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.     

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.     

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     

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.     

Therapeutic genes for cancer gene therapy

Cancer still represents a disease of high incidence and is therefore one major target for gene therapy approaches. Gene therapy for cancer implies that ideally selective tumor cell killing or inhibition of tumor cell growth can be achieved using nucleic acids (DNA and RNA) as the therapeutic agent. Therefore, the majority of cancer gene therapy strategies introduce foreign genes into tumor cells which aim at the immunological recognition and destruction, the direct killing of the target cells or the interference with tumor growth. To achieve this goal for gene therapy of cancer, a broad variety of therapeutic genes are currently under investigation in preclinical and in clinical studies. These genes are of very different origin and of different mechanisms of action, such as human cytokine genes, genes coding for immunstimulatory molecules/antigens, genes encoding bacterial or viral prodrug-activating enzymes (suicide genes), tumor suppressor genes, or multidrug resistance genes.     

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.     

Protein Information Crawler (PIC): extensive spidering of multiple protein information resources for large protein sets

Proteomic studies often produce sets of hundreds of proteins. Bioinformatic information for these large protein sets must be collected from multiple online resources. Protein Information Crawler (PIC) automatically bulk-collects such data from multiple databases and prediction servers, based on National Center for Biotechnology Information (NCBI) gi numbers or accession numbers, and summarizes them in a Microsoft Excel spreadsheet and/or HTML table. PIC greatly accelerates information procurement, helps to build customized protein information databases and drastically reduces manual database investigation in extensive proteomic studies. Availability: http://www.zoo.uni-heidelberg.de/mfa/PIC.     

Protein biomarker discovery for head and neck cancer

Squamous cell carcinoma of the head and neck (HNSCC) is the sixth most common cancer worldwide. Despite improvements in diagnosis and treatment, the five-year-survival rate of advanced HNSCC has only moderately increased, which is largely due to the high proportion of patients that present with advanced disease stage and the frequent development of relapse and second primary tumors. Protein biomarkers allowing early detection of primary HNSCC or relapse may aid to improve clinical outcome. Screening for precursor changes in the mucosal linings preceding the development of invasive tumors and for accurate prediction of risk of malignant transformation, may be propitious opportunities, which are as yet difficult. This review summarizes recent results in HNSCC proteomics for biomarker research. Despite the wide diversity of experimental designs, a few common markers have been detected. Although some of these potential biomarkers are very promising, they still have to be further clinically validated. Finally, treatment of advanced cancers of several sites within the head and neck has shifted significantly during the last decade, and also, targeted drugs have entered the clinic. This has major consequences for the research questions in HNSCC research and accordingly for the future direction of proteome research in HNSCC biomarker discovery.     

Discovering amino acid patterns on binding sites in protein complexes

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.     

Simple but powerful: phenanthroline-based small molecules for cellular imaging and cancer screening

A two-step synthetic procedure gives highly fluorescent phenanthroline molecular probes. The compounds localize in the endoplasmic reticulum and their potential as bioactive probes was evaluated. The materials are quickly taken up by living cells within 5 min. Preliminary in vitro studies have shown that these compounds are selective to esophageal cancer cells and can be used as selective markers in intracellular cancer diagnostics. The materials show a remarkable cytotoxicity towards cancer cells vs normal as 7-1.     

Biophysical delivery of peptides: applicability for cancer therapy

There is a current trend towards evaluation of molecular agents for treatment of a variety of ailments, including cancer. One class of such biomolecules is proteins, and their shortened versions, peptides. Use of peptidic entities has been hindered by poor bioavailability in vivo and the high cost involved in mass-producing these macromolecular drugs. The need for localized delivery is being met with the development of various biophysical means, which include devices and aids, mainly transdermal and invasive implants. In addition, various cell-based delivery modalities, which include the use of spore-forming bacteria and stem cells, are being explored. This review discusses these methods in turn, and examines ways by which these can be enhanced for peptide delivery to tumors.     

The human mitochondrial transport protein family: Identification and protein regions significant for transport function and substrate specificity

Protein sequence similarities and predicted structures identified 75 mitochondrial transport proteins (37 subfamilies) from among the 28,994 human RefSeq (NCBI) protein sequences. All, except two, have an E-value of less than 4e−05 with respect to the structure of the single subunit bovine ADP/ATP carrier/carboxyatractyloside complex (bAAC/CAT) (mGenThreader program). The two 30-kDa exceptions have E-values of 0.003 and 0.005. 21 have been functionally identified and belong to 14 subfamilies. A subset of subfamilies with sequence similarities for each of 12 different protein regions was identified. Many of the 12 protein regions for each tested protein yielded different size subsets. The sum of subfamilies in the 12 subsets was lowest for the phosphate transport protein (PTP) and highest for aralar 1. Transmembrane sequences are most unique. Sequence similarities are highest near the membrane center and matrix. They are highest for the region of transmembrane helices H1, H2 and connecting matrix loop 12 and smallest for transmembrane helices H3, H4 and loop 34. These sequence similarities and the predicted high similarities to the bAAC/CAT structure point to common structural/functional elements that could include subunit/subunit contact sites as they have been identified for PTP and AAC. The four residues protein segment (SerLysGlnIle) of loop 12 is the only segment projecting into the center of the funnel-like structure of the bAAC/CAT. It is present in its entirety only in the AACs and with some replacements in the large Ca2+-modulated aspartate/glutamate transporters. Other transporters have deletions and replacements in this region of loop 12. This protein segment with its central location and variation in size and composition likely contributes to the substrate specificity of the transporters.     

The cancer/testis antigen prostate-associated gene 4 (PAGE4) is a highly intrinsically disordered protein

The cancer/testis antigens (CTAs) are an important group of heterogeneous proteins that are predominantly expressed in the testis in the normal human adult but are aberrantly expressed in several types of cancers. Prostate-associated gene 4 (PAGE4) is a member of the CT-X family of CTAs that in addition to testis, is highly expressed in the fetal prostate, and may also play an important role both in benign and malignant prostate diseases. However, the function of this gene remains poorly understood. Here, we show that PAGE4 is a highly (100%) intrinsically disordered protein (IDP). The primary protein sequence conforms to the features of a typical IDP sequence and the secondary structure prediction algorithm metaPrDOS strongly supported this prediction. Furthermore, SDS-gel electrophoresis and analytical size exclusion chromatography of the recombinant protein revealed an anomalous behavior characteristic of IDPs. UV circular dichroism (CD) and NMR spectroscopy confirmed that PAGE4 is indeed a highly disordered protein. In further bioinformatic analysis, the PredictNLS algorithm uncovered a potential nuclear localization signal, whereas the algorithm DBS-Pred returned a 99.1% probability that PAGE4 is a DNA-binding protein. Consistent with this prediction, biochemical experiments showed that PAGE4 preferentially binds a GC-rich sequence. Silencing PAGE4 expression induced cell death via apoptosis and in mice carrying PCa xenografts, siRNA-mediated knockdown of the PAGE4 mRNA attenuated tumor growth in vivo. Furthermore, overexpressing PAGE4 protected cells from stress-induced death. To our knowledge, PAGE4 is the first example of a CTA that is an IDP with an anti-apoptotic function.