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An updated C. elegans nuclear body muscle transcriptome for studies in muscle formation and function

Skeletal Muscle volume 13, Article number: 4 (2023) Cite this article

Abstract

The body muscle is an important tissue used in organisms for proper viability and locomotion. Although this tissue is generally well studied and characterized, and many pathways have been elucidated throughout the years, we still lack a comprehensive understanding of its transcriptome and how it controls muscle development and function. Here, we have updated a nuclear FACS sorting-based methodology to isolate and sequence a high-quality muscle transcriptome from Caenorhabditis elegans mixed-stage animals. We have identified 2848 muscle-specific protein-coding genes, including 78 transcription factors and 206 protein-coding genes containing an RNA binding domain. We studied their interaction network, performed a detailed promoter analysis, and identified novel muscle-specific cis-acting elements. We have also identified 16 high-quality muscle-specific miRNAs, studied their function in vivo using fluorochrome-based analyses, and developed a high-quality C. elegans miRNA interactome incorporating other muscle-specific datasets produced by our lab and others.

Our study expands our understanding of how muscle tissue functions in C. elegans and

in turn provides results that can in the future be applied to humans to study muscular-related diseases.

Introduction

The body muscle is an important tissue used in organisms for proper viability and locomotion. Muscle tissue’s structure, shape, and size change depending on its function and location. In humans, there are three main types of muscles: skeletal, smooth, and cardiac muscles, each responsible for a different form of contraction. Smooth and cardiac tissue contract involuntarily and are mainly located on the surface of organs. In contrast, skeletal muscles contract voluntarily.

The contractile unit of the muscle is the sarcomere. It is composed of at least thirty different proteins, of which the most abundant are myosin and actin, and is ultimately responsible for the contraction reaction leading to movement.

The model organism Caenorhabditis elegans is ideal for studying muscle development and morphogenesis due to its well-characterized transcriptome, fully mapped cell lineage, and the availability of several methods for genetic engineering.

The basic contractile unit of the muscle is the sarcomere, which is strongly conserved in its structure and function from C. elegans to mammals [1]. C. elegans sarcomeres are composed of myosin-containing thick filaments and actin-containing thin filaments, which are responsible for the contraction reaction leading to movement through the myosin heads pulling on the actin filaments [1]. A notable difference between the worm and vertebrates is the absence of the costamere and Z-disc, which in vertebrates anchors the actin filaments to the extracellular matrix. However, worms contain a structure called the dense body, which performs the same function [2]. In addition, over 200 proteins have been identified in C. elegans to be essential for the assembly, maintenance, and function of the sarcomere, many of which have human orthologs [3]. C. elegans is also a popular choice for studying disorders affecting humans, including muscular dystrophy [4,5,6], since many human disease genes, when introduced in worms phenocopy the symptoms [7,8,9,10].

C. elegans possess two large muscles: the pharynx and the body muscle. The pharynx is localized to the front of the animal and is responsible for food intake and the physical crushing [11]. It is composed of eight layers of non-striated muscles, surrounded by epithelial and neural tissue. The body muscle tissue comprises 95 striated muscle cells localized throughout the animal’s body and is functionally equivalent to the vertebrate skeletal muscles [1]. The contractile unit of the muscle is the sarcomere. It is composed of at least 30 different proteins, of which the most abundant are myosin and actin, and is ultimately responsible for the contraction reaction leading to movement. The sarcomere is highly conserved between nematodes and vertebrates and is composed of repeated units of a complex mesh of thin (I-band) and thick (A-band) filaments. The thin filaments are mainly composed of actin and anchor each sarcomere to the dense bodies and stabilize the entire structure. Conversely, the thick filaments are mostly made of myosin and are attached to the M-line and mediate tension generated during muscle contraction.

To properly develop and maintain functional body muscle tissue, gene expression must be strictly regulated. Our group and others in the past finely characterized the transcriptome of the C. elegans body muscle in the adult stage [12, 13] and during the embryonic development [14] and identified important regulatory mechanisms used by muscle cells to maintain their homeostasis.

In addition to the identification of important genes in this tissue, our group and others recently identified that tissue-specific alternative polyadenylation (APA), a poorly characterized mechanism that produces genes with different 3′untranslated region (3′UTR) isoforms, is an important regulatory mechanism used by cells to evade the negative regulation effected by microRNAs (miRNAs) [12, 13]. Still, the complexity and depth of this form of regulation are currently unknown. Regulation of gene expression also depends on miRNAs, which are short ncRNAs that target regulatory elements in 3′UTRs and repress gene expression in the cytoplasm [15, 16].

Since their discovery, several groups, including our lab, have shown that miRNAs influence gene regulation considerably [17]. The canonical miRNA biogenesis pathway begins with the transcription of pri-miRNAs in the nucleus, which fold into a hairpin structure [17]. The microprocessor complex cleaves the pri-miRNAs into a shorter stem-loop structure, the pre-miRNAs, which are then exported to the cytoplasm [17]. Once in the cytoplasm, the pre-miRNAs are cleaved into mature miRNAs, which associate with an Argonaute protein as part of the RNA-induced silencing complex (RISC) [17,18,19]. When loaded into the RISC, miRNAs bind to complementary target sequences within the 3′UTRs of genes to be repressed [17]. This implies that the 3′UTRs and their isoforms are integral for miRNA-based regulation.

Several strategies have been developed to sequence tissue-specific transcriptomes and miRNAs in C. elegans. These methodologies utilize either fluorescence-activated cell sorting (FACS) [14, 20, 21], immunoprecipitation [12, 13, 22], or RNA modification techniques [23]. Each of these approaches has unique advantages but also presents several caveats. Methodologies utilizing FACS allow for high purity and stringency when isolating RNA from cells or nuclei. These approaches rely on tissue-specific promoters to drive fluorochromes expression in cells or nuclei. After isolation, the fluorescent cells or nuclei may be readily sorted using FACS, then either sequenced [20, 21] or processed using microarrays [14].

