Genome-scale methods converge on key mitochondrial genes for the survival of human cardiomyocytes in hypoxia

Comparative analysis of the gut microbiome of ungulate species from Qinghai-Xizang plateau

Several studies have investigated the gut bacterial composition of wild ungulates in the Qinghai-Xizang Plateau. However, the relationship between their gut microbiome dendrograms and their phylogenetic tree remains unclear. In this study, we analyzed 45 amplicons (V3-V4 region of the 16S rRNA gene) from five wild ungulates-Pseudois nayaur, Pantholops hodgsonii, Gazella subgutturosa, Bos grunniens, and Equus kiang-from the Qinghai-Xizang Plateau to clarify the relationship between their phylogenies and gut microbiome dendrograms. The unweighted pair group method with arithmetic mean analysis and hierarchical clustering analysis indicated that G. subgutturosa is closely related to P. nayaur; however, these results were inconsistent with their phylogenetic trees. Additionally, the indicator genera in the microbiome of each wild ungulate showed strong associations with the diets and habitats of their host. Thus, diet and space niche differentiation may primarily account for the differences between the gut microbiome characteristics of these wild ungulates and their phylogeny. In summary, our research provides insights into the evolutionary factors influencing the gut microbiome of wild ungulates in the Qinghai-Xizang Plateau.

Systems biology in cardiovascular disease: a multiomics approach

Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.

Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease

Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. In this study, we reconstructed a fibroblast‐specific genome‐scale model based on the recently published, FAD‐curated model, based on Recon3D reconstruction. We used transcriptomics (available via GEO database with identifier GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.

Databases

Transcriptomics data are available via GEO database with identifier GSE138379, and proteomics data are available via ProteomeXchange with identifier PXD015518.

Explore potential plasma biomarkers of acute respiratory distress syndrome (ARDS) using GC-MS metabolomics analysis

OBJECTIVES

The aim of this study was to analyse the metabolomics of patients with acute respiratory distress syndrome (ARDS) for the identification of metabolic markers with potential diagnostic and prognostic value.

METHODS

The enrolled subjects included adult patients with ARDS that met the Berlin definition and healthy controls matched based on age, gender, and body mass index (BMI). Plasma samples were collected from 37 patients with ARDS and 28 healthy controls. The plasma metabolites were detected with gas chromatography-mass spectrometry (GC-MS), and the relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

RESULTS

A total of 222 metabolites were identified in our study, of which 128 were significantly altered in patients with ARDS compared with healthy controls. Phenylalanine, aspartic acid, and carbamic acid levels were significantly different between all groups of patients with ARDS classified from mild to severe. Furthermore, four metabolites, ornithine, caprylic acid, azetidine, and iminodiacetic acid, could serve as biomarkers to potentially predict the severity of ARDS. We discovered 92 pathways that were significantly different between ARDS and control groups, including 57 pathways linked to metabolism.

CONCLUSIONS

Plasma metabolomics may improve our understanding of ARDS biology. Specific products related to hypoxia may serve as early biomarkers for ARDS prediction, while the metabolites with significant correlations with partial pressure of arterial oxygen (PaO₂)/percentage of inspired oxygen (FiO₂) may play a role in determining ARDS severity. This study suggests that metabolomic analysis in patients at risk of ARDS or those with early ARDS may provide new insight into disease pathogenesis or prognosis.

LSC 2016: from system approaches in lung disease to getting the job you want

The Lung Science Conference (LSC) 2016 generated, as in previous years, fruitful discussions and exciting networking opportunities in the breathtaking venue of the Palace Hotel, Estoril, Portugal.

Highlights of the Lung Science Conference 2016http://ow.ly/4nsKG2

Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling

High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.