Expression levels of 63 p53-related genes add up to similar values in 24 different tissues and are unified in cancer

The expression patterns of 62 genes interacting with p53 have been investigated in 24 normal and cancerous tissues using NIH's dbEST library. The expression levels of individual genes, such as the TTP53 gene itself, but also other genes, vary up to 33-fold among the 24 different tissues and no consistent pattern can be recognized. However, when expression levels for all 63 genes are summed, these "cumulated levels" are surprisingly constant over the 24 investigated normal tissues. In cancers, the variation is further reduced. Essentially, the cumulated expression levels in cancer are independent of those in normal tissue. We furthermore constructed a linear statistical classifier, i.e., a weighted sum of gene expression levels, which robustly distinguishes normal from cancer tissue independent of the particular kind of tissue. Thus, despite very large differences for individual genes and considerable changes during carcinogenesis, the cumulated expressions have narrowly defined levels.     

Protein profiling in human sera for identification of potential lung cancer biomarkers using antibody microarray

To identify potential biomarkers of lung cancer (LC), profiling of proteins in sera obtained from healthy and LC patients was determined using an antibody microarray. Based on our previous study on mRNA expression profiles between patients with LC and healthy persons, 19 proteins of interest were selected as targets for fabrication of an antibody microarray. Antibody to each protein and five nonspecific control antibodies were spotted onto a hydrogel‐coated glass slide and used for profiling of proteins in sera of LC patients in a two‐color fluorescence assay. Forty‐eight human sera samples were analyzed, and expression profiling of proteins were represented by the internally normalized ratio method. Six proteins were distinctly down‐regulated in sera of LC patients; this observation was validated by Wilcoxon test, false discovery rate, and Western blotting. Blind test of other 32 human sera using the antibody microarray followed by hierarchical clustering analysis revealed an approximate sensitivity of 88%, specificity of 80%, and an accuracy of 84%, respectively, in classifying the sera, which supports the potential of the six identified proteins as biomarkers for the prognosis of lung cancer.     

Surface plasmon resonance imaging protein arrays for analysis of triple protein interactions of HPV, E6, E6AP, and p53

We have developed a surface plasmon resonance (SPR)-based protein microarray to study protein-protein interactions in a high-throughput mode. As a model system, triple protein interactions have been explored with human papillomaviral E6 protein, tumor suppressor p53, and ubiquitin ligase E6AP. Human papillomavirus (HPV) is known to be a causative agent of cervical cancer. Upon infection, the viral E6 protein forms a heterotrimeric protein complex with p53 and E6AP. The formation of the complex eventually results in the degradation of p53. In the present study, a GST-fused E6AP protein was layered onto a glutathione (GSH)-modified gold chip surface. The specific binding of GST-E6AP protein onto the gold chip surface was facilitated through the affinity of GST to its specific ligand GSH. The interacting proteins (E6 and/or p53) were then spotted. Detection of the interaction was performed using a SPR imaging (SPRI) technique. The resulting SPRI intensity data showed that the protein-protein interactions of E6AP, E6, and p53 were detected in a concentration-dependent manner, suggesting that the SPRI-based microarray system can be an effective tool to study protein-protein interactions where multiple proteins are involved.     

How germline genes promote malignancy in cancer cells

Cancers often express hundreds of genes otherwise specific to germ cells, the germline/cancer (GC) genes. Here, we present and discuss the hypothesis that activation of a “germline program” promotes cancer cell malignancy. We do so by proposing four hallmark processes of the germline: meiosis, epigenetic plasticity, migration, and metabolic plasticity. Together, these hallmarks enable replicative immortality of germ cells as well as cancer cells. Especially meiotic genes are frequently expressed in cancer, implying that genes unique to meiosis may play a role in oncogenesis. Because GC genes are not expressed in healthy somatic tissues, they form an appealing source of specific treatment targets with limited side effects besides infertility. Although it is still unclear why germ cell specific genes are so abundantly expressed in cancer, from our hypothesis it follows that the germline's reproductive program is intrinsic to cancer development.     

