The core idea of my research is that if we cannot find the secret for aging by looking only where data may point us, we must start looking somewhere else, even if no data points in this direction. To accomplish this we must change perspectives and need to spend time reading my long comparative analogy narratives.

• Compare: What “common” things/ideas is your core idea like?

If you cannot find your favorite sweater in your closet, there is no point to keep looking for it in your closet over and over again; but instead, you may need to start looking elsewhere, e.g. in your drawers, even if you may not recall to ever have put it there, but somebody else might. So if for certain, the item or answer you are looking for cannot be found in its most likely place, then its better to start looking at less likely places despite lacking any logically seeming connections or explanations.

• Contrast: What “common” things/ideas is your core idea NOT like?

The answers are not obvious because crucial concepts are still missing. We can only find them by trying to generate new experimental data aiming to exclude initially crazy hypotheses, which may not be crazy after all, as long as they remain internally consistent.

• WHY is your research important? WHY should the audience care?

After I graduate people will forget about my intuitive insights. We may lose more than 50 years in anti-aging research and will lose billions of people unnecessarily due to aging before data may point us to look in the same directions as I am trying to propose despite the lack of reproducible evidence due to the lack of suitable datasets.

• How does your research impact the everyday person? (Be creative here)

It’s our very survival, which most of us have not yet realized lies within our handsb if we’ll reach out and grab it hard enough, long enough and creatively enough.

• WHY continued
• If you were going to tell the story of your research, what would the story sound like with each of the following plots?
• Challenge Plot: Overcoming daunting obstacles (Explain briefly)

It’s the plot of my dissertation objective, which seemed unachievable for the first 5 months, because I was looking were so many others have looked before me. Only by trying something obviously wrong simply because I ran out of right-looking rational options, my data started to make sense. Conclusion: It’s better to do wrong things than to do nothing because – in contrast to doing nothing – doing obviously-seeming wrong thing still means motion. And motion means change. And – in contrast to no actions – wrong actions may result in a comparative directional change because they allow for answering the question, whether the wrong deeming action / analysis made things worse than they were before.

• Creativity Plot: Solving a long-standing problem or innovative problem-solving (Explain briefly)

What if we replaced the most commonly used reference frame with respect to zero gene expression with a much more unconventional relative reference frame defined by periodicity measures, such as temporal phase shifts between temporal alignments between mutually exclusive, i.e. with each other interfering inhibitory biological process, such as sleep and wakefulness, fermentation and oxidation, caloric restriction (CR) and full nutrient media (YEPD), asexual reproduction by budding or sexual sporylation, i.e. by forming fruiting bodies with spores, etc.? What if we replace our linear concept of life being defined by linear gene expression trends over time, with a cyclical concept by defining life in terms of period length, synchronous and asynchronous gene expression oscillation patterns defined by reproducible amplitudes, period lengths and brief but abruptly steep changes in gene expression with durations shorter than 1/10th of the duration of the cell cycle?

We should ask the question:

Could cyclically reoccurring periodic osculation patterns and features - no matter how transient - carry more informational value when inferring functional and regulatory aspects based on time series plots compared to potentially underlying linear trends to which most of us have limited most of our attention in the past?

When looking up the same pathway for the attached 81 Time Points (TP) and 8 lifespan datasets it can be clearly shown that the 81 time points for measuring the transcription 26 times per hour. This is the only way to ensure that the time span between subsequent transcriptome measurements would never exceed 3 minutes. Only then even very brief - but nevertheless highly distinctively regulatory and functionally relevant osculating periods could be considered, especially when otherwise too many plots would look the same.

As the gaps between time points keep rising the correlations between time series curves belonging to the same GO (Gene Ontology) terms keeps gradually declining until plots of the same GO term or pathway no longer appear more correlated and similar to one another than to the remaining genome. Exactly, when this point is reached no more functional and regulatory inferences should be based on time series plots. Will therefore inferences not considering relevant cyclically recurring oscillation periods always be wrong, especially if this would cause too many plots to look too much the same?

The data, from which the attached microarray time series plots have been drawn using R, comes from:

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30053

It has the title:

Dynamics of two oscillation phenotypes in S. cerevisiae reveal a network of genome-wide transcriptional and cell cycle oscillators

The publication about this dataset is:

Chin SL, Marcus IM, Klevecz RR, Li CM. Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators. FEBS J 2012 Mar;279(6):1119-30. PMID: 22289124.

The link to it is:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3368069/

Therefore, one could hypothesize that regulatory and functional inferences based on time series plots should only be considered if the time between subsequent measurements is much shorter than the briefest - but nevertheless functionally and regulatory relevant - difference in osculation patterns.

