Eleonora Giagnorio

Eleonora Giagnorio


Nata a Monza il 27 giugno 1993, ho conseguito presso l’Università di Milano-Bicocca la laurea triennale in Scienze Biologiche nel 2016 e la laurea magistrale in Biologia nel 2018 con una tesi sull’analisi elettrofisiologica di una mutazione autosomica dominante a carico del recettore nicotinico neuronale α2β4, identificata in pazienti affetti da ADNFLE (Autosomal dominant nocturnal frontal lobe epilepsy). Da novembre 2018 frequento il dottorato di ricerca in Neuroscienze (curriculum Neuroscienze Sperimentali) dell’Università di Milano-Bicocca presso l’Unità di Neuroimmunologia e Malattie Neuromuscolari dell’Istituto Neurologico Carlo Besta di Milano, studiando il ruolo di LncRNAs e miRNAs nelle patologie SLA (Sclerosi laterale amiotrofica) e SMA (Atrofia muscolare spinale).


Role of dysregulated skeletal muscle LncRNAs/mRNA targets in Amyotrophic Lateral Sclerosis pathogenesis.

  • Curriculum: Neuroscienze sperimentali
  • Tutor: Renato Mantegazza


Aim of this project is to identify potential disease modifying mechanisms and biomarkers on patients-specific skeletal muscle cells, underlying motor neuron degeneration in ALS, through a comprehensive profiling of LncRNAs.

Specifically, aims of this poject are:

– To generate a skeletal muscle in vitro model from human induced pluripotent stem cells (hiPSCs) derived from controls and amyotrophic lateral sclerosis (ALS) patients.

– To identify Long non-coding RNAs (LncRNAs), and mRNA target pairs, differentially expressed in ALS patients’ hiPSCs-derived skeletal muscle fibers.

– To investigate the role of dysregulated skeletal muscle LncRNAs/mRNA targets in ALS pathogenesis by in vitro (hiPSCs-derived skeletal muscle fibers) and in vivo (ALS animal model) functional studies.


Amyotrophic lateral sclerosis (ALS) is an adult-onset neuromuscular disease characterized by progressive motor neuron degeneration, muscle atrophy, paralysis, and ultimately death. ALS onset has always been considered the result of alterations in nerve activity, but recent studies underline that skeletal muscles play a direct role in ALS onset, progression and severity [Dobrowolny G. 2008, Manzano R. 2012, Marcuzzo S. 2011, Pradat P. F. 2011]. The great majority of ALS cases are sporadic and 10% are inherited (familial ALS, fALS). Different causative genes have been identified in fALS patients, as SOD1, whose mutations cause mitochondrial dysfunctions, at disease onset in both neurons and skeletal muscles. Aberrant RNA processing represent another key event in ALS pathogenesis, as shown by ALS pathogenic mutations found in mRNA metabolism related genes, as SETX, FUS/TLS, ATXN2, ANG and TDP-43 [Gagliardi S. 2012]. Recent genetic analyses of the human transcriptome have revealed the importance of non-coding RNAs in many biological processes as transcription, translation, and splicing. MicroRNA implications in ALS is increasingly being recognized, and more recent studies are now focusing on Long-non-coding RNAs (LncRNAs) [Gagliardi S. 2018]. LncRNAs are 300 to thousands nucleotides long, being more similar to mRNA than miRNAs, and they seem to have a wider spectrum of functions, as transcription and translation of mRNAs, regulation of gene expression, splicing mechanisms and muscle differentiation [Kashi K. 2016, Lim Y. H. 2018]. In conclusion, LncRNAs are candidate for the study of new regulatory mechanisms involved in the control of MN differentiation or activity, which may contribute to ALS pathogenesis [Biscarini S. 2018, Nishimoto Y. 2013]. As mentioned above, mRNA processing is crucial in ALS, thus, understanding LncRNAs’ molecular implications underlying the disease, is an opportunity to clarify ALS pathogenesis and to identify innovative therapeutic interventions.


– iPSC generation and skeletal muscle fibers differentiation: iPSCs will be obtained by Sendai virus reprogramming from controls and ALS patients-derived fibroblasts. Skeletal muscle fibers will be differentiated according to a protocol adapted from Swartz, and colleagues, from iPSCs whole colonies of about 1mm in diameter on Geltrex. Maturation will be achieved at day 36 [Swartz E. 2016]. HiPSCs-derived skeletal muscle cell cultures will be characterized by qPCR analysis of key muscle development genes (PARAXIS, PAX3, PAX7, MYOD) and immunocytochemistry staining of cell cultures with markers specifically expressed during the differentiation process (Desmin, PAX3, PAX7 and Bungarotoxin) [Borchin B. 2013].

– LncRNA/mRNA analysis: Sequencing libraries will be prepared with the Illumina TruSeq Stranded RNA Library Prep, using 500-ng total RNA (Illumina) according to Gagliardi and colleagues. LncPath R package will be used to map differentially expressed lncRNAs on a lncRNA-mRNA relationship network, to evaluate the extent of each gene influenced by lncRNAs, based on a network diffusion strategy (https://CRAN.R-project.org/package=LncPath). Top-ranked genes will be validated by qPCR [Gagliardi S. 2018, Kashi K. 2016].

– In vitro studies: LncRNA mimics/inhibitors will be transfected in iPSC-derived skeletal muscles from ALS patients and controls. mRNA target genes will be analyzed by qPCR. Analysis on transfected cells will include proliferation assays, morphological assessment and functional/contractile phenotype analyses by confocal and live cells imaging.

– In vivo studies: LncRNA mimics/inhibitors and nanovectors will be administrated intrathecally or systemically in ALS transgenB6SJL-(SOD1*G93A) mice. Disease manifestations will be evaluated by 7 Tesla MRI, histological and molecular analyses of brain, spinal cord and skeletal muscles.

– Statistical evaluation: R language will be used. Differences between two group means will be assessed using two-tailed Student’s t-test, for normally distributed data, or Mann-Whitney test for non-parametric data. Multiple comparisons (to assess differences among animal groups) will be performed by one-way or two-way ANOVA followed by Bonferroni as post hoc test, for normally distributed data, or by Kruskal-Wallis test for non-parametric data. A p-value < 0.05 will be considered statistically significant. Pearson’s correlation coefficient will identify LncRNA/mRNA correlations.