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As COVID-19 spreads worldwide, governments have been implementing a wide range of measures to contain it, from movement restrictions to economy-wide shutdowns. Understanding their impacts is essential to support better policies for countries still experiencing outbreaks or in case of emergence of subsequent pandemic waves. Here we show that the cumulative decline in electricity consumption within the 5 months following the stay-home orders ranges between 3% and 12% in the most affected EU countries and USA states, except Florida, which shows no significant impact. Italy, France, Spain, California, Austria, and New York have recovered baseline consumption by the end of July, whereas Great Britain and Germany remain below baseline levels. We also show that the relationship between measures stringency and daily decline in electricity consumption is nonlinear. These results illustrate the severity of the crisis across countries and can support further research on the effect of specific measures.

Air pollution and climate change are potential drivers for the increasing burden of allergic diseases. The molecular mechanisms by which air pollutants and climate parameters may influence allergic diseases, however, are complex and elusive. This article provides an overview of physical, chemical and biological interactions between air pollution, climate change, allergens, adjuvants and the immune system, addressing how these interactions may promote the development of allergies. We reviewed and synthesized key findings from atmospheric, climate, and biomedical research. The current state of knowledge, open questions, and future research perspectives are outlined and discussed. The Anthropocene, as the present era of globally pervasive anthropogenic influence on planet Earth and, thus, on the human environment, is characterized by a strong increase of carbon dioxide, ozone, nitrogen oxides, and combustion- or traffic-related particulate matter in the atmosphere. These environmental factors can enhance the abundance and induce chemical modifications of allergens, increase oxidative stress in the human body, and skew the immune system toward allergic reactions. In particular, air pollutants can act as adjuvants and alter the immunogenicity of allergenic proteins, while climate change affects the atmospheric abundance and human exposure to bioaerosols and aeroallergens. To fully understand and effectively mitigate the adverse effects of air pollution and climate change on allergic diseases, several challenges remain to be resolved. Among these are the identification and quantification of immunochemical reaction pathways involving allergens and adjuvants under relevant environmental and physiological conditions.

In the 1990s, an uncommonly high percentage of glutathione S-transferase M1 (GSTM1) negative bladder cancer cases (70%) was reported in the greater Dortmund area. The question arose as to whether this uncommonly high percentage of GSTM1 negative bladder cancer cases was due to environmental and/or occupational exposure decades ago. Thus, 15 years later, another study on bladder cancer was performed in the same area after the coal, iron, and steel industries had finally closed in the 1990s. In total 196 bladder cancer patients from the St.-Josefs-Hospital Dortmund-Hörde and 235 controls with benign urological diseases were assessed by questionnaire and genotyped for GSTM1, glutathione S-transferase T1 (GSTT1), and the N-acetyltransferase 2 (NAT2) tag SNP rs1495741. The frequency of the GSTM1 negative genotype was 52% in bladder cancer cases and thus lower compared to a previous study performed from 1992 to 1995 in the same area (70%). NAT2 genotypes were distributed equally among cases and controls (63% slow acetylators). Fewer GSTT1 negative genotypes were present in cases (17%) than in controls (20%).

Constraint-based modeling techniques have become a standard tool for the in silico analysis of metabolic networks. To further improve their accuracy, recent methodological developments focused on integration of thermodynamic information in metabolic models to assess the feasibility of flux distributions by thermodynamic driving forces. Here we present OptMDFpathway, a method that extends the recently proposed framework of Max-min Driving Force (MDF) for thermodynamic pathway analysis. Given a metabolic network model, OptMDFpathway identifies both the optimal MDF for a desired phenotypic behavior as well as the respective pathway itself that supports the optimal driving force. OptMDFpathway is formulated as a mixed-integer linear program and is applicable to genome-scale metabolic networks. As an important theoretical result, we also show that there exists always at least one elementary mode in the network that reaches the maximal MDF. We employed our new approach to systematically identify all substrate-product combinations in Escherichia coli where product synthesis allows for concomitant net CO2 assimilation via thermodynamically feasible pathways. Although biomass synthesis cannot be coupled to net CO2 fixation in E. coli we found that as many as 145 of the 949 cytosolic carbon metabolites contained in the genome-scale model iJO1366 enable net CO2 incorporation along thermodynamically feasible pathways with glycerol as substrate and 34 with glucose. The most promising products in terms of carbon assimilation yield and thermodynamic driving forces are orotate, aspartate and the C4-metabolites of the tricarboxylic acid cycle. We also identified thermodynamic bottlenecks frequently limiting the maximal driving force of the CO2-fixing pathways. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2. Furthermore, we envision that the developed OptMDFpathway approach can be used for many other applications within the framework of constrained-based modeling and for rational design of metabolic networks.

