Predicting growth of the healthy infant using a genome scale metabolic model

Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Comprehensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism, but such models do not exist for infants. Here, we present a resource of 360 organ-resolved, sex-specific models of newborn and infant metabolism (infant-WBMs) spanning the first 180 days of life. These infant-WBMs were parameterized to represent the distinct metabolic characteristics of newborns and infants, including nutrition, energy requirements, and thermoregulation. We demonstrate that the predicted infant growth was consistent with the recommendation by the World Health Organization. We assessed the infant-WBMs' reliability and capabilities for personalization by simulating 10,000 newborns based on their blood metabolome and birth weight. Furthermore, the infant-WBMs accurately predicted changes in known biomarkers over time and metabolic responses to treatment strategies for inherited metabolic diseases. The infant-WBM resource holds promise for personalized medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

The growth rates and influencing factors of preterm and full-term infants: A birth cohort study

This study aimed to explore the growth rates and influencing factors of the length and weight of preterm and full-term infants in the urban areas of Hubei province to provide a reference for child health and related clinical fields. A birth cohort study was used to analyze the growth rates and influencing factors of the length and weight of preterm and full-term infants using a multivariate regression analysis. The growth rates of the length and weight of preterm infants were significantly lower than those of full-term infants from birth to 3 months of age (P < .05), and gradually caught up to the level of full-term infants after 3 months of age (P > .05). Meanwhile, there were some influencing factors that had significant differences by their contribution to the growth rates of the length and weight of preterm and full-term infants. This study proposed that perinatal factors were attached more importance to the growth rates of preterm infants in the urban areas of Hubei province.

Uncontrolled Thyroid during Pregnancy Alters the Circulative and Exerted Metabolome

Normal levels of thyroid hormones (THs) are essential for a normal pregnancy outcome, fetal growth and the normal function of the central nervous system. Hypothyroidism, a common endocrine disorder during pregnancy, is a significant metabolic factor leading to cognitive impairments. It is essential to investigate whether patients with thyroid dysfunction may present an altered circulative and excreted metabolic profile, even after receiving treatment with thyroxine supplements. NMR metabolomics was employed to analyze 90 serum and corresponding colostrum samples. Parallel analyses of the two biological specimens provided a snapshot of the maternal metabolism through the excretive and circulating characteristics of mothers. The metabolomics data were analyzed by performing multivariate statistical, biomarker and pathway analyses. Our results highlight the impact of hypothyroidism on metabolites’ composition during pregnancy and lactation. Thyroid disorder causing metabolite fluctuations may lead to impaired lipid and glucose metabolic pathways as well as aberrant prenatal neurodevelopment, thus posing a background for the occurrence of metabolic syndrome or neurogenerative diseases later in life. This risk applies to not only untreated but also hypothyroid women under replacement therapy since our findings in both biofluids framed a different metabolic phenotype for the latter group, thus emphasizing the need to monitor women adequately after treatment initiation.

Early-life gut microbiota and its connection to metabolic health in children: Perspective on ecological drivers and need for quantitative approach

The colonisation and development of the gut microbiota has been implicated in paediatric metabolic disorders via its powerful effect on host metabolic and immune homeostasis. Here we summarise the evidence from human studies on the early gut microbiota and paediatric overweight and obesity. Manipulation of the early gut microbiota may represent a promising target for countering the burgeoning metabolic disorders in the paediatric population, provided the assembly patterns of microbiota and their health consequences can be decoded. Therefore, in this review, we pay particular attention to the important ecological drivers affecting the community dynamics of the early gut microbiota. We then discuss the knowledge gaps in commonly studied exposures linking the gut microbiota to metabolic disorders, especially regarding maternal factors and antibiotic use. This review also attempts to give directions for future studies aiming to identify predictive and corrective measures for paediatric metabolic disorders based on the gut microbiota. Gut microbiota; Metabolism; Paediatric overweight and obesity; Ecological driver; Dynamics; Infants.

A dynamic multi-tissue model to study human metabolism

Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.

Systems Biology: A Multi-Omics Integration Approach to Metabolism and the Microbiome

The complex and dynamic nature of human physiology, as exemplified by metabolism, has often been overlooked due to the lack of quantitative and systems approaches. Recently, systems biology approaches have pushed the boundaries of our current understanding of complex biochemical, physiological, and environmental interactions, enabling proactive medicine in the near future. From this perspective, we review how state-of-the-art computational modelling of human metabolism, i.e., genome-scale metabolic modelling, could be used to identify the metabolic footprints of diseases, to guide the design of personalized treatments, and to estimate the microbiome contributions to host metabolism. These state-of-the-art models can serve as a scaffold for integrating multi-omics data, thereby enabling the identification of signatures of dysregulated metabolism by systems approaches. For example, increased plasma mannose levels due to decreased uptake in the liver have been identified as a potential biomarker of early insulin resistance by multi-omics approaches. In addition, we also review the emerging axis of human physiology and the human microbiome, discussing its contribution to host metabolism and quantitative approaches to study its variations in individuals.

