Alterations in eIF3-p48/INT6 gene expression have been implicated in murine and human mammary carcinogenesis. We examined levels of INT6 protein in human tumors and determined that breast and colon ...tumors clustered into distinct groups based on levels of INT6 expression and clinicopathological variables. We performed multiplex tissue immunoblotting of breast, colon, lung, and ovarian tumor tissues and found that INT6 protein levels positively correlated with those of TID1, Patched, p53, c-Jun, and phosphorylated-c-Jun proteins in a tissue-specific manner. INT6 and TID1 showed significant positive correlation in all tissue types tested. These findings were confirmed by immunohistochemical staining of INT6 and TID1. Further evidence supporting a cooperative role for INT6 and TID1 is the presence of endogenous INT6 and TID1 proteins as complexes. We detected co-immunoprecipitation between INT6 and TID1, as well as between INT6 and Patched. These findings suggest potential integrated roles for INT6, TID1, and Patched proteins in cell growth, development, and tumorigenesis. Additionally, these data suggest that the combination of INT6, TID1, and Patched protein levels may be useful biomarkers for the development of diagnostic assays.
Heterogeneous high-throughput biological data become readily available for various diseases. The amount of data points generated by such experiments does not allow manual integration of the ...information to design the most optimal therapy for a disease. We describe a novel computational workflow for designing therapy using Ariadne Genomics Pathway Studio software. We use publically available microarray experiments for glioblastoma and automatically constructed ResNet and ChemEffect databases to exemplify how to find potentially effective chemicals for glioblastoma--the disease yet without effective treatment. Our first approach involved construction of signaling pathway affected in glioblastoma using scientific literature and data available in ResNet database. Compounds known to affect multiple proteins in this pathway were found in ChemEffect database. Another approach involved analysis of differential expression in glioblastoma patients using Sub-Network Enrichment Analysis (SNEA). SNEA identified angiogenesis-related protein Cyr61 as the major positive regulator upstream of genes differentially expressed in glioblastoma. Using our findings, we then identified breast cancer drug Fulvestrant as a major inhibitor of glioblastoma pathway as well as Cyr61. This suggested Fulvestrant as a potential treatment against glioblastoma. We further show how to increase efficacy of glioblastoma treatment by finding optimal combinations of Fulvestrant with other drugs.
Inactivation of genes normally involved in growth suppression may lead to malignant transformation. The major goal of the present work is to establish a genetic approach to the identification and ...characterization of tumor suppressor genes. It is based on the use of genetic suppressor elements (GSEs), short fragments of cDNA which cause biological effects by inhibition of genes from which they are derived. GSEs encode either inhibitory antisense RNAs or dominant negative peptides and are isolated by expression selection from retroviral libraries of randomly fragmented cDNA. The first part of this study is focused on the application of the GSE approach to characterize the known tumor-suppressor gene p53. A retroviral GSE library was constructed from randomly fragmented p53 cDNA and delivered to mouse embryo fibroblasts. Sense-oriented GSEs with biological activity similar to that of p53 mutants were isolated and localized to four regions of p53 cDNA. Comparison of biological activities revealed major differences among the elements belonging to different p53 regions, indicating that the processes of cellular senescence, apoptosis and response to DNA damage involve different functions of p53. Some of the GSEs were derived from the 3$\sp\prime$-untranslated region of p53 mRNA, suggesting a new mechanism of p53 regulation. The p53 study provided direct evidence that short DNA sequences derived from a tumor suppressor gene can induce different phenotypes of neoplastic transformation, and demonstrated that GSEs with oncogenic properties can be selected from random fragment libraries in retroviral vector. In the second part of this work, the experience accumulated during p53 GSE work, was extended to isolation of transforming GSEs from a complex library of normalized cDNA. As a result of several large scale selections, a set of transforming GSEs was isolated, three of which were characterized in more detail. Transforming GSEs were found to represent fragments of (i) an already known gene with the properties of a tumor suppressor (type 3 inositol (1,4,5) trisphosphate receptor), (ii) a known gene, which has not been associated before with transformation (kinesin heavy chain gene), and (iii) an unknown cDNA sequence. These results show that the GSE approach can be used for the identification of genes involved in the negative regulation of cell growth. Transforming fragments of unknown cDNAs are likely to represent new tumor suppressor genes and are being used for their cloning and characterization.
Linkage Mapping of the Human CSF2 and IL3 Genes Frolova, Elena I.; Dolganov, Gregory M.; Mazo, Ilya A. ...
