Traditionally, it was thought that chimeric RNA events were exclusively characteristic of the cells of neoplasms [1
], but evidence has shown the presence of chimeric RNAs in various physiologically normal tissue [2
]. Furthermore, because of their presence in cancers, fusion RNAs were once thought to be the sole result of chromosomal translocations [4
], but other work has recently shown that they can exist without DNA arrangement and rather through two mechanisms called cis
-splicing of adjacent genes (cis
], and trans
-SAGe involves the splicing of a singular pre-mRNA molecule that results from passing through the termination site between two adjacent genes. On the other hand, trans
-splicing involves the splicing that connects separate transcripts.
Much research has been invested into elucidating the biology of aging in recent years. Many have tried to identify characteristics and understand mechanisms that contribute to aging due to its role as a major risk factor in many chronic diseases [10
], such as cardiovascular disease and cancer, that rank amongst the top causes of death in developed nations. Most indisputable is the genome damage that accompanies aging in organisms, but just how that damage affects cell and tissue function and vitality is more complex.
Previous studies have found pronounced heterochromatin loss in individuals with progeroid syndromes, which is characterized by accelerated aging in affected children [11
], and similarly, non-neuronal cell types in Caenorhabditis elegans
have shown progressive loss of heterochromatin in an age-dependent manner [12
]. Such a loss of heterochromatin causes the expression of genes that are normally repressed and, therefore, aberrant transcription that may be associated with a variety of RNA classes. Another aspect of aging is transcriptional noise, which is the differential gene expression of cells in an isogenic population, and this phenomenon is correlated with genome damage. This transcriptional noise has also been implicated in reduced organismal fitness [14
], which is essentially what is recognized as aging. Similarly, certain transcription levels of protein isoforms have been shown to change with age, and specifically, some of these isoforms cause the deregulation of mechanisms in alternative splicing [15
], which would directly increase the occurrence of abnormal splicing products. Recent work has also taken a look into the genetic level of stress, and the results support the increase of read-through transcripts under many types of biological stress, including osmotic-induced stress, heat shock, oxidative stress, and viral infection [16
We therefore hypothesize that frequency of chimeric RNA events will have a positive correlation with age due to expected age-dependent deregulation of transcription machinery, particularly in the case of cis-SAGe. With our study, we aimed to confirm the existence of chimeric RNA candidates and elucidate the presence of trends, if any, with respect to age.
In this study, we failed to establish with any strongly supporting evidence the existence of any age-biased trends in the expression of fusion RNAs. Even though certain fusion RNAs have been previously shown to be accurate biomarkers of certain diseases and these diseases affect a larger proportion of older individuals, no strong correlations could be made with age with the larger sample size.
Interestingly, not all of the fusion candidates uncovered from the RNA-Seq data were found in increasing frequency with age. This already contradicts our hypothesis and many of the aforementioned mechanisms that are associated with aging. This also suggests these candidates or fusion RNAs are not simply the byproducts of dysregulated transcription machinery. What their roles actually are would require further work to elucidate.
The lack of significant age-based trends in the expression levels of these confirmed fusions in a larger sample size, may be partially due to the complexity of aging. Aging carries countless confounding variables that are difficult to control for with de-identified samples. The blood samples used in this study were collected from various patients in the hospital, so profiles of health conditions and demographics varied greatly. Furthermore, due to the de-identification process, it was impossible to know how much healthier one sample was compared to another, which is a relationship that a numerical age may improperly represent. Thus, at this point, we had to confront the concept of difference in years since one’s birth and the current aging state one is in. In future work, it would be necessary to consider just what sample size and what demographics would be sufficient to statistically support bioinformatically predicted trend.
There are also limitations associated with the use of blood samples for studying age. Previous work has already shown that there is differential expression of fusion RNAs between tissues [21
], so contrasting results may have been collected if different tissues were used as samples. Another caveat of using blood can be realized when considering how different tissues relate to aging. Any red blood cell in the human body will stay in circulation for approximately 120 days, meaning that unlike other cell types, such as neurons, a blood cell would not age with an individual through their lifespan. However, the relative availability of blood samples compared to relatively difficult collection of any other tissues from individuals of all ages made it our first choice to probe the question.