The detection of a strong episignature for Chung–Jansen syndrome, partially overlapping with Börjeson–Forssman–Lehmann and White–Kernohan syndromes - Niels Vos; Sadegheh Haghshenas; Liselot van der Laan; Perle K. M. Russel; Kathleen Rooney; Michael A. Levy; Raissa Relator; Jennifer Kerkhof; Haley McConkey; Saskia M. Maas; Lisenka E. L. M. Vissers; Bert B. A. de Vries; et al. (Human Genetics, 2024)
Chung-Jansen syndrome is a neurodevelopmental disorder characterized by intellectual disability, behavioral problems, obesity and dysmorphic features. It is caused by pathogenic variants in the PHIP gene that encodes for the Pleckstrin homology domain-interacting protein, which is part of an epigenetic modifier protein complex. Therefore, we hypothesized that PHIP haploinsufficiency may impact genome-wide DNA methylation (DNAm). We assessed the DNAm profiles of affected individuals with pathogenic and likely pathogenic PHIP variants with Infinium Methylation EPIC arrays and report a specific and sensitive DNAm episignature biomarker for Chung–Jansen syndrome. In addition, we observed similarities between the methylation profile of Chung–Jansen syndrome and that of functionally related and clinically partially overlapping genetic disorders, White–Kernohan syndrome (caused by variants in DDB1 gene) and Börjeson–Forssman–Lehmann syndrome (caused by variants in PHF6 gene). Based on these observations we also proceeded to develop a common episignature biomarker for these disorders. These newly defined episignatures can be used as part of a multiclass episignature classifier for screening of affected individuals with rare disorders and interpretation of genetic variants of unknown clinical significance, and provide further insights into the common molecular pathophysiology of the clinically-related Chung–Jansen, Börjeson–Forssman–Lehmann and White–Kernohan syndromes.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Monitoring of ochratoxin A and ochratoxin-producing fungi in traditional salami manufactured in Northern Italy - C. Merla; G. Andreoli; C. Garino; N. Vicari; G. Tosi; M. L. Guglielminetti; A. Moretti; A. Biancardi; M. Arlorio; M. Fabbi (Mycotoxin Research, 2018)

Monopole patch antenna for in vivo exposure to nanosecond pulsed electric fields - C. Merla; F. Apollonio; A. Paffi; C. Marino; P. T. Vernier; M. Liberti (Medical & Biological Engineering & Computing, 2017)