NF-E2 p45 is important for establishing normal function of platelets.

NF-E2 is a heterodimeric transcription factor consisting of p45 and small Maf subunits. Since p45(-/-) mice display severe thrombocytopenia, p45 is recognized as a critical regulator of platelet production from megakaryocytes. To identify direct p45 target genes in megakaryocytes, we used chromatin immunoprecipitation (ChIP) sequencing to analyze the genome-wide chromatin occupancy of p45 in primary megakaryocytes. p45 target gene candidates obtained from the analysis are implicated in the production and function of platelets. Two of these genes, Selp and Myl9, were verified as direct p45 targets through multiple approaches. Since P-selectin, encoded by Selp, plays a critical role in platelet function during thrombogenesis, we tested whether p45 determines the intrinsic reactivity and potency of platelets generated from megakaryocytes. Mice expressing a hypomorphic p45 mutant instead of wild-type p45 in megakaryocytes (p45(-/-):ΔNTD-Tg mice) displayed platelet hypofunction accompanied by mild thrombocytopenia. Furthermore, lung metastasis of melanoma cells, which requires platelet activation, was repressed in p45(-/-):ΔNTD-Tg mice compared to control mice, validating the impaired function of platelets produced from p45(-/-):ΔNTD-Tg megakaryocytes. By activating genes in megakaryocytes that mediate platelet production and function, p45 determines the quantity and quality of platelets.

Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas.

Activation of the EGFR, KRAS, and ALK oncogenes defines 3 different pathways of molecular pathogenesis in lung adenocarcinoma. However, many tumors lack activation of any pathway (triple-negative lung adenocarcinomas) posing a challenge for prognosis and treatment. Here, we report an extensive genome-wide expression profiling of 226 primary human stage I-II lung adenocarcinomas that elucidates molecular characteristics of tumors that harbor ALK mutations or that lack EGFR, KRAS, and ALK mutations, that is, triple-negative adenocarcinomas. One hundred and seventy-four genes were selected as being upregulated specifically in 79 lung adenocarcinomas without EGFR and KRAS mutations. Unsupervised clustering using a 174-gene signature, including ALK itself, classified these 2 groups of tumors into ALK-positive cases and 2 distinct groups of triple-negative cases (groups A and B). Notably, group A triple-negative cases had a worse prognosis for relapse and death, compared with cases with EGFR, KRAS, or ALK mutations or group B triple-negative cases. In ALK-positive tumors, 30 genes, including ALK and GRIN2A, were commonly overexpressed, whereas in group A triple-negative cases, 9 genes were commonly overexpressed, including a candidate diagnostic/therapeutic target DEPDC1, that were determined to be critical for predicting a worse prognosis. Our findings are important because they provide a molecular basis of ALK-positive lung adenocarcinomas and triple-negative lung adenocarcinomas and further stratify more or less aggressive subgroups of triple-negative lung ADC, possibly helping identify patients who may gain the most benefit from adjuvant chemotherapy after surgical resection.

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

A signature-based method for indexing cell cycle phase distribution from microarray profiles.

BACKGROUND: The cell cycle machinery interprets oncogenic signals and reflects the biology of cancers. To date, various methods for cell cycle phase estimation such as mitotic index, S phase fraction, and immunohistochemistry have provided valuable information on cancers (e.g. proliferation rate). However, those methods rely on one or few measurements and the scope of the information is limited. There is a need for more systematic cell cycle analysis methods. RESULTS: We developed a signature-based method for indexing cell cycle phase distribution from microarray profiles under consideration of cycling and non-cycling cells. A cell cycle signature masterset, composed of genes which express preferentially in cycling cells and in a cell cycle-regulated manner, was created to index the proportion of cycling cells in the sample. Cell cycle signature subsets, composed of genes whose expressions peak at specific stages of the cell cycle, were also created to index the proportion of cells in the corresponding stages. The method was validated using cell cycle datasets and quiescence-induced cell datasets. Analyses of a mouse tumor model dataset and human breast cancer datasets revealed variations in the proportion of cycling cells. When the influence of non-cycling cells was taken into account, "buried" cell cycle phase distributions were depicted that were oncogenic-event specific in the mouse tumor model dataset and were associated with patients' prognosis in the human breast cancer datasets. CONCLUSION: The signature-based cell cycle analysis method presented in this report, would potentially be of value for cancer characterization and diagnostics.

PrognoScan: a new database for meta-analysis of the prognostic value of genes.

BACKGROUND: In cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform. DESCRIPTION: To take advantage of public resources in full, a database named "PrognoScan" has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets. CONCLUSION: PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html.

