Jiao, F. | bioRxiv Science

bioRxiv2021

Framework estimation of stochastic gene activation using transcription average level

Gene activation is usually a non-Markovian process that has been modeled as various frameworks that consist of multiple rate-limiting steps. Understanding the exact activation framework for a gene of interest is a central problem for single-cell studies. In this paper, we focus on the dynamical data of the average transcription level M (t), which is typically neglected when deciphering gene activation. Firstly, the smooth trend lines of M (t) data present rich, visually dynamic features. Secondly, tractable analysis of M (t) allows the establishment of bijections between M (t) dynamics and system parameter regions. Because of these two clear advantages, we can rule out frameworks that fail to capture M (t) features and we can further test potential competent frameworks by fitting M (t) data. We implemented this procedure to determine an exact activation framework for a large number of mouse fibroblast genes under tumor necrosis factor induction; the cross-talk between the signaling and basal pathways is crucial to trigger the first peak of M (t), while the following damped gentle M (t) oscillation is regulated by the multi-step basal pathway. Moreover, the fitted parameters for the mouse genes tested revealed two distinct regulation scenarios for transcription dynamics. Taken together, we were able to develop an efficient procedure for using traditional M (t) data to estimate the gene activation frameworks and system parameters. This procedure, together with sophisticated single-cell transcription data, may facilitate a more accurate understanding of stochastic gene activation. Author SummaryIt has been suggested that genes randomly transit between inactive and active states, with mRNA produced only when a gene is active. The gene activation process has been modeled as a framework of multiple rate-limiting steps listed sequentially, parallel, or in combination. The system step numbers and parameters can be predicted by computationally fitting sophisticated single-cell transcription data. However, current algorithms require a prior hypothetical framework of gene activation. We found that the prior estimation of the framework can be achieved using the traditional dynamical data of mRNA average level M (t) which present easily discriminated dynamical features. The theory regarding M (t) profiles allows us to confidently rule out other frameworks and to determine optimal frameworks by fitting M (t) data. We successfully applied this procedure to a large number of mouse fibroblast genes and confirmed that M (t) is capable of providing a reliable estimation of gene activation frameworks and system parameters.

biophysics↗

bioRxiv2020

Design of Biologically Active Binary Protein 2D Materials

Proteins that assemble into ordered two-dimensional arrays such as S-layers1,2 and designed analogues3-5 have intrigued bioengineers,6,7 but with the exception of a single lattice formed through non-rigid template streptavidin linkers,8 they are constituted from just one protein component. For modulating assembly dynamics and incorporating more complex functionality, materials composed of two components would have considerable advantages.9-12 Here we describe a computational method to generate de-novo binary 2D non-covalent co-assemblies by designing rigid asymmetric interfaces between two distinct protein dihedral building-blocks. The designed array components are soluble at mM concentrations, but when combined at nM concentrations, rapidly assemble into nearly-crystalline micrometer-scale p6m arrays nearly identical to the computational design model in vitro and in cells without the need of a two-dimensional support. Because the material is designed from the ground up, the components can be readily functionalized, and their symmetry reconfigured, enabling formation of ligand arrays with distinguishable surfaces to drive extensive receptor clustering, downstream protein recruitment, and signaling. Using quantitative microscopy we show that arrays assembled on living cells have component stoichiometry and likely structure similar to arrays formed in vitro, suggesting that our material can impose order onto fundamentally disordered substrates like cell membranes. We find further that in sharp contrast to previously characterized cell surface receptor binding assemblies such as antibodies and nanocages, which are rapidly endocytosed, large arrays assembled at the cell surface suppress endocytosis in a tunable manner, with potential therapeutic relevance for extending receptor engagement and immune evasion. Our work paves the way towards synthetic cell biology, where a new generation of multi-protein macroscale materials is designed to modulate cell responses and reshape synthetic and living systems. One Sentence SummaryCo-assembling binary 2D protein crystals enables robust formation of complex large scale ordered biologically active materials

synthetic biology↗