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Biology subjects

Alves, C. L.

Publications and source records attributed to Alves, C. L..

3 recordsLinked to original sources

Classifying Calcium Imaging Dynamics with Deep Learning: Multi-Frequency Analysis through Quantile-Based Time-Series Network Representations

To address the limitations of calcium imaging data, we propose a segmentation-agnostic deep learning framework that integrates Quantile-Based Time-Series Network (QTN) representations with convolutional neural networks to classify neuronal dynamics across multiple spatial resolutions and acquisition frequencies. By transforming fluorescence traces into compact, fixed-size matrices derived from quantile transitions, the method standardizes inputs across recordings while markedly reducing dimensionality and computational cost. Several QTN variants were systematically evaluated, demonstrating strong and consistent classification performance across both whole-image and grid-based preprocessing strategies. Notably, the framework maintained high accuracy under reduced temporal resolution and controlled noise perturbations, confirming that discrimination arises from meaningful temporal patterns rather than artifacts. This study establishes a robust, scalable, and generalizable approach for analyzing calcium imaging dynamics, paving the way for efficient, segmentation-independent characterization of neuronal activity in pharmacological and systems neuroscience applications.

cell biology↗

JNK pathway suppression drives resistance to combination endocrine therapy and CDK4/6 inhibition in ER+ breast cancer

PurposeEndocrine therapy in combination with CDK4/6 inhibition doubles the progression-free survival of patients with advanced ER+ breast cancer, but resistance is inevitable, leaving patients with limited treatment options. Experimental DesignWe performed unbiased genome-wide CRISPR/Cas9 knockout screens using ER+ breast cancer cells to identify novel drivers of resistance to combination endocrine therapy (tamoxifen) and CDK4/6 inhibitor (palbociclib) treatment. Screen hits were validated by CRISPR/Cas9 knockout models, mechanistic analyses and evaluation of patient samples. ResultsOur screens identified the inactivation of JNK signalling, including loss of the kinase MAP2K7, as a key driver of combination resistance. We developed multiple CRISPR/Cas9 knockout ER+ breast cancer cell lines (MCF-7 and T-47D) to investigate the effects of MAP2K7, MAPK8 and MAPK9 loss. MAP2K7 knockout increased metastatic burden in vivo and led to impaired JNK-mediated stress responses, as well as promoting cell survival and reducing senescence entry following endocrine therapy and CDK4/6 inhibitor treatment. Mechanistically, this occurred via loss of the AP-1 transcription factor c-JUN, leading to an attenuated response to combination endocrine therapy plus CDK4/6 inhibition. Furthermore, we analysed ER+ advanced breast cancer patient cohorts and found that inactivation of the JNK pathway was associated with increased metastatic burden, and low pJNKT183/Y185 activity correlated with a poorer response to systemic endocrine and CDK4/6 inhibitor therapies. ConclusionsOverall, we demonstrate that suppression of JNK signalling enables persistent growth during combined endocrine therapy and CDK4/6 inhibition. Furthermore, our data provide a pre-clinical rationale to screen patients tumours for JNK signalling deficiency prior to receiving combined endocrine therapy and CDK4/6 inhibition. STATEMENT OF TRANSLATIONAL RELEVANCEResistance to CDK4/6 inhibitors in the context of endocrine therapy resistance presents an urgent clinical challenge for the management of estrogen receptor positive (ER+) breast cancer. However, the mechanisms driving resistance to this therapeutic combination remain poorly understood. Here, we have identified inactivation of JNK signalling, specifically loss of the JNK kinase MAP2K7, as a major determinant of resistance to combined endocrine therapy and CDK4/6 inhibition in ER+ breast cancer. We uncovered that MAP2K7 loss augments tumour survival in vitro and in vivo. This occurs via disruption of activator protein-1 (AP-1) transcription factors, and thus prevents the induction of therapy-induced senescence and JNK-induced stress response. These findings reveal a critical tumour suppressor role for the JNK pathway in ER+ breast cancer, highlighting the importance of identifying patients with deficient JNK signalling and cautioning against the development of JNK inhibitors for this setting.

cancer biology↗

CHOmics: a web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines

Chinese hamster ovary (CHO) cell lines are widely used in industry for biological drug production. During cell culture development, considerable effort is invested to understand the factors that greatly impact cell growth, specific productivity and product qualities of the biotherapeutics. High-throughput omics approaches have been increasingly utilized to reveal cellular mechanisms associated with cell line phenotypes and guide process optimization, comprehensive omics data analysis and management have been a challenge. Here we developed CHOmics, a web-based tool for integrative analysis of CHO cell line omics data that provides an interactive visualization of omics analysis outputs and efficient data management. CHOmics has a built-in comprehensive pipeline for RNA sequencing data processing and multilayer statistical modules to explore relevant genes or pathways. Moreover, advanced functionalities were provided to enable users to customize their analysis and visualize the output systematically and interactively. The tool was also designed with the flexibility to allow other omics data input and thereby enabling multi-omics comparison and visualization at both gene and pathway levels. Collectively, CHOmics is an integrative platform for data analysis, visualization and management with expectations to promote the broader use of omics in CHO cell research. The open-source tool is freely available at http://www.chomics.org.

bioinformatics↗