Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic.

Synthesis and characterization of novel selective NKCC1 inhibitors for the treatment of down syndrome and brain disorders characterized by depolarizing GABAergic transmission.

Proper GABAergic transmission through Cl-permeable GABAA receptors is fundamental for physiological brain development and function. Indeed, defective GABAergic signaling – due to a high NKCC1/KCC2 expression ratio – has been implicated in several neurodevelopmental disorders (e.g., Down syndrome, DS, Autism spectrum disorders, ASD). Interestingly, NKCC1 inhibition by the FDA-approved diuretic drug bumetanide reverts cognitive deficits in the TS65Dn mouse models of DS and core symptoms in other models of brain disorders. However, the required chronic treatment with bumetanide is burdened by its diuretic side effects caused by the antagonization of the kidney Cl importer NKCC2. This may lead to hypokalemia, while jeopardizing drug compliance. Crucially, these issues would be solved by selective NKCC1 inhibitors, thus devoid of the diuretic effect of bumetanide. To this aim, starting from bumetanide’s structure, we applied a ligand-based computational approach to design new molecular entities that we tested in vitro for their capacity to selectively block NKCC1. Extensive synthetic efforts and structure-activity relationships analyses allowed us to improve in vitro potency and overall drug-like properties of the initially identified chemical hits. As a result, we identified a new highly potent NKCC1 inhibitor (ARN23746) that displayed excellent solubility, metabolic stability, and no significant effect on NKCC2 in vitro. Moreover, this novel and selective NKCC1 inhibitor was able to rescue cognitive deficits in DS mice and social/repetitive behaviors in ASD mice, with no diuretic effect and no overt toxicity upon chronic treatment in adult animals. Thus, ARN23746 a selective NKCC1 inhibitor devoid of the diuretic effect – represents a suitable and solid therapeutic strategy for the treatment of Down syndrome and all the brain neurological disorders characterized by depolarizing GABAergic transmission.

Characterization of the Caspr2 and NLGN2 ligands: a proteomic and biochemical approach

Autism Spectrum Disorder (ASD) is a range of early-onset conditions classified as neurodevelopmental disorders, characterized by deficits in social interactions and communication, as well as by restricted interest and repetitive behaviors. Among the proteins associated with this spectrum of disease there are Caspr2, α-NRXN1, NLGN1-4. Caspr2 is involved in the clustering of K+ channels at the juxtaparanodes, where it is proposed to bind TAG-1. Recent works reported a synaptic localization of Caspr2, but little is know on its role in this compartment. NRXNs and their ligand NLGNs, instead, have a well-defined role in the formation and maintenance of synapses. Among the neuroligins, NLGN2 binds NRXNs with the lowest affinity, suggesting that it could have other not yet characterized ligands. The aim of this work was to better characterize the binding of Caspr2 to TAG-1 and to identify new potential binding partner for Caspr2 and NLGN2. Unexpectedly, using Isothermal Titration Calorimetry and co-immunoprecipitation experiments the direct association of the first two proteins could not be verified and the results indicate that the first evidences reporting it were biased by false-positive artifacts. These findings, together with the uncharacterized synaptic localization of Caspr2, made the identification of new potential binding partners for this protein necessary. To find new proteins that associate with Caspr2 and NLGN2, affinity chromatography in tandem with mass spectrometry experiments were performed. Interestingly, about 25 new potential partners were found for these two proteins and NLGN1, that was originally included as a control: 5 of those, namely SFRP1, CLU, APOE, CNTN1 and TNR, were selected for further investigations. Only the association of CLU to NLGN2 was confirmed. In the future, screenings of the remaining candidates have to be carried out and the functional role for the proposed NLGN2-CLU complex has to be studied.

Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.

Role of neuroinflammation in the pathophysiology of CDKL5 deficiency disorder

Cyclin-dependent kinase-like 5 (CDKL5) deficiency disorder (CDD), a rare neurodevelopmental disease caused by mutations in the X-linked CDKL5 gene, is characterized by early-onset epilepsy, intellectual disability, and autistic features. To date, little is known about the etiology of CDD and no therapies are available. When overactivated in response to neuronal damage and genetic or environmental factors, microglia – the brain macrophages – cause damage to neighboring neurons by producing neurotoxic factors and pro-inflammatory molecules. Importantly, overactivated microglia have been described in several neurodegenerative and neurodevelopmental disorders, suggesting that active neuroinflammation may account for the compromised neuronal survival and/or brain development observed in these pathologies. Recent evidence shows a subclinical chronic inflammatory status in plasma from CDD patients. However, it is unknown whether a similar inflammatory status is present in the brain of CDD patients and, if so, whether it plays a causative or exacerbating role in the pathophysiology of CDD. Here, we show evidence of a chronic microglia overactivation status in the brain of Cdkl5 KO mice, characterized by alterations in microglial cell number/morphology and increased pro-inflammatory gene expression. We found that the neuroinflammatory process is already present in the postnatal period in Cdkl5 KO mice and worsens during aging. Remarkably, by restoring microglia alterations, treatment with luteolin, a natural anti-inflammatory flavonoid, promotes neuronal survival in the brain of Cdkl5 KO mice since it counteracts hippocampal neuron cell death and protects neurons from NMDA-induced excitotoxic damage. In addition, through the restoration of microglia alterations, luteolin treatment also increases hippocampal neurogenesis and restores dendritic spine maturation and dendritic arborization of hippocampal and cortical pyramidal neurons in Cdkl5 KO mice, leading to improved behavioral performance. These findings highlight new insights into the CDD pathophysiology and provide the first evidence that therapeutic approaches aimed at counteracting neuroinflammation could be beneficial in CDD.