Membrane Proteome of Invasive Retinoblastoma: Differential Proteins and Biomarkers

Purpose: Retinoblastoma (RB) is a pediatric ocular cancer which is caused due to the aberrations in the RB1 gene. The changes in the membrane proteomics would help in understanding the development of the retinoblastoma and could identify candidates for biomarkers and therapy. Experimental design: Quantitative proteomics is performed on the enriched membrane fractions from pooled normal retina (n = 5) and pooled retinoblastoma tissues (n = 5). The proteins are tryptic‐digested and tagged with iTRAQ labels. Orbitrap mass spectrometry is used to analyze and quantify the deregulated membrane proteins involved in the RB tumor progression. Immunohistochemistry (IHC) is used to further validate few of the differentially expressed proteins. Results: A total of 3122 proteins are identified of which, 663 proteins are found to be deregulated with ≥two fold change in the RB tumor compared to the retina. 282 proteins are upregulated and 381 are downregulated with ≥2 peptide identifications. Bioinformatic analysis revealed that, most of the proteins are involved in the transport, cellular communication, and growth. Overexpression of lamin B1 (LMNB1) and transferrin receptor (TFRC) are observed in RB tumors using IHC. Conclusion and clinical relevance: The present study, is the first comprehensive quantitative membrane proteomic atlas of the differentially regulated proteins in RB compared to the retina. LMNB1 and TFRC could be potential biomarkers for this childhood cancer.     

Multidimensional protein identification technology analysis highlights mitoxantrone‐induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas‐mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.     

Anatomic site‐specific proteomic signatures of gastrointestinal stromal tumors

The gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. Its clinical course ranges widely from a curable disorder to a highly malignant disease. Although its clinical and molecular characteristics depend on the anatomic site of origin, the molecular background of GIST arising in different anatomical site has not been studied yet. To investigate the proteomic background of GIST, we examined the proteomic features corresponding to the anatomic site of tumor origin. Comparison of the proteomic profile of gastric (23 cases) and small intestinal (9 cases) GIST by 2‐DE revealed 105 protein spots with significantly different intensity (p <0.01) between the two groups. Mass spectrometric study identified 68 distinct proteins for these 105 protein spots, including cancer‐associated ones such as prohibitin, pigment epithelium‐derived factor, and alpha‐actinin 4. The intensity of 37/105 (35.2%) protein spots was significantly concordant with the corresponding mRNA levels (p <0.01). Although both 2‐D DIGE and microarray experiments showed significant up‐regulation of vimentin expression in small intestinal GIST, Western blotting did not show a significant difference between the two groups. In conclusion, our study demonstrates the proteins specially expressed in GIST depending on their site of origin, as well as the unique advantage offered by use of proteomics to acquire such data. The identified proteins may provide clues to understanding the different characteristics of GIST depending on their site of origin.     

Analysing the localisation sites of proteins through neural networks ensembles

Scientists involved in the area of proteomics are currently seeking integrated, customised and validated research solutions to better expedite their work in proteomics analyses and drug discoveries. Some drugs and most of their cell targets are proteins, because proteins dictate biological phenotype. In this context, the automated analysis of protein localisation is more complex than the automated analysis of DNA sequences; nevertheless the benefits to be derived are of same or greater importance. In order to accomplish this target, the right choice of the kind of the methods for these applications, especially when the data set is drastically imbalanced, is very important and crucial. In this paper we investigate the performance of some commonly used classifiers, such as the K nearest neighbours and feed-forward neural networks with and without cross-validation, in a class of imbalanced problems from the bioinformatics domain. Furthermore, we construct ensemble-based schemes using the notion of diversity, and we empirically test their performance on the same problems. The experimental results favour the generation of neural network ensembles as these are able to produce good generalisation ability and significant improvement compared to other single classifier methods.     

Controlling the Evolution of Resistance

Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach.     

