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Abstract Light initiates chloroplast biogenesis inArabidopsisby eliminating PHYTOCHROME-INTERACTING transcription FACTORs (PIFs), which in turn de-represses nuclear photosynthesis genes, and synchronously, generates a nucleus-to-plastid (anterograde) signal that activates the plastid-encoded bacterial-type RNA polymerase (PEP) to transcribe plastid photosynthesis genes. However, the identity of the anterograde signal remains frustratingly elusive. The main challenge has been the difficulty to distinguish regulators from the plethora of necessary components for plastid transcription and other essential chloroplast functions, such as photosynthesis. Here, we show that the genome-wide induction of nuclear photosynthesis genes is insufficient to activate the PEP. PEP inhibition is imposed redundantly by multiple PIFs and requires PIF3’s activator activity. Among the nuclear-encoded components of the PEP holoenzyme, we identify four light-inducible, PIF-repressed sigma factors as anterograde signals. Together, our results elucidate that light-dependent inhibition of PIFs activates plastid photosynthesis genes via sigma factors as anterograde signals in parallel with the induction of nuclear photosynthesis genes. 

Warming drives ocean memory loss leading to noisier, less predictable sea surface temperature variability. 

Although ammonia is a widely used interstellar thermometer, the estimation of its rotational and kinetic temperatures can be affected by the blended hyperfine components (HFCs). We have developed a new recipe, referred to as the hyperfine group ratio (HFGR), which utilizes only direct observables, namely the intensity ratios between the grouped HFCs. As tested on the model spectra, the empirical formulae in the HFGR can derive the rotational temperature (Trot) from the HFC group ratios in an unambiguous manner. We compared the HFGR with two other classical methods, intensity ratio and hyperfine fitting, based on both simulated spectra and real data. The HFGR has three major improvements. First, it does not require modelling the HFC or fitting the line profiles, so it is more robust against the effect of HFC blending. Second, the simulation-enabled empirical formulae are much faster than fitting the spectra over the parameter space, so both computer time and human time can be saved. Third, the statistical uncertainty of the temperature ΔTrot as a function of the signal-to-noise ratio (S/N) is a natural product of the HFGR recipe. The internal error of the HFGR is ΔTrot ≤ 0.5 K over a broad parameter space of rotational temperature (10-60 K), linewidth (0.3-4 km s-1) and optical depth (0-5). When there is spectral noise, the HFGR can also maintain a reasonable uncertainty level at ΔTrot ≤ 1.0 K when S/N > 4. 

Abstract Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. We further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis.