Hindawi Publishing Corporation BioMed Research International Volume 2016, Article ID 9181702, 11 pages http://dx.doi.org/10.1155/2016/9181702

Research Article Identification of Potential Key lncRNAs and Associated with Aging Based on Microarray Data of Adipocytes from Mice

Yi Yang, Zongyan Teng, Songyan Meng, and Weigang Yu

Department of Geriatrics, The Second Affiliated Hospital ofbin Har Medical University, Harbin, Heilongjiang 150086, China

Correspondence should be addressed to Yi Yang; yangyi [email protected]

Received 22 July 2016; Revised 19 October 2016; Accepted 1 November 2016

Academic Editor: Terri L. Young

Copyright © 2016 Yi Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. This study aimed to screen potential crucial lncRNAs and genes involved in aging. Methods.Thedataof9peripheral white adipocytes, respectively, taken from male C57BL/6J mice (6 months, 14 months, and 18 months of age) in GSE25905 were used in this study. Differentially time series expressed lncRNA genes (DE-lncRNAs) and mRNA genes (DEGs) were identified. After cluster analysis of lncRNAs expression pattern, target genes of DE-lncRNAs were predicted from the DEGs, and functional analysis for target genes was conducted. Results. A total of 8301 time series-related DEGs and 43 time series-related DE-lncRNAs were identified. Among them, 41 DE-lncRNAs targeted 1880 DEGs. The DEGs positively regulated by DE-lncRNAs weremainly related to the development of blood vessel and the pathways of cholesterol biosynthesis and elastic fibre formation. Furthermore, the DEGs negatively regulated by DE-lncRNAs were correlated with metabolism. Conclusion. These DE-lncRNAs and DEGs are potentially involved in the process of aging.

1. Introduction Furthermore, the expression of MMP-3 (matrix metallopep- tidase 3) was increased in mouse subcutaneous fat cells Aging is an elevated risk of common diseases, including and human skin fibroblasts with aging [11, 12]. Additionally, obesity, hypertension, atherosclerosis, and diabetes [1–3]. decreased expression in PPAR𝛾 (peroxisome proliferator- Currently, about 800 million people are at least 60 years activated receptor gamma) through declining fat mass has old, which accounts for about 11% of the world’s population, been observed in monkey subcutaneous whole fat tissue andagingpopulationisestimatedtoincreasetomorethan [13]. In addition to these genes mentioned above, roles 2 billion by 2050 [4]. Aging is closely related to damaged of long noncoding RNAs (lncRNAs) in age-related dis- adipogenesis in various fat depots in humans [5, 6]. White eases have attracted more attention recently [14, 15]. LncR- adipose tissue (WAT) is considered as an important regulator NAs are defined as the largest transcript class in human for multiple physiological processes and highly linked to the genome longer than 200 bp that lack protein-coding potential development of multiple morbidities [7–9]. Therefore, it is [16, 17]. In aging murine aortas, mitochondrial lncRNA significantandurgenttorevealtherelationshipsofaging ASncmtRNA-2 is induced by replicative senescence [18]. and adipose, which is very important for understanding the Abnormal expression of the telomeric repeat-containing diseases in the elderly. RNA lncRNA TERRA is responsible for premature senes- Previous studies have discovered a set of genes that cence and aging through controlling telomere elongation [19]. areimplicatedintheagingprocessinanadiposedepot- In spite of much effort, the lncRNAs with known functions dependent manner. For example, age-related increase in IL- involved in aging remain rare. 6 (interleukin 6), which was related to stress responses and Microarraytechnologyhasbeenwidelyusedinmolecular cellular senescence, was observed in a fat depot-dependent studies of human diseases [20, 21]. Based on an age-related manner [5]. Sirt1 (sirtuin 1) and SOD2 (superoxide dismutase expression profile GSE25905, Liu et al. have found 2), which were correlated with mitochondrial aging, were high expression of genes involved in inflammatory response significantly decreased in aging epididymal adipocytes [10]. and low adipose-specific in bone marrow 2 BioMed Research International adipocytes, and age has a greater influence on gene expression 2.5. Prediction of DE-lncRNA Target Genes. Pearson cor- in epididymal adipocytes than bone marrow adipocytes [22]. relation coefficient (PCC) [30] was used to calculate the However, the effect of aging on expression of lncRNAs is still expression similarity of DE-lncRNAs and DEGs at differ- elusive. ent time points. For each pair of DE-lncRNA and DEGs, In the current study, to investigate the expression varia- significant correlation pairs with |PCC| > 0.95 and 𝑝 tion and functional roles of lncRNAs in aging, the microarray value < 0.05 were used to construct the DE-lncRNA/DEG data deposited by Liu et al. [22] were used to identify regulatory network which was then visualized by Cytoscape the differentially time series expressed lncRNA genes (DE- (http://js.cytoscape.org/) [31]. lncRNAs) and differentially time series expressed mRNA Furthermore, DE-lncRNA target genes that were known genes (DEGs) in the process of aging. Additionally, DEGs to be associated with aging were identified based on the infor- targeted by DE-lncRNAs and their functions were analyzed. mation in AGEMAP (Atlas of Gene Expression in Mouse The results may provide new information for the molecular Aging Project), which is a gene expression database for aging investigation of aging and a deeper insight into aging. in mice (http://cmgm.stanford.edu/∼kimlab/aging mouse) [32]. Subsequently, the regulatory network of DE-lncRNAs 2. Methods and Materials andtheknownaging-relatedtargetswasvisualizedby Cytoscape. 2.1. Tissue Samples and Data Acquisition. The gene expres- sion profile GSE25905 [22] was downloaded from the 2.6. Functional Analysis. (GO) functional Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih and pathway enrichment analyses for genes positively and .gov/geo/) database in National Center for Biotechnology negatively regulated by DE-lncRNAs were carried out using Information (NCBI). The microarray data were produced TargetMine (http://targetmine.mizuguchilab.org/) [33]. The on GPL6246 platform ([MoGene-1 0-st] Affymetrix Mouse 𝑝 value of each GO and pathway term was adjusted by Gene 1.0 ST Array, Affymetrix, CA, USA). This dataset the Holm-Bonferroni method [34], and adjusted 𝑝 value < contained 9 bone marrow adipocyte samples and 9 peripheral 0.05 was considered statistically significant. Additionally, the whiteadipocytes,respectively,takenfrommaleC57BL/6J pathway network was constructed using Cytoscape. mice (6 months, 14 months, and 18 months of age), with three replicates at each age point. In this study, only gene 3. Results microarray data of peripheral white adipocytes were used for further analysis. 3.1. Identified DE-lncRNAs and DEGs. Basedontheanno- tation information in GENCODE and GPL6246 platform, a total of 203 probes were annotated as lncRNA genes, and 2.2. Data Preprocessing. The gene expression profile of 20564 probes were annotated as mRNA genes. With the GSE25905 was preprocessed by the Robust Microarray Anal- cut-off of probability >0.9, 8301 time series DEGs and 43 ysis (RMA) algorithm [23]. The Affy package (available DE-lncRNAs were identified in peripheral white adipocyte at http://master.bioconductor.org/packages/release/bioc/html/ samples. affy.html) [24] of R. Probe IDs in CEL document was trans- lated to corresponding gene symbols. If one gene symbol was 3.2. Clusters of DE-lncRNAs Expression Pattern. To further matched by multiple probe IDs, the mean expression value explore the changes of the DE-lncRNAs expression levels wasselectedastheexpressionlevelofthisgene. at the three time points in peripheral white adipocytes, the cluster analysis was conducted. The samples at different time 2.3. Identification of DE-lncRNAs and DEGs. Based on anno- points were distinguished by DE-lncRNAs. The expression tation information of lncRNAs in GENCODE (http://www values of nearly half of DE-lncRNAs showed an uptrend .gencodegenes.org/) [25] and the array platform GPL6246, in 6–14 months and a downtrend in 14–18 months (e.g., expression data of lncRNAs were obtained. Afterwards, the ENSMUSG00000086859 and ENSMUSG00000061510); a set BETR (Bayesian Estimation of Temporal Regulation) algo- of DE-lncRNAs were expressed in a decline trend (e.g., rithm in the BETR package (http://betterpackages.com/) [26] ENSMUSG00000087540 and ENSMUSG00000032048); and was applied to identify DE-lncRNAs and DEGs at the three a small fraction of DE-lncRNAs were expressed in a rising time points, and this algorithm calculated the probability of trend (e.g., ENSMUSG00000066057) (Figure 1(a)). > differential expression for each gene. The probability 0.9 was According to the results of clustering analysis, 41 DE- set as the cut-off criterion. lncRNAs were divided into 11 clusters (Table 1). It was clearly observed that, with the increase of age in mice, DE-lncRNAs 2.4. Cluster Analysis of DE-lncRNAs Expression Pattern. Hier- in clusters 7 and 10 were expressed in a rising trend, whereas archical clustering is an analytical tool applied to discover DE-lncRNAs in clusters 2, 4, and 8 were expressed in a decline theclosestassociationsbetweengeneprofilesandspeci- trend. Clusters 5 and 11 showed an uptrend in 6–14 months mens under evaluation [27, 28]. In our study, the BHC and a downtrend in 14–18 months (Figure 1(b)). (Bayesian Hierarchical Clustering) package (http://master.bio- conductor.org/packages/release/bioc/html/BHC.html) [29] 3.3. DEGs Targeted by DE-lncRNAs. LncRNAs have critical of R was utilized to perform clustering of DE-lncRNAs and roles in the transcriptional regulation via modulating the construct the cluster heat map of DE-lncRNAs and samples. gene expressions. To further investigate the regulatory BioMed Research International 3

