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Calbrix R.G.,Laboratoire Of Biologie Chimique Isis Ulp | Beilinson V.,Bioscience | Thomas Stalker H.,North Carolina State University | Nielsen N.C.,North Carolina State University
Crop Science | Year: 2012

The diversity of seed storage proteins of cultivated peanut (Arachis hypogaea L.) was studied by analyzing nucleotide sequences encoding 2S, 7S, and 11S seed storage proteins. Alignments of sequences were performed using ClustalX, and similarity between aligned sequences was established by pairwise comparison using AlignX. Three homology groups of 2S proteins were identified, which were further divided into three, three, and two subgroups. Similarly, three homology groups of 7S proteins contained two, two, and one subgroups; and five homology groups of 11S proteins contained five, five, five, three, and two subgroups. Primer pairs were identified that allowed each member of the respective homology group to be selectively amplified by polymerase chain reaction using template complementary DNA or genomic DNA from A. hypogaea. This permitted most subgroup members to be distinguished. Peanut, like other legumes, contains small gene families encoding each type of seed storage protein. However, the diversity among these was greater than in other legume species, which reflects the allotetraploid nature of A. hypogaea. Polymerase chain reaction amplifications from diploid species were used to deduce which protein subgroup originated from the A genome and which were from the B genome. Of the 26 diploid peanut species studied, only A. duranensis Krapov. and W.C. Greg. (A genome) and A. ipaensis Krapov. and W.C. Greg. (B genome) contained the correct complement of seed storage protein coding regions. This is consistent with the hypothesis that the center of origin of the allotetraploid was southern Bolivia. © Crop Science Society of America. Source

Wehrens R.,Biometris | Hageman J.A.,Biometris | van Eeuwijk F.,Biometris | Kooke R.,Laboratory of Genetics | And 12 more authors.
Metabolomics | Year: 2016

Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana. Results: The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered. Conclusion: The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections. © 2016, The Author(s). Source

Giordanetto F.,Medicinal Chemistry | Giordanetto F.,Taros Chemicals GmbH and Co. KG | Bach P.,Medicinal Chemistry | Zetterberg F.,Medicinal Chemistry | And 11 more authors.
Bioorganic and Medicinal Chemistry Letters | Year: 2014

Modification of a series of P2Y12 receptor antagonists by replacement of the ester functionality was aimed at minimizing the risk of in vivo metabolic instability and pharmacokinetic variability. The resulting ketones were then optimized for their P2Y12 antagonistic and anticoagulation effects in combination with their physicochemical and absorption profiles. The most promising compound showed very potent antiplatelet action in vivo. However, pharmacodynamic-pharmacokinetic analysis did not reveal a significant separation between its anti-platelet and bleeding effects. The relevance of receptor binding kinetics to the in vivo profile is described. © 2014 Elsevier Ltd. All rights reserved. Source

Morley A.D.,Astrazeneca | Cook A.,Astrazeneca | King S.,Astrazeneca | Roberts B.,Astrazeneca | And 6 more authors.
Bioorganic and Medicinal Chemistry Letters | Year: 2011

A series of pyrazole inhibitors of the human FPR1 receptor have been identified from high throughput screening. The compounds demonstrate potent inhibition in human neutrophils and attractive physicochemical and in vitro DMPK profiles to be of further interest. © 2011 Elsevier Ltd. All rights reserved. Source

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