Immunoprecipitation and antibody-based approaches have also been widely used to isolate the transcriptomes of specific tissues. This approach provides considerably high yields of RNA compared to FACS-based methods and is generally much more cost-efficient.

Brosnan et al. (2021) used an epitope labeled Argonaute protein expressed in specific C. elegans tissues to immunoprecipitate tissue-specific miRNA populations from the body muscle, intestine, and neurons [22].

Alberti et al. (2018) developed a methodology named “microRNAome by methylation-dependent sequencing (Mime-seq)” to identify tissue-specific miRNA populations from complex animals at the level of single-cell specificity [23]. The Mime-seq approach expresses a transgenic methyltransferase (Ath-HEN1) from Arabidopsis thaliana in an animal system, allowing the labeling of small RNAs. Mime-seq has been utilized in C. elegans and Drosophila and improved tissue-specific miRNA identification compared to previous immunoprecipitation-based methods [23].

Our group also used an immunoprecipitation-based approach to identify transcripts targeted by miRNAs within the C. elegans intestine, and body muscle [24] and observed transcripts were targeted differentially between the two tissues. While identifying the miRNA targets provided valuable information regarding tissue-specific gene regulation, a major limitation of this study was that we could not identify the specific miRNAs involved.

Here, we describe a novel nuclear FACS sorting-based approach named “Nuc-Seq,” to allow the isolation and sequencing of high-quality muscle transcriptomes and miRNA populations from C. elegans mixed-stage animals. Using this method, we have identified 2848 muscle-specific protein-coding genes, studied their interaction network, performed a detailed promoter analysis, and identified novel muscle-specific cis-acting elements. Using fluorochrome-based analyses in vivo, we developed a high-quality muscle miRNA interactome, which incorporates other muscle-specific datasets produced by our lab and others.

Results

Nuc-Seq: an updated approach to identify muscle-specific transcriptome

We wanted to improve the annotation of the body muscle transcriptome by developing a nuclear FACS-based strategy to isolate and sequence transcriptomes from C. elegans body muscle nuclei. We named this approach “Nuc-Seq.” Our final goal was to identify the body muscle transcriptome and its miRNA population expressed explicitly in this tissue.

To test the feasibility and optimize this approach, we first performed experiments using the C. elegans strain BN452, which ubiquitously expresses the mCherry fluorochrome fused to the histone H2B ortholog gene his-58 [25] (Fig. 1). We fractionated this worm strain using mechanical stress and separated two fractions: one cytoplasmic and one enriched with mCherry fluorescent nuclei (Fig. 1A, B and the “Materials and methods” section). Using a Western blot approach, we successfully tested these two fractions for the presence of known cytoplasmic and nuclear proteins (Fig. 1C). Importantly, these nuclei are stable over time with minimal degradation after several hours (Supplemental Fig. S1). We then performed the FACS sorting step and successfully isolated a large pool of mCherry-positive nuclei, with over 34% of all the events being mCherry-positive (Fig. 1D). We named this updated approach Nuclear Sequencing (Nuc-Seq).

Fig. 1
figure 1

Nuc-Seq schematics. A Transgenic  C. elegans strains expressing mCherry fluorochrome fused to histone H2B (his-58) are (1) homogenized and (2) subjected to several rounds of mechanical filtration. (3) The resultant nuclei are collected and (4) subjected to FACS sorting. After the sorting step, (5) the mCherry-positive nuclei are sorted directly into TRIzol, and the recovered RNA is sequenced. B Top panel: The strain BN452 ubiquitously expresses the mCherry::his-58 transgene. Bottom panel: mCherry-positive nuclei recovered after step 2 described in panel A (red: mCherry, blue: DAPI). C Western blot of nuclear and cytoplasmic fractions from BN452 worms blotted with α-RFP and α-GAPDH antibodies. D Left panel: nuclear FACS profile from N2 and BN452 nuclei. The BN452 sample is a positive control that represents all nuclei. The red dots show the sorted mCherry-positive population. Right panel: pie chart showing the percentage of BN452 sorted mCherry-positive nuclei

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With these results in our hands, we decided to move forward and limit the expression of the mCherry::his-58 cassette in the body muscle tissue. We cloned the his-58 gene, fused it to the mCherry fluorochrome, and then forced its expression only in the body muscle tissue using the promoter region of the C. elegans ortholog of the myosin heavy chain gene (myo-3) (Fig. 2A upper panel). As expected, the resultant transgenic worm restricts the expression of the mCherry fluorochrome in the body muscle nuclei (Fig. 2A lower panel), with fewer fluorescent nuclei compared to the experiments in the BN452 strain (Fig. 2A). We then performed the FACS sorting step (Fig. 2B). Although the number in sorted nuclei is significantly less than those obtained with the positive control BN452, we could still isolate a large population of mCherry-positive nuclei across from each of our replicates, which we then extracted and sequenced RNA from.

Fig. 2
figure 2

FACS sorting of body muscle nuclei: A Top panel: schematics of the construct used to isolate the body muscle transcriptome. The body muscle-specific promoter myo-3 drives the expression of the mCherry:::his-58 cassette. Bottom panel: bright field and fluorescent images of the resultant transgenic worm strain. B Nuclear FACS profile from N2 and myo3p::mCherry:::his-58::unc-54 3’UTR nuclei. The red dots show the sorted mCherry-positive population. Right panel: pie chart showing the percentage of sorted mCherry-positive and body muscle-specific nuclei

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The identification of the C. elegans body muscle transcriptome

We performed three sequencing reactions in duplicates (technical triplicates), two from our body muscle-expressing nuclei (body muscle enriched), and one from nuclei isolated from BN452 (negative controls) (a total of six samples). We obtained approximately 55M mappable reads for each sample (including our technical replicates). For most of the samples, we could map more than 97% of the total reads (Supplemental Fig. S2A). The results obtained with our biological replicates correlate well (Supplemental Fig. S2B-D).

