Improved Macro- and Micronutrient Supply for Favorable Growth and Metabolomic Profile with Standardized Parenteral Nutrition Solutions for Very Preterm Infants.

Very preterm infants are at high risk for suboptimal nutrition in the first weeks of life leading to insufficient weight gain and complications arising from metabolic imbalances such as insufficient bone mineral accretion. We investigated the use of a novel set of standardized parenteral nutrition (PN; MUC PREPARE) solutions regarding improving nutritional intake, accelerating termination of parenteral feeding, and positively affecting growth in comparison to individually prescribed and compounded PN solutions. We studied the effect of MUC PREPARE on macro- and micronutrient intake, metabolism, and growth in 58 very preterm infants and compared results to a historic reference group of 58 very preterm infants matched for clinical characteristics. Infants receiving MUC PREPARE demonstrated improved macro- and micronutrient intake resulting in balanced electrolyte levels and stable metabolomic profiles. Subsequently, improved energy supply was associated with up to 1.5 weeks earlier termination of parenteral feeding, while simultaneously reaching up to 1.9 times higher weight gain at day 28 in extremely immature infants (<27 GA weeks) as well as overall improved growth at 2 years of age for all infants. The use of the new standardized PN solution MUC PREPARE improved nutritional supply and short- and long-term growth and reduced PN duration in very preterm infants and is considered a superior therapeutic strategy.

ProPores2: Web Service and Stand-Alone Tool for Identifying, Manipulating, and Visualizing Pores in Protein Structures.

Surface pockets, cavities, and tunnels in the 3D structures of proteins play integral functional roles such as enabling enzymatic catalysis, ligand binding, or transport of ions or small molecules across biomembranes. ProPores2 facilitates understanding and analysis of these processes by identifying pores and lining residues, determining their axes, and opening closed connections via side-chain rotation. The fast stand-alone tool introduces a novel mode for pore identification, improved axis determination, and additional features such as parallel batch processing and a graphical user interface. The new web service features an integrated and customizable protein viewer with an option to analyze and view more than one structure at once. This feature facilitates side-by-side comparisons of pores in different conformations of the same protein or of identified pores before and after opening gates within the same protein. ProPores2 is freely and publicly available at https://service.bioinformatik.uni-saarland.de/propores.

MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions.

Summary: Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.