Personalized whole-body models integrate metabolism, physiology, and the gut microbiome

Comprehensive molecular-level models of human metabolism have been generated on a cellular level. However, models of whole-body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole-body organ-resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter-organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host-microbiome co-metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.

Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis

We used a combination of experimental measurements and computer simulations to understand how liver cancer cells rewire their metabolism to grow faster. We observed that glutamate is excreted by the cells, and our simulations suggest that this occurs because glutamate is formed in excess in the cytoplasm, when cells rapidly synthesize nucleotides, which are required for growth. Meanwhile, glutamate that is formed in the mitochondria is, on the other hand, not excreted. Treating glutamate as two distinct pools, a cytosolic and a mitochondrial, is useful to better understand why many cancer cells rapidly consume glutamine, the precursor of glutamate. The results point toward potential drug targets that could be used to reduce growth of liver cancer cells.

Many cancer cells consume glutamine at high rates; counterintuitively, they simultaneously excrete glutamate, the first intermediate in glutamine metabolism. Glutamine consumption has been linked to replenishment of tricarboxylic acid cycle (TCA) intermediates and synthesis of adenosine triphosphate (ATP), but the reason for glutamate excretion is unclear. Here, we dynamically profile the uptake and excretion fluxes of a liver cancer cell line (HepG2) and use genome-scale metabolic modeling for in-depth analysis. We find that up to 30% of the glutamine is metabolized in the cytosol, primarily for nucleotide synthesis, producing cytosolic glutamate. We hypothesize that excreting glutamate helps the cell to increase the nucleotide synthesis rate to sustain growth. Indeed, we show experimentally that partial inhibition of glutamate excretion reduces cell growth. Our integrative approach thus links glutamine addiction to glutamate excretion in cancer and points toward potential drug targets.

An atlas of human metabolism

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

The Determinants of the Human Milk Metabolome and Its Role in Infant Health

Human milk is needed for optimal growth as it satisfies both the nutritional and biological needs of an infant. The established relationship between breastfeeding and an infant’s health is attributable to the nutritional and non-nutritional, functional components of human milk including metabolites such as the lipids, amino acids, biogenic amines and carbohydrates. These components have diverse roles, including protecting the infant against infections and guiding the development of the infant’s immature immune system. In this review, we provide an in-depth and updated insight into the immune modulatory and anti-infective role of human milk metabolites and their effects on infant health and development. We also review the literature on potential determinants of the human milk metabolome, including maternal infectious diseases such as human immunodeficiency virus and mastitis.

Early Human-Milk Metabolome in Cases of Intrauterine Growth-Restricted and Macrosomic Infants

BACKGROUND

Abnormal fetal growth is associated with short-term and long-term metabolic dysregulation and susceptibility to obesity-related disorders. Maternal milk, the ideal source of infantile nutrition, protects from metabolic diseases in adulthood. By applying nuclear magnetic resonance (NMR) metabolomics, this study investigated the metabolic profile of early human milk/colostrum (EHM/C) at the extremes of fetal-growth conditions, which could affect its nutritional value.

METHODS

From 98 mothers delivering 60 appropriate-for-gestational-age (AGA), 19 large-for-gestational-age (LGA), and 19 intrauterine growth-restricted (IUGR) full-term neonates, milk samples collected on the third to fourth day post partum were examined by NMR spectroscopy. Multivariate data analysis elicited information from NMR spectra and probed to metabolic signatures of EHM/C.

RESULTS

LGA and IUGR EHM/C samples depicted increased content in lactose, citric acid, choline, phosphocholine, and N-acetylglutamine. AGA samples exhibited increased isoleucine and valine. Metabolic pathways involved were valine, leucine/isoleucine biosynthesis and degradation, glycerophospholipid metabolism, aminoacyl-transfer RNA biosynthesis, and citrate cycle. Orthogonal projections to latent structures discriminant analysis models were validated.

CONCLUSION

This holistic metabolomics study framed an increased content of certain essential nutrients in EHM/C samples following the birth of LGA and IUGR infants prone to short- and long-term metabolic disorders, thus stressing additional benefits of early breastfeeding. Assessing the metabolic profile of EHΜ/C enables evaluation of its nutrition value, adjusted to fetal growth, and introduction of appropriate dietary interventions.

Systems biology perspective for studying the gut microbiota in human physiology and liver diseases

The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders.

