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Register a new userTranscription-dependent spatial organization of a gene locus
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There is growing appreciation that gene function is connected to the dynamic structure of the chromosome. Here we explore the interplay between three-dimensional structure and transcriptional activity at the single cell level. We show that inactive loci are spatially more compact than active ones, and that within active loci the enhancer driving transcription is closest to the promoter. On the other hand, even this shortest distance is too long to support direct physical contact between the enhancer-promoter pair when the locus is transcriptionally active. Artificial manipulation of genomic separations between enhancers and the promoter produces changes in physical distance and transcriptional activity, recapitulating the correlation seen in wild-type embryos, but disruption of topological domain boundaries has no effect. Our results suggest a complex interdependence between transcription and the spatial organization of cis-regulatory elements.
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Gene transcription is a stochastic process mostly occurring in bursts. Regulation of transcription arises from the interaction of transcription factors (TFs) with the promoter of the gene. The TFs, such as activators and repressors can interact with the promoter in a competitive or non-competitive way. Some experimental observations suggest that the mean expression and noise strength can be regulated at the transcription level. A Few theories are developed based on these experimental observations. Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength (like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier. Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolperts paradigmatic French Flag model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call Counter patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework.
Inferring functional relationships within complex networks from static snapshots of a subset of variables is a ubiquitous problem in science. For example, a key challenge of systems biology is to translate cellular heterogeneity data obtained from single-cell sequencing or flow-cytometry experiments into regulatory dynamics. We show how static population snapshots of co-variability can be exploited to rigorously infer properties of gene expression dynamics when gene expression reporters probe their upstream dynamics on separate time-scales. This can be experimentally exploited in dual-reporter experiments with fluorescent proteins of unequal maturation times, thus turning an experimental bug into an analysis feature. We derive correlation conditions that detect the presence of closed-loop feedback regulation in gene regulatory networks. Furthermore, we show how genes with cell-cycle dependent transcription rates can be identified from the variability of co-regulated fluorescent proteins. Similar correlation constraints might prove useful in other areas of science in which static correlation snapshots are used to infer causal connections between dynamically interacting components.
Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription. In this paper the features of this self-regulating process are investigated. In the here presented model the gene can be in two states. Either a protein is bound to its promoter or not. The steady state distributions of protein during and at the end of both states are analyzed. Moreover a powerful numerical method based on the corresponding master equation to compute the protein distribution in the steady state is presented and compared to an already existing method. Additionally the special case of self-regulation, in which protein can only be produced, if one of these proteins is bound to the promoter region, is analyzed. Furthermore a self-regulating gene is compared to a similar gene, which also has two states and produces the same amount of proteins but is not regulated by its protein-product.
The current pandemic of SARS-CoV-2 has caused extensive damage to society. The characterization of SARS-CoV-2 profiles has been addressed by researchers globally with the aim of resolving this disruptive crisis. This investigation process is indispensable for an understanding of how SARS-CoV-2 behaves in human host cells. However, little is known about the systematic molecular mechanisms involved in the effect of SARS-CoV-2 infection on human host cells. Here, we have presented gene-to-gene regulatory networks in response to SARS-CoV-2 using a Bayesian network. We examined the dynamic changes of the SARS-CoV-2-purturbated networks established by our proposed framework for gene network analysis, revealing that interferon signaling gradually switches to the subsequent inflammatory-cytokine signaling cascades. Furthermore, we have succeeded in capturing a COVID-19 patient-specific network in which transduction of these signalings is coincidently induced. This enabled us to explore local regulatory systems influenced by SARS-CoV-2 in host cells more precisely at an individual level. Our panel of network analyses has provided new insight into SARS-CoV-2 research from the perspective of cellular systems.
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