Correction for both common and rare cell types in blood is important to identify genes that correlate with age

Abstract

Background Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. Results Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18–81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P ≤ 2.5⨯10−6). Moreover, 511 genes (~ 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. Conclusions We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Genetic, parental and lifestyle factors influence telomere length

“…The genes are sorted by their average LFC across cell types. The row annotation bars show whether the genes are located at the subtelomeric (<4Mb) or subtelomeric long (4–10Mb) region and if the genes were previously reported in any of the following studies: Pellegrino-Coppola et al, 2021 53 , Tacutu R et al, 2018 54 , Buxton JL et al, 2014 29 or Nittis T et al, 2010 51 ( Supplementary Table 8 ). The distance to the telomeres was not calculated for the mitochondrial gene (MT-CO1) (subtelomeric (<4Mb) and subtelomeric long (4–10Mb) = NA).…”

“…NR4A2 is involved in T-cell maintenance through regulation of Treg suppressor functions and repression of aberrant Th1 induction 52 . The third line of gene–telomere interaction evidence is the overlap with leukocyte ageing– associated genes that were previously identified in whole blood after a deep correction for cell type composition (8 out of 84 testable genes overlapped; odds ratio = 3.3, p = 6×10 -3 ) 53 . Finally, four genes also overlapped with the GenAge human database, the core Human Ageing Genomic Resources (HAGR) database composed of >300 human ageing–related genes 54 .…”

“…To validate our sc-DEA findings, we compared our results to four biologically related studies that: i) revealed novel telomere proteins using in vivo cross-linking, tandem affinity purification and label-free quantitative LC-FTICR-MS 51 , ii) identified a set of genes associated with ageing in whole blood using bulk RNA-seq after correcting for cell type composition 53 , iii) created the GenAge human database as part of the HAGR databases composed of 307 human ageing–related genes 54 and iv) defined an association between telomere length and methylated cytosine levels for both blood and EBV-transformed cell-line DNA samples 29 . To this end, we overlapped the set of 97 unique genes we found to be DE with telomere length against these four previously reported lists of genes and performed a two-sided Fisher’s exact test to assess whether these overlaps were significant.…”

“…For the tested overlaps, as our background list of genes, we gather the union of all tested genes in T-, CD4T and CD8T cells. As each study's background list of genes, we considered all the tested genes in 53 and all the protein-coding genes (using Gencode v26, GRCh38 annotation) in 51 and 54 .…”

“…The column annotation bar shows all the cells from the same cell type. The row annotation bars show the DEG direction (DE_direction), whether the DEG is located at the subtelomeric (<4Mb) or subtelomeric long (4-10Mb) region, and if the DEG was previously reported in any of the following studies: Pellegrino-Coppola et al, 2021 53 , Tacutu R et al, 2018 54 , Buxton JL et al, 2014 29 or Nittis T et al, 2010 51 (literature) ( Supplementary Table 8 ). Only one DEG (PGAM1) showed a different direction among T, CD4T and CD8T DEAs (DE_direction = unconcordant).…”