David R. Goodlett

Ventilator-associated pneumonia (VAP) is a common complication in patients with acute lung injury (ALI) and can lead to increased morbidity and mortality. Identifying protein profiles specific to VAP in bronchoalveolar lavage fluid (BALF) may aid in earlier diagnosis, elucidate mechanisms of disease, and identify putative targets for therapeutic intervention. BALF was obtained from 5 normal subjects and 30 ALI patients: 14 with VAP (VAP⁺) and 16 without VAP (VAP^–). Each sample underwent shotgun proteomic analysis based on tandem mass spectrometry. Differentially expressed proteins between the groups were identified using statistical methods based on spectral counting. Mechanisms of disease were explored using functional annotation and protein interaction network analysis. Supervised classification algorithms were implemented to discover a proteomic classifier for identifying critically ill patients with VAP. ALI patients had distinct BALF proteomic profiles compared to normal controls. Within the ALI group, we identified 76 differentially expressed proteins between VAP⁺ and VAP^–. Functional analysis of these proteins suggested activation of pro-inflammatory pathways during VAP. We identified and validated a limited proteomic signature that discriminated VAP⁺ from VAP^– patients comprised of three proteins: S100A8, lactotransferrin (LTF), and actinin 1 (ACTN1). Combining proteomic with computational analyses is a powerful approach to study the BALF proteome during lung injury and development of VAP. This integrative methodology is a promising strategy to differentiate clinically relevant subsets of ALI patients, including those suffering from VAP.

Transferrin (Tf) conjugates of monomeric artemisinin (ART) and artemisinin dimer were synthesized. The two conjugates, ART-Tf and dimer-Tf, retained the original protein structure, and formed stable aggregates in aqueous buffer. ART-Tf induced declines in proteins involved in apoptosis (survivin), cell cycling (cyclin D1), oncogenesis (c-myelocytomatosis oncogene product (c-MYC)), and dysregulated WNT signaling (beta-catenin) in both the human prostate (DU145) and breast (MCF7) cancer cell lines. Both ART-Tf and dimer-Tf induced down-regulation of survivin, c-MYC and mutated human epidermal growth factor receptor-2 (ERBB2 or HER2) in the BT474 breast cancer cell line. To our knowledge, this is the first demonstration that an ART derivative can cause a decline of ERBB2 in a human cancer cell line. Potential mechanisms for the observed effects are presented. Both transferrin conjugates strongly inhibited the growth of BT474 cells in the same concentration range that the conjugates caused declines in the levels of ERBB2, survivin, and c-MYC, while showing essentially no toxicity towards MCF10A normal breast cells.

Tandem mass spectrometry experiments generate from thousands to millions of spectra. These spectra can be used to identify the presence of proteins in biological samples. In this work, we propose a new method to identify peptides, substrings of proteins, based on clustered tandem mass spectrometry data. In contrast to previously proposed approaches, which identify one representative spectrum for each cluster using traditional database searching algorithms, our method uses all available information to score all the spectra in a cluster against candidate peptides using Bayesian model selection. We illustrate the performance of our method by applying it to seven-standard-protein mixture data.

A label-free quantitative variation of the recently developed data-independent shotgun proteomic method precursor acquisition independent from ion count (PAcIFIC) was used to identify novel proteins implicated in cancer progression and resistance. Specifically, this screen identified the pro-metastatic protein anterior gradient 2 (AGR2) as significantly up-regulated in tamoxifen-treated cells. Highlighting the need for direct proteome profiling methods like PAcIFIC, neither data-dependent gas-phase fractionation nor a transcriptomic screen detected AGR2 protein/transcript at significantly up-regulated levels. Further cell-based experiments using human cancer cell lines and in vivo xenografts confirmed the PAcIFIC hypothesis that AGR2 is up-regulated in MCF-7 cells post tamoxifen treatment and that it is implicated in drug resistance mediation.

In collision-induced dissociation (CID) of peptides, it has been observed that rearrangement processes can take place that appear to permute/scramble the original primary structure, which may in principle adversely affect peptide identification. Here, an analysis of sequence permutation in tandem mass spectra is presented for a previously published proteomics study on P. aeruginosa (Scherl et al., J. Am. Soc. Mass Spectrom. 2008, 19, 891) conducted using an LTQ-orbitrap. Overall, 4878 precursor ions are matched by considering the accurate mass (i.e., < 5 ppm) of the precursor ion and at least one fragment ion that confirms the sequence. The peptides are then grouped into higher- and lower-confidence data sets, using five fragment ions as a cutoff for higher-confidence identification. It is shown that the propensity for sequence permutation increases with the length of the tryptic peptide in both data sets. A higher charge state (i.e., 3+ vs 2+) also appears to correlate with a higher appearance of permuted masses for larger peptides. The ratio of these permuted sequence ions, compared to all tandem mass spectral peaks, reaches similar to 25% in the higher-confidence data set, compared to an estimated incidence of false positives for permuted masses (maximum similar to 8%), based on a null-hypothesis decoy data set.

Background: SOX2 is a key gene implicated in maintaining the stemness of embryonic and adult stem cells. SOX2 appears to re-activate in several human cancers including glioblastoma multiforme (GBM), however, the detailed response program of SOX2 in GBM has not yet been defined. Results: We show that knockdown of the SOX2 gene in LN229 GBM cells reduces cell proliferation and colony formation. We then comprehensively characterize the SOX2 response program by an integrated analysis using several advanced genomic technologies including ChIP-seq, microarray profiling, and microRNA sequencing. Using ChIP-seq technology, we identified 4883 SOX2 binding regions in the GBM cancer genome. SOX2 binding regions contain the consensus sequence wwTGnwTw that occurred 3931 instances in 2312 SOX2 binding regions. Microarray analysis identified 489 genes whose expression altered in response to SOX2 knockdown. Interesting findings include that SOX2 regulates the expression of SOX family proteins SOX1 and SOX18, and that SOX2 down regulates BEX1 (brain expressed X-linked 1) and BEX2 (brain expressed X-linked 2), two genes with tumor suppressor activity in GBM. Using next generation sequencing, we identified 105 precursor microRNAs (corresponding to 95 mature miRNAs) regulated by SOX2, including down regulation of miR-143, -145, -253-5p and miR-452. We also show that miR-145 and SOX2 form a double negative feedback loop in GBM cells, potentially creating a bistable system in GBM cells. Conclusions: We present an integrated dataset of ChIP-seq, expression microarrays and microRNA sequencing representing the SOX2 response program in LN229 GBM cells. The insights gained from our integrated analysis further our understanding of the potential actions of SOX2 in carcinogenesis and serves as a useful resource for the research community.

Background: Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results: We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP), a commonly used open source proteomics analysis pipeline. Conclusions: We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.