The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

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.

Replication stress poses a major threat to genome integrity, yet how higher-order chromatin organization contributes to replication fork protection remains unclear^1,2. Here we show that replication stress induces the formation of transient chromatin loops that enclose de novo heterochromatin-enriched stalled replication forks³. Stressed forks preferentially stall at convergent CTCF motifs, triggering stress-dependent CTCF enrichment that constrains loop extrusion and stabilizes these structures. Loop stabilization requires both CTCF anchoring and G9a-dependent heterochromatin (trimethylation of Lys9 of histone H3 (H3K9me3)) deposition on nascent DNA within the loop body. These loops function as protective scaffolds that shield stalled and reversed forks from degradation by multiple nucleases. By contrast, combined loss of stress-induced heterochromatin and CTCF enrichment destabilizes the loop scaffold, exposing multiple entry points for nucleolytic attack and resulting in extensive nascent-strand degradation through mechanisms distinct from classical fork-reversal-dependent pathways. This protective architecture is similarly critical in BRCA2-deficient cells, in which replication-stress-associated loops predominantly safeguard replication initiation zones, while nascent DNA outside these loops undergoes massive degradation and remains highly susceptible to mutations. Our study elucidates the fundamental role of replication-stress-induced three-dimensional genome reorganization in preserving replication fork stability, thereby mitigating mutagenesis and genomic instability.

Breast cancer affects over 2 million women annually and results in 650,000 deaths. This study aimed to identify epigenetic mechanisms impacting breast cancer-related gene expression, discover potential biomarkers, and present a novel approach integrating feature selection, Natural Language Processing, and 3D chromatin structure analysis. We used The Cancer Genome Atlas database with over 800 samples and multi-omics datasets (mRNA, miRNA, DNA methylation) to select 2701 features statistically significant in cancer versus control samples, from an initial 417,486, using the Monte Carlo Feature Selection and Interdependency Discovery algorithm. Classification of cancer vs. control samples on the selected features returned very high accuracy, depending on feature-type and classifier. The cancer samples generally showed lower expression of differentially expressed genes (DEGs) and increased β-values of differentially methylated sites (DMSs). We identified mRNAs whose expression is explained by miRNA expression and β-values of DMSs. We recognized DMSs affecting NRF1 and MXI1 transcription factors binding, causing a disturbance in NKAPL and PITX1 expression, respectively. Our 3D models showed more loosely packed chromatin in cancer. This study highlights numerous possible regulatory dependencies, and the presented bioinformatic approach provides a robust framework for data dimensionality reduction, enabling the identification of key features for further experimental validation.