Cooperative RNA degradation stabilizes intermediate epithelial-mesenchymal states and supports a phenotypic continuum.

Multiple intermediate epithelial-mesenchymal transition (EMT) states reflecting hybrid epithelial and mesenchymal phenotypes were observed in physiological and pathological conditions. Previous theoretical models explaining multiple EMT states rely on regulatory loops involving transcriptional feedback, which produce three or four attractors. This is incompatible with the observed continuum-like EMT spectrum. Here, we used mass-action-based models to describe post-transcriptional regulations, finding that cooperative RNA degradation via multiple microRNA binding sites can generate four-attractor systems without transcriptional feedback. Furthermore, the newly identified intermediates-enabling circuits are common in the EMT regulatory network, and they can synergize with transcriptional feedback to support phenotypic continuum. Finally, our model predicted a role of miR-101 in multistate EMT, and we identified evidence from single-cell RNA-sequencing data that support the prediction. Our work reveals a previously unknown role of cooperative RNA degradation and microRNAs in EMT, providing a framework that can bridge the gap between mechanistic models and single-cell experiments.

Nonmodular oscillator and switch based on RNA decay drive regeneration of multimodal gene expression.

Periodic gene expression dynamics are key to cell and organism physiology. Studies of oscillatory expression have focused on networks with intuitive regulatory negative feedback loops, leaving unknown whether other common biochemical reactions can produce oscillations. Oscillation and noise have been proposed to support mammalian progenitor cells' capacity to restore heterogenous, multimodal expression from extreme subpopulations, but underlying networks and specific roles of noise remained elusive. We use mass-action-based models to show that regulated RNA degradation involving as few as two RNA species-applicable to nearly half of human protein-coding genes-can generate sustained oscillations without explicit feedback. Diverging oscillation periods synergize with noise to robustly restore cell populations' bimodal expression on timescales of days. The global bifurcation organizing this divergence relies on an oscillator and bistable switch which cannot be decomposed into two structural modules. Our work reveals surprisingly rich dynamics of post-transcriptional reactions and a potentially widespread mechanism underlying development, tissue regeneration, and cancer cell heterogeneity.

Tissue-specific alternative splicing separates the catalytic and cell signaling functions of human leucyl-tRNA synthetase.

The aminoacyl-tRNA synthetases are an ancient and ubiquitous component of all life. Many eukaryotic synthetases balance their essential function, preparing aminoacyl-tRNA for use in mRNA translation, with diverse roles in cell signaling. Herein, we use long-read sequencing to discover a leukocyte-specific exon skipping event in human leucyl-tRNA synthetase (LARS). We show that this highly expressed splice variant, LSV3, is regulated by serine-arginine-rich splicing factor 1 (SRSF1) in a cell-type-specific manner. LSV3 has a 71 amino acid deletion in the catalytic domain and lacks any tRNA leucylation activity in vitro. However, we demonstrate that this LARS splice variant retains its role as a leucine sensor and signal transducer for the proliferation-promoting mTOR kinase. This is despite the exon deletion in LSV3 including a portion of the previously mapped Vps34-binding domain used for one of two distinct pathways from LARS to mTOR. In conclusion, alternative splicing of LARS has separated the ancient catalytic activity of this housekeeping enzyme from its more recent evolutionary role in cell signaling, providing an opportunity for functional specificity in human immune cells.

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks.

BACKGROUND: Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks. RESULTS: HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition. CONCLUSIONS: HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop's usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop .