Metabolomics: quantification of intracellular metabolite dynamics

Transcriptomics integrated with metabolomics provides a new strategy for mining key genes in response to low temperature stress in Helictotrichon virescens

H. virescens is a perennial herbaceous plant with highly tolerant to cold weather, but the key genes that respond to low temperature stress still remain unclear. Hence, RNA-seq was performed using leaves of H. virescens treated at 0 °C and 25 °C for 12 h, 36 h, and 60 h, respectively, and a total of 9416 DEGs were significantly enriched into seven KEGG pathways. The LC-QTRAP platform was performed using leaves of H. virescens leaves at 0 °C and 25 °C for 12 h, 36 h, and 60 h, respectively, and a total of 1075 metabolites were detected, which were divided into 10 categories. Additionally, 18 major metabolites, two key pathways, and six key genes were mined using a multi-omics analytical strategy. The RT-PCR results showed that with the extension of treatment time, the expression levels of key genes in the treatment group gradually increased, and the difference between the treatment group and the control group was extremely significant. Notably, the functional verification results showed that the key genes positively regulated cold tolerance of H. virescens. These results can lay a foundation for the in-depth analysis of the mechanism of response of perennial herbs to low temperature stress.

Integrated transcriptomics and metabolomics analysis reveals key regulatory network that response to cold stress in common Bean (Phaseolus vulgaris L.)

Cold temperatures can be detrimental to crop survival and productivity. Breeding progress can be improved by understanding the molecular basis of low temperature tolerance. We investigated the key routes and critical metabolites related to low temperature resistance in cold-tolerant and -sensitive common bean cultivars 120 and 093, respectively. Many potential genes and metabolites implicated in major metabolic pathways during the chilling stress response were identified through transcriptomics and metabolomics research. Under chilling stress, the expression of many genes involved in lipid, amino acid, and flavonoid metabolism, as well as metabolite accumulation increased in the two bean types. Malondialdehyde (MDA) content was lower in 120 than in 093. Regarding amino acid metabolism, 120 had a higher concentration of acidic amino acids than 093, whereas 093 had a higher concentration of basic amino acids. Methionine accumulation was clearly higher in 120 than in 093. In addition, 120 had a higher concentration of many types of flavonoids than 093. Flavonoids, methionine and malondialdehyde could be used as biomarkers of plant chilling injury. Transcriptome analysis of hormone metabolism revealed considerably greater, expression of abscisic acid (ABA), gibberellin (GA), and jasmonic acid (JA) in 093 than in 120 during chilling stress, indicating that hormone regulation modes in 093 and 120 were different. Thus, chilling stress tolerance is different between 093 and 120 possibly due to transcriptional and metabolic regulation.

SiMeEx, a simplified method for metabolite extraction of adherent mammalian cells

A reliable method for metabolite extraction is central to mass spectrometry-based metabolomics. However, existing methods are lengthy, mostly due to the step of scraping cells from cell culture vessels, which restricts metabolomics in broader application such as lower cell numbers and high-throughput studies. Here, we present a simplified metabolite extraction (SiMeEx) method, to efficiently and quickly extract metabolites from adherent mammalian cells. Our method excludes the cell scraping step and therefore allows for a more efficient extraction of polar metabolites in less than 30 min per 12-well plate. We demonstrate that SiMeEx achieves the same metabolite recovery as using a standard method containing a scraping step, in various immortalized and primary cells. Omitting cell scraping does not compromise the performance of non-targeted and targeted GC-MS analysis, but enables metabolome analysis of cell culture on smaller well sizes down to 96-well plates. Therefore, SiMeEx demonstrates advantages not only on time and resources, but also on the applicability in high-throughput studies.

Effects of pH and Osmotic Changes on the Metabolic Expressions of Bacillus subtilis Strain 168 in Metabolite Pathways including Leucine Metabolism

Bacillus subtilis is often exposed to diverse culture conditions with the aim of improving hygiene or food quality. This can lead to changes in the volatile metabolite profiles related to the quality of fermented foods. To comprehensively interpret the associated metabolic expressions, changes in intracellular primary and extracellular secondary volatile metabolites were investigated by exposing B. subtilis to an alkaline pH (BP, pH 8.0) and a high salt concentration (BS, 1 M). In particular, B. subtilis was cultured in a leucine-enriched medium to investigate the formation of leucine-derived volatile metabolites. This study observed metabolic changes in several metabolic pathways, including carbohydrate metabolism, amino acid metabolism, fatty acid metabolism, and leucine degradation. The formation of proline (an osmolyte), furans, pyrrole, and monosaccharide sugars (glucose, galactose, and fructose) was enhanced in BS, whereas fatty acid derivatives (ketones and alcohols) increased in BP. In the case of leucine degradation, 3-methyl-butanal and 3-methylbutanol could be salt-specific metabolites, while the contents of 3-methylbutanoic acid and 3-methylbutylacetate increased in BP. These results show culture condition-specific metabolic changes, especially secondary volatile metabolites related to the sensory property of foods, in B. subtilis.

Metabolic control analysis of L-tryptophan producing Escherichia coli applying targeted perturbation with shikimate

L-tryptophan production from glycerol with Escherichia coli was analysed by perturbation studies and metabolic control analysis. The insertion of a non-natural shikimate transporter into the genome of an Escherichia coli L-tryptophan production strain enabled targeted perturbation within the product pathway with shikimate during parallelised short-term perturbation experiments with cells withdrawn from a 15 L fed-batch production process. Expression of the shikimate/H⁺-symporter gene (shiA) from Corynebacterium glutamicum did not alter process performance within the estimation error. Metabolic analyses and subsequent extensive data evaluation were performed based on the data of the parallel analysis reactors and the production process. Extracellular rates and intracellular metabolite concentrations displayed evident deflections in cell metabolism and particularly in chorismate biosynthesis due to the perturbations with shikimate. Intracellular flux distributions were estimated using a thermodynamics-based flux analysis method, which integrates thermodynamic constraints and intracellular metabolite concentrations to restrain the solution space. Feasible flux distributions, Gibbs reaction energies and concentration ranges were computed simultaneously for the genome-wide metabolic model, with minimum bias in relation to the direction of metabolic reactions. Metabolic control analysis was applied to estimate elasticities and flux control coefficients, predicting controlling sites for L-tryptophan biosynthesis. The addition of shikimate led to enhanced deviations in chorismate biosynthesis, revealing a so far not observed control of 3-dehydroquinate synthase on L-tryptophan formation. The relative expression of the identified target genes was analysed with RT-qPCR. Transcriptome analysis revealed disparities in gene expression and the localisation of target genes to further improve the microbial L-tryptophan producer by metabolic engineering.

Advances in the Study of Metabolomics and Metabolites in Some Species Interactions

In the natural environment, interactions between species are a common natural phenomena. The mechanisms of interaction between different species are mainly studied using genomic, transcriptomic, proteomic, and metabolomic techniques. Metabolomics is a crucial part of system biology and is based on precision instrument analysis. In the last decade, the emerging field of metabolomics has received extensive attention. Metabolomics not only provides a qualitative and quantitative method for studying the mechanisms of interactions between different species, but also helps clarify the mechanisms of defense between the host and pathogen, and to explore new metabolites with various biological activities. This review focuses on the methods and progress of interspecies metabolomics. Additionally, the prospects and challenges of interspecies metabolomics are discussed.

In silico Determination of Some Conditions Leading to Glycolytic Oscillations and Their Interference With Some Other Processes in E. coli Cells

Autonomous oscillations of species levels in the glycolysis express the self-control of this essential cellular pathway belonging to the central carbon metabolism (CCM), and this phenomenon takes place in a large number of bacteria. Oscillations of glycolytic intermediates in living cells occur according to the environmental conditions and to the cell characteristics, especially the adenosine triphosphate (ATP) recovery system. Determining the conditions that lead to the occurrence and maintenance of the glycolytic oscillations can present immediate practical applications. Such a model-based analysis allows in silico (model-based) design of genetically modified microorganisms (GMO) with certain characteristics of interest for the biosynthesis industry, medicine, etc. Based on our kinetic model validated in previous works, this paper aims to in silico identify operating parameters and cell factors leading to the occurrence of stable glycolytic oscillations in the Escherichia coli cells. As long as most of the glycolytic intermediates are involved in various cellular metabolic pathways belonging to the CCM, evaluation of the dynamics and average level of its intermediates is of high importance for further applicative analyses. As an example, by using a lumped kinetic model for tryptophan (TRP) synthesis from literature, and its own kinetic model for the oscillatory glycolysis, this paper highlights the influence of glycolytic oscillations on the oscillatory TRP synthesis through the PEP (phosphoenolpyruvate) glycolytic node shared by the two oscillatory processes. The numerical analysis allows further TRP production maximization in a fed-batch bioreactor (FBR).

