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Biomedical subjects

Jeffrey R Sachs

Publications and source records attributed to Jeffrey R Sachs.

6 recordsLinked to original sources
PubMed2006

Quantitative analysis of complex peptide mixtures using FTMS and differential mass spectrometry.

Label-free LC-MS profiling is a powerful quantitative proteomic method to study relative peptide abundances between two or more biological samples. Here we demonstrate the use of a previously described comparative LC-MS method, differential mass spectrometry (dMS), to analyze high-resolution Fourier transform mass spectrometry (FTMS) data for detection and quantification of known peptide differences between two sets of complex mixtures. Six standard peptides were spiked into a processed plasma background at fixed ratios from 1.25:1 to 4:1 to make two sets of samples. The resulting mixtures were analyzed by microcapillary LC-FTMS and dMS. dMS successfully identified five out of the six peptides as statistically significant differences (p <or= 0.005). In this experiment, the smallest fold change reliably detected by our method was 1.5:1, and the errors of estimated ratios of concentrations were less than 20% for peptides spiked at 1.5:1 to 4:1. We conclude that LC-FTMS coupled with dMS is a useful label-free quantitative MS method that can be used to detect subtle yet statistically significant peptide differences in complex protein mixtures, including plasma samples.

Amino Acid Sequence↗
PubMed2006

Quantification and identification of components in solution mixtures from 1D proton NMR spectra using singular value decomposition.

One-dimensional proton NMR spectra of complex solutions provide rich molecular information, but limited chemical shift dispersion creates peak overlap that often leads to difficulty in peak identification and analyte quantification. Modern high-field NMR spectrometers provide high digital resolution with improved peak dispersion. We took advantage of these spectral qualities and developed a quantification method based on linear least-squares fitting using singular value decomposition (SVD). The linear least-squares fitting of a mixture spectrum was performed on the basis of reference spectra from individual small-molecule analytes. Each spectrum contained an internal quantitative reference (e.g., DSS-d6 or other suitable small molecules) by which the intensity of the spectrum was scaled. Normalization of the spectrum facilitated quantification based on peak intensity using linear least-squares fitting analysis. This methodology provided quantification of individual analytes as well as chemical identification. The analysis of small-molecule analytes over a wide concentration range indicated the accuracy and reproducibility of the SVD-based quantification. To account for the contribution from residual protein, lipid or polysaccharide in solution, a reference spectrum showing the macromolecules or aggregates was obtained using a diffusion-edited 1D proton NMR analysis. We demonstrated this approach with a mixture of small-molecule analytes in the presence of macromolecules (e.g., protein). The results suggested that this approach should be applicable to the quantification and identification of small-molecule analytes in complex biological samples.

Animals↗
PubMed2006

Surface enhanced laser desorption ionization spectrometry reveals biomarkers for drug treatment but not dose.

Here we describe the use of SELDI-MS to detect dose-dependent peptide changes in plasma from mice treated with vehicle or rosiglitazone at one of two doses (10 and 30 mg/kg). SELDI features differentiating spectra from the three conditions were found and used to train classifiers. Samples treated with vehicle could be reliably distinguished from samples treated with either dose, but samples treated with the different doses could not be reliably distinguished from one another. We conclude that while SELDI-TOF mass spectra can be used to distinguish treated from untreated samples, the reproducibility and information content of SELDI-TOF are currently not sufficient as a pharmacodynamic readout to distinguish between mice treated with 10 or 30 mg/kg of rosiglitazone. This raises more general questions about whether SELDI's sensitivity is sufficient for detecting dose-dependent changes in plasma.

Animals↗
PubMed2004

Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures.

Efficiently identifying and quantifying disease- or treatment-related changes in the abundance of proteins is an important area of research for the pharmaceutical industry. Here we describe an automated, label-free method for finding differences in complex mixtures using complete LC-MS data sets, rather than subsets of extracted peaks or features. The method selectively finds statistically significant differences in the intensity of both high-abundance and low-abundance ions, accounting for the variability of measured intensities and the fact that true differences will persist in time. The method was used to compare two complex peptide mixtures with known peptide differences. This controlled experiment allowed us to assess the validity of each difference found and so to analyze the method's sensitivity and specificity. The method detects both presence versus absence and a 2-fold change in peptide concentration near the limit of detection of the instrument used, where chromatographic peaks may not be sufficiently well defined to be detected in individual samples. The method is more sensitive and gives fewer false positives than subtractive methods that ignore signal variability. Differential mass spectrometry combined with targeted MS/MS analysis of only identified differences may save both computation time and human effort compared to shotgun proteomics approaches.

Chromatography, Liquid↗
PubMed2002

Gene expression profile of adipocyte differentiation and its regulation by peroxisome proliferator-activated receptor-gamma agonists.

PPAR gamma is an adipocyte-specific nuclear hormone receptor. Agonists of PPAR gamma, such as thiazolidinediones (TZDs), promote adipocyte differentiation and have insulin-sensitizing effects in animals and diabetic patients. Affymetrix oligonucleotide arrays representing 6347 genes were employed to profile the gene expression responses of mature 3T3-L1 adipocytes and differentiating preadipocytes to a TZD PPAR gamma agonist in vitro. The expression of 579 genes was significantly up- or down-regulated by more than 1.5-fold during differentiation and/or by treatment with TZD, and these genes were organized into 32 clusters that demonstrated concerted changes in expression of genes controlling cell growth or lipid metabolism. Quantitative PCR was employed to further characterize gene expression and led to the identification of beta-catenin as a new PPAR gamma target gene. Both mRNA and protein levels for beta-catenin were down-regulated in 3T3-L1 adipocytes compared with fibroblasts and were further decreased by treatment of adipocytes with PPAR gamma agonists. Treatment of db/db mice with a PPAR gamma agonist also resulted in reduction of beta-catenin mRNA levels in adipose tissue. These results suggest that beta-catenin plays an important role in the regulation of adipogenesis. Thus, the transcriptional patterns revealed in this study further the understanding of adipogenesis process and the function of PPAR gamma activation.

3T3 Cells↗