Revealing Aging Mechanisms from Ensemble Gene Network Inference Methods

2The increasing number of sparsely and unevenly sampled gene expression experiments in aging research pose significant constraints in gene network inference. In addition, the inconsistency in predictions among gene regulatory network methods hampers the identification of true biological structure. Recent approaches show that ensemble predictions derived from community-based methods are more accurate and provide truly testable hypotheses. Such ensemble strategies combined with multiple aging datasets establish a robust cost-effective way for unraveling cross-tissue aging mechanisms and exonerate the need for refined microarray design. Over the last years, a substantial number of methods provided novel insights at different molecular aspects of aging with the use of systems biology network-based approaches. The decline in cellular function during aging is the cumulative result of changes in the expression level of a number of genes. Towards this orientation, the construction of Gene Regulatory Networks (GRNs) plays a prevalent role in the system modeling of interactions and regulations among genes. However, the choice of the network inference method still remains challenging, since recent studies have shown that no single method performs optimally across diverse data. For this reason, we propose the integration of predictions from multiple methods, since it has been proved to lead to more confident networks [1]. We downloaded two mouse gene expression datasets from GEO database with accession numbers GSE9902 (heart) and GSE9905 (liver). We retained only the male young and old mouse samples (4740 genes in total) and discarded the middle-aged ones. On second level, we applied three well-known GRN algorithms, namely ARACNE [2], MRNET [3] and CLR [4], on young and old samples separately in both experiments. The size of the resulted networks ranged between 2.1-3.3×10 6 interactions. Next, we applied a Borda count voting scheme in order to integrate the predictions inferred by each algorithm and defined the consensus network for each tissue and state (young/old), whose magnitude ranged *This research has been co-financed by the European Union (European Social Fund ± ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) Research Funding Program: Thalis. Investing in knowledge society through the European Social Fund. K.D. and A.B. are with the Medical School, University of Patras, Patras, 26500, GR (kondim@upatras.gr, correspondence author: +302610969145, bezer@upatras.gr). A.B is with SINAPSE Institute, Center of Life Sciences, National University of Singapore, Singapore 117456 G.N. D. and K.S. are with the Department of Electrical and Computer Engineering, University of Patras, Patras, 26500, GR (geodimitrak@upatras.gr , sgarbas@upatras.gr). between 1.7-1.9×10 6 . Additionally, in order to recover the cross-tissue mechanisms, we retained the overlapping networks between experiments in each state separately and ended up with 421750 (young) and 461418 (old) interactions. We examined the performance of the final network (in each state separately) compared to the single networks with regard to experimentally validated interactions. For this, we employed the available mouse protein±protein and protein-DNA interaction data. The high performance (measured with F-score metric) of the final network proves that the community-based strategy manages to reduce the network complexity without derogating from the true biological structure. Interestingly, the hub genes (with degree above 150 relations) in young state were highly enriched (P-value < E-10) LQ WKH *2 WHUPV 3PHWDEROLF SURFHVV ́ DQG 3Uesponse to VWUHVV ́ ZKHUHDV in old state ZHUH HQULFKHG LQ 3JHQH H[SUHVVLRQ ́ 3FHOO F\FOH ́ DQG 3FHOOXODU ELRV\QWKHWLF SURFHVV ́ terms. This observation indicates that different processes take over control during aging, which is partly explained by the fact that certain hub genes lost their topological status in the comparative state (Fig. 1).