Color key and histogram 20 10

Count 0 −2 −1 0 1 2 Row Z-score

ENSMUSG00000086859 ENSMUSG00000061510 ENSMUSG00000086712 ENSMUSG00000046463 ENSMUSG00000074071 ENSMUSG00000053117 ENSMUSG00000074415 ENSMUSG00000021268 ENSMUSG00000085385 ENSMUSG00000056145 ENSMUSG00000073067 ENSMUSG00000053656 ENSMUSG00000059244 ENSMUSG00000053957 ENSMUSG00000089829 ENSMUSG00000054135 ENSMUSG00000085998 ENSMUSG00000044471 ENSMUSG00000046005 ENSMUSG00000020033 ENSMUSG00000087540 ENSMUSG00000037982 ENSMUSG00000056716 ENSMUSG00000032048 ENSMUSG00000086788 ENSMUSG00000021874 ENSMUSG00000079407 ENSMUSG00000072679 ENSMUSG00000074146 ENSMUSG00000072686 ENSMUSG00000056699 ENSMUSG00000066057 ENSMUSG00000050538 ENSMUSG00000048824 ENSMUSG00000055972 ENSMUSG00000090086 ENSMUSG00000054618 ENSMUSG00000038152 ENSMUSG00000089652 ENSMUSG00000072761 ENSMUSG00000028475 GSM635893.CEL GSM635894.CEL GSM635895.CEL GSM635889.CEL GSM635887.CEL GSM635888.CEL GSM635901.CEL GSM635899.CEL GSM635900.CEL 14 months 6 months 18 months (a)

10 Cluster_10

Cluster_11 9 Cluster_9

Cluster_7 8

Cluster_8 7 Cluster_2

Cluster_1 6 Cluster_3 5 Cluster_5

Cluster_4 4 Cluster_6

6 months 14 months 18 months (b)

Figure 1: Heat maps of differentially expressed lncRNA genes. (a) Heat map of differentially expressed lncRNA genes in peripheral white adipocytes samples from male C57BL/6J mice being 6 months, 14 months, and 18 months of age. Each row represents a single gene; each column represents a sample. The gradual color change from red to green represents the changing process of expression level from upregulation to downregulation. (b) Heat map of clusters of differentially expressed lncRNA genes at 6, 14, and 18 months. Each row represents a cluster; each column represents a time point. LncRNA, long noncoding RNA. 4 BioMed Research International

Table 1: Differentially time-series expressed long noncoding RNA genes in each cluster.

Cluster Count Differentially time-series expressed long noncoding RNA gene ID number 1 3 ENSMUSG00000079407, ENSMUSG00000072686, ENSMUSG00000032048 2 2 ENSMUSG00000072679, ENSMUSG00000073067 ENSMUSG00000056145, ENSMUSG00000090086, ENSMUSG00000074071, ENSMUSG00000046463, 38 ENSMUSG00000055972, ENSMUSG00000053957, ENSMUSG00000048824, ENSMUSG00000053117 ENSMUSG00000046005, ENSMUSG00000021874, ENSMUSG00000056699, ENSMUSG00000059244, 46 ENSMUSG00000056716, ENSMUSG00000086788 5 1 ENSMUSG00000061510 6 3 ENSMUSG00000087540, ENSMUSG00000074146, ENSMUSG00000020033 7 4 ENSMUSG00000050538, ENSMUSG00000054618, ENSMUSG00000038152, ENSMUSG00000089829 ENSMUSG00000054135, ENSMUSG00000053656, ENSMUSG00000086859, ENSMUSG00000086712, 86 ENSMUSG00000074415, ENSMUSG00000021268 ENSMUSG00000085385, ENSMUSG00000066057, ENSMUSG00000037982, ENSMUSG00000028475, 96 ENSMUSG00000085998, ENSMUSG00000072761 10 1 ENSMUSG00000089652 11 1 ENSMUSG00000044471

Table 2: Results of Gene Ontology functional and pathways enrichment analyses for genes positively regulated by differentially time-series expressed lncRNAs.

Category Term Adjust 𝑝 value Gene count Genes Vasculature development Adamts1, Cdh5, Ctsh, Cxcr3, Dhcr7, Edn1, Efna1, Efnb2, GO-BP 1.33𝐸 − 08 65 [GO:0001944] Notch3, Pdgfrb,andsoon Blood vessel development Adamts1, Cdh5, Ctsh, Cxcr3, Dhcr7, Edn1, Efna1, Efnb2, GO-BP 1.34𝐸 − 07 60 [GO:0001568] Notch3, Pdgfrb, and so on Regulation of locomotion Dab2, Ifitm3, Il16, Il33, Irs2, Megf8, Myo1f, Pdgfrb, GO-BP 2.45𝐸 − 05 51 [GO:0040012] Pecam1, Pkn1, and so on Blood vessel Adamts1, Aqp1, C3, Ccr2, Edn1, Efna1, Efnb2, Egfl7, Elk3, GO-BP morphogenesis 2.55𝐸 − 05 49 Ephb4, and so on [GO:0048514] Regulation of cellular Ccl21a, Dab2, Ddr2, Dpep1, Efna1, Nup155, Pde4d, Pdgfra, GO-BP component movement 3.92𝐸 − 05 50 Pdgfrb, Pecam1, and so on [GO:0051270] Ackr3, Alcam, Cd200r1, Cd3e, Dpp4, Enpp1, Flt3l, Heg1, GO-CC Cell surface [GO:0009986] 2.46𝐸 − 05 53 Ifitm3, Il2rb, and so on Plasma membrane Ano1, Antxr1, Aqp1, Capn3, Ccr2, Itga5, Itm2c, Kcnab1, GO-CC 9.39𝐸 − 05 158 [GO:0005886] Kcnn3, Kcnt2, and so on Cell periphery Antxr1, Aqp1, Bcas3, Capn3, Eps15l1, Ezr, Krt19, Lime1, GO-CC 2.59𝐸 − 04 163 [GO:0071944] Ntn4, P2rx4, and so on Side of membrane Alcam, Ano1, Cd74, Ikbkb, Il2rb, Itga5, Kdr, Ly6a, Ly6c1, GO-CC 0.015056 33 [GO:0098552] Pkp4, and so on Plasma membrane part Klri1, Lime1, Ly6a, Npc1, P2rx4, Sema6a, Sept2, Tspan15, GO-CC 0.021127 101 [GO:0044459] Upk1b, Zdhhc2, and so on REACT 208531 Cholesterol biosynthesis 4.00𝐸 − 03 9 Cyp51, Dhcr7, Fdps, Hsd17b7, Idi1, Lss, Mvk, Sc5d, Sqle Efemp1, Efemp2, Fbln1, Fbln2, Fbln5, Furin, Itga5, Loxl1, REACT 198996 Elasticfibreformation 5.10𝐸 − 03 11 Ltbp1, Mfap3, and so on The GO-BP terms in the table are the top 5 ones with a higher adjusted 𝑝 value. DE-lncRNA, differentially time-series expressed long noncoding RNA gene; GO, Gene Ontology; BP, biological process; CC, cellular component. “REACT” terms are the pathway terms. functions of DE-lncRNAs, the DEGs regulated by DE- 9181702). The constructed regulatory network consisted of lncRNAs were analyzed by the PCC algorithm. Based on 41 DE-lncRNAs and 1880 DEGs. The DE-lncRNAs ENS- the cut-off criteria, 2313 regulatory relationships between MUSG00000066057, ENSMUSG00000086859, and ENS- DE-lncRNAs and DEGs were obtained (see Supplementary MUSG00000061510 modulated more DEGs than others. Material available online at http://dx.doi.org/10.1155/2016/ ENSMUSG00000086859 targeted genes like Efna1, Fbln1, BioMed Research International 5

