HSF1Base: A Comprehensive Database of HSF1 (Heat Shock Factor 1) Target Genes

HSF1 (heat shock factor 1) is an evolutionarily conserved master transcriptional regulator of the heat shock response (HSR) in eukaryotic cells. In response to high temperatures, HSF1 upregulates genes encoding molecular chaperones, also called heat shock proteins, which assist the refolding or degradation of damaged intracellular proteins. Accumulating evidence reveals however that HSF1 participates in several other physiological and pathological processes such as differentiation, immune response, and multidrug resistance, as well as in ageing, neurodegenerative demise, and cancer. To address how HSF1 controls these processes one should systematically analyze its target genes. Here we present a novel database called HSF1Base (hsf1base.org) that contains a nearly comprehensive list of HSF1 target genes identified so far. The list was obtained by manually curating publications on individual HSF1 targets and analyzing relevant high throughput transcriptomic and chromatin immunoprecipitation data derived from the literature and the Yeastract database. To support the biological relevance of HSF1 targets identified by high throughput methods, we performed an enrichment analysis of (potential) HSF1 targets across different tissues/cell types and organisms. We found that general HSF1 functions (targets are expressed in all tissues/cell types) are mostly related to cellular proteostasis. Furthermore, HSF1 targets that are conserved across various animal taxa operate mostly in cellular stress pathways (e.g., autophagy), chromatin remodeling, ribosome biogenesis, and ageing. Together, these data highlight diverse roles for HSF1, expanding far beyond the HSR.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor-target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor-target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor-target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/.

SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

Genetic Modulation of the GLUT1 Transporter Expression-Potential Relevance in Complex Diseases

The human GLUT1 (SLC2A1) membrane protein is the key glucose transporter in numerous cell types, including red cells, kidney, and blood-brain barrier cells. The expression level of this protein has a role in several diseases, including cancer and Alzheimer's disease. In this work, to investigate a potential genetic modulation of the GLUT1 expression level, the protein level was measured in red cell membranes by flow cytometry, and the genetic background was analyzed by qPCR and luciferase assays. We found significant associations between red cell GLUT1 levels and four single nucleotide polymorphisms (SNP) in the coding SLC2A1 gene, that in individuals with the minor alleles of rs841848, rs1385129, and rs11537641 had increased, while those having the variant rs841847 had decreased erythrocyte GLUT1 levels. In the luciferase reporter studies performed in HEK-293T and HepG2 cells, a similar SNP-dependent modulation was observed, and lower glucose, serum, and hypoxic condition had variable, cell- and SNP-specific effects on luciferase expression. These results should contribute to a more detailed understanding of the genetic background of membrane GLUT1 expression and its potential role in associated diseases.

mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. mulea is distributed as a CRAN R package downloadable from https://cran.r-project.org/web/packages/mulea/ and https://github.com/ELTEbioinformatics/mulea . It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.

Genomic surveillance as a scalable framework for precision phage therapy against antibiotic-resistant pathogens

Phage therapy is gaining increasing interest in the fight against critically antibiotic-resistant nosocomial pathogens. However, the narrow host range of bacteriophages hampers the development of broadly effective phage therapeutics and demands precision approaches. Here, we combine large-scale phylogeographic analysis with high-throughput phage typing to guide the development of precision phage cocktails targeting carbapenem-resistant Acinetobacter baumannii, a top-priority pathogen. Our analysis reveals that a few strain types dominate infections in each world region, with their geographical distribution remaining stable within 6 years. As we demonstrate in Eastern Europe, this spatiotemporal distribution enables preemptive preparation of region-specific phage collections that target most local infections. Finally, we showcase the efficacy of phage cocktails against prevalent strain types using in vitro and animal infection models. Ultimately, genomic surveillance identifies patients benefiting from the same phages across geographical scales, thus providing a scalable framework for precision phage therapy.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

Genetic Variants of the Human Thiamine Transporter (SLC19A3, THTR2)-Potential Relevance in Metabolic Diseases

Thiamine, crucial for energy metabolism, is associated with various human diseases when deficient. We studied how variations in the SLC19A3 gene, encoding THTR2, a thiamine transporter, may influence type 2 diabetes (T2DM) and gout (arthritis urica, AU). We characterized the SLC19A3 gene variants using bioinformatics and analyzed DNA samples from controls, T2DM, and gout patients to explore associations with physical/laboratory parameters. In human cells, we used a luciferase reporter assay to assess how these variants affect gene expression. We examined four large haplotypes (H1-4) in this gene, identified lead SNPs for the minor variants (MV), and explored potential transcription factor binding sites. We found that in T2DM patients, H3-MV correlated significantly with impaired glucose metabolism (pHOMA = 0.0189, pHbA1c% = 0.0102), while H4-MV correlated with altered uric acid (p = 0.0008) and white blood cell levels (p = 0.0272). In AU patients, H3-MV correlated with increased basophil granulocyte levels (p = 0.0273). In model cell lines, H3-MV presence increased gene expression (p = 0.0351), influencing responses to thiamine depletion and metformin (p = 0.0016). Although H4-MV did not directly affect luciferase expression, thiamine and fedratinib co-treatment significantly enhanced gene expression in thiamine-depleted cells (p = 0.04854). Our results suggest a connection between selected SLC19A3 variants and the severity of metabolic diseases or their response to treatment.