GC-MS-Based Metabolomics for the Smut Fungus Ustilago maydis: A Comprehensive Method Optimization to Quantify Intracellular Metabolites

Ustilago maydis, a smut fungus, is an appealing model in fundamental research and an upcoming cell factory for industrial biotechnology. The genome of U. maydis has been sequenced and some synthesis pathways were biochemically described; however, the operation of the cellular metabolic network is not well-characterized. Thus, we conducted a comprehensive study to optimize the sample preparation procedure for metabolomics of U. maydis using GC-MS/MS. Due to the unique characteristics of U. maydis cell culture, two quenching solutions, different washing steps, eight extraction methods, and three derivatization conditions have been examined. The optimal method was then applied for stable isotope-assisted quantification of low molecular weight hydrophilic metabolites while U. maydis utilized different carbon sources including sucrose, glucose, and fructose. This study is the first report on a methodology for absolute quantification of intracellular metabolites in U. maydis central carbon metabolism such as sugars, sugar phosphates, organic acids, amino acids, and nucleotides. For biotechnological use, this method is crucial to exploit the full production potential of this fungus and can also be used to study other fungi of the family Ustilaginaceae.

Escherichia coli metabolism under short-term repetitive substrate dynamics: adaptation and trade-offs

Background

Microbial metabolism is highly dependent on the environmental conditions. Especially, the substrate concentration, as well as oxygen availability, determine the metabolic rates. In large-scale bioreactors, microorganisms encounter dynamic conditions in substrate and oxygen availability (mixing limitations), which influence their metabolism and subsequently their physiology. Earlier, single substrate pulse experiments were not able to explain the observed physiological changes generated under large-scale industrial fermentation conditions.

Results

In this study we applied a repetitive feast–famine regime in an aerobic Escherichia coli culture in a time-scale of seconds. The regime was applied for several generations, allowing cells to adapt to the (repetitive) dynamic environment. The observed response was highly reproducible over the cycles, indicating that cells were indeed fully adapted to the regime. We observed an increase of the specific substrate and oxygen consumption (average) rates during the feast–famine regime, compared to a steady-state (chemostat) reference environment. The increased rates at same (average) growth rate led to a reduced biomass yield (30% lower). Interestingly, this drop was not followed by increased by-product formation, pointing to the existence of energy-spilling reactions. During the feast–famine cycle, the cells rapidly increased their uptake rate. Within 10 s after the beginning of the feeding, the substrate uptake rate was higher (4.68 μmol/g_CDW/s) than reported during batch growth (3.3 μmol/g_CDW/s). The high uptake led to an accumulation of several intracellular metabolites, during the feast phase, accounting for up to 34% of the carbon supplied. Although the metabolite concentrations changed rapidly, the cellular energy charge remained unaffected, suggesting well-controlled balance between ATP producing and ATP consuming reactions.

Conclusions

The adaptation of the physiology and metabolism of E. coli under substrate dynamics, representative for large-scale fermenters, revealed the existence of several cellular mechanisms coping with stress. Changes in the substrate uptake system, storage potential and energy-spilling processes resulted to be of great importance. These metabolic strategies consist a meaningful step to further tackle reduced microbial performance, observed under large-scale cultivations.

Microfluidic Irreversible Electroporation-A Versatile Tool to Extract Intracellular Contents of Bacteria and Yeast

Exploring the dynamic behavior of cellular metabolism requires a standard laboratory method that guarantees rapid sampling and extraction of the cellular content. We propose a versatile sampling technique applicable to cells with different cell wall and cell membrane properties. The technique is based on irreversible electroporation with simultaneous quenching and extraction by using a microfluidic device. By application of electric pulses in the millisecond range, permanent lethal pores are formed in the cell membrane of Escherichia coli and Saccharomyces cerevisiae, facilitating the release of the cellular contents; here demonstrated by the measurement of glucose-6-phosphate and the activity of the enzyme glucose-6-phosphate dehydrogenase. The successful application of this device was demonstrated by pulsed electric field treatment in a flow-through configuration of the microfluidic chip in combination with sampling, inactivation, and extraction of the intracellular content in a few seconds. Minimum electric field strengths of 10 kV/cm for E. coli and 7.5 kV/cm for yeast S. cerevisiae were required for successful cell lysis. The results are discussed in the context of applications in industrial biotechnology, where metabolomics analyses are important.