Pde7b Ranbp6

Nucks1 Tubgcp2

Fbxo25 Rhbdd1 Tprkb Lxn Imp4 Gstm7 Smc3 Ppp2r5c 2810008M24Rik Becn1 Zfp148 Ddah1 Polr3b Ern1 Zfp414 Mark3 Hvcn1 Tssk1 Rab27b Rabgap1 Zfp513 Etfdh Med6 Exoc4 Acp2 Copg Atg14 Tbc1d13 Aff2 Angel1 Uso1 Dysf Gba Olfr1428 Cyp51 1700049E17Rik1 Dennd3 Ddx20 Nsun6 Map2k1 Polr1c Gm10432 Sec23b Vti1a Nedd1 Cbara1 Cobl Itfg3 St3gal1 Mlxip Hn1 Sep15 1500011K16Rik Slu7 Il18bp Mtdh 4930528A17Rik Mblac2 Klhdc5 Cux2 Zdhhc8 Med12 Ism2 Pstk Cdc42bpb Bbs7 Tmem19 1700026J04Rik Sdhaf2 Gm4979 Zscan4-ps2 ENSMUSG00000032048 Nme7 Hecw2 Adal Zfp110 Hscb Atpbd4 BC003331 Cdk5 Ndfip1 Rb1 G6pd2 Cdc123 ENSMUSG00000020033 Sat2 Wdr75 G6pdx ENSMUSG00000056716 Epdr1 Zfp81 Rapgef1 Ctps2 Fpgt ENSMUSG00000050538 Matn2 Aasdhppt Alg3 Zfp426 Alkbh3 Slc36a2 Yipf1 Agpat4 Slc15a5 Pacs2 2900010M23Rik Agtpbp1 1110057K04Rik Adap2 Morn2 Nup88 Mcf2l Lgals8 Zfp192 Aldh4a1 Crot Sit1 Usp45 2410002O22Rik Tfg Idi1 3110048L19Rik Nudt3 Mtfmt Hexim1 Olfr1373 Adap1 Psmb4 Rars2 Nudt2 N6amt2 Serpinb9 Nupl1 Ercc2 Mn1 Pdzd11 Tsen2 Scrn3 Fap Trps1 Acad9 Uevld ENSMUSG00000053957 R3hcc1 Bptf Mtif2 Gpr4 Ube2w Ccdc104 Fgf1 Smpd2 Vac14 Kpna4 C130026I21Rik Ssr3 Decr2 Alb Ltn1 Adsl Pdia4 Ppp2r5a Gm807 Hhipl2 Fig4 Mea1 Ptcd2 Scfd1 Sqle Zfp654 Rarres2 Gm8675 Tmem44 BC048403 Pcyt2 Ankrd32 Sgcb Flot1 Shank3 Stk35 Ufsp2 Myl4 G630090E17Rik Zfp235 Nek6 Etfb 5033414D02Rik Man2a1 Olfml1 Gtf3c6 Crym Ppp2r5b Gm900 ENSMUSG00000090086 Ccdc86 Lars Gstk1 Sdc2 Mett10d Rqcd1 Nr2c1 Dhdds Gm10158 Phkg2 Eif4ebp3 Copb1 Pm20d1 Fam175a Prpsap2 Ism1 Mal2 Sptlc1 Med21 Parp9 Dock7 Tpi1 Rai2 Retnla Mlkl Eid2b Zfp277 Alg12 Rab35 5430437P03Rik Clca5 Dclre1a Gyk Pank2 Rfc4 Hsd17b7 Fam46b Alg11 Slc25a10 Chrna5 Sash1 Rxrg Dnajc15 Fkbp7 Wdr55 Eif2s1 Ppp2r2a Hoxc5 Tada1 Ptges2 Zmiz1 C530030P08Rik Lss Phkb Nutf2 Glt8d1 Htra1 4430402I18Rik Pter Psmd6 Sgce Slc12a9 Ntm Gosr1 Rmi1 Hn1l Acot7 0610009B22Rik Amz2 Crnkl1 Arl6 Tmem184c Acsl3 Lactb2 Smg1 Copg2 Hars2 Psma2 Phb Ttc9c Lman1 Zfp619 Actr10 Exoc6 Faf1 1700009P17Rik Pkp4 Nudcd1 Dido1 Hoxc10 St6galnac5 Pgls Myadm Idh3b Copz1 Bbs5 Sar1b Atp10a Dctn5 Zdhhc20 Hgf Pmpca 9030625A04Rik Pomp Rbbp9 Slc25a17 Umps Psmd5 Slc22a4 Gfpt2 Mrps6 Hapln3 Frrs1 Cd86 Tfb1m Rpe E030010A14Rik Heg1 Ccnh Rnf141 Lonp2 Snupn Cab39l Pgp Tug1 Eif3b 1110032A13Rik Gtf3c4 Creb5 Has2 Fam82a1 Il3ra Fgfr1 Plac1l Mlx C130074G19Rik ENSMUSG00000054135 Slc30a6 Extl2 AI452195 5730455P16Rik Gm13926 Gbas Cops6 Btnl9 Fastkd2 Srp72 Frmpd1 Fam158a ENSMUSG00000079407 AW209491 Ncoa4 Vps35 Ccdc85b Capza2 Fam114a2 Tmem70 Lig4 Pik3c2b Akr1e1 Cryz Tlcd2 Hba-a1 Itga5 Lima1 Mrpl16 Tie1 Ddt Tbc1d24 Fbxo36 Hdac9 Stip1 Upf3a Ltv1 Skiv2l2 Solh Ptbp1 Fmr1 Gm13154 S100a11 Ap3s2 Psmd3 Sacm1l Fam96a Mettl8 Eltd1 Synj2bp Notch2 Mr1 Zfp788 Hsbp1 2410091C18Rik Zcrb1 Usp5 1110002N22Rik Fam92a ENSMUSG00000073067 Lrrc8a Rlbp1 Wdr48 Vmn1r177 Rab11a Rell1 Ldha Prkx 6330416L07Rik Sarnp 5033411D12Rik Ptpru Lrp1 Sh3bgrl2 Mavs 2900010J23Rik Fdxacb1 Srr Gm11818 Rasgrf2 Gm10456 Ramp2 Usp39 Zdhhc2 Car13 Pafah2 Golt1b ENSMUSG00000066057 E2f6 Fdps Cnot8 Rpl17 Brms1l Rsph3a Gnpnat1 Psma4 Tatdn1 Gpr116 Tshz2 Etl4 Sub1 Mvk Fam65a Fabp5 Vps45 Tacc2 Lrrc59 Dusp19 Morn3 Uba52 Cd163 Fkbp14 Fam36a Cd97 Fancl Taco1 1110032A03Rik Ufm1 Tbc1d19 Ccdc155 Trmt2b Eif5a Tnfrsf25 Ccdc58 Vti1b 2210408I21Rik Gm6623 Cops3 D030074E01Rik Acsf2 Pten Ddx56 Sf3a3 Usp54 Med8 Srpx2 Zfml Fam175b Pot1a Il6st Large ENSMUSG00000072761 Mgat2 Mis12 Dap3 Tmem126a Tubb6 Recql Fam45a Ttc37 Zeb2 Rasip1 Nid2 Mustn1 Spaca1 Ubxn2b Ppdpf Dars Cdc16 Dgkh Hbp1 Cul5 Mup1 ENSMUSG00000072679 Ankrd11 Ggh Cd59b Emp2 Mettl4 Psmc6 Nmd3 Akr1c13 Ndrg1 Parp3 Stam2 Ube2l3 Igsf3 Gpn1 Zfp652 Hs3st1 Zyx Pkd1 Mtpap Ssbp1 Tagap Flt1 Abcb4 Rnasel Psme1 Tmem18 Wdr5b Ahr Gpatch1 BC003267 Osgepl1 Clcf1 Pqlc1 D17Wsu104e Cdc23 Arhgap10 Nt5dc1 Cinp B230120H23Rik Tax1bp1 Tnrc6c Txnl4b Pde7a Thumpd1 Acat2 Ppp2r2d Cd93 Sphkap Heyl Exoc3 Coq2 Nedd4l Leng8 Gins1 Mup2 Fam43a Ptgfr Zfand2a Ptcd3 Pts Folh1 Oat Slc16a6 l7Rn6 Zdhhc21 Pqlc3 Vkorc1 Gm7061 Acaa2 Tecr Gbp2 Ttc38 Tmf1 Noc3l BC006779 Olfr179 Hoxd8 Arfgap3 Golph3l Tdp2 Slco2b1 Tspan7 B4galt5 Ints9 Dph2 D16H22S680E B3gnt1 Slc35a1 Mina Tspan31 Pkp2 Fktn Parvb Epb4.1l2 Gm572 Ski Zfp260 Gclc Nr1d1 4933426M11Rik Plek B4galt1 Tmem183a Ppa2 Ccdc122 Dis3l Cyb5b Api5 Fam48a Gm10790 Strap Commd3 Polr3c Islr Gm10115 Dhcr7 Mrps22 Prps2 Prkaca Ednrb Ypel3 Slfn2 Sftpc Pdha1 Elovl6 Ppp1r12c Usp14 Hes1 Gfm1 Hps5 Tm4sf1 Spata13 Idh3a Myc Ophn1 C5ar1 Spopl Atg4b Ifit2 Prps1 Rnf170 Epm2aip1 Naip2 Plod3 Mrpl44 Phf12 Upf2 A630089N07Rik Rai14 Ptpn2 H3f3a Gpr89 Fgd5 Glod4 Scaf1 Eif3c Fmo3 1700021K19Rik 2700062C07Rik Alcam Snrpa1 Esf1 Rrn3 Ddx3y Csgalnact1 Ptprb Akap13 Arhgef37 Uba6 Man1a Plekho2 Pls3 Zswim1 Pnrc2 Hivep3 Mrpl38 Bag1 Rnls Ccdc80 Derl1 Bckdhb Pid1 Snx10 Bcas3 Nav1 Cldn10 Slc35f2 Fgl2 Hoxa9 Usp8 Maf Ampd1 Sema6a S1pr1 Rab43 Uba3 Ndufs1 Tlcd1 Echdc1 Chka Gnl3 Oxsm Lrrc39 Mat2b Rnf166 Pde12 AU021092 Ghdc Rpp40 Procr Fut11 Cmpk1 Muted Zfp281 Add1 Asah1 Mllt4 Efnb2 Spock3 Npc1 Dhx29 Ppp1r7 Uspl1 Bcl2l1 Lpl Cdh5 Ppp2r3c Fras1 ENSMUSG00000089829 Rhoj Wdfy3 Rabggtb 4921508M14Rik Serping1 Il17rd Mfsd8 Popdc2 Adam30 Sfswap Ugp2 Wdr1 Aard Vav3 Mmp23 Nipsnap3b Dnajc21 Fabp9 Bcl6b Secisbp2l Lyn Clic6 Srrm2 Socs3 Zfp36 Chac1 Dynll2 1190002H23Rik Tspan15 Inmt Ndufc2 Tmem135 Dock9 Glrx Apol8 Acap2 Sepp1 Fam54a Ly6e Grhl1 Pnpla6 Spcs3 Stambp Fam49a Cry2 Riok3 Ccdc71 Ndufaf2 Pacsin2 1110007C09Rik Dnali1 Gemin5 Txlna Cbx7 ENSMUSG00000053656 Csrnp1 ENSMUSG00000046463 Rnf169 Trib1 Efr3a Fkbp2 L3mbtl2 Zfp90 Kcnn3 Cxcr7 Nln Lias Dbnl Gm10694 Kcnq4 Ddx17 Fubp3 Sc5d Dlgap4 Tmem60 Arhgap24 Decr1 Dgat1 Synj2 ENSMUSG00000037982 Myl6 ENSMUSG00000074415 Cic Magi3 Rbm41 Brp44l Grn Acaca Maff Celsr2 Sh3d19 Nfyc Mrpl51 Pramef8 Serpinf1 Irak4 Baz1a Gps2 Gm11711 Dab2 Plaa Sh2b3 Gm10521 Pon3 Ifi205 H2-Eb1 Lpcat3 Pam16 Zfp187 Papln Ppargc1a Pitpnc1 Nr2c2ap Rps4y2 Eif2s3x Ddx60 Aim1 Cth ENSMUSG00000074146 1300014I06Rik Nfil3 Sema3a Parm1 Hnmt Tnfrsf11a Egr1 Plekhb2 Ina Lypla1 A230046K03Rik Ldb1 Pgd Mme Mtch2 Ptgis Kdr Fasn Ldhc Rasal2 Pex3 Wt1 Zfp59 Tirap Homer1 Suclg2 Spry1 ENSMUSG00000021268 Fhl1 Galnt2 Fam174a Nup155 Plekha1 H2-DMb2 Ccl9 Ripk3 Dusp4 Fbxo30 Pde2a Uqcrc2 2310002L09Rik Adamts1 Ints10 Pecam1 9430016H08Rik Krt19 Bcl6 Ngf Ets1 Slc25a38 Capzb Klraq1 Zfp57 Kctd12b Kctd4 Pea15a Stk4 Itpkb Alg8 Ap1s3 Tiparp Bat2l2 Npr3 Slco2a1 S1pr3 Notch3 Psmb9 Mlf1ip Abi3 Tceb3 Mcee Usp16 Nxph1 Cdc27 Ccl2 Cxcl16 Lyplal1 Angptl4 Insr Klhl12 Abhd6 Cant1 Atp11a Tmc4 Pex11a Katnal2 Csf2rb Clec7a Olfr370 Peo1 Flnb Klf3 Fbxl4 Nrip1 Casp2 1810013D10Rik