Salivary proteins from dysplastic leukoplakia and oral squamous cell carcinoma and their potential for early detection.

Dysplastic leukoplakia (LP) of the oral cavity is a potentially malignant condition for oral squamous cell carcinoma (OSCC), early detection of which remains an unmet clinical need. In an effort to develop non-invasive biomarker based method for early detection of the disease, differential proteomic profiling was carried out with the saliva from patients with risk habits and diagnosed with LP and those with lymph node negative and positive OSCC in comparison to healthy controls with risk habits. Ninety three proteins were observed at elevated level (≥1.5 fold), and 30 were prioritized based on a scoring system comprising of confidence of identification, presence in the various specimen groups, functional relevance, and their secretory potential. Verification was carried out in independent patient cohorts for 8 selected, representative, upregulated proteins using ELISA. Three of them CD44, S100A7, and S100P were significantly altered in patients with LP as well as OSCC and can be regarded as a panel of biomarker candidates for early detection of the malignancy. Other members may also be investigated in a targeted manner to expand the portfolio of biomarkers for early detection. The mass spectrometry data are available via ProteomeXchange with identifier PXD015722. SIGNIFICANCE: There is an unmet clinical need for non-invasive, biomarker based methods for the improved early detection and the subsequent management of oral cancer. The study represents differential proteome profiling of the saliva of patients with oral dysplastic leukoplakia (LP) - a potentially malignant lesion, patients diagnosed with oral squamous cell carcinoma (OSCC), and healthy controls to identify potential markers for the purpose of early detection of malignancy. From among the matched and prioritized proteins with elevated levels in the saliva of patients with LP and those with OSCC, eight were verified. Three of them - CD44, S100A7 and S100P appeared promising candidates as biomarkers for early detection of the neoplastic predisposition and may form the basis of clinical assays for this purpose.

Microsomal membrane proteome of low grade diffuse astrocytomas: Differentially expressed proteins and candidate surveillance biomarkers.

Diffuse astrocytoma (DA; WHO grade II) is a low-grade, primary brain neoplasm with high potential of recurrence as higher grade malignant form. We have analyzed differentially expressed membrane proteins from these tumors, using high-resolution mass spectrometry. A total of 2803 proteins were identified, 340 of them differentially expressed with minimum of 2 fold change and based on ≥2 unique peptides. Bioinformatics analysis of this dataset also revealed important molecular networks and pathways relevant to tumorigenesis, mTOR signaling pathway being a major pathway identified. Comparison of 340 differentially expressed proteins with the transcript data from Grade II diffuse astrocytomas reported earlier, revealed about 190 of the proteins correlate in their trends in expression. Considering progressive and recurrent nature of these tumors, we have mapped the differentially expressed proteins for their secretory potential, integrated the resulting list with similar list of proteins from anaplastic astrocytoma (WHO Grade III) tumors and provide a panel of proteins along with their proteotypic peptides, as a resource that would be useful for investigation as circulatory plasma markers for post-treatment surveillance of DA patients.

Clinicopathological Analysis and Multipronged Quantitative Proteomics Reveal Oxidative Stress and Cytoskeletal Proteins as Possible Markers for Severe Vivax Malaria.

In Plasmodium vivax malaria, mechanisms that trigger transition from uncomplicated to fatal severe infections are obscure. In this multi-disciplinary study we have performed a comprehensive analysis of clinicopathological parameters and serum proteome profiles of vivax malaria patients with different severity levels of infection to investigate pathogenesis of severe malaria and identify surrogate markers of severity. Clinicopathological analysis and proteomics profiling has provided evidences for the modulation of diverse physiological pathways including oxidative stress, cytoskeletal regulation, lipid metabolism and complement cascades in severe malaria. Strikingly, unlike severe falciparum malaria the blood coagulation cascade was not found to be affected adversely in acute P. vivax infection. To the best of our knowledge, this is the first comprehensive proteomics study, which identified some possible cues for severe P. vivax infection. Our results suggest that Superoxide dismutase, Vitronectin, Titin, Apolipoprotein E, Serum amyloid A, and Haptoglobin are potential predictive markers for malaria severity.

Metabolite profiling in posttraumatic stress disorder.

BACKGROUND: Traumatic stress does not only increase the risk for posttraumatic stress disorder (PTSD), but is also associated with adverse secondary physical health outcomes. Despite increasing efforts, we only begin to understand the underlying biomolecular processes. The hypothesis-free assessment of a wide range of metabolites (termed metabolite profiling) might contribute to the discovery of biological pathways underlying PTSD. METHODS: Here, we present the results of the first metabolite profiling study in PTSD, which investigated peripheral blood serum samples of 20 PTSD patients and 18 controls. We performed liquid chromatography (LC) coupled to Quadrupole/Time-Of-Flight (QTOF) mass spectrometry. Two complementary statistical approaches were used to identify metabolites associated with PTSD status including univariate analyses and Partial Least Squares Discriminant Analysis (PLS-DA). RESULTS: Thirteen metabolites displayed significant changes in PTSD, including four glycerophospholipids, and one metabolite involved in endocannabinoid signaling. A biomarker panel of 19 metabolites classifies PTSD with 85% accuracy, while classification accuracy from the glycerophospholipid with the highest differentiating ability already reached 82%. CONCLUSIONS: This study illustrates the feasibility and utility of metabolite profiling for PTSD and suggests lipid-derived and endocannabinoid signaling as potential biological pathways involved in trauma-associated pathophysiology.

Heterogeneous nuclear ribonucleoproteins and their interactors are a major class of deregulated proteins in anaplastic astrocytoma: a grade III malignant glioma.

Anaplastic astrocytoma is a high grade malignant glioma (WHO grade III) of the central nervous system which arises from a low grade II tumor and invariably progresses into lethal glioblastoma (WHO grade IV). We have studied differentially expressed proteins from the microsomal fraction of the clinical specimens of these tumors, using iTRAQ and high-resolution mass spectrometry followed by immunohistochemistry for representative proteins on tissue sections. A total of 2642 proteins were identified, 266 of them with minimum 2 peptide signatures and 2-fold change in expression. The major groups of proteins revealed to be differentially expressed were associated with key cellular processes such as post transcriptional processing, protein translation, and acute phase response signaling. A distinct inclusion among these important proteins is 10 heterogeneous nuclear ribonucleoproteins (hnRNPs) and their interacting partners which have regulatory functions in the cell. hnRNP-mediated post transcriptional events are known to play a major role in mRNA processing, stability, and distribution. Their altered levels have also been observed by us in lower (diffused astrocytoma) and higher (glioblastoma) grades of gliomas, and membrane localization of hnRNPs has also been documented in the literature. hnRNPs may thus be major factors underlying global gene expression changes observed in glial tumors while their differential presence in the microsomal fraction suggests yet additional and unknown roles in tumorigenesis.

Proteomic investigation of falciparum and vivax malaria for identification of surrogate protein markers.

This study was conducted to analyze alterations in the human serum proteome as a consequence of infection by malaria parasites Plasmodium falciparum and P. vivax to obtain mechanistic insights about disease pathogenesis, host immune response, and identification of potential protein markers. Serum samples from patients diagnosed with falciparum malaria (FM) (n = 20), vivax malaria (VM) (n = 17) and healthy controls (HC) (n = 20) were investigated using multiple proteomic techniques and results were validated by employing immunoassay-based approaches. Specificity of the identified malaria related serum markers was evaluated by means of analysis of leptospirosis as a febrile control (FC). Compared to HC, 30 and 31 differentially expressed and statistically significant (p<0.05) serum proteins were identified in FM and VM respectively, and almost half (46.2%) of these proteins were commonly modulated due to both of the plasmodial infections. 13 proteins were found to be differentially expressed in FM compared to VM. Functional pathway analysis involving the identified proteins revealed the modulation of different vital physiological pathways, including acute phase response signaling, chemokine and cytokine signaling, complement cascades and blood coagulation in malaria. A panel of identified proteins consists of six candidates; serum amyloid A, hemopexin, apolipoprotein E, haptoglobin, retinol-binding protein and apolipoprotein A-I was used to build statistical sample class prediction models. By employing PLS-DA and other classification methods the clinical phenotypic classes (FM, VM, FC and HC) were predicted with over 95% prediction accuracy. Individual performance of three classifier proteins; haptoglobin, apolipoprotein A-I and retinol-binding protein in diagnosis of malaria was analyzed using receiver operating characteristic (ROC) curves. The discrimination of FM, VM, FC and HC groups on the basis of differentially expressed serum proteins demonstrates the potential of this analytical approach for the detection of malaria as well as other human diseases.

Investigation of serum proteome alterations in human glioblastoma multiforme.

Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade-specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low-density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS-DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.