Robust detection and verification of linear relationships to generate metabolic networks using estimates of technical errors

Integrative metabolomics reveals unique metabolic traits in Guillain-Barré Syndrome and its variants

Guillain-Barré syndrome (GBS) is an acute fatal progressive disease caused by autoimmune mechanism mainly affecting peripheral nervous system. Although the syndrome is clinically sub-classified into several variants, specific biomarker and exact pathomechanism of each subtypes are not well elucidated yet. In current study, integrative metabolomic and lipidomic profiles were acquisitioned from cerebrospinal fluid samples of 86 GBS from three variants and 20 disease controls. And the data were systematically compared to our previous result on inflammatory demyelination disorders of central nervous system (IDDs) and healthy controls. Primary metabolite profiles revealed unique metabolic traits in which 9 and 7 compounds were specifically changed in GBS and IDD, respectively. Next, the biomarker panel with 10 primary metabolites showed a fairly good discrimination power among 3 GBS subtypes, healthy controls, and disease controls (AUCs ranged 0.849-0.999). The robustness of the biomarker panel was vigorously validated by multi-step statistical evaluation. Subsequent lipidomics revealed GBS variant-specific alteration where the significant elevations of lyso-phosphatidylcholines and sphingomyelins were unique to AIDP (acute inflammatory demyelinating polyneuropathy) and AMAN (acute motor axonal neuropathy), respectively. And metabolome-wide multivariate correlation analysis identified potential clinical association between GBS disability scale (Hughes score) and CSF lipids (monoacylglycerols, and sphingomyelins). Finally, Bayesian network analysis of covarianced structures of primary metabolites and lipids proposed metabolic hub and potential biochemical linkage associated with the pathology.

Hair sterol signatures coupled to multivariate data analysis reveal an increased 7β-hydroxycholesterol production in cognitive impairment

Altered cholesterol metabolism could be associated with cognitive impairment. The quantitative profiling of 19 hair sterols was developed using gas chromatography-mass spectrometry coupled to multivariate data analysis. The limit of quantification of all sterols ranged from 5 to 20 ng/g, while the calibration linearity was higher than 0.98. The precision (% CV) and accuracy (% bias) ranged from 3.2% to 9.8% and from 83.2% to 119.4%, respectively. Among the sterols examined, 8 were quantitatively detected from two strands of 3-cm-long scalp hair samples of female participants, including mild cognitive impairment (MCI, n=15), Alzheimer's disease (AD, n=31), and healthy controls (HC, n=36). The cognitive impairment (MCI or AD) was correlated with a higher metabolic rate than that of HCs based on 7β-hydroxycholesterol (P<0.005). Significant negative correlations (r=-0.822) were detected between Mini-Mental State Examination (MMSE) scores and hair sample metabolic ratios of 7β-hydroxycholesterol to cholesterol, which is an accepted, sensitive, and specific tool for discriminating HCs from individuals with MCI or AD. In conclusion, improved diagnostic values can be obtained using hair sterol signatures coupled with MMSE scores. This method may prove useful for predictive diagnosis in population screening of cognitive impairment.

Cognitive network neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

A metabolomic view of Staphylococcus aureus and its ser/thr kinase and phosphatase deletion mutants: involvement in cell wall biosynthesis

Little is known about intracellular metabolite pools in pathogens such as Staphylococcus aureus. We have studied a particular metabolome by means of the presented LC-MS method. By investigating the central carbon metabolism which includes most of the energy transfer molecules like nucleotides, sugar mono- and biphosphates, and cofactors, a conclusion about phenotypes and stress answers in microorganisms is possible. Quantitative metabolite levels of S. aureus grown in complex lysogeny broth and in minimal medium were compared in the wild-type S. aureus strain 8325 and the isogenic eukaryotic-like protein serine/threonine kinase (DeltapknB) and phosphatase (Deltastp) deletion mutants. Detection of several remarkable differences, e.g., in nucleotide metabolism and especially cell wall precursor metabolites, indicates a previously unreported importance of serine/threonine kinase/phosphatase on peptidoglycan and wall teichoic acid biosynthesis. These findings may lead to new insights into the regulation of staphylococcal cell wall metabolism.

Metabolomics for functional genomics, systems biology, and biotechnology

Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. Plants collectively produce a huge array of chemicals, far more than are produced by most other organisms; hence, metabolomics is of great importance in plant biology. Although substantial improvements have been made in the field of metabolomics, the uniform annotation of metabolite signals in databases and informatics through international standardization efforts remains a challenge, as does the development of new fields such as fluxome analysis and single cell analysis. The principle of transcript and metabolite cooccurrence, particularly transcriptome coexpression network analysis, is a powerful tool for decoding the function of genes in Arabidopsis thaliana. This strategy can now be used for the identification of genes involved in specific pathways in crops and medicinal plants. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis, prediction of food quality, and evaluation of genetically modified crops. Systems biology driven by metabolome data will aid in deciphering the secrets of plant cell systems and their application to biotechnology.