We have identified 2848 protein-coding genes in the C. elegans body muscle tissue, corresponding to ~14% of all C. elegans protein-coding genes (20,362 protein-coding genes; WS250) (Fig. 3 and Supplemental Table S1). Using comparative data from Kim et al. [26], we identified human orthologs for 1872 worm genes (66%).

Fig. 3
figure 3

The C. elegans body muscle transcriptome. A Left panel: heat map showing the body muscle expression levels log(fpkm) of the 20,362 protein-coding genes in WS250. a, BN452 positive control; m, body muscle transcriptome sample. The body muscle-specific transcriptome is highlighted with a dashed yellow line. Right panel: several examples of expression levels in genes identified in (a) BN452 (ubiquitously expresses GFP and mCherry in all nuclei; positive control) and (m) body muscle-specific nuclear fractions. The vertical axes mark expression levels as the log(FPKM). The body muscle-specific genes unc-54, mlc-1, unc-27, and lev-11 were all detected in our study, while the intestine-specific elt-2 and the GABA neuron-specific unc-47 were not present. (a: median log(fpkm) of the “BN452 all nuclei” dataset; m: median fpkm values of set1 and set2 muscle datasets). B The C. elegans body muscle interactome. We clustered all 2848 protein-coding genes identified in this study using previously published protein-protein interaction data. Each gene is shown as a pink dot. Within this network, we identified six smaller, highly interconnected subnetworks shown in different colors. The genes in each subnetwork were independently subjected to GO term enrichment analyses shown with colored panels above each respective subnetwork

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While some of our top hits have not been characterized yet, as expected, we detected many known components of the sarcomere, such as many of the proteins that form the thick filaments (myosin heavy chain genes, myo-3, unc-54) and myosin light chain (mlc-1), and sarcomere organization and structural factors (unc-15, unc-45), as well as those that form the thin filaments, including several actin genes (act-1, act-3, act-4) and structural factors (unc-60, unc-73) (Fig. 3A and Supplemental Fig. S3). We also detect orthologs of the tropomyosin gene lev-11, which in resting sarcomere blocks myosin binding to actin, and other notable genes previously known to be in the C. elegans body muscle tissue (Fig. 3A and Supplemental Fig. S3).

Our dataset identified 78 transcription factors, including mdl-1, skn-1, xbp-1, mxl-3, all previously known to be expressed in the body muscle tissue, and 207 non-ribosomal proteins containing an RNA binding domain (Supplemental Table S1). Within this group, we identified K08D12.3, an ortholog of the human ZNF9, which is mutated in myotonic dystrophy type 2, mbl-1, a MUSCLEBLIND-type of mRNA splicing regulator required for muscle dense body organization, locomotion, and vulval morphogenesis, and etr-1, an ortholog of the human CUG-binding protein CUGBP1, which is required for embryonic muscle development and has been implicated in myotonic dystrophy (Supplemental Table S1).

Next, we studied the interaction network of the identified C. elegans muscle genes. We used the STRING database [26] for this analysis, which contains known C. elegans predicted and functional protein-protein interactions. Using our top 2000 genes, we produced a large network containing 1995 nodes and 31,449 edges (Fig. 3B). The genes are highly interconnected (Fig. 3B), producing six large distinguished sub-networks involved in body muscle formation, maintenance, protein production, and energy storage (Fig. 3B). A GO term analysis shows functional enrichments for actin filament binding, skeletal muscle myosin filament assembly, mitochondrial functions, and ATP biosynthesis: all processes functioning in the body muscle tissue (Fig. 3B). Seventy-one percent of all genes identified in this study are protein-coding genes, followed by ncRNAs, which are also very abundant (Fig. 4A; top left). The largest functional category is receptors, followed by RNA-binding proteins, cytoskeleton, and various enzymes (Fig. 4A; top right). Based on WormBase data, 31% of the genes identified in this study show unique muscle tissue expression (Fig. 4B; top left). Almost all genes are present throughout development (Fig. 4B; top right). Most genes show cytoplasmic localization, and 36% of them are expressed in operons (Fig. 4B; bottom panels).

Fig. 4
figure 4

Comparative data analysis. A Left: pie chart showing the breakout of the RNA types identified in this study. Right: out of 2848 protein-coding genes, 1044 genes have been previously assigned to specific biological functions and shown as a pie chart. B Top left: pie chart showing the tissue localization of the genes identified in this study. Top right: gene expression profile in different C. elegans developmental stages. Bottom right: cellular localization. Bottom left: the number of genes identified in this study that are in operons. The comparative data used to prepare this analysis was extracted from WormBase

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The C. elegans body muscle PROMOTERome

Next, we sought to study the core promoters of the genes identified in this study to pinpoint potential motifs transactivated by muscle-specific transcription factors. We have extracted 500 nucleotides upstream and 100 nucleotides downstream of the transcription start site of the 2848 genes we mapped in this tissue and plotted their nucleotide distribution (Supplemental Fig. S4). We detected an enrichment of thymidine nucleotides just upstream of the transcription start site in these genes (Supplemental Fig. S4 Panel A). Repeated polythymine motifs (T-blocks) have been previously reported in C. elegans core promoters, and their presence correlates with gene expression levels [27]. We also performed a motif enrichment analysis in these core promoters and identified several novel cis-acting elements (Supplemental Fig. S4 Panel B and Supplemental Table S2). We mapped 12 members of the human Krueppel-like transcription factors family (KLF), which are important regulators of gene expression in vertebrate development and are involved in muscle health and disease [28]; Myogenin (MYOG), a muscle-specific basic-helix-loop-helix (bHLH) transcription factor involved in muscle development, myogenesis, and repair [29]; and NHLH1, a helix-loop-helix transcription factor that is involved in growth and development of a wide variety of tissues and species [30] and has been linked to muscle growth and development [31]. The complete list is shown in Supplemental Table S2.

