2018
DOI: 10.1111/acel.12785
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Extracellular RNA profiles with human age

Abstract: SummaryCirculating extracellular RNAs (exRNAs) are potential biomarkers of disease. We thus hypothesized that age‐related changes in exRNAs can identify age‐related processes. We profiled both large and small RNAs in human serum to investigate changes associated with normal aging. exRNA was sequenced in 13 young (30–32 years) and 10 old (80–85 years) African American women to identify all RNA transcripts present in serum. We identified age‐related differences in several RNA biotypes, including mitochondrial tr… Show more

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Cited by 31 publications
(31 citation statements)
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“…In addition to examining circulating miRNAs with human age, we developed a sequencing pipeline to identify both small and long RNAs in one sequencing reaction with the goal to establish a catalog of extracellular RNA (exRNA) in human aging [ 7 ]. As many previous studies have focused on exRNA profiles with a specific disease process, we believe it is important to identify age-dependent differences, which may help guide the development of references for investigating age-related disease.…”
Section: Extracellular Rna Changes With Human Agementioning
confidence: 99%
“…In addition to examining circulating miRNAs with human age, we developed a sequencing pipeline to identify both small and long RNAs in one sequencing reaction with the goal to establish a catalog of extracellular RNA (exRNA) in human aging [ 7 ]. As many previous studies have focused on exRNA profiles with a specific disease process, we believe it is important to identify age-dependent differences, which may help guide the development of references for investigating age-related disease.…”
Section: Extracellular Rna Changes With Human Agementioning
confidence: 99%
“…Recently, growing evidence shows that TERT is protective in the microcirculation against prolonged vascular stress [7,8]. Among the different biomarkers proposed, including cell-free DNA and circulating extracellular RNAs [9,10], growth differentiation factor (GDF-15) appears particularly promising due to the ease of collecting specimens and the low costs. However, little is known about how serum GDF-15 changes with age.…”
Section: Introductionmentioning
confidence: 99%
“…In particular, work on decanalization has hypothesized that gene regulatory networks evolve over many generations of stabilizing selection, and that novel environmental challenges (such as Western diets and lifestyles) may disrupt these fine-tuned connections leading to dysregulation, a breakdown in co-expression, and ultimately disease (Careau et al, 2014; Gibson, 2009a, 2009b; Hu et al, 2016; Lea et al, 2019). In support of this idea, we found diet-induced changes in the co-expression of transcription factors involved in insulin secretion and glucose tolerance (SOX4), lipid, carbohydrate, and energy metabolism (NR4A2), and BMI, HDL, and aging ( RF00283 ) (Davis et al, 2017; Dluzen et al, 2018; Goldsworthy et al, 2008; Kanai et al, 2018; Pearen & Muscat, 2010). We also observed that the transcription factor MEF2D , which has previously been implicate in the transcriptomic response to insulin signaling (Samson & Wong, 2002; Solomon et al, 2008), is a hub gene identified in 22 differentially-correlated gene pairs.…”
Section: Discussionmentioning
confidence: 55%
“…These hub genes were enriched for genes encoding transcription factors (OR = 7.40, FET p = 7.0 x 10 -3 ), including SOX4 (essential for normal insulin secretion and glucose tolerance) and NR4A2 (involved in lipid, carbohydrate, and energy metabolism (Goldsworthy et al, 2008; Pearen & Muscat, 2010)), providing further support for immunological and metabolic reprogramming induced by our diet manipulation. Interestingly, the hub gene involved in the greatest number of differentially-correlated gene pairs was RF00283, aka SCARNA18, a non-coding RNA that has been associated with BMI, HDL cholesterol, and aging in human genome-wide association studies (Davis et al, 2017; Dluzen et al, 2018; Kanai et al, 2018; Tachmazidou et al, 2017) (Fig. 3B-D).…”
Section: Resultsmentioning
confidence: 99%