Project description:We sought to compare changes in gene expression levels and pathway-level dysregulation between FUS-ALS, sporadic ALS, and healthy control patient derived fibroblasts. Gene expression profiling analysis were performed on bulk RNA-seq data obtained from 12 FUS, 11 sporadic, and 13 healthy control cell lines.
Project description:Transcriptomic analysis of lymphoblastoid cell lines from ALS patients with varying disease duration Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of ALS pathophysiology and could be used to monitor a patient’s response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs (miRNAs) are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding miRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as ALS.
Project description:Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of ALS pathophysiology and could be used to monitor a patient’s response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs (miRNAs) are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding miRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as ALS. The present study aimed to investigate the transcriptomic profile (mRNA and miRNA) of lymphoblastoid cell lines (LCLs) from ALS patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (< 12 months) compared to those that had a longer disease duration (>6 years). Transcriptional profiling of miRNA-mRNA interactions from LCL’s in ALS patients revealed dysregulation of genes involved in cell cycle, DNA damage and RNA processing in patients with longer survival from disease onset compared to those with short survival. Understanding these particular miRNA-mRNA interactions and the pathways in which they are involved may help to distinguish potential therapeutic targets that could exert neuroprotective effects to prolong the life expectancy of ALS patients.
Project description:Mapping transcriptional regulatory networks is difficult because many transcription factors (TFs) are activated only under specific conditions. We describe a generic strategy for identifying genes and pathways induced by individual TFs that does not require knowledge of their normal activation cues. Microarray analysis of 55 yeast TFs that caused a growth phenotype when overexpressed showed that the majority caused increased transcript levels of genes in specific physiological categories, suggesting a mechanism for growth inhibition. Induced genes typically included established targets and genes with consensus promoter motifs, if known, indicating that these data are useful for identifying potential new target genes and binding sites. We identified the sequence 5'-TCACGCAA as a binding sequence for Hms1p, a TF that positively regulates pseudohyphal growth and previously had no known motif. The general strategy outlined here presents a straightforward approach to discovery of TF activities and mapping targets that could be adapted to any organism with transgenic technology. Keywords: phenotypic activation