The C. elegans body muscle miRNAs

We then aimed to detect and study the body muscle-specific miRNA population identified in our study. Using stringent parameters (see the “Materials and methods” section), we identified 16 miRNAs expressed in the C. elegans body muscle tissue (Fig. 5A and Supplemental Table S3). Several of these miRNAs have also been previously described to be expressed in this tissue by other groups [32], and impairing their function either leads to defects in the muscular fiber formation or shows an uncoordinated phenotype [23, 33] (Supplemental Table S1). We validated the body muscle localization of some of these miRNAs using two independent approaches: a GFP-based approach using worm strain expressing this fluorochrome under the control of specific miRNA promoters (Fig. 5B) and a modified RT-qPCR approach using muscle-specific miRNAs immunoprecipitated from a C. elegans strain expressing a GFP::ALG-1 chimera in the body muscle tissue (Supplemental Figs. S5 and S6) [24, 34].

Fig. 5
figure 5

The C. elegans body muscle Interactome. A The 3′UTRs of the 2848 protein-coding genes identified in this study were screened for miRNA targets in their 3′UTRs using 16 high-quality body-muscle miRNAs identified in this study. We identified four subnetworks. Each miRNA is shown in grayscale and varies in size, depending on its number of predicted targets. Each target is shown in a different color, depending on the number of predicted miRNA targets in its 3′UTR, as from the miRanda algorithm. Red: one target, yellow: two targets, green: three or more targets. B Transgenic C. elegans strains expressing GFP fluorochrome under the control of three miRNAs identified in this study. Let-7 (VT1153), miR-230 (VL347), and miR-241 (VT1189). All three strains show strong body muscle expression in young adult worms

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Both approaches provided strong evidence that our Nuc-Seq approach identified bona fide muscle-specific miRNAs. Importantly, this RT qPCR approach also allowed us to determine which one of the two miRNA strands (5p or 3p) was loaded in Argonaute. We determined that the 5p strand was preferred for let-7, miR-34, miR-228, miR-239a, miR-239b, and miR-5551. The 3p strand was preferred for miR-1, miR-77, miR-80, miR-230, and miR-250. miR-392 did not have a distinct strand preference and was detected at lower levels than other miRNAs. We also performed RT qPCR with primers for miR-1, which was surprisingly below our detection threshold from our nuclear sequencing data (Supplemental Figs. S5 and S6). With this data, we constructed a body-muscle miRNA interactome. We performed MiRanda miRNA prediction using stringent parameters, querying the 16 miRNAs with the preferred 5p or 3p strand identified in this study to the body muscle-specific 3′UTRome data we published in the past [12, 13] network (Fig. 5A).

While most body-muscle genes are targeted by a single miRNA, 11 genes are targeted by two miRNAs, two genes (gld-1, daf-16) are targeted by three miRNAs, and one gene (lin-14) is targeted by four miRNAs (Fig. 5A). Our most interconnected miRNA is miR-5551, an uncharacterized C. elegans-specific miRNA predicted to target 22 muscle-expressed protein-coding genes we detected in this tissue (Fig. 5A). A GO term analysis shows that its targets control contractile fibers, sarcomere, and myofibril formation (Supplemental Fig. S3). Let-7 and the closely related miR-241 are also present. These miRNAs temporally regulate larval development and have been previously identified in the pharyngeal and vulval muscles [32, 35]. Among the predicted target genes, we identified several genes in the DAF insulin pathway that control muscle health and aging [36, 37].

Discussion

Here, we have developed an updated FACS-based nuclear sorting method we named Nuc-Seq, to identify tissue-specific transcriptomes from cell lysates and applied it to determine the C. elegans muscle transcriptome. Nuc-Seq is robust and reproducible, has minimal background noise, and allows the simultaneous identification of both muscle-specific protein-coding genes and ncRNAs, such as miRNAs (Fig. 1).

When applied to the C. elegans muscle tissue, Nuc-Seq allowed the precise identification of 2848 protein-coding genes, including 78 muscle transcription factors and 207 non-ribosomal proteins containing an RNA-binding domain. Taken together, the muscle transcriptome we identified covers ~14% of the entire C. elegans protein-coding transcriptome (Fig. 3). Some of these genes, such as unc-54, mlc-1, unc-27, and others, have been previously identified in the muscle tissue and play an important role in muscle contraction and muscle health in general. We have identified almost all genes that form the sarcomere structure and are required for muscle contraction (Supplemental Fig. S3). When clustered using previously published protein-protein interaction data [38], the C. elegans muscle transcriptome is highly interconnected (Fig. 3B), with several notable subnetworks corresponding to mRNA processing, protein synthesis, ATP metabolism, and muscle cell development (Fig. 3B), all consistent with the function of this tissue.

Recently, similar methodologies have been utilized with success in fruit flies and mice. For example, the Fly Cell Atlas published high-quality transcriptome data from 15 somatic tissues, including other tissues that cannot be directly dissected, and contain nuclei from muscle tissue obtained from whole heads or whole body [39]. Similarly, in mice, fluorescent nuclei have been purified from skeletal myofibers [40] and in syncytial skeletal muscle cells throughout development [41]. Together with ours, these studies highlight complex regulatory networks occurring in the muscle tissue.

We have also performed a promoter analysis, studying the promoter composition of the genes identified in this study (Supplemental Fig. S4). Our study identified the presence of polythymine motif (T-blocks), previously reported by others, directly upstream of the transcription start site, and using prediction software (Meme Suite [42]), we have also identified many potential elements of muscle-specific transcription factors used to control gene expression in this tissue.

