Zinc transporter gene expression is regulated by pro-inflammatory cytokines: a potential role for zinc transporters in beta-cell apoptosis?

<p>Abstract</p> <p>Background</p> <p>β-cells are extremely rich in zinc and zinc homeostasis is regulated by zinc transporter proteins. β-cells are sensitive to cytokines, interleukin-1β (IL-1β) has been associated with β-cell dysfunction and -death in both type 1 and type 2 diabetes. This study explores the regulation of zinc transporters following cytokine exposure.</p> <p>Methods</p> <p>The effects of cytokines IL-1β, interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) on zinc transporter gene expression were measured in INS-1-cells and rat pancreatic islets. Being the more sensitive transporter, we further explored ZnT8 (Slc30A8): the effect of ZnT8 over expression on cytokine induced apoptosis was investigated as well as expression of the insulin gene and two apoptosis associated genes, BAX and BCL2.</p> <p>Results</p> <p>Our results showed a dynamic response of genes responsible for β-cell zinc homeostasis to cytokines: IL-1β down regulated a number of zinc-transporters, most strikingly ZnT8 in both islets and INS-1 cells. The effect was even more pronounced when mixing the cytokines. TNF-α had little effect on zinc transporter expression. IFN-γ down regulated a number of zinc transporters. Insulin expression was down regulated by all cytokines. ZnT8 over expressing cells were more sensitive to IL-1β induced apoptosis whereas no differences were observed with IFN-γ, TNF-α, or a mixture of cytokines.</p> <p>Conclusion</p> <p>The zinc transporting system in β-cells is influenced by the exposure to cytokines. Particularly ZnT8, which has been associated with the development of diabetes, seems to be cytokine sensitive.</p

Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis

Background The infection-fatality ratio (IFR) is a metric that quantifies the likelihood of an individual dying once infected with a pathogen. Understanding the determinants of IFR variation for COVID-19, the disease caused by the SARS-CoV-2 virus, has direct implications for mitigation efforts with respect to clinical practice, non-pharmaceutical interventions, and the prioritisation of risk groups for targeted vaccine delivery. The IFR is also a crucial parameter in COVID-19 dynamic transmission models, providing a way to convert a population's mortality rate into an estimate of infections.Methods We estimated age-specific and all-age IFR by matching seroprevalence surveys to total COVID-19 mortality rates in a population. The term total COVID-19 mortality refers to an estimate of the total number of deaths directly attributable to COVID-19. After applying exclusion criteria to 5131 seroprevalence surveys, the IFR analyses were informed by 2073 all-age surveys and 718 age-specific surveys (3012 age-specific observations). When seroprevalence was reported by age group, we split total COVID-19 mortality into corresponding age groups using a Bayesian hierarchical model to characterise the non-linear age pattern of reported deaths for a given location. To remove the impact of vaccines on the estimated IFR age pattern, we excluded age-specific observations of seroprevalence and deaths that occurred after vaccines were introduced in a location. We estimated age-specific IFR with a non-linear meta-regression and used the resulting age pattern to standardise all-age IFR observations to the global age distribution. All IFR observations were adjusted for baseline and waning antibody-test sensitivity. We then modelled age-standardised IFR as a function of time, geography, and an ensemble of 100 of the top-performing covariate sets. The covariates included seven clinical predictors (eg, age-standardised obesity prevalence) and two measures of health system performance. Final estimates for 190 countries and territories, as well as subnational locations in 11 countries and territories, were obtained by predicting age-standardised IFR conditional on covariates and reversing the age standardisation.Findings We report IFR estimates for April 15, 2020, to January 1, 2021, the period before the introduction of vaccines and widespread evolution of variants. We found substantial heterogeneity in the IFR by age, location, and time. Age-specific IFR estimates form a J shape, with the lowest IFR occurring at age 7 years (0-0023%, 95% uncertainty interval [UI] 0-0015-0-0039) and increasing exponentially through ages 30 years (0-0573%, 0-0418-0-0870), 60 years (1-0035%, 0-7002-1-5727), and 90 years (20-3292%, 14-6888-28-9754). The countries with the highest IFR on July 15, 2020, were Portugal (2-085%, 0-946-4-395), Monaco (1-778%, 1-265-2-915), Japan (1-750%, 1-302-2-690), Spain (1-710%, 0-991-2-718), and Greece (1-637%, 1-155-2-678). All-age IFR varied by a factor of more than 30 among 190 countries and territories.After age standardisation, the countries with the highest IFR on July 15, 2020, were Peru (0-911%, 0-636-1-538), Portugal (0-850%, 0-386-1-793), Oman (0-762%, 0-381-1-399), Spain (0-751%, 0-435-1-193), and Mexico (0-717%, 0-426-1-404). Subnational locations with high IFRs also included hotspots in the UK and southern and eastern states of the USA. Sub-Saharan African countries and Asian countries generally had the lowest all-age and age-standardised IFRs. Population age structure accounted for 74% of logit-scale variation in IFRs estimated for 39 in-sample countries on July 15, 2020. A post-hoc analysis showed that high rates of transmission in the care home population might account for higher IFRs in some locations. Among all countries and territories, we found that the median IFR decreased from 0-466% (interquartile range 0-223-0-840) to 0-314% (0-143-0-551) between April 15, 2020, and Jan 1, 2021.Interpretation Estimating the IFR for global populations helps to identify relative vulnerabilities to COVID-19. Information about how IFR varies by age, time, and location informs clinical practice and non-pharmaceutical interventions like physical distancing measures, and underpins vaccine risk stratification. IFR and mortality risk form a J shape with respect to age, which previous research, such as that by Glynn and Moss in 2020, has identified to be a common pattern among infectious diseases. Understanding the experience of a population with COVID-19 mortality requires consideration for local factors; IFRs varied by a factor of more than 30 among 190 countries and territories in this analysis. In particular, the presence of elevated age-standardised IFRs in countries with well resourced health-care systems indicates that factors beyond health-care capacity are important. Potential extenuating circumstances include outbreaks among care home residents, variable burdens of severe cases, and the population prevalence of comorbid conditions that increase the severity of COVID-19 disease. During the pre-vaccine period, the estimated 33% decrease in median IFR over 8 months suggests that treatment for COVID-19 has improved over time. Estimating IFR for the pre-vaccine era provides an important baseline for describing the progression of COVID-19 mortality patterns.Funding Bill &amp; Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

Biochemistry and Genetics of Bacterial Bioluminescence

Bacterial light production involves enzymes-luciferase, fatty acid reductase, and flavin reductase-and substrates-reduced flavin mononucleotide and long-chain fatty aldehyde-that are specific to bioluminescence in bacteria. The bacterial genes coding for these enzymes, luxA and luxB for the subunits of luciferase; luxC, luxD, and luxE for the components of the fatty acid reductase; and luxG for flavin reductase, are found as an operon in light-emitting bacteria, with the gene order, luxCDABEG. Over 30 species of marine and terrestrial bacteria, which cluster phylogenetically in Aliivibrio, Photobacterium, and Vibrio (Vibrionaceae), Shewanella (Shewanellaceae), and Photorhabdus (Enterobacteriaceae), carry lux operon genes. The luminescence operons of some of these bacteria also contain genes involved in the synthesis of riboflavin, ribEBHA, and in some species, regulatory genes luxI and luxR are associated with the lux operon. In well-studied cases, lux genes are coordinately expressed in a population density-responsive, self-inducing manner called quorum sensing. The evolutionary origins and physiological function of bioluminescence in bacteria are not well understood but are thought to relate to utilization of oxygen as a substrate in the luminescence reaction