Deletion of Genes Encoding Arginase Improves Use of "Heavy" Isotope-Labeled Arginine for Mass Spectrometry in Fission Yeast

<div><p>The use of “heavy” isotope-labeled arginine for stable isotope labeling by amino acids in cell culture (SILAC) mass spectrometry in the fission yeast <i>Schizosaccharomyces pombe</i> is hindered by the fact that under normal conditions, arginine is extensively catabolized <i>in vivo</i>, resulting in the appearance of “heavy”-isotope label in several other amino acids, most notably proline, but also glutamate, glutamine and lysine. This “arginine conversion problem” significantly impairs quantification of mass spectra. Previously, we developed a method to prevent arginine conversion in fission yeast SILAC, based on deletion of genes involved in arginine catabolism. Here we show that although this method is indeed successful when ¹³C₆-arginine (Arg-6) is used for labeling, it is less successful when ¹³C₆¹⁵N₄-arginine (Arg-10), a theoretically preferable label, is used. In particular, we find that with this method, “heavy”-isotope label derived from Arg-10 is observed in amino acids other than arginine, indicating metabolic conversion of Arg-10. Arg-10 conversion, which severely complicates both MS and MS/MS analysis, is further confirmed by the presence of ¹³C₅¹⁵N₂-arginine (Arg-7) in arginine-containing peptides from Arg-10-labeled cells. We describe how all of the problems associated with the use of Arg-10 can be overcome by a simple modification of our original method. We show that simultaneous deletion of the fission yeast arginase genes <i>car1+</i> and <i>aru1+</i> prevents virtually all of the arginine conversion that would otherwise result from the use of Arg-10. This solution should enable a wider use of heavy isotope-labeled amino acids in fission yeast SILAC.</p></div

A maximum likelihood method for latent class regression involving a censored dependent variable

The standard tobit or censored regression model is typically utilized for regression analysis when the dependent variable is censored. This model is generalized by developing a conditional mixture, maximum likelihood method for latent class censored regression. The proposed method simultaneously estimates separate regression functions and subject membership in K latent classes or groups given a censored dependent variable for a cross-section of subjects. Maximum likelihood estimates are obtained using an EM algorithm. The proposed method is illustrated via a consumer psychology application.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45751/1/11336_2005_Article_BF02294647.pd

Vibrational spectra and modified valence force field for N,N-methylenebisacrylamide

611-616Raman and Fourier transform infrared (FTIR) spectra of N,N-methylenebisacrylamide have been recorded. A 35-parameter modified valance force field has been evaluated using 38-in-plane vibrational frequencies of the molecule. Unambiguous vibrational assignments have been made using eigenvectors and potential energy distributions computed in the process