bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Cholesterol metabolism is a potential therapeutic target in

Duchenne Muscular Dystrophy

F. Amor1,2*, A. Vu Hong1,2*, G. Corre1,2, M. Sanson1,2, L. Suel1,2, S. Blaie1, L. Servais3, T. Voit4,

I. Richard1,2 and D. Israeli1,2

1 Généthon, 91000, Evry, France

2 Université Paris-Saclay, Univ Evry, Inserm, Genethon, Integrare research unit UMR_S951,

91000, Evry, France

3 MDUK Oxford Neuromuscular Center, Department of Paediatrics, University of Oxford, UK

& Division of Child Neurology, Centre de Référence des Maladies Neuromusculaires,

Department of Paediatrics, University Hospital of Liège & University of Liège, Belgium

4 NIHR Great Ormond Street Hospital Biomedical Research Centre and Great Ormond Street

Institute of Child Health, University College London, UK

*These authors equally contributed

Key words: Duchenne Muscular dystrophy; Host gene; biological interpretation of miRNA dysregulation; SREBP1; SREBP2; lipid metabolism; Cholesterol; Simvastatin, DLK1-DIO3

Corresponding Author:

David Israeli

Généthon, 1 rue de l’Internationale, 91000 Evry, France

Tel: +33-1 69 47 29 67

E-mail: [email protected]

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Abstract

Background: Duchenne Muscular Dystrophy (DMD) is a lethal muscle disease detected in approximately 1:5000 male births. DMD is caused by mutations in the DMD gene, encoding a critical protein that link the cytoskeleton and the extracellular matrix in skeletal and cardiac muscles. The primary consequence of the disrupted link between the extracellular matrix and the myofiber actin cytoskeleton is thought to involve sarcolemma destabilization, perturbation of Ca+2 homeostasis, activation of proteases, mitochondrial damage and tissue degeneration. A recently emphasized secondary aspect of the dystrophic process is a progressive metabolic change of the dystrophic tissue; however, the mechanism and nature of the metabolic dysregulation is yet poorly understood. In this study, we characterized a molecular mechanism of metabolic perturbation in DMD.

Methods: We sequenced plasma miRNA in a DMD cohort, comprising of 54 DMD patients treated or not by glucocorticoid, compared to 27 healthy controls, in three age groups. We developed an original approach for the biological interpretation of miRNA dysregulation, and produced a novel hypothesis concerning metabolic perturbation in DMD. We then used the mdx mouse model for DMD for the investigation of this hypothesis.

Results: We identified 96 dysregulated miRNAs, of which 74 were up- and 22 down-regulated in DMD. We confirmed the dysregulation in DMD of Dystro-miRs, Cardio-miRs and a large number of the DLK1-DIO3 miRNAs. We also identified numerous dysregulated miRNAs, yet unreported in DMD. Bioinformatics analysis of both target and host genes for dysregulated miRNAs predicted that lipid metabolism might be a critical metabolic perturbation in DMD. Investigation of skeletal muscles of the mdx mouse uncovered dysregulation of transcription factors of cholesterol and fatty acid metabolism (SREBP1 and SREBP2), perturbation of the mevalonate pathway, and accumulation of cholesterol. Elevated cholesterol level was also found in muscle biopsies of DMD patients. Treatment of mdx mice with Simvastatin, a cholesterol-reducing agent, normalized these perturbations and partially restored the dystrophic parameters.

Conclusion: This investigation supports that cholesterol metabolism and the mevalonate pathway are potential therapeutic targets in DMD.

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Introduction

Duchenne muscular dystrophy (DMD) is the most common inherited pediatric muscle disorder.

It is an X-linked genetic progressive myopathy characterized by muscle wasting and weakness,

which leads to loss of motor functions, cardiac and respiratory involvement, and premature

death 1,2. DMD occurs at a rate of approximately 1:5000 male births and arises due to mutations

in the dystrophin gene. The disease is caused by a deficiency of functional dystrophin, a critical

protein component of the dystrophin glycoprotein complex acting as a link between the

cytoskeleton and the extracellular matrix in skeletal and cardiac muscles 3.

The only routinely used medication for DMD patients is glucosteroid drugs, which can at best

only slightly delay the progression of the disease 4. Experimental therapeutic approaches, based

on gene therapy, cell therapy and drug discovery, are focused on the restoration of dystrophin

expression (review in 5,6). However, despite increasing efficiency in the restoration of

dystrophin expression in recent clinical trials, only modest muscle functional improvement has

been achieved 7–9.

The primary direct consequence of the disrupted link between the extracellular matrix and the

myofiber actin cytoskeleton due to lack of dystrophin is thought to involve sarcolemma

destabilization, perturbation of Ca+2 homeostasis, activation of proteases, mitochondrial

damage and tissue degeneration. The tissue damage activates a regenerative response, resulting

in repeated cycles of myofiber degeneration and regeneration. This ongoing process evolves

into a cascade of downstream pathological events, including chronic inflammation, oxidative

damage, and the replacement of contractile tissue by fibrotic and fatty tissues 10. A gradual

decline in the capacity of the stem cells for a compensatory proliferation, differentiation, and

tissue regeneration 11 participates also in the pathophysiology of the disease. A recently

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emphasized aspect of the dystrophic process is a progressive metabolic change of affected

muscle 12,13,22,23,14–21.

Profiling miRNA can be useful for diagnosis, monitoring and understanding mechanisms of

diseases. DMD has been the subject of a large number of miRNA profiling studies 24,25,34–36,26–

33. However, these studies have been hampered by one or a combination of limitations,

including the use of animal models rather than human patients, the relatively small size of the

studied cohorts, the detection of a small number of predefined miRNAs, or the use of miRNA

profiling technologies with low sensitivities. In the present study, we overcame these limitations

by profiling circulating miRNAs in the plasma of a relatively large patient cohort, which was

composed of three age subgroups between the ages of 4 and 20 years old, and included both

glucocorticoids treated and untreated DMD patients, as well as an age-match control group.

Additionally, the profiling technology was of miRNA sequencing, maximizing the detection

sensitivity of the entire spectrum of expressed miRNAs. Finally, an original bioinformatics

model for the interpretation of miRNA dysregulation was employed.

Interestingly, the bioinformatics analysis of plasma miRNAs dysregulation identified lipid

metabolism as the most important metabolic perturbation in DMD. In order to validate this

prediction, we screened muscle biopsies of young mdx mice for the expression of factors that

are related to lipid metabolism, and identified their dysregulation as early as at the age of 5-

weeks. Specifically, the mevalonate pathway that controls the synthesis of cholesterol was

highly affected and cholesterol level was increased in the dystrophic muscles. A recent paper

reported a positive effect of simvastatin on the dystrophic parameters and muscle function in

the mdx mouse. Surprisingly, considering the known target of simvastatin, its effect was

reported to be unrelated to blood cholesterol 37. We treated mdx mice with simvastatin and

confirmed improved dystrophic muscle parameters. We demonstrated that components of the

muscle mevalonate pathway and cholesterol levels were dysregulated in the muscle of mdx

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mouse compared to those of the healthy control, and returned to normal after simvastatin

treatment. In addition, the normalization of lipid metabolism correlates with improved

dystrophic parameters in mdx dystrophic muscle. In conclusion, besides to the discovery of a

large number of dysregulated miRNA in the plasma in DMD patients, the present study

provides new understanding of the metabolic perturbation in DMD and thus, opens up new

perspectives for the treatment of DMD.

MATERIALS AND METHODS

Ethical declaration

The human study (DMD patients and controls) was conducted according to the principles of

the declaration of Helsinki “ethical principles for medical research”, and was specifically

approved by the ethical committee CPP Ile de France VI, on July 20, 2010, and the Comité

d’Ethique (412) du CHR La Citadelle (Liège, Belgium) January 26, 2011.

Human patients and cohort composition

DMD patients were admitted from 10 European medical centers, two of them from Belgium

(n=35), one from Romania (n=16), and seven from France (n=49). Healthy control patients

were admitted from the same medical centers, Belgium (n=28), Romania (n=45) and France

(n=50). Patients were divided into three age groups 4-8, 8-12, and 8-20 years old. In the

youngest age group, the same donors contributed the GC-treated and untreated samples, with

untreated samples that were obtained before, and treated samples after their first GC treatment,

at interval of less than 6 month. GC-treated and untreated samples for the age groups of 8-12

and 12-20 years old were obtained from distinct DMD patients.

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Human skeletal muscle tissues were obtained from the Myobank, the tissue bank of the

Association Francaise contre les Myopathies (AFM). Open skeletal muscle biopsies were

performed after informed consent, according to the Declaration of Helsinki. Muscle biopsies

included in this study were derived from the paravertebral (two controls and two DMD),

Gluteus, and the tensor fasciae latae (one each control and one DMD) from the group aged 8-

12 (DMD) and 12-20 (control) years old.

Blood collection

Samples were collected from individuals with a written informed consent of parents or legal

guardians. Human blood samples were collected from male subjects of at least 3 years-old and

15kg body mass, in both control and DMD patients. Peripheral blood samples were collected

into 5 ml K3EDTA tubes (Greiner Bio-One). Plasma was separated from buffy coat and red

blood cells after 10 minutes centrifugation at 1800 g and stored at -80°C until further

processing.

Choice of RNA Samples for the miRNA profiling

Following the RNA extraction, a sub-cohort of 81 RNA samples was selected for the high

throughput sequencing (HTS) cohort (Supplemental table 2). We employed an optimization

process for the selection of the most suited RNA samples. This included the selection of (i)

plasma samples of OD 414 > 0.2 (indicative of absence of hemolytic contamination 38, (ii)

removal of RNA preparation of low concentration (< 30 ng/µl, conferring improved RNA

stability), (iii) samples with optimized patients’ age-distribution (inside age-groups), and (iv)

age-matching between group types (i. e. between DMD, DMD non treated and healthy

controls). Thus, the entire studied HTS cohort was composed of 54 DMD patients and 27

healthy controls (N=81) divided into 9 groups of 9 subject, composed of three age groups as

described in Figure 1A.

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MiRNA Sequencing

MiRNA sequencing was performed by Integragen (Evry, France). Libraries cloning was

modified from 39, for improved efficiency in small samples. Briefly, a 3' adenylated DNA

adaptor was ligated in the presence of 12% PEG and the absence of ATP, avoiding miRNAs

self-ligation. A 5' RNA adaptor was ligated in the presence of ATP. RT primer complementary

to the 3' adaptor was added, forming a duplex to reduce adapter dimer formation. RT reaction

was carried out with 1.75 pmol adaptors (3' adaptor / 5' adaptor / RT primer), and cDNAs were

amplified by 13 PCR cycles with primers complementary to the 3' and 5' adaptors. During this

PCR step, a specific barcode was incorporated for individual sample recognition. PCR samples

band quantification was carried out with Fragment Analyzer (AATI). An equimolar pool of ten

different samples was migrated on PAGE, and the miRNA band was extracted (Qiagen

MinElute column). Libraries were quantified by qPCR, to load precisely 7pM pool per line of

HiSeq Flow-Cell. The HiSeq 36b and index (barcode) sequencing was carried out as instructed

(Illumina) with a SBS V3 kit leading on 150 million passing filter clones.

All clean reads were compared to the Rfam database (http://rfam.xfam.org/), repeatmasker

(http://www.repeatmasker.org/, UCSC download 01/04/2014), and the NCBI RefSeq 40,

download 10/04/2014) for the annotation of the rRNA, snoRNA, piRNA 41, and tRNA 42.

Unique miR reads and their copy numbers were aligned with miRanalyzer online software 43,

using Ensembl human gene browser (genome assembly GRCh38) and (miRbase v20, June 2013

44). MiR count raw data was normalized and processed for differential expression by the Deseq2

R package ggplot2 45. The significance of miRNAs differential expression was ranked by T-

test, with a false discovery rate (FDR) correction according 46.

MicroRNA target genes enrichment analysis

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MiR-target gene enrichment analysis was performed with the online mirPath v.3 47 http://snf-

515788.vm.okeanos.grnet.gr/, based on predicted miRNA to mRNA interaction in human.

MicroRNA host genes analysis

For this analysis we considered all the dysregulated miRNAs p≤0.05 in DMD patients of the

aged 4-12 years old, compared to their age related healthy controls (Supplemental table 2).

MicroRNA host genes were retrieved from the miRIAD database 48 and were manually

validated on the Ensembl human genome browser GRCh38.p7. We considered all miRNAs

embedded on the sense strand of introns and exons of protein coding genes. We assigned to the

host-genes the fold change (FC) and p values of their embedded miRNAs. When both 3p and

5p isoform of the same premiRNA were dysregulated their host-genes were considered only

once, with the lower p value miR isoform. Similarly host genes for a number of different

miRNAs in a miRNA cluster was considered only once, with the highest dysregulated miRNA,

(according p value). In miRNAs hosted on more than one host-gene, both host-genes were

considered. The table of host-genes with their assigned dysregulation values is in supplemental

table 8. Host genes data were assigned to the core analysis of Ingenuity® Pathway Analysis

(IPA®, QIAGEN Redwood City,www.qiagen.com/ingenuity). Pathway enrichment of the gene

set was performed by ReactomePA and ClusterProfiler R/Bioconductor packages 49.

RESULTS

DMD patient characterization and cohort composition

To perform miRNA profiling in the serum of DMD patients, we collected samples of 100 DMD

patients and 123 healthy controls from 10 European medical centers (Supplemental table 1).

The study was approved by central and local Institutional Review Board (IRB), and recorded

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on clinicaltrials.gov (NCT NCT01380964). All patients or parents for minors signed an

informed consent. Following RNA extraction from the plasma and quality control validation,

we selected 81 RNA samples and constituted nine groups of nine patients. The groups consist

of DMD patients, either untreated (DMD-UT) or receiving treatment with glucocorticoid

(DMD-T) and healthy controls, in three age categories of 4-8, 8-12 or 12-20 years old. (Figure

1a). The mean age of disease onset of the corresponding cohort was 3 years of age. Normal

walking capacity was preserved in all patients of the first age group while three patients of the

second group (33%) and all but 2 in the last age group (88%) had lost their ability to walk

(Supplemental table 2). Overall, the mean age of loss of walking ability was 9.6 years.

