Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.     

Effective Identification of Bacterial Type III Secretion Signals Using Joint Element Features

Type III secretion system (T3SS) plays important roles in bacteria and host cell interactions by specifically translocating type III effectors into the cytoplasm of the host cells. The N-terminal amino acid sequences of the bacterial type III effectors determine their specific secretion via type III secretion conduits. It is still unclear as to how the N-terminal sequences guide this specificity. In this work, the amino acid composition, secondary structure, and solvent accessibility in the N-termini of type III and non-type III secreted proteins were compared and contrasted. A high-efficacy mathematical model based on these joint features was developed to distinguish the type III proteins from the non-type III ones. The results indicate that secondary structure and solvent accessibility may make important contribution to the specific recognition of type III secretion signals. Analysis also showed that the joint feature of the N-terminal 6^th–10^th amino acids are especially important for guiding specific type III secretion. Furthermore, a genome-wide screening was performed to predict Salmonella type III secreted proteins, and 8 new candidates were experimentally validated. Interestingly, type III secretion signals were also predicted in gram-positive bacteria and yeasts. Experimental validation showed that two candidates from yeast can indeed be secreted through Salmonella type III secretion conduit. This research provides the first line of direct evidence that secondary structure and solvent accessibility contain important features for guiding specific type III secretion. The new software based on these joint features ensures a high accuracy (general cross-validation sensitivity of ∼96% at a specificity of ∼98%) in silico identification of new type III secreted proteins, which may facilitate our understanding about the specificity of type III secretion and the evolution of type III secreted proteins.     

Mapping of interactions between human macrophages and Staphylococcus aureus reveals an involvement of MAP kinase signaling in the host defense

Staphylococcus aureus is a dangerous opportunistic human pathogen that causes serious invasive diseases when it reaches the bloodstream. Recent studies have shown that S. aureus is highly resistant to killing by professional phagocytes and that such cells even provide a favorable environment for intracellular survival of S. aureus. Importantly, the reciprocal interactions between phagocytes and S. aureus have remained largely elusive. Here we have employed kinase profiling to define the nature and time resolution of the human THP-1 macrophage response toward S. aureus and proteomics to identify the response of S. aureus toward macrophages. The results of these studies reveal major macrophage signaling pathways triggered by S. aureus and proteomic signatures of the responses of S. aureus to macrophages. We also identify human proteins bound to S. aureus that have potential roles in bacterial killing and internalization. Most noticeably, our observations challenge the classical concept that macrophage responses are mainly mediated through Toll-like receptor 2 and NF-κB signaling and highlight the important role of the stress-activated MAP kinase signaling in orchestrating the host defense.     

InXy and SeXy, compact heterologous reporter proteins for mammalian cells

Mammalian reporter proteins are essential for gene-function analysis, drugscreening initiatives and as model product proteins for biopharmaceutical manufacturing. Bacillus subtilis can maintain its metabolism by secreting Xylanase A (XynA), which converts xylan into shorter xylose oligosaccharides. XynA is a family 11 xylanase monospecific for D-xylose containing substrates. Mammalian cells transgenic for constitutive expression of wild-type xynA showed substantial secretion of this prokaryotic enzyme. Deletion analysis confirmed that a prokaryotic signal sequence encoded within the first 81 nucleotides was compatible with the secretory pathway of mammalian cells. Codon optimization combined with elimination of the prokaryotic signal sequence resulted in an exclusively intracellular mammalian Xylanase A variant (InXy) while replacement by an immunoglobulin-derived secretion signal created an optimal secreted Xylanase A derivative (SeXy). A variety of chromogenic and fluorescence-based assays adapted for use with mammalian cells detected InXy and SeXy with high sensitivity and showed that both reporter proteins resisted repeated freeze/thaw cycles, remained active over wide temperature and pH ranges, were extremely stable in human serum stored at room temperature and could independently be quantified in samples also containing other prominent reporter proteins such as the human placental alkaline phosphatase (SEAP) and the Bacillus stearothermophilus-derived secreted alpha-amylase (SAMY). Glycoprofiling revealed that SeXy produced in mammalian cells was N- glycosylated at four different sites, mutation of which resulted in impaired secretion. SeXy was successfully expressed in a variety of mammalian cell lines and primary cells following transient transfection and transduction with adeno-associated virus particles (AAV) engineered for constitutive SeXy expression. Intramuscular injection of transgenic AAVs into mice showed significant SeXy levels in the bloodstream. InXy and SeXy are highly sensitive, compact and robust reporter proteins, fully compatible with pre-existing marker genes and can be assayed in high-throughput formats using very small sample volumes.     

Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.     

NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins

Background  

Most predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.     

