Genomic network analysis of environmental and livestock F-type plasmid populations

F-type plasmids are diverse and of great clinical significance, often carrying genes conferring antimicrobial resistance (AMR) such as extended-spectrum β-lactamases, particularly in Enterobacterales. Organising this plasmid diversity is challenging, and current knowledge is largely based on plasmids from clinical settings. Here, we present a network community analysis of a large survey of F-type plasmids from environmental (influent, effluent and upstream/downstream waterways surrounding wastewater treatment works) and livestock settings. We use a tractable and scalable methodology to examine the relationship between plasmid metadata and network communities. This reveals how niche (sampling compartment and host genera) partition and shape plasmid diversity. We also perform pangenome-style analyses on network communities. We show that such communities define unique combinations of core genes, with limited overlap. Building plasmid phylogenies based on alignments of these core genes, we demonstrate that plasmid accessory function is closely linked to core gene content. Taken together, our results suggest that stable F-type plasmid backbone structures can persist in environmental settings while allowing dramatic variation in accessory gene content that may be linked to niche adaptation. The association of F-type plasmids with AMR may reflect their suitability for rapid niche adaptation.Subject terms: Environmental microbiology, Genomics     

A 12 kb multi-allelic copy number variation encompassing a GC gene enhancer is associated with mastitis resistance in dairy cattle

Clinical mastitis (CM) is an inflammatory disease occurring in the mammary glands of lactating cows. CM is under genetic control, and a prominent CM resistance QTL located on chromosome 6 was reported in various dairy cattle breeds. Nevertheless, the biological mechanism underpinning this QTL has been lacking. Herein, we mapped, fine-mapped, and discovered the putative causal variant underlying this CM resistance QTL in the Dutch dairy cattle population. We identified a ~12 kb multi-allelic copy number variant (CNV), that is in perfect linkage disequilibrium with a lead SNP, as a promising candidate variant. By implementing a fine-mapping and through expression QTL mapping, we showed that the group-specific component gene (GC), a gene encoding a vitamin D binding protein, is an excellent candidate causal gene for the QTL. The multiplicated alleles are associated with increased GC expression and low CM resistance. Ample evidence from functional genomics data supports the presence of an enhancer within this CNV, which would exert cis-regulatory effect on GC. We observed that strong positive selection swept the region near the CNV, and haplotypes associated with the multiplicated allele were strongly selected for. Moreover, the multiplicated allele showed pleiotropic effects for increased milk yield and reduced fertility, hinting that a shared underlying biology for these effects may revolve around the vitamin D pathway. These findings together suggest a putative causal variant of a CM resistance QTL, where a cis-regulatory element located within a CNV can alter gene expression and affect multiple economically important traits.     

C_4 Plants as Biofuel Feedstocks: Optimising Biomass Production and Feedstock Quality from a Lignocellulosic Perspective

The main feedstocks for bioethanol are sugarcane (Saccharum officinarum) and maize (Zea mays),both of which are C4 grasses,highly efficient at converting solar energy into chemical energy,and both are food crops.As the systems for lignocellulosic bioethanol production become more efficient and cost effective,plant biomass from any source may be used as a feedstock for bioethanol production.Thus,a move away from using food plants to make fuel is possible,and sources of biomass such as wood from forestry and ...     

How Can Inequalities in Mortality Be Reduced? A Quantitative Analysis of 6 Risk Factors in 21 European Populations

Background

Socioeconomic inequalities in mortality are one of the greatest challenges for health policy in all European countries, but the potential for reducing these inequalities is unclear. We therefore quantified the impact of equalizing the distribution of six risk factors for mortality: smoking, overweight, lack of physical exercise, lack of social participation, low income, and economic inactivity.

