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Garrett Smith's Genomics Web Assignment 2

A Review of Song, et al's "Deep RNA Sequencing Reveals Novel Cardiac Transcriptomic Signatures for Physiological and Pathological Hypertrophy"

(Note: summary highlights for certain divisions of this review  are presented in in bold. Further elaborations of the study and interpretations of figures are presented in normal type).


    This study by Song, et al. (2012) addressed the differential expression of RNA between cells of hearts (i.e. the heart cells' transcriptomes) having experienced either physiological hypertrophy (PHH) or pathological hypertrophy (PAH). Both of these types of hypertrophy lead to similar morphological changes in cardiac tissue; however, PHH shows beneficial contributions to cardiac function, whereas PAH is associated with progressive declines in cardiac function. This study thus sought a deeper understanding of the molecular differences in the two hypertrophy processes, with the hope of potentially highlighting certain key molecular players in PAH pathology.

    This study expanded upon studies of transcription patterns in these cells found using microarray – a technique with certain limitations such as low sensitivity – by utilizing RNA sequencing (RNA-seq), a newer technique found to be superior to more traditional microarray techniques in sensitivity, accuracy, and reproducibility.  RNA-seq, also called whole transcriptome shotgun sequencing (Iacobucci, et al., 2012), involves sequencing of cDNAs obtained from RNAs using high-throughput sequencing technologies to precisely discern the magnitude of expression of certain RNAs and thus genes (Nagalakshmi, et al., 2010).

    Cardiac tissue from four groups of mice were analyzed in the study. In the exercise group, mice were made to swim for progressively longer periods over four weeks. Mice in the transaortic constriction (TAC) group underwent surgery involving ligation of the transverse aortic arch (Cha, et al., 2008), and sham mice went through the same surgical preparation as the TAC group with the exception of aortic ligation. Sedentary mice did not undergo any forced exercise or operation of any kind. RNA sequencing was performed utilizing all heart tissue from a given animal.

    Analysis of differentially expressed genes (DEGs) between PAH and PHH cardiac cells showed that genes and signaling pathways involved in PHH were highly distinct from PAH, with PAH cells showing a marked increase in DEGs relative to PHH cells. The results from RNA-seq as used in the study, were found mirror the results of RT-PCR, substantiating the utility of RNA-seq, but comparisons of RNA-seq to microarray results revealed significant fdifferences between the two, with microarray being much less sensitive to the variety of genes found through RNA-seq. Additionally RNA-seq was able to identify variations in alternative splicing on top of DEG, using the same single approach - a feat that would be challenging for microarrays.
    The transcription factor FOXM1 was implicated in the PAH pathogenesis, as was found through various manifestations of its associations with other genes also found to be linked to the PAH transcriptomic profile. Patterns in alternative splicing (AS) of RNAs were found to differ between the two hypertrophied  tissue types as well, which may have resulted from differential splicing factor expression. Groupings of DEGs and alternatively spliced genes by function revealed interesting qualitative trends as well, revealing, for example, that genes upregulated in PAH tended to be involved in immune function and cell cycles.
    Overall these findings provide a rather comprehensive overview of differential transcriptomics of PAH and PHH – information predicted to be of potential use in understanding the mechanisms of each of these types of cardiac hypertrophies, potentially leading to new avenues for disease management or promotion of greater cardiovascular health.

Personal Opinion

    I found this report to be an excellent example of the utility of RNA sequencing as a more sensitive means of identifying DEGs between two tissue types. The RNA-seq technique was reported to be more sensitive than and consistent with microarray, a technology assumed reliable in studies of genomics, just as previous studies found with RNA-seq (Nagalakshmi, et al. 2010; Song, et al., 2012), and demonstration of the technique's utility compared to that of preexisting techniques seemed well supported. In most cases analyses were both thorough and multifaceted (both qualitative and quantitative on several levels), inspecting patterns ranging from genes' promoter segments to their alternative splicing. A more thorough set of comparisons to older techniques (as was given in figure 3C), however, could have been conducted or at least referenced to more clearly verify RNA-seq's utility relative to more trusted techniques, as only 8 genes were reported to have been verified by both techniques.
     I imagine the same RNA-seq approach can and will be used on a larger scale to identify molecular differences between normal and diseased (or disease susceptible) tissue types for other conditions in which morphological differences in normal versus diseased tissue are not obvious (Chohn's disease, for example). The fact that RNA-seq could be used to sequence both differentially expressed genes and alternatively spliced genes using the same assay was particularly noteworthy, especially considering how accurate and complete the RNA-seq methods was in comparison to older techniques such as microarray. I also imagine that the limitations addressed in the study for the RNA-seq approach (limited available software, bioinformatic algorithms, and standardization) were not particularly substantial in that they will likely resolve in time as this technique becomes more widely practiced and a greater infrastructure develops around the use of RNA-seq.
The RNA-seq technique, which, as reported,  can even detect de novo transcripts and long, non-coding RNAs (unlike most microarrays) may show promise for completely supplanting microarray where applicable, providing an elaborate yet elegant approach to understanding and comparing the genomics both within and between systems.

    There were a number of mostly minor points of concern I had regarding the reporting of some information through the figures, however, listed below in the order in which they appeared in the article:
    Overall these criticisms were relatively minor, not greatly obscuring the central message of the article: that despite morphological similarities in two different tissues – given in the case study between PAH and PHH cardiac tissues – differences in functional outcomes (i.e. health) may be largely explained through differential gene expression, and RNA-seq provides a thorough and accurate way of elucidating these genetic differences relative to previously available techniques. The progression of ideas throughout the study is reflected in the progression of its figures and tables. Click below to visit the first figure and see an overview of genetic differences identified between cardiac tissue of PAH and PHH models.

Proceed to Figure 1

Figure 2

Table 1

Figure 3

Figure 4


Iacobucci, I., et al. (2012). Application of the whole-transcriptome shotgun sequencing approach to the study of Philadelphia-positive acute lymphoblastic leukemia. Blood Cancer Journal, 2(3), e61.

Nagalakshmi, U., Waern, K. & Snyder, M. (2010). RNA-Seq: a method for comprehensive transcriptome analysis. Current Protocols in Molecular Biology, Chapter 4: Unit 4.11.1-13.

Song, H. K., Hong, S. E., Kim, T., Kim D. H., et al. (2012). Deep RNA sequencing reveals novel cardiac transcriptomic signatures for physiological and pathological hypertrophy. PLoS One, 7, e35552.

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