Not all cancers are created equal: Tissue specificity in cancer genes and pathways

Tumors arise through waves of genetic alterations and clonal expansion that allow tumor cells to acquire cancer hallmarks, such as genome instability and immune evasion. Recent genomic analyses showed that the vast majority of cancer driver genes are mutated in a tissue-dependent manner, that is, are altered in some cancers but not others. Often the tumor type also affects the likelihood of therapy response. What is the origin of tissue specificity in cancer? Recent studies suggest that both cell-intrinsic and cell-extrinsic factors play a role. On one hand, cell type–specific wiring of the cell signaling network determines the outcome of cancer driver gene mutations. On the other hand, the tumor cells’ exposure to tissue-specific microenvironments (e.g. immune cells) also contributes to shape the tissue specificity of driver genes and of therapy response. In the future, a more complete understanding of tissue specificity in cancer may inform methods to better predict and improve therapeutic outcomes.     

A novel procedure for protein extraction from formalin-fixed paraffin-embedded tissues

Most of the archived pathological specimens in hospitals are kept as formalin-fixed paraffin-embedded tissues (FFPE) for long-term preservation. Up to now, these samples are only used for immunohistochemistry in a clinical routine as it is difficult to recover intact protein from these FFPE tissues. Here, we report a novel, short time-consuming and cost-effective method to extract full-length, non-degraded proteins from FFPE tissues. This procedure is combined with an effective and non-toxic deparaffinisation process and an extraction method based on antigen-retrieval, high concentration of SDS and high temperature. We have obtained enough intact protein to be detected by Western blotting analysis. This technique will allow utilising these stored FFPE tissues in several applications for protein analysis helping to advance the translational studies in cancer and other diseases.     

Affinity-capture reagents for protein arrays

The simultaneous identification and quantitative measurement of the production levels of thousands of different proteins in a biological specimen remains an unachieved goal of modern proteomic research. Advances in the development of microarray-based platforms for highly parallel detection of proteins have therefore received a considerable impulse during the last few years. Here, we review the existing reagents for affinity capture of protein targets, as well as the techniques used for their immobilization on solid supports and methods for the detection of binding events, underlining the problems and the opportunities in this continuously evolving research field.     

Quality criteria for finding genes with high mRNA-protein expression correlation and coexpression correlation

mRNA expression is widely used as a proxy for protein expression. However, their true relation is not known and two genes with the same mRNA levels might have different abundances of respective proteins. A related question is whether the coexpression of mRNA for gene pairs is reflected by the corresponding protein pairs. We examined the mRNA-protein correlation for both expression and coexpression. This analysis yielded insights into the relationship between mRNA and protein abundance, and allowed us to identify subsets of greater mRNA-protein coherence. The correlation between mRNA and protein was low for both expression and coexpression, 0.12 and 0.06 respectively. However, applying the best-performing quality measure, high-quality subsets reached a Spearman correlation of 0.31 for expression, 0.34 for coexpression and 0.49 for coexpression when restricted to functionally coupled genes. Our methodology can thus identify subsets for which the mRNA levels are expected to be the strongest correlated with protein levels.     

6-Dimethyl amino purine and 2-amino purine inhibit the induction of expresion of milk protein genes by prolactin

Two protein kinase-inhibitors, 6-dimethyl amino purine and 2-amino purine inhibited induction of -casein synthesis by prolactin when added to the culture medium of rabbit mammary explant and cells. The accumulation of the mRNA for _s1- and -caseins and for whey acidic protein did not take place in the presence of the inhibitors whereas -actin mRNA concentration was not altered. In the same experimental conditions, H7, an inhibitor of protein kinase C and, to a lower extent, of protein kinase A did not prevent prolactin from acting. These data suggest for the first time that specific protein kinases are involved in the transduction of the prolactin signal to milk protein genes.Abbreviations 6-DMAP 6-dimethyl amino purine - 2-AP 2-amino purine - H7 1-(5 isoquinoline sulfonyl)-2-methyl-piperazine dihydrochloride - WAP whey acidic protein - PRL prolactin     

Eukaryotic expression vectors containing genes encoding plant proteins for killing of cancer cells

Background: Gene therapy has attracted attention for its potential to specifically and efficiently target cancer cells with minimal toxicity to normal cells. At present, it offers a promising direction for the treatment of cancer patients. Numerous vectors have been engineered for the sole purpose of killing cancer cells, and some have successfully suppressed malignant tumours. Many plant proteins have anticancer properties; consequently, genes encoding some of these proteins are being used to design constructs for the inhibition of multiplying cancer cells. Results: Data addressing the function of vectors harbouring genes specifically encoding ricin, saporin, lunasin, linamarase, and tomato thymidine kinase 1 under the control of different promoters are summarised here. Constructs employing genes to encode cytotoxic proteins as well as constructs employing genes of enzymes that convert a nontoxic prodrug into a toxic drug are considered here. Conclusion: Generation of eukaryotic expression vectors containing genes encoding plant proteins for killing of cancer cells may permit the broadening of cancer gene therapy strategy, particularly because of the specific mode of action of anticancer plant proteins.     

