PROJECT DESCRIPTION Although biological systems are highly adapted and characterized by sets of purposeful molecular interactions, they are also subject to stochastic fluctuations. In aging, stochastic variation is obvious in the large individual variation in aging rate and patterns of aging-related pathology, even in genetically homogenous animals. Stochasticity is also apparent at the molecular level. Random molecular fluctuations creating variability in gene expression within a cell population have been demonstrated in bacteria and yeast. Increased stochastic noise in gene transcription and translation could have important consequences for organismal fitness. We have recently demonstrated increased cell-to-cell variation in the expression of a number of genes in the heart of old as compared to young mice, indicating increased transcriptional noise with age. Similar increased variation was induced in cultured fibroblasts by a genotoxic agent, suggesting that increased transcriptional noise can be caused by damage to the genome. To study the possible causes and consequences of increased transcriptional noise with age or upon genotoxic treatment, we propose to use microarray chips for transcription profiling of single cells. We will use a variety of statistical techniques and computational tools to determine and characterize the changes in transcriptional noise arising from the aging process itself or the treatment with the genotoxic agent. In particular, we will determine and characterize the distributions of transcript levels in cardiomyocytes from young and old mice, as well as in mouse embryonic fibroblasts exposed or not exposed to hydrogen peroxide, and examine the genomic contexts, pre-defined pathways, literature-derived network motifs and network structures associated with genes of interest. Such low- and high- level analysis of single-cell gene expression profiling data should provide insight into the possible causes of aging-related transcriptional noise and its consequences in terms of organ-specific functional decline and increased disease risk. The results obtained in the proposed project would provide us with new insights regarding the impact of gene transcriptional noise in aging and disease. ? ? ?
| Li, Wenge; Calder, R Brent; Mar, Jessica C et al. (2015) Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles. Mutat Res 772:55-62 |
| Gundry, Michael; Li, Wenge; Maqbool, Shahina Bano et al. (2012) Direct, genome-wide assessment of DNA mutations in single cells. Nucleic Acids Res 40:2032-40 |
| Gundry, Michael; Vijg, Jan (2012) Direct mutation analysis by high-throughput sequencing: from germline to low-abundant, somatic variants. Mutat Res 729:1-15 |
