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Screening for Biomarkers of Aging

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Identification of biomarkers for aging based on DNA microarray data

Highlights:

  1. Totally, 43 time series-related lncRNAs were screened.
  2. A total of 11 clusters of 41 lncRNAs were identified.
  3. CYP51 and FDPS were mainly enriched in pathway of cholesterol biosynthesis.
  4. MFAP3 and MFAP5 were significantly enriched in pathway of elastic fibre formation.

Abstract

Background: The age-related disorders including cancers, chronic inflammatory and neurodegenerative diseases become a burden on health care provision in the developed countries. The objective of this study was to screen for possible lncRNAs and target genes of aging and to explore the mechanisms of aging. Methods: GSE25905 was downloaded from the Gene Expression Omnibus database. In this analysis, 3 samples of gene microarray data (peripheral white adipocytes isolated from male C57BL/6J mice of 6 months, 14 months and 18 months of age) with 3 replicates were obtained. Identification of differentially expressed lncRNAs and mRNAs were performed at three time points. Then, lncRNA target genes were predicted. Subsequently, cluster analysis of lncRNAs expression pattern was performed, following by the functional analysis for positive- and negative-regulation target genes of lncRNAs. Results: A total of 8301 time series-related mRNAs and 43 time series-related lncRNAs were identified in peripheral white adipocytes samples. Additionally, CYP51 (lanosterol 14-demethylase) and FDPS (farnesyl diphosphate synthase), the positive-regulation potential target genes of lncRNAs, were mainly enriched in pathway of cholesterol biosynthesis. Moreover, MFAP3 (microfibrillar associated protein 3) and MFAP5 were significantly enriched in pathway of elastic fibre formation. However, the negative-regulation potential target genes of lncRNAs were mainly enriched in pathways such as metabolism of proteins. Conclusion: CYP51, FDPS, MFAP3 and MFAP5 may be pivotal genes for the process of aging.

Key words: aging; long non-coding RNAs; target genes; Gene Ontology; pathway

Introduction

Aging is related with damaged adipogenesis in various fat depots in humans [1, 2]. In addition, aging is connected with increased generation of pro-inflammatory signals in visceral white adipose tissue (WAT)[3]. Currently, about 800 million people are at least 60 years old, which accouts about 11% of the world’s population; by 2050, aging population is expected to increase to more than 2 billion, representing 22% of the population [4]. Moreover, aging remains an elevated risk of common diseases, including hypertension, atherosclerosis and diabetes [5, 6]. Notably, WAT is considered as an important regulator for multiple physiological processes and highly linked to the development of multiple morbidities [7-9]. Therefore, understanding the aging-adipose interactions is very important for understanding the basis of disease in the elderly.

Several studies have exhibited some genes that are implicated in aging process in an adipose depot-dependent manner. For example, age-related increase in IL-6 (interleukin 6), which was related to stress responses and cellular senescence, was observed in a fat depot-dependent manner, [1]. Sirt1 (sirtuin 1) and SOD2 (superoxide dismutase 2), which were correlated with mitochondrial aging, were significantly reduced in epididymal adipocytes with age [10]. Additionally, the expression of MMP-3 (matrix metallopeptidase 3 (stromelysin 1, progelatinase)) was increased in mouse subcutaneous fat cells and human skin fibroblasts with aging [11, 12]. Furthermore, decreased expression in PPARγ (peroxisome proliferator-activated receptor gamma) through declining fat mass has been observed in monkey subcutaneous whole fat tissue [13]. Cartwright et.al reported that the levels of adipogenic transcription factors, such as C/EBPa (CCAAT/enhancer binding protein (C/EBP), alpha), C/EBPδ and PPARg (peroxisome proliferator-activated receptor gamma), were lower in differentiating adipocytes isolated from old than that of young rats [14]. Krishnamurthy et.al also have demonstrated that the expression of the Ink4a/Arf tumor suppressor locus is a robust biomarker and potential effector of mammalian aging [15]. In addition to these genes mentioned above, long non-coding RNAs (lncRNAs), which are defined as largest transcript class in human genome longer than 200 bp that lack protein-coding potential[16, 17], may play a key role in a variety of biological cellular processes and diseases development [18, 19]. In spite of much effort, the lncRNAs with known functions remains rare. Thus, efficient prediction of lncRNAs functions is still a considerable challenge.

