Background: Rare diseases represent a significant research challenge due to the limited availability of data, small patient cohorts, and heterogeneous phenotypes. Data augmentation and synthetic data generation are increasingly adopted to mitigate these limitations. Methods: This scoping review maps the application of data augmentation and synthetic data generation methods as strategies to address these limitations. A total of 118 studies published between 2018 and 2025 were identified through PubMed, Scopus, and Electronics Engineers (IEEE) Xplore. Results: Imaging data headed the field, followed by clinical and omics datasets. Classical augmentation, mainly geometric and photometric transformations, emerged as the most frequent approach, while deep generative models have rapidly expanded since 2021. Rule- and model-based methods were less common but demonstrated high interpretability in small datasets. Conclusions: Overall, these techniques enabled dataset expansion and improved model robustness. However, both approaches require rigorous validation to confirm biological plausibility. Together, these methods can transform data scarcity from a barrier into a driver of methodological innovation, enabling more inclusive rare disease research.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the missense mutations found in the 2,535 proteins involved in Mendelian rare diseases (MRD), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRD show different mutation site distributions at different three-dimensional protein depths. We then predicted the depth-dependent effects of mutations for the 20,248 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

The growing availability of protein structural data from experimental methods and accurate predictive models provides the opportunity to investigate the molecular origins of rare diseases (RDs) reviewed in the Orpha.net database. In this study, we analyzed the topology of 5728 missense mutation sites involved in Mendelian RDs (MRDs), forming the basis of our structural bioinformatics investigation. Each mutation site was characterized by side-chain position within the overall 3D protein structure and side-chain orientation. Atom depth quantitation, achieved by using SADIC v2.0, allowed the classification of all the mutation sites listed in our database. Particular attention was given to mutations where smaller amino acids replaced bulky, outward-oriented residues in the outer structural layers. Our findings reveal that structural features that could lead to the formation of void spaces in the outer protein region are very frequent. Notably, we identified 722 cases where MRD-associated mutations could generate new surface pockets with the potential to accommodate pharmaceutical ligands. Molecular dynamics (MD) simulations further supported the prevalence of cryptic pocket formation in a subset of drug-binding protein candidates, underscoring their potential for structure-based drug discovery in RDs.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Alkaptonuria (AKU) is an ultra-rare inherited metabolic disorder caused by impaired activity of homogentisate 1,2-dioxygenase (HGD), a Fe(II)-dependent enzyme that catalyzes the oxidative cleavage of homogentisic acid in the tyrosine degradation pathway. Although high-resolution structures of human HGD have been solved, a fundamental mechanistic question has remained unresolved: how molecular oxygen reaches the deeply buried catalytic iron required for catalysis. Here, we identify a previously unreported AKU-associated HGD variant, c.925G>A (p.G309R), and use it as a mechanistic perturbation reference to dissect the structural determinants of oxygen access. By integrating replicated classical and steered molecular dynamics simulations with transient pocket detection, tunnel mapping, O₂ spatial-occupancy analysis, residue-level tunnel composition, and PCA/tICA-based dynamic validation, we identify a structurally accessible and dynamically supported O₂-translocation architecture connecting the central pore of the hexameric enzyme to the non-heme Fe(II) active sites. This pathway is not intrinsic to a single subunit but emerges from a cooperative arrangement of residues contributed by three protomers, generating six symmetry-related O₂-access routes per hexamer. The G309R substitution perturbs the architecture and continuity of this tunnel system, providing a mechanistic explanation for enzyme dysfunction without evidence of active-site structural perturbation or global destabilization. Together, our findings support oxygen-tunnel integrity as a previously unrecognized mechanistic requirement for human HGD activity and introduce disruption of oxygen trafficking as an additional pathogenic mode in AKU, with implications for structure-guided variant interpretation and precision-medicine strategies.

Alkaptonuria (AKU) is a rare metabolic disorder caused by homogentisic acid accumulation and characterised by ochronosis, oxidative stress, chronic inflammation, and progressive connective tissue damage. This study aimed to define the circulating proteomic alterations associated with AKU and assess their relationship with nitisinone treatment. Plasma samples from 11 patients with AKU and 6 age- and sex-matched healthy controls were analysed by liquid chromatography coupled to tandem mass spectrometry using label-free quantification. Differentially abundant proteins were identified using thresholds of |log₂FC| ≥ 1 and Benjamini–Hochberg false discovery rate ≤ 0.01, followed by functional enrichment and treatment-stratified analyses. Twenty-two proteins were differentially abundant between AKU patients and controls. Complement components (C1R, C1S, C9, C4BPA, CPN2), fibronectin, clusterin, PGLYRP2, and haemoglobin subunits showed increased abundance, whereas most immunoglobulin chains, kallikrein, apolipoprotein A2, and alpha-1-antitrypsin showed decreased abundance. Functional enrichment highlighted complement activation, B-cell-mediated and humoral immune responses, immunoglobulin-related functions, platelet activation, and erythrocyte gas-exchange pathways. Correlation analysis linked several proteins, particularly CPN2, APOA2, C1R and C1S, to core biochemical parameters of disease activity. Treatment-stratified analysis identified fourteen proteins that remained significantly altered in both treated and untreated patients, forming a treatment-resistant core of the signature, while several complement-, coagulation-, and lipid-related proteins were significant only in one treatment subgroup. These findings define an AKU plasma proteomic signature dominated by complement activation and humoral immune alterations, together with extracellular matrix, erythrocyte-, and coagulation-associated changes. The persistence of most alterations across treatment groups suggests that residual systemic proteomic dysregulation remains despite nitisinone treatment.