Metabolic engineering of Arabidopsis for butanetriol production using bacterial genes

Includes bibliographical references (pages 119-120).1,2,4-butanetriol (butanetriol) is a useful precursor for the synthesis of the energetic material butanetriol trinitrate and several pharmaceutical compounds. Bacterial synthesis of butanetriol from xylose or arabinose takes place in a pathway that requires four enzymes. To produce butanetriol in plants by expressing bacterial enzymes, we cloned native bacterial or codon optimized synthetic genes under different promoters into a binary vector and stably transformed Arabidopsis plants. Transgenic lines expressing introduced genes were analyzed for the production of butanetriol using gas chromatography coupled to mass spectrometry (GC-MS). Soil-grown transgenic plants expressing these genes produced up to 20 µg/g of butanetriol. To test if an exogenous supply of pentose sugar precursors would enhance the butanetriol level, transgenic plants were grown in a medium supplemented with either xylose or arabinose and the amount of butanetriol was quantified. Plants expressing synthetic genes in the arabinose pathway showed up to a forty-fold increase in butanetriol levels after arabinose was added to the medium. Transgenic plants expressing either bacterial or synthetic xylose pathways, or the arabinose pathway showed toxicity symptoms when xylose or arabinose was added to the medium, suggesting that a by-product in the pathway or butanetriol affected plant growth. Furthermore, the metabolite profile of plants expressing arabinose and xylose pathways was altered. Our results demonstrate that bacterial pathways that produce butanetriol can be engineered into plants to produce this chemical. This proof-of-concept study for phytoproduction of butanetriol paves the way to further manipulate metabolic pathways in plants to enhance the level of butanetriol production.Published with support from the Colorado State University Libraries Open Access Research and Scholarship Fund

Secreted metabolome of porcine blastocysts encapsulated with in \u3ci\u3ein vitro\u3c/i\u3e 3D alginate hydrogel culture systems under going morphological changes provides insights into specific mechanisms involved in the initiation of porcine conceptus elongation

Context. The exact mechanisms regulating the initiation of porcine conceptus elongation are not known due to the complexity of the uterine environment. Aims. To identify contributing factors for initiation of conceptus elongation in vitro, this study evaluated differential metabolite abundance within media following culture of blastocysts within unmodified alginate (ALG) or Arg-Gly-Asp (RGD)-modified alginate hydrogel culture systems. Methods. Blastocysts were harvested from pregnant gilts, encapsulated within ALG or RGD or as non-encapsulated control blastocysts (CONT), and cultured. At the termination of 96 h culture, media were separated into blastocyst media groups: non-encapsulated control blastocysts (CONT); ALG and RGD blastocysts with no morphological change (ALG− and RGD−); ALG and RGD blastocysts with morphological changes (ALG+ and RGD+) and evaluated for non-targeted metabolomic profiling by liquid chromatography (LC)–mass spectrometry (MS) techniques and gas chromatography– (GC–MS). Key results. Analysis of variance identified 280 (LC–MS) and 1 (GC–MS) compounds that differed (P \u3c 0.05), of which 134 (LC–MS) and 1 (GC–MS) were annotated. Metabolites abundance between ALG+ vs ALG−, RGD+ vs RGD−, and RGD+ vs ALG+ were further investigated to identify potential differences in metabolic processes during the initiation of elongation. Conclusions. This study identified changes in phospholipid, glycosphingolipid, lipid signalling, and amino acid metabolic processes as potential RGD-independent mechanisms of elongation and identified changes in lysophosphatidylcholine and sphingolipid secretions during RGD-mediated elongation. Implications. These results illustrate changes in phospholipid and sphingolipid metabolic processes and secretions may act as mediators of the RGD-integrin adhesion that promotes porcine conceptus elongation

Metabolic compounds within the porcine uterine environment are unique to the type of conceptus present during the early stages of blastocyst elongation