Additional clinical and functional characterization of the patients is shown in Supplemental

table 2 and the spectrum of dystrophin mutations present in the patients is depicted in Figure

1b.

9 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted DecemberVu Hong_Figure2, 2020. The copyright 1holder for this preprint1a (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Age (Year)

1b

1 1 2 2 3 3 4 4 5 5 6 6 7 7 D 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 Dys Exons D1/D11 del 53-54 D2/D13 del 46-48 D3/D12 del 51 D4/D14 del 74 D5/D10 del 46-48 D6/D15 Poi 23 D7/D16 Spl 67 D8 del 48-54 4-8 Years old D9/D18 del 3-7 D17 del 51-55 Patients D1 to D18 D21 St 2 D20 dup 19 D19 del 50 x D22 del 45 D23 del62 D24 St 2 D28 dup 13-17 D25 del 45D27 dup 52-54 x D26 St 34 x D30 del 40 D29 del 48-50 D32 del 8-9 D31 del 68 8-12 Years old D33 dup 5-6 Patients D19 to D36 D34 del 50 D35 dup 3-9 D36 dup 18-44

D37 del 65 D42 del 5-7 D39 del 30 D38 del 48-50 D40 del 3-42 D41 del 45-50 D45 del 22 D43 dup 55-63 D46 del 8-12 D44 del 45-52 D47 dup 3-42 D49 del 46-52 12-20 Years old D50 del 50-52 D48 dup 8-11 Patients D37 to D54 D51 del 45-50 D52 del 45-50 D54 del 46-47 D53 dup 53

C C H R R R H R R R R R R R R R R R R R R R R HR R R R R HW E E Z C Dys Protein domains H H 1 1 2 3 2 4 5 6 7 8 9 1 1 1 1 1 1 1 1 1 1 3 2 2 2 2 2 4W F F Z - 1 2 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 1 2 T e Deletion Duplication Point mut. x Stop mut. rSpl Splice mut.

Figure 1. DMD cohort characterization. (1a) Cohort subgroups and age composition. DMD patients and healthy controls were classified into three age groups of 4-8, 8-12 and, 12-20 years old. DMD patients were glucocorticoid treated (DMD T) or untreated (DMD UT). The figurine symbols represent the ages of individual patients on the horizontal axis, black for untreated DMD, grey figures for treated DMD, and brown figures for healthy controls. Treated and untreated DMD of the 4-8 group of age are the same patients before and after GC treatment (except one patient). (1b) A graphical presentation of the spectrum of dystrophin mutations by age group. Dystrophin’s gene 79 exons and protein domains are presented on respectively the upper and lower vertical bands. Patients of the 4-8 age years old group are represented twice in the cohort (with the exceptions of D8 and D17), before and after Glucocorticoid treatment, by samples D1 to D9 and D10 to D18 respectively. Del (blue) =deletion; Dup (red) = duplication; St (black) = stop codon mutation; Poi (in black) = point mutation.

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RNA profile dysregulation in the plasma in DMD patients is age-dependent

After size selection, the 81 RNA samples were all individually sequenced using the Illumina

technique. All mapped reads were matched to the hg19 (GRCh38) human genome assembly

and were assigned to RNA classes, including rRNA snoRNA, piRNA tRNA, and microRNA

(miRbase v20, June 2013 44) (Figure 2a). We obtained 9.37, 3.68, and 2.81 million sequences

on average per studied sample for total high quality (high quality-Reads), human genome

mapped (Mapped reads), and mature miRNA sequences, respectively (Figure 2b). A Principal

component analysis (PCA) was used for the identification of overall miRNA profile

dysregulation between the different cohort subgroups. PCA failed to separate DMD from

control samples (Figure 2C upper left). In contrast, PCA primary and secondary components

separated the DMD (orange dots) from the control samples (blue dots) of the 4-8 and 8-12 age

groups (Figure 2C, upper middle and right respectively), but not of the 12-20 age group

(bottom left). In the 4-12 years old group (combined 4-8 and 8-12 groups), DMD patients were

separated from healthy controls (bottom middle). However, PCA failed to separate GC-treated

from untreated DMD patients (bottom right). These results indicate a robust disease effect, but

not such a robust GC effect, on circulating miRNA in DMD patients below the age of 12. This

effect is age-dependent as it is no longer observed beyond the age of 12.

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81 plasma samples 2a Small RNA extraction and sequencing

High quality reads

Unmapped reads Mapped reads

Other RNA classes Mature miRNAs

PCA analysis 2b

High quality reads 1.6 E+07 Mapped reads Mature miRNA 1.2 E+07

0.8 E+06 JB data version) 0.4 E+06

0.0 E+06 2c

4-20 YO 4-8 YO 8-12 YO

Control DMD

12-20 YO 4-12 YO 4-12 YO

DMD UT DMD Treated

Figure 2. Characterization circulating RNA by high throughput sequencing.

(2a) Schematic presentation plasma sample processing. Small RNA high quality reads (high quality Reads) were mapped on the human genome (Mapped reads). Mapped reads were classified to miRNA and other small RNA classes. (2b) Graphical presentation high quality Reads (blue), Mapped reads (red), and miRNA (green) in the cohort subgroups are presented as average ± SEM. (2c) PCA analysis of cohort segregation according to miRNA expression in DMD (orange dots) and healthy control (green dots) by age groups (panels 1 to 5) and treated (red dots) versus untreated (black dots) DMD patients (panel 6).

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Differentially expressed plasmatic miRNAs in DMD patients

The above data indicates that the overall miRNA dysregulation in DMD patients is drastically

reduced beyond the age of 12. In accordance, upon stratification on age and treatment, we

noticed that the largest set of dysregulated miRNAs was identified by comparing the combined

GC- treated and untreated 4-12 years old patient group to their age related healthy controls

(Supplemental figure 1), including 65 up and 25 down-regulated miRNAs (FDR<0.1) in DMD

patients (Supplemental tables 3 and 4, respectively), and Figure 3a. Among the dysregulated

miRNAs, we validated the dysregulation in DMD of the dystromiRs 24–27,29,32, the heart-

enriched cardiomiRs and a large number of miRNAs belonging to the DLK1-DIO3 cluster 27.

Of the newly identified dysregulated miRNAs, we noticed in particular a large number of the

Let-7 family members, the entire miR-320 family, and many miRNAs which are known

modulators of diverse biological functions in skeletal and cardiac muscles, including miR-128

50, miR-199a 51,52, miR-223 53,54, miR-486 55,56, and members of the miR-29 57 and miR-30 58

families. The proximity between miR class members on the Volcano plot, suggested their

similar dysregulation. Indeed, highly significant expression correlations were identified among

miR members within the distinct miR classes (Supplemental figure 2), supporting their

coordinated dysregulation. The dysregulation pattern of a selected number of miRNAs are

presented graphically (Figure 3), including the upregulated miR-206, miR-208a-3p, miR-128-

3p, miR-199a-3p and miR-369-5p (Figure 3b), the downregulated miR-342-3p, miR-320a,

miR-361-3p, miR-29b-3p and miR-30e-5p (figure 3c), and the GC-responsive miR-223-3p,

miR-379-5p, miR-27b-5p and Let-7d-5p (Figure 3d). To analyze the glucocorticoid response,

we compared miRNA expression before and after glucocorticoid treatment in the 4-12 age

group DMD patients and identified 11 miRNAs which were dysregulated in untreated DMD

patients compared to healthy controls, and for which the expression was significantly changed

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by the glucocorticoid treatment. Expression of nine of these miRNAs shifted toward

normalization by the GC-treatment. Of note, miR-27b-5p, let-7i-5p and miR-379-5p were no

longer dysregulated in the group of treated DMD versus healthy control (Supplemental table

5). Similarly, miR-27b-5p and miR-379-5p were not statistically different from healthy controls

when all DMD patients (treated + untreated) are grouped together.

3a

128-3p

7 199a-3p DystromiRs CardiomiRs Dlk1-Dio3 miRNAs 30e-3p 6 Let-7 family 299-5p 1277 miR-320 family 369-5p Unclassified L7d-5p 598

5 487b-3p

) 23a-5p 208b-3p

fdr ( 4 487b-5p 499-5p 484 381-3p 154-5p L7g-5p 208a-5p 494-3p 409-5p 323a-3p 889-3p 208a-3p

3 493-5p Log 10 p p 10 Log 95 206

342-3p 320a L7b-3p

L7d-3p 133a-3p 2 320b -1 320d L7a-5p 1290 320e 320c 1

0 -10 -5 0 5 10 Log2 fold change

Figure 3. Plasma miRNA profiling in the DMD cohort.

(3a) A volcano plot of miRNA dysregulation in the plasma of 4-12 years old DMD patients (treated & untreated together, n=36) versus healthy control (n=18). Upregulated miRNA in DMD are on the right side and downregulated on the left side of the threshold lines (FC± 1.5). MiRNAs above the horizontal line are differentially expressed with a p value <0.1 (FDR). DystromiRs in yellow, cardiomiRs in red, DLK1-DIO3 miRs in green, Let7 family miR in blue, miR-320 family in brown, unclassified miR in black.

14

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Control

3b DMD Normalized count Normalized

Control

3c DMD Normalized count Normalized

Control 3d DMD untreated DMD Glucocorticoid

let-7d-5p Normalized count Normalized

Figure 3. Plasma miRNA profiling in the DMD cohort.

(3b) A graphical presentation of miRNA upregulated in DMD. (3c) A graphical presentation of miRNA down-regulated in DMD. (3d) Glucocorticoid responsive miRNAs in DMD. Each dot is one patient, n=9, error bar = SEM.

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Bioinformatics interpretation of miRNA dysregulation

Investigation of predicted target genes for dysregulated miRNAs: MicroRNAs are controlling

the expression and/or stability of target genes, and consequently the biological functions of

miRNAs are mediated by their targets. A common method for the investigation of miRNAs

functions consists of the identification of signaling pathways and cellular functions of these

target genes. For the identification of pathways that might be controlled by dysregulated

miRNAs in DMD, we used the KEGG function of DIANA TOOLS miRPath V3 47, considering

all up and down miRNAs (fdr <0.1) of 4-12 years old DMD compared to age-matched controls.

The analysis identified 20 distinct significantly enriched (p<0.05) pathways, including

pathways already suspected of participating in the DMD disease such as Extra-cellular matrix

(ECM)-receptor interaction, fatty acid biosynthesis, focal adhesion, PI3k-AKt signaling, and

Foxo signaling (Supplemental table 6)

Investigation of host-genes for dysregulated miRNAs in DMD: We also attempted at the

development of a complementary procedure for the biological interpretation of miRNA

dysregulation. More than 1/2 of human miRNAs are embedded within introns and exons of

protein coding genes 48. The embedded miRNAs are often co-expressed with their host genes

and can regulate their expression and activity 48,59–62, which support functional relations

between the miRNA host-gene, target genes, and biological activity 63,64. Indeed, we found that

twelve of the identified dysregulated miRNAs were embedded in seven host genes that are

known to be dysregulated in DMD models and patients (Supplemental table 7). Accordingly,

we explored the possibility that analysis of host genes for dysregulated miRNAs may provide

an insight into functional causes and consequences of miRNA dysregulation. The Ingenuity

pathway analysis (IPA) package (Qiagen-bioinformatics) was used for the algorithmic

prediction of host-gene networks, assigning the fold change and p value of their embedded

miRNAs (Supplemental table 8). Three networks were identified, all of which including the

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term Lipid Metabolism, and Small Molecule Biochemistry (Table 1). Of interest, the three

networks integrate a number of biomolecules that are known to be involved in DMD

pathophysiology, some of which are recognized as therapeutic targets, supporting the relevance

of the analysis. This included, in network-1, insulin, MAPK, Erk1/2, PI3K and Akt, all

members of the canonical muscle hypertrophy pathway 65,66, NF-ĸB 67–69, H3/H4 histones,

PDGF and PDGFR 70,71, Hox9A 72, and the Mediator complex 73. The SREBp-1 gene was

identified as the most connected in the first and largest network (Network 1, Figure 4a).

Network 2 includes cholesterol 37, EGFR 74,75, the  and  estradiol and their estrogen receptor

76. Beta estradiol was the most connected molecule in this network (Network 2, Figure 4b).

Network 3 includes TGF-beta 77 and the hepatocyte nuclear factor HNF4a, which is its most

connected molecule (Network 3, Figure 4c). Finally, upon integration the three networks,

using the IPA software, the SREBp-1 molecule was the most connected molecule of the

combined network (Figure 4d). This basic-helix-loop-helix transcription factor, as well as its

family member SREBP2, can bind specific sterol regulatory element DNA sequences, to

upregulate the synthesis of enzymes involved in sterol biosynthesis. Thus, the SREBs are

master regulators of lipid metabolism 78.

In a complementary approach, the same 71 host genes (Supplemental table 8) were subjected

to a gene ontology (GO) terms enrichment analysis, using the ReactomePA R algorithm.

Particular enrichment was identified for terms that are related to lipid metabolism, including

the activation of gene expression by SREBp (at the upper position by p value), regulation of

lipid metabolism by Peroxisome Proliferator Activated Receptor Alpha (PPARA), COPI

mediated transport, regulation of cholesterol biosynthesis by SREBp and metabolism of

steroids (Figure 4e). Considering that a large proportion of host genes are involved in lipid

metabolism, the ClusterProfiler R package 49 was used on a sub-selection of the host genes

which are known to participate in lipid metabolism. This analysis suggested the involvement of

17 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

4a 4b

4c 4d

Figure 4. Bioinformatic analysis of host-genes for dysregulated miRNAs in DMD plasma.

(4a) Network 1: Lipid Metabolism, Molecular Transport, Small Molecule Biochemistry, neurological disease, cancer, organismal, injury and abnormalities, (4b) Network 2: Lipid Metabolism, Small Molecule Biochemistry, Molecular Transport. (4c) Network 3, lipid metabolism, small molecule biochemistry, Dermatological Diseases and Conditions. (4d) Merged networks 1-3, Lipid metabolism. The connected molecules in each network are in blue. Red is upregulated, green is downregulated, continuous and discontinuous arrows are respectively direct and indirect relations, except in 4d. Round circle = complex, rhombus = enzyme, inverse triangle = kinase, flat (horizontally oriented) circle = transcription regulator, vertically oriented circle = transmembrane receptor, trapeze = transporter.