Comparison of Five Extraction Methods for Intracellular Metabolites of Salmonella typhimurium

Salmonella enterica serovar typhimurium (S. typhimurium) causes food poisoning in human and animals. Its infection rate is the highest among all salmonella serotypes. Metabolomics is a potential way to study the pathogenesis of S. typhimurium via analysis of various small molecular substances. Due to the lack of a uniform protocol for the extraction of metabolites, we evaluated five commonly used extraction methods including cold methanol (CM), hot ethanol (HE), chloroform-methanol cocktail (CMC), perchloric acid (PCA), and alkali (AL) for their efficacy in extracting the intracellular metabolites of S. typhimurium. Samples were quenched in 60% methanol at - 40 °C, and then the five methods were used to extract the metabolites. After derivatization, all samples were analyzed on a gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS). Our results suggest that CM and HE extraction methods provide the best compromise allowing identification of 98 and 95 metabolites in a single analysis. For targeted metabolome analysis, the optimal extraction method for alcohols and organic acids is HE. CMC preferentially extracted lipid metabolites. PCA is suitable for extraction of small molecular carbohydrates. The optimal extraction method for macromolecular carbohydrates is the CM method.

Insight into the surfactin production of Bacillus velezensis B006 through metabolomics analysis

Bacillus velezensis B006 is a biocontrol agent which functions through effective colonization and surfactin production. To reveal the surfactin-producing mechanism, gas chromatography–mass spectrometry based untargeted metabolomics was performed to compare the metabolite profiles of strain B006 grown in industrial media M3 and M4. Based on the statistical and pathway topology analyses, a total of 31 metabolites with a fold change of less than − 1.0 were screened as the significantly altered metabolites, which distributed in 15 metabolic pathways. Fourteen amino acids involving in the metabolisms of alanine/aspartate/glutamate, glycine/serine/threonine, arginine/proline, glutathione/cysteine/methionine and valine/leucine/isoleucine as well as succinic acid in TCA cycle were identified to be the hub metabolites. Aminoacyl-tRNA biosynthesis, glycerolipid metabolism, and pantothenate/CoA biosynthesis also contributed to surfactin production. To the best of our knowledge, this study is the first to investigate the metabolic pathways of B. velezensis on surfactin production, and will benefit the optimization of commercial fermentation for higher surfactin yield.

[Link between sortase A function and cariogenicity of Streptococcus mutans: a preliminary metabolomics analysis]

OBJECTIVE

This study intends to explore the mechanism underlying the support of sortase A (SrtA) of the cariogenicity of Streptococcus mutans (S. mutans).

METHODS

We performed a metabonomics study based on ¹H nuclear magnetic resonance spectroscopy (NMR), in which we compared the extracellular metabolites of wild-type S. mutans UA159 with those of its SrtA-deficient strain. Metabolite differences among strains were identified using a combination of principal component analysis and orthogonality partial least square discriminant analysis.

RESULTS

Several differences corresponding mostly to unknown metabolites were identified. Some amino acids such as leucine and valine (δ 0.92×10⁻⁶-1.20×10⁻⁶), lactic acid ( δ1.28×10⁻⁶), oxoglutaric acid (δ 3.00×10⁻⁶), and glycine (δ 3.60×10⁻⁶) differed among strains.

CONCLUSIONS

This work establishes the feasibility of using ¹H NMR-based metabonomics to provide leads for research into molecular factors that promote caries. The database of microbial metabolites should be also improved in further studies.

Comprehensive optimization of the metabolomic methodology for metabolite profiling of Corynebacterium glutamicum

Metabolomics has been a potential tool for strain improvement through analyzing metabolite changes in the context of different conditions. However, the availability of a universal metabolite profiling analysis is still a big challenge. In this study, we presented an optimized liquid chromatography-tandem mass spectrometry-based metabolomics methodology for Corynebacterium glutamicum, an important industrial workhorse. It was found that quenching the cellular metabolism with 5-fold volume of - 20 °C 40% methanol was highly recommended due to its lower cell damage rate and higher intracellular metabolite recovery rate. For extracting intracellular metabolites, ethanol/water (3:1, v/v) at 100 °C combined with acidic acetonitrile/water (1:1, v/v, with 0.1% formic acid) at - 20 °C achieved the unbiased metabolite profiling of C. glutamicum. The established methodology was then applied to investigate the intracellular metabolite differences between C. glutamicum ATCC 13032 and an mscCG-deleted mutant under biotin limitation condition. It was observed that in the presence of the functional L-glutamate exporter MscCG, biotin limitation led to accumulation of intracellular 2-oxoglutarate but not L-glutamate. Deletion of mscCG severely inhibited L-glutamate excretion and resulted in a dramatical increase of intracellular L-glutamate, which in turn affected the metabolite profile. The optimized metabolomics methodology holds promise for promoting studies on metabolic mechanism of C. glutamicum.