Proceedings of the National Academy of Sciences - PNAS,
06/1991, Letnik:
88, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Interleukin 3 (encoded by the IL3 gene) and granulocyte-macrophage colony-stimulating factor (encoded by the CSF2 gene) are small secreted polypeptides that bind to specific cell surface receptors ...and regulate the growth, gene expression, and differentiation of many of the hematopoietic cell lineages, particularly nonlymphoid cells. The IL3 and CSF2 genes have been cloned and mapped to human chromosome bands 5q23-31. Only 10 kilobases of DNA separates the two genes, suggesting that they have a common origin and/or regulation. We have cloned 70 kilobases of genomic DNA that includes the IL3 and CSF2 genes, as well as flanking sequences, and report a physical map of this region. Several unique-sequence DNA segments have been identified in this region, and one of these fragments detects two restriction fragment length polymorphisms in DNA from unrelated Caucasians. Segregation of these DNA polymorphisms was followed in the Centre Etude du Polymorphisme Humaine (CEPH) panel of 40 large three-generation pedigrees, and linkage was detected with 17 genetic markers previously typed in these families. Multipoint linkage analysis permits the placement of the region containing the IL3 and CSF2 structural genes on the recombination-genetic linkage map of chromosome 5q and thereby allows the role of these genes in leukemogenesis to be more critically examined.
We develop a matrix-based approach to predict and verify indirect interactions in gene and protein regulatory networks. It is based on the approximate transitivity of indirect regulations (e.g. A ...regulates B and B regulates C often implies that A regulates C) and optimally takes into account the length of a cascade and signs of intermediate interactions. Our method is at its most powerful when applied to large and densely interconnected networks. It successfully predicts both the yet unknown indirect regulations, as well as the sign (activation or repression) of already known ones. The reliability of sign predictions was calibrated using the gold-standard sets of positive and negative interactions. We fine-tuned the parameters of our algorithm by maximizing the area under the Receiver Operating Characteristic (ROC) curve. We then applied the optimized algorithm to large literature-derived networks of all direct and indirect regulatory interactions in several model organisms (Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster).
Nucleic Acids Research 2005 33(11):3629-3635 We demonstrate that Protein-Protein Interaction (PPI) networks in several
eucaryotic organisms contain significantly more self-interacting proteins than
...expected if such homodimers randomly appeared in the course of the evolution.
We also show that on average homodimers have twice as many interaction partners
than non-self-interacting proteins. More specifically the likelihood of a
protein to physically interact with itself was found to be proportional to the
total number of its binding partners. These properties of dimers are are in
agreement with a phenomenological model in which individual proteins differ
from each other by the degree of their ``stickiness'' or general propensity
towards interaction with other proteins including oneself. A duplication of
self-interacting proteins creates a pair of paralogous proteins interacting
with each other. We show that such pairs occur more frequently than could be
explained by pure chance alone. Similar to homodimers, proteins involved in
heterodimers with their paralogs on average have twice as many interacting
partners than the rest of the network. The likelihood of a pair of paralogous
proteins to interact with each other was also shown to decrease with their
sequence similarity. This all points to the conclusion that most of
interactions between paralogs are inherited from ancestral homodimeric
proteins, rather than established de novo after the duplication. We finally
discuss possible implications of our empirical observations from functional and
evolutionary standpoints.
We demonstrate that Protein-Protein Interaction (PPI) networks in several eucaryotic organisms contain significantly more self-interacting proteins than expected if such homodimers randomly appeared ...in the course of the evolution. We also show that on average homodimers have twice as many interaction partners than non-self-interacting proteins. More specifically the likelihood of a protein to physically interact with itself was found to be proportional to the total number of its binding partners. These properties of dimers are are in agreement with a phenomenological model in which individual proteins differ from each other by the degree of their ``stickiness'' or general propensity towards interaction with other proteins including oneself. A duplication of self-interacting proteins creates a pair of paralogous proteins interacting with each other. We show that such pairs occur more frequently than could be explained by pure chance alone. Similar to homodimers, proteins involved in heterodimers with their paralogs on average have twice as many interacting partners than the rest of the network. The likelihood of a pair of paralogous proteins to interact with each other was also shown to decrease with their sequence similarity. This all points to the conclusion that most of interactions between paralogs are inherited from ancestral homodimeric proteins, rather than established de novo after the duplication. We finally discuss possible implications of our empirical observations from functional and evolutionary standpoints.
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