Fkbp10 Tmem67 Pf4 Rufy3 Scoc Fdx1 Adamts9 AI607873 Armcx5 Fos Tmem86a Cd3e Syde2 Trim12a Ctage5 Mccc2 S100pbp Vit Snord95 Galm 4931406C07Rik Dgkd Slc25a26 Cd9 Gbgt1 Trafd1 Cpeb2 Eif4e3 Ola1 Acn9 9230110C19Rik Siah2 Abhd15 Ankfy1 Mll1 Il22ra2 Rps6ka3 Myh9 Atp5h Slc25a43 Tank Efemp2 Inpp4a Gas6 Fes Olr1 Atxn1l Trp53inp1 Daglb Ddah2 Cma1 Zbtb11 Mgmt Rabl3 Zdhhc18 Snx2 Rab20 Bach1 Tbc1d23 Tbc1d15 Lap3 Sfrs18 Sept8 Psd4 Sult1e1 Ccbp2 Spg21 Sema3b Bcs1l C1galt1c1 Prdm1 Taf1a Csad Rnf220 Thnsl1 Uba7 Crkl Lepre1 Nkain4 Gadd45b Aifm1 Alpk1 C1qtnf7 ENSMUSG00000044471 Ppp1r10 Cd74 Tcf21 Thoc3 Kdm6b Trim34 Fmod Ank2 Tmem134 BC017643 Cdyl2 Tnfrsf12a BC088983 Lmo7 Robo4 Sept2 Cox7b Cyb561d1 Ranbp2 Rgs7 Cbr3 Gng2 Pam Prkch D10Ertd610e Fxr1 Anapc11 Gbp6 Golga1 Bloc1s2 Letm1 Taf4b Abca6 Opa1 Rft1 Sbno2 Papolg Slc22a5 Gimap6 Rnase4 Fam83f Lonrf1 Adcy4 Tmem150b Slc44a2 Snord15b St13 Abcc3 Aspn Sgtb Zbtb6 Nfe2l1 Endou Pdcd11 Acadvl Pitrm1 Zmiz2 Fbln5 Ptprq Icam2 AI317395 Mdk Bag3 Zfp84 Bbs10 Mecr Serpine2 Tatdn2 Sulf1 Dennd4c Zbtb38 Dstyk Mrpl27 Pla2g6 Man2a2 Trp53inp2 Megf8 Rapgef3 Arih2 1110036O03Rik Ephx1 Aqp1 Igf1 Dgka Rbm14 Dcun1d5 Dda1 Mfap5 Tmem88 Mtap9 Pxdn Etv3 Kbtbd7 Irs2 Cd209d Lgals9 A730017L22Rik Spg7 Siglec1 Srxn1 Slc7a11 Agpat6 Ephx3 Grap Mcc Mmp14 Chst11 Slamf9 Ghr Itgb1bp1 Trim69 Spry3 Zfp563 Plekha2 Prorsd1 4933409K07Rik Fgl1 Tmed2 Mrc2 Klhl2 4930518I15Rik Lrrfip1 Midn Picalm Tbrg1 Dlg4 Ell2 Cd209f Fry Spg20 Jund Rnf219 Fas Pla2g4a Hmox1 Cox19 Mynn Zfp131 Kcne4 Trim21 Cxcl9 B3galtl Gm12942 Etv1 Pnp2 Lpxn Slc2a1 Sdc4 Cd109 Dhrs7b Cd2ap Prokr1 Rab3gap2 A930001N09Rik Plxna2 Zfp87 Gm6377 Agpat3 Scn3b Il16 Cebpb Stk40 Atp5l Tshr Hsdl2 Cenpb Cish Plekhf1 Ier5 Lifr Trfr2 1300010F03Rik Chd1 Ptrf ENSMUSG00000087540 Thyn1 Afap1 Antxr2 Osr1 Elp2 Tpsb2 Rasd1 Katna1 Zfp386 Cyp21a2-ps Chst1 Orc6 H2-Ke6 Zfp655 Ddr2 Fchsd2 Tlr8 Fosl2 Ano1 Gars Wasl Prss23 A4galt Stat3 Gcnt2 Gm10046 Gpr21 Dot1l Stxbp3a Zfp143 Add2 Itga11 Pisd-ps1 Tram1 Dnahc3 Ralgds 2900073G15Rik Snrpd2 Plekhh2 Car7 Zranb1 Oasl2 Elmo2 Smc6 Dsel Pi15 Dnaja3 Gclm Ms4a6d Vegfa Gm7609 Aff1 Capn3 Stat2 Mtmr11 Foxred2 Pamr1 Adamtsl5 Cd81 Aff4 Apobec1 Ccdc43 Dagla Auts2 Slc12a2 ENSMUSG00000085998 Cxcl10 Dnajb14 Kcnab1 Jak1 Wdr91 Mast3 Naaladl2 ENSMUSG00000089652 Eif2ak3 Echdc2 Ms4a8a Rrs1 Mettl3 Igf2bp2 Vat1l Serpine1 Upk1b Vasn Tlr4 Slc1a5 Etfa Mfap3 Retsat ENSMUSG00000085385 Egr2 Papola Txnrd1 2010001K21Rik Psap ENSMUSG00000061510 C2 Slc25a25 Zfp101 Sfrp1 Plekho1 Nop58 Atp8a1 Klra5 Itpkc Ltbp3 Slc41a1 Ptgs2 Anxa3 Prcp Efhd1 Cct7 Nav3 Fads2 Iah1 Brcc3 Edn1 Arl5b Mbip Ms4a4d Zfand3 Jak2 Morc3 Slc2a12 Loxl1 Rbm12b Col14a1 Lgi2 Dcn Itpr3 Tob1 Ptprm Rprd1a Cd209g Mocs1 Dusp8 Gpx1 Slc10a6 Ptgr1 Crip1 Mrs2 Slc35b4 Cs R3hdm2 Slc22a23 Gpr108 Orc5 Pmp22 Rnf125 Syngr2 Sphk1 Iqsec1 Csprs Vegfc Plxnb2 Brd2 Rtn2 3010026O09Rik Ugdh Tmem8b Acvr1b Gadd45a Suclg1 Hk2 Penk Ccbl2 Xpnpep2 Arhgef3 Avpr1a Dpp4 Metrnl Adcy5 Ogn Ncoa1 Abca8a Gm16848 Irak1bp1 Dbn1 Gsta3 Vwa5a 1700029I01Rik Cdon Col4a1 Larp1 Ccnl2 Mkl2 Reck Uap1 Pdzd2 Il15ra Rgs10 Sema3c Zfp955a Thbs1 Huwe1 Nfkbiz Pcdh18 Dennd5b 1700017B05Rik Pias1 Cobll1 4930466F19Rik Pla1a Ntn4 Arid5a Arl13b Samm50 Tnfrsf23 Enpp3 Ak3 Ppp1r14a Dpysl2 Myd88 Gm13139 Cpa3 Amy1 Rnf44 Ppm1m Prrx1 Wdr93 Exoc6b Ezr Thnsl2 Immt Cpt1b 1110054O05Rik Rnd1 Zc3h12a Atp1b3 Slit2 Pcolce Ccl8 Ifitm3 Ccl4 Psmb8 C7 Hspb6 Sele Mcam Selm Tmem229b Hibch Plce1 Tbxas1 Cd2 Slc37a1 Rela Ptplad1 P4ha2 Bcar1 Chdh Snord32a Cdc42se2 Prkd1 Rtn1 Olfml3 Abca9 Hspd1 Btbd3 Pex13 Prdm5 Gm10419 Esyt2 Tram2 Crtc2 Zfp54 Ptplad2 Psma1 Airn Oxsr1 C87436 2610008E11Rik Mef2c Hic1 Lmna Acyp2 Clpx Ace Rab11fip5 AW549877 Trex1 Abca2 Dpep1 Sfrp4 Ell Vat1 ENSMUSG00000072686 Des Tmem214 A530099J19Rik Cfh Gja5 Cdkl2 Slc43a3 Mmp11 Zfp322a Tle3 Prkab2 Npy1r Axl Stard8 Tmem48 Lrp5 Scamp2 Tbc1d14 Scara3 Lrig1 Bcar3 Cnnm2 Rbp1 Tnfrsf1a Smoc2 Cxcr3 Cass4 Wdr85 Fkbp9 Setd7 Gabra3 Rnpepl1 P2rx7 Snx29 Ticam2 Adamts4 Pyhin1 Mcpt4 Lyrm5 Mtap7d1 Taok2 Fndc3a Sstr4 Bnc1 Sigirr Lrrfip2 Pdgfra Lgi4 Egflam Pi4ka Tmpo Antxr1 Bank1 Podnl1 Tmbim1 Nubp2 Cd68 Cysltr1 Fam180a Zfp60 Ccl3 Kcnt2 Nova1 Iars2 Camk1d Dnajb1 Sorbs2 Sp100 Plvap Top1 Tmem38b Furin Slc9a9 Eny2 Sfxn3 Mmrn2 Timp3 Prkacb Slc16a2 Fgf18 Vegfb Fam165b Tgoln1 Ehd4 Gbp4 St6galnac4 Zfp869 Fam189a2 Plac9 Flt3l 6030429G01Rik Clcn6 Ms4a14 Rpl7a Sgcd Slc9a3r1 Dnase1l2 F3 Tnfsf10 Litaf Ly6c1 Limk1 S100a6 Cyp4b1 Cacng7 Itgb7 Fnbp1 Susd2 Itgbl1 Gadd45g Sephs2 Lime1 Ttc14 C630004H02Rik Etf1 Gas1 Ubash3b Rab9 Agtrap Gfpt1 C1rb Stard9 ENSMUSG00000086712 March3 Gnb4 P2ry13 Ppl Ly6a 6530401D17Rik Gpc4 Met ENSMUSG00000086859 Ano10 Flrt2 Capg Nfatc2 Mobkl2b Slc18a2 Ccl21a 4930578C19Rik Asap3 Tcf4 Sdha 9930111J21Rik2 Itm2c Isyna1 Igfbp7 Apol9a Atrn Efna1 Thbs3 AK129341 Abr Lmo4 Zfp142 Cyth4 Gm9975 Ltbp1 Rundc2a Ggta1 Sirpa Ms4a6b Pip4k2a Ppp2r3a Wisp2 Sfrp2 Cygb Serpinb8 Lonp1 Arhgap31 Hdgfrp2 Egfl7 Uap1l1 Slc44a3 Ptpro Mapk7 Lpcat1 Fbln2 Lhx1 Sccpdh Mical1 Mmgt1 Senp5 0610011L14Rik Arntl2 Zfp691 Tmem45a Gpr183 Rpl36al Tarsl2 Ikbkb Nes Ankrd10 Fcrls 2510012J08Rik Il10ra BC096441 Ephb4 Fbln1 Rab8b Fgf10 Cpxm2 C3 Enpp1 1190002A17Rik Sytl2 Sod2 Gimap3 Cln8 Lpar1 Trip10 Map3k3 Kmo Igl-V2 Pdgfrb Slc27a3 Gm1077 ENSMUSG00000038152 C330018D20Rik V165-D-J-C Creld2 Zfp746 Tbc1d4 ENSMUSG00000056699 Cd209a Scarf2 Chkb Plxdc2 St6gal1 Zdhhc1 ENSMUSG00000086788 Art3 D2hgdh Fst B9d1 Il9 ENSMUSG00000048824 Elk3 Sox18 Faim3 Pde3a Sept1 ENSMUSG00000056145 Nphp1 Efemp1 Calcrl Leprel2 Ggct Txnrd3 Abcc4 Rgs7bp Cd37 Klri1 Ctsh Ly6d Caprin2 Rfwd2 Rbm5 Bnip3 Tmem181a Col6a3 Tln1 Clec2d 1110031I02Rik Coro1c Tpm1 Sh3bp2 Cln3 Setd6 Evi2a Eps15l1 Cd3g Lum Myo1f Rftn1 Pnpla8 Scpep1 Ranbp9 Zfp521 Tmem204 Pgm5 1110001A16Rik Lrrc49 Igj Obfc2a Atp6v1h Gm26 Map4k1 Abcd3 Slc25a37 Gimap1 Olfr398 Tns3 Acin1 Dpep2 4930506M07Rik Spon1 Mgp Col1a2 Atp8b2 Tinagl1 Ankrd23 Sh3gl1 Aldh1a3 P2rx4 Cd80 Ccr2 Ttll4 Meox2 Srgap2 Aldh1a2 Pcdh11x Phldb2 March5 Rcn3 Got1 Gpd2 2010110P09Rik Nr1h4 Smap2 Cybasc3 Unkl Ctsz Herpud1 Myh11 Sept11 Pak1 Man1c1 Trim2 2900062L11Rik Mtmr12 Nceh1 Adssl1 Pkn1 Tshz1 Trim24 Naip5 Slc37a2 Gba2 Arc Myl9 Cd200r1 Pou2af1 Gmip Hpcal1 Klb Mfsd6 Il10rb Cox6b1 Ly9 Pja1 Vsig4 Mif Rpl10 St8sia4 Wdr82 Rhog ENSMUSG00000053117 Mpp6 Ccr5 Klhl6 Plagl1 Arhgap25 Il2rb Spib Trim3 Cd300lb Gm10808 Myct1 Stat4 Ppargc1b Ptpn6 Hint2 Gcnt1 Ndst2 ENSMUSG00000046005 Gm9900 ENSMUSG00000028475 ENSMUSG00000055972 ENSMUSG00000059244 ENSMUSG00000021874 ENSMUSG00000054618 Pigl Tmsb10 ENSMUSG00000074071 Pik3cg Rasa4 Csf2ra Mir22 Abhd5 Snai2 Acsm3 Fkbp15 Bmf Steap3 Cabin1 Zfp710

