Johannes A. Hewel

The Proteomes of Human Parotid and Submandibular/Sublingual Gland Salivas Collected as the Ductal Secretions

Saliva is a body fluid with important functions in oral and general health. A consortium of three research groups catalogued the proteins in human saliva collected as the ductal secretions: 1166 identifications--914 in parotid and 917 in submandibular/sublingual saliva--were made. The results showed that a high proportion of proteins that are found in plasma and/or tears are also present in saliva along with unique components. The proteins identified are involved in numerous molecular processes ranging from structural functions to enzymatic/catalytic activities. As expected, the majority mapped to the extracellular and secretory compartments. An immunoblot approach was used to validate the presence in saliva of a subset of the proteins identified by mass spectrometric approaches. These experiments focused on novel constituents and proteins for which the peptide evidence was relatively weak. Ultimately, information derived from the work reported here and related published studies can be used to translate blood-based clinical laboratory tests into a format that utilizes saliva. Additionally, a catalogue of the salivary proteome of healthy individuals allows future analyses of salivary samples from individuals with oral and systemic diseases, with the goal of identifying biomarkers with diagnostic and/or prognostic value for these conditions; another possibility is the discovery of therapeutic targets.

Adaptation of Energy Metabolism in Breast Cancer Brain Metastases

Brain metastases are among the most feared complications in breast cancer, as no therapy exists that prevents or eliminates breast cancer spreading to the brain. New therapeutic strategies depend on specific knowledge of tumor cell properties that allow breast cancer cell growth within the brain tissue. To provide information in this direction, we established a human breast cancer cell model for brain metastasis based on circulating tumor cells from a breast cancer patient and variants of these cells derived from bone or brain lesions in immunodeficient mice. The brain-derived cells showed an increased potential for brain metastasis in vivo and exhibited a unique protein expression profile identified by large-scale proteomic analysis. This protein profile is consistent with either a selection of predisposed cells or bioenergetic adaptation of the tumor cells to the unique energy metabolism of the brain. Increased expression of enzymes involved in glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation pathways suggests that the brain metastatic cells derive energy from glucose oxidation. The cells further showed enhanced activation of the pentose phosphate pathway and the glutathione system, which can minimize production of reactive oxygen species resulting from an enhanced oxidative metabolism. These changes promoted resistance of brain metastatic cells to drugs that affect the cellular redox balance. Importantly, the metabolic alterations are associated with strongly enhanced tumor cell survival and proliferation in the brain microenvironment. Thus, our data support the hypothesis that predisposition or adaptation of the tumor cell energy metabolism is a key element in breast cancer brain metastasis, and raise the possibility of targeting the functional differentiation in breast cancer brain lesions as a novel therapeutic strategy.

ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity

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. This article is part of a Special Issue entitled: Computational Proteomics.

A Lentiviral Functional Proteomics Approach Identifies Chromatin Remodeling Complexes Important for the Induction of Pluripotency

Protein complexes and protein-protein interactions are essential for almost all cellular processes. Here, we establish a mammalian affinity purification and lentiviral expression (MAPLE) system for characterizing the subunit compositions of protein complexes. The system is flexible (i.e. multiple N- and C-terminal tags and multiple promoters), is compatible with GatewayTM cloning, and incorporates a reference peptide. Its major advantage is that it permits efficient and stable delivery of affinity-tagged open reading frames into most mammalian cell types. We benchmarked MAPLE with a number of human protein complexes involved in transcription, including the RNA polymerase II-associated factor, negative elongation factor, positive transcription elongation factor b, SWI/SNF, and mixed lineage leukemia complexes. In addition, MAPLE was used to identify an interaction between the reprogramming factor Klf4 and the Swi/Snf chromatin remodeling complex in mouse embryonic stem cells. We show that the SWI/SNF catalytic subunit Smarca2/Brm is up-regulated during the process of induced pluripotency and demonstrate a role for the catalytic subunits of the SWI/SNF complex during somatic cell reprogramming. Our data suggest that the transcription factor Klf4 facilitates chromatin remodeling during reprogramming.

Biomarkers for Early and Late Stage Chronic Allograft Nephropathy by Proteogenomic Profiling of Peripheral Blood

Background Despite significant improvements in life expectancy of kidney transplant patients due to advances in surgery and immunosuppression, Chronic Allograft Nephropathy (CAN) remains a daunting problem. A complex network of cellular mechanisms in both graft and peripheral immune compartments complicates the non-invasive diagnosis of CAN, which still requires biopsy histology. This is compounded by non-immunological factors contributing to graft injury. There is a pressing need to identify and validate minimally invasive biomarkers for CAN to serve as early predictors of graft loss and as metrics for managing long-term immunosuppression. Methods We used DNA microarrays, tandem mass spectroscopy proteomics and bioinformatics to identify genomic and proteomic markers of mild and moderate/severe CAN in peripheral blood of two distinct cohorts (n = 77 total) of kidney transplant patients with biopsy-documented histology. Findings Gene expression profiles reveal over 2400 genes for mild CAN, and over 700 for moderate/severe CAN. A consensus analysis reveals 393 (mild) and 63 (moderate/severe) final candidates as CAN markers with predictive accuracy of 80% (mild) and 92% (moderate/severe). Proteomic profiles show over 500 candidates each, for both stages of CAN including 302 proteins unique to mild and 509 unique to moderate/severe CAN. Conclusions This study identifies several unique signatures of transcript and protein biomarkers with high predictive accuracies for mild and moderate/severe CAN, the most common cause of late allograft failure. These biomarkers are the necessary first step to a proteogenomic classification of CAN based on peripheral blood profiling and will be the targets of a prospective clinical validation study.