Comparative proteomic analysis of low stage and high stage endometrioid ovarian adenocarcinomas

Ovarian cancer, the second most common gynecological malignancy, accounts for 3% of all cancers among women in the United States, and has a high mortality rate, largely because existing therapies for widespread disease are rarely curative. Ovarian endometrioid adenocarcinoma (OEA) accounts for about 20% of the overall incidence of all ovarian cancer. We have used proteomics profiling to characterize low stage (FIGO stage 1 or 2) versus high stage (FIGO stage 3 or 4) human OEAs. In general, the low stage tumors lacked p53 mutations and had frequent CTNNB1, PTEN, and/or PIK3CA mutations. The high stage tumors had mutant p53, were usually high grade, and lacked mutations predicted to deregulate Wnt/β‐catenin and PI3K/Pten/Akt signaling. We utilized 2‐D liquid‐based separation/mass mapping techniques to elucidate molecular weight and pI measurements of the differentially expressed intact proteins. We generated 2‐D protein mass maps to facilitate the analysis of protein expression between both the low stage and high stage tumors. These mass maps (over a pI range of 5.6–4.6) revealed that the low stage OEAs demonstrated protein over‐expression at the lower pI ranges (pI 4.8–4.6) in comparison to the high stage tumors, which demonstrated protein over‐expression in the higher pI ranges (pI 5.4–5.2). These data suggest that both low and high stage OEAs have characteristic pI signatures of abundant protein expression probably reflecting, at least in part, the different signaling pathway defects that characterize each group. In this study, the low stage OEAs were distinguishable from high stage tumors based upon the proteomic profiles. Interestingly, when only high‐grade (grade 2 or 3) OEAs were included in the analysis, the tumors still tended to cluster according to stage, suggesting that the altered protein expression was not solely dependent upon tumor cell differentiation. Further, these protein profiles clearly distinguish OEA from other types of ovarian cancer at the protein level.     

Formation of 3-nitrotyrosines in carbonic anhydrase III is a sensitive marker of oxidative stress in skeletal muscle

Oxidation of skeletal muscle proteins has been reported to occur following contractions, with ageing, and with a variety of disease states, but the nature of the oxidised proteins has not been identified. A proteomics approach was utilised to identify major proteins that contain carbonyls and/or 3‐nitrotyrosine (3‐NT) groups in the gastrocnemius (GTN) muscles of adult (5–11 months of age) and old (26–28 months of age) wild type (WT) mice and adult mice lacking copper, zinc superoxide dismutase (Sod1^?/? mice), manganese superoxide dismutase (Sod2^+/? mice) or glutathione peroxidase 1 (GPx1^?/? mice). In quiescent GTN muscles of adult and old WT mice, protein carbonylation and/or formation of 3‐NT occurred in several proteins involved in glycolysis, as well as creatine kinase and carbonic anhydrase III. Following contractions, the 3‐NT intensity was increased in specific protein bands from GTN muscles of both adult and old WT mice. In quiescent GTN muscles from adult Sod1^?/?, Sod2^+/? or GPx1^?/? mice compared with age‐matched WT mice only carbonic anhydrase III showed a greater 3‐NT content. We conclude that formation of 3‐NT occurs readily in response to oxidative stress in carbonic anhydrase III and this may provide a sensitive measure of oxidative damage to muscle proteins.     

Discovery of diagnostic serum biomarkers of gastric cancer using proteomics

Gastric cancer has significant morbidity and mortality worldwide and locally. Good prognosis relies on an early diagnosis. However, this remains a challenge due to the lack of specific and sensitive serum biomarkers for early detection. Hence, there is a constant search for these biomarkers for screening purposes. Proteomic profiling enables a new approach to the discovery of biomarkers in disease. This review presents recent attempts in search of gastric cancer serum biomarker using proteomics. Different methodologies and different types of samples were employed by different groups of researchers. Major difficulties were encountered in the discovery processes, including interference from abundant proteins and continuous changing serum proteomes from different individuals.     

