Pakrasi, H. | Search bioRxiv

bioRxiv2024

Network for Knowledge Organization (NEKO): an AI knowledge mining workflow for synthetic biology research

Large language models (LLMs) can complete general scientific question-and-answer, yet they are constrained by their pretraining cut-off dates and lack the ability to provide specific, cited scientific knowledge. Here, we introduce Network for Knowledge Organization (NEKO), a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining. When user inputs a keyword of interest, NEKO can generate knowledge graphs and comprehensive summaries from PubMed search. NEKO has immediate applications in daily academic tasks such as education of young scientists, literature review, paper writing, experiment planning/troubleshooting, and new hypothesis generation. We exemplified this workflows applicability through several case studies on yeast fermentation and cyanobacterial biorefinery. NEKOs output is more informative, specific, and actionable than GPT-4s zero-shot Q&A. NEKO offers flexible, lightweight local deployment options. NEKO democratizes artificial intelligence (AI) tools, making scientific foundation model more accessible to researchers without excessive computational power.

bioengineering↗

bioRxiv2018

Engineering Nitrogen Fixation Activity in an Oxygenic Phototroph

Biological nitrogen fixation is catalyzed by nitrogenase, a complex metalloenzyme found only in prokaryotes. N2 fixation is energetically highly expensive, and an energy generating process such as photosynthesis can meet the energy demand of N2 fixation. However, synthesis and expression of nitrogenase is exquisitely sensitive to oxygen. Thus, engineering nitrogen fixation activity in photosynthetic organisms that produce oxygen is challenging. Cyanobacteria are oxygenic photosynthetic prokaryotes, and some of them also fix N2. Here, we demonstrate a feasible way to engineer nitrogenase activity in the non-diazotrophic cyanobacterium Synechocystis sp. PCC 6803 through the transfer of 35 nitrogen fixation (nif) genes from the diazotrophic cyanobacterium Cyanothece sp. ATCC 51142. In addition, we have identified the minimal nif cluster required for such activity in Synechocystis 6803. Moreover, nitrogenase activity was significantly improved by increasing the expression levels of nif genes. Importantly, the O2 tolerance of nitrogenase was enhanced by introduction of uptake hydrogenase genes, showing this to be a functional way to improve nitrogenase enzyme activity under micro-oxic conditions. To date, our efforts have resulted in engineered Synechocystis 6803 strains that remarkably have more than 30% N2-fixation activity compared to that in Cyanothece 51142, the highest such activity established in any non-diazotrophic oxygenic photosynthetic organism. This study establishes a baseline towards the ultimate goal of engineering nitrogen fixation ability in crop plants.\n\nIMPORTANCEApplication of chemically synthesized nitrogen fertilizers has revolutionized agriculture. However, the energetic costs of such production processes as well as the wide spread application of fertilizers have raised serious environmental issues. A sustainable alternative is to endow crop plants the ability to fix atmospheric N2 in situ. One long-term approach is to transfer all nif genes from a prokaryote to plant cells, and express nitrogenase in an energy-producing organelle, chloroplast or mitochondrion. In this context, Synechocystis 6803, the non-diazotrophic cyanobacterium utilized in this study, provides a model chassis for rapid investigation of the necessary requirements to establish diazotrophy in an oxygenic phototroph.

synthetic biology↗