Molecular Mechanisms of Chronic Kidney Transplant Rejection via Large-Scale Proteogenomic Analysis of Tissue Biopsies

The most common cause of kidney transplant failure is the poorly characterized histopathologic entity interstitial fibrosis and tubular atrophy (IFTA). There are no known unifying mechanisms, no effective therapy, and no proven preventive strategies. Possible mechanisms include chronic immune rejection, inflammation, drug toxicity, and chronic kidney injury from secondary factors. To gain further mechanistic insight, we conducted a large-scale proteogenomic study of kidney transplant biopsies with IFTA of varying severity. We acquired proteomic data using tandem mass spectrometry with subsequent quantification, analysis of differential protein expression, validation, and functional annotations to known molecular networks. We performed genome-wide expression profiling in parallel. More than 1400 proteins with unique expression profiles traced the progression from normal transplant biopsies to biopsies with mild to moderate and severe disease. Multiple sets of proteins were mapped to different functional pathways, many increasing with histologic severity, including immune responses, inflammatory cell activation, and apoptosis consistent with the chronic rejection hypothesis. Two examples include the extensive population of the alternative rather than the classical complement pathway, previously not appreciated for IFTA, and a comprehensive control network for the actin cytoskeleton and cell signaling of the acute-phase response. In summary, this proteomic effort using kidney tissue contributes mechanistic insight into several biologic processes associated with IFTA.

Isolation and Angiogenesis by Endothelial Progenitors in the Fetal Liver

Endothelial progenitor cells (EPCs) have significant therapeutic potential. However, the low quantity of such cells available from bone marrow and their limited capacity to proliferate in culture make their use difficult. Here, we present the first definitive demonstration of the presence of true EPCs in murine fetal liver capable of forming blood vessels in vivo connected to the hosts vasculature after transplantation. This population is particularly interesting because it can be obtained at high yield and has a high angiogenic capacity as compared with bone marrow–derived EPCs. The EPC capacity is contained within the CD31+Sca1+ cell subset. We demonstrate that these cells are dependent for survival and proliferation on a feeder cell monolayer derived from the fetal liver. In addition, we describe a novel and easy method for the isolation and ex vivo proliferation of these EPCs. Finally, we used gene expression profiling and tandem mass spectrometry proteomics to examine the fetal liver endothelial progenitors and the feeder cells to identify possible proangiogenic growth factor and endothelial differentiation‐associated genes.

Evaluation of data-dependent versus targeted shotgun proteomic approaches for monitoring transcription factor expression in breast cancer.

In breast cancer, there is a significant degree of molecular diversity among tumors. Multiple perturbations in signal transduction pathways impinge on transcriptional networks that in turn dictate malignant transformation and metastatic progression. Detailed knowledge of the sequence-specific transcription factors that become activated or repressed within a tumor and comparison of their relative levels of expression in cancer versus normal tissue should therefore provide insight into disease mechanisms, improving patient stratification and facilitating personalized treatment. While high-throughput tandem mass spectrometry methods for global proteome profiling have been developed, existing approaches have limited sensitivity and are often unable to detect low-abundance transcription factors in a complex biological specimen like a biopsy or tumor cell extract. To this end, we have undertaken a systematic comparative evaluation of three MS/MS methods for the ability to detect reference transcription factors spiked in known amounts into a cell-free breast cancer nuclear extract: Data-Dependent Acquisition (DDA), wherein precursor ion intensity dictates selection for fragmentation; Targeted Peptide Monitoring (TPM), a directed approach using successive isolation and fragmentation of predefined m/ z ratios; and Multiple Reaction Monitoring (MRM), in which specific precursor ion to product ion transitions are selectively monitored. Through a series of controlled, parallel benchmarking experiments, we have determined the relative figures-of-merit of each approach, and have established that prior knowledge of signature proteotypic peptides markedly improves overall detection sensitivity, reliability, and quantification.

Human-Chromatin-Related Protein Interactions Identify a Demethylase Complex Required for Chromosome Segregation

Chromatin regulation is driven by multicomponent protein complexes, which form functional modules. Deciphering the components of these modules and their interactions is central to understanding the molecular pathways these proteins are regulating, their functions, and their relation to both normal development and disease. We describe the use of affinity purifications of tagged human proteins coupled with mass spectrometry to generate a protein-protein interaction map encompassing known and predicted chromatin-related proteins. On the basis of 1,394 successful purifications of 293 proteins, we report a high-confidence (85% precision) network involving 11,464 protein-protein interactions among 1,738 different human proteins, grouped into 164 often overlapping protein complexes with a particular focus on the family of JmjC-containing lysine demethylases, their partners, and their roles in chromatin remodeling. We show that RCCD1 is a partner of histone H3K36 demethylase KDM8 and demonstrate that both are important for cell-cycle-regulated transcriptional repression in centromeric regions and accurate mitotic division.

Target Identification by Chromatographic Co-elution: Monitoring of Drug-Protein Interactions without Immobilization or Chemical Derivatization

Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (Kd range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.