Bergen, H. Robert

BACKGROUNDα-1-Antitrypsin (A1AT) deficiency results from a genetic disorder at 2 common loci. Diagnosis requires quantification of A1AT and subsequent identification of the specific variant. The current algorithm of laboratory testing for the diagnosis of A1AT deficiency uses a combination of quantification (nephelometry), genotyping, and/or phenotyping. We developed a multiple reaction monitoring liquid chromatography–tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of A1AT and identification of the 2 most common deficiency alleles present in 95% of the patients with A1AT deficiency.METHODSSerum samples (n = 40) were digested with trypsin, and appropriate 13C/15N-labeled standard peptides were added. We performed LC-MS/MS analysis with a 0.5- by 150-mm C18 column and H2O:acetonitrile:n-propanol:formic acid (A:98:1:1:0.2 and B:10:80:10:0.2; flow 12 μL/min) mobile phase in positive ion mode on a TSQ Quantum triple quadrupole MS system. We measured the A1AT concentration by comparison to a calibration curve and determined the phenotype by the presence or absence of variant peptides. We compared the results to the current phenotyping assay by isoelectric focusing (IEF) and the immunonephelometry quantitative assay.RESULTSFor A1AT allele detection, in 39 of 40 samples the LC-MS/MS results were identical to those obtained by IEF gel electrophoresis. The single discrepant result was rerun by IEF at a lower dilution, and the results were in concordance. The A1AT quantification by LC-MS/MS also compared favorably with nephelometry.CONCLUSIONSThe LC-MS/MS method correlates well with current phenotyping and nephelometric assays and has the potential to improve the laboratory diagnosis of genetic A1AT deficiency.

AbstractThe clinical management of amyloidosis is based on the treatment of the underlying etiology, and accurate identification of the protein causing the amyloidosis is of paramount importance. Current methods used for typing of amyloidosis such as immunohistochemistry have low specificity and sensitivity. In this study, we report the development of a highly specific and sensitive novel test for the typing of amyloidosis in routine clinical biopsy specimens. Our approach combines specific sampling by laser microdissection (LMD) and analytical power of tandem mass spectrometry (MS)–based proteomic analysis. We studied 50 cases of amyloidosis that were well-characterized by gold standard clinicopathologic criteria (training set) and an independent validation set comprising 41 cases of cardiac amyloidosis. By use of LMD/MS, we identified the amyloid type with 100% specificity and sensitivity in the training set and with 98% in validation set. Use of the LMD/MS method will enhance our ability to type amyloidosis accurately in clinical biopsy specimens.

Abstract The c-jun-NH2-kinases (JNK) play a critical role in tumor promoter–induced cell transformation and apoptosis. Here, we showed that the nuclear factor of activated T3 (NFAT3) is phosphorylated by JNK1 or JNK2 at Ser213 and Ser217, which are located in the conserved SP motif. The transactivation domain of NFAT3 is found between amino acids (aa) 113 and 260 and includes the phosphorylation targets of JNK1 and JNK2. NFAT3 transactivation activity was suppressed in JNK1−/− or JNK2−/− mouse embryonic fibroblast (MEF) cells compared with wild-type MEF cells. Moreover, a 3xNFAT-luc reporter gene assay indicated that NFAT3 transcriptional activity was increased in a dose-dependent manner by JNK1 or JNK2. Double mutations at Ser213 and Ser217 suppressed NFAT3 transactivation activity; and SP600125, a JNK inhibitor, suppressed NFAT3-induced 3xNFAT-luciferase activity. Knockdown of JNK1 or JNK2 suppressed foci formation in NIH3T3 cells. Importantly, ectopic expression of NFAT3 inhibited AP-1 activity and suppressed foci formation. Furthermore, knockdown of NFAT3 enhanced Ras-JNK1 or JNK2-induced foci formation in NIH3T3 cells. Taken together, these results provided direct evidence for the anti-oncogenic potential of the NFAT3 transcription factor. [Cancer Res 2007;67(18):8725–35]

AbstractMany congenital disorders of glycosylation (CDG) can be diagnosed by observing the extent of glycosylation of the abundant serum glycoprotein transferrin (Trf). Trf is an N‐glycosylated protein with two asparagine glycation sites. CDG types I are those genetic defects which occur prior to transfer of the complex oligosaccharide to the acceptor asparagine in the cotranslated polypeptide chain. CDG Ia constitutes by far the most frequent form of CDG and is the result of mutations in the phosphomannomutase gene. CDG Ia and the Ib subtype (Phosphomannoisomerase deficiency) result in low cellular mannose‐1‐phosphate levels, a required precursor for oligosaccharide assembly in the endoplasmic reticulum. The deficiency in oligosaccharides with branched mannose structures is thereafter expressed by the appearance of glycoproteins with unoccupied N‐glycosylation sites (hypoglycosylation). Currently, there have been at least 11 Type I defects, type Ia being by far the most frequently occurring. Most, if not all type I defects result in unoccupied N‐glycation sites. Hypoglycosylated Trf, also known as carbohydrate‐deficient Trf (CDT), can be detected using mass spectrometry (MS) to measure the masses of the serum Trf. The methods for sample preparation using affinity chromatography and MS analysis are described in this unit. Curr. Protoc. Hum. Genet. 54:17.4.1‐17.4.9 © 2007 by John Wiley & Sons, Inc.

Abstract Motivation: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for 16O/18O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of 16O/18O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies. Contact: eckel@mayo.edu