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A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H, Phillips BE, Cederholm T, Jensen T, van Loon LJ, Lannfelt L, Kraus WE, Atherton PJ, Howard R, Gustafsson T, Hodges A, Timmons JA - Genome Biol. (2015)

Bottom Line: Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity.We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample.This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

View Article: PubMed Central - PubMed

Affiliation: XRGenomics Ltd, London, UK.

ABSTRACT

Background: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

Results: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

Conclusions: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

No MeSH data available.


Related in: MedlinePlus

Gene ontology profile and chromosomal positional enrichment analysis. Pathway analysis and GO analysis indicate that the 150 healthy ageing genes are not related to a few specific biological processes but rather originate from across many biological processes. a Density curves of raw p values for each of the 10,000 hypergeometric tests using randomly sampled probe-sets from the U133+2 gene-chip (n = 150 each time; black) and the density curve of the raw p values from a hypergeometric test using the 150 healthy ageing gene classifier probe-sets (red). A similar result was obtained when the top 670 genes were utilized as the input and compared with randomly generated gene sets of 670 genes. b Positional gene enrichment analysis for the top 670 genes from the prototype classifier (670 probes from which the top 150 probes, with performance >90 %, were selected) found over-representation at 7q22, 11q13 and 11q23. Results were consistent using positional gene enrichment analysis and the ToppGene algorithm; both identified 3, 12 and 3 genes at each loci, respectively, with p < 0.001 or less. Those for 11q13 and 11q23 in particular were most significant, and contained genetic variants that influence the age of onset of various cancers
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Fig6: Gene ontology profile and chromosomal positional enrichment analysis. Pathway analysis and GO analysis indicate that the 150 healthy ageing genes are not related to a few specific biological processes but rather originate from across many biological processes. a Density curves of raw p values for each of the 10,000 hypergeometric tests using randomly sampled probe-sets from the U133+2 gene-chip (n = 150 each time; black) and the density curve of the raw p values from a hypergeometric test using the 150 healthy ageing gene classifier probe-sets (red). A similar result was obtained when the top 670 genes were utilized as the input and compared with randomly generated gene sets of 670 genes. b Positional gene enrichment analysis for the top 670 genes from the prototype classifier (670 probes from which the top 150 probes, with performance >90 %, were selected) found over-representation at 7q22, 11q13 and 11q23. Results were consistent using positional gene enrichment analysis and the ToppGene algorithm; both identified 3, 12 and 3 genes at each loci, respectively, with p < 0.001 or less. Those for 11q13 and 11q23 in particular were most significant, and contained genetic variants that influence the age of onset of various cancers

Mentions: We were interested in whether the healthy ageing diagnostic identified any particular biological processes that might be open to therapeutic targeting. The 150-gene list (Additional file 1) was evaluated using both Ingenuity pathway analysis and R-based gene ontology (GO) analysis. Ingenuity analysis (where a total of 127 genes were annotated in the database) revealed a few marginal functional associations (e.g., nervous system development genes) but these did not remain significant following Benjamini and Hochberg correction. The top ranked database network (genes with published interactions) was defined as ‘cell death and survival’ and contained 31 molecules. In Fig. 6a the density curves of p values for each one of 10,000 hypergeometric tests using a randomly sampled gene set (n = 150 in size) are plotted (black), along with the density curve of the p values from the healthy ageing 150-gene set (red). The profile of ontological enrichment in the healthy ageing diagnostic was not different from a random sample of 150 genes from the gene-chip, of which more than 99 % of the 54,000 probe-sets had no ability to discriminate tissue age in our training model. Manual searching of PubMed and the Online Mendelian Inheritance in Man (OMIM) database yielded some plausible connections with age-related and disease processes (Additional file 1) but such analysis is subjective and it cannot be concluded that these biological functions appear in the ‘healthy ageing’ diagnostic more than by simple proportionality. We did note that the 150 genes included some previously identified ‘ageing’ genes, for example, LMNA (linked with Hutchinson-Gilford progeria syndrome), Unc-13 homolog (UNC13C; linked with beta-amyloid biology), as well as COL1A1 (thought to change in skin ageing).Fig. 6


A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status.

