Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms

Classical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time distributions, implying the existence of multiple promoter states and molecular memory in the promoter switching dynamics. Here we analytically solve a gene expression model with random bursting and complex promoter switching, and derive the time-dependent distributions of the mRNA and protein copy numbers, generalizing the steady-state solutions obtained in [SIAM J. Appl. Math. 72, 789-818 (2012)] and [SIAM J. Appl. Math. 79, 1007-1029 (2019)]. Using multiscale simplification techniques, we find that molecular memory has no influence on the time-dependent distribution when promoter switching is very fast or very slow, while it significantly affects the distribution when promoter switching is neither too fast nor too slow. By analyzing the dynamical phase diagram of the system, we also find that molecular memory in the inactive gene state weakens the transient and stationary bimodality of the copy number distribution, while molecular memory in the active gene state enhances such bimodality.

Translation initiation from sequence variants of the bacteriophage T7 g10RBS in Escherichia coli and Agrobacterium fabrum

Background The bacteriophage T7 gene 10 ribosome binding site (g10RBS) has long been used for robust expression of recombinant proteins in Escherichia coli. This RBS consists of a Shine–Dalgarno (SD) sequence augmented by an upstream translational “enhancer” (Enh) element, supporting protein production at many times the level seen with simple synthetic SD-containing sequences. The objective of this study was to dissect the g10RBS to identify simpler derivatives that exhibit much of the original translation efficiency. Methods and results Twenty derivatives of g10RBS were tested using multiple promoter/reporter gene contexts. We have identified one derivative (which we call “CON_G”) that maintains 100% activity in E. coli and is 33% shorter. Further minimization of CON_G results in variants that lose only modest amounts of activity. Certain nucleotide substitutions in the spacer region between the SD sequence and initiation codon show strong decreases in translation. When testing these 20 derivatives in the alphaproteobacterium Agrobacterium fabrum, most supported strong reporter protein expression that was not dependent on the Enh. Conclusions The g10RBS derivatives tested in this study display a range of observed activity, including a minimized version (CON_G) that retains 100% activity in E. coli while being 33% shorter. This high activity is evident in two different promoter/reporter sequence contexts. The array of RBS sequences presented here may be useful to researchers in need of fine-tuned expression of recombinant proteins of interest.

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

Accurate reconstruction of dynamic gene expression and growth rate profiles from noisy measurements

Cells face changing environments to which they sense and respond in complex ways, changing their rates of gene expression and growth. Measuring these dynamics is therefore essential to understanding natural and synthetic regulatory networks that give rise to functional phenotypes. However, reconstruction of gene expression and growth rate profiles from typically noisy measurements of cell populations is difficult due to the effects of noise at low cell densities among other factors. We present here a method for estimation of dynamic gene expression rates and biomass growth rates from noisy measurement data, and show that it is several times more accurate than current approaches. We applied our method to multiple promoter-reporter fusion genes. Gene expression rates of such promoter-reporter fusions are typically used as a proxy for transcription rates. However, using our method we show that fusion gene expression rate dynamics are determined at least by the promoter of interest and the downstream reporter.

Exp2Ipynb: A general machine-learning workflow for the analysis of promoter libraries

Strain engineering in biotechnology modifies metabolic pathways in microorganisms to overproduce target metabolites. To modify metabolic pathway activity in bacteria, gene expression is an effective and easy manipulated process, specifically the promoter sequence recognized by sigma factors. Promoter libraries are generated to scan the expression activity of different promoter sequences and to identify sequence positions that predict activity. To maximize information retrieval, a well-designed experimental setup is required. We present a computational workflow to analyse promoter libraries; by applying this workflow to seven libraries, we aim to identify critical design principles. The workflow is based on a Python Jupyter Notebook and covers the following steps: (i) statistical sequence analysis, (ii) sequence-input to expression-output predictions, (iii) estimator performance evaluation, and (iv) new sequence prediction with defined activity. The workflow can process multiple promoter libraries, across species or reporter proteins, and classify or regress expression activity. The strongest predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. A trade-off between sample size and sequence diversity reduces prediction quality, and we present a relationship to estimate the minimum sample size. The workflow guides the user through analysis and machine-learning training, is open source and easily adaptable to include alternative machine-learning strategies and to process sequence libraries from other expression-related problems. The workflow is a contribution to increase insight to the growing application of high-throughput experiments and provides support for efficient strain engineering.

