The pathways that distinguish transport of folded and misfolded cargo through the exocytic (secretory) pathway of eukaryotic cells remain unknown. Using proteomics to assess global cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein interactions (the CFTR interactome), we show that Hsp90 cochaperones modulate Hsp90-dependent stability of CFTR protein folding in the endoplasmic reticulum (ER). Cell-surface rescue of the most common disease variant that is restricted to the ER, DeltaF508, can be initiated by partial siRNA silencing of the Hsp90 cochaperone ATPase regulator Aha1. We propose that failure of DeltaF508 to achieve an energetically favorable fold in response to the steady-state dynamics of the chaperone folding environment (the "chaperome") is responsible for the pathophysiology of CF. The activity of cargo-associated chaperome components may be a common mechanism regulating folding for ER exit, providing a general framework for correction of misfolding disease.

To take advantage of the potential quantitative benefits offered by tandem mass spectrometry, we have modified the method in which tandem mass spectrum data are acquired in 'shotgun' proteomic analyses. The proposed method is not data dependent and is based on the sequential isolation and fragmentation of precursor windows (of 10 m/z) within the ion trap until a desired mass range has been covered. We compared the quantitative figures of merit for this method to those for existing strategies by performing an analysis of the soluble fraction of whole-cell lysates from yeast metabolically labeled in vivo with (15)N. To automate this analysis, we modified software (RelEx) previously written in the Yates lab to generate chromatograms directly from tandem mass spectra. These chromatograms showed improvements in signal-to-noise ratio of approximately three- to fivefold over corresponding chromatograms generated from mass spectrometry scans. In addition, to demonstrate the utility of the data-independent acquisition strategy coupled with chromatogram reconstruction from tandem mass spectra, we measured protein expression levels in two developmental stages of Caenorhabditis elegans.

Chagas disease, leishmaniasis, and sleeping sickness affect 20 million people worldwide and lead to more than 50,000 deaths annually1. The diseases are caused by infection with the kinetoplastid parasites Trypanosoma cruzi, Leishmania spp. and Trypanosoma brucei spp., respectively. These parasites have similar biology and genomic sequence, suggesting that all three diseases could be cured with drug(s) modulating the activity of a conserved parasite target2. However, no such molecular targets or broad spectrum drugs have been identified to date. Here we describe a selective inhibitor of the kinetoplastid proteasome (GNF6702) with unprecedented in vivo efficacy, which cleared parasites from mice in all three models of infection. GNF6702 inhibits the kinetoplastid proteasome through a non-competitive mechanism, does not inhibit the mammalian proteasome or growth of mammalian cells, and is well-tolerated in mice. Our data provide genetic and chemical validation of the parasite proteasome as a promising therapeutic target for treatment of kinetoplastid infections, and underscore the possibility of developing a single class of drugs for these neglected diseases.

We describe Census, a quantitative software tool compatible with many labeling strategies as well as with label-free analyses, single-stage mass spectrometry (MS1) and tandem mass spectrometry (MS/MS) scans, and high-and low-resolution mass spectrometry data. Census uses robust algorithms to address poor-quality measurements and improve quantitative efficiency, and it can support several input file formats. We tested Census with stable-isotope labeling analyses as well as label-free analyses. Keywords mass spectrometry; quantification; label free; metabolic labelingIn recent years, global quantification using mass spectrometry has garnered a significant level of interest due to the emergence of fields that rely on large scale profiling of peptides/ proteins (proteomics) and small molecules (metabolomics). In the field of proteomics, the identification of large numbers of peptides has become commonplace with the advent of new instrumentation(1-7) and informatics tools(8-11), however, progress with regards to the quantification process has been hampered by the extreme analytical challenges.In general, peptide/protein quantification by mass spectrometry is achieved via either stable isotope labeling or a label free approach. Stable isotope labeling has become the core technology for high throughput peptide quantification efforts employing mass spectrometry. Quantification is typically achieved by comparison of an unlabeled or "light" peptide (i.e., comprised of naturally abundant stable isotopes) to an internal standard that is chemically identical with the exception of atoms that are enriched with a "heavy" stable isotope. While the stable isotope labeling approach has been the most commonly employed over the past several years, label free approaches have been gaining momentum recently due to the inherent simplicity, increased throughput, and low cost. Several strategies for label free differential expression analysis have emerged and can generally be divided into two groups; those that are fundamentally based on identification of peptides prior to quantification and those that rely on first stage MS data alone. (Fig. 1). Census is based on a program previously written in our lab called RelEx(12), but has been re-written with many new features that significantly improve the accuracy and precision of resulting measurements and drastically improves computational performance (Supplementary Information online and Table 1). Census is capable of quantification from either MS or MS/MS scans and is thus able to process data generated from data-independent acquisition(13), SRM, or MRM analyses. Other features incorporated into Census include the ability to use high resolution and high mass accuracy MS data for improved quantification, as well as the ability to perform quantitative analyses based on both spectral counting and an LC-MS peak area approach utilizing chromatogram alignment. To minimize false positive measurements and improve protein/peptide ratio accuracy Census incorporates multiple algorithms such as...

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster.

DAF-2, an insulin receptor-like protein, regulates metabolism, development, and aging in Caenorhabditis elegans. In a quantitative proteomic study, we identified 86 proteins that were more or less abundant in long-lived daf-2 mutant worms than in wild-type worms. Genetic studies on a subset of these proteins indicated that they act in one or more processes regulated by DAF-2, including entry into the dauer developmental stage and aging. In particular, we discovered a compensatory mechanism activated in response to reduced DAF-2 signaling, which involves the protein phosphatase calcineurin.

As the speed with which proteomic labs generate data increases along with the scale of projects they are undertaking, the resulting data storage and data processing problems will continue to challenge computational resources. This is especially true for shotgun proteomic techniques that can generate tens of thousands of spectra per instrument each day. One design factor leading to many of these problems is caused by storing spectra and the database identifications for a given spectrum as individual files. While these problems can be addressed by storing all of the spectra and search results in large relational databases, the infrastructure to implement such a strategy can be beyond the means of academic labs. We report here a series of unified text file formats for storing spectral data (MS1 and MS2) and search results (SQT) that are compact, easily parsed by both machine and humans, and yet flexible enough to be coupled with new algorithms and data-mining strategies.

We previously reported the metabolic 15 N labeling of a rat where enrichment ranged from 94% to 74%. We report here an improved labeling strategy which generates 94% 15 N enrichment throughout all tissues of the rat. A high 15 N enrichment of the internal standard is necessary for accurate quantitation, and thus, this approach will allow quantitative mass spectrometry analysis of animal models of disease targeting any tissue.