Anthropometry before Day 46 and Growth Velocity before 6 Months of Guatemalan Breastfed Infants Are Associated with Subclinical Mastitis and Milk Cytokines, Minerals, and Trace Elements

BACKGROUND

Subclinical mastitis (SCM) is an inflammatory condition of the mammary gland, but its association with human milk composition and infant growth is not well described.

OBJECTIVES

We investigated whether SCM, milk cytokines, and/or estimated intakes of milk minerals and trace elements were associated with infant anthropometry or growth velocity before 6 mo.

METHODS

Breast milk was collected from Mam-Mayan mothers (n = 114) at both early (2-46 d) and established (4-6 mo) lactation. Concentrations of 9 elements (sodium, potassium, calcium, magnesium, copper, iron, manganese, selenium, zinc) analyzed by inductively-coupled plasma mass spectrometry were used to estimate infants' daily intakes. Concentrations of 4 cytokines were measured. Milk concentrations and infants' estimated elemental intakes and anthropometric measurements during early and established lactation were compared by SCM status. Multiple regression was used to identify factors associated with infant growth attainment (<46 d) for infant weight- (WAZ), length- (LAZ), and head circumference-for-age (HCAZ) and weight-for-length (WLZ) z scores and with growth velocity (expressed as Δ/d) from early to established lactation.

RESULTS

SCM prevalence was higher in early (30%) than established (10%) lactation. Breast milk of SCM mothers had higher cytokine concentrations and higher magnesium in early and higher selenium concentrations in both early and established lactation (Padj < 0.0121). At day 46, regression models showed inverse associations of SCM with WLZ and IL-1β with LAZ (Padj < 0.0150). In contrast, linear growth velocity was positively associated with IL-1β measured in early lactation (Padj < 0.0124), whereas cranial growth velocity was positively associated with IL-8 measured during established lactation ( Padj < 0.0124).

CONCLUSIONS

Despite a high prevalence of inadequate intakes, only infants' intake of milk magnesium during early lactation was associated with linear growth velocity in breastfed infants <6 mo. Evidence shows that SCM, breast-milk cytokines, and infants' estimated intakes of select elements are independently associated with growth attainment and growth velocity during lactation.

Metabolic Models of Protein Allocation Call for the Kinetome

The flux of metabolites in the living cell depend on enzyme activities. Recently, many metabolic phenotypes have been explained by computer models that incorporate enzyme activity data. To move further, the scientific community needs to measure the kinetics of all enzymes in a systematic way.

Gut microbiota dysbiosis is associated with malnutrition and reduced plasma amino acid levels: Lessons from genome-scale metabolic modeling

Malnutrition is a severe non-communicable disease, which is prevalent in children from low-income countries. Recently, a number of metagenomics studies have illustrated associations between the altered gut microbiota and child malnutrition. However, these studies did not examine metabolic functions and interactions between individual species in the gut microbiota during health and malnutrition. Here, we applied genome-scale metabolic modeling to model the gut microbial species, which were selected from healthy and malnourished children from three countries. Our analysis showed reduced metabolite production capabilities in children from two low-income countries compared with a high-income country. Additionally, the models were also used to predict the community-level metabolic potentials of gut microbes and the patterns of pairwise interactions among species. Hereby we found that due to bacterial interactions there may be reduced production of certain amino acids in malnourished children compared with healthy children from the same communities. To gain insight into alterations in the metabolism of malnourished (stunted) children, we also performed targeted plasma metabolic profiling in the first 2 years of life of 25 healthy and 25 stunted children. Plasma metabolic profiling further revealed that stunted children had reduced plasma levels of essential amino acids compared to healthy controls. Our analyses provide a framework for future efforts towards further characterization of gut microbial metabolic capabilities and their contribution to malnutrition.

Worldwide Variation in Human Milk Metabolome: Indicators of Breast Physiology and Maternal Lifestyle?

Human milk provides essential substrates for the optimal growth and development of a breastfed infant. Besides providing nutrients to the infant, human milk also contains metabolites which form an intricate system between maternal lifestyle, such as the mother's diet and the gut microbiome, and infant outcomes. This study investigates the variation of these human milk metabolites from five different countries. Human milk samples (n = 109) were collected one month postpartum from Australia, Japan, the USA, Norway, and South Africa and were analyzed by nuclear magnetic resonance. The partial least squares discriminant analysis (PLS-DA) showed separation between either maternal countries of origin or ethnicities. Variation between countries in concentration of metabolites, such as 2-oxoglutarate, creatine, and glutamine, in human milk, between countries, could provide insights into problems, such as mastitis and/or impaired functions of the mammary glands. Several important markers of milk production, such as lactose, betaine, creatine, glutamate, and glutamine, showed good correlation between each metabolite. This work highlights the importance of milk metabolites with respect to maternal lifestyle and the environment, and also provides the framework for future breastfeeding and microbiome studies in a global context.

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.