Biology subjects

Deng, Y.-H.

Publications and source records attributed to Deng, Y.-H..

3 recordsLinked to original sources

bioRxiv2026

Tailoring Precise Genomic Integration Toward Isolate-to-Industry Strain Development for Scalable High-Titer Production of Polyhydroxyalkanoate

Halophilic chassis has emerged as a promising biomanufacturing platform for industrial polyhydroxyalkanoate (PHA) production. However, challenges still remain in improving the production capacity, scalability and robustness, thereby lowering cost to meet market demands. Here, a high-performing halophilic strain Halomonas LY03 was isolated with over 38% glucose- to-PHA conversion rate and broad non-grain substrate utilization capability. Multidimensional tools, including algorithm-guided high-expression neutral integration site (HENIS) screening toolkit designated SiteSeek, stop codon (TAA)-dependent enhancement of gene expression and recombinase-mediated large-fragment (> 9 kb) genomic integration, were then developed to enable precise, efficient and interference-free genomic integrative expression. Using these tools, various chromosomally engineered strains were rapidly constructed to achieve high-level production of poly-3-hydroxybutyrate (PHB, 151 g L-{superscript 1}) and poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P34HB, 139 g L-{superscript 1}) under high cell-density fermentation (up to 186 g L-1 cell dry weight) in a 5-L bioreactor. Scalability was demonstrated at 2-m3 and 20-m3 industry-scale fermentations, yielding up to 134 g L-{superscript 1} PHB and 127 g L-{superscript 1} P34HB (6.1 mol% 4HB). Building on the proven robustness, a two-stage continuous fermentation (TCF) process was developed using a twin-bioreactor system at 5-L and 20-m3 scales, where stable and sustained PHA production lasted over 260 h and 160 h, respectively. Techno-economic analysis revealed a substantial cost-reduction space of 48% compared with conventional fed-batch process. This study demonstrates a successful paradigm for engineering a newly isolated strain toward robust, high-titer and cost-competitive PHA production across lab-to-industry scales.

synthetic biology↗

bioRxiv2026

Engineering xylose metabolism for diverse polyhydroxyalkanoates synthesis in Halomonas TD

Engineering the biosynthesis of fully biodegradable polyhydroxyalkanoates (PHAs) from non-food and renewable feedstocks like lignocellulose is becoming an attractive strategy for sustainable biomanufacturing. However, the efficiency and diversity of PHA synthesis from lignocellulosic hydrolysate (LH), mainly containing glucose and xylose, still remains challenge. Here, Halomonas TD, a cost-effective PHA-producing chassis, was developed to utilize glucose and xylose (or LH) for effective production of poly-3-hydroxybutyrate (PHB) and poly(3-hydroxybutyrate-co-4-hydroxybutyrate) [P(3HB-co-4HB)] by engineering the phosphoketolase pathway-dependent xylose metabolism in the genome, yielding 50.1 g L-1 PHB and 42.6 g L-1 P(3HB-co- 11.4 mol% 4HB) under fed-batch condition. Subsequently, the introduction of Weimberg pathway was found be able to synthesize terpolymer consisting of 3-hydroxybutyrate (3HB), 4-hydroxybutyrate (4HB) and 3-hydroxyvalerate (3HV), namely [P(3HB-co-4HB-co-3HV)], from glucose and xylose only due to the isoenzyme activity of keto-acid decarboxylase encoded by kivD, which converts xylose-derived intermediate 2,5-dioxopentanoate and -ketoglutarate into butanedial (4HB synthesis) and 2-ketobutyrate (3HV synthesis), respectively. Finally, a tailored-made xylose-induced system was constructed to achieve exquisite xylose transmembrane transportation control for improved synthesis of terpolymer P(3HB-co-4.5 mol% 4HB-co- 3.0 mol% 3HV), reaching to 6.3 g L-1 under shake-flask condition, together with the co-expression of fine-tuned phosphoketolase and Weimberg pathways. This study provides a feasible and sustainable alternative for lignocellulosic resources valorization powered by the engineered Halomonas TD capable of efficient xylose utilization and diverse PHAs synthesis.

bioengineering↗

bioRxiv2020

Rapid SARS-CoV-2 Detection and Classification Using Phase Imaging with Computational Specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A), HAdV (adenovirus), and ZIKV (Zika). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. One Sentence SummaryThis work proposes a rapid (<1 min.), label-free testing method for SARS-CoV-2 detection, using quantitative phase imaging and deep learning.

bioengineering↗