The structural basis for high‐affinity uptake of lignin‐derived aromatic compounds by proteobacterial TRAP transporters

The organic polymer lignin is a component of plant cell walls, which like (hemi)-cellulose is highly abundant in nature and relatively resistant to degradation. However, extracellular enzymes released by natural microbial consortia can cleave the β-aryl ether linkages in lignin, releasing monoaromatic phenylpropanoids that can be further catabolised by diverse species of bacteria. Biodegradation of lignin is therefore important in global carbon cycling, and its natural abundance also makes it an attractive biotechnological feedstock for the industrial production of commodity chemicals. Whilst the pathways for degradation of lignin-derived aromatics have been extensively characterised, much less is understood about how they are recognised and taken up from the environment. The purple phototrophic bacterium Rhodopseudomonas palustris can grow on a range of phenylpropanoid monomers and is a model organism for studying their uptake and breakdown. R. palustris encodes a tripartite ATP-independent periplasmic (TRAP) transporter (TarPQM) linked to genes encoding phenylpropanoid-degrading enzymes. The periplasmic solute-binding protein component of this transporter, TarP, has previously been shown to bind aromatic substrates. Here, we determine the high-resolution crystal structure of TarP from R. palustris as well as the structures of homologous proteins from the salt marsh bacterium Sagittula stellata and the halophile Chromohalobacter salexigens, which also grow on lignin-derived aromatics. In combination with tryptophan fluorescence ligand-binding assays, our ligand-bound co-crystal structures reveal the molecular basis for high-affinity recognition of phenylpropanoids by these TRAP transporters, which have potential for improving uptake of these compounds for biotechnological transformations of lignin

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

User`s manual for the radioactive decay and accumulation code RADAC

The RADAC computer code calculates radioactive decay and accumulation of decayed products using an algorithm based on the direct use of the Bateman equations and referred to here as the yield factor method. This report explains the yield factor method, gives an overview of the various modules in the RADAC code system, and describes the decay and accumulation code in detail. The RADAC code has capacity for two waste types and can accommodate up to 60 years of annual waste inputs. Decay times as high as 1 million years can be calculated. The user supplies the undecayed composition and radioactivity of the waste placed in storage each year. The code calculates the decayed composition, radioactivity, and thermal power of the accumulated waste at the end of each year and gives the results in terms of grams and curies of individual radionuclides. Calculations can be made for up to 19 waste storage sites in a single run. For each site and each waste type, calculations can be made by 1-year steps up to 60 years, by 10-year steps to 160 years, and by 6 discrete steps to 1 million years. Detailed outputs can be printed for each waste site and each time step by individual radionuclides. Summarized outputs are also available. Excluding data-preparation time, RADAC requires about 2 min to run 19 waste sites with two types of transuranic waste at each site, using a 486 DX computer with a clock speed of 33 MHz. Because RADAC uses a preselected set of decay times and does not make in-reactor calculations, it should not be viewed as a substitute for ORIGEN2. RADAC is intended for use in applications in which accumulations at the decay times provided by the code are sufficient for the user`s purposes

Quantitative laser-induced fluorescence: some recent developments in combustion diagnostics

Kohse-Höinghaus K. Quantitative laser-induced fluorescence: some recent developments in combustion diagnostics. Applied Physics, B. 1990;50(6):455-461.This report summarizes several recent applications of quantitative laser-induced fluorescence techniques for the determination of species concentrations and temperature in combustion processes. Several lines of further development are discusse