Drupad K. Trivedi

New cofactor supports α,β-unsaturated acid decarboxylation via 1,3-dipolar cycloaddition

The bacterial ubiD and ubiX or the homologous fungal fdc1 and pad1 genes have been implicated in the non-oxidative reversible decarboxylation of aromatic substrates, and play a pivotal role in bacterial ubiquinone (also known as coenzyme Q) biosynthesis or microbial biodegradation of aromatic compounds, respectively. Despite biochemical studies on individual gene products, the composition and cofactor requirement of the enzyme responsible for in vivo decarboxylase activity remained unclear. Here we show that Fdc1 is solely responsible for the reversible decarboxylase activity, and that it requires a new type of cofactor: a prenylated flavin synthesized by the associated UbiX/Pad1. Atomic resolution crystal structures reveal that two distinct isomers of the oxidized cofactor can be observed, an isoalloxazine N5-iminium adduct and a N5 secondary ketimine species with markedly altered ring structure, both having azomethine ylide character. Substrate binding positions the dipolarophile enoic acid group directly above the azomethine ylide group. The structure of a covalent inhibitor–cofactor adduct suggests that 1,3-dipolar cycloaddition chemistry supports reversible decarboxylation in these enzymes. Although 1,3-dipolar cycloaddition is commonly used in organic chemistry, we propose that this presents the first example, to our knowledge, of an enzymatic 1,3-dipolar cycloaddition reaction. Our model for Fdc1/UbiD catalysis offers new routes in alkene hydrocarbon production or aryl (de)carboxylation.

UbiX is a flavin prenyltransferase required for bacterial ubiquinone biosynthesis

Ubiquinone (also known as coenzyme Q) is a ubiquitous lipid-soluble redox cofactor that is an essential component of electron transfer chains. Eleven genes have been implicated in bacterial ubiquinone biosynthesis, including ubiX and ubiD, which are responsible for decarboxylation of the 3-octaprenyl-4-hydroxybenzoate precursor. Despite structural and biochemical characterization of UbiX as a flavin mononucleotide (FMN)-binding protein, no decarboxylase activity has been detected. Here we report that UbiX produces a novel flavin-derived cofactor required for the decarboxylase activity of UbiD. UbiX acts as a flavin prenyltransferase, linking a dimethylallyl moiety to the flavin N5 and C6 atoms. This adds a fourth non-aromatic ring to the flavin isoalloxazine group. In contrast to other prenyltransferases, UbiX is metal-independent and requires dimethylallyl-monophosphate as substrate. Kinetic crystallography reveals that the prenyltransferase mechanism of UbiX resembles that of the terpene synthases. The active site environment is dominated by π systems, which assist phosphate-C1′ bond breakage following FMN reduction, leading to formation of the N5–C1′ bond. UbiX then acts as a chaperone for adduct reorientation, via transient carbocation species, leading ultimately to formation of the dimethylallyl C3′–C6 bond. Our findings establish the mechanism for formation of a new flavin-derived cofactor, extending both flavin and terpenoid biochemical repertoires.

Taking your breath away: metabolomics breathes life in to personalized medicine

Breath-based metabolomics (breathomics) is an exciting developing area of biotechnology that centers on the capture, identification, and quantification of volatile organic compound (VOC) patterns in human breath and their utilization as tools in the diagnosis of a broad spectrum of medical problems. With the age of personalized medicines demanding rapid bespoke diagnosis and treatment, this area of molecular diagnostics is beginning to see an upsurge in biotechnological advancement. Here, we discuss recent improvements and directions in the development of breath VOC analysis and diagnosis platforms that offer the potential for disease biomarker discovery and disease prognosis.

Electronic cigarette exposure triggers neutrophil inflammatory responses

BackgroundThe use of electronic cigarettes (e-cigs) is increasing and there is widespread perception that e-cigs are safe. E-cigs contain harmful chemicals; more research is needed to evaluate the safety of e-cig use. Our aim was to investigate the effects of e-cigs on the inflammatory response of human neutrophils.MethodsNeutrophils were exposed to e-cig vapour extract (ECVE) and the expression of CD11b and CD66b was measured by flow cytometry and MMP-9 and CXCL8 by ELISA. We also measured the activity of neutrophil elastase (NE) and MMP-9, along with the activation of inflammatory signalling pathways. Finally we analysed the biochemical composition of ECVE by ultra-high performance liquid chromatography mass spectrometry.ResultsECVE caused an increase in the expression of CD11b and CD66b, and increased the release of MMP-9 and CXCL8. Furthermore, there was an increase in NE and MMP-9 activity and an increase in p38 MAPK activation. We also identified several harmful chemicals in ECVE, including known carcinogens.ConclusionsECVE causes a pro-inflammatory response from human neutrophils. This raises concerns over the safety of e-cig use.