Antibody screening database for protein kinetic modeling

Knowledge-based proteomic studies rely on the availability of quality antibodies. The increasing number of commercially available antibodies covers a wide range of protein networks; however, performance of each antibody can vary, depending on what type of cells, treatments, and time points are studied. Here, we describe an antibody database in which we screened 279 antibodies against multiple cell lysates after various treatments and from different time points. We applied these quality-confirmed antibodies on protein arrays, showing their utility for protein kinetic modeling.     

Robust discriminant analysis and its application to identify protein coding regions of rice genes

Identification of protein coding regions is fundamentally a statistical pattern recognition problem. Discriminant analysis is a statistical technique for classifying a set of observations into predefined classes and it is useful to solve such problems. It is well known that outliers are present in virtually every data set in any application domain, and classical discriminant analysis methods (including linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA)) do not work well if the data set has outliers. In order to overcome the difficulty, the robust statistical method is used in this paper. We choose four different coding characters as discriminant variables and an approving result is presented by the method of robust discriminant analysis.     

Quality control for a large-scale study using protein arrays and protein beads to measure immune response in serum and plasma

We report on quality control performed in the context of a large-scale, multi-institutional study of the immune response in blood samples from prostate cancer patients. The measurements were performed by two commercially available techniques/services: protein arrays and an automated bead-based ELISA-like technique on 871 patient samples. The project started with a wide screen using standard arrays with 8302 protein sequences for 113 patients, followed by three studies using custom arrays with 215 selected protein sequences. These studies were followed up by three studies using the bead-based approach on 57 protein sequences chosen from the 215 selected before. We find similar responses in plasma and serum samples. Samples from the two European projects from which the samples originated also appeared comparable. In the data from the high-density standard arrays, we see for ～12% of the protein sequences high cross-correlation (R(2) > 0.8) with signals from unrelated protein sequences that are physically nearby on the array, suggesting production issues. The custom array and bead-based techniques both have good reproducibility, but the techniques do not agree with each other for ～50% of the protein sequences measured. We discuss the consequences of the observed data quality for the design and interpretation of the study.     

Microarray-based kinetic colorimetric detection for quantitative multiplex protein phosphorylation analysis

Commonly used colorimetric detection applied to protein microarrays with enzymatic signal amplification leads to non‐linear signal production upon increase in analyte concentration, thereby considerably limiting the range and accuracy of quantitative readout interpretation. To extend the detection range, we developed a kinetic colorimetric detection protocol for the analysis of ELISA microarrays designed to measure multiple phosphorylated proteins using the platforms ArrayTube? and ArrayStrip?. With our novel quantification approach, microarrays were calibrated over a broad concentration range spanning four orders of magnitude of analyte concentration with picomolar threshold. We used this design for the simultaneous quantitative measurement of 15 phosphorylated proteins on a single chip.     

Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model

Understanding the mechanism of the protein stability change is one of the most challenging tasks. Recently, the prediction of protein stability change affected by single point mutations has become an interesting topic in molecular biology. However, it is desirable to further acquire knowledge from large databases to provide new insights into the nature of them. This paper presents an interpretable prediction tree method (named iPTREE-2) that can accurately predict changes of protein stability upon mutations from sequence based information and analyze sequence characteristics from the viewpoint of composition and order. Therefore, iPTREE-2 based on a regression tree algorithm exhibits the ability of finding important factors and developing rules for the purpose of data mining. On a dataset of 1859 different single point mutations from thermodynamic database, ProTherm, iPTREE-2 yields a correlation coefficient of 0.70 between predicted and experimental values. In the task of data mining, detailed analysis of sequences reveals the possibility of the compositional specificity of residues in different ranges of stability change and implies the existence of certain patterns. As building rules, we found that the mutation residues in wild type and in mutant protein play an important role. The present study demonstrates that iPTREE-2 can serve the purpose of predicting protein stability change, especially when one requires more understandable knowledge.