Identifying metabolites from protein identifiers with P2M

The identification of metabolites from complex biological samples often involves matching experimental mass spectrometry data to signatures of compounds derived from massive chemical databases. However, misidentifications may result due to the complexity of potential chemical space that leads to databases containing compounds with nearly identical structures. Prior knowledge of compounds that may be enzymatically consumed or produced by an organism can help reduce misidentifications by restricting initial database searching to compounds that are likely to be present in a biological system. While databases such as UniProt allow for the identification of small molecules that may be consumed or generated by enzymes encoded in an organism's genome, currently no tool exists for identifying SMILES strings of metabolites associated with protein identifiers and expanding R-containing substructures to fully defined, biologically relevant chemical structures. Here we present Proteome2Metabolome (P2M), a tool that performs these tasks using external database querying behind a simple command line interface. Beyond mass spectrometry based applications, P2M can be generally used to identify biologically relevant chemical structures likely to be observed in a biological system

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

Alternatively activated macrophage-derived RELM-α is a negative regulator of type 2 inflammation in the lung

Differentiation and recruitment of alternatively activated macrophages (AAMacs) are hallmarks of several inflammatory conditions associated with infection, allergy, diabetes, and cancer. AAMacs are defined by the expression of Arginase 1, chitinase-like molecules, and resistin-like molecule (RELM) α/FIZZ1; however, the influence of these molecules on the development, progression, or resolution of inflammatory diseases is unknown. We describe the generation of RELM-α–deficient (Retnla−/−) mice and use a model of T helper type 2 (Th2) cytokine-dependent lung inflammation to identify an immunoregulatory role for RELM-α. After challenge with Schistosoma mansoni (Sm) eggs, Retnla−/− mice developed exacerbated lung inflammation compared with their wild-type counterparts, characterized by excessive pulmonary vascularization, increased size of egg-induced granulomas, and elevated fibrosis. Associated with increased disease severity, Sm egg–challenged Retnla−/− mice exhibited elevated expression of pathogen-specific CD4+ T cell–derived Th2 cytokines. Consistent with immunoregulatory properties, recombinant RELM-α could bind to macrophages and effector CD4+ Th2 cells and inhibited Th2 cytokine production in a Bruton's tyrosine kinase–dependent manner. Additionally, Retnla−/− AAMacs promoted exaggerated antigen-specific Th2 cell differentiation. Collectively, these data identify a previously unrecognized role for AAMac-derived RELM-α in limiting the pathogenesis of Th2 cytokine-mediated pulmonary inflammation, in part through the regulation of CD4+ T cell responses

Extending our scientific reach in arboreal ecosystems for research and management

The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers (n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems

GWAS meta-analysis of intrahepatic cholestasis of pregnancy implicates multiple hepatic genes and regulatory elements

Intrahepatic cholestasis of pregnancy (ICP) is a pregnancy-specific liver disorder affecting 0.5–2% of pregnancies. The majority of cases present in the third trimester with pruritus, elevated serum bile acids and abnormal serum liver tests. ICP is associated with an increased risk of adverse outcomes, including spontaneous preterm birth and stillbirth. Whilst rare mutations affecting hepatobiliary transporters contribute to the aetiology of ICP, the role of common genetic variation in ICP has not been systematically characterised to date. Here, we perform genome-wide association studies (GWAS) and meta-analyses for ICP across three studies including 1138 cases and 153,642 controls. Eleven loci achieve genome-wide significance and have been further investigated and fine-mapped using functional genomics approaches. Our results pinpoint common sequence variation in liver-enriched genes and liver-specific cis-regulatory elements as contributing mechanisms to ICP susceptibility