The Application of SIMCA P+ in Shotgun Metabolomics Analysis of ZICHILIC-MS Spectra of Human Urine - Experience with the Shimadzu IT-TOF and Profiling Solutions Data Extraction Software

The search for bio markers of disease have moved from metabolites to proteins and to genes and back again as technology which was developed. Bio-analytical scientists have been trained for decades to methodically isolate, identify and measure specific molecules. Rapid separations coupled to mind blowing rapid mass spectral characterization and generating analytical profiles within minutes which contain incomprehensible amounts of data. As the old saying goes we “can’t see the wood for the trees”. The realization is that in this data rich age we no longer have to methodically isolate characterize and measure specific molecules. What is important is to identify which of the 100’s or 1000’s of resolved and measured “unknown” molecules is associated with the pathology for which we are interested. The goal is markers of clinical relevance and statistical examination of patterns of association is the new mantra of the biomedical analyst - so called shotgun analysis. However, as any clinical biomedical scientist will tell you; a biomarker has to be robust, easily, rapidly and cheaply measured if it is to be adopted in a hospital laboratory. Any shotgun analysis has to recognize these constraints and not just identify statistical outliers that are seen only sometimes in a few samples and not consistently in the majority of the pathology in question. We have applied the SIMCA P+ plus software in shotgun metabolomic analysis of pregnancy urine.

High-throughput metabolic screening of microalgae genetic variation in response to nutrient limitation

Microalgae produce metabolites that could be useful for applications in food, biofuel or fine chemical production. The identification and development of suitable strains require analytical methods that are accurate and allow rapid screening of strains or cultivation conditions. We demonstrate the use of Fourier transform infrared (FT-IR) spectroscopy to screen mutant strains of Chlamydomonas reinhardtii. These mutants have knockdowns for one or more nutrient starvation response genes, namely PSR1, SNRK2.1 and SNRK2.2. Limitation of nutrients including nitrogen and phosphorus can induce metabolic changes in microalgae, including the accumulation of glycerolipids and starch. By performing multivariate statistical analysis of FT-IR spectra, metabolic variation between different nutrient limitation and non-stressed conditions could be differentiated. A number of mutant strains with similar genetic backgrounds could be distinguished from wild type when grown under specific nutrient limited and replete conditions, demonstrating the sensitivity of FT-IR spectroscopy to detect specific genetic traits. Changes in lipid and carbohydrate between strains and specific nutrient stress treatments were validated by other analytical methods, including liquid chromatography–mass spectrometry for lipidomics. These results demonstrate that the PSR1 gene is an important determinant of lipid and starch accumulation in response to phosphorus starvation but not nitrogen starvation. However, the SNRK2.1 and SNRK2.2 genes are not as important for determining the metabolic response to either nutrient stress. We conclude that FT-IR spectroscopy and chemometric approaches provide a robust method for microalgae screening.

Do not just do it, do it right: urinary metabolomics--establishing clinically relevant baselines.

Metabolomics is currently being adopted as a tool to understand numerous clinical pathologies. It is essential to choose the best combination of techniques in order to optimize the information gained from the biological sample examined. For example, separation by reverse-phase liquid chromatography may be suitable for biological fluids in which lipids, proteins and small organic compounds coexist in a relatively nonpolar environment, such as serum. However, urine is a highly polar environment and metabolites are often specifically altered to render them polar suitable for normal phase/hydrophilic interaction liquid chromatography. Similarly, detectors such as high-resolution mass spectrometry (MS) may negate the need for a pre-separation but specific detection and quantification of less abundant analytes in targeted metabolomics may require concentration of the ions by methods such an ion trap MS. In addition, the inherent variability of metabolomic profiles need to be established in appropriately large sample sets of normal controls. This review aims to explore various techniques that have been tried and tested over the past decade. Consideration is given to various key drawbacks and positive alternatives published by active research groups and an optimum combination that should be used for urinary metabolomics is suggested to generate a reliable dataset for baseline studies.