Human duodenal proteome modulations by glutamine and antioxidants

Purpose : Glutamine (Gln) has protective, anti‐inflammatory effects in animal models and humans. Antioxidant nutrients may exert synergistic effects on intestinal functions. Therefore, these combined nutrients may have a therapeutic potential during intestinal inflammation. This study was designed to investigate in humans the effects of a supplement composed of Gln and high‐dosed antioxidant micronutrients compared to isomolar Gln only, on duodenal proteome. Experimental design : Enteral perfusion of Gln (0.8 mmol . kg^?1. h^?1) or supplement was performed in two groups of six healthy volunteers during 5 h before taking endoscopic duodenal biopsies. Protein expression was analyzed by 2‐DE and the relevant proteins identified by MS/MS. Results : About 1500 protein spots were revealed in both supplement and Gln conditions. Comparative proteomics analysis indicated that 11 proteins were differentially and significantly (p≤0.05) expressed in response to the supplement. These proteins were essentially implicated in metabolism pathways, e.g. fatty acid binding protein‐1 and 40S ribosomal protein SA expressions were downregulated while manganese superoxide dismutase and retinal dehydrogenase‐1 expressions were upregulated. Conclusions and clinical relevance : This study provides new information on human duodenal proteome and its nutritional modulation, and supports further clinical investigations designed to evaluate the effects of Gln plus antioxidants during intestinal inflammation and cancer.     

A comparative proteomic study to characterize the vinblastine resistance in human ovarian cancer cells

Drug resistance is a major impediment to the successful treatment of human cancers, including ovarian cancer. Vinblastine (VLB), an antimicrotubule agent, is one of the chemotherapeutic drugs that exhibit resistance in ovarian cancer patients. To determine the protein factors that are involved in vinblastine resistance in human ovarian cancer cells, a combination of sample pre-fractionation and high-resolution 2-DE proteomic analysis was performed. Approximately 1200 proteins were detected and quantitatively compared in both nuclear/membrane and cytosolic fractions. Sixty-nine proteins from the nuclear/membrane fraction showed altered expression levels, whereas 59 were altered in the cytosolic fraction between SKOV3 (vinblastine-sensitive) and SKVLB (vinblastine-resistant) cell lines. These proteins include membrane-associated, chromatin remodeling, cytoskeletal, and microtubule-associated proteins as well as others that regulate signal transduction. This study not only demonstrates a novel understanding of the mechanism of drug resistance but also provides a valuable resource for future studies on drug resistance to vinblastine. In addition, it also represents a good example of how to increase the protein dynamic range and reduce sample complexity using currently available tools.     

Post-transplant proteinuria associated with everolimus: Definition of main features with proteomics

Little is known on both the composition and mechanism(s) of proteinuria associated with the use of mTOR inhibitors, in particular of Everolimus (E). We characterized urinary proteins utilizing an integrated proteomics approach (quantitative essays, 2‐DE, MALDI‐TOF, Western blot) in 48 renal transplant recipients who were alternatively treated with E (n = 31) or with enteric coated mycophenolic acid (EC‐MPA) (n = 17). Twelve E patients (39%) developed high (>3 g/day) or intermediate proteinuria (1–3 g) compared to four (23%) of the EC‐MPA group. Urinary proteins (p<0.001), β2 microglobulin (p<0.001) and α1microglobulin (p<0.025) were higher in E than in EC‐MPA, appeared more rapidly and were inversely correlated with the day of treatment. Proteomics showed a marked increase of all urinary components in E and EC‐MPA patients, major changes involving typical components of glomerular damage (albumin, α1‐Zn glycoprotein, α2HS glycoprotein, leucin‐richα2‐glycoprotein) and specific bio‐markers for E (clusters of α1‐antitrypsin fragments and monoclonal λ chains). Finally, inter‐α‐trypsin‐inhibitor heavy chain H4 precursor was decreased in E and EC‐MPA urine compared to normal urine. In conclusion, E induced massive and generalized proteinuria of mixed glomerular and tubular origin that was correlated with the start of treatment and reached a nephrotic range in few cases. Specific urinary markers reflect renal alterations related to the transplant or specific alterations associated with the drug.     

