A graph-based representation of Gene Expression profiles in DNA microarrays

This paper proposes a new and very flexible data model, called gene expression graph (GEG), for genes expression analysis and classification. Three features differentiate GEGs from other available microarray data representation structures: (i) the memory occupation of a GEG is independent of the number of samples used to built it; (ii) a GEG more clearly expresses relationships among expressed and non expressed genes in both healthy and diseased tissues experiments; (iii) GEGs allow to easily implement very efficient classifiers. The paper also presents a simple classifier for sample-based classification to show the flexibility and user-friendliness of the proposed data structur

Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome

Escherichia coli has long been regarded as a model organism in the study of codon usage bias (CUB). However, most studies in this organism regarding this topic have been computational or, when experimental, restricted to small datasets; particularly poor attention has been given to genes with low CUB. In this work, correspondence analysis on codon usage is used to classify E.coli genes into three groups, and the relationship between them and expression levels from microarray experiments is studied. These groups are: group 1, highly biased genes; group 2, moderately biased genes; and group 3, AT-rich genes with low CUB. It is shown that, surprisingly, there is a negative correlation between codon bias and expression levels for group 3 genes, i.e. genes with extremely low codon adaptation index (CAI) values are highly expressed, while group 2 show the lowest average expression levels and group 1 show the usual expected positive correlation between CAI and expression. This trend is maintained over all functional gene groups, seeming to contradict the E.coli–yeast paradigm on CUB. It is argued that these findings are still compatible with the mutation–selection balance hypothesis of codon usage and that E.coli genes form a dynamic system shaped by these factors

PTOMSM: A modified version of Topological Overlap Measure used for predicting Protein-Protein Interaction Network

A variety of methods are developed to integrating diverse biological data to predict novel interaction relationship between proteins. However, traditional integration can only generate protein interaction pairs within existing relationships. Therefore, we propose a modified version of Topological Overlap Measure to identify not only extant direct PPIs links, but also novel protein interactions that can be indirectly inferred from various relationships between proteins. Our method is more powerful than a naïve Bayesian-network-based integration in PPI prediction, and could generate more reliable candidate PPIs. Furthermore, we examined the influence of the sizes of training and test datasets on prediction, and further demonstrated the effectiveness of PTOMSM in predicting PPI. More importantly, this method can be extended naturally to predict other types of biological networks, and may be combined with Bayesian method to further improve the prediction

Myocyte enhancer factor 2C: an osteoblast transcription factor identified by DMSO enhanced mineralization

Free to read on publisher website Rapid mineralization of cultured osteoblasts could be a useful characteristic in stem-cell mediated therapies for fracture and other orthopaedic problems. Dimethyl sulfoxide (DMSO) is a small amphipathic solvent molecule capable of simulating cell differentiation. We report that, in primary human osteoblasts, DMSO dose-dependently enhanced the expression of osteoblast differentiation markers alkaline phosphatase (ALP) activity and extracellular matrix mineralization. Furthermore, similar DMSO mediated mineralization enhancement was observed in primary osteoblast-like cells differentiated from mouse mesenchymal cells derived from fat, a promising source of starter cells for cell-based therapy. Using a convenient mouse pre-osteoblast model cell line MC3T3-E1 we further investigated this phenomenon showing that numerous osteoblast-expressed genes were elevated in response to DMSO treatment and correlated with enhanced mineralization. Myocyte enhancer factor 2c (Mef2c) was identified as the transcription factor most induced by DMSO, among numerous DMSO-induced genes, suggesting a role for Mef2c in osteoblast gene regulation. Immunohistochemistry confirmed expression of Mef2c in osteoblast-like cells in mouse mandible, cortical and trabecular bone. shRNAi-mediated Mef2c gene silencing resulted in defective osteoblast differentiation, decreased ALP activity and matrix mineralization and knockdown of osteoblast specific gene expression, including osteocalcin and bone sialoprotein. Flow on knockdown of bone specific transcription factors, Runx2 and osterix by shRNAi knockdown of Mef2c suggests that Mef2c lies upstream of these two important factors in the cascade of gene expression in osteoblasts

Estrogen-dependent dynamic profile of eNOS-DNA associations in prostate cancer

In previous work we have documented the nuclear translocation of endothelial NOS (eNOS) and its participation in combinatorial complexes with Estrogen Receptor Beta (ERβ) and Hypoxia Inducible Factors (HIFs) that determine localized chromatin remodeling in response to estrogen (E2) and hypoxia stimuli, resulting in transcriptional regulation of genes associated with adverse prognosis in prostate cancer (PCa). To explore the role of nuclear eNOS in the acquisition of aggressive phenotype in PCa, we performed ChIP-Sequencing on chromatin-associated eNOS from cells from a primary tumor with poor outcome and from metastatic LNCaP cells. We found that: 1. the eNOS-bound regions (peaks) are widely distributed across the genome encompassing multiple transcription factors binding sites, including Estrogen Response Elements. 2. E2 increased the number of peaks, indicating hormone-dependent eNOS re-localization. 3. Peak distribution was similar with/without E2 with ≈ 55% of them in extragenic DNA regions and an intriguing involvement of the 5′ domain of several miRs deregulated in PCa. Numerous potentially novel eNOS-targeted genes have been identified suggesting that eNOS participates in the regulation of large gene sets. The parallel finding of downregulation of a cluster of miRs, including miR-34a, in PCa cells associated with poor outcome led us to unveil a molecular link between eNOS and SIRT1, an epigenetic regulator of aging and tumorigenicity, negatively regulated by miR-34a and in turn activating eNOS. E2 potentiates miR-34a downregulation thus enhancing SIRT1 expression, depicting a novel eNOS/SIRT1 interplay fine-tuned by E2-activated ER signaling, and suggesting that eNOS may play an important role in aggressive PCa

Characterisation of Embryonic Dermal Precursor Cells

Skin is an attractive organ for the acquisition of stem cells due to its accessibility, size and potential for autologous transplants. Research into skin development has implications for the isolation of stem cell populations, for example skin-derived precursors (SKPs), as well as the treatment of skin conditions, such as fibrosis. This study centred on the early development, differentiation and stem cell potential of the dermis in embryonic mouse skin. Based on microarray data, the expression of specific Wnt family members was examined using RT-PCR and immunohistochemistry. No evidence of Wnt protein expression was observed in the dermis, but more embryonic stages and Wnt family members need to be explored to better understand Wnt signalling and its role in dermal development. Our main focus was to investigate the plasticity of the common dermal fibroblast precursor population present at E13.5. We hypothesised that the dermis contains a common precursor capable of producing all cell types of the dermis and could harbour a high proportion of mesenchymal stem cell (MSC)-like precursors. This E13.5 dermal cell (DC) population was investigated by exploring its differentiation potential when cultured in adipogenic and osteogenic media. These experiments indicated the E13.5 DCs contained a small subpopulation of MSC-like progenitors. However, when E13.5 DCs were cultured to produce SKPs and, subsequently, pushed to adipogenic and osteogenic lineages, they differentiated less than expected. Most research regarding SKPs has used adult and older embryonic skin, therefore the findings here are novel in that SKPs were not expected at a younger age. However, RT-PCR revealed differences between the gene expression profiles of early and late embryonic dermal SKPs. Moreover, neither displayed the expected differentiation potential. The possible reasons for these unexpected findings include the potential role of hair follicle induction and/or a later migration of neural crest progenitors into the dermis

The identification of diagnostic serological autoantibodies for Cutaneous Melanoma

Abstract not available