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. 2013 Dec 9:6:55.
doi: 10.1186/1755-8794-6-55.

Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain

Weiguo Li, Lina Chen  1 , Wan Li, Xiaoli Qu, Weiming He, Yuehan He, Chenchen Feng, Xu Jia, Yanyan Zhou, Junjie Lv, Binhua Liang, Binbin Chen, Jing Jiang
Affiliations

Unraveling the characteristics of microRNA regulation in the developmental and aging process of the human brain

Weiguo Li et al. BMC Med Genomics. .

Abstract

Background: Structure and function of the human brain are subjected to dramatic changes during its development and aging. Studies have demonstrated that microRNAs (miRNAs) play an important role in the regulation of brain development and have a significant impact on brain aging and neurodegeneration. However, the underlining molecular mechanisms are not well understood. In general, development and aging are conventionally studied separately, which may not completely address the physiological mechanism over the entire lifespan. Thus, we study the regulatory effect between miRNAs and mRNAs in the developmental and aging process of the human brain by integrating miRNA and mRNA expression profiles throughout the lifetime.

Methods: In this study, we integrated miRNA and mRNA expression profiles in the human brain across lifespan from the network perspective. First, we chose the age-related miRNAs by polynomial regression models. Second, we constructed the bipartite miRNA-mRNA regulatory network by pair-wise correlation coefficient analysis between miRNA and mRNA expression profiles. At last, we constructed the miRNA-miRNA synergistic network from the miRNA-mRNA network, considering not only the enrichment of target genes but also GO function enrichment of co-regulated target genes.

Results: We found that the average degree of age-related miRNAs was significantly higher than that of non age-related miRNAs in the miRNA-mRNA regulatory network. The topological features between age-related and non age-related miRNAs were significantly different, and 34 reliable age-related miRNA synergistic modules were identified using Cfinder in the miRNA-miRNA synergistic network. The synergistic regulations of module genes were verified by reviewing miRNA target databases and previous studies.

Conclusions: Age-related miRNAs play a more important role than non age-related mrRNAs in the developmental and aging process of the human brain. The age-related miRNAs have synergism, which tend to work together as small modules. These results may provide a new insight into the regulation of miRNAs in the developmental and aging process of the human brain.

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Figures

Figure 1
Figure 1
The workflow of procedure. I) The workflow to construct the miRNA-mRNA regulatory network. The bipartite miRNA-mRNA regulatory network was constructed by pair-wise correlation coefficient analysis between miRNA and mRNA expression profiles. II) The workflow to construct the miRNA-miRNA synergistic network. The miRNA-miRNA synergistic network was constructed based on co-regulation of target genes and GO function enrichment of co-regulated target genes. III) The analysis of the miRNA-miRNA synergistic network. We compared topological measurements between age-related-miRNAs and non age–related–miRNAs, and identified miRNA synergistic modules from the miRNA-miRNA synergistic network by Cfinder.
Figure 2
Figure 2
Degree distribution of the miRNA-mRNA network. A) out-degree distribution of the miRNA-mRNA network. Most of miRNAs are lowly connected and only a few are relatively highly connected. The examination of the out-degree distribution of the miRNA-mRNA network reveals a power law with a exponent of -0.6985 and R2 =0.8324. B) in-degree distribution of the miRNA-mRNA network. In-degree is defined as the number of regulatory miRNAs for each target, signifying an exponential distribution with an exponent of -0.3866 and R2 = 0.9729. The exponent described the distribution of degrees. R2 is the coefficient of determination, which indicates how well data points fit a line or curve. An R2 of 1 indicates that the line perfectly fits the data. The larger R2 is, the better the data fit the line.
Figure 3
Figure 3
Scatter plot of miRNAs degree distribution in the miRNA-mRNA network. Y-axis represents miRNA degree, and x-axis is all miRNAs in the miRNA-mRNA network. Red and blue dots represent age-related miRNAs and non age-related miRNAs, respectively. Age-related miRNAs had higher degrees than non age-related miRNAs, which indicated that age-related miRNAs (e.g. hsa-mir-130a, has-mir-330-3p and hsa-mir-29a) regulated much more mRNAs than non age-related miRNAs in the miRNA-mRNA network.
Figure 4
Figure 4
The number of connected components compared for networks deleting age-related miRNAs and non age-related miRNAs. The lower and upper lines of the boxes are the 25th and 75th percentiles of the measure. The lines in the middle of the boxes are the median. Lines extending above and below the boxes show the extent of the rest of the sample. The plus signs at the top and bottom of the figures are indications of outliers.
Figure 5
Figure 5
The layout of the miRNA-miRNA synergistic network. The miRNA-miRNA synergistic network generated by the procedure described in ‘Methods’. This network consists of 324 miRNAs and 3141 co-regulatory links. A node represents a miRNA, and an edge represents a synergistic action. Green and yellow circles represent age-related miRNAs and non age-related miRNAs, respectively. Age-related miRNAs are closer to each other in the red rectangle of the network.
Figure 6
Figure 6
The characteristic path lengths among age-related miRNAs is shorter than those in randomization tests. The red arrow represents the characteristic path length of the age-related miRNAs in the actual network.
Figure 7
Figure 7
The miRNA clique 103. All miRNAs in the clique were fully connected with each other in the miRNA-miRNA synergistic network. All miRNAs in this clique are age-related and related with neuronal development, neurodegenerative diseases and aging-related disorders.
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