Sood S, Gallagher IJ, Lunnon K, Rullman E, Keohane A, Crossland H, Phillips BE, Cederholm T, Jensen T, van Loon LJ, Lannfelt L, Kraus WE, Atherton PJ, Howard R, Gustafsson T, Hodges A, Timmons JA - Genome Biol. (2015)

Gene ontology profile and chromosomal positional enrichment analysis. Pathway analysis and GO analysis indicate that the 150 healthy ageing genes are not related to a few specific biological processes but rather originate from across many biological processes. a Density curves of raw p values for each of the 10,000 hypergeometric tests using randomly sampled probe-sets from the U133+2 gene-chip (n = 150 each time; black) and the density curve of the raw p values from a hypergeometric test using the 150 healthy ageing gene classifier probe-sets (red). A similar result was obtained when the top 670 genes were utilized as the input and compared with randomly generated gene sets of 670 genes. b Positional gene enrichment analysis for the top 670 genes from the prototype classifier (670 probes from which the top 150 probes, with performance >90 %, were selected) found over-representation at 7q22, 11q13 and 11q23. Results were consistent using positional gene enrichment analysis and the ToppGene algorithm; both identified 3, 12 and 3 genes at each loci, respectively, with p < 0.001 or less. Those for 11q13 and 11q23 in particular were most significant, and contained genetic variants that influence the age of onset of various cancers
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4561473&req=5

Fig6: Gene ontology profile and chromosomal positional enrichment analysis. Pathway analysis and GO analysis indicate that the 150 healthy ageing genes are not related to a few specific biological processes but rather originate from across many biological processes. a Density curves of raw p values for each of the 10,000 hypergeometric tests using randomly sampled probe-sets from the U133+2 gene-chip (n = 150 each time; black) and the density curve of the raw p values from a hypergeometric test using the 150 healthy ageing gene classifier probe-sets (red). A similar result was obtained when the top 670 genes were utilized as the input and compared with randomly generated gene sets of 670 genes. b Positional gene enrichment analysis for the top 670 genes from the prototype classifier (670 probes from which the top 150 probes, with performance >90 %, were selected) found over-representation at 7q22, 11q13 and 11q23. Results were consistent using positional gene enrichment analysis and the ToppGene algorithm; both identified 3, 12 and 3 genes at each loci, respectively, with p < 0.001 or less. Those for 11q13 and 11q23 in particular were most significant, and contained genetic variants that influence the age of onset of various cancers
Mentions: We were interested in whether the healthy ageing diagnostic identified any particular biological processes that might be open to therapeutic targeting. The 150-gene list (Additional file 1) was evaluated using both Ingenuity pathway analysis and R-based gene ontology (GO) analysis. Ingenuity analysis (where a total of 127 genes were annotated in the database) revealed a few marginal functional associations (e.g., nervous system development genes) but these did not remain significant following Benjamini and Hochberg correction. The top ranked database network (genes with published interactions) was defined as ‘cell death and survival’ and contained 31 molecules. In Fig. 6a the density curves of p values for each one of 10,000 hypergeometric tests using a randomly sampled gene set (n = 150 in size) are plotted (black), along with the density curve of the p values from the healthy ageing 150-gene set (red). The profile of ontological enrichment in the healthy ageing diagnostic was not different from a random sample of 150 genes from the gene-chip, of which more than 99 % of the 54,000 probe-sets had no ability to discriminate tissue age in our training model. Manual searching of PubMed and the Online Mendelian Inheritance in Man (OMIM) database yielded some plausible connections with age-related and disease processes (Additional file 1) but such analysis is subjective and it cannot be concluded that these biological functions appear in the ‘healthy ageing’ diagnostic more than by simple proportionality. We did note that the 150 genes included some previously identified ‘ageing’ genes, for example, LMNA (linked with Hutchinson-Gilford progeria syndrome), Unc-13 homolog (UNC13C; linked with beta-amyloid biology), as well as COL1A1 (thought to change in skin ageing).Fig. 6

Bottom Line: Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity.We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample.This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

View Article: PubMed Central - PubMed

Affiliation: XRGenomics Ltd, London, UK.

ABSTRACT

Background: Diagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.

Results: One hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83-0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is 'up-regulated' in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case-control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA 'disease signature', the healthy ageing RNA classifier is diagnostic for AD.

Conclusions: We identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

No MeSH data available.


Related in: MedlinePlus