Dcaf7 Il33 Igk-V1 Ms4a1 Ap2m1 Pik3ap1 Irs1 Cd22 Naip6 Cd52 Ephb3 Ebp Frzb Sap30 Supt3h Cables2 Slc6a6 Crlf3 A630001G21Rik Sos2 Acox1 Csk Cdan1 Prkd2 Six2 Snx5 Nfxl1 Clec4a2 Coro7 Cklf Sla

Optc Sipa1 Cd19 Tbc1d1 Dusp7

Figure 2: The regulatory network of 41 differentially expressed lncRNA genes and their target mRNA genes. The diamonds represent lncRNA gene IDs, and rectangles represent mRNA genes. The purple nodes represent the target genes of ENSMUSG00000066057; the blue nodes represent the target genes of ENSMUSG00000061510; the green nodes represent the target genes of ENSMUSG00000086859. LncRNA, long noncoding RNA. and Fbln2. ENSMUSG00000066057 regulated the DEGs, vessel, such as vasculature development and blood vessel such as CYP51, FDPS,andEif2s1 (Figure 2). The expression morphogenesis (Table 2). A series of positively regulated changes over time of ENSMUSG00000086859 and ENS- target genes were significantly enriched in the pathways of MUSG00000066057, as well as some targets of them, were cholesterol biosynthesis (e.g., Cyp51 and Fdps)andelastic shown in Figure 3. fibre formation (e.g., Fbln1, Fbln2, and Fbln5)(Table2). Based on the AGEMAP database, a total of 51 DE- Furthermore, the negatively regulated target genes of DE- lncRNA target genes had been discovered to be correlated lncRNAsweremainlyenrichedinasetofbiologyprocesses, with aging in mice. There were 16 DE-lncRNAs that reg- such as metabolic process (e.g., Abi3 and Acaca)andmito- ulated these genes, and 61 regulatory relationships were chondrion organization (e.g., Acaa2 and Bnip3), as well as included in the network. Both ENSMUSG00000086859 and pathways like metabolism of (e.g., Eif2s1, Eif2s3x,and ENSMUSG00000061510 targeted Slc16a2 and Ifitm3;ENS- Eif3b)(Table3). MUSG00000066057 regulated aging-related DEGs like Wdr1 Additionally, the pathway of metabolism of proteins was (Figure 4(a)). predicted to interact with five other pathways, such as post- translational protein modification and asparagine N-linked glycosylation (Figure 4(b)). 3.4. Enrichment Analysis of DE-lncRNA Targets. To further reveal the potential functions mediated by DE-lncRNAs, the 4. Discussion GO and pathway enrichment analyses of DE-lncRNA targets were performed, respectively. The DEGs positively regulated The increased occurrence of age-related diseases, such as can- by DE-lncRNAs (e.g., Efna1 and Efnb2)weremainlyenriched cers, chronic inflammatory, and neurodegenerative diseases, in a set of biology processes about the development of blood becomes a burden on health care provision in the developed 6 BioMed Research International

