Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples

Age-Related Alteration of Arginase Activity Impacts on Severity of Leishmaniasis

It is well documented that ageing alters many aspects of immune responses; however, a causal relation between impaired immune functions in ageing individuals and the response to infection has not been established. Experimental leishmaniasis is an excellent model to analyse protective and pathological immune responses. Leishmania parasites are obligate intracellular pathogens and invade mainly macrophages, which have dual function: they can kill the parasites or promote their growth. We have recently shown that arginase, an enzyme induced in infected macrophages, is a key factor for parasite survival. Here, we show that ageing reduces the expression levels of arginase in macrophages, resulting in more efficient control of parasite growth. Our results suggest that age-related differences in the metabolism of arginase in macrophages might contribute to the higher susceptibility of children to leishmaniasis

ARIA-Versorgungspfade für die Allergenimmuntherapie 2019 = 2019 ARIA Care pathways for allergen immunotherapy

Allergen immunotherapy (MT) is a proven therapeutic option for the treatment of allergic rhinitis and/or asthma. Many guidelines or national practice guidelines have been produced but the evidence- based method varies, many are complex and none propose care pathways. This paper reviews care pathways for AIT using strict criteria and provides simple recommendations that can be used by all stakeholders including health professionals. The decision to prescribe MT for the patient should be individualized and based on the relevance of the allergens, the persistence of symptoms despite appropriate medications according to guidelines as well as on the availability of good-quality and efficacious extracts. Allergen extracts cannot be regarded as generics. Immunotherapy is selected by specialists for stratified patients. There are no currently available validated biomaikers that can predict MT success. In adolescents and adults, AIT should be reserved for patients with moderate/severe rhinitis or for those with moderate asthma who, despite appropriate phannacotherapy and adherence, continue to exhibit exacerbations that appear to be related to allergen exposure, except in some specific cases. Immunotherapy may be even more advantageous in patients with multimorbidity. In children, AIT may prevent asthma onset in patients with rhinitis. mHealth tools are promising for the stratification and follow up of patients