Bioproduction of methylated phenylpropenes and isoeugenol in Escherichia coli.

Phenylpropenes are a class of natural products that are synthesised by a vast range of plant species and hold considerable promise in the flavour and fragrance industries. Many in vitro studies have been carried out to elucidate and characterise the enzymes responsible for the production of these volatile compounds. However, there is a scarcity of studies demonstrating the in vivo production of phenylpropenes in microbial cell factories. In this study, we engineered Escherichia coli to produce methylchavicol, methyleugenol and isoeugenol from their respective phenylacrylic acid precursors. We achieved this by extending and modifying a previously optimised heterologous pathway for the biosynthesis of chavicol and eugenol. We explored the potential of six S-adenosyl l-methionine (SAM)-dependent O-methyltransferases to produce methylchavicol and methyleugenol from chavicol and eugenol, respectively. Additionally, we examined two isoeugenol synthases for the production of isoeugenol from coniferyl acetate. The best-performing strains in this study were able to achieve titres of 13 mg L-1 methylchavicol, 59 mg L-1 methyleugenol and 361 mg L-1 isoeugenol after feeding with their appropriate phenylacrylic acid substrates. We were able to further increase the methyleugenol titre to 117 mg L-1 by supplementation with methionine to facilitate SAM recycling. Moreover, we report the biosynthesis of methylchavicol and methyleugenol from l-tyrosine through pathways involving six and eight enzymatic steps, respectively.

A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity.

Although small cell lung cancer (SCLC) is treated as a homogeneous disease, biopsies and preclinical models reveal heterogeneity in transcriptomes and morphology. SCLC subtypes were recently defined by neuroendocrine transcription factor (NETF) expression. Circulating-tumor-cell-derived explant models (CDX) recapitulate donor patients' tumor morphology, diagnostic NE marker expression and chemotherapy responses. We describe a biobank of 38 CDX models, including six CDX pairs generated pretreatment and at disease progression revealing complex intra- and intertumoral heterogeneity. Transcriptomic analysis confirmed three of four previously described subtypes based on ASCL1, NEUROD1 and POU2F3 expression and identified a previously unreported subtype based on another NETF, ATOH1. We document evolution during disease progression exemplified by altered MYC and NOTCH gene expression, increased 'variant' cell morphology, and metastasis without strong evidence of epithelial to mesenchymal transition. This CDX biobank provides a research resource to facilitate SCLC personalized medicine.

Mapping biological process relationships and disease perturbations within a pathway network.

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at https://data.mendeley.com/datasets/3pbwkxjxg9/1.