Classifying Aging Genes into DNA Repair or Non-DNA Repair-Related Categories

The elderly population in almost every country is growing faster than ever before. However, our knowledge about the aging process is still limited despite decades of studies on this topic. In this report, we focus on the gradual accumulation of DNA damage in cells, which is a key aspect of the aging process and one that underlies age-dependent functional decline in cells, tissues, and organs. To achieve the goal of discriminating DNA-repair from non-DNA-repair genes among currently known genes related to human aging, four machine learning methods were employed: Decision Trees, Naive Bayes, Support Vector Machine, and Random Forest (RF). Among the four methods, the RF algorithm achieved a total accuracy (ACC) of 97.32% and an area under receiver operating characteristic (AUC) of 0.98. These estimates were based on 18 selected attributes, including 10 Gene Ontology and 8 Protein-Protein Interaction (PPI) attributes. A predictive model built with only 15 PPI attributes achieved performance levels of ACC= 96.56% and AUC=0.95. Systems biology analyses showed that the features of these attributes were related to cancer, genetic, developmental, and neurological disorders, as well as DNA replication/recombination/repair, cell cycle, cell death, and cell function maintenance. The results of this study indicate that genes indicative of aging may be successfully classified into DNA repair and non-DNA repair genes and such successful classification may help identify pathways and biomarkers that are important to the aging process.

[1]  Ronald A. DePinho,et al.  Linking functional decline of telomeres, mitochondria and stem cells during ageing , 2010, Nature.

[2]  Jianwen Fang,et al.  Feature Selection in Validating Mass Spectrometry Database Search Results , 2008, J. Bioinform. Comput. Biol..

[3]  Frederick W. Alt,et al.  DNA Repair, Genome Stability, and Aging , 2005, Cell.

[4]  Manuel Serrano,et al.  The common biology of cancer and ageing , 2007, Nature.

[5]  Arie Budovsky,et al.  The Human Ageing Genomic Resources: online databases and tools for biogerontologists , 2009, Aging cell.

[6]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[7]  Olga Vasieva,et al.  A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related , 2011, BMC Genomics.

[8]  Sandy Chang,et al.  WRN at telomeres: implications for aging and cancer , 2007, Journal of Cell Science.

[9]  D. Jackson,et al.  Characterisation of the interaction between WRN, the helicase/exonuclease defective in progeroid Werner's syndrome, and an essential replication factor, PCNA , 2003, Mechanisms of Ageing and Development.

[10]  Jianwen Fang,et al.  Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins. , 2010, Biochemical and biophysical research communications.

[11]  Stephen P. Jackson,et al.  Conserved modes of recruitment of ATM, ATR and DNA-PKcs to sites of DNA damage , 2005, Nature.

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  S. Emmert,et al.  Functional DNA repair system analysis in haematopoietic progenitor cells using host cell reactivation , 2007, Scandinavian journal of clinical and laboratory investigation.

[14]  Bo Xu,et al.  ATM phosphorylates p95/nbs1 in an S-phase checkpoint pathway , 2000, Nature.

[15]  Man Tsuey Tse.  Brain ageing: A fine balance , 2012, Nature Reviews Neuroscience.

[16]  I. Kohane,et al.  Gene regulation and DNA damage in the ageing human brain , 2004, Nature.

[17]  C. Kenyon.  The genetics of ageing , 2010, Nature.

[18]  Monika Aggarwal,et al.  Inhibition of helicase activity by a small molecule impairs Werner syndrome helicase (WRN) function in the cellular response to DNA damage or replication stress , 2011, Proceedings of the National Academy of Sciences.

[19]  Jan Vijg,et al.  Accelerating aging by mouse reverse genetics: a rational approach to understanding longevity , 2004, Aging cell.

[20]  Marco Foiani,et al.  Regulation of DNA repair throughout the cell cycle , 2008, Nature Reviews Molecular Cell Biology.

[21]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[22]  Richard D. Wood,et al.  Human DNA Repair Genes , 2001, Science.

[23]  Kara Dolinski,et al.  The BioGRID Interaction Database: 2011 update , 2010, Nucleic Acids Res..

[24]  Kristian Vlahovicek,et al.  Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests , 2009, PLoS Comput. Biol..

[25]  J. Bartek,et al.  DNA damage checkpoints: from initiation to recovery or adaptation. , 2007, Current opinion in cell biology.

[26]  Alexander A. Ishchenko,et al.  The hMsh2-hMsh6 complex acts in concert with monoubiquitinated PCNA and Pol η in response to oxidative DNA damage in human cells. , 2011, Molecular cell.

[27]  Tomas Lindahl,et al.  Human DNA repair genes, 2005. , 2005, Mutation research.

[28]  Hao Jiang,et al.  Classifying DNA repair genes by kernel-based support vector machines , 2011, Bioinformation.

[29]  Anastasia Nijnik,et al.  DNA repair is limiting for haematopoietic stem cells during ageing , 2007, Nature.

[30]  Sandhya Rani,et al.  Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..

[31]  Junko Oshima,et al.  WRN, the protein deficient in Werner syndrome, plays a critical structural role in optimizing DNA repair , 2003, Aging cell.

[32]  Jianwen Fang,et al.  Predicting residue-residue contacts using random forest models , 2011, Bioinform..

[33]  Liangjiang Wang,et al.  Prediction of DNA-binding residues from protein sequence information using random forests , 2009, BMC Genomics.