015 Modulating melatonin dynamics at transcription level using virtual knockout approaches: an advanced perspective in sleep medicine

Abstract

Abstract Introduction Sleep disorders, the most neglected public-health issues, are threatening overall health. It is tightly associated with the individual’s exposures to the light/dark (LD)-cycles linked to their circadian rhythms. Lifestyle, shift-work, frequent travels, and post-pandemic-stress lead unintentional compromise of sleep, thus circadian homeostasis. Melatonin (MT), a pivotal natural hormone for circadian and sleep health, attains acrophase in the dark. MT mediates LD-triggered circadian rhythms through its dynamic expressions. Circadian phases are greatly reflected by MT-dynamics. Dim-light-MT-onsets (DLMO) act as a marker, reporting internal circadian-timing in mammals. Estimating this is essential in therapeutic-designing against misaligned circadian conditions. Despite many experimental approaches, there is still a slit hacking the MT-dynamics from molecules to systems. Inclusive perspectives on endogenous factors affecting MT’s synthesis, secretions and bioavailability over time-course are not extensively exposed. MT-dynamics has multiplexed stochastic interactions across numerous genes, TFs, and regulators, and they are coupled non-linearly. Small changes used to compound through the genetic networks and reflected in systems-wide events marking distinct signalling response dynamics. Understanding such responses is challenging yet imperative. A robust quantitative model is inevitable to investigate such stochastic intricacy. Methods We proposed a quantitative framework to model MT signalling networks using diverse kinetic parameters linked in its genetic circuits and perturbing them must alter the MT-dynamics. We used a robust computational approach, LogicTRN to decode the systematic controls of the MT-dynamics. It combines multi-layered transcriptome-wide data as input. Computing this returned the regulatory TF-logics in the transcriptional regulatory networks for MT. We developed transcriptional simulations with virtual-knockout mutants and performed genetic network perturbation study. Results The results showed the reconstruction of robust quantitative regulatory networks decrypting transcriptional controls for MT-dynamics to estimate the influence of the multiple kinetically distinct inputs affecting those dynamics. This offered competitive advantages in terms of scalability, robustness, and iterations to characterize the effective molecular-targets to modulate the genetic circuit of MT-dynamics effectually. Conclusion Quantitative reconstruction and characterization of the regulatory interactome of MT may facilitate us to strategize the adjustments of regulatory controls to effectively modulate MT-dynamics. This foundation may enhance the advancement of circadian and sleep medicine in future. Support (if any):