The maximally acceptable time span between subsequent measurements, which would still allow to make meaningful functional and regulatory inferences based on time series plot similarities, still needs to be experimentally determined because it depends on the overall duration of the cell cycle for each species.

This, in turn, implies that the cyclical nature of periodically reoccurring oscillation patterns tends to over-shadow and thus disguises any potentially underlying linear gene expression trend over time, to which most anti-aging investigators limited most of their attention.

Another informational dimension to highly interdependent time series plots as described above could be added by dividing the Gene Expression Dynamic Inspector (GEDI) tool into different expression trend regions for better replicating and validating Janssen's et. al.’s conclusion that the proteome will be less similar to the transcriptome later in life when compared to its beginning.

See:

Gabriel S. Eichler, Sui Huang, Donald E. Ingber; Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles. Bioinformatics 2003; 19 (17): 2321-2322. doi: 10.1093/bioinformatics/btg307

But when I tried to validate this claim by Janssen et. al. I found about as many converging as diverging genes. This requires to carefully reproduce Janssen's understandings and concepts of the terms "divergence" and "convergence" and how they were quantified.

See:

Janssens, G. E., Meinema, A. C., González, J., Wolters, J. C., Schmidt, A., Guryev, V., Bischoff, R., Wit, E. C., Veenhoff, L. M., and Heinemann, M. (2015). Protein biogenesis machinery is a driver of replicative aging in yeast. eLife, 4:e08527+. (see https://elifesciences.org/articles/08527)

Could all this lead to the more general conclusion that functional and regulatory inferences based on time series similarities must be flawed when the measuring time points are to far apart for capturing functionally and regulatory relevant periodically reoccurring transient osculation patterns without which it would have been impossible to discern between otherwise identical plots because this would result in functionally unrelated genes to be erroneously placed into the same group?

• HOW is your research being conducted? How is it working?

I am only an almost blind bioinformatics student. I can only analyze data but I cannot generate datasets. I can only hope that researchers in wet-labs read my writings and generate cell cycle data less than 3 minutes apart throughout all cell cycles of the yeast, i.e. spanning its entire lifespan to test my hypothesis that aging could - at least in part - be caused by a gradual increase of temporal misalignments of gene expression between genes forming imperative unconditionally life-essential (i.e. always functional) functioning units, e.g. life-essential molecular functions) and conditionally functional units and by gradually losing the imposing phase shifts between groups of genes, which are part of mutually exclusive life processes, such as being asleep or awake, fermenting or oxidating, asexually or sexually reproducing, calorically restricted (CR) or at libidos (access to glucose), etc., which interfere with each other if no longer separated by temporal gaps (i.e. periodic phase shifts)? Ruling out this non-data driven aging hypothesis is as valuable as confirming it because it would at least help to describe a way by which the yeast IS NOT aging since it reduces the options by which the yeast could be aging by at least one possibility.

• Data gathering process

Generating microarray and RNA time series data is expensive but it is essential for our survival. The wild type (WT) yeast can replicate between 25-26 times. Everything above 30 replication is considered long-lived. Up to 60 replications have been observed. But I could only find datasets with up to 3 out of the maximally possible 60 replicative cycles. But since the most valuable datasets for time series yeast gene expression data is from 2005 it implies that the wet-lab experiment designers did not see any values in spanning the entire yeast lifespan with extremely high temporal resolution. We had the means more than 10 years ago but nobody must have seen any value in using them for generating the kind of data I am proposing.

• Results

To be determined (TBD) as soon as suitable experimental data become available.

• Implications (Explain briefly)

If this hypothesis gets confirmed we could cause rejuvenation by restoring youthful temporal alignments within groups of genes belonging to the same functional unit (e.g. molecular function (MF)), as well as temporal and relative gene expression ratios between different groups of genes, which form different functional units. This kind of option to counteract the adverse effects of aging would have been overlook if we’d keep conceptualizing life and aging as being defined only by linear trends (e.g. up- and down-regulated gene expression instead of also considering to conceptualize life and aging as being defined by relative changes in its reproducible periodicity parameters, such as period length, amplitude, oscillation patterns, temporal phase shifts, etc.

If this hypothesis gets proven wrong then we must remain creative and keep looking elsewhere for the secrets of aging by imagining scenarios, which could explain why we could not unravel the secrets of aging by our past methods approaches to understand, control and permanently reverse it.

Who feels that my writings make sense and should be published? Thanks for your time reading all the way until here and for your attention. I welcome your feedback.

Best regards,

Thomas Hahn

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