Color key and histogram 20 10

Count 0 −2 −1 0 1 2 Row Z-score ENSMUSG00000086859

Fbln1

Fbln2

Efna1

Fbln5

Eif3b

ENSMUSG00000066057

Fdps

Eif2s3x

Cyp51

Eif2s1 GSM635893 GSM635894 GSM635895 GSM635901 GSM635899 GSM635900 GSM635887 GSM635888 GSM635889 14 months 18 months 6 months Figure 3: Heat map showing the expression changes over time of ENSMUSG00000086859 and ENSMUSG00000066057, as well as some targets of them. Each row represents a single gene or lncRNA; each column represents a sample. The gradual color change from red to green represents the changing process of expression level from upregulation to downregulation. LncRNA, long noncoding RNA.

and developing countries [35]. In this study, gene expression balance of alternatively expressed isoforms in Efna1 is dis- profile GSE25905 was downloaded and analyzed using bi- rupted in peripheral blood leukocytes of human population oinformatics methods to explore the potential mechanisms with advancing age [39]. Furthermore, the expression of of aging. A total of 8301 time series DEGs and 43 time Efnb2 is significantly decreased during the aging of the rat series DE-lncRNAs were identified in peripheral white retina [40]. In this study, Efna1 was predicted to be regulated adipocyte samples. In the DE-lncRNAs/DEGs regulatory by the DE-lncRNA ENSMUSG00000086859 (gene name: network, the DE-lncRNAs ENSMUSG00000066057, ENS- 2810008D09Rik). There is no study that reports the role MUSG00000086859, and ENSMUSG00000061510 regulated of ENSMUSG00000086859 in aging so far. Collectively, we multiple DEGs. The DEGs positively regulated by DE- speculate that ENSMUSG00000086859 may play key roles in lncRNAsweremainlyenrichedinthefunctionsaboutthe aging through genes related to blood vessel development (e.g., development of blood vessel (e.g., Efna1 and Efnb2), as well Efna1). as the pathways of cholesterol biosynthesis (e.g., Cyp51 and In this study, several other DEGs positively regulated by Fdps) and elastic fibre formation (e.g., Fbln1, Fbln2,and DE-lncRNAs (e.g., Fbln1, Fbln2,andFbln5)weresignificantly Fbln5). enriched in the pathway of elastic fibre formation. During The function of blood vessel development was signif- cutaneous aging, elastic fibres exhibited disintegration and icantly enriched by a set of DE-lncRNA genes, such as appeared to be loose [41]. All of Fbln1, Fbln2,andFbln5 Efna1 and Efnb2. During aging, angiogenesis is delayed, encode a secreted that is incorporated into a and capillary density as well as newly deposited collagen is fibrillar [42]. During aging, the balance decreased [36]. Cardiovascular structure and function are between proteases and their inhibitors involved in extra- altered during aging, with elongated and stiffer aorta, as cellular matrix formation is destroyed [43]. In this study, well as changed arterial baroreflex [37]. Both Efna1 and Fbln1 and Fbln2 were discovered to be an age-regulated gene Efnb2 encode members of the ephrin family, which mediates and regulated by the DE-lncRNA ENSMUSG00000086859 developmental events [38]. It has been confirmed that the (gene name: 2810008D09Rik). The association of Fbln2 BioMed Research International 7

Sp100 Bnip3

Fbln2

ENSMUSG00000086859 Cops3 Brd2 Pacs2 Tpm1 Ddah2 Slc16a2 Procr

S100a6 Fabp5 Atp5l Ifitm3 Coro1c Dgkh Slc25a17 ENSMUSG00000061510

Tram1 ENSMUSG00000066057 Etfa Gbas Cct7 Wdr1 Car7 ENSMUSG00000073067 Sema3a ENSMUSG00000074415 Hba-a1

Dab2

ENSMUSG00000089829

L3mbtl2

Eif2ak3 Cdk5 Gpr108

Dctn5 ENSMUSG00000087540 ENSMUSG00000089652 ENSMUSG00000032048

A230046K03Rik ENSMUSG00000072679 Man2a2 Tnfrsf12a Lgals8 Lman1 Arfgap3 Ncoa1 ENSMUSG00000037982

Ank2 ENSMUSG00000085385 Pkp2 Col1a2 ENSMUSG00000046463 ENSMUSG00000086712 Tiparp ENSMUSG00000021268

ENSMUSG00000079407 ENSMUSG00000053117

1700021K19Rik Mocs1 Nutf2 Enpp1 Eif5a Rbm14 Opa1 Uba52 Rpl10 (a)

Post translational protein modification

APC/C:Cdc20 mediated degradation of Securin Metabolism of proteins

Asparagine N-linked glycosylation

Metabolism

Autodegradation of Cdh1 by Cholesterol biosynthesis Cdh1:APC/C [REACT_208531] Cyp51

(b)

Figure 4: Networks of genes and pathways. (a) The regulatory network of differentially expressed lncRNA genes and age-related target genes. The diamonds represent lncRNA gene IDs, and rectangles represent mRNA genes. (b) The network of pathways that are enriched bytarget genes. Nodes represent pathways, and lines indicate that there are common genes related to the two pathways. The larger the thickness of line is, the more the common genes are. LncRNA, long noncoding RNA. 8 BioMed Research International

Table 3: Results of Gene Ontology functional and pathways enrichment analyses for genes negatively regulated by differentially time-series expressed lncRNAs.

Category Term Adjust 𝑝 value Gene count Genes Metabolic process Abi3, Acaca, Acadvl, Brd2, Capn15, Cxcl9, D2hgdh, Dagla, GO-BP 8.05𝐸 − 10 380 [GO:0008152] Gemin5, Gfm1, and so on Organic substance Acox1, Acsm3, Capn15, Casp2, Ddx20, Decr1, Dennd3, GO-BP metabolic process 5.74𝐸 − 09 360 Frzb, Fxr1, Gadd45a, and so on [GO:0071704] Primary metabolic process Acadvl, Acot7, Babam1, Bach1, Eif3c, Ell, Gbp2, Gclm GO-BP 3.62𝐸 − 08 343 [GO:0044238] Mtdh, Nabp1, and so on Cellular metabolic process Acadvl, Acot7, Babam1, Bach1, Eif3c, Ell, Gbp2, Gclm GO-BP 5.03𝐸 − 08 344 [GO:0044237] Mtdh, Nabp1, and so on Mitochondrion Acaa2, Bnip3, Cln8, Dap3, March5, Mrpl44, Mtch2, GO-BP 2.30𝐸 − 04 32 organization [GO:0007005] Mtfr2, Ptcd2, Slc22a5, and so on Abi3, Acaa2, Acyp2, Adap2, Capn15, Ggct, Ggh, Itgb1bp1, GO-CC Intracellular [GO:0005622] 2.01𝐸 − 16 478 Jak2, Katna1, and so on Intracellular part Abi3, Acaa2, Acyp2, Adap2, Capn15, Ggct, Ggh, Itgb1bp1, GO-CC 3.17𝐸 − 16 475 [GO:0044424] Jak2, Katna1, and so on Atp5l, B4galt1, Dusp8, Dynll2, Exoc4, Faf1, Hint2, Hmox1, GO-CC Cell [GO:0005623] 4.05𝐸 − 15 526 Rtn2, S100a11, and so on Atp5l, B4galt1, Dusp8, Dynll2, Exoc4, Faf1, Hint2, Hmox1, GO-CC Cell part [GO:0044464] 4.05𝐸 − 15 526 Rtn2, S100a11, and so on Mitochondrion Atp5l, Bcs1l, Hspd1, Iars2, Ptrf, Rab11a, Sugct, Tango2, GO-CC 7.42𝐸 − 12 134 [GO:0005739] Trmt2b, Uqcc2, and so on Catalytic activity Abhd6, Acaca, Dusp8, Ebp, Htra1, Huwe1, Itpkc, Lypla1, GO-MF 1.50𝐸 − 03 180 [GO:0003824] Man2a1, Nek6, and so on Agpat4, Akr1c13, Cth, D2hgdh, Gstm7, Helz2, Ogn, Pank2, REACT 188937 Metabolism 2.32𝐸 − 04 125 Psma1, Suclg2, and so on Eif2s1, Eif2s3x, Eif3b, Hspd1, Igf1, Man2a1, Nfyc, Pam16, REACT 247926 Metabolism of proteins 0.004417 58 Rft1, Slc30a6, and so on Asparagine N-linked Alg11, Gfpt2, Gnpnat1, Lman1, Mgat2, Rft1, Slc35a1, REACT 237472 0.004713 19 glycosylation St3gal1, St6galnac5, Uap1, and so on Posttranslational protein Alg11, Eif5a, Galnt2, Gfpt1, Gfpt2, Man2a1, Senp5, Slc35a1, REACT 236283 0.010703 27 modification St3gal1, St6galnac5, and so on Autodegradation of Cdh1 Cdc16, Cdc23, Cdc27, Psma1, Psma2, Psma4, Psmb4, REACT 225686 0.012911 13 by Cdh1:APC/C Psmb8, Psmb9, Psmc6, and so on APC/C:Cdc20 mediated Cdc16, Cdc23, Cdc27, Psma1, Psma2, Psma4, Psmb4, REACT 219897 0.027596 13 degradation of Securin Psmb8, Psmb9, Psmc6, and so on The GO-BP and GO-CC terms in the table are the top 5 ones with a higher adjusted 𝑝 value. DE-lncRNA, differentially time-series expressed long noncoding RNA gene; GO, Gene Ontology; BP, biological process; CC, cellular component. “REACT” terms are the pathway terms. with human aging has also been discovered by previ- member of cytochrome P450 gene superfamily, participates ous studies [44, 45]. Moreover, Fbln5 was predicted to in the late portion of cholesterol biosynthesis [49]. In aging be targeted by ENSMUSG00000061510. Therefore, ENS- peripheral nervous system and liver, CYP51 is also detected MUSG00000086859 may also exert functions in aging via to be involved in the deregulation of cholesterol biosynthesis regulating the genes involved in elastic fibre formation (e.g., [50, 51]. FDPS encodes farnesyl diphosphate synthase, which Fbln1 and Fbln2). ENSMUSG00000061510 may function in is a key intermediate in cholesterol and sterol biosynthesis aging via regulating the expression of genes like Fbln5. [52]. Previous studies have reported that FDPS is associated A previous study has demonstrated that aging is associ- with bone mineral density of aging bone [53, 54]. During ated with altered cholesterol metabolism in T cells, causing aging, cholesterol synthesis is reduced in human hippocam- increased cholesterol levels in lipid rafts [46]. Furthermore, pus [55]. For example, the concentration of three cholesterol cholesterol transport and lipid catabolism have been iden- precursors (lathosterol, lanosterol, and desmosterol) is signif- tified to be upregulated in normally aging rats [47, 48]. In icantly decreased in the hippocampus [56]. Currently, there the present study, CYP51 and FDPS, the positively regulated is no experimental evidence that ENSMUSG00000066057 is target genes of the DE-lncRNA ENSMUSG00000066057 involved in aging. Therefore, this DE-lncRNA may play a role (gene name: Gm1976), were significantly enriched in choles- in aging via the genes related to cholesterol synthesis (e.g., terol biosynthesis. CYP51, the most evolutionarily conserved Cyp51 and Fdps). BioMed Research International 9