Annexin A4: A novel molecular marker for gastric cancer with Helicobacter pylori infection using proteomics approach

Helicobacter pylori was reported to be an important risk factor for the carcinogenesis of gastric cancer. Here, we used a proteomic approach to find differentially expressed proteins between the normal and tumor tissue of gastric cancer patients infected with H. pylori. In our results, we found annexin A4 was over-expressed in patients infected with H. pylori and was found in tumor cells, and over-expressed in gastric cancer SCM-1 cells after H. pylori infection. Ca(2+ ) can be induced by H. pylori and interact with annexin A4 Ca(2+) binding site to block the calmodulin-activated chloride conductance activation; therefore, it produces a new environment that benefits the malignant existence of H. pylori and raises the risk for gastric cancer. We also found interleuken-8 (IL-8) expression levels were increased in H. pylori infected SCM-1 cells. Combined with previous reports and our results, we summarize that the over-expression of annexin A4 in SCM-1 cells with H. pylori infection may subsequently induce IL-8 which can further cause tumor angiogenesis. In this paper, we show that annexin A4 is a potential novel molecular marker for gastric cancer with H. pylori infection, and our results may provide a new insight in the development of new anti-cancer drugs.     

Diagnostic protein marker patterns in squamous cervical cancer

Purpose: Cervical cancer is the second most prevalent malignancy of women. Our aim was to identify additional marker protein patterns for objective diagnosis of squamous cervical cancer (SCC). Experimental design: Collected tissue biopsies of SCC, squamous vaginal cancer (SVC), normal cervical and vaginal mucosa were subjected to 2‐DE, SameSpot analysis, MALDI‐TOF‐MS protein identification, and analysis of the expression of selected proteins by immunohistochemistry. Results: In 148 protein spots selected by the difference in expression 99 proteins were identified. A differential protein pattern for SCC was, e.g. over‐expressed (OE) eukaryotic translation initiation factor 3‐2β, neutrophil cytosolic factor 2, annexin A6 (ANXA6), for SVC it was OE cathepsin D, γ‐catenin, RAB2A, for both cancers it was OE apolipoprotein E, tropomyosin 3, HSPA8, and underexpressed cytokeratin 13, osteoglycin. In SCC nuclear expression of neutrophil cytosolic factor 2, PRDX2, HSP27 (nine of ten cases), ANXA6 (nine of ten cases) was observed while tropomyosin 4 was expressed only in two of ten cases. There was 81.1% (43/53) agreement between the expression of protein spots and the immune expression of proteins ( www.proteinatlas.org ). Conclusions and clinical relevance: SCC is characterized by specific tissue marker protein patterns that allow objective detection of the disease. They can become a basis for objective automated cytology‐based screening and improve current diagnostics of SCC.     

Online Higher-Order Error Correction of Nonlinear Diffusion Generalized Perturbation Theory Using Neural Networks

An online scheme has been developed for the reduction of errors associated with a second order generalized perturbation theory (GPT) approximation of the neutron diffusion fundamental-mode eigenvalue (1/k _eff.). The primary application of this work is nuclear fuel loading optimization, where the noted calculation estimates the eigenvalue response as a function of fuel material perturbations (i.e., fuel assembly shuffles) relative to a reference (unperturbed) core loading pattern. The implementation of GPT for approximating k _eff can reduce the computational time requirements by a factor of 10 or greater, however, the nature of GPT is such that the errors in the second order approximation grow as the perturbations get larger. Therefore, the main emphasis of this study is to achieve improved approximations of the End-of-cycle (EOC) k _eff with minimal increase in the computational time. Specifically, this study shows that an online neural network-based scheme can be applied to reduce the average errors while still maintaining a computational edge over direct eigenvalue calculations.     

Discovery of putative oocyte quality markers by comparative ExacTag proteomics

Purpose: Identification of the biomarkers of oocyte quality, and developmental and reprogramming potential is of importance to assisted reproductive technology in humans and animals. Experimental design: PerkinElmer ExacTag? Kit was used to label differentially proteins in pig oocyte extracts (oocyte proteome) and pig oocyte‐conditioned in vitro maturation media (oocyte secretome) obtained with high‐ and low‐quality oocytes. Results: We identified 16 major proteins in the oocyte proteome that were expressed differentially in high‐ versus low‐quality oocytes. More abundant proteins in the high‐quality oocyte proteome included kelch‐like ECH‐associated protein 1 (an adaptor for ubiquitin‐ligase CUL3), nuclear export factor CRM1 and ataxia‐telangiectasia mutated protein kinase. Dystrophin (DMD) was more abundant in low‐quality oocytes. In the secretome, we identified 110 proteins, including DMD and cystic fibrosis transmembrane conductance regulator, two proteins implicated in muscular dystrophy and cystic fibrosis, respectively. Monoubiquitin was identified in the low‐quality‐oocyte secretome. Conclusions and clinical implications: A direct, quantitative proteomic analysis of small oocyte protein samples can identify potential markers of oocyte quality without the need for a large amount of total protein. This approach will be applied to discovery of non‐invasive biomarkers of oocyte quality in assisted human reproduction and in large animal embryo transfer programs.     

