From Mechanism to Observation and Back Again

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Abstract

I like to think of genomes like my (almost) two-year-old's wooden train set. She can build layouts in a variety of ways, and the number of possible combinations grows along with the size of the train set. Stations, switches, and crossing gates provide ''regulation,'' and these parts, along with the tracks, are incredibly dynamic, much like genomes. Of course, a toddler, who likes to destroy things, is also a great inducer of genomic instability. When studying this set-up, you might like to know how two trains interact at a crossing gate, or how often they are found in the same station. But to truly understand how the whole system works, it is very helpful to be able to see the whole picture. In the field of genomics, obtaining such a system-wide snapshot is indeed possible, to a point. Due to rapidly paced technological development over the last 15 years or so, driven at least in part by next-generation sequencing technologies (Reuter et al., 2015), the breadth of genome-wide data we can now obtain is remarkable. However, during this exciting time the field of genome biology and gene regulation has in many ways moved away from its mechanistic beginnings in the work of people like Jacob and Monod (1961), Ptashne and Hopkins (Ptashne 1967), and Gilbert and Mü ller-Hill (1967). Instead, the field has moved into areas that were almost inconceivable in the 1960s, some of which are covered in this issue—such as mapping mutation rates genome-wide to determine tumor genome evolution (Kass et al., 2016), or even tracking DNA methylation and histone modifications over the lifetime of multi-cellular organisms (Booth and Brunet, 2016). For several years, however, these data were often more correlative than mechanistic. It may come as no surprise to you that as a journal focused on molecular mechanisms, we are most excited to see the field turning back to its mechanistic roots. An experiment that generates large amounts of genome-wide data often comes with the weakness of only capturing a snapshot of the train set, and that snapshot is more often than not an average across a population of cells. The last few years have brought incred-ible advances in single-cell technologies (Wang and Navin, 2015; Kolodziejczyk et al., 2015), but these are still often that snapshot. One picture shows all the trains in one station; another shows them spread across the track. On any given day, the track could be damaged (by a rampaging toddler) or repaired (by either parent, with surprising differences in fidelity). Some days the track is a circle, another a figure eight complete with a plastic bridge. Observing these scenarios provides new knowledge, but it does not tell us the details of the underlying mechanism and function of the trains. Sophisticated analysis must be performed to normalize and correlate in order to draw conclusions. The reason we are publishing this special issue is to highlight the genome, along with the accompanying transcriptomes, methyl-omes, and epigenomes, as the dynamic entity it is. Each facet of the genome is increasingly being studied with time and stimulus as variables in the experiment in order for us to understand the underlying regulatory mechanisms at work. Which transcription factors move where in response to a given signal? What happens to the chromatin or methylation when they get there (if you are an organism that has nucleosomes and methylation, of course), and how does that regulate transcription? What are the mechanisms of heritabil-ity? For example, there is still a huge amount of work to be done in resolving what our common histone variants and modifications really do, and more modifications are being discovered all the time (Soshnev et al., 2016, this issue). These are the kinds of studies that now find their home here at Molecular Cell. Genome-editing and super-resolution microscopy techniques have tremendous potential to help us answer more of the outstanding questions about the complexity of our genome and the factors that read, package, and replicate it. In the future I expect Molecular Cell papers to go even further to test the hypotheses that come from genome-wide population data. So your favorite tran-scription factor goes there, and the chromatin changes in response to stimulus X? Tell us what happens when it can't, please, or when the chromatin is unable to adjust in response, in an individual cell. And, perhaps most importantly, can you separate cause and effect? As always, when we publish a special review issue, it is the authors and reviewers who make it happen, and the credit is all theirs. We thank them for their thoughtful and interesting contributions, and we hope you all enjoy the issue. REFERENCES

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APA

Gaskell, E. (2016, June 2). From Mechanism to Observation and Back Again. Molecular Cell, 62(5), 649. https://doi.org/10.1016/j.molcel.2016.05.022

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