Furthermore, the DEGs that were negatively regulated by [2]A.Rodriguez,D.C.Muller,M.Engelhardt,andR.Andres, ENSMUSG00000066057 (e.g., Eif2s1)weremainlyenriched “Contribution of impaired glucose tolerance in subjects with the in the pathways about protein metabolism along with Eif2s3x metabolic syndrome: Baltimore Longitudinal Study of Aging,” and Eif3b, and several pathways about protein metabolism Metabolism: Clinical and Experimental,vol.54,no.4,pp.542– interacted with each other. In aging humans, the balance 547, 2005. between muscle protein synthesis and degradation is dis- [3] J. Harrington and T. Lee-Chiong, “Obesity and aging,” Clinics in rupted, which leads to the loss of skeletal muscle mass [57]. Chest Medicine, vol. 30, no. 3, pp. 609–614, 2009. All of Eif2s1, Eif2s3x,andEif3b encode subunits of eukaryotic [4]C.B.NewgardandN.E.Sharpless,“Comingofage:molecular translation initiation factors (EIFs), which regulate protein drivers of aging and therapeutic opportunities,” Journal of synthesis [58]. Decreased eIF2𝛼 phosphorylation has been Clinical Investigation,vol.123,no.3,pp.946–950,2013. detected in aged tissues and it is responsible for a higher [5]M.J.Cartwright,K.Schlauch,M.E.Lenburgetal.,“Aging, level of protein phosphatase 1 and other proapoptotic proteins depot origin, and preadipocyte gene expression,” The Journals [59, 60]. There is no evidence to prove the roles of Eif2s1, of Gerontology Series A: Biological Sciences and Medical Sciences, Eif2s3x,andEif3b in aging so far. We speculate that the vol.65,no.3,pp.242–251,2010. ENSMUSG00000066057 may also play critical roles in aging [6] T. Tchkonia, D. E. Morbeck, T. Von Zglinicki et al., “Fat tissue, via regulating protein metabolism through Eif2s1.TheDEGs aging, and cellular senescence,” Aging Cell,vol.9,no.5,pp.667– Eif2s1, Eif2s3x,andEif3b may also be involved in aging via 684, 2010. protein metabolism. [7]S.A.Farr,K.A.Yamada,D.A.Butterfieldetal.,“Obe- Despite the aforementioned results, there were several sity and hypertriglyceridemia produce cognitive impairment,” limitations in this study. The predicted results should be Endocrinology,vol.149,no.5,pp.2628–2636,2008. confirmed by laboratory data. Furthermore, the included [8] R. M. Uranga, A. J. Bruce-Keller, C. D. Morrison et al., “Intersec- samples for analysis should be more. In our further studies, tion between metabolic dysfunction, high fat diet consumption, more samples of aging will be included to validate the and brain aging,” Journal of Neurochemistry,vol.114,no.2,pp. expression levels and functions of the potential key lncRNAs 344–361, 2010. and genes. [9] C. D. Morrison, P. J. Pistell, D. K. Ingram et al., “High fat diet In conclusion, based on the gene expression data of pe- increases hippocampal oxidative stress and cognitive impair- ripheral white adipocytes taken from mice at different ages, a ment in aged mice: implications for decreased Nrf2 signaling,” totalof8301timeseriesDEGsand43timeseriesDE-lncRNAs Journal of Neurochemistry,vol.114,no.6,pp.1581–1589,2010. were identified. Among them, 41 DE-lncRNAs targeted 1880 [10] G. N. Landis and J. Tower, “Superoxide dismutase evolution and DEGs. The DE-lncRNAs ENSMUSG00000066057, ENS- life span regulation,” Mechanisms of Ageing and Development, MUSG00000086859, and ENSMUSG00000061510 regulated vol. 126, no. 3, pp. 365–379, 2005. multiple DEGs. Furthermore, the DEGs positively regulated [11] F. Hazane, K. Valenti, S. Sauvaigo et al., “Ageing effects on by DE-lncRNAs (e.g., ENSMUSG00000066057 and ENS- the expression of cell defence genes after UVA irradiation in MUSG00000086859) were mainly related to the functions human male cutaneous fibroblasts using cDNA arrays,” Journal of Photochemistry and Photobiology B: Biology,vol.79,no.3,pp. about the development of blood vessel (e.g., Efna1 and Efnb2), 171–190, 2005. as well as the pathways of cholesterol biosynthesis (e.g., Cyp51 and Fdps) and elastic fibre formation (e.g., Fbln1, Fbln2, [12] M. Gasparrini, D. Rivas, A. Elbaz, and G. Duque, “Differential expression of cytokines in subcutaneous and marrow fat of and Fbln5). Additionally, the DEGs (e.g., Eif2s1, Eif2s3x, aging C57BL/6J mice,” Experimental Gerontology,vol.44,no. and Eif3b) that were negatively regulated by DE-lncRNAs 9, pp. 613–618, 2009. were correlated with the pathways about protein metabolism. These DE-lncRNAs and DEGs may be involved in aging, [13] K. Hotta, N. L. Bodkin, T. A. Gustafson, S. Yoshioka, H. K. Ortmeyer, and B. C. Hansen, “Age-related adipose tissue mRNA which provides novel information for the study of aging. expression of ADD1/SREBP1, PPAR𝛾,lipoproteinlipase,and GLUT4 glucose transporter in rhesus monkeys,” The Journals of Competing Interests Gerontology—Series A Biological Sciences and Medical Sciences, vol.54,no.5,pp.B183–B188,1999. All authors declare that they have no conflict of interests to state. [14] I. Grammatikakis, A. C. Panda, K. Abdelmohsen, and M. Gorospe, “Long noncoding RNAs (lncRNAs) and the molecular Acknowledgments hallmarks of aging,” Aging,vol.6,no.12,pp.992–1009,2014. [15] J. Kim, K. M. Kim, J. H. Noh, J.-H. Yoon, K. Abdelmohsen, This study was funded by the science and technology andM.Gorospe,“LongnoncodingRNAsindiseasesofaging,” research project from Department of Education, Hei- Biochimica et Biophysica Acta—Gene Regulatory Mechanisms, longjiang Province (Grant no. 12531423). vol.1859,no.1,pp.209–221,2016. [16] M. Guttman, I. Amit, M. Garber et al., “Chromatin signature References reveals over a thousand highly conserved large non-coding RNAs in mammals,” Nature, vol. 458, no. 7235, pp. 223–227, [1] M. J. Cartwright, T. Tchkonia, and J. L. Kirkland, “Aging in 2009. adipocytes: potential impact of inherent, depot-specific mech- [17] A. Fatica and I. Bozzoni, “Long non-coding RNAs: new players anisms,” Experimental Gerontology,vol.42,no.6,pp.463–471, in cell differentiation and development,” Nature Reviews Genet- 2007. ics,vol.15,no.1,pp.7–21,2014. 10 BioMed Research International

