Elena Rojano

Integrative analysis of multiomic datasets in rare diseases is often constrained by small-n cohorts, impeding the identification of robust and biologically meaningful disease mechanisms. We developed an end-to-end framework that combines multiomic integration, robust statistical inference and network representation learning to extract clinically relevant molecular signatures from limited sample sizes. The framework prioritizes informative omic layers using Multiple Factor Analysis, identifies stable severity-associated features through stability selection and projects these features onto the human interactome using deep network embeddings to uncover functionally coherent molecular modules. We demonstrate the utility of this approach in PMM2-CDG, an ultra-rare congenital disorder of glycosylation, by integrating transcriptomic, miRNA, metabolomic and phenotypic data from ten patient-derived fibroblast cell lines spanning the clinical severity spectrum. Network modules derived from the severity-associated dimension showed significant separation between severe and mild phenotypes and revealed biological processes related to proteostasis, neurodevelopmental signaling and extracellular matrix organization. These results show that combining multiomic integration with network embedding can help address the key challenges of rare disease research, providing a computationally scalable systems biology framework for the discovery of clinically relevant disease mechanisms in small and heterogeneous cohorts.

Neuromuscular diseases (NMDs) like Duchenne muscular dystrophy (DMD), limb–girdle muscular dystrophy (LGMD), and amyotrophic lateral sclerosis (ALS) are rare, progressive disorders with complex molecular mechanisms. Traditional transcriptomic analyses often struggle to capture systems-level dysregulation, especially given the small sample sizes typical of rare disease studies. Our differential expression analysis of eight public RNA-seq datasets from various cell types in DMD, LGMD, and ALS revealed not only disease-relevant pathways but also unexpected enrichments, such as renal development, suggesting systemic impacts beyond muscle tissue. To address limitations in capturing broader molecular mechanisms, we applied an integrative systems biology approach combining differential expression data, protein–protein interaction (PPI) networks, and network embedding techniques. Comparative functional enrichment revealed shared pathways, including glycosaminoglycan binding in both DMD and FUS-related ALS, implicating extracellular matrix–protein interactions in FUS mutation effects. Mapping DEGs onto the human PPI network and assessing their proximity to causal genes uncovered dysregulated non-coding RNAs, such as PAX8-AS1, SBF2-AS1, and NEAT1, potentially indicating common regulatory roles. We also found candidate genes within disease-proximal clusters, like HS3ST3A1, which may contribute to pathogenesis. Overall, this integrative approach reveals shared transcriptional programs and novel targets, advancing our understanding and potential treatment strategies for NMDs.

Understanding and predicting biological processes from protein–protein interaction (PPI) networks requires accurate and efficient representations of their structure. However, many existing methods fail to capture the complex, overlapping modular structure of biological systems. To address this, we propose a network embedding strategy that improves both biological interpretability and predictive power. By transforming networks into a low-dimensional space while preserving key topological properties, embedding enables the discovery of novel functional relationships. Pre-clustering a network before embedding enhances representation quality, i.e. the ability to preserve meaningful structural and functional properties in the embedding space. However, traditional non-overlapping clustering methods can introduce bias by ignoring the overlapping nature of biological communities. We overcome this limitation by integrating the Hierarchical Link Clustering (HLC) algorithm into an embedding workflow tailored for large, weighted, undirected networks. First, we introduce two optimized HLC implementations for Python and R, both outperforming existing methods in clustering accuracy and scalability. Then, by restricting random walks to HLC-defined communities, we improve the representation of biological pathways, as shown using Reactome on the human PPI network. We also apply our full cluster embedding workflow to analyze RASopathies, a group of interrelated disorders with a diverse range of phenotypes, caused by mutations in genes from the RAS/MAPK pathway. This approach was used not only to represent known pathways, but also to identify potential novel gene candidates associated with RASopathies, including Noonan and Costello syndrome. HLC implementations are available in the CDLIB library (https://github.com/GiulioRossetti/cdlib), and at https://github.com/jimrperkins/linkcomm for Python and R, respectively.

β-hydroxybutyrate (BHB) serves as an alternative cellular fuel during states of low glucose availability, such as fasting or carbohydrate restriction, when the body shifts to using fats and ketone bodies for energy. While BHB has shown potential metabolic benefits, its mechanisms of action in the context of obesity are not fully understood. In this study, we examined the effects of BHB supplementation on subcutaneous adipose tissue (SAT) metabolism in a diet-induced obesity (DIO) mouse model. Adult male mice were first fed a high-fat diet for six weeks, followed by a standard diet with or without BHB supplementation for an additional six weeks. BHB supplementation led to significant body weight loss independent of food intake. This weight reduction was associated with decreased adipocyte differentiation, reflected by reduced peroxisome proliferator-activated receptor gamma (PPARγ) protein levels and lower uncoupling protein 1 (UCP1) expression, indicating altered SAT function. Transcriptomic analysis of SAT revealed upregulation of genes involved in fatty acid activation and transport (e.g., Slc27a2, Plin5, Acot4, Acsm3, Rik). Functional enrichment highlighted the activation of the PPAR signaling pathway and enrichment of peroxisomal components in the BHB group. Together, these results suggest that BHB promotes lipid remodeling in SAT, enhancing fatty acid metabolism while suppressing thermogenic pathways, and thus may represent a novel mechanism contributing to adiposity reduction and metabolic improvement.

Hirschsprung’s disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein–protein interaction (PPI) and miRNA–target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene–disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

A wide range of approaches can be used to detect micro RNA (miRNA)–target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Background: Angiogenesis is regulated by multiple genes whose variants can lead to different disorders. Among them, rare diseases are a heterogeneous group of pathologies, most of them genetic, whose information may be of interest to determine the still unknown genetic and molecular causes of other diseases. In this work, we use the information on rare diseases dependent on angiogenesis to investigate the genes that are associated with this biological process and to determine if there are interactions between the genes involved in its deregulation. Results: We propose a systemic approach supported by the use of pathological phenotypes to group diseases by semantic similarity. We grouped 158 angiogenesis-related rare diseases in 18 clusters based on their phenotypes. Of them, 16 clusters had traceable gene connections in a high-quality interaction network. These disease clusters are associated with 130 different genes. We searched for genes associated with angiogenesis througth ClinVar pathogenic variants. Of the seven retrieved genes, our system confirms six of them. Furthermore, it allowed us to identify common affected functions among these disease clusters. Availability: https://github.com/ElenaRojano/angio_cluster. Contact: seoanezonjic@uma.es and elenarojano@uma.es