2.2 RNA Abundance and Transcriptional Activity:
2 Data Sets on Mouse Liver and Kidney
All further analyses in the following sections will examine two data sets. In this section I will first characterize the properties regarding circadian gene expression. The first data set is derived from mouse liver and is published by Menet et al. [5], the second data set was generated from mouse kidney by our group in collaboration with the group of Achim Kramer (Charité) and Roman-Ulrich Müller from University of Cologne. Both data sets are based on RNA sequenc-ing. To access transcriptional activity, the second feature of the data sets, different methods are utilized. Menet et al. [5] make use of the so-termed “nascent-seq” method, explained in Section 1.2.3. Here, they separate experimentally pre-mature mRNA from mature mRNA fol-lowed by sequencing. In contrast, transcriptional activity in kidney is obtained from the same RNA sequencing data set which provides mRNA abundance. Separation between transcriptional activity and mRNA abundance is performed computationally by individually quantifying tran-scripts with (pre-RNA) or without (mature mRNA) introns, a method which possibly provides not as “clean” results as the method applied by Menet et al. [5] as described earlier. Further-more, the sampling frequency is different between the two data sets. Mouse liver was sampled every 4th hour, 6 time points per period and 12 time points in total, mouse kidney was sampled with a slightly higher frequency, every 3rd hour, 8 time points per period and 16 in total.
In the following 3 sections, there will be a lot of numbers arising regarding these two sets. For the convenience of the reader these numbers are summarized in a table in Appendix F.2.
In mouse liver 13698 genes are expressed with information on both, mRNA and transcriptional activity, in kidney 14324 genes are expressed. I consider circadian genes as genes with mRNA or transcriptional activity with a 24 hour-periodic pattern (detected with RAIN [200] and a false discovery rate ≤ 0.25) and a relative amplitude larger than 0.1, see also Section 1.3.3.
With these cut-offs I find 3813 (30%) of expressed mRNA to be rhythmic in liver and 4137 (29%) in kidney. If I include transcriptional activity into this analysis I find that 5581 (43%) of expressed genes have a circadian rhythm in either their RNA abundance or transcriptional activity, for kidney this is true for 6489 (47%) of expressed genes. Compared to other studies these percentages of circadianly expressed genes are more than twice as high [94, 5]. This can be in part explained by a higher sampling frequency and the different experimental methods. RNA sequencing employed in the present studies produce data less prone to technical noise compared to microarrays [214]. Both, sampling with higher frequency and less noise, increases the detection of rhythms. Furthermore, RAIN, not employed by the other studies, is able to detect rhythms which other known detection algorithms miss [200]. However, this is not enough to explain the large discrepancy, also the parameter thresholds which separate circadian from non-circadian gene expression play an important role. Here, rather the choice of relative amplitude than a different false discovery rate affects the proportion, see Appendix C.1.2. Hence, many genes I classify as circadian have a low relative amplitude, see also Figure 2.2A. One may argue, that a large proportion of these genes are not able to fulfill a circadian function in the cell. However, the main purpose of this study is to investigate rhythmic PTR. Not asking for specific gene functions but rather for fundamental regulation principles justifies rather loose parameters for gene classification in order to keep the test set as large as possible.
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A different phase distributions of mRNA abundance and transcriptional activity for liver and kidney suggest that circadian gene expression is organ specific, see Figure 2.2B. A clear difference between both organs is also found for the phase difference of transcriptional activity and mRNA abundance, see Figure 2.2C. In kidney most of the mRNA abundance peaks within 6 hours after its transcriptional activity, while in liver a broader distribution of phase differences is observed.
The theoretical boundary for a phase difference is 6 hours when rhythms in mRNA abundance stem only from rhythmic transcriptional activity, see previous section. Hence, one would expect the proportion of genes where rhythms in mRNA are only generated by a transcriptional activity to be much higher in liver than in kidney. However, this needs further validation.
Consistent with the organ specific circadian transcriptome, I find that most of the genes (12148) are expressed in both organs, but only a small proportion (1361 genes, 11% of genes expressed in both organs) is found to be circadian in both organs.
High relative amplitudes suggest that rhythmicity is important for gene function. With that in mind it is not surprising that genes with a high relative amplitudes in their mRNA abdunance in both kidney and liver are almost exclusively core clock genes, see Figure 2.3A. For some other genes no connection to the circadian clock has yet been investigated, despite their strong rhythmicity, see Figure 2.3A. It seems that, to date, we have only scratched the tip of the iceberg when it comes to knowledge of output and consequences of the circadian clock. Furthermore, there are mRNA which show a relative amplitude larger than 1, which would include theoretically (see Section 1.3.1) negative RNA abundances. However, the time series of these mRNAs have a distinct, rather pointy shape and a sine fit results in larger relative amplitudes, see Figure 2.3C.
It might be desirable to introduce a different fit to these time series. This is beyond the scope of this thesis.
The phases of genes expressed in both organs often differ among organs, see Figure 2.3B. In contrast, phases of genes with a high relative amplitude correlate quite well, see Figure 2.3B.
Interestingly however, we observe a systematic phase shift between those genes whose phases correlate. Transcript abundances peak later, while transcriptional activities peak earlier in kidney than in liver. For the latter, the experimental setup may be blamed. Nascent-seq applied for transcriptional activity in liver captures a broad range of newly synthesized RNA including already spliced RNA. The computational separation of exons, proxy for the mature mRNA, from introns accounting for transcriptional activity captures only unspliced RNA. Since RNA is often spliced very early in its life time, the phase of transcriptional activity in kidney appears earlier than in liver.
However, the systematic phase difference in mRNA abundance - especially in core clock genes - is rather odd. It could mean that the core clock oscillates with a phase difference of about 5 hours in both organs. But we must take into consideration that the two data sets were generated in different laboratories, each with its own routines, mouse strains etc.. Although both experiments used mice housed in LD 12:12, probably the most significant influence on possible phase differences, it would still require an investigation of both organs in one laboratory in order to exclude any other experimental influences.
Two organs, two distinct circadian transcriptomes with some similarities, especially in core clock gene expression, leads to the question: How much of the circadian transcriptome can be explained by the transcriptional activity? There seems to be a difference between the two organs because many more mRNA in liver in comparison to kidney have phase differences between transcriptional activity and mRNA abundance larger than 6 hours, the theoretical boundary for constant PTR, see also Figure 2.2C. In the following section I examine both data sets by comparing transcriptional activity and transcript abundance with respect to the model from Section 2.1.