Hendricks Corbett (cavegauge6)

Engineering microorganisms into biological factories that convert renewable feedstocks into valuable materials is a major goal of synthetic biology; however, for many nonmodel organisms, we do not yet have the genetic tools, such as suites of strong promoters, necessary to effectively engineer them. In this work, we developed a computational framework that can leverage standard RNA-seq data sets to identify sets of constitutive, strongly expressed genes and predict strong promoter signals within their upstream regions. The framework was applied to a diverse collection of RNA-seq data measured for the methanotroph Methylotuvimicrobium buryatense 5GB1 and identified 25 genes that were constitutively, strongly expressed across 12 experimental conditions. For each gene, the framework predicted short (27-30 nucleotide) sequences as candidate promoters and derived -35 and -10 consensus promoter motifs (TTGACA and TATAAT, respectively) for strong expression in M. buryatense. CF-102 agonist This consensus closely matches the canonical E. coli sigma-70 motif and was found to be enriched in promoter regions of the genome. A subset of promoter predictions was experimentally validated in a XylE reporter assay, including the consensus promoter, which showed high expression. The pmoC, pqqA, and ssrA promoter predictions were additionally screened in an experiment that scrambled the -35 and -10 signal sequences, confirming that transcription initiation was disrupted when these specific regions of the predicted sequence were altered. These results indicate that the computational framework can make biologically meaningful promoter predictions and identify key pieces of regulatory systems that can serve as foundational tools for engineering diverse microorganisms for biomolecule production.Mutations of the Amyloid Precursor Protein, from which the amyloid β peptide Aβ42 is cleaved, are associated with familial Alzheimer's disease. The disease-relevant familial mutations include the Arctic (E22G), Iowa (D23N), Italian (E22K), Dutch (E22Q), Japanese (D7N), English (D6R), and Flemish (A21G) variants. A detailed mechanistic understanding of the aggregation behavior of the mutant peptides at the residue level is, however, still lacking. We report here a study of the aggregation kinetics of these mutants in vitro by pulsed hydrogen-deuterium exchange mass spectrometry (HDX-MS) to obtain a temporally and sequence resolved picture of their self-assembly. For all variants, HDX occurs to give a bimodal distribution representing two soluble classes of aggregates, one protected and one solvent-exposed. There is no evidence of other classes of structural intermediates within the detection limits of the HDX approach. The fractional changes in the bimodal exchange profiles for several regions of Aβ42 reveal that the central and C-terminal peptides gain protection upon fibril formation, whereas the N-terminal regions remain largely solvent-accessible. For these mutants, all peptide fragments follow the same kinetics, acquiring solvent protection at the same time, further supporting that there are no significant populations of intermediate species under our experimental conditions. The results demonstrate the potential of pulsed HDX-MS for resolving the region-specific aggregation behavior of Aβ42 isoforms in solution where X-ray crystallography and solid-state NMR (ssNMR) are challenged.Strain engineering of perovskite quantum dots (pQDs) enables widely tunable photonic device applications. However, manipulation at the single-emitter level has never been attempted. Here, we present a tip-induced control approach combined with tip-enhanced photoluminescence (TEPL) spectroscopy to engineer strain, bandgap, and the emission quantum yield of a single pQD. Single CsPbBrxI3-x pQDs are clearly resolved through hyperspectral TEPL imaging with ∼10 nm spatial resolution. The plasmonic tip then directly applies pressure to a single pQD to facilitate a bandgap shift u