Characterization of tissue expression and full-length coding sequence of a novel human gene mapping at 3q12.1 and transcribed in oligodendrocytes

EGF-Dependent Activation of ELK1 Contributes to the Induction of CLDND1 Expression Involved in Tight Junction Formation

Claudin proteins are intercellular adhesion molecules. Increased claudin domain-containing 1 (CLDND1) expression is associated with the malignant transformation of estrogen receptor-negative breast cancer cells with low sensitivity to hormone therapy. Abnormal CLDND1 expression is also implicated in vascular diseases. Previously, we investigated the regulatory mechanism underlying CLDND1 expression and identified a strong enhancer region near the promoter. In silico analysis of the sequence showed high homology to the ETS domain-containing protein-1 (ELK1)-binding sequence which is involved in cell growth, differentiation, angiogenesis, and cancer. Transcriptional ELK1 activation is associated with the mitogen-activated protein kinase (MAPK) signaling cascade originating from the epidermal growth factor receptor (EGFR). Here, we evaluated the effect of gefitinib, an EGFR tyrosine kinase inhibitor, on the suppression of CLDND1 expression using ELK1 overexpression in luciferase reporter and chromatin immunoprecipitation assays. ELK1 was found to be an activator of the enhancer region, and its transient expression increased that of CLDND1 at the mRNA and protein levels. CLDND1 expression was increased following EGF-induced ELK1 phosphorylation. Furthermore, this increase in CLDND1 was significantly suppressed by gefitinib. Therefore, EGF-dependent activation of ELK1 contributes to the induction of CLDND1 expression. These findings open avenues for the development of new anticancer agents targeting CLDND1.

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data. Assuming that the scRNA-seq data exist in multiple subspaces, the low-rank model is used to find the lowest rank representation of the data in the subspace. It is worth noting that the penalty factor of the low-rank kernel function is uncertain, and different penalty factors correspond to different low-rank structures. Moreover, the single cluster model is difficult to find the cellular structure of all datasets. To strengthen the correlation between model solutions, we construct a new ensemble clustering framework LRSEC by using the low-rank model as the basic learner. The LRSEC framework captures the global structure of data through low-rank subspaces, which has better clustering performance than a single clustering model. We validate the performance of the LRSEC framework on seven small datasets and one large dataset and obtain satisfactory results.

Down Regulation of CLDND1 Induces Apoptosis in Breast Cancer Cells

Identification of targets for apoptosis induction is important to provide novel therapeutic approaches in breast cancer. Our earlier studies showed that down regulation of protein kinase C δ (PKCδ) induces death in breast cancer cells. In this study we set out to identify previously unrecognized apoptosis regulators in breast cancer cells. To identify candidates, global expression analysis with microarray was performed after down regulation of PKCδ in the basal-like breast cancer cell lines MDA-MB-231, MDA-MB-468 and BT-549. Genes that were down regulated in all cell lines were further studied for survival-supporting effects. The claudin-like CLDND1 was singled out since several independent siRNAs targeting CLDND1 induced cell death in several cell lines. The cell death induced by CLDND1 knockdown was caspase-dependent, suggesting induction of apoptosis. Nuclear fragmentation, cleavage of caspase-3 and PARP and release of cytochrome C from the mitochondria upon CLDND1 depletion demonstrated involvement of the intrinsic apoptotic pathway. Inhibition of MEK1/2 and JNK further potentiated the cell death induction by CLDND1 knockdown. However, CLDND1 down regulation augmented ERK1/2 phosphorylation, which thereby may protect against the apoptosis inducing effects of CLDND1 down regulation. A concomitant inhibition of MEK1/2 suppresses the ERK1/2 phosphorylation and markedly potentiates the cell death following CLDND1 siRNA treatment. There is today little information on the function of CLDND1. These data provide novel information on CLDND1 and highlight it as a novel survival factor in basal-like breast cancer cell lines.

IN SILICO SEARCH FOR NATURAL ANTISENSE TRANSCRIPTS REVEALS THEIR DIFFERENTIAL EXPRESSION IN HUMAN TUMORS

We created an algorithm that allows high-throughput mapping of sense-antisense (SA) pairs of transcripts. By this method we mapped approximately 32 000 SA pairs of human mRNAs. Collected SA pairs were divided into three groups: SA pairs based on two or more UniGene clusters (17% of all sense-antisense pairs), SA pairs based on ESTs that belong to the same UniGene cluster (42%), and SA pairs formed by UniGene cluster and non-unique unclustered transcripts (41%). To study expression patterns of natural SA pairs we created a software application "Antisense Cluster Filter". We retrieved tissue expression data for all the transcripts forming identified SA pairs, including clustered and unclustered ones. After that, we selected 108 SA pairs represented by transcripts differentially regulated in human tumors. For each of these SA pairs one of the transcripts was expressed only in tumors, another one was expressed both in non-malignant and malignant tissues. Indicated SA pairs may represent a new class of tumor markers. An example of the tumor-specific natural antisense to C3orf4 mRNA is detailed.