De novo variants of CSNK2B cause a new intellectual disability-craniodigital syndrome by disrupting the canonical Wnt signaling pathway

CSNK2B encodes for casein kinase II subunit beta (CK2b), the regulatory subunit of casein kinase II (CK2), which is known to mediate diverse cellular pathways. Variants in this gene have been recently identified as a cause of Poirier-Bienvenu neurodevelopmental syndrome (POBINDS), but functional evidence is sparse. Here, we report five unrelated individuals: two of them manifesting POBINDS, while three are identified to segregate a new intellectual disability-craniodigital syndrome (IDCS), distinct from POBINDS. The three IDCS individuals carried two different de novo missense variants affecting the same codon of CSNK2B. Both variants, NP_001311.3; p.Asp32His and NP_001311.3; p.Asp32Asn, lead to an upregulation of CSNK2B expression at transcript and protein level, along with global dysregulation of canonical Wnt signaling. We found impaired interaction of the two key players DVL3 and b-catenin with mutated CK2b. The variants compromise the kinase activity of CK2 as evident by a marked reduction of phosphorylated b-catenin and consequent absence of active b-catenin inside nuclei of the patient-derived lymphoblastoid cell lines (LCLs). In line with these findings, whole-transcriptome profiling of patient-derived LCLs harboring the NP_001311.3; p.Asp32His variant confirmed a marked difference in expression of genes involved in the Wnt signaling pathway. In addition, whole-phosphoproteome analysis of the LCLs of the same subject showed absence of phosphorylation for 313 putative CK2 substrates, enriched in the regulation of nuclear b-catenin and transcription of the target genes. Our findings suggest that discrete variants in CSNK2B cause dominant-negative perturbation of the canonical Wnt signaling pathway, leading to a new craniodigital syndrome distinguishable from POBINDS

MAU2 and NIPBL variants impair the heterodimerization of the cohesin loader subunits and cause Cornelia de Lange syndrome

The NIPBL/MAU2 heterodimer loads cohesin onto chromatin. Mutations inNIPBLaccount for most cases ofthe rare developmental disorder Cornelia de Lange syndrome (CdLS). Here we report aMAU2 variant causing CdLS, a deletion of seven amino acids that impairs the interaction between MAU2 and the NIPBL N terminus.Investigating this interaction, we discovered that MAU2 and the NIPBL N terminus are largely dispensable fornormal cohesin and NIPBL function in cells with a NIPBL early truncating mutation. Despite a predicted fataloutcome of an out-of-frame single nucleotide duplication inNIPBL, engineered in two different cell lines,alternative translation initiation yields a form of NIPBL missing N-terminal residues. This form cannot interactwith MAU2, but binds DNA and mediates cohesin loading. Altogether, our work reveals that cohesin loading can occur independently of functional NIPBL/MAU2 complexes and highlights a novel mechanism protectiveagainst out-of-frame mutations that is potentially relevant for other genetic conditions

WHOLE TRANSCRIPTOME ANALAYSIS BY NEXT GENERATION SEQUENCING (NGS) IN AUTISM SPECTRUM DISORDERS (ASDs)

