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. 2007:3:147.
doi: 10.1038/msb4100189. Epub 2007 Dec 4.

A modular network model of aging

Huiling Xue  1 , Bo Xian, Dong Dong, Kai Xia, Shanshan Zhu, Zhongnan Zhang, Lei Hou, Qingpeng Zhang, Yi Zhang, Jing-Dong J Han
Affiliations

A modular network model of aging

Huiling Xue et al. Mol Syst Biol. 2007.

Abstract

Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein-protein interaction (PPI) networks during fruitfly and human brain aging. In both networks, there are two modules associated with the cellular proliferation to differentiation temporal switch that display opposite aging-related changes in expression. During fly aging, another couple of modules are associated with the oxidative-reductive metabolic temporal switch. These network modules and their relationships demonstrate (1) that aging is largely associated with a small number, instead of many network modules, (2) that some modular changes might be reversible and (3) that genes connecting different modules through PPIs are more likely to affect aging/longevity, a conclusion that is experimentally validated by Caenorhabditis elegans lifespan analysis. Network simulations further suggest that aging might preferentially attack key regulatory nodes that are important for the network stability, implicating a potential molecular basis for the stochastic nature of aging.

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Figures

Figure 1
Figure 1
Network modules during human brain and fruitfly aging. (A) Flow diagram of the NP analysis arriving at the aging-related NP network and modularized NP network. The first step of the NP analysis is to obtain all the PPIs between interactors that are negatively or positively correlated at the transcription level (∣PCC∣>0.4) during aging. Once the subnetwork is found, the genes in the subnetwork are clustered by their expression profiles using hierarchical clustering, then the best separated modules on the clustering tree are found judged by the percentage of negatively correlated interacting gene pairs within a module (<1%). PCC stands for Pearson correlation coefficient. (B) Transcriptional relationships among the modules of the human brain aging NP network. Human D (differentiation), P (proliferation), PP (protein processing) and I (immunity) modules are represented with nodes of lavender, green, brown and dark blue, respectively. Pearson correlation coefficient (PCC) between the two serials of the average expression levels of a pair of the modules over different samples are used to measure the similarity of expression patterns between the modules. The PCC for each module pair is marked at the line connecting the two modules. Solid red and green lines represent strong transcriptional correlations and anticorrelations, respectively (∣PCC∣>=0.7), whereas dotted red and green lines indicate weak correlations and anticorrelations, respectively (0.4<∣PCC∣<0.7). Gray dotted lines represent no obvious transcriptional relationships (∣PCC∣<=0.4). The number of genes of each network module is indicated below the name of the module. (C, D) Transcriptional relationships among the modules of the fruitfly aging under normal (C) and CR (D) conditions. (E) Average expression intensities are plotted against the age of the human subjects for the human brain module genes. The gray vertical line marks age 85. (F, G) Average expression intensities are plotted against fly age for the fly module genes under normal (E) and CR condition (F), respectively. Polynomial fits of the fly module expression level across different populations over age are also displayed, indicating perfect linearity of O and R module expression level with age. Linear regression R2 of the O and R modular changes with age are also displayed. Only the expression levels of the genes overlapping between the corresponding normal diet and CR modules are plotted. Those for the nonoverlapping genes are shown in Supplementary Figure 4. (H) Relationship of O and R modules to metabolic cycle. The average expression levels of the yeast orthologs of the fly R and O genes oscillate with the metabolic cycles and reach the highest levels in the antiphases of reductive and oxidative metabolisms, respectively (marked by the arrows). The P and D modules are also included for comparison. Genes for each module are the overlapping genes between the corresponding modules under normal diet and CR.
Figure 2
Figure 2
Aging regulatory role of the interface genes. (A) Aging genes and transcription regulators are more enriched on the module interfaces than in the cores. * indicates insignificant differences. Proportion test P-values for the differences can be found in Supplementary Table V. (B) Worm lifespan upon RNAi inactivation of worm orthologs of selected human module interface genes. Wild-type N2 worms were fed bacteria expressing dsRNA that target the genes indicated on the right of the panel. The figure shows the results of one representative experiment carried out at 20°C. See Table II for statistical differences. (C) Worm lifespan upon RNAi inactivation of randomly selected worm genes that have human orthologs.
Figure 3
Figure 3
Correlations of the percentage of aging genes and regulatory genes to PPI degree, betweeness of the proteins and the AvgPCC of the hubs. (A, B) The percentage of aging genes (A) or transcription regulators (B) among the NP or HPRD proteins of distinct PPI degrees are plotted against the PPI degrees of the proteins. The linear regression significance P-values are indicated above the polynomial fitted trend lines. (C, D) The percentage of aging genes (C) or transcription regulators (D) among the NP or HPRD proteins that are within each betweeness value interval of 2000 is plotted against minimal betweeness value of the interval. The linear regression significance P-values are indicated above the polynomial fitted trend lines. (E, F) The percentage of aging genes (E) or transcription regulators (F) among the NP or HPRD hubs that are within each AvgPCC value interval of 0.2 is plotted against minimal AvgPCC value of the interval. The linear regression significance P-values are indicated above the connected lines in (E).
Figure 4
Figure 4
Aging genes and interface genes are important to network stability. (A) Attacking the aging genes in the NP network increases the CPL of the network more rapidly than attacking randomly selected non-aging genes or removal of random nodes (‘failure'), but slower than specific attacks on hubs. Degree-matched attacks on aging and non-aging genes in the NP network are shown in the inset. (B) Attacking aging genes belonging to the NP network increases CPL of the HPRD network more rapidly than attacking aging genes not in the NP network or random removal of genes in the HPRD network. Degree-matched attacks on NP and non-NP aging genes in the HPRD network are shown in the inset. (C) Attacking the aging (or all) genes on the module interfaces increases CPL of the NP network more rapidly than attacking their counterparts in the cores. Degree-matched attacks on the core and interface genes are shown in the inset. Only the first 2% of the attacks are shown in the inset for the interface and core genes, the trend continues for the rest.
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