Design of synthetic promoters for cyanobacteria with generative deep-learning model.

Deep generative models, which can approximate complex data distribution from large datasets, are widely used in biological dataset analysis. In particular, they can identify and unravel hidden traits encoded within a complicated nucleotide sequence, allowing us to design genetic parts with accuracy. Here, we provide a deep-learning based generic framework to design and evaluate synthetic promoters for cyanobacteria using generative models, which was in turn validated with cell-free transcription assay. We developed a deep generative model and a predictive model using a variational autoencoder and convolutional neural network, respectively. Using native promoter sequences of the model unicellular cyanobacterium Synechocystis sp. PCC 6803 as a training dataset, we generated 10 000 synthetic promoter sequences and predicted their strengths. By position weight matrix and k-mer analyses, we confirmed that our model captured a valid feature of cyanobacteria promoters from the dataset. Furthermore, critical subregion identification analysis consistently revealed the importance of the -10 box sequence motif in cyanobacteria promoters. Moreover, we validated that the generated promoter sequence can efficiently drive transcription via cell-free transcription assay. This approach, combining in silico and in vitro studies, will provide a foundation for the rapid design and validation of synthetic promoters, especially for non-model organisms.

Exploring ideal operative time for best outcomes in gastric cancer surgery: A multi-institutional study based on KLASS-07 database.

Objective: While a rushed operation can omit essential procedures, prolonged operative time results in higher morbidity. Nevertheless, the optimal operative time range remains uncertain. This study aimed to estimate the ideal operative time range and evaluate its applicability in laparoscopic cancer surgery. Methods: A prospectively collected multicenter database of 397 patients who underwent laparoscopic distal gastrectomy were retrospectively reviewed. The ideal operative time range was statistically calculated by separately analyzing the operative time of uneventful surgeries. Finally, intraoperative and postoperative outcomes were compared among the shorter, ideal, and longer operative time groups. Results: The statistically calculated ideal operative time was 135.4-165.4 min. The longer operative time (LOT) group had a lower rate of uneventful, perfect surgery than the ideal or shorter operative time (IOT/SOT) group (2.8% vs. 8.8% and 2.2% vs. 13.4%, all P<0.05). Longer operative time increased bleeding, postoperative morbidities, and delayed diet and discharge (all P<0.05). Particularly, an uneventful, perfect surgery could not be achieved when the operative time exceeded 240 min. Regardless of ideal time range, SOT group achieved the highest percentage of uneventful surgery (13.4%), which was possible by surgeon's ability to retrieve a higher number of lymph nodes and perform ≥150 gastrectomies annually. Conclusions: Operative time longer than the ideal time range (especially ≥240 min) should be avoided. If the essential operative procedure were faithfully conducted without compromising oncological safety, an operative time shorter than the ideal range leaded to a better prognosis. Efforts to minimize operative time should be attempted with sufficient surgical experience.

Cell-Free Transcription-Coupled CRISPR/Cas12a Assay for Prototyping Cyanobacterial Promoters.

Cyanobacteria are promising microbial hosts for the production of diverse biofuels and biochemicals. However, compared to other model microbial hosts such as Escherichia coli and yeast, it takes a long time to genetically modify cyanobacteria. One way to efficiently engineer cyanobacteria while minimizing genetic engineering would be to develop a fast, high-throughput prototyping tool for cyanobacteria. In this study, we developed a CRISPR/Cas12a-based assay coupled with cyanobacteria cell-free systems to rapidly prototype promoter characteristics. Using this newly developed assay, we demonstrated cyanobacteria cell-free transcription for the first time and confirmed a positive correlation between the in vitro and in vivo transcription performance. Furthermore, we generated a synthetic promoter library and evaluated the characteristics of promoter subregions by using the assay. Varied promoter strength derived from random mutations were rapidly and effectively measured in a high-throughput way. We believe that this study offers an easily applicable and rapid prototyping platform to characterize promoters for cyanobacterial engineering.