The contribution of proteomics towards deciphering the enigma of Coxiella burnetii

Coxiella burnetii, the causative agent of Q fever, is an intracellular bacterium and a potential weapon for bioterrorism. The widespread throughout the world, zoonosis is manifested clinically as a self-limited febrile illness, as pneumonia (acute Q fever) or as a chronic illness with endocarditis being its major complication. The recent Netherlands Q fever outbreak has driven the bacterium from a relatively cryptic, underappreciated, “niche” microorganism on the sideline of bacteriology, to one of possibly great impact on public health. Advances in the study of this microorganism proceeded slowly, primarily due to the, until recently, obligatory intracellular nature of the pathogen that in its virulent phase I must be manipulated under biosafety level-3 conditions. Proteomic studies, in particular, have generated a vast amount of information concerning several aspects of the bacterium such as virulence factors, detection/diagnostic and immunogenic biomarkers, inter-/intraspecies variation, resistance to antibiotics, and secreted effector proteins with significant clinical impact. The phenomenon observed following the genomics era, that of generation and accumulation of huge amount of data that ultimately end up unexploited on several databases, begins to emerge in the proteomics field as well. This review will focus on the advances in the field of C. burnetii proteomics through MS, attempting in parallel to utilize some of the proteomics findings by suggesting future directions for the improvement of Q fever diagnosis and therapy.     

Quantification of Biomarker Proteins Using Reverse‐Phase Protein Arrays

It is expected that antibody‐based proteomics will soon occupy a pivotal position in the discovery and validation of biomarkers and therapeutic targets. The reverse‐phase protein array (RPPA) is an antibody‐based proteomic method that can quantify the expression of multiple posttranslationally modified proteins (such as those that have been phosphorylated) across a large number of protein samples. RPPA is highly sensitive and requires only very small protein samples. This feature, in combination with large antibody libraries, makes RPPA ideal for clinical proteomics, as well as the fact that it is an expandable multiplex assay. In Volume 14, Issue 1 of Proteomics Clinical Applications, Suzuki and colleagues report for the first time a study comparing RPPA and immunohistochemistry for quantification of seven biomarker proteins used for subtyping of diffuse large B‐cell lymphoma. Such combination of multiple biomarkers is likely to increase diagnostic accuracy and can be used for precise classification of this heterogeneous disease.     

MS‐Based Biomarker Discovery in Bronchoalveolar Lavage Fluid for Lung Cancer

Bronchoalveolar lavage fluid (BALF) is a lung fluid. BALF is extracted from the lungs by a bronchoscope. The first step is to instill saline liquid into the lungs followed by extraction. The extracted liquid is depleted from cells by low spin centrifugation. The biochemical content of BALF is mainly composed of phospholipids and proteins and to less extent nucleic acids (DNA, miRNA, mRNA). The proteins, mRNAs, miRNAs, and lipids mirror the pathophysiological state of the patient and are consequently regarded as a rich source of biomarkers with already some examples of established clinical applications. Recently, in Proteomics Clin. Appl. 2019 , 13, 1900028, Sim et al. established a novel MS‐based proteomics protocol for BALF lung cancer sample analysis by combining antibody‐based depletion of high abundant BALF proteins, high pH peptide fractionation, and label free quantitation on a high resolution Orbitrap Fusion instrument. They demonstrate an improvement in BALF sample coverage compared with some previous published methodologies. Notably, the result from the study supports the hypothesis that BALF more than serum reflects the lung cancer proteome and for this reason is a promising source for lung cancer biomarkers.