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Multiple aspects of the local extracellular environment profoundly affect cell phenotype and function. Physical and chemical cues in the environment trigger intracellular signaling cascades that ultimately activate transcription factors (TFs) - powerful regulators of the cell phenotype. TRACER (TRanscriptional Activity CEll aRrays) was employed for large-scale, dynamic quantification of TF activity in human fibroblasts cultured on hydrogels with a controlled elastic modulus and integrin ligand density. We identified three groups of TFs: responders to alterations in ligand density alone, substrate stiffness or both. Dynamic networks of regulatory TFs were constructed computationally and revealed distinct TF activity levels, directionality (i.e., activation or inhibition), and dynamics for adhesive and mechanical cues. Moreover, TRACER networks predicted conserved hubs of TF activity across multiple cell types, which are significantly altered in clinical fibrotic tissues. Our approach captures the distinct and overlapping effects of adhesive and mechanical stimuli, identifying conserved signaling mechanisms in normal and disease states.

Multiple aspects of the local extracellular environment profoundly affect cell phenotype and function. Physical and chemical cues in the environment trigger intracellular signaling cascades that ultimately activate transcription factors (TFs) - powerful regulators of the cell phenotype. TRACER (TRanscriptional Activity CEll aRrays) was employed for large-scale, dynamic quantification of TF activity in human fibroblasts cultured on hydrogels with a controlled elastic modulus and integrin ligand density. We identified three groups of TFs: responders to alterations in ligand density alone, substrate stiffness or both. Dynamic networks of regulatory TFs were constructed computationally and revealed distinct TF activity levels, directionality (i.e., activation or inhibition), and dynamics for adhesive and mechanical cues. Moreover, TRACER networks predicted conserved hubs of TF activity across multiple cell types, which are significantly altered in clinical fibrotic tissues. Our approach captures the distinct and overlapping effects of adhesive and mechanical stimuli, identifying conserved signaling mechanisms in normal and disease states.

Geminal (gem−) disubstitution in heterocyclic monomers is an effective strategy to enhance polymer chemical recyclability by lowering their ceiling temperatures. However, the effects of specific substitution patterns on the monomer's reactivity and the resulting polymer's properties are largely unexplored. Here we show that, by systematically installing gem‐dimethyl groups onto ϵ‐caprolactam (monomer of nylon 6) from the α to ϵ positions, both the redesigned lactam monomer's reactivity and the resulting gem‐nylon 6's properties are highly sensitive to the substitution position, with the monomers ranging from non‐polymerizable to polymerizable and the gem‐nylon properties ranging from inferior to far superior to the parent nylon 6. Remarkably, the nylon 6 with the gem‐dimethyls substituted at the γ position is amorphous and optically transparent, with a higher Tg (by 30 °C), yield stress (by 1.5 MPa), ductility (by 3×), and lower depolymerization temperature (by 60 °C) than conventional nylon 6. [ABSTRACT FROM AUTHOR]

BACKGROUND:In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. DESCRIPTION:Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. CONCLUSIONS:MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]