The objective of this study was to identify metabolites within the porcine uterine milieu during the early stages of blastocyst elongation. At Days 9, 10, or 11 of gestation, reproductive tracts of White cross‐bred gilts (n = 38) were collected immediately following harvest and flushed with Roswell Park Memorial Institute‐1640 medium. Conceptus morphologies were assessed from each pregnancy and corresponding uterine flushings were assigned to one of five treatment groups based on these morphologies: (a) uniform spherical (n = 8); (b) heterogeneous spherical and ovoid (n = 8); (c) uniform ovoid (n = 8); (d) heterogeneous ovoid and tubular (n = 8); and (e) uniform tubular (n = 6). Uterine flushings from these pregnancies were submitted for nontargeted profiling by gas chromatography–mass spectrometry (GC–MS) and ultra performance liquid chromatography (UPLC)–MS techniques. Unsupervised multivariate principal component analysis (PCA) was performed using pcaMethods and univariate analysis of variance was performed in R with false discovery rate (FDR) adjustment. PCA analysis of the GC–MS and UPLC–MS data identified 153 and 104 metabolites, respectively. After FDR adjustment of the GC–MS and UPLC–MS data, 38 and 59 metabolites, respectively, differed (p \u3c .05) in uterine flushings from pregnancies across the five conceptus stages. Some metabolites were greater (p \u3c .05) in abundance for uterine flushings containing earlier stage conceptuses (i.e., spherical), such as uric acid, tryptophan, and tyrosine. In contrast, some metabolites were greater (

Non-targeted urine metabolomics and associations with prevalent and incident type 2 diabetes

Better risk prediction and new molecular targets are key priorities in type 2 diabetes (T2D) research. Little is known about the role of the urine metabolome in predicting the risk of T2D. We aimed to use non-targeted urine metabolomics to discover biomarkers and improve risk prediction for T2D. Urine samples from two community cohorts of 1,424 adults were analyzed by ultra-performance liquid chromatography/mass spectrometry (UPLC-MS). In a discovery/replication design, three out of 62 annotated metabolites were associated with prevalent T2D, notably lower urine levels of 3-hydroxyundecanoyl-carnitine. In participants without diabetes at baseline, LASSO regression in the training set selected six metabolites that improved prediction of T2D beyond established risk factors risk over up to 12 years' follow-up in the test sample, from C-statistic 0.866 to 0.892. Our results in one of the largest non-targeted urinary metabolomics study to date demonstrate the role of the urine metabolome in identifying at-risk persons for T2D and suggest urine 3-hydroxyundecanoyl-carnitine as a biomarker candidate.Peer reviewe

Plant neighbor identity influences plant biochemistry and physiology related to defense

<p>Abstract</p> <p>Background</p> <p>Chemical and biological processes dictate an individual organism's ability to recognize and respond to other organisms. A small but growing body of evidence suggests that plants may be capable of recognizing and responding to neighboring plants in a species specific fashion. Here we tested whether or not individuals of the invasive exotic weed, <it>Centaurea maculosa</it>, would modulate their defensive strategy in response to different plant neighbors.</p> <p>Results</p> <p>In the greenhouse, <it>C. maculosa </it>individuals were paired with either conspecific (<it>C. maculosa</it>) or heterospecific (<it>Festuca idahoensis</it>) plant neighbors and elicited with the plant defense signaling molecule methyl jasmonate to mimic insect herbivory. We found that elicited <it>C. maculosa </it>plants grown with conspecific neighbors exhibited increased levels of total phenolics, whereas those grown with heterospecific neighbors allocated more resources towards growth. To further investigate these results in the field, we conducted a metabolomics analysis to explore chemical differences between individuals of <it>C. maculosa </it>growing in naturally occurring conspecific and heterospecific field stands. Similar to the greenhouse results, <it>C. maculosa </it>individuals accumulated higher levels of defense-related secondary metabolites and lower levels of primary metabolites when growing in conspecific versus heterospecific field stands. Leaf herbivory was similar in both stand types; however, a separate field study positively correlated specialist herbivore load with higher densities of <it>C. maculosa </it>conspecifics.</p> <p>Conclusions</p> <p>Our results suggest that an individual <it>C. maculosa </it>plant can change its defensive strategy based on the identity of its plant neighbors. This is likely to have important consequences for individual and community success.</p

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef