Abstract Background Titinopathies are inherited muscular diseases triggered by genetic mutations in the titin gene. Muscular dystrophy with myositis ( mdm ) is one such disease caused by a LINE repeat insertion, leading to exon skipping and an 83-amino acid residue deletion in the N2A-PEVK region of mouse titin. This region has been implicated in a number of titin—titin ligand interactions, hence are important for myocyte signaling and health. Mice with this mdm mutation develop a severe and progressive muscle degeneration. The range of phenotypic differences observed in mdm mice shows that the deletion of this region induces a cascade of transcriptional changes extending to numerous signaling pathways affected by the titin filament. Previous research has focused on correlating phenotypic differences with muscle function in mdm mice. These studies have provided understanding of the downstream physiological effects resulting from the mdm mutation but only provide insights on processes that can be physiologically observed and measured. We used differential gene expression (DGE) to compare the transcriptomes of extensor digitorum longus (EDL), psoas and soleus muscles from wild-type and mdm mice to develop a deeper understand of these tissue-specific responses. Results The overall expression pattern observed shows a well-differentiated transcriptional signature in mdm muscles compared to wild type. Muscle-specific clusters observed within the mdm transcriptome highlight the level of variability of each muscle to the deletion. Differential gene expression and weighted gene co-expression network analysis showed a strong directional response in oxidative respiration-associated mitochondrial genes, which aligns with the poor shivering and non-shivering thermogenesis previously observed. Sln, which is a marker associated with shivering and non-shivering thermogenesis, showed the strongest expression change in fast-fibered muscles. No drastic changes in MYH expression levels were reported, which indicated an absence of major fiber-type switching events. Overall expression shifts in MYH isoforms, MARPs, and extracellular matrix associated genes demonstrated the transcriptional complexity associated with mdm mutation. The expression alterations in mitochondrial respiration and metabolism related genes in the mdm muscle dominated over other transcriptomic changes, and likely account for the late stage cellular responses in the mdm muscles. Conclusions We were able to demonstrate that the complex nature of mdm mutation extends beyond a simple rearrangement in titin gene. EDL, psoas and soleus exemplify unique response modes observed in skeletal muscles with mdm mutation. Our data also raises the possibility that failure to maintain proper energy homeostasis in mdm muscles may contribute to the pathogenesis of the degenerative phenotype in mdm mice. Understanding the full disease-causing molecular cascade is difficult using bulk RNA sequencing techniques due to intricate nature of the disease. The development of the mdm phenotype is temporally and spatially regulated, hence future studies should focus on single fiber level investigations.
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Evaluation of the Common Molecular Basis in Alzheimer’s and Parkinson’s Diseases
Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most common neurodegenerative disorders related to aging. Though several risk factors are shared between these two diseases, the exact relationship between them is still unknown. In this paper, we analyzed how these two diseases relate to each other from the genomic, epigenomic, and transcriptomic viewpoints. Using an extensive literature mining, we first accumulated the list of genes from major genome-wide association (GWAS) studies. Based on these GWAS studies, we observed that only one gene (HLA-DRB5) was shared between AD and PD. A subsequent literature search identified a few other genes involved in these two diseases, among which SIRT1 seemed to be the most prominent one. While we listed all the miRNAs that have been previously reported for AD and PD separately, we found only 15 different miRNAs that were reported in both diseases. In order to get better insights, we predicted the gene co-expression network for both AD and PD using network analysis algorithms applied to two GEO datasets. The network analysis revealed six clusters of genes related to AD and four clusters of genes related to PD; however, there was very low functional similarity between these clusters, pointing to insignificant similarity between AD and PD even at the level of affected biological processes. Finally, we postulated the putative epigenetic regulator modules that are common to AD and PD.
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- Award ID(s):
- 1802588
- NSF-PAR ID:
- 10171819
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 20
- Issue:
- 15
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 3730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the most common type of dementia. AD is characterized by a decline of cognitive function and brain atrophy, and is highly heritable with estimated heritability ranging from 60 to 80 $$\%$$ % . The most straightforward and widely used strategy to identify AD genetic basis is to perform genome-wide association study (GWAS) of the case-control diagnostic status. These GWAS studies have identified over 50 AD related susceptibility loci. Recently, imaging genetics has emerged as a new field where brain imaging measures are studied as quantitative traits to detect genetic factors. Given that many imaging genetics studies did not involve the diagnostic outcome in the analysis, the identified imaging or genetic markers may not be related or specific to the disease outcome. Results We propose a novel method to identify disease-related genetic variants enriched by imaging endophenotypes, which are the imaging traits associated with both genetic factors and disease status. Our analysis consists of three steps: (1) map the effects of a genetic variant (e.g., single nucleotide polymorphism or SNP) onto imaging traits across the brain using a linear regression model, (2) map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model, and (3) detect SNP-diagnosis association via correlating the SNP effects with the diagnostic effects on the brain-wide imaging traits. We demonstrate the promise of our approach by applying it to the Alzheimer’s Disease Neuroimaging Initiative database. Among 54 AD related susceptibility loci reported in prior large-scale AD GWAS, our approach identifies 41 of those from a much smaller study cohort while the standard association approaches identify only two of those. Clearly, the proposed imaging endophenotype enriched approach can reveal promising AD genetic variants undetectable using the traditional method. Conclusion We have proposed a novel method to identify AD genetic variants enriched by brain-wide imaging endophenotypes. This approach can not only boost detection power, but also reveal interesting biological pathways from genetic determinants to intermediate brain traits and to phenotypic AD outcomes.more » « less
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Methods Here, for the first time, we performed a comparative polygenic risk score (PRS) - Phenome Wide Association Study (PheWAS) for 11 different ADs (Juvenile Idiopathic Arthritis, Primary Sclerosing Cholangitis, Celiac Disease, Multiple Sclerosis, Rheumatoid Arthritis, Psoriasis, Myasthenia Gravis, Type 1 Diabetes, Systemic Lupus Erythematosus, Vitiligo Late Onset, Vitiligo Early Onset) and 3,254 phenotypes available in the UK Biobank that include a wide range of socio-demographic, lifestyle and health-related outcomes. Additionally, we investigated the genetic relationships of the studied ADs, calculating their genetic correlation and conducting cross-disorder GWAS meta-analyses for the observed AD clusters.
Results In total, we identified 508 phenotypes significantly associated with at least one AD PRS. 272 phenotypes were significantly associated after excluding variants in the HLA region from the PRS estimation. Through genetic correlation and genetic factor analyses, we identified four genetic factors that run across studied ADs. Cross-trait meta-analyses within each factor revealed pleiotropic genome-wide significant loci.
Discussion Overall, our study confirms the association of different factors with genetic susceptibility for ADs and reveals novel observations that need to be further explored.
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Abstract Background There is growing evidence indicating that a number of functional connectivity networks are disrupted at each stage of the full clinical Alzheimer’s disease spectrum. Such differences are also detectable in cognitive normal (CN) carrying mutations of AD risk genes, suggesting a substantial relationship between genetics and AD-altered functional brain networks. However, direct genetic effect on functional connectivity networks has not been measured. Methods Leveraging existing AD functional connectivity studies collected in NeuroSynth, we performed a meta-analysis to identify two sets of brain regions: ones with altered functional connectivity in resting state network and ones without. Then with the brain-wide gene expression data in the Allen Human Brain Atlas, we applied a new biclustering method to identify a set of genes with differential co-expression patterns between these two set of brain regions. Results Differential co-expression analysis using biclustering method led to a subset of 38 genes which showed distinctive co-expression patterns between AD-related and non AD-related brain regions in default mode network. More specifically, we observed 4 sub-clusters with noticeable co-expression difference, where the difference in correlations is above 0.5 on average. Conclusions This work applies a new biclustering method to search for a subset of genes with altered co-expression patterns in AD-related default mode network regions. Compared with traditional differential expression analysis, differential co-expression analysis yielded many more significant hits with extra insights into the wiring mechanism between genes. Particularly, the differential co-expression pattern was observed between two sets of genes, suggesting potential upstream genetic regulators in AD development.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijms20153730