Metabolomics Analysis Reveals that Ethylene and Methyl Jasmonate Regulate Different Branch Pathways to Promote the Accumulation of Terpenoid Indole Alkaloids in Catharanthus roseus

The medicinal plant Catharanthus roseus accumulates large numbers of terpenoid indole alkaloids (TIAs), including the pharmaceutically important vinblastine, vincristine, ajmalicine, and serpentine. The phytohormone ethylene or methyl jasmonate (MeJA) can markedly enhance alkaloid accumulation. The interaction between ethylene or MeJA in the regulation of TIA biosynthesis in C. roseus is unknown. Here, a metabolomics platform is reported that is based on liquid chromatography (LC) coupled with time-of-flight mass spectrometry to study candidate components for TIA biosynthesis, which is controlled by ethylene or MeJA in C. roseus. Multivariate analysis identified 16 potential metabolites mostly associated with TIA metabolic pathways and seven targeted metabolites, outlining the TIA biosynthesis metabolic networks controlled by ethylene or MeJA. Interestingly, ethylene and MeJA regulate the 2-C-methyl-d-erythritol 4-phosphate (MEP) and acetate-mevalonate (MVA) pathways through AACT and HMGS and through DXS, respectively, to induce TIA biosynthesis in C. roseus. Overall, both nontargeted and targeted metabolomics, as well as transcript analysis, were used to reveal that MeJA and ethylene control different metabolic networks to induce TIA biosynthesis.

New Protocol Based on UHPLC-MS/MS for Quantitation of Metabolites in Xylose-Fermenting Yeasts

Xylose fermentation is a bottleneck in second-generation ethanol production. As such, a comprehensive understanding of xylose metabolism in naturally xylose-fermenting yeasts is essential for prospection and construction of recombinant yeast strains. The objective of the current study was to establish a reliable metabolomics protocol for quantification of key metabolites of xylose catabolism pathways in yeast, and to apply this protocol to Spathaspora arborariae. Ultra-high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) was used to quantify metabolites, and afterwards, sample preparation was optimized to examine yeast intracellular metabolites. S. arborariae was cultivated using xylose as a carbon source under aerobic and oxygen-limited conditions. Ion pair chromatography (IPC) and hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS) were shown to efficiently quantify 14 and 5 metabolites, respectively, in a more rapid chromatographic protocol than previously described. Thirteen and eleven metabolites were quantified in S. arborariae under aerobic and oxygen-limited conditions, respectively. This targeted metabolomics protocol is shown here to quantify a total of 19 metabolites, including sugars, phosphates, coenzymes, monosaccharides, and alcohols, from xylose catabolism pathways (glycolysis, pentose phosphate pathway, and tricarboxylic acid cycle) in yeast. Furthermore, to our knowledge, this is the first time that intracellular metabolites have been quantified in S. arborariae after xylose consumption. The results indicated that fine control of oxygen levels during fermentation is necessary to optimize ethanol production by S. arborariae. The protocol presented here may be applied to other yeast species and could support yeast genetic engineering to improve second generation ethanol production. Graphical Abstract ᅟ.

CHO-Omics Review: The Impact of Current and Emerging Technologies on Chinese Hamster Ovary Based Bioproduction

CHO cells are the most prevalent platform for modern bio-therapeutic production. Currently, there are several CHO cell lines used in bioproduction with distinct characteristics and unique genotypes and phenotypes. These differences limit advances in productivity and quality that can be achieved by the most common approaches to bioprocess optimization and cell line engineering. Incorporating omics-based approaches into current bioproduction processes will complement traditional methodologies to maximize gains from CHO engineering and bioprocess improvements. In order to highlight the utility of omics technologies in CHO bioproduction, the authors discuss current applications as well as limitations of genomics, transcriptomics, proteomics, metabolomics, lipidomics, fluxomics, glycomics, and multi-omics approaches and the potential they hold for the future of bioproduction. Multiple omics approaches are currently being used to improve CHO bioprocesses; however, the application of these technologies is still limited. As more CHO-omic datasets become available and integrated into systems models, the authors expect significant gains in product yield and quality. While individual omics technologies provide incremental improvements in bioproduction, the authors will likely see the most significant gains by applying multi-omics and systems biology approaches to individual CHO cell lines.