Abstract Background Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world’s population or the world’s climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments. Description The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided. Conclusions With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw.

BackgroundAlcohol dependence (AD) shows evidence for genetic liability, but genes influencing risk remain largely unidentified.MethodsWe conducted a genomewide association study in 706 related AD cases and 1,748 unscreened population controls from Ireland. We sought replication in 15,496 samples of European descent. We used model organisms (MOs) to assess the role of orthologous genes in ethanol (EtOH)‐response behaviors. We tested 1 primate‐specific gene for expression differences in case/control postmortem brain tissue.ResultsWe detected significant association in COL6A3 and suggestive association in 2 previously implicated loci, KLF12 and RYR3. None of these signals are significant in replication. A suggestive signal in the long noncoding RNA LOC339975 is significant in case:control meta‐analysis, but not in a population sample. Knockdown of a COL6A3 ortholog in Caenorhabditis elegans reduced EtOH sensitivity. Col6a3 expression correlated with handling‐induced convulsions in mice. Loss of function of the KLF12 ortholog in C. elegans impaired development of acute functional tolerance (AFT). Klf12 expression correlated with locomotor activation following EtOH injection in mice. Loss of function of the RYR3 ortholog reduced EtOH sensitivity in C. elegans and rapid tolerance in Drosophila. The ryanodine receptor antagonist dantrolene reduced motivation to self‐administer EtOH in rats. Expression of LOC339975 does not differ between cases and controls but is reduced in carriers of the associated rs11726136 allele in nucleus accumbens (NAc).ConclusionsWe detect association between AD and COL6A3, KLF12, RYR3, and LOC339975. Despite nonreplication of COL6A3, KLF12, and RYR3 signals, orthologs of these genes influence behavioral response to EtOH in MOs, suggesting potential involvement in human EtOH response and AD liability. The associated LOC339975 allele may influence gene expression in human NAc. Although the functions of long noncoding RNAs are poorly understood, there is mounting evidence implicating these genes in multiple brain functions and disorders.

The mass, chemical composition and toxicological properties of fine particulates (PM2.5) emitted from cooking activities in three Hong Kong based restaurants and two simulated cooking experiments were characterized. Extracts from the PM2.5 samples elicited significant biological activities [cell viability, generation of reactive oxygen species (ROS), DNA damage and inflammation effect (TNF-α)] in a dose-dependent manner. The composition of PAHs, oxygenated PAHs (OPAHs) and azaarenes (AZAs) mixtures differed between samples. The concentration ranges of the Σ30PAHs, Σ17OPAHs and Σ4AZAs and Σ7Carbonyls in the samples were 9627-23,452 pg m-3, 503-3700 pg m-3, 33-263 pg m-3 and 158 - 5328 ng m-3, respectively. Cell viability caused by extracts from the samples was positively correlated to the concentration of benzo[a]anthracene, indeno[1,2,3-cd]pyrene and 1,4-naphthoquinone in the PM2.5 extracts. Cellular ROS production (upon exposure to extracts) was positively correlated with the concentrations of PM2.5, decaldehyde, acridine, Σ17OPAHs and 7 individual OPAHs. TNF-α showed significant positive correlations with the concentrations of most chemical species (elemental carbon, 16 individual PAHs including benzo[a]pyrene, Σ30PAHs, SO42-, Ca2+, Ca, Na, K, Ti, Cr, Mn, Fe, Cu and Zn). The concentrations of Al, Ti, Mn, Σ30PAHs and 8 individual PAHs including benzo[a]pyrene in the samples were positively correlated with DNA damage caused by extracts from the samples. This study demonstrates that inhalation of PM2.5 emitted from cooking could result in adverse human health effects.