Health and development from preconception to 20 years of age and human capital.

Optimal health and development from preconception to adulthood are crucial for human flourishing and the formation of human capital. The Nurturing Care Framework, as adapted to age 20 years, conceptualises the major influences during periods of development from preconception, through pregnancy, childhood, and adolescence that affect human capital. In addition to mortality in children younger than 5 years, stillbirths and deaths in 5-19-year-olds are important to consider. The global rate of mortality in individuals younger than 20 years has declined substantially since 2000, yet in 2019 an estimated 8·6 million deaths occurred between 28 weeks of gestation and 20 years of age, with more than half of deaths, including stillbirths, occurring before 28 days of age. The 1000 days from conception to 2 years of age are especially influential for human capital. The prevalence of low birthweight is high in sub-Saharan Africa and even higher in south Asia. Growth faltering, especially from birth to 2 years, occurs in most world regions, whereas overweight increases in many regions from the preprimary school period through adolescence. Analyses of cohort data show that growth trajectories in early years of life are strong determinants of nutritional outcomes in adulthood. The accrual of knowledge and skills is affected by health, nutrition, and home resources in early childhood and by educational opportunities in older children and adolescents. Linear growth in the first 2 years of life better predicts intelligence quotients in adults than increases in height in older children and adolescents. Learning-adjusted years of schooling range from about 4 years in sub-Saharan Africa to about 11 years in high-income countries. Human capital depends on children and adolescents surviving, thriving, and learning until adulthood.

Human and preclinical studies of the host-gut microbiome co-metabolite hippurate as a marker and mediator of metabolic health.

OBJECTIVE: Gut microbial products are involved in regulation of host metabolism. In human and experimental studies, we explored the potential role of hippurate, a hepatic phase 2 conjugation product of microbial benzoate, as a marker and mediator of metabolic health. DESIGN: In 271 middle-aged non-diabetic Danish individuals, who were stratified on habitual dietary intake, we applied 1H-nuclear magnetic resonance (NMR) spectroscopy of urine samples and shotgun-sequencing-based metagenomics of the gut microbiome to explore links between the urine level of hippurate, measures of the gut microbiome, dietary fat and markers of metabolic health. In mechanistic experiments with chronic subcutaneous infusion of hippurate to high-fat-diet-fed obese mice, we tested for causality between hippurate and metabolic phenotypes. RESULTS: In the human study, we showed that urine hippurate positively associates with microbial gene richness and functional modules for microbial benzoate biosynthetic pathways, one of which is less prevalent in the Bacteroides 2 enterotype compared with Ruminococcaceae or Prevotella enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate concentration, independently of gene richness, accounts for links with metabolic health. In the high-fat-fed mice experiments, we demonstrate causality through chronic infusion of hippurate (20 nmol/day) resulting in improved glucose tolerance and enhanced insulin secretion. CONCLUSION: Our human and experimental studies show that a high urine hippurate concentration is a general marker of metabolic health, and in the context of obesity induced by high-fat diets, hippurate contributes to metabolic improvements, highlighting its potential as a mediator of metabolic health.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.

MOTIVATION: Data processing is a key bottleneck for 1H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D 1H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA ("pJRES Binning Algorithm"), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma 1H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring the Genetic Landscape of Metabolic Phenotypes with MetaboSignal.

MetaboSignal is an R/Bioconductor package designed to explore the relationships between genes and metabolites, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) as its primary database. It is a network-based approach that allows overlaying metabolic and signaling pathways and exploring the topological relationship between genes (signaling or metabolic genes) and metabolites. MetaboSignal is ideally suited to identify candidate genes in metabolome genome-wide association studies (mGWAS), particularly in the case of trans-acting associations. It can also be used to provide mechanistic explanations of perturbed metabolic patterns observed in genetic models, as well as to identify novel target metabolic pathways of signaling genes. © 2018 by John Wiley & Sons, Inc.

MWASTools: an R/bioconductor package for metabolome-wide association studies.

Summary: MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. Availability and implementation: The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact: m.dumas@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis.

1H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D 1H NMR spectra, we have investigated the use of 2D J-resolved (JRES) 1H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the J-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows.

MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways.

Summary: MetaboSignal is an R package that allows merging metabolic and signaling pathways reported in the Kyoto Encyclopaedia of Genes and Genomes (KEGG). It is a network-based approach designed to navigate through topological relationships between genes (signaling- or metabolic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape of metabolic phenotypes. Availability and Implementation: MetaboSignal is available from Bioconductor: https://bioconductor.org/packages/MetaboSignal/. Contact: m.dumas@imperial.ac.uk . Supplementary information: Supplementary data are available at Bioinformatics online.

Preparation for a first-in-man lentivirus trial in patients with cystic fibrosis.

We have recently shown that non-viral gene therapy can stabilise the decline of lung function in patients with cystic fibrosis (CF). However, the effect was modest, and more potent gene transfer agents are still required. Fuson protein (F)/Hemagglutinin/Neuraminidase protein (HN)-pseudotyped lentiviral vectors are more efficient for lung gene transfer than non-viral vectors in preclinical models. In preparation for a first-in-man CF trial using the lentiviral vector, we have undertaken key translational preclinical studies. Regulatory-compliant vectors carrying a range of promoter/enhancer elements were assessed in mice and human air-liquid interface (ALI) cultures to select the lead candidate; cystic fibrosis transmembrane conductance receptor (CFTR) expression and function were assessed in CF models using this lead candidate vector. Toxicity was assessed and 'benchmarked' against the leading non-viral formulation recently used in a Phase IIb clinical trial. Integration site profiles were mapped and transduction efficiency determined to inform clinical trial dose-ranging. The impact of pre-existing and acquired immunity against the vector and vector stability in several clinically relevant delivery devices was assessed. A hybrid promoter hybrid cytosine guanine dinucleotide (CpG)- free CMV enhancer/elongation factor 1 alpha promoter (hCEF) consisting of the elongation factor 1α promoter and the cytomegalovirus enhancer was most efficacious in both murine lungs and human ALI cultures (both at least 2-log orders above background). The efficacy (at least 14% of airway cells transduced), toxicity and integration site profile supports further progression towards clinical trial and pre-existing and acquired immune responses do not interfere with vector efficacy. The lead rSIV.F/HN candidate expresses functional CFTR and the vector retains 90-100% transduction efficiency in clinically relevant delivery devices. The data support the progression of the F/HN-pseudotyped lentiviral vector into a first-in-man CF trial in 2017.