Indirect readout in drug-DNA recognition: role of sequence-dependent DNA conformation.

DNA-binding drugs have numerous applications in the engineered gene regulation. However, the drug-DNA recognition mechanism is poorly understood. Drugs can recognize specific DNA sequences not only through direct contacts but also indirectly through sequence-dependent conformation, in a similar manner to the indirect readout mechanism in protein-DNA recognition. We used a knowledge-based technique that takes advantage of known DNA structures to evaluate the conformational energies. We built a dataset of non-redundant free B-DNA crystal structures to calculate the distributions of adjacent base-step and base-pair conformations, and estimated the effective harmonic potentials of mean force (PMF). These PMFs were used to calculate the conformational energy of drug-DNA complexes, and the Z-score as a measure of the binding specificity. Comparing the Z-scores for drug-DNA complexes with those for free DNA structures with the same sequence, we observed that in several cases the Z-scores became more negative upon drug binding. Furthermore, the specificity is position-dependent within the drug-bound region of DNA. These results suggest that DNA conformation plays an important role in the drug-DNA recognition. The presented method provides a tool for the analysis of drug-DNA recognition and can facilitate the development of drugs for targeting a specific DNA sequence.

Characterization of BRCAA1 and its novel antigen epitope identification.

Looking for novel breast cancer antigen epitopes is helpful for its treatment, diagnosis, and prevention. brcaa1 gene is mapped at 1q42.1-q43, its whole genome is 93.857 kb, including 18 exons and 17 introns. BRCAA1 protein is composed of 1,214 amino acids with 10 glycosylate sites, and shares 37% amino acid identity and an identical antigen epitope with Rb binding protein 1. The novel antigen epitope, SSKKQKRSHK, was predicted to locate in the region 610 to 619 sites, was synthesized, and its antibody was fabricated. Competent inhibition analysis showed that SSKKQKRSHK is the shortest effective peptide. The antigen epitope was mapped in the cytoplasm of MCF-7 cells. Immunohistochemistry analysis showed that the antigen epitope exhibited positive expression in 65% (39 of 60) breast cancer specimens and negative expression in 60 non-cancerous tissues. Statistical analysis shows that its expression is closely associated with status of ER and PR, with sensitivity of 100% and specificity of 81%, and confidence interval of 85.9% to 96.9%. ELISA analysis showed that the mean absorbance of sera antibody titers from breast cancer patients and healthy donors were 0401 +/- 0.163 SD and 0.137 +/- 0.121 SD, respectively. Sixty-four percent breast cancer patient sera and 13% healthy donor sera had higher titer than mean titer of healthy donors, and there exists significant difference between breast cancer patients and healthy donors (P < 0.001). In this study, a novel breast cancer antigen epitope, SSKKQKRSHK, is identified. Its expression is associated with characteristics that are themselves associated with prognosis of breast cancer, and its sera antibody level may be helpful for breast cancer diagnosis.

Determinants in the sequence specific binding of two plant transcription factors, CBF1 and NtERF2, to the DRE and GCC motifs.

Arabidopsis ERF proteins such as DREB1, DREB2, and CBF1 bind to the dehydration-responsive element (DRE), which has the sequence TACCGACAT. Mutation analyses reveal that a central 5 bp CCGAC core of the DRE is the minimal sequence motif (designated as the DRE motif in this paper), to which the ERF domain fragment of CBF1 (CBF1-F) binds specifically with a binding K(d) at the nanomolar level. In contrast, the ERF domain fragment of the tobacco ERF2 (NtERF2-F) does not interact with the DRE motif, but restrictedly recognizes the sequence containing a minimal 6 bp GCCGCC motif (designated as the GCC motif in this paper). However, CBF1-F binds to the GCC motif with a binding activity similar to its binding activity for the DRE motif. These in vitro binding variations were further demonstrated through reporter cotransformation assays, suggesting that the DRE and GCC motifs are two similar sequence motifs sharing a common core region of CCGNC with a discriminating guanine base at the 5'-end of the GCC motif. Binding analyses with the mutated ERF domain show that such a unique binding of NtERF2-F to the GCC motif can be altered by the substitution of A14 with valine in beta-strand 2 of its ERF domain, the mutant NtERF2-F, ERFav, acquiring a binding to the DRE motif with a K(d) comparable to that for CBF1-F binding to the DRE motif. This demonstrates that A14 is an important determinant of the NtERF2-F binding specificity. A possible mechanism of the binding specificity determination is discussed.