Chemokine (C-C motif) receptor 7 (CCR7), a class A subtype G-Protein Coupled Receptor (GPCR), is involved in the migration, activation and survival of multiple cell types including dendritic cells, T cells, eosinophils, B cells, endothelial cells and different cancer cells. Together, CCR7 signaling system has been implicated in diverse biological processes such as lymph node homeostasis, T cell activation, immune tolerance, inflammatory response and cancer metastasis. CCL19 and CCL21, the two well-characterized CCR7 ligands, have been established to be differential in their signaling through CCR7 in multiple cell types. Although the differential ligand signaling through single receptor have been suggested for many receptors including GPCRs, there exists no resource or platform to analyse them globally. Here, first of its kind, we present the cell-type-specific differential signaling network of CCL19/CCL21-CCR7 system for effective visualization and differential analysis of chemokine/GPCR signaling. Database URL:http:// www. netpath. org/ pathways? path_ id= NetPath_ 46 .

HER2-positive breast cancer is one of the most prevalent forms of cancer among women worldwide. Generally, the molecular characteristics of this breast cancer include activation of human epidermal growth factor receptor-2 (HER2) and hormone receptor activation. HER2-positive is associated with a higher death rate, which led to the development of a monoclonal antibody called trastuzumab, specifically targeting HER2. The success rate of HER2-positive breast cancer treatment has been increased; however, drug resistance remains a challenge. This fact motivated us to explore the underlying molecular mechanisms of trastuzumab resistance. For this purpose, a two-fold approach was taken by considering well-known breast cancer cell lines SKBR3 and BT474. In the first fold, trastuzumab treatment doses were optimized separately for both cell lines. This was done based on the proliferation rate of cells in response to a wide variety of medication dosages. Thereafter, each cell line was cultivated with a steady dosage of herceptin for several months. During this period, six time points were selected for further in vitro analysis, ranging from the untreated cell line at the beginning to a fully resistant cell line at the end of the experiment. In the second fold, nucleic acids were extracted for further high throughput-based microarray experiments of gene and microRNA expression. Such expression data were further analyzed in order to infer the molecular mechanisms involved in the underlying development of trastuzumab resistance. In the list of differentially expressed genes and miRNAs, multiple genes (e.g., BIRC5, E2F1, TFRC, and USP1) and miRNAs (e.g., hsa miR 574 3p, hsa miR 4530, and hsa miR 197 3p) responsible for trastuzumab resistance were found. Downstream analysis showed that TFRC, E2F1, and USP1 were also targeted by hsa-miR-8485. Moreover, it indicated that miR-4701-5p was highly expressed as compared to TFRC in the SKBR3 cell line. These results unveil key genes and miRNAs as molecular regulators for trastuzumab resistance.

The BioAI Hackathon at the Centre of New Technologies at the University of Warsaw convened 43 international researchers to collaboratively explore artificial intelligence (AI) approaches for solving complex challenges in computational biology. Nine interdisciplinary and multi-institutional teams addressed the following problems: disease-gene prioritization, microbiome analysis, drug-protein interactions, alternative splicing prediction, chromatin architecture study and toxicological profiling. Using cutting-edge tools such as graph neural networks (GNNs), large language models (LLMs), and multi-omics integration frameworks, participants developed scalable and reproducible analytical pipelines. The results include: a disease gene prioritization framework using GNNs, a microbiome dynamics analysis for poultry health prediction and the construction of chromatin structure-aware regulatory networks. All projects follow the open science principles and display translational potential. This hackathon underscores the transformative role of AI in biomedicine and the value of collaborative, time-bounded innovation for accelerating discovery in life sciences. All projects are publicly available on GitHub: https://github.com/SFGLab

The improvements in sequencing technology make the development of new tools for the detection of structural variance more and more common. However, the tools available for the long-read Oxford Nanopore sequencing are limited, and it is hard to choose one, which is the best. That is why there is a need to create a tool based on consensus that combines existing work in order to discover a set of high-quality, reliable structural variants that can be used for further downstream analysis. The field has also been subject to revolution in machine learning techniques, especially deep learning. In the spirit of the aforementioned need and developments, we propose a novel, fully automated ConsensuSV-ONT algorithm. The method uses six independent, state-of-the-art structural variant callers for long-read sequencing along with a convolutional neural network for filtering high-quality variants. We provide a runtime environment in the form of a docker image, wrapping a nextflow pipeline for efficient processing using parallel computing. The solution is complete in its form and is ready to use not only by computer scientists but accessible and easy to use for everyone working with Oxford Nanopore long-read sequencing data.