Lumi-Map, a Real-Time Luciferase Bioluminescence Screen of Mutants Combined with MutMap, Reveals Arabidopsis Genes Involved in PAMP-Triggered Immunity

Plants recognize pathogen-associated molecular patterns (PAMPs) to activate PAMP-triggered immunity (PTI). However, our knowledge of PTI signaling remains limited. In this report, we introduce Lumi-Map, a high-throughput platform for identifying causative single-nucleotide polymorphisms (SNPs) for studying PTI signaling components. In Lumi-Map, a transgenic reporter plant line is produced that contains a firefly luciferase (LUC) gene driven by a defense gene promoter, which generates luminescence upon PAMP treatment. The line is mutagenized and the mutants with altered luminescence patterns are screened by a high-throughput real-time bioluminescence monitoring system. Selected mutants are subjected to MutMap analysis, a whole-genome sequencing-based method of rapid mutation identification, to identify the causative SNP responsible for the luminescence pattern change. We generated nine transgenic Arabidopsis reporter lines expressing the LUC gene fused to multiple promoter sequences of defense-related genes. These lines generate luminescence upon activation of FLAGELLIN-SENSING 2 (FLS2) by flg22, a PAMP derived from bacterial flagellin. We selected the WRKY29-promoter reporter line to identify mutants in the signaling pathway downstream of FLS2. After screening 24,000 ethylmethanesulfonate-induced mutants of the reporter line, we isolated 22 mutants with altered WRKY29 expression upon flg22 treatment (abbreviated as awf mutants). Although five flg22-insensitive awf mutants harbored mutations in FLS2 itself, Lumi-Map revealed three genes not previously associated with PTI. Lumi-Map has the potential to identify novel PAMPs and their receptors as well as signaling components downstream of the receptors. [Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Lumi-Map, a real-time luciferase bioluminescence screen of mutants combined with MutMap, reveals Arabidopsis genes involved in PAMP-triggered immunity

Plants recognize pathogen-associated molecular patterns (PAMPs) to activate PAMP-triggered immunity (PTI). However, our knowledge of PTI signaling remains limited. In this report, we introduce Lumi-Map, a high-throughput platform for identifying causative single nucleotide polymorphisms (SNPs) to studying PTI signaling components. In Lumi-Map, a transgenic reporter plant line is produced that contains a firefly luciferase (LUC) gene driven by a defense gene promoter, which generates luminescence upon PAMP treatment. The line is mutagenized and the mutants with altered luminescence patterns are screened by a high-throughput real-time bioluminescence monitoring system. Selected mutants are subjected to MutMap analysis, a whole genome sequencing (WGS)-based method of rapid mutation identification, to identify the causative SNP responsible for the luminescence pattern change. We generated nine transgenic Arabidopsis reporter lines expressing LUC gene fused to multiple promoter sequences of defense-related genes. These lines generate luminescence upon activation of FLAGELLIN-SENSING 2 (FLS2) by flg22, a PAMP derived from bacterial flagellin. We selected the WRKY29-promoter reporter line to identify mutants in the signaling pathway downstream of FLS2. After screening 24,000 ethylmethanesulfonate (EMS)-induced mutants of the reporter line, we isolated 22 mutants with altered WRKY29 expression upon flg22 treatment (abbreviated as awf mutants). While five flg22-insensitive awf mutants harbored mutations in FLS2 itself, Lumi-Map revealed three genes not previously associated with PTI. Lumi-Map has the potential to identify novel PAMPs and their receptors as well as signaling components downstream of the receptors.

Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy

Improving efficacy of oncolytic virotherapy remains challenging due to difficulty increasing specificity and immune responses against cancer and limited understanding of its population dynamics. Here, we construct programmable and modular synthetic gene circuits to control adenoviral replication and release of immune effectors selectively in hepatocellular carcinoma cells in response to multiple promoter and microRNA inputs. By performing mouse model experiments and computational simulations, we find that replicable adenovirus has a superior tumor-killing efficacy than non-replicable adenovirus. We observe a synergistic effect on promoting local lymphocyte cytotoxicity and systematic vaccination in immunocompetent mouse models by combining tumor lysis and secretion of immunomodulators. Furthermore, our computational simulations show that oncolytic virus which encodes immunomodulators can exert a more robust therapeutic efficacy than combinatorial treatment with oncolytic virus and immune effector. Our results provide an effective strategy to engineer oncolytic adenovirus, which may lead to innovative immunotherapies for a variety of cancers.

Linking dynamical complexities from activation signals to transcription responses

The transcription of inducible genes involves signalling pathways that induce DNA binding of the downstream transcription factors to form functional promoter states. How the transcription dynamics is linked to the temporal variations of activation signals is far from being fully understood. In this work, we develop a mathematical model with multiple promoter states to address this question. Each promoter state has its own activation and inactivation rates and is selected randomly with a probability that may change in time. Under the activation of constant signals, our analysis shows that if only the activation rates differ among the promoter states, then the mean transcription level m ( t ) displays only a monotone or monophasic growth pattern. In a sharp contrast, if the inactivation rates change with the promoter states, then m ( t ) may display multiphasic growth patterns. Upon the activation of signals that oscillate periodically, m ( t ) also oscillates later, almost periodically at the same frequency, but the magnitude decreases with frequency and is almost completely attenuated at high frequencies. This gives a surprising indication that multiple promoter states could filter out the signal oscillation and the noise in the random promoter state selection, as observed in the transcription of a gene activated by p53 in breast carcinoma cells. Our approach may help develop a theoretical framework to integrate coherently the genetic circuit with the promoter states to elucidate the linkage from the activation signal to the temporal profile of transcription outputs.