Our approach also allowed us to identify 16 high-quality muscle-specific miRNAs. Unfortunately, since Nuc-Seq identifies nuclear pre-miRNAs, we can only identify the miRNAs, but not which strand (5p or 3p or both) is loaded in the Argonaute protein. To bypass this limitation, we used a modified miRNA-CLIP approach coupled with RT qPCR analysis to determine which miRNA strand, the 5p or the 3p, was loaded into the Argonaute complex. We tested 11 miRNAs we identified from nuclear sequencing and additionally tested miR-1, which was not identified from nuclear sequencing. Interestingly, we found that miR-1-3p was highly expressed from our pulldown of ALG-1 in the body muscle. In contrast, some miRNAs that had high FPKM values, such as miR-5551, had noticeably lower expression levels from our RT qPCR data (Supplemental Fig. S6). One critical difference between our two methods of miRNA profiling was that the miRNAs were sequenced from the nucleus instead of the cytoplasm. A possible explanation for the differences in expression is that certain miRNAs may localize to the nucleus or not be exported and associate with Argonaute proteins as mature miRNAs. Previous studies have also described nuclear-localized miRNAs [43,44,45] and characterized their functions [46]. Future studies investigating the miRNAs found in high abundance in our nuclear sorting but not from our cytoplasmic RT qPCR could help explain these discrepancies and if these miRNAs have biological functions.

To strengthen our miRNA interactome results, we also ignore miRNAs described in miRbase with less than 1000 reads. Another stringent filter we used excluded the miRNAs located within introns of other genes (mirtrons). The C. elegans WS250 release contains 257 miRNAs, including 118 mirtrons. We removed the mirtrons because the density of reads within their genomic regions in genes with small introns made it too difficult to map them with high confidence. This step automatically removed 46% of all C. elegans miRNAs from our analysis and may explain why several body muscle miRNAs identified by others [23] are not present in our list. In addition, we used very stringent filters during the miRNA identification step using the miRanda algorithm, which may have also reduced the number of target predictions.

We believe that all these stringent filters and rules, while significantly reducing the number of miRNAs and their identified target genes, increase the quality of our hits.

We have validated the body muscle localization of several miRNAs identified in our study (Fig. 5B and Supplemental Figs. S5 and S6).

The body muscle miRNA interactome produced by clustering our 16 high-quality miRNAs and the miRanda predictions using 3′UTRs data from our 2848 protein-coding genes identified in this study showed seven subnetworks. One large subnetwork contained 12 miRNAs (Fig. 5). Eighty-eight genes in this large subnetwork are connected to single miRNAs, but exceptions occur with gld-1 and daf-16, each targeted by three miRNAs (let-7-5p, miR-392-5p and miR-4937-5p for gld-1, and let-7-5p, miR-241-5p, and miR-793-5p for daf-16). Interestingly, the heterochronic gene lin-14 is targeted by four miRNAs in this tissue. More experiments must be performed to validate these hits and understand how and why these genes are specifically regulated in the body-muscle tissue. It is also important to note that our approach did not allow us to identify the pharyngeal muscle transcriptome, which represents the second-largest muscle tissue in C. elegans and could provide an important new angle to study the complete muscle tissue transcriptome and miRNAome in this organism.

In conclusion, we have produced an updated C. elegans body muscle transcriptome and miRNA interactome, which will allow future studies to better understand the function of this tissue in normal states and diseases.

Materials and methods

Preparation of the transgenic strains

The C. elegans strain bqSi189 II; mel-28(bq5[GFP::mel-28]) III, or BN452, which ubiquitously expresses GFP and mCherry in all nuclei, was obtained from the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). The genomic DNA was extracted from mixed-stage BN452 worms, and the mCherry-tagged his-58 sequence was cloned using PCR using BP Gateway-flanked primers specific for the Gateway entry vector pDONR221. The mCherry::his-58 sequence was successfully cloned using Gateway technology into the entry vector, pDONR221, as evidenced by sequencing data. To assemble the finalized clone, we performed a Gateway LR Clonase II plus reaction (cat. 12538-013; Invitrogen) using the destination vector pCFJ150 (Frøkjær-Jensen et al. 2012) and entry clones containing the body-muscle-specific promoter myo-3, the mCherry::his-58 sequence, and the unc-54 3′UTR as previously published (Blazie et al. 2017). The pCFJ150 construct and pCFJ601 (50 ng/μl), which contains a Mos1 transposon, were injected into the C. elegans strain EG6699 [ttTi5605 II; unc-119(ed3) III; oxEx1578] (Frøkjær-Jensen et al. 2012), which is designed for MosI-mediated single-copy integration (MosSCI), using standard injection techniques.

Nuclei extraction

To obtain a mixed-stage population, we synchronized a population by bleaching gravid adult C. elegans as described previously [47]. We allowed worms to reproduce several times over approximately 2 weeks, which resulted in a population containing every developmental stage. Plates were inspected before nuclei isolation to confirm all life stages were present in approximately equal ratios. Six plates (100 × 15mm) were washed per replicate. Several medium plates of mixed-stage worms were washed four times at 1500 rpm for 3 min and resuspended in 10 mL of ice-cold NPB buffer (10 mM HEPES pH 7.6, 10 mM KCl, 1.5 mM MgCl2, 1 mM EGTA, 0.25 mM sucrose) per 250 μL volume of worm pellet. Nuclei were released by douncing 8–12 times in a chilled Wheaton stainless-steel tissue grinder (clearance 0.0005 inches = 12.5 µm) in 5-mL batches. The homogenate was sequentially filtered through 40-µm, 20-µm, and 10-µm nylon filters (pluriStrainer), then centrifuged for 10 min at 2,500 g at 4 °C to pellet the nuclei. The supernatant was aspirated, and the pellets containing nuclei were resuspended in 4 volumes of ice-cold NPB buffer. To remove larger debris, the resuspended nuclei pellets were centrifuged at 300 g for 1 min, then the supernatant containing the nuclei was transferred to a new tube on ice.

Nuclei stability

The nuclei were isolated from the myo-3::mCherry::his-58::unc-54 strain as described above. Nuclei were imaged in triplicates using DAPI and mCherry filters using a Leica DMi8 inverted microscope over 8 h. Images were obtained using 1-s exposure times. The result of this experiment is shown in Supplemental Fig. S1.

