Glycans unique to the relapse-prone subset within triple-negative breast cancer as revealed by lectin array-based analysis of surgical specimens

Introduction Molecular and cellular characteristics of the relapse-prone subset within triple-negative breast cancer (TNBC) remain unclear. Aberrant glycosylation is involved in the malignant behavior of cancer cells. In the present study, we aimed to reveal glycan profiles unique to relapsed TNBC patients. Methods Thirty TNBC patients who did not undergo neoadjuvant chemotherapy but postoperative standard adjuvant therapy from 2009 through 2016 at Juntendo Hospital were investigated. TNBC cells were resected from primary breast cancer sections of formalin-fixed surgical specimens using laser-assisted microdissection. The binding intensities of the extracted glycoproteins to 45 lectins were quantified using lectin microarray and compared between relapsed and non-relapsed patients. Immunohistochemical staining with TJA-II lectin in specimen sections was performed. Results Five patients relapsed during the follow-up (range 37–123 months). Lectin microarray analysis revealed that 7 out of 45 lectins showed significant differences in binding intensity between the relapsed and the non-relapsed group. TJA-II, ACA, WFA, and BPL showed stronger binding in the relapsed group. PNGase F treatment of TNBC cell lysates suggested that TJA-II and ACA bind O-glycans. TJA-II staining of tissue sections revealed strong binding to cell surface membranes and to the cytoplasm of TNBC cells, but not to other types of cells. Significantly more TNBC cells were stained in tissue sections from relapsed than non-relapsed patients. Conclusions TNBC cells from relapsed patients showed a unique lectin reactivity, with higher levels of TJA-II (also WFA and BPL) binding than in non-relapsed patients. The results are potentially useful to develop new prognostic and therapeutic tools.

A noncanonical AR addiction drives enzalutamide resistance in prostate cancer

Resistance to next-generation anti-androgen enzalutamide (ENZ) constitutes a major challenge for the treatment of castration-resistant prostate cancer (CRPC). By performing genome-wide ChIP-seq profiling in ENZ-resistant CRPC cells we identify a set of androgen receptor (AR) binding sites with increased AR binding intensity (ARBS-gained). While ARBS-gained loci lack the canonical androgen response elements (ARE) and pioneer factor FOXA1 binding motifs, they are highly enriched with CpG islands and the binding sites of unmethylated CpG dinucleotide-binding protein CXXC5 and the partner TET2. RNA-seq analysis reveals that both CXXC5 and its regulated genes including ID1 are upregulated in ENZ-resistant cell lines and these results are further confirmed in patient-derived xenografts (PDXs) and patient specimens. Consistent with the finding that ARBS-gained loci are highly enriched with H3K27ac modification, ENZ-resistant PCa cells, organoids, xenografts and PDXs are hyper-sensitive to NEO2734, a dual inhibitor of BET and CBP/p300 proteins. These results not only reveal a noncanonical AR function in acquisition of ENZ resistance, but also posit a treatment strategy to target this vulnerability in ENZ-resistant CRPC.

pyBedGraph: a python package for fast operations on 1D genomic signal tracks

Motivation Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. Results We developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or a bigWig file. When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average 260 times faster than the existing program pyBigWig. On average, pyBedGraph can look up the exact mean signal of 1 million regions in ∼0.26 s and can compute their approximate means in <0.12 s on a conventional laptop. Availability and implementation pyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

Presence of N-acetylgalactosamine/galactose residues on bronchioloalveolar cells during rat postnatal development

