Species-specific design of artificial promoters by transfer-learning based generative deep-learning model

Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.

Concentration Recognition-Based Auto-Dynamic Regulation System (CRUISE) Enabling Efficient Production of Higher Alcohols

Microbial factories lacking the ability of dynamically regulating the pathway enzymes overexpression, according to in situ metabolite concentrations, are suboptimal, especially when the metabolic intermediates are competed by growth and chemical production. The production of higher alcohols (HAs), which hijacks the amino acids (AAs) from protein biosynthesis, minimizes the intracellular concentration of AAs and thus inhibits the host growth. To balance the resource allocation and maintain stable AA flux, this work utilizes AA-responsive transcriptional attenuator ivbL and HA-responsive transcriptional activator BmoR to establish a concentration recognition-based auto-dynamic regulation system (CRUISE). This system ultimately maintains the intracellular homeostasis of AA and maximizes the production of HA. It is demonstrated that ivbL-driven enzymes overexpression can dynamically regulate the AA-to-HA conversion while BmoR-driven enzymes overexpression can accelerate the AA biosynthesis during the HA production in a feedback activation mode. The AA flux in biosynthesis and conversion pathways is balanced via the intracellular AA concentration, which is vice versa stabilized by the competition between AA biosynthesis and conversion. The CRUISE, further aided by scaffold-based self-assembly, enables 40.4 g L-1 of isobutanol production in a bioreactor. Taken together, CRUISE realizes robust HA production and sheds new light on the dynamic flux control during the process of chemical production.

Selection and validation of appropriate reference genes for RT-qPCR analysis of Nitraria sibirica under various abiotic stresses

BACKGROUND

Nitraria sibirica Pall. is a halophytic shrub with strong environmental adaptability that can survive in extremely saline-alkali and drought-impacted environments. Gene expression analysis aids in the exploration of the molecular mechanisms of plant responses to abiotic stresses. RT-qPCR is the most common technique for studying gene expression. Stable reference genes are a prerequisite for obtaining accurate target gene expression results in RT-qPCR analysis.

RESULTS

In this study, a total of 10 candidate reference genes were selected from the transcriptome of N. sibirica, and their expression stability in leaves and roots under different treatment conditions (salt, alkali, drought, cold, heat and ABA) was evaluated with the geNorm, NormFinder, BestKeeper, comparative ΔCt and RefFinder programs. The results showed that the expression stability of the candidate reference genes was dependent on the tissue and experimental conditions tested. ACT7 combined with R3H, GAPDH, TUB or His were the most stable reference genes in the salt- or alkali-treated leaves, salt-treated roots and drought-treated roots, respectively; R3H and GAPDH were the most suitable combination for drought-treated leaves, heat-treated root samples and ABA-treated leaves; DIM1 and His maintained stable expression in roots under alkali stress; and TUB combined with R3H was stable in ABA-treated roots. TBCB and GAPDH exhibited stable expression in heat-treated leaves; TBCB, R3H, and ERF3A were stable in cold-treated leaves; and the three most stable reference genes for cold-treated roots were TBCB, ACT11 and DIM1. The reliability of the selected reference genes was further confirmed by evaluating the expression patterns of the NsP5CS gene under the six treatment conditions.

CONCLUSION

This study provides a theoretical reference for N. sibirica gene expression standardization and quantification under various abiotic stress conditions and will help to reveal the molecular mechanisms that confer stress tolerance to N. sibirica.