains.
Figure 4.
Protein domain enrichment of the differentially expressed proteins. The value of −log10 (Fisher's exact test p value) represents the degree of enrichment
Functional phenotyping of the proteome (Enrichment-based Clustering)
To systematically understand the differences in the proteome samples with different ages, proteomic phenotyping was employed based on the functional enrichment results. When Young was compared with Middle or Old, GO enrichment-based clustering revealed that 41 biological processes, 18 cellular components and 23 molecular functions were enriched among the downregulated proteins but were not enriched when Middle was compared with Old. Moreover, 5 biological processes, 3 cellular components and 15 molecular functions were enriched in the upregulated proteins when Young was compared with Middle or Old (Figure 5).
Figure 5.
Functional phenotyping of the proteome. GO enrichment was further clustered for the z-transformed p values. Red — significant enrichment; blue — no significant enrichment
KEGG pathway enrichment-based clustering revealed that 4 pathways (“Platelet activation”, “Neuroactive ligand-receptor interaction”, “Staphylococcus aureus infection”, “Complement and coagulation cascades”) were enriched in the downregulated proteins when Young was compared with Middle or Old (Figure 6A).
Figure 6.
Functional phenotyping of the proteome. (A) KEGG pathway enrichment and (B) protein domain enrichment were further clustered for the z-transformed p values. Red — significant enrichment; blue — no significant enrichment
Protein domain enrichment-based clustering revealed that when Young was compared with Middle or Old, 13 domains and 2 domains were enriched in the downregulated proteins and upregulated proteins, respectively, which were not enriched when Middle was compared with Old (Figure 6B).
Proteins with age-specific changes in expression
In our study, we found 10 proteins with age-specific changes in expression, which indicates that the expression of the proteins changed consecutively from Young to Old. The expression patterns for the 10 proteins are shown in Figure 7. These proteins were categorized into four types: (1) upregulation (Young to Middle)-upregulation (Middle to Old) type: cartilage oligomeric matrix protein (COMP), ubiquitin-60S ribosomal protein L40 (UBA52) and cystatin-A (CSTA); (2) downregulation-downregulation type: apolipoprotein C-1 (APOC1) and insulin-like growth factor 1 (IGF-1); (3) upregulation-downregulation type: insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) and neuroblast differentiation-associated protein AHNAK (AHNAK); and (4) downregulation-upregulation type: ribonuclease pancreatic (RNASE1), profilin-1 (PFN1) and lysozyme C (LYZ).
Figure 7.
Proteins with age-specific changes in expression. These proteins were categorized into four types: (A) upregulation (Young to Middle)-upregulation (Middle to Old) type, (B) downregulation-downregulation type, (C) upregulation-downregulation type and (D) downregulation-upregulation type
In addition, we validated the expression pattern of IGF-1 both in rats and humans and demonstrated that the plasma IGF-1 levels were significantly decreased with age in our previous study. We also demonstrated that plasma from young adults ameliorates aging-related acute brain injury after intracerebral hemorrhage, and this phenomenon might be explained by IGF-1, which was abundant in the plasma from young adults (15).
Discussion
Although several studies have assessed age-specific changes in the human plasma proteome, these studies had limitations (14), and studies on age-dependent changes in the plasma proteome are still needed. To identify age biomarkers to predict/monitor age-related physiological decline and disease, to find new treatments for diseases, and to provide further insight into the molecular mechanisms of human aging, we performed TMT-LC-MS/MS on healthy adults of different ages to screen for differentially expressed plasma proteins. In our study, we successfully identified age-dependent differentially expressed proteins; 37 proteins were upregulated, while 62 proteins were downregulated in Young plasma compared with Old plasma. As aging is considered a process of functional decline in cells, tissues and organs, it was interesting that more upregulated proteins were found in the plasma from older humans.
However, these upregulated proteins in the plasma from older humans might cause disorders. For example, receptor-type tyrosine-protein phosphatase zeta (PTPRZ1), which was identified as the top upregulated protein in Old plasma (Table S4, Young/Old ratio = 0.198), has already been confirmed to be overexpressed in several tumors, such as lung cancer, cervical cancer, hepatocarcinoma, renal cancer, and glioblastoma (18). Moreover, PTPRZ1 may be a novel risk factor for a poor prognosis of triple-negative breast cancer (19). Inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), the second upregulated protein in Old plasma (Table S4, Young/Old ratio = 0.288), which can be found in the extracellular matrix of various organs and in blood circulation, was also demonstrated to play an important role in extracellular matrix remodeling during tumor progression and could be used as a novel predictive circulating biomarker for pancreatic carcinoma (20).
On the other hand, many proteins that are beneficial for human health were found to be downregulated in old plasma. Solute carrier family 25 member 33 (SLC25A33) is an insulin/ IGF-1-responsive mitochondrial carrier protein and is essential for mitochondrial maintenance (21). In our study, SLC25A33 was the most downregulated protein among the differentially expressed proteins when Young was compared with Old (Table S4, Young/Old ratio = 2.82). Insulin-like growth factor 1 (IGF-1) and apolipoprotein C-1 (APOC1), which have important roles in neuron protection (15), brain injury recovery (22, 23, 24) and human aging (25, 26), were also identified as two of the top five downregulated proteins when Young was compared with Old (Table S4, IGF-1 Young/Old ratio = 1.806, APOC1 Young/ Old ratio = 1.798). The downregulation of these proteins, which were also called “youth factors” (15), might lead to the decline in biological function and finally result in disease.
