Table_1_DMD deletions underlining mild dystrophinopathies: literature review highlights phenotype-related mutation clusters and provides insights about genetic mechanisms and prognosis.docx

DMD gene pathogenic variations cause a spectrum of phenotypes, ranging from severe Duchenne muscular dystrophy, the Becker milder cases, the intermediate or very mild muscle phenotypes invariably characterized by high CK, and the ultrarare fully-asymptomatic cases. Besides these phenotypes, X-linked dilated cardiomyopathy is also caused by DMD mutations. Males carrying DMD deletions with absent or very mild phenotypes have been sparsely described. We performed a horizon scan on public datasets to enroll males with the above phenotypes and carrying DMD deletions to delineate myopathic genotype-phenotype relationships. We inventoried 81 males, who were divided into the following clinical categorization: fully-asymptomatic males aged >43 years (A, N = 22); isolated hyperCKemia (CK, N = 35); and mild weakness (any age) with or without high CK (WCK, N = 24). In all cases, deleted intervals were exons 2 to 55, and no downstream exons were ever involved, apart from an exon 78 deletion in a WCK patient. All deletions were in-frame apart from the known exception to the rule of exon 2 and exon 78. We correlated the mild phenotypes (A and CK) to deleted exons, intronic breakpoints, exon-exon junctions, 3′ isoforms rule, and protein epitopes, and we found that some genetic profiles are exclusively/mainly occurring in A/CK phenotypes, suggesting they are compatible with a quasi-normal muscular performance. We discussed diverse pathogenic mechanisms that may contribute to mild dystrophinopathic phenotypes, and we tried to address some “critical” genetic configurations or exon content needed to preserve a semi-functional DMD gene.</p

Table_2_DMD deletions underlining mild dystrophinopathies: literature review highlights phenotype-related mutation clusters and provides insights about genetic mechanisms and prognosis.docx

DMD gene pathogenic variations cause a spectrum of phenotypes, ranging from severe Duchenne muscular dystrophy, the Becker milder cases, the intermediate or very mild muscle phenotypes invariably characterized by high CK, and the ultrarare fully-asymptomatic cases. Besides these phenotypes, X-linked dilated cardiomyopathy is also caused by DMD mutations. Males carrying DMD deletions with absent or very mild phenotypes have been sparsely described. We performed a horizon scan on public datasets to enroll males with the above phenotypes and carrying DMD deletions to delineate myopathic genotype-phenotype relationships. We inventoried 81 males, who were divided into the following clinical categorization: fully-asymptomatic males aged >43 years (A, N = 22); isolated hyperCKemia (CK, N = 35); and mild weakness (any age) with or without high CK (WCK, N = 24). In all cases, deleted intervals were exons 2 to 55, and no downstream exons were ever involved, apart from an exon 78 deletion in a WCK patient. All deletions were in-frame apart from the known exception to the rule of exon 2 and exon 78. We correlated the mild phenotypes (A and CK) to deleted exons, intronic breakpoints, exon-exon junctions, 3′ isoforms rule, and protein epitopes, and we found that some genetic profiles are exclusively/mainly occurring in A/CK phenotypes, suggesting they are compatible with a quasi-normal muscular performance. We discussed diverse pathogenic mechanisms that may contribute to mild dystrophinopathic phenotypes, and we tried to address some “critical” genetic configurations or exon content needed to preserve a semi-functional DMD gene.</p

Overall workflow of the analysis.

<p>See corresponding section for detailed description.</p

Novel Approach to Meta-Analysis of Microarray Datasets Reveals Muscle Remodeling-related Drug Targets and Biomarkers in Duchenne Muscular Dystrophy

<div><p>Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.</p> <p>The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA) to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.</p> <p>We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD). In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1), regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A) correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.</p> </div

Fraction of common genes in top-k rankings for different types of gene expression.

<p>For each of six datasets and for each type of regulation gene ranking procedure was performed and overlap between six top-k lists was calculated. Fraction of common genes in top-k reaches saturation for k roughly equal to 500, hence adding more genes will not increase overlap between six rankings.</p

GEO datasets used for the meta-analysis.

<p>GEO datasets used for the meta-analysis.</p

Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.

<p>Consistent regulators of differentially expressed genes plus regulators from SNEA of reference dataset.</p

Regulators of down-regulated genes.

<p>Most of SNEA-derived regulators of down-regulated genes regulate the processes related to myotube formation, fast-to-slow fiber type switch (including changes in myofiber composition, mitochondria content and insulin sensitivity) and metabolic changes in DMD affected muscles. Relations are described in text. Catalytic subunit of AMPK, PRKAA2, is shown next to AMPK. Functional class - class of proteins, such as enzyme families. Complex - a group of two or more proteins linked by non-covalent protein-protein interactions. Expression - protein members of one class regulate expression of proteins in another class. DirectRegulation - protein members of one class bind and regulate proteins in another class. Regulation - protein members of one class indirectly regulate proteins in another class. ProteinModification - protein members of the regulator class phosphorylate or otherwise modify proteins in the target class. PromoterBinding - protein members of one class bind promoters of genes encoding proteins in another class.</p

Gene Ontology groups enriched by consistent differentially expressed genes.

<p>Biological processes from Gene Ontology associated with consistently differentially expressed genes were found by applying “Find groups enriched with selected entities” tool embedded in Ariadne Pathway Studio to the list of 431 genes. Resulting significant (p-value<0.05) biological processes were sorted by number of genes involved in a process. Top 10 processes are shown.</p

CU values in <i>Homo sapiens</i> muscle, skin, and kidney genes.

Heat plots were generated using R package gplots. Rows were clustered based on Euclidean distance. The color coding varies from dark blue to red with low to high CU values, respectively. Hierarchical clustering of synonymous codon usage in all studied genes in different tissues (muscle, skin and kidney) in HSA was generated. The heat plot graphs show that the clustering of frequently used codons (in red) and rarely used codons (dark blue) greatly varies among genes and tissues. In muscle genes, extremely biased codons (low CU values, key color dark blue, or high CU values, key color dark red) are tightly clustered in terms of both gene and codon types, while intermediate CU values (key color light blue to yellow) are more dispersed in the trees (panel A). Among muscle genes, only DMD does not show any extreme CUB, since no red spots (corresponding to higher CU values) occur (Fig 1A). The CUB fingerprint of skin genes (panel B) shows a prevalent low CU values codons (dark blue), with a few dispersed, not clustered, inhomogeneous distributed yellow-red spots (intermediate and high CU values). CU clusters are less defined compared to muscle. In kidney genes (panel C), the CUB fingerprint also differs from the other two gene groups. The vast majority of genes have low or intermediate CU values (wide and diffuse yellow-blue spot clusters) with a cluster of high CU values closely clustered and related to UMOD, BSND, SLC22A8, MIOX, AQP6, PKD1, SLC12A3, and GGACT genes. Interestingly all of these genes are DC.</p