First name
Shikha
Last name
Vashisht
Year of Study
Research Center
Thesis Title
Computational Approaches in the Estimation and Analysis of Transcripts Differential Expression and Splicing: Application to Spinal Muscular Atrophy
Thesis Abstract
Spinal Muscular Atrophy (SMA) is among the most common genetic neurological diseases causing
infant mortality. SMA is caused by deletion or mutations in survival motor neuron 1 gene (SMN1), which
are expected to generate mRNA-splicing alterations and reductions in mRNA-transport within the motor
neurons (MNs). SMA ultimately results in the selective degeneration of MNs, but the underlying reason
is still ambiguous. The aim of this study is to investigate splicing abnormalities and to identify genes
presenting differential splicing events, possibly involved in SMA pathogenesis at genome-wide level.
We performed RNA-Sequencing data analysis on two SMA-patients and two healthy-controls, with 2
biological replicates each sample, derived from their iPSC-differentiated-MNs. Three analyses were
executed. Firstly, differential expression analysis was performed to identify possibly mis-regulated
genes. Secondly, alternative splicing analysis was conducted to find differentially-used exons (DUEs),
as splicing patterns are notably altered in MNs by SMN protein suboptimal levels. Thirdly, we did RNAbinding
protein (RBP) - motif discovery for an identified set of DUEs, to pinpoint MN-specific
mechanisms underlying such alterations. Gene Ontology enrichment analysis of identified significantly
differentially expressed genes and DUEs revealed various GO-terms related to axon-guidance, musclecontraction,
microtubule-based transport, axon-cargo transport etc. suggesting their involvement in
SMA. We obtained promising results from motif analysis that has identified 22 RBPs where 5 RBPs
from the PABP family are known to interact with SMN protein, enhancing mRNA-stabilization and
mRNA-transport in MNs. To validate our results wet-lab experiments involving precise recognition of
RNA-binding sites are required which might offer potential therapeutic role towards treating SMA.
Further we observed, the current methods for an effective understanding of differential splicing events
within the transcriptomic landscape are insufficient. To address this problem, we developed a
computational model having a potential to precisely estimate “transcript expression levels” within a
given gene locus by disentangling mature and nascent transcription contributions for each transcript at
per base resolution. Exonic and intronic read coverages were modeled and transcript expressions were
estimated, which best approximated the observed expression in total RNA-Seq data. Our model has an
application in the detection of differential splicing events. At exon level, differences in the ratio of the
sum of mature and the sum of nascent transcripts over all the transcripts in a gene locus gives an
indication of differential splicing. We have implemented our model in R-statistical language.
infant mortality. SMA is caused by deletion or mutations in survival motor neuron 1 gene (SMN1), which
are expected to generate mRNA-splicing alterations and reductions in mRNA-transport within the motor
neurons (MNs). SMA ultimately results in the selective degeneration of MNs, but the underlying reason
is still ambiguous. The aim of this study is to investigate splicing abnormalities and to identify genes
presenting differential splicing events, possibly involved in SMA pathogenesis at genome-wide level.
We performed RNA-Sequencing data analysis on two SMA-patients and two healthy-controls, with 2
biological replicates each sample, derived from their iPSC-differentiated-MNs. Three analyses were
executed. Firstly, differential expression analysis was performed to identify possibly mis-regulated
genes. Secondly, alternative splicing analysis was conducted to find differentially-used exons (DUEs),
as splicing patterns are notably altered in MNs by SMN protein suboptimal levels. Thirdly, we did RNAbinding
protein (RBP) - motif discovery for an identified set of DUEs, to pinpoint MN-specific
mechanisms underlying such alterations. Gene Ontology enrichment analysis of identified significantly
differentially expressed genes and DUEs revealed various GO-terms related to axon-guidance, musclecontraction,
microtubule-based transport, axon-cargo transport etc. suggesting their involvement in
SMA. We obtained promising results from motif analysis that has identified 22 RBPs where 5 RBPs
from the PABP family are known to interact with SMN protein, enhancing mRNA-stabilization and
mRNA-transport in MNs. To validate our results wet-lab experiments involving precise recognition of
RNA-binding sites are required which might offer potential therapeutic role towards treating SMA.
Further we observed, the current methods for an effective understanding of differential splicing events
within the transcriptomic landscape are insufficient. To address this problem, we developed a
computational model having a potential to precisely estimate “transcript expression levels” within a
given gene locus by disentangling mature and nascent transcription contributions for each transcript at
per base resolution. Exonic and intronic read coverages were modeled and transcript expressions were
estimated, which best approximated the observed expression in total RNA-Seq data. Our model has an
application in the detection of differential splicing events. At exon level, differences in the ratio of the
sum of mature and the sum of nascent transcripts over all the transcripts in a gene locus gives an
indication of differential splicing. We have implemented our model in R-statistical language.
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