The expression profile GSE25905 [20] was offered by Liu et al. who analyzed differentially expressed genes (DEGs) in bone marrow adipocytes and epididymal adipocytes and determined the effects of aging on genes associated with mitochondria function and inflammation in bone marrow adipocytes. However, the effects of lncRNAs on aging were not performed.

Therefore, in the current study, we performed an extensive analysis using the bioinformatics methods to identify the lncRNAs and explore the molecular alteration in the process of aging. Moreover, functional analysis of target genes of lncRNAs was carried out. The results might provide a deeper insight into the development of aging.

2. Methods and materials

2.1. Tissue samples and data acquisition

The gene expression profile was downloaded at the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) database , which was accessible through GSE25905 [20]. The samples were based on GPL6246 platform of ([MoGene-1_0-st] Affymetrix Mouse Gene 1.0 ST Array. In this analysis, 3 samples of gene microarray data (peripheral whiteadipocytes isolated from male C57BL/6J mice of 6 months, 14 months and 18 months of age) with 3 replicates were obtained.

2.2. Data preprocessing and profiling of long non-coding RNAs (lncRNAs)

The gene expression profile of GSE25905 was preprocessed by the Affy package [21] provided by Brain Array Lab. Expression data of probe in CEL document were processed to corresponding genes according to the annotation of GPL6246 platform, and normalization was carried out using the robust multiarray average (RMA) algorithm [22]. Then, the expression matrix was obtained. The expression values of multi-probes probes for a given gene were reduced to a single value by computing the average expression value.

Then, lncRNAs were obtained from the authoritative data sources of GENCODE (http://www.gencodegenes.org/) [23].

2.3. Identification of differentially expressed lncRNAs and mRNAs at three time points

The BETR (Bayesian Estimation of Temporal Regulation) algorithm of BETR package [24]was applied to identify differentially expressed lncRNAs and mRNAs at three time points, which calculated the probability of differential expression for each lncRNA and gene. The probability > 0.9 was selected as the criteria.

2.4. LncRNA target prediction

Differentially expressed lncRNAs were chosen for target prediction in order to determine whether lncRNAs might play roles via regulating the corresponding mRNAs. Pearson correlation coefficient (PCC) was performed to calculate the expression similarity of lncRNAs and mRNAs at different time series. For each pair of lncRNA-mRNA, significant correlation pairs with| PCC | more than 0.95 were used to construct the lncRNA-mRNA regulatory network displayed by Cytoscape [25].

2.5 Cluster analysis of lncRNAs expression pattern

Hierarchical clustering [26, 27] is an analytical tool applied to discover the closest associations between gene profiles and specimens under evaluation. In our study, to analyze the changes of lncRNAs expression pattern, the BHC (Bayesian Hierarchical Clustering) package [28] was preformed to construct the cluster heat map of lncRNAs and samples.

2.6 Functional analysis

Gene Ontology (GO) and pathway functional analysis for positive- and negative-regulation target genes of lncRNAs above were carried out using TargetMine (http://targetmine.nibio.go.jp) [29] which was a successful approach towards the identification of candidate genes for further investigation. The adjusted p value < 0.05 was considered statistically significant which was calculated by the Holm-Bonferroni [30] method.

3. Results

3.1. Data preprocessing and profiling of lncRNAs

Based on the annotation information of GPL6246 platform, a total of 203 probes were identified and annotated as lncRNAs. Moreover, 20564 genes were screened. The data before and after normalization were shown in Figure 1A and 1B.

3.2 Identification of differentially expressed lncRNAs and mRNAs at three time points

Based on the probability > 0.9, 8301 time series-related mRNAs and 43 time series-related lncRNAs were identified in peripheral white adipocytes samples.

