La Nova Cançó a Mallorca.

A foundation for the building and interpretation of a comprehensive atlas of the mammalian circadian transcriptome has been presented here. To characterize the role of the circadian clock in mouse physiology and behavior, we used RNA-seq and DNA arrays to quantify the transcriptomes of twelve mouse organs over time. We found 43% of all protein coding genes showed circadian rhythms in transcription somewhere in the body, largely in an organ-specific manner. In most organs, we noticed the expression of many oscillating genes peaked during transcriptional “rush hours” preceding dawn and dusk. Looking at the genomic landscape of rhythmic genes, we saw that they clustered together, were longer, and had more spliceforms than non-oscillating genes. Systems- level analysis revealed intricate rhythmic orchestration of gene pathways throughout the body. We also found oscillations in the expression of more than one thousand known and novel non-coding RNAs (ncRNAs). Supporting their potential role in mediating clock function, ncRNAs conserved between mouse and human showed rhythmic expression in similar proportions as protein coding genes. Importantly, we also found that the majority of best-selling drugs and World Health Organization essential medicines directly target the products of rhythmic genes. Many of these drugs have short half-lives and may benefit from timed dosage. In sum, this study highlights critical, systemic, and surprising roles of the mammalian circadian clock and provides a blueprint for advancement in chronotherapy.

A computational algorithm to discover gene-phase set enrichment in circadian data was also developed. This algorithm overcomes shortcomings of existing gene-set enrichment analysis tools that do not allow the handling of data from a cyclic domain. Applying the algorithm to several in-house and published datasets, we find that activity of many biological pathways comprised of circadian genes cluster temporally.

Importantly, clustering often occurred at critical times of the day, such as dawn, which were missed by existing analysis methods. We also found that restricted feed time leads to complete phase-shifts in physiology, while restricted sleep time does not. Restriction of feed time appeared to disrupt coupling between timing of degradation and synthesis processes. Separately, we discovered that circadian genes uniquely bound to by the core clock factor, CLOCK, cluster in phase whereas those for other core clock factors do not. Finally, we showed that gene-phase set enrichment could be easily adapted to analyze cell-cycle data.

Future directions

The atlas of the mammalian circadian transcriptome that we have generated is an enormous dataset that captures biological space and time of the mouse at a very high resolution. The results presented in this dissertation scratch the surface of what can be gleamed from this data, but there is enough data to power perhaps hundreds of future studies. Currently, we have catalogued twelve organs, but we would eventually like to catalogue every organ and tissue in the body. Work on cataloguing the SCN is currently underway in our lab at the time of this writing.

Aside from cataloguing more and more organs, it is also natural to imagine the cataloguing of many other species. Drosophila, Arabidopsis, zebrafish and worm are popular model organisms that come to mind. Due to invasiveness of current RNA collection methods, humans are unsuitable subjects at this time but remain the ultimate future goal. Our hope is also that technology will one day advance to the point where proteins can also be quantified with the level of ease and accuracy as is possible for DNA and RNA today.

Our findings regarding widespread targeting of circadian genes by blockbuster drugs obviously point in a hugely impactful future direction. Here, we have essentially performed an initial screen for noteworthy drugs that may benefit from time-of-day based dosing, but clinical trials examining efficacy and toxicity dependent on time of

administration for individual drugs will be required to ascertain the optimal dosing

schedule for any given treatment. It will be important to examine time as an independent variable at all stages of future clinical trials.

In phase 0, where the aim is to characterize pharmacokinetics and

pharmacodynamics using sub-therapeutic doses, trials must systematically distinguish absorption, distribution, metabolization, excretion, and interactions of drugs within the body at multiple circadian time points over the course of multiple circadian cycles (days) in order to establish adequate temporal profiling. Similarly, in phase 1, where the aim is to determine dose safety, the drug in question must be administered to different groups of subjects at different time points of the day and side effects monitored independently for each group. In phase 2, the aim is to establish efficacy of a drug, and here the drug in question must again be administered to different groups of subjects at different time points of the day with efficacy evaluated independently for each group. Finally, during

phase 3 and eventually phase 4, data must be stratified systematically by circadian time in order to formulate guidelines for proper temporal dosing. Awareness of circadian rhythm effects on clinical diagnosis and treatment will likely increase by much in the coming years as more work along these lines is reported.

Final thoughts

The study of circadian rhythms is a field currently experiencing explosive growth. Though we feel that the work presented here is highly impactful at this stage, there is no doubt that it is but a small piece to a much larger story than could be captured by any one, or even multiple, dissertations. The author’s intent is that the findings and methodologies described here may catalyze continued research interest in this area, especially towards the improvement of clinical practice in the real world.

APPENDIX

To conserve manuscript space, lengthier data produced from this work have been collected as supplementary digital files. Below are legends for these datasets.

Supplementary_dataset_S1.xls:

Dataset S1. Drugs from the top-100 best-selling drugs list that target circadian genes. Extended and verbose version of Table 2.1. All drugs from the top-100 best-selling drugs list are recorded, regardless of half-life.

Supplementary_dataset_S2.xls:

Dataset S2. Drugs from the WHO Essential Medicines list that target circadian genes. As Dataset S1 except for drugs listed under the World Health Organization’s official list of essential medicines.

Supplementary_dataset_S3.xls:

Dataset S3. Half-life notes on all drugs from Datasets S1 and S2.

Supplementary_dataset_S4.xls:

Dataset S4: Oscillation phases of all conserved, non-coding RNAs.

Annotation data includes genomic coordinates from the mouse genome, the assigned RefSeq IDs and gene symbols from mouse and human, the direction of the alignment between the ncRNA and RefSeq sequences (sense or antisense), and the functional

group assigned to each ncRNA. Additionally, this file lists the peak phase in expression for each tissue, as well as the number of tissues in which each ncRNA oscillates.

Supplementary_dataset_S5.xls:

Dataset S5. Antisense transcripts.

Annotation data includes the genomic coordinates for each transcript, as well as the Ensembl gene ID and symbol for the overlapping sense transcript. Additionally, this file lists the difference between peak expression phase in the sense and antisense

transcripts for each tissue. If these columns list "antisense_osc_only", or

"sense_osc_only", it means that in the given tissue, only the antisense or sense

transcript oscillated, respectively. Lastly, this file contains four summary columns that list the number of tissues in which the antisense transcript oscillated, the number in which the sense transcript oscillated, the number in which both oscillated, and the maximum difference in peak expression phase across all tissues.

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