Kaiser, M. S. | bioRxiv Science

bioRxiv2021

Dual roles of mTORC1-dependent activation of the ubiquitin-proteasome system in muscle proteostasis

Muscle size is controlled by the PI3K-PKB/Akt-mTORC1-FoxO pathway, which integrates signals from growth factors, energy and amino acids to activate protein synthesis and inhibit protein breakdown. While mTORC1 activity is necessary for PKB/Akt-induced muscle hypertrophy, its constant activation alone induces muscle atrophy. Here we show that this paradox is based on mTORC1 activity promoting protein breakdown through the ubiquitin-proteasome system (UPS) by simultaneously inducing ubiquitin E3 ligase expression via feedback inhibition of PKB/Akt and proteasome biogenesis via Nuclear Factor Erythroid 2-Like 1 (Nrf1). Muscle growth was restored by reactivation of PKB/Akt, but not by Nrf1 knockdown, implicating ubiquitination as the limiting step. However, both PKB/Akt activation and proteasome depletion by Nrf1 knockdown led to an immediate disruption of proteome integrity with rapid accumulation of damaged material. These data highlight the physiological importance of mTORC1-mediated PKB/Akt inhibition and point to juxtaposed roles of the UPS in atrophy and proteome integrity.

physiology↗

bioRxiv2019

Hierarchical Markov Random Field model captures spatial dependency in gene expression, demonstrating regulation via the 3D genome

AO_SCPLOWBSTRACTC_SCPLOWHiC technology has revealed many details about the eukaryotic genomes complex 3D architecture. It has been shown that the genome is separated into organizational structures which are associated with gene expression. However, to the best of our knowledge, no studies have quantitatively measured the level of gene expression in the context of the 3D genome. Here we present a novel model that integrates data from RNA-seq and HiC experiments, and determines how much of the variation in gene expression can be accounted for by the genes spatial locations. We used Poisson hierarchical Markov Random Field (PhiMRF), to estimate the level of spatial dependency among protein-coding genes in two different human cell lines. The inference of PhiMRF follows a Bayesian framework, and we introduce the Spatial Interaction Estimate (SIE) to measure the strength of spatial dependency in gene expression. We find that the quantitative expression of genes in some chromosomes show meaningful positive intra-chromosomal spatial dependency. Interestingly, the spatial dependency is much stronger than the dependency based on linear gene neighborhoods, suggesting that 3D chromosome structures such as chromatin loops and Topologically Associating Domains (TADs) are strongly associated with gene expression levels. In some chromosomes the spatial dependency in gene expression is only detectable when the spatial neighborhoods are confined within TADs, suggesting TAD boundaries serve as insulating barriers for spatial gene regulation in the genome. We also report high inter-chromosomal spatial correlations in the majority of chromosome pairs, as well as the whole genome. Some functional groups of genes show strong spatial dependency in gene expression as well, providing new insights into the regulation mechanisms of these molecular functions. This study both confirms and quantifies widespread spatial correlation in gene expression. We propose that, with the growing influx of HiC data complementing gene expression data, the use of spatial dependence should be an integral part of the toolkit in the computational analysis of the relationship between chromosome structure and gene expression.

bioinformatics↗