Reconstructing curcumin biosynthesis in yeast reveals the implication of caffeoyl-shikimate esterase in phenylpropanoid metabolic flux.

Curcumin is a polyphenolic natural product from the roots of turmeric (Curcuma longa). It has been a popular coloring and flavoring agent in food industries with known health benefits. The conventional phenylpropanoid pathway is known to proceed from phenylalanine via p-coumaroyl-CoA intermediate. Although hydroxycinnamoyl-CoA: shikimate hydroxycinnamoyl transferase (HCT) plays a key catalysis in the biosynthesis of phenylpropanoid products at the downstream of p-coumaric acid, a recent discovery of caffeoyl-shikimate esterase (CSE) showed that an alternative pathway exists. Here, the biosynthetic efficiency of the conventional and the alternative pathway in producing feruloyl-CoA was examined using curcumin production in yeast. A novel modular multiplex genome-edit (MMG)-CRISPR platform was developed to facilitate rapid integrations of up to eight genes into the yeast genome in two steps. Using this MMG-CRISPR platform and metabolic engineering strategies, the alternative CSE phenylpropanoid pathway consistently showed higher titers (2-19 folds) of curcumin production than the conventional pathway in engineered yeast strains. In shake flask cultures using a synthetic minimal medium without phenylalanine, the curcumin production titer reached up to 1.5 mg/L, which is three orders of magnitude (∼4800-fold) improvement over non-engineered base strain. This is the first demonstration of de novo curcumin biosynthesis in yeast. Our work shows the critical role of CSE in improving the metabolic flux in yeast towards the phenylpropanoid biosynthetic pathway. In addition, we showcased the convenience and reliability of modular multiplex CRISPR/Cas9 genome editing in constructing complex synthetic pathways in yeast.

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.

Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.