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Azusa Tanaka

Publications and source records attributed to Azusa Tanaka.

2 recordsLinked to original sources

Integrative analysis of ATAC-seq and RNA-seq for cells infected by human T-cell leukemia virus type 1

Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy (HAM) after a long latent period in a fraction of infected individuals. These HTLV-1-infected cells typically have phenotypes similar to that of CD4${^+}$ T cells, but the cell status is not well understood. To extract the inherent information of HTLV-1-infected CD4$^+$ cells, we integratively analyzed the ATAC-seq and RNA-seq data of infected cells. Compared to CD4${^+}$ T cells from healthy donors, we found anomalous chromatin accessibility in HTLV-1-infected CD4${^+}$ cells derived from ATL cases in terms of location and sample-to-sample fluctuations in open chromatin regions. Further, by focusing on systematically selected genes near the open chromatin regions, all the gene expressions in ATL cases were found to be distinct from those of healthy CD4$^+$ T cells. Based on a further analysis of chromatin accessibility, we detected TLL1 (Tolloid Like 1) as one of the key genes that exhibit unique gene expressions in ATL cases. A luciferase assay indicated that TLL1 has a strong regulatory effect on TGF-$\beta$. Overall, this study provides results about the status of HTLV-1 infected cells, which are qualitatively consistent across the different scales of chromatin accessibility, transcription, and immunophenotype.

q-bio.GN

Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells

The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of $1$s and $0$s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.

q-bio.GN