I disordini dello spettro autistico (ASDs) sono caratterizzati dalla compromissione dell\u2019interazione sociale e della comunicazione verbale e non verbale, e da comportamenti ripetitivi e stereotipati. L\u2019autismo \ue8 una delle pi\uf9 frequenti tra le malattie complesse ereditabili, tuttavia, solo pochi geni implicati nell\u2019eziologia sono stati identificati. Negli ultimi anni, in diversi studi, sono stati individuati polimorfismi a singolo nucleotide (SNP), anomalie cromosomiche e rare varianti genetiche associati al fenotipo autistico. Inoltre, in studi di ibridazione genomica comparativa (CGH), come fattori di rischio sono state individuate alcune variazioni del numero di copie (CNV). Altri studi hanno evidenziato che le cellule linfoblastoidi possono discriminare tra soggetti con ASD e campioni di controllo. Il presente studio \ue8 parte di un progetto Telethon, avviato nel 2009, che coinvolge diversi gruppi italiani di clinica e di ricerca. Questo progetto ha l\u2019obiettivo di analizzare le variazioni di espressione genica in 27 soggetti con ASD e 23 controlli sani. I probandi selezionati presentano CNV potenzialmente coinvolte nell'insorgenza dell'autismo. Il trascrittoma di 27 probandi e 23 controlli sani \ue8 stato analizzato attraverso la tecnica di sequenziamento di nuova generazione dell\u2019RNA (RNA sequencing). L\u2019analisi di arricchimento del gruppo di geni (GSEA) risultati differenzialmente espressi, compiuta sull\u2019intera coorte e su un sottogruppo con una delezione 22q13.3, ha rilevato che i principali pathway arricchiti appartengono ai disturbi autoimmuni e al pathway di presentazione dell'antigene. La sottoanalisi compiuta sui campioni con la delezione evidenzia il coinvolgimento di geni appartenenti al pathway di orientamento degli assoni, confermando che le linee cellulari linfoblastoidi possono presentare biomarcatori rilevanti per l'autismo. Inoltre, abbiamo dimostrato che tre geni \u201coutlier\u201d clusterizzano all'interno di una CNV sul cromosoma 16p13.1, suggerendo che questo \ue8 un potenziale candidato locus per autismo. Questo studio fornisce la prova che le varianti strutturali, potenzialmente causative di ASD, hanno un impatto funzionale attraverso alterazioni del trascrittoma e dimostra l'utilit\ue0 di integrare i dati di espressione genica con i dati genomici. Ulteriori analisi sui geni differenzialmente espressi e su CNVs, non selezionate in questo studio, contribuiranno a mettere in rilievo le basi genetiche e fisiopatologiche di ASD e ad evidenziare nuovi potenziali pathway coinvolti nei disordini dello spettro autistico.Autism Spectrum Disorders (ASDs) represent a group of childhood neurodevelopmental and neuropsychiatric disorders characterized by deficits in verbal communication, impairment of social interaction, and restricted and repetitive patterns of interests and behaviours. Evidences indicate that ASDs have strong genetic bases. Known chromosomal anomalies, rare genetic variants and single nucleotide polymorphisms (SNPs) have been related to ASD phenotypes in many studies. Furthermore Comparative Genomic Hybridization (CGH) studies have revealed copy number variations (CNVs) as risk factors. Recently, several studies have suggested that lymphoblastoid cells (LCLs) can discriminate between ASDs and control samples. This study is part of a Telethon project which has been started in 2009 and involves different Italian clinical and research groups; it aims to analyze gene expression variations in ASD subjects, characterized for CNVs potentially involved in the onset of autism. Transcriptome from LCLs of 27 ASD probands and 23 health controls have been analyzed through Next Generation Sequencing technology (RNA Sequencing). Gene set enrichment analysis (GSEA), on the total cohort and on a subgroup with a 22q13.3 deletion, revealed that autoimmune disorders and antigen processing and presentation pathways are the most enriched ones. Subgroup\u2019s GSEA highlights the involvement of axon guidance pathway, confirming that LCLs could exhibit biomarkers relevant to autism. Moreover, we demonstrate that three outlier genes cluster within a CNV on 16p13.1, suggesting that this is a potential candidate ASD region. This study provides evidence that potentially causative structural variants have a functional impact via transcriptome alterations in ASDs at a genome wide level and demonstrates the utility of integrating gene expression with mutation data. Further analysis of differentially expressed genes and CNVs not selected in this study will help understanding the genetic bases for ASD pathophysiology and unravelling potential new pathways involved in ASDs

Automatic detection of pathological regions in medical images

Medical images are an essential tool in the daily clinical routine for the detection, diagnosis, and monitoring of diseases. Different imaging modalities such as magnetic resonance (MR) or X-ray imaging are used to visualize the manifestations of various diseases, providing physicians with valuable information. However, analyzing every single image by human experts is a tedious and laborious task. Deep learning methods have shown great potential to support this process, but many images are needed to train reliable neural networks. Besides the accuracy of the final method, the interpretability of the results is crucial for a deep learning method to be established. A fundamental problem in the medical field is the availability of sufficiently large datasets due to the variability of different imaging techniques and their configurations. The aim of this thesis is the development of deep learning methods for the automatic identification of anomalous regions in medical images. Each method is tailored to the amount and type of available data. In the first step, we present a fully supervised segmentation method based on denoising diffusion models. This requires a large dataset with pixel-wise manual annotations of the pathological regions. Due to the implicit ensemble characteristic, our method provides uncertainty maps to allow interpretability of the model’s decisions. Manual pixel-wise annotations face the problems that they are prone to human bias, hard to obtain, and often even unavailable. Weakly supervised methods avoid these issues by only relying on image-level annotations. We present two different approaches based on generative models to generate pixel-wise anomaly maps using only image-level annotations, i.e., a generative adversarial network and a denoising diffusion model. Both perform image-to-image translation between a set of healthy and a set of diseased subjects. Pixel-wise anomaly maps can be obtained by computing the difference between the original image of the diseased subject and the synthetic image of its healthy representation. In an extension of the diffusion-based anomaly detection method, we present a flexible framework to solve various image-to-image translation tasks. With this method, we managed to change the size of tumors in MR images, and we were able to add realistic pathologies to images of healthy subjects. Finally, we focus on a problem frequently occurring when working with MR images: If not enough data from one MR scanner are available, data from other scanners need to be considered. This multi-scanner setting introduces a bias between the datasets of different scanners, limiting the performance of deep learning models. We present a regularization strategy on the model’s latent space to overcome the problems raised by this multi-site setting