A new paradigm for known metabolite identification in metabonomics/metabolomics: Metabolite identification efficiency

A new paradigm is proposed for assessing confidence in the identification of known metabolites in metabonomics studies using NMR spectroscopy approaches. This new paradigm is based upon the analysis of the amount of metabolite identification information retrieved from NMR spectra relative to the molecular size of the metabolite. Several new indices are proposed: metabolite identification efficiency (MIE) and metabolite identification carbon efficiency (MICE), both of which can be easily calculated. These indices, together with some guidelines, can be used to provide a better indication of known metabolite identification confidence in metabonomics studies than existing methods. Since known metabolite identification in untargeted metabonomics studies is one of the key bottlenecks facing the science currently, it is hoped that these concepts based on molecular spectroscopic informatics, will find utility in the field

Academic drug discovery: Current status and prospects

Introduction. The contraction in pharmaceutical drug discovery operations in the past decade has been counter-balanced by a significant rise in the number of academic drug discovery groups. In addition, pharmaceutical companies that used to operate in completely independent, vertically-integrated operations for drug discovery are now collaborating more with each other, and with academic groups. We are in a new era of drug discovery. Areas Covered. This review provides an overview of the current status of academic drug discovery groups, their achievements and the challenges they face,, together with perspectives on ways to achieve improved outcomes. Expert Opinion. Academic groups have made important contributions to drug discovery, from its earliest days and continue to do so today. However, modern drug discovery and development is exceedingly complex, and has high failure rates, principally because human biology is complex and poorly understood. Academic drug discovery groups need to play to their strengths and not just copy what has gone before. However, there are lessons to be learnt from the experiences of the industrial drug discoverers and four areas are highlighted for attention: (i) increased validation of targets, (ii) elimination of false hits from HTS, (iii) increasing the quality of molecular probes and (iv) investing in a high quality informatics infrastructure

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area

Metabonomics study of the effects of single copy mutant KRAS in the presence or absence of WT allele using human HCT116 isogenic cell lines.

INTRODUCTION: KRAS was one of the earliest human oncogenes to be described and is one of the most commonly mutated genes in different human cancers, including colorectal cancer. Despite KRAS mutants being known driver mutations, KRAS has proved difficult to target therapeutically, necessitating a comprehensive understanding of the molecular mechanisms underlying KRAS-driven cellular transformation. OBJECTIVES: To investigate the metabolic signatures associated with single copy mutant KRAS in isogenic human colorectal cancer cells and to determine what metabolic pathways are affected. METHODS: Using NMR-based metabonomics, we compared wildtype (WT)-KRAS and mutant KRAS effects on cancer cell metabolism using metabolic profiling of the parental KRAS G13D/+ HCT116 cell line and its isogenic, derivative cell lines KRAS +/- and KRAS G13D/-. RESULTS: Mutation in the KRAS oncogene leads to a general metabolic remodelling to sustain growth and counter stress, including alterations in the metabolism of amino acids and enhanced glutathione biosynthesis. Additionally, we show that KRASG13D/+ and KRASG13D/- cells have a distinct metabolic profile characterized by dysregulation of TCA cycle, up-regulation of glycolysis and glutathione metabolism pathway as well as increased glutamine uptake and acetate utilization. CONCLUSIONS: Our study showed the effect of a single point mutation in one KRAS allele and KRAS allele loss in an isogenic genetic background, hence avoiding confounding genetic factors. Metabolic differences among different KRAS mutations might play a role in their different responses to anticancer treatments and hence could be exploited as novel metabolic vulnerabilities to develop more effective therapies against oncogenic KRAS

Metabolic characterization of colorectal cancer cells harbouring different KRAS mutations in codon 12, 13, 61 and 146 using human SW48 isogenic cell lines

Introduction: KRAS (Kirsten Rat Sarcoma Viral Oncogene Homolog) mutations occur in approximately one-third of colorectal (CRC) tumours and have been associated with poor prognosis and resistance to some therapeutics. In addition to the well-documented pro-tumorigenic role of mutant Ras alleles, there is some evidence suggesting that not all KRAS mutations are equal and the position and type of amino acid substitutions regulate biochemical activity and transforming capacity of KRAS mutations. Objectives: To investigate the metabolic signatures associated with different KRAS mutations in codons 12, 13, 61 and 146 and to determine what metabolic pathways are affected by different KRAS mutations. Methods: We applied an NMR-based metabonomics approach to compare the metabolic profiles of the intracellular extracts and the extracellular media from isogenic human SW48 CRC cell lines with different KRAS mutations in codons 12 (G12D, G12A, G12C, G12S, G12R, G12V), 13 (G13D), 61 (Q61H) and 146 (A146T) with their wild-type counterpart. We used false discovery rate (FDR)-corrected analysis of variance (ANOVA) to determine metabolites that were statistically significantly different in concentration between the different mutants. Results: CRC cells carrying distinct KRAS mutations exhibited differential metabolic remodelling, including differences in glycolysis, glutamine utilization and in amino acid, nucleotide and hexosamine metabolism. Conclusions: Metabolic differences among different KRAS mutations might play a role in their different responses to anticancer treatments and hence could be exploited as novel metabolic vulnerabilities to develop more effective therapies against oncogenic KRAS