Analysis of Intracellular Metabolites from Microorganisms: Quenching and Extraction Protocols

Sample preparation is one of the most important steps in metabolome analysis. The challenges of determining microbial metabolome have been well discussed within the research community and many improvements have already been achieved in last decade. The analysis of intracellular metabolites is particularly challenging. Environmental perturbations may considerably affect microbial metabolism, which results in intracellular metabolites being rapidly degraded or metabolized by enzymatic reactions. Therefore, quenching or the complete stop of cell metabolism is a pre-requisite for accurate intracellular metabolite analysis. After quenching, metabolites need to be extracted from the intracellular compartment. The choice of the most suitable metabolite extraction method/s is another crucial step. The literature indicates that specific classes of metabolites are better extracted by different extraction protocols. In this review, we discuss the technical aspects and advancements of quenching and extraction of intracellular metabolite analysis from microbial cells.

Sensors for disposable bioreactors

Modern bioprocess monitoring demands sensors that provide on-line information about the process state. In particular, sensors for monitoring bioprocesses carried out in single-use bioreactors are needed because disposable systems are becoming increasingly important for biotechnological applications. Requirements for the sensors used in these single-use bioreactors are different than those used in classical reusable bioreactors. For example, long lifetime or resistance to steam and cleaning procedures are less crucial factors, while a requirement of sensors for disposable bioreactors is a cost that is reasonable on a per-use basis. Here, we present an overview of current and emerging sensors for single-use bioreactors, organized by the type of interface of the sensor systems to the bioreactor. A major focus is on non-invasive, in-situ sensors that are based on electromagnetic, semiconducting, optical, or ultrasonic measurements. In addition, new technologies like radio-frequency identification sensors or free-floating sensor spheres are presented. Notably, at this time there is no standard interface between single-use bioreactors and the sensors discussed here. In the future, manufacturers should address this shortcoming to promote single-use bioprocess monitoring and control.

Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by 13C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

OBJECTIVE

Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.

METHODS

In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.

RESULTS

To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.

CONCLUSION

Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.

SIGNIFICANCE

Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

Biomarker analysis of liver cells exposed to surfactant-wrapped and oxidized multi-walled carbon nanotubes (MWCNTs)

Carbon nanotubes (CNTs) have great potential in industrial, consumer, and mechanical applications, based partly on their unique structural, optical and electronic properties. CNTs are commonly oxidized or treated with surfactants to facilitate aqueous solution processing, and these CNT surface modifications also increase possible human and ecological exposures to nanoparticle-contaminated waters. To determine the exposure outcomes of oxidized and surfactant-wrapped multiwalled carbon nanotubes (MWCNTs) on biochemical processes, metabolomics-based profiling of human liver cells (C3A) was utilized. Cells were exposed to 0, 10, or 100ng/mL of MWCNTs for 24 and 48h; MWCNT particle size distribution, charge, and aggregation were monitored concurrently during exposures. Following MWCNT exposure, cellular metabolites were extracted, lyophilized, and buffered for (1)H NMR analysis. Acquired spectra were subjected to both multivariate and univariate analysis to determine the consequences of nanotube exposure on the metabolite profile of C3A cells. Resulting scores plots illustrated temporal and dose-dependent metabolite responses to all MWCNTs tested. Loadings plots coupled with t-test filtered spectra identified metabolites of interest. XPS analysis revealed the presence of hydroxyl and carboxyl functionalities on both MWCNTs surfaces. Metal content analysis by ICP-AES indicated that the total mass concentration of the potentially toxic impurities in the exposure experiments were extremely low (i.e. [Ni]≤2×10(-10)g/mL). Preliminary data suggested that MWCNT exposure causes perturbations in biochemical processes involved in cellular oxidation as well as fluxes in amino acid metabolism and fatty acid synthesis. Dose-response trajectories were apparent and spectral peaks related to both dose and MWCNT dispersion methodologies were determined. Correlations of the significant changes in metabolites will help to identify potential biomarkers associated with carbonaceous nanoparticle exposure.

Homeostasis of metabolites in Escherichia coli on transition from anaerobic to aerobic conditions and the transient secretion of pyruvate

We have developed a method for rapid quenching of samples taken from chemostat cultures of Escherichia coli that gives reproducible and reliable measurements of extracellular and intracellular metabolites by ¹H NMR and have applied it to study the major central metabolites during the transition from anaerobic to aerobic growth. Almost all metabolites showed a gradual change after perturbation with air, consistent with immediate inhibition of pyruvate formate-lyase, dilution of overflow metabolites and induction of aerobic enzymes. Surprisingly, although pyruvate showed almost no change in intracellular concentration, the extracellular concentration transiently increased. The absence of intracellular accumulation of pyruvate suggested that one or more glycolytic enzymes might relocate to the cell membrane. To test this hypothesis, chromosomal pyruvate kinase (pykF) was modified to express either PykF-green fluorescent protein or PykF-FLAG fusion proteins. Measurements showed that PykF-FLAG relocates to the cell membrane within 5 min of aeration and then slowly returns to the cytoplasm, suggesting that on aeration, PykF associates with the membrane to facilitate secretion of pyruvate to maintain constant intracellular levels.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

