The initiator methionine tRNA drives secretion of type II collagen from stromal fibroblasts to promote tumor growth and angiogenesis

Summary: Expression of the initiator methionine tRNA (tRNAi Met) is deregulated in cancer. Despite this fact, it is not currently known how tRNAi Met expression levels influence tumor progression. We have found that tRNAi Met expression is increased in carcinoma-associated fibroblasts, implicating deregulated expression of tRNAi Met in the tumor stroma as a possible contributor to tumor progression. To investigate how elevated stromal tRNAi Met contributes to tumor progression, we generated a mouse expressing additional copies of the tRNAi Met gene (2+tRNAi Met mouse). Growth and vascularization of subcutaneous tumor allografts was enhanced in 2+tRNAi Met mice compared with wild-type littermate controls. Extracellular matrix (ECM) deposited by fibroblasts from 2+tRNAi Met mice supported enhanced endothelial cell and fibroblast migration. SILAC mass spectrometry indicated that elevated expression of tRNAi Met significantly increased synthesis and secretion of certain types of collagen, in particular type II collagen. Suppression of type II collagen opposed the ability of tRNAi Metoverexpressing fibroblasts to deposit pro-migratory ECM. We used the prolyl hydroxylase inhibitor ethyl- 3,4-dihydroxybenzoate (DHB) to determine whether collagen synthesis contributes to the tRNAi Met-driven pro-tumorigenic stroma in vivo. DHB had no effect on the growth of syngeneic allografts in wild-type mice but opposed the ability of 2+tRNAi Met mice to support increased angiogenesis and tumor growth. Finally, collagen II expression predicts poor prognosis in high-grade serous ovarian carcinoma. Taken together, these data indicate that increased tRNAi Met levels contribute to tumor progression by enhancing the ability of stromal fibroblasts to synthesize and secrete a type II collagen-rich ECM that supports endothelial cell migration and angiogenesis

Refining Pheromone Lures for the Invasive Halyomorpha halys (Hemiptera: Pentatomidae) Through Collaborative Trials in the United States and Europe

Brown marmorated stink bug, Halyomorpha halys, is native to Asia and has invaded North America and Europe inflicting serious agricultural damage to specialty and row crops. Tools to monitor the spread of H. halys include traps baited with the two-component aggregation pheromone (PHER), (3S,6S,7R,10S)-10,11-epoxy-1-bisabolen-3-ol and (3R,6S,7R,10S)-10,11-epoxy-1-bisabolen-3-ol, and pheromone synergist, methyl (2E,4E,6Z)-decatrienoate (MDT). Here, an international team of researchers conducted trials aimed at evaluating prototype commercial lures for H. halys to establish relative attractiveness of: 1) low and high loading rates of PHER and MDT for monitoring tools and attract and kill tactics; 2) polyethylene lure delivery substrates; and 3) the inclusion of ethyl (2E,4E,6Z)-decatrieonate (EDT), a compound that enhances captures when combined with PHER in lures. In general, PHER loading rate had a greater impact on overall trap captures compared with loading of MDT, but reductions in PHER loading and accompanying lower trap captures could be offset by increasing loading of MDT. As MDT is less expensive to produce, these findings enable reduced production costs. Traps baited with lures containing PHER and EDT resulted in numerically increased captures when EDT was loaded at a high rate, but captures were not significantly greater than those traps baited with lures containing standard PHER and MDT. Experimental polyethylene vial dispensers did not outperform standard lure dispensers; trap captures were significantly lower in most cases. Ultimately, these results will enable refinement of commercially available lures for H. halys to balance attraction and sensitivity with production cost

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 hospitalization2–4 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

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

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

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

The enzymatic conversion of major algal and cyanobacterial carbohydrates to bioethanol

The production of fuels from biomass is categorized as first-, second- or third-generation depending upon the source of raw materials, either food crops, lignocellulosic material, or algal biomass, respectively. Thus far, the emphasis has been on using food crops creating several environmental problems. To overcome these problems, there is a shift toward bioenergy production from non-food sources. Algae, which store high amounts of carbohydrates, are a potential producer of raw materials for sustainable production of bioethanol. Algae store their carbohydrates in the form of food storage sugars and structural material. In general, algal food storage polysaccharides are composed of glucose subunits, however they vary in the glycosidic bond that links the glucose molecules. In starch-type polysaccharides (starch, floridean starch, and glycogen), the glucose subunits are linked together by α-(1→4) and α-(1→6) glycosidic bonds. Laminarin-type polysaccharides (laminarin, chrysolaminarin, and paramylon) are made of glucose subunits that are linked together by β-(1→3) and β-(1→6) glycosidic bonds. In contrast to food storage polysaccharides, structural polysaccharides vary in composition and glycosidic bond. The industrial production of bioethanol from algae requires efficient hydrolysis and fermentation of different algal sugars. However, the hydrolysis of algal polysaccharides employs more enzymatic mixes in comparison to terrestrial plants. Similarly, algal fermentable sugars display more diversity than plants, and therefore more metabolic pathways are required to produce ethanol from these sugars. In general, the fermentation of glucose, galactose, and glucose isomers is carried out by wild type strains of Saccharomyces cerevisiae and Zymomonas mobilis. In these strains, glucose enters glycolysis, where is it converted to pyruvate through either Embden-Meyerhof-Parnas pathway or Entner-Doudoroff pathway. Other monosaccharides must be converted to fermentable sugars before entering glycolysis. In contrast, microbial wild type strains are not capable of producing ethanol from alginate, and therefore the production of bioethanol from alginate was achieved by using genetically engineered microbial strains, which can simultaneously hydrolyze and ferment alginate to ethanol. In this review, we emphasize the enzymatic hydrolysis processes of different algal polysaccharides. Additionally, we highlight the major metabolic pathways that are employed to ferment different algal monosaccharides to ethanol

Purification and Characterization of the AAA+ Domain of Sinorhizobium meliloti DctD, a σ(54)-Dependent Transcriptional Activator

Activators of σ(54)-RNA polymerase holoenzyme couple ATP hydrolysis to formation of an open complex between the promoter and RNA polymerase. These activators are modular, consisting of an N-terminal regulatory domain, a C-terminal DNA-binding domain, and a central activation domain belonging to the AAA+ superfamily of ATPases. The AAA+ domain of Sinorhizobium meliloti C(4)-dicarboxylic acid transport protein D (DctD) is sufficient to activate transcription. Deletion analysis of the 3′ end of dctD identified the minimal functional C-terminal boundary of the AAA+ domain of DctD as being located between Gly-381 and Ala-384. Histidine-tagged versions of the DctD AAA+ domain were purified and characterized. The DctD AAA+ domain was significantly more soluble than DctD(()(Δ)(1-142)), a truncated DctD protein consisting of the AAA+ and DNA-binding domains. In addition, the DctD AAA+ domain was more homogeneous than DctD(()(Δ)(1-142)) when analyzed by native gel electrophoresis, migrating predominantly as a single high-molecular-weight species, while DctD(()(Δ)(1-142)) displayed multiple species. The DctD AAA+ domain, but not DctD(()(Δ)(1-142)), formed a stable complex with σ(54) in the presence of the ATP transition state analogue ADP-aluminum fluoride. The DctD AAA+ domain activated transcription in vitro, but many of the transcripts appeared to terminate prematurely, suggesting that the DctD AAA+ domain interfered with transcription elongation. Thus, the DNA-binding domain of DctD appears to have roles in controlling the oligomerization of the AAA+ domain and modulating interactions with σ(54) in addition to its role in recognition of upstream activation sequences