Targeting LINC00152 activates cAMP/Ca2+/ferroptosis axis and overcomes tamoxifen resistance in ER+ breast cancer.

Tamoxifen has been the mainstay therapy to treat early, locally advanced, and metastatic estrogen receptor-positive (ER + ) breast cancer, constituting around 75% of all cases. However, the emergence of resistance is common, necessitating the identification of novel therapeutic targets. Here, we demonstrated that long-noncoding RNA LINC00152 confers tamoxifen resistance by blocking tamoxifen-induced ferroptosis, an iron-mediated cell death. Mechanistically, inhibiting LINC00152 reduces the mRNA stability of phosphodiesterase 4D (PDE4D), leading to activation of the cAMP/PKA/CREB axis and increased expression of the TRPC1 Ca2+ channel. This causes cytosolic Ca2+ overload and generation of reactive oxygen species (ROS) that is, on the one hand, accompanied by downregulation of FTH1, a member of the iron sequestration unit, thus increasing intracellular Fe2+ levels; and on the other hand, inhibition of the peroxidase activity upon reduced GPX4 and xCT levels, in part by cAMP/CREB. These ultimately restore tamoxifen-dependent lipid peroxidation and ferroptotic cell death which are reversed upon chelating Ca2+ or overexpressing GPX4 or xCT. Overexpressing PDE4D reverses LINC00152 inhibition-mediated tamoxifen sensitization by de-activating the cAMP/Ca2+/ferroptosis axis. Importantly, high LINC00152 expression is significantly correlated with high PDE4D/low ferroptosis and worse survival in multiple cohorts of tamoxifen- or tamoxifen-containing endocrine therapy-treated ER+ breast cancer patients. Overall, we identified LINC00152 inhibition as a novel mechanism of tamoxifen sensitization via restoring tamoxifen-dependent ferroptosis upon destabilizing PDE4D, increasing cAMP and Ca2+ levels, thus leading to ROS generation and lipid peroxidation. Our findings reveal LINC00152 and its effectors as actionable therapeutic targets to improve clinical outcome in refractory ER+ breast cancer.

Improving Cleaning Efficiency through the Measurement of Food Fouling Adhesive Strength.

This study aims to investigate the impacts of factors, including textural properties, surface roughness, and contact angle, on the cleaning performance of food soils and develop a preliminary mathematical model to predict the cleaning score, depending on the soil-surface properties. The force required to remove soil from the surface was determined by a texture analyzer equipped with a newly designed probe. Potato puree and egg yolk soils showed high adhesive forces compared to other deposits. Margarine required the lowest force to detach from the surfaces. A soil-surface characteristic number (SSCN) was constructed from the results of contact angle, roughness, and textural analysis to predict the cleaning score depending on the soil-surface properties. The experimental work presented indicates that a higher SSCN was associated with lower cleaning scores for soil-surface combinations. Furthermore, a predictive model was developed to define the relationship between cleaning scores and SSCN. The applicability of the model was validated by measuring the cleaning performance of caramel and pudding soils on glass, porcelain, and stainless-steel household surfaces by using an automatic method. Therefore, it can be concluded that the SSCN approach can be improved in further studies to predict cleaning scores of soil-surface combinations in the experimental rig or automatic dishwasher.

CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters.

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.

Small RNA-seq dataset of wild type and 16C Nicotiana benthamiana leaves sprayed with naked dsRNA using the high-pressure spraying technique.

Double-stranded RNA (dsRNA) applications have emerged as promising alternatives to chemical plant pesticides. It has been proposed that the protective effect of dsRNA is mediated by the RNA interference (RNAi) mechanism. Small RNAs (sRNAs) are one of the landmarks of RNAi mechanisms. Two classes of sRNAs appear upon RNAi, triggered by dsRNA: The cleavage products of the dsRNA mapping directly to the dsRNA sequence and the transitive sRNAs mapping to the target transcript outside of the dsRNA sequence. Therefore, the sRNA-seq data obtained from dsRNA-treated plants have been exclusively analysed in the context of the target genes and the outcome has been considered essential to evaluate the underlying mechanism of dsRNA mediated plant protection. Using high-pressure spraying technology (HPST), we have applied a GFP targeting 139bp-long dsRNA on wild type (WT) and GFP expressing (16C) Nicotiana benthamiana plants in biological triplicates. As a control, we applied water with HPST on 16C N. benthamiana. We have acquired sRNA-seq data on the treated and control leaves 5 days post spraying. In this dataset, we have expanded our sRNA-seq analysis from the target GFP transgene sequence to the whole transcriptome of N. benthamiana to provide the community with a resource for the small RNA landscape after high-pressure spraying in 16C and WT samples. Furthermore, we have provided a comparison of sRNA landscape between WT and 16C lines.