Biology subjects

Pippucci, T.

Publications and source records attributed to Pippucci, T..

3 recordsLinked to original sources

bioRxiv2021

Third Generation Cytogenetic Analysis (TGCA): diagnostic application of long-read sequencing.

Unbalanced Structural Variants (uSVs) play important roles in the pathogenesis of several genetic syndromes. Traditional and molecular karyotyping are considered the first-tier diagnostic tests to detect macroscopic and cryptic deletions/duplications. However, their time-consuming and laborious experimental protocols protract diagnostic times from three to fifteen days. Long read sequencing approaches, such as Oxford Nanopore Technologies (ONT), have the ability to reduce time to results for the detection of uSVs with the same resolution of current state-of-the-art diagnostic tests. Here we compared ONT to molecular karyotyping for the detection of pathogenic uSVs of 7 patients with previously diagnosed causative CNVs of different sizes and allelic fractions. Larger chromosomal anomalies included trisomy 21 and mosaic tetrasomy 12p. Among smaller CNVs we tested two reciprocal genomic imbalances in 7q11.23 (1.367 Mb), a 170 kb deletion encompassing NRXN1 and mosaic 6q27 (1.231 Mb) and 2q23.1 (408 kb) deletions. DNA libraries were prepared following ONT standard protocols and sequenced on the GridION device for 48 h. Data generated during runs were analysed in online mode, using NanoGLADIATOR. We were capable to identify all pathogenic CNVs with detection time inversely proportional to size and allelic fraction. Aneuploidies were called after only 30 minutes of sequencing, while 30 hours were needed to call CNVs < 500 kb also in mosaic state (44%). These results demonstrate the clinical utility of our approach that allows the molecular diagnosis of genomic disorders within a 30 minutes to 30 hours time-frame.

genomics↗

bioRxiv2020

unCOVERApp: an interactive graphical application for clinical assessment of sequence coverage at the base-pair level

MotivationNext Generation Sequencing (NGS) is increasingly adopted in the clinical practice largely thanks to concurrent advancements in bioinformatic tools for variant detection and annotation. Despite improvements in available approaches, the need to assess sequencing quality down to the base-pair level still poses challenges for diagnostic accuracy. One of the most popular quality parameters of diagnostic NGS is the percentage of targeted bases characterized by low depth of coverage (DoC). These regions potentially hide a clinically-relevant variant, but no annotation is usually returned for them. However, visualizing low-DoC data with their potential functional and clinical consequences may be useful to prioritize inspection of specific regions before re-sequencing all coverage gaps or making assertions about completeness of the diagnostic test. To meet this need we have developed unCOVERApp, an interactive application for graphical inspection and clinical annotation of low-DoC genomic regions containing genes. ResultsunCOVERApp is a suite of graphical and statistical tools to support clinical assessment of low-DoC regions. Its interactive plots allow to display gene sequence coverage down to the base-pair level, and functional and clinical annotations of sites below a user-defined DoC threshold can be downloaded in a user-friendly spreadsheet format. Moreover, unCOVERApp provides a simple statistical framework to evaluate if DoC is sufficient for the detection of somatic variants, where the usual 20x DoC threshold used for germline variants is not adequate. A maximum credible allele frequency calculator is also available allowing users to set allele frequency cut-offs based on assumptions about the genetic architecture of the disease instead of applying a general one (e.g. 5%). In conclusion, unCOVERApp is an original tool designed to identify sites of potential clinical interest that may be hidden in diagnostic sequencing data. AvailabilityunCOVERApp is a freely available application written in R and developed with Shiny packages and available in GitHub.

bioinformatics↗

bioRxiv2018

In Troyer Syndrome Spartin loss induces Complex I impairments and alters pyruvate metabolism

Growth delay and retardation are complex phenotypes which can results by a range of factors including genetics variants. We identified a novel homozygous frameshift mutation, c.892dupA, in SPART gene, in two brothers with short stature and psychomotor retardation, born from healthy consanguineous parents. Mutations in SPART are the cause of Troyer syndrome, an autosomal recessive form of spastic paraplegia resulting in muscle weakness, short stature and cognitive defects. SPART encodes for Spartin, a protein with different cellular functions, such as endosomal trafficking and mitochondrial stability.\n\nWe evaluated the effects of Spartin loss by transiently silencing SPART in human neural stem cells (hNSCs) and by generating an SH-SY5Y cell line model carrying the c.892dupA mutation via CRISPR/Cas9. In both models, we observed an altered neuronal growth and an increase in neurite outgrowth. In the SH-SY5Y cell line carrying the c.892dupA mutation, Spartin absence led to an altered distribution of mitochondria, and to a severe decrease in the NADH-dehydrogenase activity of mitochondrial Complex I. These impairments determined an energetic failure with a decrease in ATP synthesis due to a halt in mitochondrial oxidative phosphorylation, increased reactive oxygen species production, and alteration in intracellular Ca2+ homeostasis. Transient re-expression of Spartin in mutant cells restored an intracellular Ca2+ level. Mutant cells presented a significant increase in extracellular pyruvate, which may result from increased glycolysis due to impaired Complex I activity. Consistently, Spartin loss led to an over-activation of Signal Transducer and Activator of Transcription 3 (STAT3) factor, a key regulator of glycolysis.\n\nThese data demonstrate that Spartin loss leads to a profound bioenergetics imbalance with defective OXPHOS activity, and this altered metabolism might underlie Troyer syndrome and neurodevelopmental delays.

cell biology↗