CompMoby: comparative MobyDick for detection of cis-regulatory motifs

Oct4 upregulates osteopontin via Egr1 and is associated with poor outcome in human lung cancer

Background

Roles of cancer stem cells and early growth response gene 1 (Egr1) in carcinogenesis have been extensively studied in lung cancer. However, the role of Egr1 in the metastasis of lung cancer remains undetermined, especially in regard to stem cell-related pathways.

Methods

Egr1, osteopontin (OPN) and Oct4 expression in human lung cancer was determined by performing immunohistochemistry. Immunoblotting, ELISA, luciferase reporter assay, chromatin immunoprecipitation assay and RT-PCR were performed to validate the regulation of Oct4-Egr1-OPN axis. Moreover, the effect of Oct4-Egr1-OPN axis on lung cancer progression was evaluated by cell migration assay and mice study.

Results

We detected Oct4, Egr1, and OPN expression in clinical specimens from 79 lung cancer patients, including 72 adenocarcinomas and 7 squamous cell carcinomas. High expression of Oct4, Egr1, and OPN accounted for 53, 51, and 57% of the patients, respectively. All of the three biomarkers were positively correlated in clinical human lung cancer. Patients with high expression of OPN were significantly associated with shorter disease-free survivals than those with low expression of OPN (p < 0.05). In lung cancer cells, Oct4 transactivated the Egr1 promoter and upregulated Egr1 expression. In a human lung cancer xenograft model, Oct4-overexpressing tumors expressed elevated levels of Egr1. Furthermore, overexpression of Oct4 in lung cancer cells increased the metastatic potential.

Conclusions

Egr1 exerts a promoting effect on cancer metastasis in Oct4-overexpressing lung cancer. Thus, therapeutic strategies targeting the Oct4/Egr1/OPN axis may be further explored for the treatment of lung cancer, especially when lung cancer is refractory to conventional treatment due to cancer stem cells.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-6014-5) contains supplementary material, which is available to authorized users.

Full text clustering and relationship network analysis of biomedical publications

Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP) to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

In silico discovery of novel transcription factors regulated by mTOR-pathway activities

The mammalian target of rapamycine (mTOR) pathway is a key regulator of cellular growth, development, and ageing, and unraveling its control is essential for understanding life and death of biological organisms. A motif-discovery workbench including nine tools was used to identify transcription factors involved in five basic (Insulin, MAPK, VEGF, Hypoxia, and mTOR core) activities of the mTOR pathway. Discovered transcription factors are classified as “process-specific” or “pathway-ubiquitous” with highlights toward their regulating/regulated activities within the mTOR pathway. Our transcription regulation results will facilitate further research on investigating the control mechanism in mTOR pathway.

Evolution of gene regulation of pluripotency--the case for wiki tracks at genome browsers

Background

Experimentally validated data on gene regulation are hard to obtain. In particular, information about transcription factor binding sites in regulatory regions are scattered around in the literature. This impedes their systematic in-context analysis, e.g. the inference of their conservation in evolutionary history.

Results

We demonstrate the power of integrative bioinformatics by including curated transcription factor binding site information into the UCSC genome browser, using wiki and custom tracks, which enable easy publication of annotation data. Data integration allows to investigate the evolution of gene regulation of the pluripotency-associated genes Oct4, Sox2 and Nanog. For the first time, experimentally validated transcription factor binding sites in the regulatory regions of all three genes were assembled together based on manual curation of data from 39 publications. Using the UCSC genome browser, these data were then visualized in the context of multi-species conservation based on genomic alignment. We confirm previous hypotheses regarding the evolutionary age of specific regulatory patterns, establishing their "deep homology". We also confirm some other principles of Carroll's "Genetic theory of Morphological Evolution", such as "mosaic pleiotropy", exemplified by the dual role of Sox2 reflected in its regulatory region.

Conclusions

We were able to elucidate some aspects of the evolution of gene regulation for three genes associated with pluripotency. Based on the expected return on investment for the community, we encourage other scientists to contribute experimental data on gene regulation (original work as well as data collected for reviews) to the UCSC system, to enable studies of the evolution of gene regulation on a large scale, and to report their findings.

Reviewers

This article was reviewed by Dr. Gustavo Glusman and Dr. Juan Caballero, Institute for Systems Biology, Seattle, USA (nominated by Dr. Doron Lancet, Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel), Dr. Niels Grabe, TIGA Center (BIOQUANT) and Medical Systems Biology Group, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Germany (nominated by Dr. Mikhail Gelfand, Department of Bioinformatics, Institute of Information Transfer Problems, Russian Academy of Science, Moscow, Russian Federation) and Dr. Franz-Josef Müller, Center for Regenerative Medicine, The Scripps Research Institute, La Jolla, CA, USA and University Hospital for Psychiatry and Psychotherapy (part of ZIP gGmbH), University of Kiel, Germany (nominated by Dr. Trey Ideker, University of California, San Diego, La Jolla CA, United States).

Evolution of gene regulation--on the road towards computational inferences

If fragments of DNA are transcribed (expressed), they deserve to be called (parts of) a gene. Whether transcription takes place depends on the 'gene regulatory network'. This network is defined as the complex interplay of the sequence, biochemical modifications and structure of the chromosomal DNA with the regulatory proteins/RNA (transcription factors, co-factors, regulating RNA and the transcriptional apparatus itself). Gene regulatory networks play a role in various stages of development as well as in the maintenance of the organism; in this review we will concentrate on the former. Their evolutionary reconstruction is daunting (to say the least), and bioinformatics tools are in their infancy. However, gain of understanding offers a reward beyond itself, since evolutionary considerations can enable discoveries in the first place, e.g. the computational identification of conserved transcription factor binding sites. We discuss the evolution of gene regulation in the context of the 'Genetic Theory of Morphological Evolution' as described by Carroll, identifying those parts of the theory that are relevant for bioinformatics, and their implications. We discuss the important question of how bioinformatics analysis results on the evolution of gene regulation may be validated. Finally, we briefly exemplify use of the UCSC genome browser, exploiting its pre-computed alignments to describe the evolution of gene regulation.