3.3 LncRNA target prediction

As shown in Figure 2, the regulatory network of 41 lncRNAs and corresponding to mRNAs was constructed, which was involved in 1880 genes and 2313 regulatory pairs.

3.4 Cluster analysis of lncRNAs expression pattern

To further explore the changes of the lncRNAs expression levels at three time points in peripheral white adipocytes, we performed the cluster analysis. Our results demonstrated that the expression values of most lncRNAs were higher in peripheral white adipocytes isolated from male C57BL/6J mice of 14 months old than that of 6 and 18 months old. The cluster heat map of 41 lncRNAs was shown in Figure 3.

According to the results of clustering analysis, 11 clusters were identified. The cluster heat map of 11 clusters (Figure 4) presented a decline trend of lncRNAs expression at three time points.

3.3 Functional enrichment analysis

We used the TargetMine to identify GO enriched functions and pathways for positive- and negative-regulation potential target genes of lncRNAs. The positive-regulation potential target genes of lncRNAs were mainly enriched in biology process such as vasculature development and pathways such as cholesterol biosynthesis as well as elastic fibre formation (Table 1). The negative-regulation potential target genes of lncRNAs were mainly enriched in biology process such as metabolic process and pathways such as metabolism of proteins (Table 2).

Discussion

The increased occurrence of cancers, chronic inflammatory and neurodegenerative diseases related with age becomes a burden on health care provision in the developed countries [31]. In this study, gene expression profile GSE25905 was downloaded and investigated to explore the potential mechanisms of aging applying bioinformatics methods. A total of 8301 time series-related mRNAs and 43 time series-related lncRNAs were identified in peripheral white adipocytes samples. Additionally, CYP51 (lanosterol 14-demethylase) and FDPS (farnesyl diphosphate synthase), the positive-regulation potential target genes of lncRNAs, were mainly enriched in pathway of cholesterol biosynthesis. Moreover, MFAP3 (microfibrillar associated protein 3) and MFAP5 were significantly enriched in pathway of elastic fibre formation.

A former study has demonstrated that aging is associated with altered cholesterol metabolism in T cells, causing increased cholesterol levels in lipid rafts [32]. Other researches also identified several aging-dependent up-regulated processes, such as cholesterol transport , lipid catabolism and proteolysis in normally aging rats [33, 34]. In the present study, CYP51 and FDPS, the positive-regulation potential target genes of lncRNA, was significantly enriched in cholesterol biosynthesis. Moreover, the expression of CYP51 and FDPS were down-regulated with aging. CYP51, the most evolutionarily conserved member of CYP (cytochrome P450) gene superfamily, participates in the late portion of cholesterol biosynthesis [35]. Moreover, cholesterol biosynthesis is mediated via the SREBPs (sterol regulatory element binding protein transcription factors) which are regarded as the key elements in controlling cellular cholesterol homeostasis [36]. Notably, the co-regulatory of SREBPs and cAMP-dependent pathway is of great importance for maintaining the cellular cholesterol level [37]. The network of insulin/insulin-like growth factor 1, AMP-activated protein kinase/target of rapamycin and cAMP/PKA pathways modulates the organismal lifespan [38-40]. FDPS, encoded by FDPS, is a crucial enzyme in the isoprene biosynthetic pathway, which offers the cell with cholesterol. Besides, FDPS was observed to be involved in cholesterol biosynthesis in aging peripheral nervous system [41]. Therefore, we infer that CYP51 and FDPS might provide some support for a role of further cholesterol-related genes in aging.

Another significant pathway, elastic fibre formation was identified involved in MFAP3 and MFAP5 which were down-regulated with aging. Elastic fibre is a major insoluble extracellular matrix that ensures connective tissues with resilience, allowing long-range deformability as well as passive recoil and these properties are of significant importance to the function of arteries, lungs, skin and other dynamic connective tissues [42]. However, the loss of elasticity is a main contributing factor in aging [43]. In ageing and immune states, microfibrils are related with amyloid deposits and the accumulation of adhesive glycoproteins [44]. MFAP3 and MFAP5 are two members of microfibril-associated proteins. MFAP-3 and elastic fibres colocalise in skin and other tissues [45]. MFAP5 is participated in the rearrangement of elastic fibres in the extracellular space via interacting with the FBN1 (fibrillin 1) and FBN2 proteins [46, 47]. Moreover, MFAP5 had an age-dependent weakening of blood vessels [48]. In light of these conclusions, we infer MFAP3 and MFAP5 may play a critical role in the process of aging via regulation of elastic fibre formation.

