BACKGROUND: The molecular basis of renal aging is not completely understood. METHODS: We used global gene expression monitoring by cDNA microarrays to identify age associated genes in human kidney samples. Our samples included young (8 weeks-8 years, N= 4), adult (31-46 years, N= 7), and old kidneys (71-88 years, N= 9). RESULTS: Old kidneys had more glomerulosclerosis, tubular atrophy, interstitial fibrosis, and fibrous intimal thickening in small arteries. We identified approximately 500 genes that were differentially expressed among the three age groups. Old kidneys appeared to have increased extracellular matrix turnover and a nonspecific inflammatory response, combined with a reduction in processes dependent on energy metabolism and mitochondrial function. Quantitative supervised bioinformatics analyses of adult and old kidney expression data correlated the expression of 255 gene profiles with renal pathology scores. Microarray class prediction analysis (PAM) identified 50 unique genes that segregated old kidneys into two distinct clusters: those more similar within age class (OO, N= 5) versus old kidneys more similar to adult kidneys (OA, N= 4). The expression of six functionally significant genes was further validated by quantitative reverse transcription-polymerase chain reaction (RT-PCR) (FN1, MMP7, TNC, SERPIN3A, BPHL, CSPG2) in the experiment group and, subsequently, confirmed independently in 17 additional old and adult age-stratified test kidney samples. The p53 inducible gene, CSPG2, performed best in separating OO kidneys from adults and OA samples in this analysis. CONCLUSION: The method described in this study using independent validation samples can be envisioned to test utility of the identified genes in assessing age-related changes that contribute to decline in renal function.
BACKGROUND: The molecular basis of renal aging is not completely understood. METHODS: We used global gene expression monitoring by cDNA microarrays to identify age associated genes in human kidney samples. Our samples included young (8 weeks-8 years, N= 4), adult (31-46 years, N= 7), and old kidneys (71-88 years, N= 9). RESULTS: Old kidneys had more glomerulosclerosis, tubular atrophy, interstitial fibrosis, and fibrous intimal thickening in small arteries. We identified approximately 500 genes that were differentially expressed among the three age groups. Old kidneys appeared to have increased extracellular matrix turnover and a nonspecific inflammatory response, combined with a reduction in processes dependent on energy metabolism and mitochondrial function. Quantitative supervised bioinformatics analyses of adult and old kidney expression data correlated the expression of 255 gene profiles with renal pathology scores. Microarray class prediction analysis (PAM) identified 50 unique genes that segregated old kidneys into two distinct clusters: those more similar within age class (OO, N= 5) versus old kidneys more similar to adult kidneys (OA, N= 4). The expression of six functionally significant genes was further validated by quantitative reverse transcription-polymerase chain reaction (RT-PCR) (FN1, MMP7, TNC, SERPIN3A, BPHL, CSPG2) in the experiment group and, subsequently, confirmed independently in 17 additional old and adult age-stratified test kidney samples. The p53 inducible gene, CSPG2, performed best in separating OO kidneys from adults and OA samples in this analysis. CONCLUSION: The method described in this study using independent validation samples can be envisioned to test utility of the identified genes in assessing age-related changes that contribute to decline in renal function.
Authors: Yuliang Wang; Diana G Eng; Natalya V Kaverina; Carol J Loretz; Abbal Koirala; Shreeram Akilesh; Jeffrey W Pippin; Stuart J Shankland Journal: Kidney Int Date: 2020-06-25 Impact factor: 10.612
Authors: Maria João Pinho; José Miguel Cabral; Elisabete Silva; Maria Paula Serrão; Patrício Soares-da-Silva Journal: Mol Cell Biochem Date: 2010-12-02 Impact factor: 3.396
Authors: G Chen; E A Bridenbaugh; A D Akintola; J M Catania; V S Vaidya; J V Bonventre; A C Dearman; H W Sampson; D C Zawieja; R C Burghardt; A R Parrish Journal: Am J Physiol Renal Physiol Date: 2007-08-01
Authors: Valeria R Mas; Mariano J Scian; Kellie J Archer; Jihee L Suh; Krystle G David; Qing Ren; Todd W B Gehr; Anne L King; Marc P Posner; Thomas F Mueller; Daniel G Maluf Journal: Mol Med Date: 2011-09-06 Impact factor: 6.354