Role of Enhancers in Development and Diseases

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

Reversal of high-glucose-induced transcriptional and epigenetic memories through NRF2 pathway activation

Diabetes complications such as nephropathy, retinopathy, or cardiovascular disease arise from vascular dysfunction. In this context, it has been observed that past hyperglycemic events can induce long-lasting alterations, a phenomenon termed "metabolic memory." In this study, we evaluated the genome-wide gene expression and chromatin accessibility alterations caused by transient high-glucose exposure in human endothelial cells (ECs) in vitro. We found that cells exposed to high glucose exhibited substantial gene expression changes in pathways known to be impaired in diabetes, many of which persist after glucose normalization. Chromatin accessibility analysis also revealed that transient hyperglycemia induces persistent alterations, mainly in non-promoter regions identified as enhancers with neighboring genes showing lasting alterations. Notably, activation of the NRF2 pathway through NRF2 overexpression or supplementation with the plant-derived compound sulforaphane, effectively reverses the glucose-induced transcriptional and chromatin accessibility memories in ECs. These findings underscore the enduring impact of transient hyperglycemia on ECs' transcriptomic and chromatin accessibility profiles, emphasizing the potential utility of pharmacological NRF2 pathway activation in mitigating and reversing the high-glucose-induced transcriptional and epigenetic alterations.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Decoding gene regulatory circuitry underlying TNBC chemoresistance reveals biomarkers for therapy response and therapeutic targets

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype characterised by extensive intratumoral heterogeneity, high rates of metastasis and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of chemotherapy resistance in TNBC patients remains poorly understood. Here, leveraging single-cell transcriptome datasets of matched longitudinal TNBC chemoresponsive and chemoresistant patient cohorts, we unravel distinct cell subpopulations intricately associated with chemoresistance and the signature genes defining these populations. Notably, using genome-wide mapping of the H3K27ac mark, we show that the expression of these chemoresistance genes is driven via a set of TNBC super-enhancers and associated transcription factor networks across TNBC subtypes. Furthermore, genetic screens reveal that a subset of these transcription factors is essential for the survival of TNBC cells, and their loss increases sensitivity to chemotherapeutic agents. Overall, our study has revealed epigenetic and transcription factor networks underlying chemoresistance and suggests novel avenues to stratify and improve the treatment of patients with a high risk of developing resistance.

A Kaleidoscope of Keratin Gene Expression and the Mosaic of Its Regulatory Mechanisms

Keratins are a family of intermediate filament-forming proteins highly specific to epithelial cells. A combination of expressed keratin genes is a defining property of the epithelium belonging to a certain type, organ/tissue, cell differentiation potential, and at normal or pathological conditions. In a variety of processes such as differentiation and maturation, as well as during acute or chronic injury and malignant transformation, keratin expression undergoes switching: an initial keratin profile changes accordingly to changed cell functions and location within a tissue as well as other parameters of cellular phenotype and physiology. Tight control of keratin expression implies the presence of complex regulatory landscapes within the keratin gene loci. Here, we highlight patterns of keratin expression in different biological conditions and summarize disparate data on mechanisms controlling keratin expression at the level of genomic regulatory elements, transcription factors (TFs), and chromatin spatial structure.

Analysis of the landscape of human enhancer sequences in biological databases

The process of gene regulation extends as a network in which both genetic sequences and proteins are involved. The levels of regulation and the mechanisms involved are multiple. Transcription is the main control mechanism for most genes, being the downstream steps responsible for refining the transcription patterns. In turn, gene transcription is mainly controlled by regulatory events that occur at promoters and enhancers. Several studies are focused on analyzing the contribution of enhancers in the development of diseases and their possible use as therapeutic targets. The study of regulatory elements has advanced rapidly in recent years with the development and use of next generation sequencing techniques. All this information has generated a large volume of information that has been transferred to a growing number of public repositories that store this information. In this article, we analyze the content of those public repositories that contain information about human enhancers with the aim of detecting whether the knowledge generated by scientific research is contained in those databases in a way that could be computationally exploited. The analysis will be based on three main aspects identified in the literature: types of enhancers, type of evidence about the enhancers, and methods for detecting enhancer-promoter interactions. Our results show that no single database facilitates the optimal exploitation of enhancer data, most types of enhancers are not represented in the databases and there is need for a standardized model for enhancers. We have identified major gaps and challenges for the computational exploitation of enhancer data.