Temporal changes in plasma membrane lipid content induce endocytosis to regulate developmental epithelial-to-mesenchymal transition.

Epithelial-to-mesenchymal transition (EMT) is a dramatic change in cellular physiology during development and metastasis, which requires coordination between cell signaling, adhesion, and membrane protrusions. These processes all involve dynamic changes in the plasma membrane; yet, how membrane lipid content regulates membrane function during EMT remains incompletely understood. By screening for differential expression of lipid-modifying genes over the course of EMT in the avian neural crest, we have identified the ceramide-producing enzyme neutral sphingomyelinase 2 (nSMase2) as a critical regulator of a developmental EMT. nSMase2 expression begins at the onset of EMT, and in vivo knockdown experiments demonstrate that nSMase2 is necessary for neural crest migration. We find that nSMase2 promotes Wnt and BMP signaling and is required to activate the mesenchymal gene expression program. Mechanistically, we show that nSMase2-dependent ceramide production is necessary for and sufficient to up-regulate endocytosis and is required for Wnt co-receptor internalization. Finally, inhibition of endocytosis in the neural crest mimics the loss of migration and Wnt signaling observed following nSMase2 knockdown. Our results support a model in which nSMase2 is expressed at the onset of neural crest EMT to produce ceramide and facilitate receptor-mediated endocytosis of Wnt and BMP signaling complexes, thereby activating promigratory gene expression. These results highlight the critical role of plasma membrane lipid metabolism in regulating transcriptional changes during developmental EMT programs.

Cross-evaluation of E. coli's operon structures via a whole-cell model suggests alternative cellular benefits for low- versus high-expressing operons.

Many bacteria use operons to coregulate genes, but it remains unclear how operons benefit bacteria. We integrated E. coli's 788 polycistronic operons and 1,231 transcription units into an existing whole-cell model and found inconsistencies between the proposed operon structures and the RNA-seq read counts that the model was parameterized from. We resolved these inconsistencies through iterative, model-guided corrections to both datasets, including the correction of RNA-seq counts of short genes that were misreported as zero by existing alignment algorithms. The resulting model suggested two main modes by which operons benefit bacteria. For 86% of low-expression operons, adding operons increased the co-expression probabilities of their constituent proteins, whereas for 92% of high-expression operons, adding operons resulted in more stable expression ratios between the proteins. These simulations underscored the need for further experimental work on how operons reduce noise and synchronize both the expression timing and the quantity of constituent genes. A record of this paper's transparent peer review process is included in the supplemental information.