Methods

We collected and harmonized data on mortality and risk factors by educational level for 21 European populations in the early 2000s. The impact of the risk factors on mortality in each educational group was determined using Population Attributable Fractions. We estimated the impact on inequalities in mortality of two scenarios: a theoretical upward levelling scenario in which inequalities in the risk factor were completely eliminated, and a more realistic best practice scenario, in which inequalities in the risk factor were reduced to those seen in the country with the smallest inequalities for that risk factor.

Findings

In general, upward levelling of inequalities in smoking, low income and economic inactivity hold the greatest potential for reducing inequalities in mortality. While the importance of low income is similar across Europe, smoking is more important in the North and East, and overweight in the South. On the basis of best practice scenarios the potential for reducing inequalities in mortality is often smaller, but still substantial in many countries for smoking and physical inactivity.

Interpretation

Theoretically, there is a great potential for reducing inequalities in mortality in most European countries, for example by equity-oriented tobacco control policies, income redistribution and employment policies. Although it is necessary to achieve substantial degrees of upward levelling to make a notable difference for inequalities in mortality, the existence of best practice countries with more favourable distributions for some of these risk factors suggests that this is feasible.     

Robust and Sensitive Analysis of Mouse Knockout Phenotypes

A significant challenge of in-vivo studies is the identification of phenotypes with a method that is robust and reliable. The challenge arises from practical issues that lead to experimental designs which are not ideal. Breeding issues, particularly in the presence of fertility or fecundity problems, frequently lead to data being collected in multiple batches. This problem is acute in high throughput phenotyping programs. In addition, in a high throughput environment operational issues lead to controls not being measured on the same day as knockouts. We highlight how application of traditional methods, such as a Student’s t-Test or a 2-way ANOVA, in these situations give flawed results and should not be used. We explore the use of mixed models using worked examples from Sanger Mouse Genome Project focusing on Dual-Energy X-Ray Absorptiometry data for the analysis of mouse knockout data and compare to a reference range approach. We show that mixed model analysis is more sensitive and less prone to artefacts allowing the discovery of subtle quantitative phenotypes essential for correlating a gene’s function to human disease. We demonstrate how a mixed model approach has the additional advantage of being able to include covariates, such as body weight, to separate effect of genotype from these covariates. This is a particular issue in knockout studies, where body weight is a common phenotype and will enhance the precision of assigning phenotypes and the subsequent selection of lines for secondary phenotyping. The use of mixed models with in-vivo studies has value not only in improving the quality and sensitivity of the data analysis but also ethically as a method suitable for small batches which reduces the breeding burden of a colony. This will reduce the use of animals, increase throughput, and decrease cost whilst improving the quality and depth of knowledge gained.     

Analysis of a zebrafish VEGF receptor mutant reveals specific disruption of angiogenesis

Blood vessels form either by the assembly and differentiation of mesodermal precursor cells (vasculogenesis) or by sprouting from preexisting vessels (angiogenesis). Endothelial-specific receptor tyrosine kinases and their ligands are known to be essential for these processes. Targeted disruption of vascular endothelial growth factor (VEGF) or its receptor kdr (flk1, VEGFR2) in mouse embryos results in a severe reduction of all blood vessels, while the complete loss of flt1 (VEGFR1) leads to an increased number of hemangioblasts and a disorganized vasculature. In a large-scale forward genetic screen, we identified two allelic zebrafish mutants in which the sprouting of blood vessels is specifically disrupted without affecting the assembly and differentiation of angioblasts. Molecular cloning revealed nonsense mutations in flk1. Analysis of mRNA expression in flk1 mutant embryos showed that flk1 expression was severely downregulated, while the expression of other genes (scl, gata1, and fli1) involved in vasculogenesis or hematopoiesis was unchanged. Overexpression of vegf(121+165) led to the formation of additional vessels only in sibling larvae, not in flk1 mutants. We demonstrate that flk1 is not required for proper vasculogenesis and hematopoiesis in zebrafish embryos. However, the disruption of flk1 impairs the formation or function of vessels generated by sprouting angiogenesis.