18 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

4e

Gene count

Transport to Golgi 4f PPARA activates gene expression

Regulation of lipid metabolism by PPARA

Regulation of cholesterol biosynthesis by SREBP

COPI-mediated anterograde transport

Retinoid metabolism and transport

Metabolism of fat-soluble vitamins

Metabolism of steroids

Transcriptional regulation of white adipocyte differentiation

Scavenging of heme from plasma

RAB GEFs exchange GTP for GDP on RABs

Figure 4. Bioinformatic analysis of host-genes for dysregulated miRNAs in DMD plasma.

(4e) GO terms of host genes for dysregulated miRNAs, classified by p value. (4f) Lipid dysregulation network in DMD. A graphical presentation of a sub selection of miRNA host genes, which are participating in lipid metabolism, and their related GO terms.

19 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

this network in the functions of transport to Golgi, PPARA transcriptional activity and

regulation of lipid metabolism, regulation of cholesterol synthesis, COPI mediated anterograde

transport, metabolism of fat-soluble vitamins (vitamin D) and of steroids (Figure 4f). Thus,

two different bioinformatics analyses identified the SERBp-dependent transcription program

and the metabolism of lipid and cholesterol in the center of a network of host genes for

dysregulated miRNAs in DMD.

Evidence for lipid metabolism dysregulation in the mdx model

The miRNA host gene bioinformatics analysis predicted the dysregulation of the SREBp

pathway in DMD. To investigate the relevance of this prediction, we quantified the expression

of mRNA and proteins of lipid metabolism components, with a focus on the SREBp pathway

(Figure 5a). In addition to SREBp-1 and SREBp-2, this analysis included the SREBp upstream

regulator, SCAP, 79, the SREBP-1 target gene, FASN, and the SREBP-2 target genes, HMGCR

and LDLR, 80,81. Four and three (out of the six) transcripts were significantly upregulated in

gastrocnemius (GA) and diaphragm, respectively (Figures 5b and 5c). At the protein level,

FASN, HMGCR, and SREBp-1 were upregulated in GA (Figures 5d and 5e) whereas the

FASN, HMGCR, SCAP and SREBP1 were significantly upregulated in diaphragm (Figures 5f

and 5g). Thus, this analysis supported the upregulation of both SREBp-1 and SREBP-2

expressions and activities in the dystrophic muscle of the mdx mouse.

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 5a SREBP-1 FASN Fatty acid synthesis &metabolism SCAP LDLR SREBP-2 Mevalonate pathway & cholesterol synthesis HMGCR

5b Gastrocnemius 5c Diaphragm 2.0

wt ** 4.0 ** wt *

** * 1.0 2.0

**

FC FC mdx/ mRNA FC FC mdx/ mRNA

1.0 0.5

1

2

1 2

-

- -

-

LDLR

LDLR SCAP

SCAP

FASN FASN

HMGCR

HMGCR

SREBp

SREBp SREBp SREBp

5d mdx Control 5e FASN Gastrocnemius **

HMGCR wt 4.0 * *** SCAP 2.0

SREBp-1 1.0 protein mdx/

FC FC 0.5

1

Gastrocnemius

- SCAP

GAPDH FASN

HMGCR SREBp

5f mdx Control 5g FASN Diaphragm 8.0 HMGCR wt 4.0 ** * ** SCAP 2.0 1.0 SREBp-1 protein mdx/

FC FC 0.5

Diaphragm 1

- SCAP

GAPDH FASN

HMGCR SREBp

Figure 5. SREBp pathway in the mdx muscle

(5a) A schematic presentation of the components in the SREBp pathway (see text for details). (5b-c) Fold change SRBp pathway transcripts in the Gatsrocnemius (5b) and the diaphragm (5c) in the mdx versus healthy control mouse. (5d-g) A Western blot analysis of SREBP pathway protein expression in the gastrocnemius (5d) and its graphical quantification (5e), and of the diaphragm muscle (5f) and its graphical quantification (5g). SREBP1= Sterol regulatory binding element, SREBBP2= sterol binding regulatory element 2, HMGCR= HMG-CoA reductase, FASN= Fatty acid synthase, LDLR= Low density lipoprotein receptor, SCAP=SREBP Cleavage-Activating Protein.

21 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Simvastatin alleviated the dystrophic phenotype and normalized muscle cholesterol

content in mdx mouse

A positive effect of the cholesterol synthesis inhibitor simvastatin on the dystrophic muscle was

previously reported in the mdx mouse model 37,82. The observed beneficial effect was attributed

to reduction of oxidative stress, fibrosis and inflammation. Interestingly, a recent study reported

an abnormally higher cholesterol content in muscles of DMD patients 14. Therefore, we aimed

at validating the beneficial effect of simvastatin in the mdx mouse, and its possible relation with

modulation of muscle cholesterol. Orally administrated simvastatin during a 3 week period

significantly reduced the level of serum myomesin-3, a sensitive serum biomarker of muscular

dystrophy 83 (Figures 6a and 6b). The level of mCK was reduced as well in the simvastatin

treated mdx mice (Figure 6c). A histological analysis of the diaphragm muscle revealed

significantly reduced fibrosis in treated mdx mice (Figure 6d and 6e).

The level of serum cholesterol was not reduced by simvastatin (Supplemental figure 3), in

agreement with the observation made in a previous study 37. We thought of investigating the

consequences of simvastatin treatment at the level of the muscle tissue. Filipin is a naturally

fluorescent polyene antibiotic that binds to free but not esterified sterols, and is useful therefore

for the detection of free cholesterol in biological membranes 84. The specificity of filipin

staining to cholesterol was demonstrated by treating C2C12 cells with the cholesterol-

trafficking inhibitor U18666A (Supplemental figure 4). Importantly, filipin staining of

transversal sections confirmed increased free cholesterol content in the diaphragm of 10 weeks

old mdx mice as compared to their WT controls (Figure 6f). Quantification of confocal images

of the same sections revealed over 2-fold (P<0.001) increased cholesterol expression in the in

mdx compared to wild type controls, and a complete normalization of cholesterol expression in

the simvastatin treated group (p<0.001) (Figures 6g and 6h). Similar reduced cholesterol

content in treated mdx mice were observed also in the GA and TA muscles (Supplemental

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

figures 5). Finally, the quantification of the free cholesterol content in muscle biopsies in a

small cohort of human DMD patients (n=4) confirmed about two fold (p<0.035) increased free

cholesterol in DMD muscles compared to controls (Figure 6i). A western blot analysis

demonstrated that simvastatin treatment normalized the expression of SREBP1, SREBP2 and

HMGCR in the diaphragm of the mdx mouse (Figures 6j and 6k). Finally, we employed

confocal microscopy for the investigation of SREBP-1 and SREBP-2 expression in transversal

sections of the diaphragm. Higher levels of both proteins were observed in mdx, as compared

to the wild type control mouse, and this expression was reversed by simvastatin (Figures 6l

and 6m). Importantly, we noticed the expression of SRBP-1 (Figure 6l) and a more clearly

high expression of SREBP-2 (Figure 6m and supplemental figure 6) inside myonucleus in

the mdx mice. The majority of the SRBP-1 and SREBP-2 positive myonucleus were in a central

position in the myofibers, indicating regenerated myofibers. Of particular interest, the

expression of SREBP-1 and SREBP-2 was only rarely detected in the nuclei of regenerated

myofibers of the simvastatin treated mdx mice. Taken together, these results indicate that (1)

muscles of the mdx mouse are characterized by the upregulation of the transcriptionally active

nuclear forms of SREBP-1 and SREBP-2, in centrally positioned myonucleus of regenerating

myofibers. (2), muscle fibers of the mdx mouse are characterized by increased cholesterol

content, and (3), simvastatin treated mdx mice presented improved dystrophic phenotype in

correlation with normalized muscle SREBp1 and SERBP-2 expression, and cholesterol content.

23 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 6a 6b 6c *** ** ** * *** * 40000 2.0 e6

mCK 30000 Myom3 1.5 e6 20000 1.0 e6

mdx mdx Simva Serum wt 0.5 e6 10000

0.0 Myom3 intensity Myom3

S 0

S

-

wt

-

wt

mdx

mdx

mdx mdx 6d WT mdx mdx Simva 6e *** *** 30 * 20 10

5

Sirius red Sirius Sirius red Sirius

Mean intensity Mean 0

wt

mdx mdx S

6f WT mdx mdx Simva Filipin

1000 µm 1000 µm 1000 µm Filipin

200µm 200µm 200µm

6g WT mdx mdx Simva 6h

*** ***

filipin 15

10 Filipin 5

0

25µm 25µm 25µm S

-

wt

Mean intensity Mean

mdx mdx

Figure 6. Simvastatin effect on skeletal muscle in the mdx mouse. 7-week old (young adult) control mice, and mdx mice untreated (mdx) or treated (mdx-Simva) by Simvastatin during a three weeks period (n=6). (6a-b) Serum myomesin-3 (Myom3) and its graphical presentation. (6c) Muscle Creatine Kinase (mCK) in the blood serum. (6d-e) Fibrosis staining (Sirius red) of diaphragm transversal sections and its graphical presentation. (6f) Images of free cholesterol staining (Filipin) of transversal sections of a whole diaphragm. (6g-h). Confocal images of diaphragm transversal sections (6g) and its quantification (6h).

24 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

6i 6j * Control mdx mdx Simva HMGCR 1.5 SREBP2 SREBP1

1.0 Total Protein

/mg tissue) /mg 0.5 nmol

( 0.0

muscle free cholesterol cholesterol freemuscle

DMD Healthy

6k *** *** ** ** *** 1.6 *** *** * 1.4 1.2 WT 1.0 mdx 0.8 mdx Simva 0.6

relative Intensity relative 0.4

0.2 Prot 0.0 SREBP1 SREBP2 HMGCR

Figure 6. Simvastatin effect on skeletal muscle in the mdx mouse.

(6i) Cholesterol content of skeletal muscle biopsies of DMD patients and their healthy controls. (6j and 6k). A Western blot analysis (6j) and its graphical presentation (6k) of SREBP -1 SREBP-2 and HMGCR in the diaphragm muscle of control, mdx, and treated mdx mice

25

bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

6l

WGA SREBP1 WGA + SREBP1

C57BL/10

mdx mdx + Simvastatin + mdx

26 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

6m

WGA SREBP2 WGA + SREBP2

C57BL/10

mdx mdx + Simvastatin + mdx

Figure 6. Simvastatin effect on skeletal muscle in the mdx mouse.

(6l and 6m). Confocal microscopy images of SREBp -1, SREBp-2 in the diaphragms of the same mice. Notice that the wheat germ agglutinin (WGA) lectin stains both the sarcolemma and the myonuclear membranes. Blue arrows denote SREBP positive myonucleus inside regenerated myofibers (in the center of the myofiber). Pink arrows denote SREBP negative myonuclei in regenerated myofibers.

27 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

DISCUSSION

In previous investigations, we profiled circulating miRNA in the serum of animal models for

DMD 26,27,34. Following these studies, we are reporting the profiling of miRNA in the plasma

of DMD patients. We validated and confirmed the dysregulation of many of the previously

identified dysregulated circulating miRNAs in muscular dystrophy 26,27,34. In addition, we are

reporting many newly identified dysregulated miRNA. We reasoned that an analysis of overall

miRNA dysregulation can provide new information on molecular mechanisms in DMD. An

original bioinformatics approach was employed, based both on target and host genes for

dysregulated miRNAs. This analysis predicted a central role for the perturbation of lipid

metabolism and particularly of the SREB / mevalonate / cholesterol synthesis pathway in the

DMD pathology. Analysis of skeletal muscle biopsies of the mdx mouse confirmed a

dysregulation of the SREBP pathway and increased cholesterol content. Treatment of mdx mice

with simvastatin, an inhibitor of the mevalonate pathway and cholesterol synthesis, partially

normalized the SREBP pathway, reduced the accumulation of cholesterol in the dystrophic

muscles, and improved dystrophic parameters of the treated mice.

The DMD cohort and miRNA dysregulation. To our knowledge, this is the first study to

report global miRNA profiling in the plasma of DMD patients over a large age range, including

GC-treated versus untreated patients. The overall miRNA dysregulation decreased in subjects

above the age of 12. Thus, this age-dependent dysregulation pattern, which was shown

previously with the mCK and the myomiRs, was extended in the present study to a large

majority of the dysregulated miRNAs in DMD. This pattern may reflect both the reduced

muscle mass and physical activity of the older DMD age group (quantitative changes), as well

pathophysiological evolution, independently of muscle mass and activity (qualitative changes).

We confirmed the upregulation of many miRNAs that were identified in previous

investigations. These include the dystromiRs, the cardiomiRs, and the DLK1-DIO3 clustered

28 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

miRNAs 26,27,85. Of interest, we are reporting here, to our knowledge for the first time in DMD,

the dysregulation of many members of the Let-7 and miR-320 families. Among the newly

identified dysregulated miRNA in DMD plasma, we identified many miRNAs that are known

to play diverse roles in muscle pathophysiology. Among these are miR-128 50,86, miR-199a 51,

miR-223 53, miR-486 55,56, and the miR-29 87 and miR-30 58,88 family members. As in previous

investigations, we identified a higher number of upregulated rather than downregulated

circulating miRNA in DMD. It is thought the upregulation in DMD of circulating miRNAs

results principally from increased passive leakiness from damaged fibers and the active passage

of miRNAs through the dystrophic sarcolemma. Of interest, however, in the present study, is

the large number of downregulated miRNAs which we identified in the plasma in DMD. The

downregulation of circulating miRNA is not explained by the alteration of sarcolemma

permeability. Thus, it is likely to result from transcriptional adaptation in dystrophic tissues. Of

the downregulated circulating miRNAs, we noticed in particular miR-342 and miR-185, both

of which target the SREBp mevalonate pathway and the synthesis of cholesterol 89–91.

Additionally, we noted the downregulation of the entire (5 members) miR-320 family, of which

the biological consequences in the context of muscular dystrophy are as yet unknown.

A holistic bioinformatics analysis of miRNA dysregulation. The conventional method for

the biological interpretation of miRNA dysregulation is based on the analysis of the

consequences of miRNAs dysregulation. This interpretation can be carried out by a pathway

enrichment analysis of mRNA targets for the dysregulated miRNAs. The miRNA target genes

analysis predicted enrichment for the KEGG pathways, of the proteoglycan ECM-receptor

interaction 92,93, focal adhesion 94, fatty acid biosynthesis 13,14, as well as several signaling

pathways : Hippo 95, PI3K-Akt 96, estrogen 97, FoxO 98 and ErbB 99, all of which are known to

be dysregulated in DMD. The identification of the dysregulation of these pathway is well-

known in the field and thus supports the pertinence of our miRNA dysregulation results.

29 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

However, the target genes analysis failed to produce novel hypotheses or an explanation for the

DMD pathophysiology. Importantly, among the dysregulated miRNAs, we noticed some which

are positioned inside mRNA transcripts that are known to be dysregulated in the dystrophic

muscle in DMD. This observation supports a hypothesis that, in DMD, miRNAs and their host-

gene might be coordinately dysregulated, and thus, that miRNA dysregulation may indicate a

host-gene dysregulation. As mentioned above, in the analysis which is based on miRNAs target

genes, the focus is on the consequences of the dysregulation of the miRNAs. In contrast, in the

host gene approach, the focus is on the causes of the dysregulation of the miRNAs, i.e. on the

upstream events of miRNA dysregulation, thus providing different information. The host gene

approach is of particular interest for the interpretation of miRNA dysregulation in the

serum/plasma, since mRNA profiling cannot be carried out in this compartment (that express

principally remnant of degraded mRNAs). Taking into account all these considerations, we

hypothesized that a biological interpretation approach for circulating miRNA that combine both

target and host genes (described schematically in supplemental Figure 7), may provide an

improved interpretation of DMD pathophysiology. To predict the possible consequences of

dysregulation of the host genes, we used two different approaches. In the first, we used the IPA

algorithm for the construction of gene networks, and in the second, we used the ReactomePA

and ClusterProfiler R algorithms for the identification and illustration of GO terms, which are

associated with the host genes. The IPA-based gene-interaction analysis predicted three

dysregulated networks, all of which contain the term lipid metabolism. At the central positions

of the three networks, which identify the most connected molecules, we found SREBP1, Beta

estradiol, and HNFA4. The SREBp 1 molecule was identified as being central to the merged

network of dysregulation in DMD. Similarly, the analysis of GO terms by the ReactomPA

algorithm predicted a key role for the SREBp transcription factors. Importantly, the GO term

analysis supports the hypothesis that the dysregulation of the SREBp pathway may happen in

30 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

the context of a broader level lipid metabolism dysregulation as observed with the Reactome

IPA analysis.

Lipid metabolism dysregulation in the mdx mouse. The SREBP family of transcription

factors is composed of three members. The two isoforms SREBP1c and SREBP1a, result from

two distinct promoters of the SREBPF1 gene. SREBP-1 isoforms control primarily lipogenic

gene expression, while SREBP-2 regulates the transcription of genes related to cholesterol

metabolism 81. Indeed, in agreement with the bioinformatics prediction, gene expression

analysis confirmed the significant upregulation of members of the SREBp pathway in the

gastrocnemius and the diaphragm muscles in the young mdx mouse, providing supporting

experimental data from the dystrophic muscle for the hypothesis of SREB pathway

dysregulation.

Treating mice by simvastatin. The mevalonate pathway and cholesterol synthesis are inhibited

by statins, by acting directly on the HMGCR, the rate limiting enzyme of the cholesterol

synthesis pathway. The treatment of mdx mice with the FDA-approved drug simvastatin was

reported to affect positively the mdx mice, which presented reduced dystrophic symptoms and

improved muscle function. Simvastatin is a pleotropic drug and its positive effect on mdx was

reported to be independent of lipid metabolism in accordance with the absence of modification

of the seric cholesterol level 37. Importantly, we identified an overexpressed mevalonate

pathway in the skeletal muscle of the mdx mouse, as well as increased cholesterol content, both

of which returned toward normal after simvastatin treatment. Thus, while the positive effect of

simvastatin was repeatedly suggested, the molecular basis for this effect was not clear 37,82. In

the present investigation, we identified a positive correlation between the effect of simvastatin

on the muscle mevalonate pathway, and its alleviated dystrophic parameters in the mdx mouse.

This correlation supports that HMGCR and accumulation of muscle cholesterol are the

31 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

therapeutic targets of simvastatin in the dystrophic muscle. The molecular mechanism by which

the increased cholesterol level affecting the dystrophic muscle in DMD is yet unknown.

However, our investigation is in agreement with earlier reports in support of lipids and

particularly of cholesterol metabolism abnormalities in DMD 13,14,106–112,15,20,100–105. Of

particular interest, Steen and colleagues reported the improved dystrophic parameters in an mdx

mouse that overexpress the NPC1 gene, an accelerator of intracellular cholesterol trafficking

113, while Milad and colleagues reported the correlation between increased plasma cholesterol

and accentuated muscular dystrophy in the mdx mouse 112, which together supporting that

perturbation of cholesterol metabolism affects the DMD phenotype. These early reports failed,

however, to provide a molecular hypothesis for the causes and origin of this dysregulation. Not

only does our study concords with these previous findings, it also provides a molecular

framework for this dysregulation for the first time. In summary, from the plasma samples of a

large DMD cohort, the present study identified that perturbation of lipid metabolism, and in

particular of the cholesterol homeostasis, play an important role in the pathophysiology of

DMD. These findings are opening novel perspective for clinical interventions in DMD.

AUTHOR CONTRIBUTION

FA and AVH designed and performed experiments, analyzed results and wrote the paper. MS

and LSu performed experiments and analyzed results. GC performed bioinformatics analyses.

LSe and TV designed experiments, managed the clinical trial and analyzed results. SB

coordinated the clinical trial. IR and DI designed experiments, managed the project and wrote

the manuscript.

ACKNOWLEDGEMENT

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

We are grateful to the In vivo evaluation, Imaging Histology services of Genethon and to Siân

Cronin for critical reading of the manuscript. This study was financially supported by the

Association Francais contre les Myopathies (AFM); by ADNA (Advanced Diagnostics for New

Therapeutic Approaches); the Institut National de la Sante et de la Recherche Medicale

(INSERM); the Universite Pierre et Marie Curie Paris 06; the Centre National de la Recherche

Scientifique (CNRS). The authors wish to thank patients and parents for participating, and all

staff involved in sample collections in the different medical centers. The authors of this

manuscript certify that they comply with the ethical guidelines for authorship and publishing in

the Journal of Cachexia, Sarcopenia and Muscle.

Competing interests:

LS is member of the SAB or has performed consultancy for Sarepta, Dynacure, Santhera,

Avexis, Biogen, Cytokinetics and Roche, Audentes Therapeutics and Affinia Therapeutics.

TV is the Chief Scientific Officer of DiNAQOR AG. He also serves on the data safety

monitoring board for trials sponsored by Italfarmaco and Sarepta. He is a consultant for

Antisense Therapeutics, BioPhytis, Catabasis, Constant Therapeutics, Italfarmaco, Prosensa,

Sarepta, Solid Biosciences and Syneos.

All other authors declare no competing interest

References

1. Mercuri E, Muntoni F. Muscular dystrophies. Lancet 2013;381:845–60.

2. Guiraud S, Aartsma-Rus A, Vieira NM, Davies KE, van Ommen G-JB, Kunkel LM. The Pathogenesis and Therapy of Muscular Dystrophies. Annu Rev Genomics Hum Genet 2015;16:281–308.

3. Ervasti JM, Campbell KP. A role for the dystrophin-glycoprotein complex as a transmembrane

33 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

linker between laminin and actin. J Cell Biol 1993;122:809–23.

4. Goemans N, Buyse G. Current Treatment and Management of Dystrophinopathies. Curr Treat Options Neurol 2014;16:287.

5. Abdul-Razak H, Malerba A, Dickson G. Advances in gene therapy for muscular dystrophies. F1000Research 2016;5:2030.

6. Chamberlain JRJSJR, Chamberlain JRJSJR. Progress toward Gene Therapy for Duchenne Muscular Dystrophy. American Society of Gene and Cell Therapy; 2017.

7. Dalakas MC. Gene therapy for Duchenne muscular dystrophy: balancing good science, marginal efficacy, high emotions and excessive cost. SAGE Publications Ltd; 2017.

8. McNally EM, Wyatt EJ. Mutation-Based Therapy for Duchenne Muscular Dystrophy. Circulation 2017;136:979–981.

9. Randeree L, Eslick GD. Eteplirsen for paediatric patients with Duchenne muscular dystrophy: A pooled-analysis. J Clin Neurosci 2017 doi:10.1016/j.jocn.2017.10.082.

10. Allen DG, Whitehead NP, Froehner SC. Absence of Dystrophin Disrupts Skeletal Muscle Signaling: Roles of Ca 2+ , Reactive Oxygen Species, and Nitric Oxide in the Development of Muscular Dystrophy. Physiol Rev 2016;96:253–305.

11. Dumont NA, Wang YX, von Maltzahn J, Pasut A, Bentzinger CF, Brun CE et al. Dystrophin expression in muscle stem cells regulates their polarity and asymmetric division. Nat Med 2015;21:1455–1463.

12. Saure C, Caminiti C, Weglinski J, de Castro Perez F, Monges S. Energy expenditure, body composition, and prevalence of metabolic disorders in patients with Duchenne muscular dystrophy. Diabetes Metab Syndr Clin Res Rev 2017;12:81–85.

13. Le Borgne F, Guyot S, Logerot M, Beney L, Gervais P, Demarquoy J. Exploration of Lipid Metabolism in Relation with Plasma Membrane Properties of Duchenne Muscular Dystrophy Cells: Influence of L-Carnitine. PLoS One 2012;7:e49346.

14. Srivastava NK, Yadav R, Mukherjee S, Pal L, Sinha N. Abnormal lipid metabolism in skeletal muscle tissue of patients with muscular dystrophy: In vitro, high-resolution NMR spectroscopy based observation in early phase of the disease. Magn Reson Imaging 2017;38:163–173.

15. Srivastava NK, Pradhan S, Mittal B, Gowda GAN. High resolution NMR based analysis of serum lipids in Duchenne muscular dystrophy patients and its possible diagnostic significance. NMR Biomed 2010;23:13–22.

16. Rodríguez-Cruz M, Sanchez R, Escobar RE, Cruz-Guzmán O del R, López-Alarcón M, Bernabe García M et al. Evidence of Insulin Resistance and Other Metabolic Alterations in Boys with Duchenne or Becker Muscular Dystrophy. Int J Endocrinol 2015;2015:1–8.

17. Rodríguez-Cruz M, Cruz-Guzmán OR, Escobar RE, López-Alarcón M. Leptin and metabolic syndrome in patients with Duchenne/Becker muscular dystrophy. Acta Neurol Scand 2016;133:253–260.

18. Stapleton DI, Lau X, Flores M, Trieu J, Gehrig SM, Chee A et al. Dysfunctional Muscle and Liver Glycogen Metabolism in mdx Dystrophic Mice. PLoS One 2014;9:e91514.

19. Amaral AR, Brunetto MA, Brólio MP, Cima DS, Miglino MA, Santos JPF et al. Abnormal

34 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

carbohydrate metabolism in a canine model for muscular dystrophy. J Nutr Sci 2017;6:e57.

20. Srivastava NK, Yadav R, Mukherjee S, Sinha N. Perturbation of muscle metabolism in patients with muscular dystrophy in early or acute phase of disease: In vitro, high resolution NMR spectroscopy based analysis. Clin Chim Acta 2017 doi:10.1016/j.cca.2017.12.036.

21. Tuazon MA, Henderson GC. Fatty acid profile of skeletal muscle phospholipid is altered in mdx mice and is predictive of disease markers. Metabolism 2012;61:801–811.

22. Paran CW, Zou K, Ferrara PJ, Song H, Turk J, Funai K. Lipogenesis mitigates dysregulated sarcoplasmic reticulum calcium uptake in muscular dystrophy. Biochim Biophys Acta - Mol Cell Biol Lipids 2015;1851:1530–1538.

23. Timpani CA, Hayes A, Rybalka E. Revisiting the dystrophin-ATP connection: How half a century of research still implicates mitochondrial dysfunction in Duchenne Muscular Dystrophy aetiology. Med Hypotheses 2015;85:1021–1033.

24. Cacchiarelli D, Legnini I, Martone J, Cazzella V, D’Amico A, Bertini E et al. miRNAs as serum biomarkers for Duchenne muscular dystrophy. EMBO Mol Med 2011;3:258–265.

25. Zaharieva IT, Calissano M, Scoto M, Preston M, Cirak S, Feng L et al. Dystromirs as serum biomarkers for monitoring the disease severity in duchenne muscular dystrophy. PLoS One 2013;8:e80263.

26. Vignier N, Amor F, Fogel P, Duvallet A, Poupiot J, Charrier S et al. Distinctive serum miRNA profile in mouse models of striated muscular pathologies. PLoS One 2013;8:e55281.

27. Jeanson-Leh L, Lameth J, Krimi S, Buisset J, Amor F, Le Guiner C et al. Serum Profiling Identifies Novel Muscle miRNA and Cardiomyopathy-Related miRNA Biomarkers in Golden Retriever Muscular Dystrophy Dogs and Duchenne Muscular Dystrophy Patients. Am J Pathol 2014;184:2885–98.

28. Li X, Li Y, Zhao L, Zhang D, Yao X, Zhang H et al. Circulating Muscle-specific miRNAs in Duchenne Muscular Dystrophy Patients. Mol Ther Nucleic Acids 2014;3:e177.

29. Hu J, Kong M, Ye Y, Hong S, Cheng L, Jiang L. Serum miR-206 and other muscle-specific microRNAs as non-invasive biomarkers for Duchenne muscular dystrophy. J Neurochem 2014;129:877–83.

30. Matsuzaka Y, Kishi S, Aoki Y, Komaki H, Oya Y, Takeda S-I et al. Three novel serum biomarkers, miR-1, miR-133a, and miR-206 for Limb-girdle muscular dystrophy, Facioscapulohumeral muscular dystrophy, and Becker muscular dystrophy. Environ Health Prev Med 2014;19:452–8.

31. Llano-Diez M, Ortez CI, Gay JA, Álvarez-Cabado L, Jou C, Medina J et al. Digital PCR quantification of miR-30c and miR-181a as serum biomarkers for Duchenne muscular dystrophy. Neuromuscul Disord 2017;27:15–23.

32. Mizuno H, Nakamura A, Aoki Y, Ito N, Kishi S, Yamamoto K et al. Identification of muscle- specific microRNAs in serum of muscular dystrophy animal models: promising novel blood- based markers for muscular dystrophy. PLoS One 2011;6:e18388.

33. Roberts TC, Blomberg KE, McClorey G, Andaloussi SE, Godfrey C, Betts C et al. Expression Analysis in Multiple Muscle Groups and Serum Reveals Complexity in the MicroRNA Transcriptome of the mdx Mouse with Implications for Therapy. Mol Ther Nucleic Acids 2012;1:e39.

35 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

34. Israeli D, Poupiot J, Amor F, Charton K, Lostal W, Jeanson-Leh L et al. Circulating miRNAs are generic and versatile therapeutic monitoring biomarkers in muscular dystrophies. Sci Rep 2016;6:28097.

35. Perry MM, Muntoni F. Noncoding RNAs and Duchenne muscular dystrophy. Epigenomics 2016;8:1527–1537.

36. Coenen-Stass AML, Wood MJA, Roberts TC. Biomarker Potential of Extracellular miRNAs in Duchenne Muscular Dystrophy. Trends Mol Med 2017;23:989–1001.

37. Whitehead NP, Kim MJ, Bible KL, Adams ME, Froehner SC. A new therapeutic effect of simvastatin revealed by functional improvement in muscular dystrophy. Proc Natl Acad Sci 2015;112:12864–12869.

38. Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N et al. Haemolysis during sample preparation alters microRNA content of plasma. PLoS One 2011;6:e24145.

39. Vigneault F, Ter-Ovanesyan D, Alon S, Eminaga S, C Christodoulou D, Seidman JG et al. High-throughput multiplex sequencing of miRNA. Curr Protoc Hum Genet 2012;Chapter 11:Unit 11.12.1-10.

40. Pruitt KD, Tatusova T, Brown GR, Maglott DR. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 2012;40:D130-5.

41. Sai Lakshmi S, Agrawal S. piRNABank: a web resource on classified and clustered Piwi- interacting RNAs. Nucleic Acids Res 2008;36:D173-7.

42. Chan PP, Lowe TM. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res 2009;37:D93-7.

43. Hackenberg M, Rodríguez-Ezpeleta N, Aransay AM, Rodriguez-Ezpeleta N, Aransay AM. miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments. Nucleic Acids Res 2011;39:W132-8.

44. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014;42:D68–D73.

45. Anders S, McCarthy DJ, Chen Y, Okoniewski M, Smyth GK, Huber W et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat Protoc 2013;8:1765–1786.

46. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. J R Stat Soc 1995;57:289–300.

47. Vlachos IS, Zagganas K, Paraskevopoulou MD, Georgakilas G, Karagkouni D, Vergoulis T et al. DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res 2015;43:W460–W466.

48. Hinske LC, Franca GS, Torres HAM, Ohara DT, Lopes-Ramos CM, Heyn J et al. miRIAD-- integrating microRNA inter- and intragenic data. Database 2014;2014:bau099–bau099.

49. Yu G, He QY. ReactomePA: An R/Bioconductor package for reactome pathway analysis and visualization. Mol Biosyst 2016;12:477–479.

50. Motohashi N, Alexander MS, Shimizu-Motohashi Y, Myers J a, Kawahara G, Kunkel LM.

36 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Regulation of IRS1/Akt insulin signaling by microRNA-128a during myogenesis. J Cell Sci 2013;126 Pt 12:2678–91.

51. Alexander MS, Kawahara G, Motohashi N, Casar JC, Eisenberg I, Myers J a et al. MicroRNA- 199a is induced in dystrophic muscle and affects WNT signaling, cell proliferation, and myogenic differentiation. Cell Death Differ 2013;1–15.

52. Chen X, Gao Y-Q, Zheng Y-Y, Wang W, Wang P, Liang J et al. The intragenic microRNA miR199A1 in the dynamin 2 gene contributes to the pathology of X-linked centronuclear myopathy. J Biol Chem 2020;jbc.RA119.010839.

53. Greco S, De Simone M, Colussi C, Zaccagnini G, Fasanaro P, Pescatori M et al. Common micro-RNA signature in skeletal muscle damage and regeneration induced by Duchenne muscular dystrophy and acute ischemia. Faseb J 2009;23:3335–3346.

54. Cheng N, Liu C, Li Y, Gao S, Han Y-C, Wang X et al. MicroRNA-223-3p promotes skeletal muscle regeneration by regulating inflammation in mice. J Biol Chem 2020 doi:10.1074/jbc.RA119.012263.

55. Alexander MS, Casar JC, Motohashi N, Myers JA, Eisenberg I, Gonzalez RT et al. Regulation of DMD pathology by an ankyrin-encoded miRNA. Skelet Muscle 2011;1:27.

56. Small EM, O’Rourke JR, Moresi V, Sutherland LB, McAnally J, Gerard RD et al. Regulation of PI3-kinase/Akt signaling by muscle-enriched microRNA-486. Proc Natl Acad Sci U S A 2010;107:4218–4223.

57. Li J, Chan MC, Yu Y, Bei Y, Chen P, Zhou Q et al. miR-29b contributes to multiple types of muscle atrophy. Nat Commun 2017;8:15201.

58. Guess MG, Barthel KKB, Harrison BC, Leinwand LA. miR-30 Family microRNAs Regulate Myogenic Differentiation and Provide Negative Feedback on the microRNA Pathway. PLoS One 2015;10:e0118229.

59. Baskerville S, Bartel DP. Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA 2005;11:241–7.

60. Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A. Identification of mammalian microRNA host genes and transcription units. Genome Res 2004;14:1902–10.

61. Hinske L, Galante PA, Kuo WP, Ohno-Machado L. A potential role for intragenic miRNAs on their hosts’ interactome. BMC Genomics 2010;11:533.

62. Zhu Y, Xie J, Sun H. Three miRNAs cooperate with host genes involved in human cardiovascular disease. Hum Genomics 2019;13:40.

63. Boivin V, Deschamps-Francoeur G, Scott MS. Protein coding genes as hosts for noncoding RNA expression. Semin. Cell Dev. Biol. 2018;75:3–12.

64. Lutter D, Marr C, Krumsiek J, Lang EW, Theis FJ. Intronic microRNAs support their host genes by mediating synergistic and antagonistic regulatory effects. BMC Genomics 2010;11:224.

65. Schiaffino S, Mammucari C. Regulation of skeletal muscle growth by the IGF1-Akt/PKB pathway: insights from genetic models. Skelet Muscle 2011;1:4.

66. Alexander MS, Casar JC, Motohashi N, Vieira NM, Eisenberg I, Marshall JL et al. MicroRNA-

37 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

486–dependent modulation of DOCK3/PTEN/AKT signaling pathways improves muscular dystrophy–associated symptoms. J Clin Invest 2014;124:2651–2667.

67. Monici MC, Aguennouz M, Mazzeo A, Messina C, Vita G. Activation of nuclear factor- kappaB in inflammatory myopathies and Duchenne muscular dystrophy. Neurology 2003;60:993–7.

68. Acharyya S, Villalta SA, Bakkar N, Bupha-Intr T, Janssen PML, Carathers M et al. Interplay of IKK/NF-κB signaling in macrophages and myofibers promotes muscle degeneration in Duchenne muscular dystrophy. J Clin Invest 2007;117:889–901.

69. Hammers DW, Sleeper MM, Forbes SC, Coker CC, Jirousek MR, Zimmer M et al. Disease- modifying effects of orally bioavailable NF-κB inhibitors in dystrophin-deficient muscle. JCI Insight 2016;1:e90341.

70. Uezumi A, Fukada S, Yamamoto N, Ikemoto-Uezumi M, Nakatani M, Morita M et al. Identification and characterization of PDGFRα+ mesenchymal progenitors in human skeletal muscle. Cell Death Dis 2014;5:e1186.

71. Mozzetta C, Consalvi S, Saccone V, Tierney M, Diamantini A, Mitchell KJ et al. Fibroadipogenic progenitors mediate the ability of HDAC inhibitors to promote regeneration in dystrophic muscles of young, but not old Mdx mice. EMBO Mol Med 2013;5:626–639.

72. Schwörer S, Becker F, Feller C, Baig AH, Köber U, Henze H et al. Epigenetic stress responses induce muscle stem-cell ageing by Hoxa9 developmental signals. Nature 2016;540:428–432.

73. Baskin KK, Winders BR, Olson EN. Muscle as a “Mediator” of Systemic Metabolism. Cell Metab 2015;21:237–248.

74. D’Uva G, Aharonov A, Lauriola M, Kain D, Yahalom-Ronen Y, Carvalho S et al. ERBB2 triggers mammalian heart regeneration by promoting cardiomyocyte dedifferentiation and proliferation. Nat Cell Biol 2015;17:627–638.

75. Wang YX, Feige P, Brun CE, Hekmatnejad B, Dumont NA, Renaud J-M et al. EGFR-Aurka Signaling Rescues Polarity and Regeneration Defects in Dystrophin-Deficient Muscle Stem Cells by Increasing Asymmetric Divisions. Cell Stem Cell 2019;24:419-432.e6.

76. Dorchies OM, Reutenauer-Patte J, Dahmane E, Ismail HM, Petermann O, Patthey-Vuadens O et al. The Anticancer Drug Tamoxifen Counteracts the Pathology in a Mouse Model of Duchenne Muscular Dystrophy. Am J Pathol 2013;182:485–504.

77. Burks TN, Cohn RD, Gordon K, Blobe G, Guo X, Wang X et al. Role of TGF-β signaling in inherited and acquired myopathies. Skelet Muscle 2011;1:19.

78. Brown MS, Goldstein JL. The SREBP pathway: Regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor. Cell. 1997;89:331–340.

79. Yang T, Espenshade PJ, Wright ME, Yabe D, Gong Y, Aebersold R et al. Crucial step in cholesterol homeostasis: sterols promote binding of SCAP to INSIG-1, a membrane protein that facilitates retention of SREBPs in ER. Cell 2002;110:489–500.

80. Jeon T-I, Osborne TF. SREBPs: metabolic integrators in physiology and metabolism. Trends Endocrinol Metab 2012;23:65–72.

81. Shimano H, Sato R. SREBP-regulated lipid metabolism: convergent physiology — divergent pathophysiology. Nat Rev Endocrinol 2017;13:710–730.

38 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

82. Kim MJ, Bible KL, Regnier M, Adams ME, Froehner SC, Whitehead NP. Simvastatin provides long‐term improvement of left ventricular function and prevents cardiac fibrosis in muscular dystrophy. Physiol Rep 2019;7:e14018.

83. Rouillon J, Poupiot J, Zocevic A, Amor F, Léger T, Garcia C et al. Serum proteomic profiling reveals fragments of MYOM3 as potential biomarkers for monitoring the outcome of therapeutic interventions in muscular dystrophies. Hum Mol Genet 2015;24:4916–32.

84. Maxfield FR, Wüstner D. Analysis of Cholesterol Trafficking with Fluorescent Probes. In: Methods in cell biology. 2012. pp. 367–393.

85. Sanson M, Hog Vu A, Massourides E, Bourg N, Suel L, Amor F et al. miR-379 links glucocorticoid treatment with mitochondrial response in Duchenne muscular dystrophy. Sci Rep 2020;10:9139.

86. Farina FM, Hall IF, Serio S, Zani SM, Climent M, Salvarani N et al. miR-128-3p Is a Novel Regulator of Vascular Smooth Muscle Cell Phenotypic Switch and Vascular Diseases. Circ Res 2020;126.

87. Zanotti S, Gibertini S, Curcio M, Savadori P, Pasanisi B, Morandi L et al. Opposing roles of miR-21 and miR-29 in the progression of fibrosis in Duchenne muscular dystrophy. Biochim Biophys Acta 2015;1852:1451–64.

88. Trifunov S, Natera-de Benito D, Exposito Escudero JM, Ortez C, Medina J, Cuadras D et al. Longitudinal Study of Three microRNAs in Duchenne Muscular Dystrophy and Becker Muscular Dystrophy. Front Neurol 2020;11:304.

89. Robertson KA, Hsieh WY, Forster T, Blanc M, Lu H, Crick PJ et al. An Interferon Regulated MicroRNA Provides Broad Cell-Intrinsic Antiviral Immunity through Multihit Host-Directed Targeting of the Sterol Pathway. PLoS Biol 2016;14:e1002364.

90. Yang M, Liu W, Pellicane C, Sahyoun C, Joseph BK, Gallo-Ebert C et al. Identification of miR-185 as a regulator of de novo cholesterol biosynthesis and low density lipoprotein uptake. J Lipid Res 2014;55:226–238.

91. Li X, Chen YT, Josson S, Mukhopadhyay NK, Kim J, Freeman MR et al. MicroRNA-185 and 342 Inhibit Tumorigenicity and Induce Apoptosis through Blockade of the SREBP Metabolic Pathway in Prostate Cancer Cells. PLoS One 2013;8:e70987.

92. Carmen L, Maria V, Morales-Medina JC, Vallelunga A, Palmieri B, Iannitti T. Role of proteoglycans and glycosaminoglycans in Duchenne muscular dystrophy. Glycobiology 2019;29:110–123.

93. Fallon JR, McNally EM. Non-Glycanated Biglycan and LTBP4: Leveraging the extracellular matrix for Duchenne Muscular Dystrophy therapeutics. Matrix Biol. 2018;68–69:616–627.

94. Burkin DJ, Wallace GQ, Nicol KJ, Kaufman DJ, Kaufman SJ. Enhanced expression of the α7β1 integrin reduces muscular dystrophy and restores viability in dystrophic mice. J Cell Biol 2001;152:1207–1218.

95. Vita GL, Polito F, Oteri R, Arrigo R, Ciranni AM, Musumeci O et al. Hippo signaling pathway is altered in Duchenne muscular dystrophy. PLoS One 2018;13:e0205514.

96. Kim MH, Kay DI, Rudra RT, Chen BM, Hsu N, Izumiya Y et al. Myogenic Akt signaling attenuates muscular degeneration, promotes myofiber regeneration and improves muscle function in dystrophin-deficient mdx mice. Hum Mol Genet 2011;20:1324–38.