In mammals, the alveolarization process develops predominantly after birth. Airway cells display a complex assemblage of glycans on their surface. These glycans, particularly terminal glycan extensions, are important effective carriers of information that change during the differentiation process. Nevertheless, few systematic data are reported about the cell surface sugar residue content during post-natal lung development. In the present work, we aimed to identify and semi-quantify N-acetylgalactosamine (GalNAc)/galactose (Gal) residues on the bronchioloalveolar cell surface in rat lung sections from 1-, 4-, 8- day old and adult animals and link these data with the lung glycocalyx composition. Horseradish peroxidase-conjugated lectin from Glycine max (soybean agglutinin, SBA) was used, and light microscopy methodologies were performed. SBA labelling intensity was studied before and after sialidase pre-treatment, at one-, four- and eight-day-old animals and adult animals. For semi-quantitative evaluation of SBA binding intensity, two investigators performed the analysis independently, blinded to the type of experiment. Reactivity of the lectin was assessed in bronchiolar and respiratory portion/alveolar epithelial cell surfaces. We evidenced a stronger positive reaction when lung sections were pre-treated with neuraminidase before incubation with the lectin in one- and four-day-old animals and adult animals. These results were not so manifest in eight-day-old animals. This binding pattern, generally points towards the presence of terminal but mainly sub-terminal GalNAc/Gal residues probably capped by sialic acids on the rat bronchiolar/respiratory tract epithelial cells. As this glycan extension is common in O- and N-glycans, our results suggest that these glycan classes can be present in bronchioloalveolar cells immediately after birth and exist during the postnatal period. The results observed in eight-day-old rat lung sections may be due to the dramatic lung morphologic changes and the possible underlying biological mechanisms that occur during this age-moment.

pyBedGraph: a Python package for fast operations on 1-dimensional genomic signal tracks

MotivationModern genomic research relies heavily on next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed.ResultsWe developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph file. When tested on 8 ChIP-seq and ATAC-seq datasets, pyBedGraph is on average 245 times faster than the existing program. Notably, pyBedGraph can look up the exact mean signal of 1 million regions in ~0.26 second on a conventional laptop. An approximate mean for 10,000 regions can be computed in ~0.0012 second with an error rate of less than 5 percent.AvailabilitypyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license.

Transcription Factor IRF4 Dysfunction Affects the Immunosuppressive Function of Treg Cells in Patients with Primary Immune Thrombocytopenia

Background. Th17/Treg balance skews towards Th17 in ITP patient. IRF4 has been highlighted for its close relationship to the immunosuppressive function of Treg cells and the IL-17 synthesis in CD4+ T cells. This study was aimed at examining the effects of IRF4 to the Th17/Treg cells in patients with ITP. Methods. Treg and Teff cells were isolated from PBMCs of newly diagnosed ITP patients. The percentages of CD4+CD25hiFoxp3+Treg cells and the CD3+CD4+IL-17+Th17 cells were detected by flow cytometry. After being cultured, the supernatants of Tregs were collected for IL-10 concentration test. The IRF4 levels of Tregs were measured. Teffs were cultured alone or with Tregs for 24 hours. Then the supernatants were collected for IL-17 concentration test. The binding intensity of IRF4 to the gene IL-10 in Treg cells was detected by ChIP-qPCR. Metabolic assays for Teffs and Tregs were performed with Agilent Seahorse XF96 Analyzer. Results. The secretion of IL-10 by Tregs was decreased in ITP patients. The intensity of IRF4 binding to IL-10 DNA of Tregs in patients was higher than that of normal controls and Teffs in ITP patients. The expressions of IRF4 of Tregs in ITP patients were remarkably lower than that of healthy controls. The percentage of Th17 cells in healthy controls was significantly increased after IRF4 mRNA silencing. Abnormal metabolism of Treg and Teff cells was found in ITP patients. Conclusion. The skewed ratio of Th17/Treg cells and dysfunction of Treg cells in newly diagnosed ITP patients was at least partly caused by IRF4 dysfunction. The underlying mechanism might be the impact of IRF4 on the metabolism of Treg and Teff cells.

Removal of heavy metals with sequential sludge washing techniques using saponin: optimization conditions, kinetics, removal effectiveness, binding intensity, mobility and mechanism

Testing of sequential sludge washing in triplicate using typical biosurfactant saponin was conducted to remove heavy metals.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.