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Strigolactones (SLs) are a class of phytohormones playing diverse roles in plant growth and development, yet the limited access to SLs is largely impeding SL-based foundational investigations and applications. Here, we developed Escherichia coli – Saccharomyces cerevisiae consortia to establish a microbial biosynthetic platform for the synthesis of various SLs, including carlactone, carlactonoic acid, 5-deoxystrigol (5DS; 6.65 ± 1.71 μg/liter), 4-deoxyorobanchol (3.46 ± 0.28 μg/liter), and orobanchol (OB; 19.36 ± 5.20 μg/liter). The SL-producing platform enabled us to conduct functional identification of CYP722Cs from various plants as either OB or 5DS synthase. It also allowed us to quantitatively compare known variants of plant SL biosynthetic enzymes in the microbial system. The titer of 5DS was further enhanced through pathway engineering to 47.3 μg/liter. This work provides a unique platform for investigating SL biosynthesis and evolution and lays the foundation for developing SL microbial production process. 

Biomedical named entity recognition (BioNER) is a fundamental step for mining COVID-19 literature. Existing BioNER datasets cover a few common coarse-grained entity types (e.g., genes, chemicals, and diseases), which cannot be used to recognize highly domain-specific entity types (e.g., animal models of diseases) or emerging ones (e.g., coronaviruses) for COVID-19 studies. We present CORD-NER, a fine-grained named entity recognized dataset of COVID-19 literature (up until May 19, 2020). CORD-NER contains over 12 million sentences annotated via distant supervision. Also included in CORD-NER are 2,000 manually-curated sentences as a test set for performance evaluation. CORD-NER covers 75 fine-grained entity types. In addition to the common biomedical entity types, it covers new entity types specifically related to COVID-19 studies, such as coronaviruses, viral proteins, evolution, and immune responses. The dictionaries of these fine-grained entity types are collected from existing knowledge bases and human-input seed sets. We further present DISTNER, a distantly supervised NER model that relies on a massive unlabeled corpus and a collection of dictionaries to annotate the COVID-19 corpus. DISTNER provides a benchmark performance on the CORD-NER test set for future research.