Integrated Proteomic and Metabolomic Analysis of an Artificial Microbial Community for Two-Step Production of Vitamin C

An artificial microbial community consisted of Ketogulonicigenium vulgare and Bacillus megaterium has been used in industry to produce 2-keto-gulonic acid (2-KGA), the precursor of vitamin C. During the mix culture fermentation process, sporulation and cell lysis of B. megaterium can be observed. In order to investigate how these phenomena correlate with 2-KGA production, and to explore how two species interact with each other during the fermentation process, an integrated time-series proteomic and metabolomic analysis was applied to the system. The study quantitatively identified approximate 100 metabolites and 258 proteins. Principal Component Analysis of all the metabolites identified showed that glutamic acid, 5-oxo-proline, L-sorbose, 2-KGA, 2, 6-dipicolinic acid and tyrosine were potential biomarkers to distinguish the different time-series samples. Interestingly, most of these metabolites were closely correlated with the sporulation process of B. megaterium. Together with several sporulation-relevant proteins identified, the results pointed to the possibility that Bacillus sporulation process might be important part of the microbial interaction. After sporulation, cell lysis of B. megaterium was observed in the co-culture system. The proteomic results showed that proteins combating against intracellular reactive oxygen stress (ROS), and proteins involved in pentose phosphate pathway, L-sorbose pathway, tricarboxylic acid cycle and amino acids metabolism were up-regulated when the cell lysis of B. megaterium occurred. The cell lysis might supply purine substrates needed for K. vulgare growth. These discoveries showed B. megaterium provided key elements necessary for K. vulgare to grow better and produce more 2-KGA. The study represents the first attempt to decipher 2-KGA-producing microbial communities using quantitative systems biology analysis

Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method

Late diagnosis of hepatocarcinoma (HCC) is one of the most primary factors for the poor survival of patients. Thereby, identification of sensitive and specific biomarkers for HCC early diagnosis is of great importance in biological medicine to date. In the present study, serum metabolites of the HCC patients and healthy controls were investigated using the improved liquid chromatography–mass spectrometry (LC/MS). A wavelet-based method was utilized to find and align peaks of LC–MS. The characteristic peaks were selected by performing a two-sample t test statistics (p value <0.05). Clustering analysis based on principal component analysis showed a clear separation between HCC patients and healthy individuals. The serum metabolite, namely 1-methyladenosine, was identified as the characteristic metabolite for HCC. Moreover, receiver–operator curves were calculated with 1-methyladenosine and/or alpha fetal protein (AFP). The higher area under curve value was achieved in 1-methyladenosine group than AFP group (0.802 vs. 0.592), and the diagnostic model combining 1-methyladenosine with AFP exhibited significant improved sensitivity, which could identify those patients who missed the diagnosis of HCC by determining serum AFP alone. Overall, these results suggested that LC/MS-based metabonomic study is a potent and promising strategy for identifying novel biomarkers of HCC

Glucosinolates, myrosinase hydrolysis products, and flavonols found in rocket (Eruca sativa and Diplotaxis tenuifolia)

Rocket species have been shown to have very high concentrations of glucosinolates and flavonols, which have numerous positive health benefits with regular consumption. In this review we highlight how breeders and processors of rocket species can utilize genomic and phytochemical research to improve varieties and enhance the nutritive benefits to consumers. Plant breeders are increasingly looking to new technologies such as HPLC, UPLC, LC-MS and GC-MS to screen populations for their phytochemical content to inform plant selections. Here we collate the research that has been conducted to-date in rocket, and summarise all glucosinolate and flavonol compounds identified in the species. We emphasize the importance of the broad screening of populations for phytochemicals and myrosinase degradation products, as well as unique traits that may be found in underutilized gene bank resources. We also stress that collaboration with industrial partners is becoming essential for long-term plant breeding goals through research

Endophytic fungi from the roots of horseradish (Armoracia rusticana) and their interactions with the defensive metabolites of the glucosinolate myrosinase – isothiocyanate system

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A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism

Biochemical­­– and biophysical–induced barriergenesis in the blood brain barrier: a review of barriergenic factors for use in in vitro models

Central nervous system (CNS) pathologies are a prevalent problem in aging populations, creating a need to understand the underlying events in these diseases and develop efficient CNS‐targeting drugs. The importance of the blood‐brain barrier (BBB) has become evident, acting both as a physical barrier to drug entry into the CNS, and potentially as the cause or aggravator of CNS diseases. The development of a biomimetic BBB in vitro model is required for the understanding of BBB‐related pathologies and in the screening of drugs targeting the CNS. There is currently a great interest in understanding the influence of biochemical and biophysical factors, as these have the potential to greatly improve the barrier function of brain microvascular endothelial cells (BMECs). Recent advances in understanding how these may regulate barriergenesis in BMECs can help promote the development of improved BBB in vitro models, and therefore novel interventional therapies for pathologies related to its disruption. This review provides an overview of specific biochemical and biomechanical cues in the formation of the BBB, with a focus on in vitro models and how these might recapitulate BBB function

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules