SUMMARY We investigated the potential of in-depth quantitative proteomics to reveal plasma protein signatures that reflect lung tumor biology. We compared plasma protein profiles of four mouse models of lung cancer with profiles of models of pancreatic, ovarian, colon, prostate, and breast cancer and two models of inflammation. A protein signature for Titf1/Nkx2-1, a known lineage-survival oncogene in lung cancer, was found in plasmas of mouse models of lung adenocarcinoma. An EGFR signature was found in plasma of an EGFR mutant model, and a distinct plasma signature related to neuroendocrine development was uncovered in the small-cell lung cancer model. We demonstrate relevance to human lung cancer of the protein signatures identified on the basis of mouse models.

BackgroundThe filamentous fungus Trichoderma reesei is a major producer of lignocellulolytic enzymes utilized by bioethanol industries. However, to achieve low cost second generation bioethanol production on an industrial scale an efficient mix of hydrolytic enzymes is required for the deconstruction of plant biomass. In this study, we investigated the molecular basis for lignocellulose-degrading enzyme production T. reesei during growth in cellulose, sophorose, and glucose.ResultsWe examined and compared the transcriptome and differential secretome (2D-DIGE) of T. reesei grown in cellulose, sophorose, or glucose as the sole carbon sources. By applying a stringent cut-off threshold 2,060 genes were identified as being differentially expressed in at least one of the respective carbon source comparisons. Hierarchical clustering of the differentially expressed genes identified three possible regulons, representing 123 genes controlled by cellulose, 154 genes controlled by sophorose and 402 genes controlled by glucose. Gene regulatory network analyses of the 692 genes differentially expressed between cellulose and sophorose, identified only 75 and 107 genes as being specific to growth in sophorose and cellulose, respectively. 2D-DIGE analyses identified 30 proteins exclusive to sophorose and 37 exclusive to cellulose. A correlation of 70.17% was obtained between transcription and secreted protein profiles.ConclusionsOur data revealed new players in cellulose degradation such as accessory proteins with non-catalytic functions secreted in different carbon sources, transporters, transcription factors, and CAZymes, that specifically respond in response to either cellulose or sophorose.

Posttranslational protein modification by ubiquitin (Ub) is a central eukaryotic mechanism that regulates a plethora of physiological processes. Recent studies unveiled an unconventional type of ubiquitination mediated by the SidE family of Legionella pneumophila effectors, such as SdeA, that catalyzes the conjugation of Ub to a serine residue of target proteins via a phosphoribosyl linker (hence named PR-ubiquitination). Comparable to the deubiquitinases in the canonical ubiquitination pathway, here we show that 2 paralogous Legionella effectors, Lpg2154 (DupA; deubiquitinase for PR-ubiquitination) and Lpg2509 (DupB), reverse PR-ubiquitination by specific removal of phosphoribosyl-Ub from substrates. Both DupA and DupB are fully capable of rescuing the Golgi fragmentation phenotype caused by exogenous expression of SdeA in mammalian cells. We further show that deletion of these 2 genes results in significant accumulation of PR-ubiquitinated species in host cells infected with Legionella. In addition, we have identified a list of specific PR-ubiquitinated host targets and show that DupA and DupB play a role in modulating the association of PR-ubiquitinated host targets with Legionella-containing vacuoles. Together, our data establish a complete PR-ubiquitination and deubiquitination cycle and demonstrate the intricate control that Legionella has over this unusual Ub-dependent posttranslational modification.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brainspecific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins. Huntington disease (HD)1 is an inherited neurodegenerative disorder characterized by progressive cognitive decline and psychiatric and movement symptoms. The cause of the disease is the expansion of trinucleotide (CAG) repeats in the coding region of the htt gene that translates into a polyglutamine tract in the huntingtin protein (1). Currently no treatment has been shown to delay the onset of the disease or slow its progression in patients. To speed assessment of therapies in clinical trials, it is critical to identify biological markers that can accurately monitor disease progression.Several genomics and proteomics approaches to identifying biomarkers for HD have been undertaken previously. Genomics studies have determined the molecular phenotype of human HD brain (2) and different tissues of HD mouse models at the mRNA level (3-6). Proteomics approaches have been applied to brain tissues of HD mouse models and humans to identify candidate markers (7-9). Blood plasma in particular has received considerable attention recently because of its ready accessibility clinically (10, 11). The candidate protein biomarkers identified in the blood proteomics studies are largely known inflammatory markers. Because HD is regarded primarily as a neurodegenerative disease, it is not entirely clear how directly general markers of neuroinflammation relate to the pathophysiology of HD, although astrocytosis and microgliosis (12) are prominent components of HD in its mid-to late stages (13). Another concern regarding markers discovered primarily in blood is that the blood-brain barrier may restrict brain proteins from entering plasma, and so plasma candidates may not directly reflect HD progression in the brain.Cerebrospinal fluid (CSF) is a more relevant biomaterial for biomarker discovery because it is proximal to the brain; it occupies the subarachnoid space of the central nervous system and the ventricular system around and inside the b...

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1–2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide’s parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.