FACS sorting

Two replicates of resuspended nuclei samples for the body muscle tissue were FACS isolated into TRIzol solution using a BD Biosciences FACSAria III cell sorter with a 70-µm nozzle, and a 560-nm laser with a temperature-controlled tube holder at 4°C. The Eppendorf tubes were filled until a 1:1 TRIzol reagent and sorted nuclei prep were achieved after periodic gentle inversion. We set the gating parameters with 10,000 events from our positive control BN452 and negative control N2 and 30,000 events for the myo-3::mCherry::his-58::unc-54 strain. We sorted 239,000 and 353,000 nuclei for the sample and the replicate for the set1 and 340,000 and 353,000 sorted nuclei for the sample and the replicate of the set2.

RNA extraction and library preparation

The RNA from FACS-isolated nuclei was extracted using an RNA MiniPrep kit (ZR2070, Zymo Research) per the manufacturer’s protocol. RNA was quantified by using a bioanalyzer. The cDNA libraries for the two replicates were generated using Nextera XT RNA sequencing. We prepared six mixed-stages cDNA libraries from the following worm strains: BN452 (two replicates), myo-3::mCherry::his-58::unc-54 (four replicates). The libraries were depleted of ribosomal RNA contaminants using the Low Input RiboMinus Eukaryotic System v2 (ThermoFisher Scientific Catalog # A15027). The libraries were prepared from the ribosomal RNA depleted samples using the Nextflex Rapid Directional RNAseq Kit (PerkinElmer Catalog # NOVIA-5138-08) as per the manufacturer’s protocol and sequenced on the Nextseq 500 instrument (Illumina, San Diego, CA), using 75-bp SE chemistry. The sequencing runs were performed at Girihlet Inc. Oakland (CA). We obtained between 50M to 60M of mappable reads across all six datasets.

Bioinformatics analysis

The FASTQ files corresponding to the six datasets and the controls (total of six datasets) were mapped to the C. elegans gene model WS250 using the Bowtie 2 algorithm [48] with the following parameters: --local -D 20 -R 3 -L 11 -N 1 -p 40 --gbar 1 -mp 3. The mapped reads were converted into a bam format and sorted using SAMtools software using standard parameters [49]. We processed ~285M reads from all our datasets combined and obtained a median mapping success of ~93%. We used the Cufflinks software [50] to estimate the expression levels of the genes obtained in each dataset as per their BAM files. We calculated the fragment per kilobase per million bases (FPKM) number in each experiment and performed the comparison using the Cuffdiff algorithm [50]. We have pooled the four experimental samples into two sets, named set1 and set2, and the two replicates using the BN452 into a new set named “BN452 nuclear.” We used the median FPKM value ≥5 in the set1 and set2 dataset as a threshold to define positive gene expression levels. The results are shown in Supplemental Fig. S2 and Supplemental Table S1 using scores obtained by the Cuffdiff algorithm [50] and plotted using the CummeRbund package.

miRNA target identification

The predicted miRNA targeting network was constructed by extracting the longest 3′UTR sequence for each of the 2848 protein-coding genes identified in our study from the body-muscle transcriptome dataset we identified in past studies [12, 13]. We converted the 3′UTR sequences to FASTA format and parsed the file using the miRanda algorithm [51] using stringent parameters (-strict -sc -1.2). We used either the 5p or the 3p strand identified in our IP-coupled RT-qPCR approach shown in Supplemental Figs. S5 and S6. All 16 miRNAs used were identified in this study with an FPKM value ≥1 (Supplemental Table S1). This value represents the top 5% of miRSVR scores produced by the miRanda algorithm. We also restrict the analysis to pairing scores >150 and energy score < −7. We only used miRNAs that have been previously detected in C. elegans with more than 1000 reads in the miRbase database (www.mirbase.org) and that are not present in introns of protein-coding genes. The miRanda algorithm produced 118 high-quality predicted targets for 16 miRNAs. The networks were then built using the Cytoscape software [52] and uploaded to the Network Analyst online software [53] to produce the images shown in Fig. 5.

miRNA validation

To validate the RNA sequencing data shown in Fig. 3B, three specific transgenic C. elegans strains were purchased from the CGC (VT1153, VL347, and VT1189). These strains drive GFP expression under the control of let-7, miR-230, or miR-241 promoters, respectively. Each strain was imaged using identical settings for brightfield and GFP expression. These transgenic strains were imaged using a Leica DMi8 Inverted microscope. The miRNA validation in Supplemental Fig. S5 and S6 was performed using miRNA immunoprecipitated from a C. elegans strain expressing ALG-1 fused to the GFP fluorochrome expressed specifically in the body muscle tissue [24]. The immunoprecipitation step was performed using anti-GFP antibodies (Chromotek GTMA-10) as previously described [24]. Briefly, six mixed-stage 100×15mm confluent plates of transgenic C. elegans strains expressing the pmyo-3::ALG-1::GFP construct were washed 3× at 1500 rpm for 3 min. Crosslinking and RNA extraction was performed as previously described [24]. RNA was quantified; then, 465 ng of immunoprecipitated RNA was polyuridylated (New England Biolabs, cat no. M0337S) and reverse transcribed (Invitrogen; cat: no. 8080044) using a stem-loop poly-A reverse primer for the first strand reaction as previously described [34]. Eleven miRNA-specific forward primers were designed and shown in Supplemental Table S4. Primers with low melting temperatures were not used. Polyuridynated cDNA was diluted to a 1:2 or 1:10 ratio depending on the miRNAs’ abundance. We used a 1:10 cDNA dilution for let-7, miR-34, miR-77, miR-80, miR-228, and miR-1 and a 1:2 cDNA dilution for miR-230, miR-239a, miR-239b, miR-250, miR-392, and miR-5551. The relative abundance of each miRNA was calculated based on an exogenous positive control.

Western blot experiments

The western blot validation experiments shown in Fig. 1C are performed using total protein for each fraction, and the input was measured using a Bradford assay; 0.75 ng of protein was used for both the nuclear and cytoplasmic fractions, and 3 ng of protein was used for the input. Primary monoclonal antibodies for RFP (6G6-100, Chromotek) (1:1000) and GAPDH (ab125247, Abcam) (1:2000) were used, followed by IRDye 800CW goat: mouse secondary antibodies (LI-COR, 925–32210) (1:5000). The membrane was imaged using the ODYSSEY CLX system (LI-COR Biosciences, NE).