Shotgun metabolomic profiles in maternal urine identify potential mass spectral markers of abnormal fetal biochemistry – dihydrouracil and progesterone in the metabolism of Down syndrome

In Down syndrome (DS) in particular, the precise cellular mechanisms linking genotype to phenotype is not straightforward despite a clear mapping of the genetic cause. Metabolomic profiling might be more revealing in understanding molecular-cellular mechanisms of inborn errors of metabolism/syndromes than genomics alone and also result in new prenatal screening approaches. The urinary metabolome of 122 maternal urine from women with and without an aneuploid pregnancy (predominantly Down syndrome) were compared by both zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) and reversed-phase liquid chromatography (RPLC) coupled to hybrid ion trap time of flight mass spectral analysis. ZIC-HILIC mass spectrometry resolved 10-fold more unique molecular ions than RPLC mass spectrometry, of which molecules corresponding to ions of m/z 114.07 and m/z 314.20 showed maternal urinary level changes that significantly coincided with the presence of a DS fetus. The ion of m/z 314.20 was identified as progesterone and m/z 114.07 as dihydrouracil. A metabolomics profiling-based maternal urinary screening test modelled from this separation data would detect approximately 87 and 60.87% (using HILIC-MS and RPLC-MS, respectively) of all DS pregnancies between 9 and 23 weeks of gestation with no false positives.

Development of Zwitterionic Hydrophilic Liquid Chromatography (ZICⓇHILIC-MS) metabolomics method for Shotgun analysis of human urine

Urine is a product of the body’s metabolism and the majority of the metabolic products exiting via the renal system are rendered polar in order to be water soluble. Resolution of urinary metabolites for metabolomic studies requires the development of HPLC separation techniques that match this feature of biological chemistry. ZIC –HILIC is an ideal candidate to take forward resolution of such metabolites where reverse phase is unable to give adequate separation. Metabolomic data has to be processed by Shotgun multivariate analysis to sift through thousands of analytes and their variables such as ion intensity. In the development of ZIC-HILIC separation with mass spectrometric (IT-ToF) detection, methodological variability have to be minimized so that any Shotgun data analysis does not reveal potential biomarker analytes that are artifacts or are adversely affected of the separation and detection technique. Here, we report the development of a ZIC-HILIC mass spectrometry method that is suitable for SIMCA P+ data analysis of urine. Variables such as resolution, run reproducibility and sample storage temperature were evaluated in tandem with SIMCA P+ data analysis and quality control pre-processing. The developed method couples quality control runs that pre-process and exclude analytes that are insufficiently robust for further candidate biomarker studies. This meant labile analytes that could not be reproduced in 70% of QC runs (which are pools of all samples run that day) were excluded. However, urine samples stored at 4°C for more than 9 months will contain metabolites that will alter and produce small molecule marker artifacts when compared to samples stored at -20°C. In conclusion, the developed method is a robust method of ZIC-HILIC mass spectrometry shotgun analysis suitable for urinary metabolome discovery of robust biomarkers.

Metabolomics for the masses: The future of metabolomics in a personalized world

Current clinical practices focus on a small number of biochemical directly related to the pathophysiology with patients and thus only describe a very limited metabolome of a patient and fail to consider the interations of these small molecules. This lack of extended information may prevent clinicians from making the best possible therapeutic interventions in sufficient time to improve patient care. Various post-genomics ‘(’omic)’ approaches have been used for therapeutic interventions previously. Metabolomics now a well-established’omics approach, has been widely adopted as a novel approach for biomarker discovery and in tandem with genomics (especially SNPs and GWAS) has the potential for providing systemic understanding of the underlying causes of pathology. In this review, we discuss the relevance of metabolomics approaches in clinical sciences and its potential for biomarker discovery which may help guide clinical interventions. Although a powerful and potentially high throughput approach for biomarker discovery at the molecular level, true translation of metabolomics into clinics is an extremely slow process. Quicker adaptation of biomarkers discovered using metabolomics can be possible with novel portable and wearable technologies aided by clever data mining, as well as deep learning and artificial intelligence; we shall also discuss this with an eye to the future of precision medicine where metabolomics can be delivered to the masses.