39 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

97. Maani N, Sabha N, Rezai K, Ramani A, Groom L, Eltayeb N et al. Tamoxifen therapy in a murine model of myotubular myopathy. Nat Commun 2018;9:4849.

98. Parolo S, Marchetti L, Lauria M, Misselbeck K, Scott-Boyer M-P, Caberlotto L et al. Combined use of protein biomarkers and network analysis unveils deregulated regulatory circuits in Duchenne muscular dystrophy. PLoS One 2018;13:e0194225.

99. Handschin C, Kobayashi YM, Chin S, Seale P, Campbell KP, Spiegelman BM. PGC-1α regulates the neuromuscular junction program and ameliorates Duchenne muscular dystrophy. Genes Dev 2007;21:770–783.

100. Touboul D, Piednoël H, Voisin V, De La Porte S, Brunelle A, Halgand F et al. Changes in Phospholipid Composition within the Dystrophic Muscle by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry and Mass Spectrometry Imaging. Eur J Mass Spectrom 2004;10:657–664.

101. Kunze D, Reichmann G, Egger E, Olthoff D, Döhler K. Fatty Acid Pattern of Lipids in Normal and Dystrophic Human Muscle. Eur J Clin Invest 1975;5:471–475.

102. Ionasescu V, Monaco L, Sandra A, Ionasescu R, Burmeister L, Deprosse C et al. Alterations in lipid incorporation in Duchenne muscular dystrophy. Studies of fresh and cultured muscle. J Neurol Sci 1981;50:249–257.

103. Hughes BP. Lipid changes in Duchenne muscular dystrophy. J Neurol Neurosurg Psychiatry 1972;35:658–663.

104. Chen R, Runswick MJ, Carroll J, Fearnley IM, Walker JE. Association of two proteolipids of unknown function with ATP synthase from bovine heart mitochondria. FEBS Lett 2007;581:3145–3148.

105. Carroll JE, Villadiego A, Brooke MH. Increased long chain acyl CoA in duchenne muscular dystrophy. Neurology 1983;33:1507–1510.

106. Nishio H, Wada H, Matsuo T, Horikawa H, Takahashi K, Nakajima T et al. Glucose, free fatty acid and ketone body metabolism in Duchenne muscular dystrophy. Brain Dev 1990;12:390– 402.

107. Gehrig SM, van der Poel C, Sayer T a, Schertzer JD, Henstridge DC, Church JE et al. Hsp72 preserves muscle function and slows progression of severe muscular dystrophy. Nature 2012;484:394–8.

108. Momose M, Iguchi N, Imamura K, Usui H, Ueda T, Miyamoto K et al. Depressed myocardial fatty acid metabolism in patients with muscular dystrophy. Neuromuscul Disord 2001;11:464– 469.

109. Piperi C, Papapanagiotou A, Kalofoutis C, Zisaki K, Michalaki V, Tziraki A et al. Altered long chain fatty acids composition in Duchenne muscular dystrophy erythrocytes. In Vivo (Brooklyn) 2004;18:799–802.

110. Strakova J, Kamdar F, Kulhanek D, Razzoli M, Garry DJ, Ervasti JM et al. Integrative effects of dystrophin loss on metabolic function of the mdx mouse. Sci Rep 2018;8.

111. Lee‐McMullen B, Chrzanowski SM, Vohra R, Forbes SC, Vandenborne K, Edison AS et al. Age‐dependent changes in metabolite profile and lipid saturation in dystrophic mice. NMR Biomed 2019;32:e4075.

40 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

112. Milad N, White Z, Tehrani AY, Sellers S, Rossi FMV, Bernatchez P. Increased plasma lipid levels exacerbate muscle pathology in the mdx mouse model of Duchenne muscular dystrophy. Skelet Muscle 2017;7:19.

113. Steen MS, Adams ME, Tesch Y, Froehner SC. Amelioration of muscular dystrophy by transgenic expression of niemann-pick C1. Mol Biol Cell 2009;20:146–152.

41 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Tables

Table 1: Network analysis of host genes for dysregulated miRNAs

A pathway analysis was used for the construction of gene networks, using the core analysis of Ingenuity® Pathway Analysis (IPA) with a threshold of 70 molecule / network. The constructed networks are composed of host-genes for dysregulated miRNAs (p<0.05) in the plasma of the 4-12 years old DMD patients. The P value denotes the “probability” of the coincidental construction of a network out of its components. The Focus molecule (3rd column) denotes the number of the network’s principal molecules, and the identity of the most connected molecule of each network is provided in the 4th column. The 4rth network was obtained by the merging all networks function of IPA algorithm.

Network Score Number Most connected Focus molecule Molecules 1 Lipid Metabolism, 1 e-64 31 SREBF1 Small Molecule Biochemistry Molecular Transport 2 Lipid Metabolism, 1 e-41 22 Beta estradiol Small Molecule Biochemistry, (Estrogen) Molecular Transport 3 Lipid Metabolism, 1 e-32 18 HNFA4 Small Molecule Biochemistry, Dermatological Diseases and Conditions

4 Lipid Metabolism SREBF1 (Merged 1-3) Molecular transport

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Cholesterol metabolism is a potential therapeutic target in

Duchenne Muscular Dystrophy

F. Amor1,2*, A. Vu Hong1,2*, G. Corre1,2, M. Sanson1,2, L. Suel1,2, S. Blaie1, L. Servais3, T. Voit4,

I. Richard1,2 and D. Israeli1,2

SUPPLEMENTAL DATA

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Supplemental table 1.

Cohort’s medical center composition

City medical center DMD Control principal investigator 1 Liege CHR Citadelle 22 24 Dr Laurent SERVAIS 2 Gent UZ Ghent 13 4 Dr Nicolas DECONINCK 3 Bucarest Al Obregia Hospital 16 45 Dr Dana CRAIU 4 Paris AIM 22 17 Dr Laurent SERVAIS 5 Amiens Hôpital Nord 3 9 Dr Anne Gaëlle LE MOING 6 Rennes Hôpital Pontchaillou 2 0 Dr Hélène RAUSCENT 7 Lille Hôpital Roger Salengro 3 18 Dr Jean Marie CUISSET 8 Toulouse Hôpital des Enfants CHU Purpan 10 0 Dr Claude CANCES 9 Nantes CHU Hôtel Dieu 7 5 Pr Yann PEREON 10 Garches Hôpital Raymond-Poincaré 2 1 Dr Brigitte ESTOURNET Total 100 123

44

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Supplemental table 2

Clinical features of the DMD patients. Glucocorticoid treated (T) patients are in blue. Untreated patients (UT) are in black. (*) from Martinique; (**) Mother from the Reunion island and Martinique, Father Caucasian, (***) Mother from Brazil, Father Caucasian. NA: not available. ND: not done.

HTS ID Age of Age onset of ability Cardiac first Loss of to walk ejection Age Weight Height Gene mutation confirming symptoms ability lost fraction Group Country Ethnic origin (Years) (kg) (cm) diagnosis (years) to walk (years) Walton scale Brooke scale CPK (EF) D1 4-8 UT France Caucasian 4 16.8 98 Del exons 53 - 54 3 No . 1 1 9860 65 D2 4-8 UT Belgium Caucasian 4 16.9 102 Del exons 46 - 48 2 No . 1 1 22158 ND D3 4-8 UT France Asian 4 19.1 107 Del exon 51 3 No . 3 1 20391 ND D4 4-8 UT France Caucasian 4 16.9 108 Del T in exon 74 2 No . 2 1 16067 65 D5 4-8 UT France Caucasian 4 17.2 107 Del exons 46 - 48 3 No . 2 1 18408 ND D6 4-8 UT France Caucasian 6 23.8 119 Point mutation exon 23 2 No . 3 1 1392 ND D7 4-8 UT Belgium African 7 18 110 Splicing mutation exon 67 <1 No . 3 1 5597 65 D8 4-8 UT Belgium Caucasian 7 23.2 116 Del exons 48 - 54 3 No . 1 1 14002 ND D9 4-8 UT Belgium Caucasian 7 21.5 122 Del exons 3 - 7 NA No . 3 1 5997 67 D10 4-8 T Belgium Caucasian 4 17.2 105 Del exons 46 - 48 2 No . 1 1 14157 71 D11 4-8 T France Caucasian 4 17.5 101 Del exons 53 - 54 3 No . 4 1 17808 ND D12 4-8 T France Asian 4 20.5 109 Del exon 51 3 No . 1 18924 ND D13 4-8 T France Caucasian 4 17.3 109 Del exons 46 - 48 3 No . 3 1 17077 ND D14 4-8 T France Caucasian 4 18.7 111 Del T in exon 74 2 No . 1 1 29539 ND D15 4-8 T France Caucasian 6 26.9 122 Point mutation exon 23 2 No . 1 1 17334 ND D16 4-8 T Belgium African 7 19 115 Splicing mutation exon 67 <1 No . 3 1 5877 ND D17 4-8 T Belgium Caucasian 5 16.8 108 Del exons 51 - 55 3 No . 1 1 23349 ND D18 4-8 T Belgium Caucasian 7 23 122 Del exons 3 - 7 NA No . 1 1 7887 65 D19 8-12 UT France Caucasian 8 23 116 Del exon 50 1 No . 3 1 14115 ND D20 8-12 UT France Caucasian 8 35.6 134 Dup exon 17 2 Yes 8 7 2 5763 ND D21 8-12 UT France Caucasian 8 34 116 Stop exon 26 2 Yes 7 6 5 3031 ND D22 8-12 UT Romania Caucasian 8 30 132 Del exon 45 4 No . 3 1 15324 ND D23 8-12 UT Belgium Caucasian 9 24 127 Del 4 bp in exon 62 3 No . 3 1 12055 65 D24 8-12 UT France Caucasian 9 26 122 Stop exon 2 4 No . 4 1 14211 68

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D25 8-12 UT Romania Caucasian 10 47 140 Del exon 45 3 No . 3 2 10407 60 D26 8-12 UT France African 10 27 128 Stop exon 34 4 No . 3 1 12544 63 D27 8-12 UT France Caucasian 10 30 132 Dup exon 52 - 54 3 Yes 9 6 6 3796 ND D28 8-12 T France Caucasian 8 25.4 119 Dup exon 13 - 17 6 No . 3 1 12800 64 D29 8-12 T France Caucasian 8 24.6 120 Del exons 48-50 2 No . 3 1 24120 ND D30 8-12 T France Mixed (*) 8 29 129 Del 5 bp in exon 40 5 No . 3 1 11729 ND D31 8-12 T France Caucasian 9 27 127 Del 22 bp in exon 68 1 No . 3 1 NA ND D32 8-12 T Romania Caucasian 9 21 115 Del exons 8 - 9 4 No . 4 1 9420 60 D33 8-12 T Romania Caucasian 10 23 124 Dup exons 5 - 6 2 No . 4 1 9950 65 D34 8-12 T Romania Caucasian 10 45 136 Del exon 50 6 No . 4 2 5985 ND D35 8-12 T Belgium Caucasian 11 36.5 134 Dup exons 3 - 9 3 No . 0 1 7001 ND D36 8-12 T France Mixed (**) 12 27.9 139 Dup exons 18 - 44 3 No . 4 1 NA 66 D37 12-20 UT Belgium Caucasian 12 48 156 Del exon 65 NA Yes 10 7 6 1687 68 D38 12-20 UT Belgium Caucasian 13 50 162 Del exons 48 - 50 4 Yes 10.5 7 4 1601 66 D39 12-20 UT France Caucasian 14 29 146 Del exon 30 4 Yes 10 5 5 3660 ND D40 12-20 UT Romania Caucasian 15 56 150 Del exons 3 - 42 3 Yes 9 5 5 3210 50 D41 12-20 UT France Caucasian 16 52 175 Del exons 45 - 50 2 Yes 8 5 5 658 40 D42 12-20 UT Belgium Caucasian 17 70 152 Del exons 5 - 7 1 Yes 10 6 5 1391 A D43 12-20 UT Belgium Caucasian 18 63 165 Dup exons 56 - 63 3 Yes 10 6 5 688 ND D44 12-20 UT France Caucasian 19 61 166 Del exons 45- 52 1 Yes 7 6 5 1435 ND D45 12-20 UT Belgium Caucasian 20 85 165 Del exon 22 4 Yes 9 5 5 1779 45 D46 12-20 T Romania Caucasian 12 38 146 Del exons 8 - 12 6 Yes 10 7 6 4138 65 D47 12-20 T France Mixed (***) 13 45 165 Dup exons 3 - 42 3 Yes 10 7 6 NA 63 D48 12-20 T Belgium Caucasian 13 60.8 170 Dup exons 8 - 11 3 Yes 9 7 6 1945 ND D49 12-20 T Belgium Caucasian 13 46.5 139 Del exons 46 - 52 3 Yes 12 4 1 6902 ND D50 12-20 T Romania Caucasian 13 45 133 Del exons 50 - 52 5 No . 4 6 1254 60 D51 12-20 T Romania Caucasian 13 34 132 Del exons 45 - 50 5 Yes 12 7 6 7547 ND D52 12-20 T Romania Caucasian 14 42.5 143 Del exons 45 - 50 6 No . 3 1 4249 ND D53 12-20 T France Caucasian 14 55.5 147 Dup G in exon 53 4 Yes 13 7 1 NA 55 D54 12-20 T Belgium Caucasian 17 49 160 Del exons 46 - 47 4 Yes 10 6 4 1159 ND

46 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 3

Up-regulated plasma miRNA 4-12 years old DMD versus control. MiRNA were ranked by FDR adjusted p-value. MiR categories include dystromiRs, heart-enriched miRNA (CardiomiR), miRNAs residing in the DLK1-Dio3 genomic locus, and Let-7 or miR-320 family members. MiRNAs of none of these categories were designed as unclassified.