Imaging analysis

Confocal images of isolated nuclei pre-FACS stained with DAPI for the BN452 strain were acquired in the Biodesign Imaging Core, Division of the Arizona State University Bioimaging Facility. Fluorescent microscopy images of isolated nuclei pre-FACS stained with DAPI for the myo-3::MH58::unc-54 strain were acquired using a Leica DMi8 inverted microscope.

Network analysis

The network shown in Fig. 3B is constructed by parsing the top 2000 hits identified in this study using the STRING algorithm (v. 11.5) [54], run with standard parameters using only “protein-protein interactions” as input and set the minimum required interaction score to “high-confidence” (0.700). The produced network possesses 1995 nodes and 31,449 edges, with an average node degree of 31.5 and an average local clustering coefficient of 0.356. The network was then uploaded in Cytoscape [55] to prepare the figure.

Promoter analysis

We extracted 600 nt from the transcription start site for the top 100 genes identified in this study. We then used different custom Perl scripts to calculate the nucleotide distribution. The transcription factor predictions were produced by parsing these promoters to the Simple Enrichment Analysis script from the MEME suite software [42]. The results are shown in Supplemental Fig. S4 and Supplemental Table S2.

Expression analysis

The expression analysis shown in Fig. 4 is performed using the SimpleMine tool in WormBase Field [46], which retrieves essential gene information in this database. We used as input the 2848 protein-coding genes identified in this study. The output was then parsed with simple Perl scripts to extract and assign gene function (GO Term output), previously published tissue localization data, larval stage presence, cellular localization, and presence in operons.

Availability of data and materials

Raw reads were submitted to the NCBI Sequence Read Archive (http://trace.ncbi.nlm.nih.gov/Traces/sra/) with BioProject ID: PRJNA820874 and Submission ID: SUB11236444. The results of our analyses are available in Excel format as Supplemental Table S1.

References

  1. Gieseler K, Qadota H, Benian GM. Development, structure, and maintenance of C. elegans body wall muscle. WormBook. 2017;2017:1–59.

    Article  PubMed  Google Scholar 

  2. Lecroisey C, Segalat L, Gieseler K. The C. elegans dense body: anchoring and signaling structure of the muscle. J Muscle Res Cell Motil. 2007;28(1):79–87.

    Article  CAS  PubMed  Google Scholar 

  3. Benian GM, Epstein HF. Caenorhabditis elegans muscle: a genetic and molecular model for protein interactions in the heart. Circ Res. 2011;109(9):1082–95.

    Article  CAS  PubMed  Google Scholar 

  4. Hrach HC, O’Brien S, Steber HS, Newbern J, Rawls A, Mangone M. Transcriptome changes during the initiation and progression of Duchenne muscular dystrophy in Caenorhabditis elegans. Hum Mol Genet. 2020;29(10):1607–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chaya T, Patel S, Smith EM, Lam A, Miller EN, Clupper M, Kervin K, Tanis JE. A C. elegans genome-wide RNAi screen for altered levamisole sensitivity identifies genes required for muscle function. G3 (Bethesda). 2021;11(4):jkab047.

    Article  CAS  PubMed  Google Scholar 

  6. Ellwood RA, Hewitt JE, Torregrossa R, Philp AM, Hardee JP, Hughes S, van de Klashorst D, Gharahdaghi N, Anupom T, Slade L, et al. Mitochondrial hydrogen sulfide supplementation improves health in the C. elegans Duchenne muscular dystrophy model. Proc Natl Acad Sci U S A. 2021;118(9):e2018342118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yue Y, Li S, Shen P, Park Y. Caenorhabditis elegans as a model for obesity research. Curr Res Food Sci. 2021;4:692–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chandler RJ, Cogo S, Lewis PA, Kevei E. Modelling the functional genomics of Parkinson’s disease in Caenorhabditis elegans: LRRK2 and beyond. Biosci Rep. 2021;41(9):BSR20203672.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Natale C, Barzago MM, Diomede L. Caenorhabditis elegans models to investigate the mechanisms underlying tau toxicity in tauopathies. Brain Sci. 2020;10(11):838.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Markaki M, Tavernarakis N. Caenorhabditis elegans as a model system for human diseases. Curr Opin Biotechnol. 2020;63:118–25.

    Article  CAS  PubMed  Google Scholar 

  11. Song BM, Avery L. The pharynx of the nematode C. elegans: a model system for the study of motor control. Worm. 2013;2(1):e21833.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Blazie SM, Babb C, Wilky H, Rawls A, Park JG, Mangone M. Comparative RNA-Seq analysis reveals pervasive tissue-specific alternative polyadenylation in Caenorhabditis elegans intestine and muscles. BMC Biol. 2015;13:4.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Blazie SM, Geissel HC, Wilky H, Joshi R, Newbern J, Mangone M. Alternative polyadenylation directs tissue-specific miRNA targeting in Caenorhabditis elegans somatic tissues. Genetics. 2017;206(2):757–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fox RM, Watson JD, Von Stetina SE, McDermott J, Brodigan TM, Fukushige T, Krause M, Miller DM 3rd. The embryonic muscle transcriptome of Caenorhabditis elegans. Genome Biol. 2007;8(9):R188.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75(5):843–54.

    Article  CAS  PubMed  Google Scholar 

  16. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature. 2000;403(6772):901–6.

    Article  CAS  PubMed  Google Scholar 

  17. Bartel DP. Metazoan microRNAs. Cell. 2018;173(1):20–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bernstein E, Caudy AA, Hammond SM, Hannon GJ. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature. 2001;409(6818):363–6.

