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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"
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
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
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
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.
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
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.
- It was a bit disorienting for figure 1 to begin a third row of the
flow chart that did not clearly link it with the far right side of the
upper rows. It would have been helpful for the figure to have been
arranged as a single continuous flowchart. If arranged vertically, this
chart could have probably used about as much space as figure 4.
- Figures 2B and 3A do not include labels for their charts' y-axes, and
though the "degree of expression" as the figure caption describes the
vertical measures can be used to surmise the meaning of the data
portrayed, I feel it would have helped to include a more explicit
description of what this figure axis was portraying.
- The table left a few items vague as well. It is not obvious as to
whether the "function" column refers to the function of the normal
splice variant in control cases (presumably so) or the induced function
of the splice variant in one of the hypertrophy states, or both. "Normal
gene function" may have been preferable here. Other aspects of the chart
generally fall into understanding once patterns are observed and when
relevant terms (e.g. "domain") are defined. Given that the table was
constructed almost entirely from previous studies' data, and only a
subset of its data was directly relevant for other figures (specifically
figure 3 which only references a few of the AS genes from the table, and
includes mostly AS genes uncovered in the study itself) it also seemed
that the table could have been made supplementary.
- Using Figure 3 and the methods, I could not discern how the 8 genes
selected for further analysis and verification for alternative splicing
were selected. The paper did not reveal a particular reason for choosing
these particular genes from the subset of 470 and 387 genes containing
alternative exons in PAH and PHH respectively. This may mean the genes
were chosen randomly, the authors neglected to either report their full
findings for all of the genes, or a rationale for selecting these genes
existed but was not reported.
- Also in figure 3, I was left curious as to why all four experimental
groups were not included in every histogram in B, as they were in the
final two histograms; perhaps these data were not notable to the
authors, but this is not clear. Additionally an inconsistency is also
apparent in the continuity of these figures' labels; the histogram
corresponding to the gene Cxxc5 has labels for TAC and sham,
but the adjacent RT-PCR blot contains labels for sedentary and exercise.
This is opposite of the pattern for other rows, where TAC and sham were
labeled similarly in the histograms and blots.
The figure caption notes that Cxxc5 variants were
identified in PHH, but I believe this information was just inexplicably
excluded from the histogram.
- Figure 4 makes what I thought was a minor error in not explicitly
noting whether "multilateral pathway analysis" (a term I could not find
used in any other published paper) refers to analysis of KEGG (Kyoto
Encyclopedia of Genes and Genomes) information. Though I believe that it
likely does, this led to some confusion during interpretation.
- FOXM1 was implicated in a number of figures to be involved in
the pathogenesis of PAH, but an unsatisfying discussion was provided
regarding follow up studies that might be performed to manipulate this
gene or its protein to alter the outcome of PAH.
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),
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.
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,
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