A GC-MS method for determination of amino acid uptake by plants

In this study, we present a rapid, robust and sensitive method for quantification of plant amino acid uptake using universally (U) (¹³C, ¹⁵N)-labelled amino acids and gas chromatography-mass spectrometry (GC-MS). Amino acids were analysed as their tert -butyldimethylsilyl (tBDMS) derivatives and displayed detection limits in the range 10–100 fmol on column, depending on the amino acid. The technique allows for simultaneous detection and quantification of both unlabelled and isotopically labelled species of amino acids. This makes simple quantification of plant amino acid uptake from an isotopically labelled source possible. The analytical variation was low, concerning total amino acid concentrations (relative standard deviation, rsd , less than 5.3%) as well as enrichment of U-¹³C, ¹⁵N-labelled glycine (Gly), arginine (Arg) and glutamic acid (Glu) ( rsd <2.1%). An application of the GC-MS method was conducted on non-mycorrhizal Pinus sylvestris roots supplied with U-¹³C, ¹⁵N-labelled amino acids. Intact, labelled amino acids were traced in root extracts. This provided conclusive evidence of plant root uptake of intact amino acids. Uptake rates of the three amino acids Gly, Glu and Arg in the range 0.5–37.9 μmol g⁻¹ dry weight h⁻¹ were recorded. These rates are comparable with those recorded in earlier studies of amino acid uptake, using other methods, as well as uptake rates measured for nitrate and ammonium.     

A novel method for nucleic acid sequence determination

We describe a novel sequencing methodology which should be readily and completely automated. The method relies on fragmentation of a nucleotide or deoxynucleotide sequence into short fragments, and subsequent quantitation of the fragments by hybridization to oligo-deoxynucleotides on a solid support. The original sequence may be reconstructed from the resulting table of fragment frequencies. We present a specific protocol which would allow practical implementation of this approach.     

Structure determination of a protein assembly by amino acid selective cross-saturation

Amino acid selective cross-saturation (ASCS) method not only provides information about the interface of a protein assembly by the spin relaxation experiment, but also identifies the amino acid residues in the acceptor protein, which are located close to the selectively labeled amino acid residues in the donor protein. Here, a new method was developed to build a precise structural model of a protein assembly, which satisfies the experimental ASCS values, using simulated annealing computation. This method was applied to the ubiquitin-yeast ubiquitin hydrolase 1 (Ub-YUH1) complex to build a precise complex structure compatible with that determined by X-ray crystallography.     

A high-precision selective determination method for free N-acetylneuraminic acid in human serum

We established a high-precision selective determination method for free N-acetylneuraminic acid in human serum, involving capillary gas chromatography-mass spectrometry with selected ion monitoring and specific separation on a Sep Pak silica cartridge. In contrast to the value of 800 ng/ml of N-acetylneuraminic acid previously reported by Haverkamp et al. (J. Haverkamp, R. Schauer, and M. Wember (1976) Hoppe-Seyler's Z. Physiol. Chem. 357, 1699), who used the thiobarbituric acid method, the present method gave a value of 194 +/- 96 ng/ml for 22 serum samples from normal Japanese male volunteers aged between 20 and 30 years. The mass fragmentogram of serum showed a good signal/noise ratio, and the measurement was very specific, accurate, and reproducible.     

A simple method for N-15 labelling of exocyclic amino groups in synthetic oligodeoxynucleotides

The use of the ammonia deprotection step to introduce ¹⁵N labels at specific exocyclic amino positions of adenine, cytosine, guanine or 2-aminopurine of oligodeoxynucleotides is described.     

A novel method for predicting protein subcellular localization based on pseudo amino acid composition

In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.