Omics-Based Biomarkers: Application of Metabolomics in Neuropsychiatric Disorders

One of the major concerns of modern society is to identify putative biomarkers that serve as a valuable early diagnostic tool to identify a subset of patients with increased risk to develop neuropsychiatric disorders. Biomarker identification in neuropsychiatric disorders is proposed to offer a number of important benefits to patient well-being, including prediction of forthcoming disease, diagnostic precision, and a level of disease description that would guide treatment choice. Nowadays, the metabolomics approach has unlocked new possibilities in diagnostics of devastating disorders like neuropsychiatric disorders. Metabolomics-based technologies have the potential to map early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can be used as indicators of pathological abnormalities prior to development of clinical symptoms of neuropsychiatric disorders. This review highlights different -omics strategies for biomarker discovery in neuropsychiatric disorders. We also highlight initial outcomes from metabolomics studies in psychiatric disorders such as schizophrenia, bipolar disorder, and addictive disorders. This review will also present issues and challenges regarding the implementation of the metabolomics approach as a routine diagnostic tool in the clinical laboratory in context with neuropsychiatric disorders.

Gas chromatography-mass spectrometry-based metabolite profiling of Salmonella enterica serovar Typhimurium differentiates between biofilm and planktonic phenotypes

The aim of this study was to utilize gas chromatography coupled with mass spectrometry (GC-MS) to compare and identify patterns of biochemical change between Salmonella cells grown in planktonic and biofilm phases and Salmonella biofilms of different ages. Our results showed a clear separation between planktonic and biofilm modes of growth. The majority of metabolites contributing to variance between planktonic and biofilm supernatants were identified as amino acids, including alanine, glutamic acid, glycine, and ornithine. Metabolites contributing to variance in intracellular profiles were identified as succinic acid, putrescine, pyroglutamic acid, and N-acetylglutamic acid. Principal-component analysis revealed no significant differences between the various ages of intracellular profiles, which would otherwise allow differentiation of biofilm cells on the basis of age. A shifting pattern across the score plot was illustrated when analyzing extracellular metabolites sampled from different days of biofilm growth, and amino acids were again identified as the metabolites contributing most to variance. An understanding of biofilm-specific metabolic responses to perturbations, especially antibiotics, can lead to the identification of novel drug targets and potential therapies for combating biofilm-associated diseases. We concluded that under the conditions of this study, GC-MS can be successfully applied as a high-throughput technique for "bottom-up" metabolomic biofilm research.

Alkaline conditions in hydrophilic interaction liquid chromatography for intracellular metabolite quantification using tandem mass spectrometry

Modeling of metabolic networks as part of systems metabolic engineering requires reliable quantitative experimental data of intracellular concentrations. The hydrophilic interaction liquid chromatography-electrospray ionization-tandem mass spectrometry (HILIC-ESI-MS/MS) method was used for quantitative profiling of more than 50 hydrophilic key metabolites of cellular metabolism. Without prior derivatization, sugar phosphates, organic acids, nucleotides, and amino acids were measured under alkaline and acidic mobile phase conditions with pre-optimized multiple reaction monitoring (MRM) transitions. Irrespective of the polarity mode of the acquisition method used, alkaline conditions achieved the best quantification limits and linear dynamic ranges. Fully 90% of the analyzed metabolites presented detection limits better than 0.5pmol (on column), and 70% presented 1.5-fold higher signal intensities under alkaline mobile phase conditions. The quality of the method was further demonstrated by absolute quantification of selected metabolites in intracellular extracts of Escherichia coli. In addition, quantification bias caused by matrix effects was investigated by comparison of calibration strategies: standard-based external calibration, isotope dilution, and standard addition with internal standards. Here, we recommend the use of alkaline mobile phase with polymer-based zwitterionic hydrophilic interaction chromatography (ZIC-pHILIC) as the most sensitive scenario for absolute quantification for a broad range of metabolites.