[18] V. Bianchessi, I. Badi, M. Bertolotti et al., “The mitochondrial [36] E. Sadoun and M. J. Reed, “Impaired angiogenesis in aging lncRNA ASncmtRNA-2 is induced in aging and replicative is associated with alterations in vessel density, matrix compo- senescence in Endothelial Cells,” Journal of Molecular and sition, inflammatory response, and growth factor expression,” Cellular Cardiology,vol.81,pp.62–70,2015. Journal of Histochemistry and Cytochemistry,vol.51,no.9,pp. [19] T.-Y. Yu, Y.-W. Kao, and J.-J. Lin, “Telomeric transcripts stim- 1119–1130, 2003. ulate telomere recombination to suppress senescence in cells [37] A. U. Ferrari, A. Radaelli, and M. Centola, “Invited review: aging lacking telomerase,” Proceedings of the National Academy of and the cardiovascular system,” Journal of Applied Physiology, Sciences of the United States of America, vol. 111, no. 9, pp. 3377– vol. 95, no. 6, pp. 2591–2597, 2003. 3382, 2014. [38] N. Holder and R. Klein, “Eph receptors and ephrins: effectors of [20]J.Khan,M.L.Bittner,Y.Chen,P.S.Meltzer,andJ.M.Trent, morphogenesis,” Development,vol.126,no.10,pp.2033–2044, “DNA microarray technology: the anticipated impact on the 1999. study of human disease,” Biochimica et Biophysica Acta— [39] L. W. Harries, D. Hernandez, W. Henley et al., “Human aging Reviews on Cancer,vol.1423,no.2,pp.M17–M28,1999. is characterized by focused changes in gene expression and [21]W.E.Bunney,B.G.Bunney,M.P.Vawteretal.,“Microarray deregulation of alternative splicing,” Aging Cell,vol.10,no.5, technology: a review of new strategies to discover candidate pp. 868–878, 2011. vulnerability genes in psychiatric disorders,” American Journal [40] C. P. Smith and J. J. Steinle, “Changes in growth factor of Psychiatry,vol.160,no.4,pp.657–666,2003. expression in normal aging of the rat retina,” Experimental Eye [22] L.-F. Liu, W.-J. Shen, M. Ueno, S. Patel, and F. B. Kraemer, Research,vol.85,no.6,pp.817–824,2007. “Characterization of age-related gene expression profiling in [41] I. M. Braverman and E. Fonferko, “Studies in cutaneous aging: I. bone marrow and epididymal adipocytes,” BMC Genomics,vol. The elastic fiber network,” Journal of Investigative Dermatology, 12, article 212, 2011. vol.78,no.5,pp.434–443,1982. [23] R. A. Irizarry, B. Hobbs, F.Collin et al., “Exploration, normaliza- tion, and summaries of high density oligonucleotide array probe [42] R. O. Hynes and A. Naba, “Overview of the matrisome—an level data,” Biostatistics,vol.4,no.2,pp.249–264,2003. inventory of extracellular matrix constituents and functions,” Cold Spring Harbor Perspectives in Biology,vol.4,no.1,Article [24] L. Gautier, L. Cope, B. M. Bolstad, and R. A. Irizarry, “Affy— ID a004903, 2012. analysis of affymetrix genechip data at the probe level,” Bioin- formatics,vol.20,no.3,pp.307–315,2004. [43] M. P. Jacob, “Extracellular matrix remodeling and matrix [25]J.Harrow,A.Frankish,J.M.Gonzalezetal.,“GENCODE:the metalloproteinases in the vascular wall during aging and in reference annotation for the ENCODE project,” pathological conditions,” Biomedicine and Pharmacotherapy, Genome Research,vol.22,no.9,pp.1760–1774,2012. vol. 57, no. 5-6, pp. 195–202, 2003. [26] M. J. Aryee, J. A. Gutierrez-Pabello,´ I. Kramnik, T. Maiti, and J. [44] E. B. van den Akker, W. M. Passtoors, R. Jansen et al., “Meta- Quackenbush, “An improved empirical bayes approach to esti- analysis on blood transcriptomic studies identifies consistently mating differential gene expression in microarray time-course coexpressed protein-protein interaction modules as robust data: BETR (Bayesian Estimation of Temporal Regulation),” markers of human aging,” Aging Cell,vol.13,no.2,pp.216–225, BMC Bioinformatics,vol.10,article409,2009. 2014. [27] U. Yamaguchi, R. Nakayama, K. Honda et al., “Distinct gene [45] E. B. V. d. Akker, Computational Biology in Human Aging: expression-defined classes of gastrointestinal stromal tumor,” An Omics Data Integration Approach, Department of Medical Journal of Clinical Oncology,vol.26,no.25,pp.4100–4108,2008. Statistics and Bioinformatics, Faculty of Medicine, Leiden [28] G. J. Szekely and M. L. Rizzo, “Hierarchical clustering via joint University Medical Center (LUMC), Leiden University, 2015. between-within distances: extending Ward’sminimum variance [46] A. Larbi, C. Fortin, G. Dupuis, H. Berrougui, A. Khalil, and T. method,” Journal of Classification,vol.22,no.2,pp.151–183, Fulop, “Immunomodulatory role of high-density lipoproteins: 2005. impact on immunosenescence,” Age,vol.36,no.5,p.9712,2014. [29] R. S. Savage, BHC: Bayesian Hierarchical Clustering,2010. [47] E. M. Blalock, K.-C. Chen, K. Sharrow et al., “Gene microar- [30] J.Benesty,J.Chen,Y.Huang,andI.Cohen,“Pearsoncorrelation rays in hippocampal aging: statistical profiling identifies novel coefficient,” in Noise Reduction in Speech Processing,pp.1–4, processes correlated with cognitive impairment,” The Journal of Springer, Berlin, Germany, 2009. Neuroscience,vol.23,no.9,pp.3807–3819,2003. [31] M. E. Smoot, K. Ono, J. Ruscheinski, P.-L. Wang, and T. Ideker, [48] E. M. Blalock, K.-C. Chen, A. J. Stromberg et al., “Harnessing “Cytoscape 2.8: new features for data integration and network the power of gene microarrays for the study of brain aging visualization,” Bioinformatics,vol.27,no.3,pp.431–432,2011. and Alzheimer’s disease: statistical reliability and functional [32] J. M. Zahn, S. Poosala, A. B. Owen et al., “AGEMAP: a gene correlation,” Ageing Research Reviews,vol.4,no.4,pp.481–512, expression database for aging in mice,” PLoS Genetics,vol.3,no. 2005. 11, pp. 2326–2337, 2007. [49] T. Rezen,N.Debeljak,D.Kordiˇ ˇs, and D. Rozman, “New [33] Y.-A. Chen, L. P. Tripathi, and K. Mizuguchi, “TargetMine, an aspects on lanosterol 14𝛼-demethylase and cytochrome P450 integrated data warehouse for candidate gene prioritisation and evolution: lanosterol/cycloartenol diversification and lateral target discovery,” PLoS ONE,vol.6,no.3,ArticleIDe17844, transfer,” Journal of Molecular Evolution,vol.59,no.1,pp.51– 2011. 58, 2004. [34] G. Van Belle, L. D. Fisher, P. J. Heagerty, and T. Lumley, [50] V. Verdier, G. Csardi,´ A.-S. de Preux-Charles et al., “Aging of Biostatistics: A Methodology for the Health Sciences,JohnWiley myelinating glial cells predominantly affects lipid metabolism & Sons, New York, NY, USA, 2004. and immune response pathways,” Glia,vol.60,no.5,pp.751– [35] A. Larbi, C. Franceschi, D. Mazzatti, R. Solana, A. Wikby, and 760, 2012. G. Pawelec, “Aging of the immune system as a prognostic factor [51] F. Capel, G. Rolland-Valognes, C. Dacquet et al., “Analysis for human longevity,” Physiology,vol.23,no.2,pp.64–74,2008. of sterol-regulatory element-binding protein 1c target genes BioMed Research International 11

in mouse liver during aging and high-fat diet,” Journal of Nutrigenetics and Nutrigenomics,vol.6,no.2,pp.107–122,2013. [52] D. Peschel, R. Koerting, and N. Nass, “Curcumin induces changes in expression of genes involved in cholesterol home- ostasis,” The Journal of Nutritional Biochemistry,vol.18,no.2, pp. 113–119, 2007. [53] M. E. Levy, R. A. Parker, R. E. Ferrell, J. M. Zmuda, and S. L. Greenspan, “Farnesyl diphosphate synthase: a novel genotype association with bone mineral density in elderly women,” Maturitas,vol.57,no.3,pp.247–252,2007. [54] L. J. Dominguez, G. D. Bella, M. Belvedere, and M. Barbagallo, “Physiology of the aging bone and mechanisms of action of bisphosphonates,” Biogerontology,vol.12,no.5,pp.397–408, 2011. [55]K.M.Thelen,P.Falkai,T.A.Bayer,andD.Lutjohann,¨ “Choles- terol synthesis rate in human hippocampus declines with aging,” Neuroscience Letters,vol.403,no.1-2,pp.15–19,2006. [56] K. Smiljanic, T. Vanmierlo, A. M. Djordjevic et al., “Aging induces tissue-specific changes in cholesterol metabolism in rat brain and liver,” Lipids,vol.48,no.11,pp.1069–1077,2013. [57] R. Koopman and L. J. C. Van Loon, “Aging, exercise, and muscle protein metabolism,” Journal of Applied Physiology,vol.106,no. 6, pp. 2040–2048, 2009. [58] B. He, M. Gross, and B. Roizman, “The 𝛾134.5 protein of herpes simplex virus 1 complexes with protein phosphatase 1𝛼 to dephosphorylate the 𝛼 subunit of the eukaryotic translation initiation factor 2 and preclude the shutoff of protein synthesis by double-stranded RNA-activated protein kinase,” Proceedings of the National Academy of Sciences of the United States of America,vol.94,no.3,pp.843–848,1997. [59] S. G. Hussain and K. V. A. Ramaiah, “Reduced eIF2𝛼 phos- phorylation and increased proapoptotic proteins in aging,” Biochemical and Biophysical Research Communications,vol.355, no. 2, pp. 365–370, 2007. [60] S. E. Wells, P. E. Hillner, R. D. Vale, and A. B. Sachs, “Circular- ization of mRNA by eukaryotic translation initiation factors,” Molecular Cell,vol.2,no.1,pp.135–140,1998. International Journal of Peptides

Advances in BioMed Stem Cells International Journal of Research International International Genomics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Virolog y http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

Journal of Nucleic Acids

Zoology International Journal of Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

Submit your manuscripts at http://www.hindawi.com

Journal of The Scientific World Journal Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

Genetics Anatomy International Journal of Biochemistry Advances in Research International Research International Microbiology Research International Bioinformatics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

Enzyme International Journal of Molecular Biology Journal of Archaea Research Evolutionary Biology International Marine Biology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014