FDR, FC Student p- miR-ID miR Category adjusted, p- DMD/control value value

1 miR-128-3p Unclassified 3.17 5.09E-11 7.59E-08 2 miR-199a-3p = miR-199b-3p Unclassified 3.22 1.09E-10 8.10E-08 3 miR-30e-3p Unclassified 2.93 2.40E-09 1.19E-06 4 miR-299-5p DLK1-DIO3 9.58 4.11E-09 1.53E-06 5 miR-1277-3p Unclassified 3.33 1.37E-08 3.41E-06 6 miR-369-5p DLK1-DIO3 5.70 1.21E-08 3.41E-06 7 let-7d-5p Let7 2.78 3.26E-08 6.16E-06 8 miR-598-3p Unclassified 3.40 3.30E-08 6.16E-06 9 miR-487b-5p DLK1-DIO3 5.34 1.11E-07 1.85E-05 10 miR-23a-3p Unclassified 2.54 2.42E-07 3.62E-05 11 miR-208b-3p CardiomiR 494.90 4.42E-07 5.99E-05 12 miR-487b-3p DLK1-DIO3 4.21 8.52E-07 1.06E-04 13 miR-499a-5p CardiomiR 101.27 1.38E-06 1.58E-04 14 miR-484 Unclassified 2.48 2.52E-06 2.51E-04 15 miR-139-5p Unclassified 2.08 3.98E-06 3.71E-04 16 let-7g-5p Let7 1.92 5.40E-06 4.03E-04 17 miR-106b-3p Unclassified 2.14 5.04E-06 4.03E-04 18 miR-208a-5p CardiomiR 218.94 5.41E-06 4.03E-04 19 miR-374b-3p Unclassified 3.36 4.78E-06 4.03E-04 20 miR-221-5p Unclassified 2.90 5.73E-06 4.07E-04 21 miR-103a-3p Unclassified 1.71 9.10E-06 5.57E-04 22 miR-22-5p Unclassified 4.26 8.68E-06 5.57E-04 23 miR-28-5p Unclassified 1.84 1.00E-05 5.57E-04 24 miR-323a-3p DLK1-DIO3 2.80 9.79E-06 5.57E-04 25 miR-208a-3p CardiomiR 199.19 1.34E-05 7.12E-04 26 miR-3120-3p Unclassified 2.51 1.82E-05 9.35E-04 27 miR-95-3p Unclassified 6.86 2.64E-05 1.27E-03 28 miR-206 DystromiR 211.52 2.74E-05 1.28E-03 29 miR-381-3p DLK1-DIO3 2.57 4.50E-05 2.04E-03 30 miR-32-3p Unclassified 3.07 6.84E-05 2.76E-03 31 miR-10a-3p Unclassified 3.31 7.28E-05 2.86E-03 32 miR-494-3p DLK1-DIO3 3.21 9.33E-05 3.57E-03 33 miR-340-3p Unclassified 4.69 1.06E-04 3.96E-03

47 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

34 let-7b-3p Let7 3.88 1.26E-04 4.60E-03 35 miR-889-3p DLK1-DIO3 2.70 1.65E-04 5.25E-03 36 miR-92b-3p Unclassified 2.38 1.92E-04 5.97E-03 37 miR-223-3p Unclassified 1.91 2.26E-04 6.89E-03 38 let-7d-3p Let7 2.18 2.77E-04 7.40E-03 39 miR-154-5p DLK1-DIO3 2.81 2.60E-04 7.40E-03 40 miR-374b-5p Unclassified 2.45 2.74E-04 7.40E-03 41 miR-409-5p DLK1-DIO3 2.08 2.61E-04 7.40E-03 42 miR-493-5p DLK1-DIO3 4.75 2.68E-04 7.40E-03 43 miR-98-5p Let7 2.32 3.09E-04 7.96E-03 44 miR-133a-3p DystromiR 6.60 3.27E-04 8.26E-03 45 miR-3613-5p Unclassified 3.87 3.55E-04 8.69E-03 46 miR-501-3p Unclassified 2.33 4.08E-04 9.14E-03 47 miR-532-5p Unclassified 2.16 4.01E-04 9.14E-03 48 miR-625-5p Unclassified 2.39 4.11E-04 9.14E-03 49 miR-10b-3p Unclassified 3.98 4.20E-04 9.21E-03 50 miR-1 DystromiR 14.56 5.88E-04 1.19E-02 51 miR-486-3p Unclassified 2.42 6.90E-04 1.37E-02 52 miR-16-5p Unclassified 2.21 9.54E-04 1.85E-02 53 let-7a-5p Let7 2.07 1.06E-03 1.95E-02 54 miR-23b-5p Unclassified 2.61 1.20E-03 2.14E-02 55 miR-556-5p Unclassified 1.83 1.20E-03 2.14E-02 56 miR-629-5p Unclassified 1.60 1.40E-03 2.40E-02 57 miR-30a-3p Unclassified 1.85 1.99E-03 3.22E-02 58 miR-1296-5p Unclassified 2.93 2.46E-03 3.81E-02 59 miR-183-5p Unclassified 2.03 4.30E-03 5.77E-02 60 miR-145-3p Unclassified 1.95 5.25E-03 6.69E-02 61 miR-132-3p Unclassified 1.70 5.91E-03 7.35E-02 62 miR-196b-5p Unclassified 2.12 6.27E-03 7.54E-02 63 miR-424-5p Unclassified 1.55 7.38E-03 8.68E-02 64 miR-1180-3p Unclassified 1.78 7.71E-03 8.84E-02

65 miR-543 DLK1-DIO3 1.90 8.11E-03 9.03E-02

48 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 4

Down-regulated plasma miRNA 4-12 years old DMD versus control. MiRNA were ranked by FDR adjusted p-value. MiR categories include the miR-320 family members and unclassified miRNAs.

miR-id miR-class FC DMD/ Student p- FDR, control value adjusted, p-value

1 miR-342-3p Unclassified -2.26 1.62E-04 5.25E-03 2 miR-320a miR-320 -4.35 2.94E-04 7.69E-03 3 miR-320b miR-320 -3.70 1.04E-03 1.95E-02 4 miR-320d miR-320 -6.07 1.60E-03 2.69E-02 5 miR-2110 Unclassified -2.40 1.95E-03 3.19E-02 6 miR-361-3p Unclassified -3.36 2.23E-03 3.51E-02 7 miR-320e miR-320 -12.69 2.47E-03 3.81E-02 8 miR-1290 Unclassified -69.06 2.89E-03 4.35E-02 9 miR-29b-3p Unclassified -3.64 3.05E-03 4.42E-02 10 miR-30e-5p Unclassified -1.85 3.68E-03 5.18E-02 11 miR-320c miR-320 -3.13 3.66E-03 5.18E-02 12 miR-769-5p Unclassified -2.52 4.26E-03 5.77E-02 13 miR-181c-3p Unclassified -2.78 5.16E-03 6.64E-02 14 miR-191-5p Unclassified -1.70 5.82E-03 7.30E-02 15 miR-185-5p Unclassified -3.67 6.18E-03 7.54E-02 16 miR-192-5p Unclassified -2.92 6.28E-03 7.54E-02 17 miR-29c-5p Unclassified -2.79 6.37E-03 7.54E-02 18 miR-425-5p Unclassified -1.62 6.34E-03 7.54E-02 19 miR-147b Unclassified -6.85 7.54E-03 8.78E-02 20 miR-3613-3p Unclassified -2.32 7.62E-03 8.81E-02 21 miR-362-5p Unclassified -7.44 7.85E-03 8.90E-02 22 miR-1307-3p Unclassified -2.38 8.71E-03 9.56E-02 23 miR-339-3p Unclassified -1.82 8.67E-03 9.56E-02 24 miR-142-3p Unclassified -2.48 9.13E-03 9.75E-02 25 miR-152-3p Unclassified -1.64 9.40E-03 9.88E-02

49 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 5: dysregulation of glucocorticoid responsive miRNAs in 4-12 years

old DMD patients

(5a). Eleven DMD dysregulated miRNAs were GC-responsive. MiRNAs are classified by order of p value in treated versus untreated DMD patients. All but miR-30e-5p and miR-425-5p were upregulated in DMD and reversed by the GC treatment toward normalization. In contrast, GC- treatment increased the downregulation in DMD of miR-30e-5p and miR-425-5p. Three miRNAs, miR-27-5p, miR-379-5p and let-7i-5p were no longer dysregulated after GC treatment. Similarly, miR-27-5p, miR-379-5p were not dysregulated in the group of all DMD patients versus healthy controls (p > 0.05 in red). FC= fold change, UT = untreated, Tr = Treated. (5b). Heat map of miRNA dysregulation. The relative levels of the miRNAs are increased from blue to red

UT DMD / Treated DMD Treated DMD miR-name All DMD /Healthy Healthy /Untreated DMD /Healthy

FC p value FC p value FC p value FC p value miR-223-3p 2,14 1,62E-05 -1,27 3,15E-03 1,68 5,06E-03 1,91 2,26E-04 miR-221-5p 3,55 1,45E-06 -1,57 3,19E-03 2,26 1,54E-03 2,90 5,73E-06 miR-27b-5p 1,52 3,04E-02 -1,47 8,03E-03 1,03 8,89E-01 1,27 2,04E-01 let-7d-5p 3,34 6,32E-06 -1,50 1,78E-02 2,22 1,69E-04 2,78 3,26E-08 miR-28-5p 2,08 3,94E-05 -1,31 3,28E-02 1,60 9,26E-04 1,84 1,00E-05 miR-30e-5p -1,44 1,07E-02 -1,27 3,55E-02 -2,10 1,44E-03 -1,85 3,68E-03 miR-139-5p 2,44 1,91E-04 -1,41 4,12E-02 1,73 2,36E-04 2,08 3,98E-06 let-7i-5p 1,52 2,61E-03 -1,25 4,36E-02 1,22 1,51E-01 1,37 1,13E-02 miR-1277- 3,89 3,70E-06 -1,40 4,68E-02 2,78 3,97E-05 3,33 1,37E-08 miR3p -379-5p 1,88 2,19E-02 -1,60 4,73E-02 1,18 5,62E-01 1,53 7,91E-02 miR-425-5p -1,44 2,33E-02 -1,28 4,93E-02 -1,84 2,40E-03 -1,62 6,34E-03

Supplemental 5b

50 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 6

Pathway enrichment analysis of target genes for dysregulated miRNAs, considering up and

down regulated miRNAs in the 4-12 years old DMD/control samples, (fdr < 0.1). Pathways

are classified according p values (FDR). Shown are the 20 most enriched pathways.

KEGG pathway p (fdr) No Genes No miRNA 1 Proteoglycans in cancer (hsa05205) 1.95E-11 114 58 Signaling pathways regulating 88 2 1.14E-10 54 pluripotency of stem cells (hsa04550) 3 ECM-receptor interaction (hsa04512) 1.87E-10 43 42 4 Morphine addiction (hsa05032) 4.29E-09 51 42 5 Fatty acid biosynthesis (hsa00061) 8.06E-09 5 12 6 GABAergic synapse (hsa04727) 6.16E-08 46 40 7 Hippo signaling pathway (hsa04390) 2.00E-07 85 50 8 Hedgehog signaling pathway (hsa04340) 1.91E-05 37 37 9 Axon guidance (hsa04360) 1.91E-05 71 49 10 Cocaine addiction (hsa05030) 2.08E-05 28 42 11 Gap junction (hsa04540) 2.08E-05 50 47 12 Prostate cancer (hsa05215) 2.08E-05 55 53 13 Focal adhesion (hsa04510) 2.08E-05 113 57 14 PI3K-Akt signaling pathway (hsa04151) 4.80E-05 170 64 15 Estrogen signaling pathway (hsa04915) 1.19E-04 53 55 16 Circadian entrainment (hsa04713) 1.39E-04 48 47 17 FoxO signaling pathway (hsa04068) 1.39E-04 75 52 18 ErbB signaling pathway (hsa04012) 1.54E-04 54 52 19 Pathways in cancer (hsa05200) 2.17E-04 191 58 Thyroid hormone signaling pathway 61 20 2.89E-04 55 (hsa04919)

51

bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 7

List of the host genes of the identified dysregulated intragenic miRNAs that are known to be dysregulated in DMD models and patients. Both 3’ (3p) and 5’ (5p) mature miRNA forms of a given pre-miRNA were considered, when both are dysregulated. The reference column refers to the study that described the dysregulation of the host gene.

Knowledge of dysregulation of miRNA Chrom. Gene Host gene the host gene in dmd models Reference name location symbol Function and patients

Up in dystrophin-knockdown mouse 1 miR-128-1-3p 2q21 R3HDM1 unknown myoblast Ghahramani Seno Down in dystrophin-knockdown mouse 2010 1* miR-128-2-3p 3p22 ARPP21/R3HDM3 unknown myoblast Higher in muscle biopsies of moderate 2 miR-1307-3p Mitochondrial complex 5 Pegoraro 2011 10q24 USMG5/DAPIT versus severe DMD patients. associated subunit Sanson 2020 3 miR-1307-5p Downregulated in dystrophic mdx muscle Heparan sulfate 4 miR-149-5p 2q37 GPC1 Up in DMD quadriceps biopsies Fadic R 2006 proteoglycan paternally imprinted 5 miR-335-3p 7q32 MEST mesoderm-specific miR-335 and MEST higher in mdx muscle Hiramuki 2015 transcript 6 ** miR-188-5p 7 ** miR-362-5p 8 ** miR-501-3p Xp11 CLCN5 Chloride ion channel Up in mdx muscle Mizbani 2016 9 ** miR-532-3p 10 ** miR-532-5p Nitric oxide synthase 1 11 miR-556-3p Ségalat 2005 adaptor protein, a 1q23 NOS1AP Up in mdx heart and skeletal muscle Treuer 2014 cytosolic peptide that 12 miR-556-5p binds nNOS

* The mature forms of miR-128-1-3p and miR-128-2-3p are identical and it is unknown which (or both) is dysregulated in the 4-12 DMD group. Of note, both host genes R3HDM1 and ARPP21/R3HDM3 are dysregulated in the referred dystrophic model.

** These dysregulated miRNAs are clustered together at Xp11 on an intron of the CLCN5 gene

52 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

References for Supplemental table 7

Fadic, R. et al. (2006) ‘Increase in decorin and biglycan in Duchenne muscular dystrophy: Role of fibroblasts as cell source of these proteoglycans in the disease’, Journal of Cellular and Molecular Medicine, 10(3), pp. 758–769. doi: 10.1111/j.1582-4934.2006.tb00435.x.