In the meantime, we should also note that some of the upregulated proteins in the old might be beneficial for health, and these proteins might play a key role in maintenance of health. For example, ADAMTS-like protein 2 (ADAMTSL2, Table S4, Young/Old ratio = 0.379), might be important for maintaining the vascular and Brain-Blood Barrier (BBB) stability (27). Proteins like this may act as “Anti-aging factors”, which should be identified and further studied.
To further understand the functions in which the differentially expressed proteins were involved, a functional enrichment study was performed. The GO enrichment analysis revealed that the upregulated proteins in the plasma from young adults (i.e., downregulated proteins in old plasma) were involved in enzyme activities (“endonuclease activity”, “ribonuclease activity” and “nuclease activity”, etc.) and cellular complex assembly (“cellular protein complex assembly”, “cellular macromolecular complex assembly”, etc.; Figure 2), which indicated that cell and organ function in the Young group was better than that in the Old group. We also found that the upregulated proteins in the plasma from young adults were involved in the negative regulation of biological function (“negative regulation of smooth muscle cell proliferation”, “negative regulation of translation”, etc.; Figure 2). It was reported that most of the atherosclerotic lesions were derived from smooth muscle cells (SMCs), and the abnormal proliferation and migration of SMCs promoted the development, expansion, and reorganization of atherosclerotic lesions (28, 29). A previous report also demonstrated that the negative regulation of a specific protein/pathway might be a crucial step to prevent and cure cancer (30). Therefore, the negative regulation of biological function in the plasma of young adults might aid in the homeostasis of the internal environment and provide insight into the molecular mechanisms of diseases in old people, such as atherosclerosis and cancer.
We also identified the most enriched pathways with KEGG pathway enrichment analysis. To characterize the differences in different proteome samples, we further performed proteomic phenotyping (i.e., enrichment-based clustering). This method provides an unbiased global portrait of representative biological functions, enabling visual interpretation of the phenotype in terms of aggregate functional modules (17). The results revealed that “Complement and coagulation cascades” was a notable enriched pathway among the differentially expressed proteins (Figure 3 and Figure 6). The proteins involved in this pathway included fibronectin (FN1), coagulation factor (V, IX, etc.), prothrombin (F2), fibrinogen gamma chain (FGG), fibrinogen alpha chain (FGA), and fibrinogen beta chain (FGB), which were significantly upregulated in the plasma from older adults. The data indicated that coagulation function changed with age, and a hypercoagulation state might exist for older people, which could cause thrombus formation. Our results were also consistent with previous studies (31, 32).
The quantitation of age-specific variability in plasma proteins has clear and relevant applications in clinical practice. In our previous study (15), we demonstrated that plasma from young adults could be a novel therapeutic approach for the treatment of aging-related acute brain injury. The underlying mechanisms might be the neuroprotective effects of “Youth factors” such as IGF-1, which was abundant in plasma from young adults but absent in plasma from older people (Table S4).
In conclusion, in our study, TMT-LC-MS/MS was utilized to screen for differentially expressed plasma proteins in 118 healthy adults of different ages. Differentially expressed proteins were identified; moreover, we also identified the most highly enriched functions, pathways and protein domains in which the differentially expressed proteins were involved. Future studies should be directed at further elucidating the links between age-specific variabilities in protein expression and disease processes.
Conclusions
In the present study, TMT-LC-MS/MS was utilized to screen differentially expressed plasma proteins in 118 healthy adults of different ages. Participants were divided into three groups: 21–30 years of age (Young), 41–50 years of age (Middle) and ≥60 years of age (Old). The number of differentially expressed proteins in the comparisons of Young vs Middle, Middle vs Old and Young vs Old were 82, 22 and 99, respectively. These proteins were involved in numerous physiological processes, such as “negative regulation of smooth muscle cell proliferation” and “blood coagulation”. Moreover, when Young was compared with Middle or Old, “Complement and coagulation cascades” was confirmed to be the top enriched pathway by KEGG pathway enrichment analysis. Functional phenotyping of the proteome demonstrated that the plasma proteomic profiles of young adults were strikingly dissimilar to middle-aged or older adults. The results of this study mapped the variation in the expression of plasma proteins and provided information about possible biomarkers/treatments for different age-related functional disorders. Future studies should be directed at further elucidating the links between age-specific variabilities in protein expression and disease processes.
Author contributions
R.X., C.X.G., X.Y.X. and Q.W.Y., designed the experiments, performed the statistical analyses and drafted the manuscript; C.M.D., J.C.H., G.Q.Y., J.J.Y., and Q.Z. performed the experiments. X.Y.X. and Q.W.Y., supervised throughout the study. All authors read and approved the final manuscript.
Funding
This work was supported by National Natural Science Fund for Distinguish Young Scholars (No. 81525008); National Natural Science Foundation for Young Scientists of China (No. 81901271); Miao Pu project of Army Medical University (2017R016).
Acknowledgments
The authors thank all the participants in the present study.
Contributor Information
Xiaoyi Xiong, Email: xiongxy1989@hotmail.com.
Qingwu Yang, Email: yangqwmlys@hotmail.com.
Ethical standards
The study complies with the current laws of the country in which it was performed.
Conflict of interest
The authors declare no conflicts of interests.
Data availability
The datasets for this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016199. Reviewer account details: Username: reviewer53396@ebi.ac.uk; Password: HpSbIf0s
Electronic Supplementary Material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-020-1392-6 and is accessible for authorized users.
Supplementary material, approximately 1.29 KB.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material, approximately 1.29 KB.
Data Availability Statement
The datasets for this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016199. Reviewer account details: Username: reviewer53396@ebi.ac.uk; Password: HpSbIf0s