New methodology for known metabolite identification in metabonomics / metabolomics: topological metabolite identification carbon efficiency (tMICE)

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analysed and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Since known metabolite identification is one of the key bottlenecks in either NMR spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility

Detection of Planetary and Stellar Companions to Neighboring Stars via a Combination of Radial Velocity and Direct Imaging Techniques

13 pages, 6 figures, 4 tables, accepted for publication in the Astronomical Journal (submitted 25 Feb 2019; accepted 28 April 2019). Machine readable tables and Posteriors from the RadVel fits are available here: http://stephenkane.net/rvfits.tarThe sensitivities of radial velocity (RV) surveys for exoplanet detection are extending to increasingly longer orbital periods, where companions with periods of several years are now being regularly discovered. Companions with orbital periods that exceed the duration of the survey manifest in the data as an incomplete orbit or linear trend, a feature that can either present as the sole detectable companion to the host star, or as an additional signal overlain on the signatures of previously discovered companion(s). A diagnostic that can confirm or constrain scenarios in which the trend is caused by an unseen stellar rather than planetary companion is the use of high-contrast imaging observations. Here, we present RV data from the Anglo-Australian Planet Search (AAPS) for 20 stars that show evidence of orbiting companions. Of these, six companions have resolved orbits, with three that lie in the planetary regime. Two of these (HD 92987b and HD 221420b) are new discoveries. Follow-up observations using the Differential Speckle Survey Instrument (DSSI) on the Gemini South telescope revealed that 5 of the 20 monitored companions are likely stellar in nature. We use the sensitivity of the AAPS and DSSI data to place constraints on the mass of the companions for the remaining systems. Our analysis shows that a planetary-mass companion provides the most likely self-consistent explanation of the data for many of the remaining systems.Peer reviewedFinal Accepted Versio

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare

Treatment of wild-type mice with 2,3-butanediol, a urinary biomarker of Fmo5-/- mice, decreases plasma cholesterol and epididymal fat deposition

We previously showed that Fmo5-/- mice exhibit a lean phenotype and slower metabolic ageing. Their characteristics include lower plasma glucose and cholesterol, greater glucose tolerance and insulin sensitivity, and a reduction in age-related weight gain and whole-body fat deposition. In this paper, nuclear magnetic resonance (NMR) spectroscopy-based metabolite analyses of the urine of Fmo5-/- and wild-type mice identified two isomers of 2,3-butanediol as discriminating urinary biomarkers of Fmo5-/- mice. Antibiotic-treatment of Fmo5-/- mice increased plasma cholesterol concentration and substantially reduced urinary excretion of 2,3-butanediol isomers, indicating that the gut microbiome contributed to the lower plasma cholesterol of Fmo5-/- mice, and that 2,3-butanediol is microbially derived. Short- and long-term treatment of wild-type mice with a 2,3-butanediol isomer mix decreased plasma cholesterol and epididymal fat deposition but had no effect on plasma concentrations of glucose or insulin, or on body weight. In the case of long-term treatment, the effects were maintained after withdrawal of 2,3-butanediol. Short-, but not long-term treatment, also decreased plasma concentrations of triglycerides and non-esterified fatty acids. Fecal transplant from Fmo5-/- to wild-type mice had no effect on plasma cholesterol, and 2,3-butanediol was not detected in the urine of recipient mice, suggesting that the microbiota of the large intestine was not the source of 2,3-butanediol. However, 2,3-butanediol was detected in the stomach of Fmo5-/- mice, which was enriched for Lactobacillus genera, known to produce 2,3-butanediol. Our results indicate a microbial contribution to the phenotypic characteristic of Fmo5-/- mice of decreased plasma cholesterol and identify 2,3-butanediol as a potential agent for lowering plasma cholesterol