Ghahramani Seno, M. M. et al. (2010) ‘Transcriptomic analysis of dystrophin RNAi knockdown reveals a central role for dystrophin in muscle differentiation and contractile apparatus organization’, BMC Genomics, 11(1), p. 345. doi: 10.1186/1471-2164-11-345.

Hiramuki, Y. et al. (2015) ‘Mest but Not MiR-335 Affects Skeletal Muscle Growth and Regeneration’, PLOS ONE. Edited by V. Mouly, 10(6), p. e0130436. doi: 10.1371/journal.pone.0130436.

Mizbani, A. et al. (2016) ‘MicroRNA deep sequencing in two adult stem cell populations identifies miR-501 as a novel regulator of myosin heavy chain during muscle regeneration’, Development, 143(22), pp. 4137–4148. doi: 10.1242/dev.136051.

Pegoraro, E. et al. (2011) ‘SPP1 genotype is a determinant of disease severity in Duchenne muscular dystrophy’, Neurology, 76(3), pp. 219–226. doi: 10.1212/WNL.0b013e318207afeb.

Sanson, M. et al. (2020) ‘miR-379 links glucocorticoid treatment with mitochondrial response in Duchenne muscular dystrophy’, Scientific Reports. Sceientific Reports, 10(1), p. 9139. doi: 10.1038/s41598-020-66016-7.

Ségalat, L. et al. (2005) ‘CAPON expression in skeletal muscle is regulated by position, repair, NOS activity, and dystrophy’, Experimental Cell Research, 302(2), pp. 170–179. doi: 10.1016/j.yexcr.2004.09.007.

Treuer, A. V and Gonzalez, D. R. (2014) ‘NOS1AP modulates intracellular Ca(2+) in cardiac myocytes and is up-regulated in dystrophic cardiomyopathy.’, International journal of physiology, pathophysiology and pharmacology, 6(1), pp. 37–46. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24665357 (Accessed: 16 January 2017).

53 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental table 8

Dysregulated intragenic miRNA in DMD plasma and their related host-gene, considering all miRNAs embedded on the sense strand of introns and exons of protein coding genes. FC and p (student T test) values are of miR expression in the blood plasma of 4-12 old DMD patients versus healthy controls. When both 3p and 5p isoforms of the same pre-miRNA were dysregulated, their host-genes was considered only once, with the lower p value miR isoform. Host genes for a number of different miRNAs were considered only once, taking into account FC and P values of the miRNA with the lowest p value.

FC Gene FC Gene DMD/ symbol of DMD/ symbol of miR name control P value host gene miR name control P value host gene 1 let-7g-5p 1.92 5.40E-06 WDR82 37 miR-320e -12.69 2.47E-03 PRKD2 2 miR-103a-3p 1.71 9.10E-06 PANK3 38 miR-32-3p 3.07 6.84E-05 TMEM245 3 miR-106b-3p 2.14 5.04E-06 MCM7 39 miR-335-3p 2.04 2.51E-02 MEST 4 miR-107 1.82 2.55E-02 PANK1 40 miR-339-3p -1.82 8.67E-03 C7orf50 5 miR-10a-3p 3.31 7.28E-05 HOXB3 41 miR-33a-5p -5.28 2.06E-02 SREBF2 6 miR-10b-3p 3.98 4.20E-04 HOXD3 42 miR-33b-5p -13.52 5.19E-02 SREBF1 7 miR-1180-3p 1.78 7.71E-03 B9D1 43 miR-340-3p 4.69 1.06E-04 RNF130 8 miR-1228-5p -2.73 9.95E-03 LRP1 44 miR-342-3p -2.26 1.62E-04 EVL 9 miR-126-5p 1.67 2.39E-02 EGFL7 45 miR-361-3p -3.36 2.23E-03 CHM 10 miR-1277-3p 3.33 1.37E-08 WDR44 46 miR-3615 1.6 1.53E-02 SLC9A3R1 11 miR-128-1-3p 3.17 5.09E-11 R3HDM1 47 miR-3656 -9.52 2.03E-02 TRAPPC4 12 miR-128-2-3p 3.17 5.09E-11 ARPP21 48 miR-378a-5p 2.16 2.81E-02 PPARGC1B 13 miR-1290 -69.06 2.89E-03 ALDH4A1 49 miR-4532 -4.02 2.73E-02 NOP56 14 miR-1294 -3.03 1.40E-02 GALNT10 50 miR-484 2.48 2.52E-06 NDE1 15 miR-1296-5p 2.93 2.46E-03 JMJD1C 51 miR-486-3p 2.42 6.90E-04 ANK1 16 miR-1301-3p -1.77 2.18E-02 DNMT3A 52 miR-5010-5p -2.44 3.97E-02 ATP6V0A1 17 miR-1307-3p -2.38 8.71E-03 USMG5 53 miR-505-5p -1.78 2.87E-02 ATP11C 18 miR-139-5p 2.08 3.98E-06 PDE2A 54 miR-532-5p 2.16 4.01E-04 CLCN5 19 miR-140-3p -3.03 5.10E-02 WWP2 55 miR-548ax 1.78 1.31E-02 ARHGAP6 20 miR-147b -6.85 7.54E-03 C15orf48 56 miR-550a-5p -2.4 4.61E-02 ZNRF2 21 miR-148b-3p -3 3.71E-02 COPZ1 57 miR-556-3p 3.6 2.18E-03 NOS1AP 22 miR-149-5p 2.03 1.49E-02 GPC1 58 miR-590-3p 2.02 2.09E-02 EIF4H 23 miR-152-3p -1.64 9.40E-03 COPZ2 59 miR-598-3p 3.4 3.30E-08 XKR6 24 miR-1537-3p -2.86 1.61E-02 LYST 60 miR-625-5p 2.39 4.11E-04 FUT8 25 miR-15b-3p -2.08 2.21E-02 SMC4 61 miR-627-5p -2.69 1.57E-02 VPS39 26 miR-185-5p -3.67 6.18E-03 TANGO2 62 miR-628-5p 1.97 2.15E-02 CCPG1 27 miR-191-5p -1.7 5.82E-03 DALRD3 63 miR-629-5p 1.6 1.40E-03 TLE3 28 miR-196b-5p 2.12 6.27E-03 HOXA9 64 miR-6515-5p -2.3 4.15E-02 CALR 29 miR-2110 -2.4 1.95E-03 C10orf118 65 miR-652-3p 1.4 1.15E-02 TMEM164 30 miR-23b-5p 2.61 1.20E-03 C9orf3 66 miR-671-3p 1.61 4.23E-02 CHPF2 31 miR-26b-5p 1.4 3.52E-02 CTDSP1 67 miR-6852-5p -3.54 1.54E-02 TLN1 32 miR-28-5p 1.84 1.00E-05 LPP 68 miR-7-1-3p 1.62 4.53E-02 HNRNPK 33 miR-30e-3p 2.93 2.40E-09 NFYC 69 miR-7704 -5.46 2.40E-02 HOXD1 34 miR-3120-3p 2.51 1.82E-05 DNM3 70 miR-95-3p 6.86 2.64E-05 ABLIM2 35 miR-3200-5p -2.43 3.33E-02 OSBP2 71 miR-98-5p 2.32 3.09E-04 HUWE1 36 miR-320b -3.7 1.04E-03 NVL

54 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 1.

Venn diagram analysis of differentially expressed miRNAs in age and treatment stratified groups (a) Venn diagram of age-specific and age overlapping dysregulated miRNAs between DMD and healthy control samples and a table presentation of (a) is shown in (b). (c) Dysregulated miRNAs in the 4-20 years old. (d) Specific and overlapping dysregulated miRNAs in the 4-12 YO group between treated and untreated DMD versus healthy control. Thirty four and 49 miRNAs are dysregulated in the 4-8 and 8-12 years old group, respectively (a and b). No miRNA (fdr<0.1) is differentially-expressed between DMD and healthy control in the 12-20 YO group. When age groups are combined, 90 and 45 miRNAs are dysregulated in the 4-12 and 4-20 years old groups, respectively (a and b). Dysregulated miRNAs in the 4- 12 years old DMD patients included all but one (miR-122) of the dysregulated miRNAs in the 4-20 years old group (c). When looking only at the 4-12 years old group, the classification into GC-treated and untreated as compared to the healthy control samples reduced the number of dysregulated miRNAs from 90 to 77 (d).

a b 12-20 4-12 4-8 8-12 Up in Down Total DMD in DMD dysreg 4-8 YO 33 1 34 8-12 YO 48 1 49 12-20 YO 0 0 0 4-12 YO 65 25 90 4-20 YO 42 3 45

c d 4-12 YO DMD T + DMD UT 4-12 YO 4-12 YO DMD T 18 DMD UT 4-12 4-20

8 22 1 42 4 46 44 1

55 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 2.

Correlogram of plasma miRNA expression in DMD patients of the 4-12 years old age group

Expression correlations were analyzed among members of miRNA classes that were identified as dysregulated in the plasma in DMD patients. Dysregulated miRNA classes included the (a) MyomiRs, (b) CardiomiR, (c) Let-7 miRs, (d) miR-320 and (e) DLK1-DIO3 cluster miRNAs. Presented only significant Spearman correlations (r <0.05) whereas “X” represent absence of correlation. The elliptic shape and the relative intensity of the blue color are proportional to the level of positive correlation. Correlation r values are presented inside the elliptic shapes. The r values for the correlations in the DLK1-DIO3 miRNAs (e) are presented in (f).

a. Myo-miRs b. Cardio-miRs

c. Let-7 miR family d. miR-320 family

e. DLK1-DIO3 miRs f. DLK1-DIO3 miRs

miR- miR- miR- miR- miR- miR- miR- miR- miR- miR- miR- 154- 299- 323a 369- 381- 409- 487b 487b 493- 494- miR- 889- 5p 5p -3p 5p 3p 5p -3p -5p 5p 3p 543 3p miR-154-5p / 0,39 0,56 0,48 0,57 0,70 0,67 0,59 0,40 0,51 0,67 0,37 miR-299-5p / 0,54 0,62 0,59 0,43 0,59 0,50 0,54 0,50 0,42 0,61 miR-323a-3p / 0,70 0,59 0,36 0,75 0,52 0,70 0,61 0,67 0,77 miR-369-5p / 0,45 0,40 0,77 0,57 0,83 0,63 0,58 0,83 miR-381-3p / 0,50 0,50 0,46 0,36 0,37 0,44 0,46 miR-409-5p / 0,48 0,70 x x 0,53 x miR-487b-3p / 0,59 0,87 0,78 0,80 0,82 miR-487b-5p / 0,50 0,55 0,50 0,44 56 miR-493-5p / 0,78 0,61 0,89 miR-494-3p / 0,59 0,77 miR-543 / 0,57 miR-889-3p / bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 3

mCK and cholesterol in the serum of mdx mice treated by Simvastatin.

Serum cholesterol in a 7-week old (young adult) mdx and control mice treated by Simvastatin during three weeks

57 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 4

Filipin staining of free cholesterol in C2C12 cells

C2C2 myoblast were treated by the cholesterol trafficking inhibitor U18666A (Lu et al., 2015), without (middle) or with (right) 24 hours treatment by the cholesterol synthesis inhibitor, simvastatin. Nucleus were stained by Syto13. Accumulated free cholesterol was stained with filipin.

C2C12 C2C12 C2C12 1uM U18666A untreated 1uM U18666A 20uM Simvastatin

Syto13 Syto13 Syto13 Filipin Filipin

10µm 10µm 10µm

58 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 5

Confocal analysis of cholesterol expression in transversal sections of the Tibialis anterior and the Gastrocnemius muscles

Transversal sections of mdx and control healthy C57Bl/10 mice, Gastrocnemius (GA) and Tibialis Anterior (TA), were labeled by Cyto 13 (nucleus)Vu Hong_Supplemental in blue, and Filipins (cholesterol) Figure 5 in red. Mdx mice were untreated (center; mdx), or treated (right; mdx + Simva) by Simvastatin.

C57Bl/10 mdx mdx + Simv GA

25µm 25µm 25µm TA

25µm 25µm 25µm

Syto13 (Nucleus) Filipin

Confocal images of Filipin cholesterol in the TA and GA

59

bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 6

Confocal analysis of SRBP-2 expression in the mouse diaphragm

Transversal sections were immunostained with wheat germ agglutinin (WGA) in green, which stains the sarcolemma and the myonucleus membrane, with DAPI for nuclear staining, and with the anti-SREBP-2 antibody. White arrows indicating myonucleus in a central position in regenerated myofibres. Note that mdx nucleus are positive to all three colors (Pink), while only residual faint red dotes can be observed in the nucleus (blue) of the mdx treated by simvastatin, demonstrating the downregulation of nuclear SRBP-2.

C57Bl/10 mdx untreated mdx Simvastatin

No Centronucleation, Centronucleation with high Centronucleation with low (or occasional faint SREBP-2 SREBP-2 expression without) SREBP-2 expression expression

60 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.01.405910; this version posted December 2, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplemental figure 7

Approach for the biological interpretation of miRNA dysregulation, based on both target and host genes of the dysregulated miRNAs

Transcriptional adaptation in the diseased tissue is affecting both intergenic miRNAs (miRNA between genes, on the left), and intragenic miRNAs (miRNA inside genes, on the right). Intergenic miRNA are often co-transcribed with their host genes, cooperatively affecting downstream events. The functional link between miRNA to a host gene is thought to be strong, while the link between miRNA to their many predicted target gens is questionable. Dysregulation of intergenic miRNAs and their host genes provides information on upstream signaling in the disease, which causes transcriptional dysregulation, while the analysis of target genes may provide information on downstream events, which are the consequences of miRNA dysregulation.

Transcriptional adaptation in DMD

Dysregulated upstream signals

Intragenic miRNAs Intergenic miRNAs and their host genes

Exon Intron

miRNA’s host genes miRNA’s target genes Downstream effects Approximatively half of the All dysregulated miRNAs dysregulated miRNAs Many targets per miRNA One host gene per miRNA Specificity between miR-host co-regulation? miRNA and target gene? miR-host functional relation?

Target genes dysregulation Host genes dysregulation biological interpretation biological interpretation

Holistic interpretation of miRNA dysregulation, integrating both upstream signals and downstream effects miRNA between genes

miRNA inside host gene

miRNA target gene

miRNA host gene

miRNAs effects on target genes

Biological functions of predicted target gene Biological functions of confirmed host gene 61