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Additional file 7: Figure S7. Differentially methylated regions (DMRs) in DYT28. For each significant differentially methylated genomic region, the plot displays methylation levels calculated for 8 pathogenic variants (Pt. 1-8), used to define the episignature, versus 56 control samples. Mean difference, along with statistically significance according to Fisher’s and Stouffer’s methods, are reported for each region.

Additional file 5: Figure S5. DYT28 episignature identification excluding samples with missense KMT2B variants. (A) Hierarchical clustering with Ward’s method on Euclidean distance was performed. In the heatmap plot, each row illustrates a selected CpG site, and each column depicts a sample. The heatmap color scale indicates the range of methylation level; from blue (no methylation or 0) to red (full methylation or 1). The detected episignature clearly differentiates between samples with pathogenic KMT2B variants and controls. (B) The first two dimensions of a MDS plot using the selected probes separate the samples with pathogenic variants in KMT2B from control samples. Blue circles represent control subjects and red circles indicate subjects with pathogenic KMT2B variants and a confirmed diagnosis of DYT28. Ellipses indicate 95% confidence interval.

Additional file 2. Fig. S2. MALDI-TOF mass spectra of permethylated transferrin N-glycans in SLC39A8-CDG Patient-3 compared to a reference control. Transferrin N-glycan analysis in a pediatric representative control (a). Transferrin N-glycosylation analysis of Patient-3 (b), showing very slight unspecific glycosylation changes with minor increases of hyposialylated and fucosylated glycoforms. Compared to control, the observed changes are outlined by red marks. Glycan structures were assigned following consortium for functional glycomics guidelines: N-acetylglucosamine, blue square; mannose, green circle; galactose, yellow circle; sialic acid, purple lozenge; fucose, red triangle.

Additional file 8: Figure S8. DMRs methylation levels distribution throughout different genomic regions. Histograms show the DNA methylation levels (as beta values) for different genomic regions (TSS1500, TSS200, 5’UTR, 1stExon, Body, 3’UTR, IGR) in all the probes contained in DYT28’s DMRs. Blue track displays median beta values in DYT28 (Pt.1-8); red track, median values for controls; black tracks, subjects with pathogenic KMT2B variants; grey tracks, individuals with KMT2B VUS not related to DYT28. The numbers on the right show the percentage of CpG probes having a beta value more similar to the median in DYT28 patients compared to controls, calculated for each patient i as ABS(Bi – control median) – ABS(Bi – DYT28 median), where ABS is the absolute value of the beta difference for the inspected probe. “TSS” indicates transcription start sites, “IGR” indicates intergenic regions, “Body” indicates genomic regions encompassing gene bodies.

Additional file 6: Figure S6. DNA methylation pattern analysis excluding samples with missense KMT2B variants yields full sensitivity and specificity in classifying individuals affected by DYT28. Hierarchical clustering analysis (A) and MDS (B) plots are used to classify VUS/and likely pathogenic missense variants (used as the testing set) with respect to pathogenic KMT2B variants in Pt.1-4,6,7 and control samples (used as the training set). (C) A support vector machine (SVM) was used to classify samples and calculate probability scores reaching full sensitivity and specificity for identifying pathogenic KMT2B variants. The classifier was trained using bona fide pathogenic KMT2B variants, controls and other NDDs/RDs. 75% of controls and NDD/RD samples used for training (blue), 25% for testing (grey). Ellipses indicate 95% confidence intervals.

Additional file 3: Figure S3. Mean methylation difference between patients carrying pathogenic KMT2B variants and control samples versus individual probes.

Additional file 4: Figure S4. Leave-1-out cross validation carried out by means of MDS plots based on the episignature analysis. For each round of validation, seven of the eight samples with bona fide pathogenic KMT2B variants were used for probe selection along with control samples and the one remaining was saved for testing. MDS was used to cluster the samples. Each time, the testing sample clustered with the other KMT2B mutated samples.

The discussion concerning the measure of the quality of a biobank should focus not only on the number of stored samples and their quality but also on the assessment of their access arrangements and governance. This article aims at contributing to the ongoing debate on samples and data access governance in biobanking by presenting the case of the Telethon Network of Genetic Biobanks (TNGB). We attempt to contribute to the need for clear and available access criteria and harmonization in access arrangements to maximize the influence of biobanks in the progress of biomedical research. We reviewed all the sample requests submitted to the TNGB from 2008 to 2020, focusing on those rejected by the Access Committee and the reasons behind the rejections. The analysis of the reasons behind the rejected requests allowed us to analyze how those relate to the issues of scientific misconduct, prioritization, and noncompliance with the biobank's mission. We discuss those issues in light of the actions and motivations used by TNGB in the access decision-making process. Based on this analysis, we suggest that a cross-implementation of a checklist for access assessment would improve the whole access process, ensuring a more transparent and smoother governance. Finally, we conclude that the TNGB's Charter and approach toward access governance could contribute as an important reference point to deal with the issues that have emerged in the international discussion on the topic.

Additional file 1. Fig. S1. Serum transferrin capillary zone electrophoresis in patients with SLC39A8 variants and healthy control. Patient -1 and -3 show an increase of 3-sialo transferrin,14,7% and 8% respectively (n.v.≤5.5%), and a decrease of 4-sialo transferrin, 75% and 82% respectively (n.v. ≥84%)

Additional file 1: Figure S1. Chromatograms showing the KMT2B variants identified in the 18 patients included in the study.

Additional file 2: Figure S2. Volcano plot of differences in the methylation status of individual probes between patients carrying pathogenic KMT2B variants and controls versus statistical significance (-log p-value) of individual probes. Red dots represent selected, significant differentially methylated probes (DMPs) in Pt. 1-8. Positive and negative mean methylation difference show hypermethylation and hypomethylation, respectively.

Objectives\ud The majority of people with suspected genetic dystonia remain undiagnosed after maximal investigation, implying that a number of causative genes have not yet been recognised. We aimed to investigate this paucity of diagnoses.\ud \ud Methods\ud We undertook weighted burden analysis of whole‐exome sequencing data from 138 individuals with unresolved generalised dystonia of suspected genetic aetiology, followed by additional case‐finding from international databases, first for the gene implicated by the burden analysis (VPS16), then for other functionally related genes. Electron microscopy was performed on patient‐derived cells.\ud \ud Results\ud Analysis revealed a significant burden for VPS16 (Fisher's exact test p‐value, 6.9x10−9). VPS16 encodes a subunit of the homotypic fusion and vacuole protein sorting (HOPS) complex, which plays a key role in autophagosome‐lysosome fusion. A total of 18 individuals harbouring heterozygous loss‐of‐function VPS16 variants, and one with a microdeletion, were identified. These individuals experienced early‐onset progressive dystonia with predominant cervical, bulbar, orofacial and upper limb involvement. Some patients had a more complex phenotype with additional neuropsychiatric and/or developmental comorbidities. We also identified biallelic loss‐of‐function variants in VPS41, another HOPS‐complex encoding genes, in an individual with infantile‐onset generalised dystonia. Electron microscopy of patient‐derived lymphocytes and fibroblasts from both VPS16 and VPS41 patients showed vacuolar abnormalities suggestive of impaired lysosomal function.\ud \ud Interpretation\ud Our study strongly supports a role for HOPS complex dysfunction in the pathogenesis of dystonia, though variants in different subunits display different phenotypic and inheritance characteristics.