C-terminal antibodies (CTAbs): a simple and broadly applicable approach for the rapid generation of protein-specific antibodies with predefined specificity

Recent advances in proteomic techniques have resulted in an ever-increasing need to produce antibodies. Here, to address this problem, a technically simple approach of targeting the extreme C-termini of proteins with antibodies (CTAbs) was investigated in proteins secreted by the human pathogen Streptococcus pyogenes. Target proteins were identified by a conventional proteomic approach and CTAbs produced against synthetic five amino acid peptides representing the C-terminus of each target protein. In every case where protein secretion was demonstrated (n = 20), CTAbs were successfully produced and bound specifically to the target protein (100% success rate). The apparent specificity was consistent with the structural heterogeneity of the C-termini of S. pyogenes proteins. The global specificity of CTAb binding was defined using a combinatorial library of synthetic peptides representing structural variants of the original synthetic immunogen. This is a systematic and comprehensive approach for the development of antibodies with defined specificity that can be used in a range of applications.     

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.     

Proteomic profiling of exosomes: current perspectives

Exosomes are 40-100 nm membrane vesicles of endocytic origin secreted by most cell types in vitro. Recent studies have shown that exosomes are also found in vivo in body fluids such as blood, urine, amniotic fluid, malignant ascites, bronchoalveolar lavage fluid, synovial fluid, and breast milk. While the biological function of exosomes is still unclear, they can mediate communication between cells, facilitating processes such as antigen presentation and in trans signaling to neighboring cells. Exosome-like vesicles identified in Drosophila (referred to as argosomes) may be potential vehicles for the spread of morphogens in epithelia. The advent of current MS-based proteomic technologies has contributed significantly to our understanding of the molecular composition of exosomes. In addition to a common set of membrane and cytosolic proteins, it is becoming increasingly apparent that exosomes harbor distinct subsets of proteins that may be linked to cell-type associated functions. The secretion of exosomes by tumor cells and their implication in the transport and propagation of infectious cargo such as prions and retroviruses such as HIV suggest their participation in pathological situations. Interestingly, the recent observation that exosomes contain both mRNA and microRNA, which can be transferred to another cell, and be functional in that new environment, is an exciting new development in the unraveling exosome saga. The present review aims to summarize the physical properties that define exosomes as specific cell-type secreted membrane vesicles.     

Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest

We present our approach to classifying the processed proteomic data that were made available to the participants of the classification competition. Although classification of the spectra was the goal of the competition we feel that proteomic applications to cancer biomarker studies make certain additional demands. For example, one such requirement should be identification of certain features which collectively could differentiate the two groups of samples. Also ideally, the size of the feature set should be small. To that end we propose a linear discriminant classifier based on nine m/z intensity values. Construction and performance of this classifier are discussed.     

Application of β-Lactamase Reporter Fusions as an Indicator of Effector Protein Secretion during Infections with the Obligate Intracellular Pathogen Chlamydia trachomatis

Chlamydia spp. utilize multiple secretion systems, including the type III secretion system (T3SS), to deploy host-interactive effector proteins into infected host cells. Elucidation of secreted proteins has traditionally required ectopic expression in a surrogate T3SS followed by immunolocalization of endogenous candidate effectors to confirm secretion by chlamydiae. The ability to transform Chlamydia and achieve stable expression of recombinant gene products has enabled a more direct assessment of secretion. We adapted TEM-1 β-lactamase as a reporter system for assessment of chlamydial protein secretion. We provide evidence that this system facilitates visualization of secretion in the context of infection. Specifically, our findings provide definitive evidence that C. trachomatis CT695 is secreted during infection. Follow-up indirect immunofluorescence studies confirmed CT695 secretion and indicate that this effector can be secreted at multiple points during the chlamydial developmental cycle. Our results indicate that the BlaM-fusion reporter assay will allow efficacious identification of novel secreted proteins. Moreover, this approach can easily be adapted to enable more sophisticated studies of the secretion process in Chlamydia.     

Mapping cyclosporine-induced changes in protein secretion by renal cells using stable isotope labeling with amino acids in cell culture (SILAC)

Nephrotoxicity is an adverse event that strongly limits the use of the immunosuppressant cyclosporine in solid organ transplantation and the precise molecular mechanisms underlying this toxicity remain unclear. MS-based proteomic analysis of the secretome of HEK-293 renal cells exposed to cyclosporine was performed to identify changes in protein secretion, as a first step to discover potential biomarkers of such nephrotoxicity. To detect and quantify the perturbed proteins in the culture medium we used SILAC and nano-scale liquid chromatography followed by MALDI-TOF/TOF mass spectrometry. Among 106 proteins identified, 80 were quantified in both forward/reverse SILAC experiments and quantitative proteomic analysis revealed altered levels of expression for 24 secreted proteins. These included the down-regulation of a number of extracellular matrix/cell adhesion components, and the up-regulation of secreted cyclophilins A and B, macrophage inhibition factor and phosphatidylethanolamine-binding protein 1. These changes in protein secretion were not prevented by co-incubation with the antioxidant N-acetylcysteine, suggesting that they were not triggered by cyclosporine-induced oxidative stress. The results from the present study provide important new knowledge to gain insights into the molecular mechanisms of cyclosporine-related toxicity. Some of the proteins identified here should be tested as potential biomarkers of cyclosporine nephrotoxicity in subsequent clinical studies.     

