Broadly neutralizing anti-HIV-1 monoclonal antibodies, such as PG9, and its derivative RSH hold great promise in AIDS therapy and prevention. An important feature related to the exceptional efficacy of PG9 and RSH is the presence of sulfated tyrosine residues in their antigen-binding regions. To maximize antibody functionalities, we have now produced glycan-optimized, fucose-free versions of PG9 and RSH in Nicotiana benthamiana. Both antibodies were efficiently sulfated in planta on coexpression of an engineered human tyrosylprotein sulfotransferase, resulting in antigen-binding and virus neutralization activities equivalent to PG9 synthesized by mammalian cells ( CHO PG9). Based on the controlled production of both sulfated and nonsulfated variants in plants, we could unequivocally prove that tyrosine sulfation is critical for the potency of PG9 and RSH. Moreover, the fucose-free antibodies generated in N. benthamiana are capable of inducing antibody-dependent cellular cytotoxicity, an activity not observed for CHO PG9. Thus, tailoring of the antigen-binding site combined with glycan modulation and sulfoengineering yielded plant-produced anti-HIV-1 antibodies with effector functions superior to PG9 made in CHO cells.onoclonal antibodies (mAbs) offer great promise for AIDS treatment (1). In particular, the recent discovery of broadly neutralizing anti-HIV-1 mAbs (bNAbs) with extraordinary potency as exemplified by the antibodies PG9, PG16 (2), or those of the PGT series (3) creates hope for effective therapy by passive antibody transfer. PG9 and its close relative PG16 neutralize ∼80% of HIV-1 isolates across all clades (2, 4). The recognized epitopes are within the hypervariable and heavily glycosylated V1/V2 loops of the viral envelope glycoprotein gp120 and preferentially displayed in its trimeric state (2). Both mAbs use their unusually long complementarity-determining region (CDR) H3 domains (4-6) to penetrate the glycan shield of the virus and make contact with the underlying protein backbone (7). In addition, PG9 and PG16 recognize two highly conserved gp120 N-glycans attached to Asn 160 and Asn 156/173 , which flank the peptide epitope (7-9). Remarkably, the glycan-binding properties of the two antibodies could be combined by modification of the PG9 light chain with R L94

Ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was used for geographical origin discrimination of hazelnuts (Corylus avellana L.). Four different LC-MS methods for polar and nonpolar metabolites were evaluated with regard to best discrimination abilities. The most suitable method was used for analysis of 196 authentic samples from harvest years 2014 and 2015 (Germany, France, Italy, Turkey, Georgia), selecting and identifying 20 key metabolites with significant differences in abundancy (5 phosphatidylcholines, 3 phosphatidylethanolamines, 4 diacylglycerols, 7 triacylglycerols, and γ-tocopherol). Classification models using soft independent modeling of class analogy (SIMCA), linear discriminant analysis based on principal component analysis (PCA-LDA), support vector machine classification (SVM), and a customized statistical model based on confidence intervals of selected metabolite levels were created, yielding 99.5% training accuracy at its best by combining SVM and SIMCA. Forty nonauthentic hazelnut samples were subsequently used to estimate as realistically as possible the prediction capacity of the models.

A total of 262 authentic samples was analyzed by 1H NMR spectroscopy for the geographical discrimination of hazelnuts (Corylus avellana L.) covering samples from five countries (Germany, France, Georgia, Italy, and Turkey) and the harvest years 2013–2016. This article describes method development starting with an extraction protocol suitable for separation of polar and nonpolar metabolites in addition to reduction of macromolecular components. Using the polar fraction for data analysis, principle component analysis was applied and used to monitor sample preparation and measurement. Several machine learning algorithms were tested to build a classification model. The best results were obtained by a linear discrimination analysis applying a random subspace algorithm. The division of the samples in a trainings set and a test set yielded a cross validation accuracy of 91% for the training set and an accuracy of 96% for the test set. The identification of key features was carried out by Kruskal–Wallis test and t test. A feature assigned to betaine exhibits a significant level for the classification of all five countries and is considered a possible candidate for the development of targeted approaches. Further, the results were compared to a previously published study based on LC–MS analysis of nonpolar metabolites. In summary, this study shows the robustness and high accuracy of a discrimination model based on NMR analysis of polar metabolites.

The reaction of some 1,1'-dialkynylferrocenes with a variety of phenols in the presence as well as in the absence of [Mo(CO)(6)] yields good to high yields of phenoxy[4]ferrocenophanedienes. Similar reactivity was observed with a thiophenol and with acetic acid. Reaction under basic reaction conditions led to the formation of the [4]ferrocenophanone 17. The phenoxy[4]ferrocenophanedienes obtained show dynamic behavior as a result of a torsional twist of the carbon bridge as indicated by the (1)H and (13)C NMR spectra. The reaction mechanism is discussed in view of recent related results of Sato et al. as well as of Pudelski et al. A vinyl cation intermediate is postulated in this context, whose relative stability is evident from the mass spectra of the compounds prepared.