Simultaneous parameters identifiability and estimation of an E. coli metabolic network model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of the Escherichia coli K-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

Mass-spectrometry-based microbial metabolomics: recent developments and applications

Metabolomics is an omics technique aiming at qualitatively and quantitatively describing a metabolome by various analytical platforms. It is an indispensable component of modern systems biology. Microbial metabolomics can be roughly classified as metabolic footprint analysis and metabolic fingerprint analysis depending on the analyte origins. Both of them have been beneficial to microbiological research for different reasons. Mass spectrometry and nuclear magnetic resonance spectroscopy techniques are popular analytical strategies prevailing in the metabolomics field. In this review, chromatography-mass-spectrometry-based microbial metabolomic analysis steps are summarized, including sample collection, metabolite extraction, instrument analysis, and data analysis. Moreover, their applications in some representative fields are discussed as examples. The aim of this review is to present briefly recent technical advances in mass-spectrometry-based analysis, and to highlight the value of modern applications of microbial metabolomics.

Molecular extraction in single live cells by sneaking in and out magnetic nanomaterials

Extraction of intracellular molecules is crucial to the study of cellular signal pathways. Disruption of the cellular membrane remains the established method to release intracellular contents, which inevitably terminates the time course of biological processes. Also, conventional laboratory extractions mostly use bulky materials that ignore the heterogeneity of each cell. In this work, we developed magnetized carbon nanotubes that can be sneaked into and out of cell bodies under a magnetic force. Using a testing model with overexpression of GFP, the nanotubes successfully transported the intracellular GFP out at the single-cell level. The confined nanoscale invasiveness did not change cell viability or proliferation. This study presents the proof of concept of a previously unidentified real-time and single-cell approach to investigate cellular biology, signal messengers, and therapeutic effects with nanomaterials.

Molecular physiology of the dynamic regulation of carbon catabolite repression in Escherichia coli

We report on the use of the chemostat as an optimal device to create time-invariant conditions that allow accurate sampling for various omics assays in Escherichia coli, in combination with recording of the dynamics of the physiological transition in the organism under study that accompany the initiation of glucose repression. E. coli cells respond to the addition of glucose not only with the well-known transcriptional response, as was revealed through quantitative PCR analysis of the transcript levels of key genes from the CRP (cAMP receptor protein) regulon, but also with an increased growth rate and a transient decrease in the efficiency of its aerobic catabolism. Less than half of a doubling time is required for the organism to recover to maximal values of growth rate and efficiency. Furthermore, calculations based on our results show that the specific glucose uptake rate (qs) and the H(+)/e(-) ratio increase proportionally, up to a growth rate of 0.4 h(-1), whilst biomass yield on glucose (Yx / s) drops during the first 15 min, followed by a gradual recovery. Surprisingly, the growth yields after the recovery phase show values even higher than the maximum theoretical yield. Possible explanations for these high yields are discussed.

Metabolic flux pattern of glucose utilization by Xanthomonas campestris pv. campestris: prevalent role of the Entner–Doudoroff pathway and minor fluxes through the pentose phosphate pathway and glycolysis

Complex metabolic flux pattern ofX. campestris.

Stable isotope-labeled tracers for metabolic pathway elucidation by GC-MS and FT-MS

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), over the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly widespread metabolic diseases such as cancer, diabetes, and obesity. Emerging NMR and MS atom-tracking technologies and informatics are poised to revolutionize metabolomics-based research because they deliver the high information throughput (HIT) that is needed for deciphering systems biochemistry. In particular, stable isotope-resolved metabolomics (SIRM) enables unambiguous tracking of individual atoms through compartmentalized metabolic networks in a wide range of experimental systems, including human subjects. MS offers a wide range of instrumental capabilities involving different levels of initial capital outlay and operating costs, ranging from gas-chromatography (GC) MS that is affordable by many individual laboratories to the HIT-supporting Fourier-transform (FT) class of MS that rivals NMR in cost and infrastructure support. This chapter focuses on sample preparation, instrument, and data processing procedures for these two extremes of MS instrumentation used in SIRM.

SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks

Metabolomics analysis tools can provide quantitative information on the concentration of metabolites in an organism. In this paper, we propose the minimum pathway model generator tool for simulating the dynamics of metabolite concentrations (SS-mPMG) and a tool for parameter estimation by genetic algorithm (SS-GA). SS-mPMG can extract a subsystem of the metabolic network from the genome-scale pathway maps to reduce the complexity of the simulation model and automatically construct a dynamic simulator to evaluate the experimentally observed behavior of metabolites. Using this tool, we show that stochastic simulation can reproduce experimentally observed dynamics of amino acid biosynthesis in Arabidopsis thaliana. In this simulation, SS-mPMG extracts the metabolic network subsystem from published databases. The parameters needed for the simulation are determined using a genetic algorithm to fit the simulation results to the experimental data. We expect that SS-mPMG and SS-GA will help researchers to create relevant metabolic networks and carry out simulations of metabolic reactions derived from metabolomics data.