In sum, the identified positive-regulation potential target genes of lncRNA, especially CYP51, FDPS, MFAP3 and MFAP5, may be pivotal genes for the process of aging. However, there remain shortcomings in this study. The results were obtained using bioinformatics methods and have not been verified by relevant experiments yet. Further experiments are needed to prove the effects and mechanisms of CYP51, FDPS, MFAP3 and MFAP5 in aging.

Figures Legends

Figure 1 A: Box plot of gene expressions in peripheral white adipocytes samples at three time points before normalization. B: Box plot of gene expressions in peripheral white adipocytes samples at three time points after normalization. The X axis stands for samples while the Y axis stands for expression level of genes. The black line in the center was the median of expression value, and the consistent distribution indicated a good standardization.

Figure 2 The regulatory network of 41 lncRNAs and their corresponding to mRNAs. The diamond nodes stand for lncRNAs; arrows represent the positive regulation; non-arrows represent the negative regulation.

Figure 3 The cluster heat map of the 41 long non-coding RNAs (lncRNAs). The color scale represents the relative levels of lncRNAs; horizontal axis represents samples; vertical coordinate represents lncRNAs.

Figure 4 The expression pattern and heat map of 11 clusters.

Table 1 Gene Ontology (GO) and pathways functional enrichment analysis of positive-regulation potential target genes of lncRNAs

Ontology

term

adjust-P

count

GO-BP

vasculature development [GO:0001944]

1.33E-08

65

GO-BP

blood vessel development [GO:0001568]

1.34E-07

60

GO-BP

regulation of locomotion [GO:0040012]

2.45E-05

51

GO-BP

blood vessel morphogenesis [GO:0048514]

2.55E-05

49

GO-BP

regulation of cellular component movement [GO:0051270]

3.92E-05

50

GO-CC

cell surface [GO:0009986]

2.46E-05

53

GO-CC

plasma membrane [GO:0005886]

9.39E-05

158

GO-CC

cell periphery [GO:0071944]

2.59E-04

163

GO-CC

side of membrane [GO:0098552]

0.015056

33

GO-CC

plasma membrane part [GO:0044459]

0.021127

101

REACT_208531

Cholesterol biosynthesis

4.00E-03

9

REACT_198996

Elastic fibre formation

5.10E-03

11

Table 2 Gene Ontology (GO) and pathways functional enrichment analysis of negative-regulation potential target genes of lncRNAs

Ontology

term

adjust-P

count

GO-BP

metabolic process [GO:0008152]

8.05E-10

380

GO-BP

organic substance metabolic process [GO:0071704]

5.74E-09

360

GO-BP

primary metabolic process [GO:0044238]

3.62E-08

343

GO-BP

cellular metabolic process [GO:0044237]

5.03E-08

344

GO-BP

mitochondrion organization [GO:0007005]

2.30E-04

32

GO-CC

intracellular [GO:0005622]

2.01E-16

478

GO-CC

intracellular part [GO:0044424]

3.17E-16

475

GO-CC

cell [GO:0005623]

4.05E-15

526

GO-CC

cell part [GO:0044464]

4.05E-15

526

GO-CC

mitochondrion [GO:0005739]

7.42E-12

134

GO-MF

catalytic activity [GO:0003824]

1.50E-03

180

REACT_188937

Metabolism

2.32E-04

125

REACT_247926

Metabolism of proteins

0.004417

58

REACT_237472

Asparagine N-linked glycosylation

0.004713

19

REACT_236283

Post-translational protein modification

0.010703

27

REACT_225686

Autodegradation of Cdh1 by Cdh1:APC/C

0.012911

13

1


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