A proteomic approach for identification of secreted proteins during the differentiation of 3T3-L1 preadipocytes to adipocytes

We have undertaken a systematic proteomic approach to purify and identify secreted factors that are differentially expressed in preadipocytes versus adipocytes. Using one-dimensional gel electrophoresis combined with nanoelectrospray tandem mass spectrometry, proteins that were specifically secreted by 3T3-L1 preadipocytes or adipocytes were identified. In addition to a number of previously reported molecules that are up- or down-regulated during this differentiation process (adipsin, adipocyte complement-related protein 30 kDa, complement C3, and fibronectin), we identified four secreted molecules that have not been shown previously to be expressed differentially during the process of adipogenesis. Pigment epithelium-derived factor, a soluble molecule with potent antiangiogenic properties, was found to be highly secreted by preadipocytes but not adipocytes. Conversely, we found hippocampal cholinergic neurostimulating peptide, neutrophil gelatinase-associated lipocalin, and haptoglobin to be expressed highly by mature adipocytes. We also used liquid chromatography-based separation followed by automated tandem mass spectrometry to identify proteins secreted by mature adipocytes. Several additional secreted proteins including resistin, secreted acidic cysteine-rich glycoprotein/osteonectin, stromal cell-derived factor-1, cystatin C, gelsolin, and matrix metalloprotease-2 were identified by this method. To our knowledge, this is the first study to identify several novel secreted proteins by adipocytes by a proteomic approach using mass spectrometry.     

TatC is a specificity determinant for protein secretion via the twin-arginine translocation pathway

The recent discovery of a ubiquitous translocation pathway, specifically required for proteins with a twin-arginine motif in their signal peptide, has focused interest on its membrane-bound components, one of which is known as TatC. Unlike most organisms of which the genome has been sequenced completely, the Gram-positive eubacterium Bacillus subtilis contains two tatC-like genes denoted tatCd and tatCy. The corresponding TatCd and TatCy proteins have the potential to be involved in the translocation of 27 proteins with putative twin-arginine signal peptides of which approximately 6-14 are likely to be secreted into the growth medium. Using a proteomic approach, we show that PhoD of B. subtilis, a phosphodiesterase belonging to a novel protein family of which all known members are synthesized with typical twin-arginine signal peptides, is secreted via the twin-arginine translocation pathway. Strikingly, TatCd is of major importance for the secretion of PhoD, whereas TatCy is not required for this process. Thus, TatC appears to be a specificity determinant for protein secretion via the Tat pathway. Based on our observations, we hypothesize that the TatC-determined pathway specificity is based on specific interactions between TatC-like proteins and other pathway components, such as TatA, of which three paralogues are present in B. subtilis.     

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.     

Proteomics of filamentous fungi

Proteomic analysis, defined here as the global assessment of cellular proteins expressed in a particular biological state, is a powerful tool that can provide a systematic understanding of events at the molecular level. Proteomic studies of filamentous fungi have only recently begun to appear in the literature, despite the prevalence of these organisms in the biotechnology industry, and their importance as both human and plant pathogens. Here, we review recent publications that have used a proteomic approach to develop a better understanding of filamentous fungi, highlighting sample preparation methods and whole-cell cytoplasmic proteomics, as well as subproteomics of cell envelope, mitochondrial and secreted proteins.     

Osteogenic differentiation of adipose derived stem cells promoted by overexpression of osterix

Secreted proteins, which may be involved in the regulation of various biological processes, are the potential targets for diagnosis and treatment of diverse diseases. In this study, to identify the human hepatoma HepG2 cells-derived secreted proteins more extensively, we applied the protein sample preparations using the combinations of denaturation methods and molecular-mass cutoff via ultrafiltration to the two-dimensional liquid chromatography coupled with tandem mass spectrometry (2D LC–MS/MS) analysis. We were able to identify a total of 86 proteins containing widely known secreted proteins of HepG2 such as alpha-fetoprotein, of which 73 proteins including 27 signal peptide-containing proteins have never been reported to be secreted from HepG2 cells in other proteomic studies. Among the identified signal peptide-containing proteins, ten proteins such as growth differentiation factor 15, osteopontin and stanniocalcin 2 were discovered as new secreted proteins of HepG2 cells. These observations suggest that the combinations of different sample preparation methods and 2D LC–MS/MS analysis are useful for identifying a wider range of low-abundance proteins and that the secreted proteins from HepG2 identified in this study may be useful as liver-specific biomarkers for diagnosis and treatment.     

An empirical assessment of validation practices for molecular classifiers

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.