    Article  CAS  PubMed  Google Scholar 

  19. Kawamata T, Tomari Y. Making RISC. Trends Biochem Sci. 2010;35(7):368–76.

    Article  CAS  PubMed  Google Scholar 

  20. Haenni S, Ji Z, Hoque M, Rust N, Sharpe H, Eberhard R, Browne C, Hengartner MO, Mellor J, Tian B, et al. Analysis of C. elegans intestinal gene expression and polyadenylation by fluorescence-activated nuclei sorting and 3’-end-seq. Nucleic Acids Res. 2012;40(13):6304–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Serizay J, Dong Y, Janes J, Chesney M, Cerrato C, Ahringer J. Distinctive regulatory architectures of germline-active and somatic genes in C. elegans. Genome Res. 2020;30(12):1752–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brosnan CA, Palmer AJ, Zuryn S. Cell-type-specific profiling of loaded miRNAs from Caenorhabditis elegans reveals spatial and temporal flexibility in Argonaute loading. Nat Commun. 2021;12(1):2194.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Alberti C, Manzenreither RA, Sowemimo I, Burkard TR, Wang J, Mahofsky K, Ameres SL, Cochella L. Cell-type specific sequencing of microRNAs from complex animal tissues. Nat Methods. 2018;15(4):283–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kotagama K, Schorr AL, Steber HS, Mangone M. ALG-1 influences accurate mRNA splicing patterns in the Caenorhabditis elegans intestine and body muscle tissues by modulating splicing factor activities. Genetics. 2019;212(3):931–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gomez-Saldivar G, Fernandez A, Hirano Y, Mauro M, Lai A, Ayuso C, Haraguchi T, Hiraoka Y, Piano F, Askjaer P. Identification of conserved MEL-28/ELYS domains with essential roles in nuclear assembly and chromosome segregation. PLoS Genet. 2016;12(6):e1006131.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kim W, Underwood RS, Greenwald I, Shaye DD. OrthoList 2: a new comparative genomic analysis of human and Caenorhabditis elegans genes. Genetics. 2018;210(2):445–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Grishkevich V, Hashimshony T, Yanai I. Core promoter T-blocks correlate with gene expression levels in C. elegans. Genome Res. 2011;21(5):707–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Prosdocimo DA, Sabeh MK, Jain MK. Kruppel-like factors in muscle health and disease. Trends Cardiovasc Med. 2015;25(4):278–87.

    Article  CAS  PubMed  Google Scholar 

  29. Nabeshima Y, Hanaoka K, Hayasaka M, Esumi E, Li S, Nonaka I, Nabeshima Y. Myogenin gene disruption results in perinatal lethality because of severe muscle defect. Nature. 1993;364(6437):532–5.

    Article  CAS  PubMed  Google Scholar 

  30. Cogliati T, Good DJ, Haigney M, Delgado-Romero P, Eckhaus MA, Koch WJ, Kirsch IR. Predisposition to arrhythmia and autonomic dysfunction in Nhlh1-deficient mice. Mol Cell Biol. 2002;22(14):4977–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Miao W, Ma Z, Tang Z, Yu L, Liu S, Huang T, Wang P, Wu T, Song Z, Zhang H, et al. Integrative ATAC-seq and RNA-seq analysis of the longissimus muscle of Luchuan and Duroc pigs. Front Nutr. 2021;8:742672.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Martinez NJ, Ow MC, Reece-Hoyes JS, Barrasa MI, Ambros VR, Walhout AJ. Genome-scale spatiotemporal analysis of Caenorhabditis elegans microRNA promoter activity. Genome Res. 2008;18(12):2005–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Miska EA, Alvarez-Saavedra E, Abbott AL, Lau NC, Hellman AB, McGonagle SM, Bartel DP, Ambros VR, Horvitz HR. Most Caenorhabditis elegans microRNAs are individually not essential for development or viability. PLoS Genet. 2007;3(12):e215.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Mei Q, Li X, Meng Y, Wu Z, Guo M, Zhao Y, Fu X, Han W. A facile and specific assay for quantifying microRNA by an optimized RT-qPCR approach. PLoS One. 2012;7(10):e46890.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Andachi Y, Kohara Y. A whole-mount in situ hybridization method for microRNA detection in Caenorhabditis elegans. RNA. 2016;22(7):1099–106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mallick A, Ranawade A, van den Berg W, Gupta BP. Axin-mediated regulation of lifespan and muscle health in C. elegans requires AMPK-FOXO signaling. iScience. 2020;23(12):101843.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Basu A, Behera S, Bhardwaj S, Dey S, Ghosh-Roy A. Regulation of UNC-40/DCC and UNC-6/Netrin by DAF-16 promotes functional rewiring of the injured axon. Development. 2021;148(11):dev198044.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Remmelzwaal S, Boxem M. Protein interactome mapping in Caenorhabditis elegans. Curr Opin Syst Biol. 2019;13:1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Li H, Janssens J, De Waegeneer M, Kolluru SS, Davie K, Gardeux V, Saelens W, David FPA, Brbic M, Spanier K, et al. Fly Cell Atlas: a single-nucleus transcriptomic atlas of the adult fruit fly. Science. 2022;375(6584):eabk2432.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Petrany MJ, Swoboda CO, Sun C, Chetal K, Chen X, Weirauch MT, Salomonis N, Millay DP. Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers. Nat Commun. 2020;11(1):6374.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kim M, Franke V, Brandt B, Lowenstein ED, Schowel V, Spuler S, Akalin A, Birchmeier C. Single-nucleus transcriptomics reveals functional compartmentalization in syncytial skeletal muscle cells. Nat Commun. 2020;11(1):6375.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bailey TL, Johnson J, Grant CE, Noble WS. The MEME Suite. Nucleic Acids Res. 2015;43(W1):W39-49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liao JY, Ma LM, Guo YH, Zhang YC, Zhou H, Shao P, Chen YQ, Qu LH. Deep sequencing of human nuclear and cytoplasmic small RNAs reveals an unexpectedly complex subcellular distribution of miRNAs and tRNA 3’ trailers. PLoS One. 2010;5(5):e10563.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Reyes-Gutierrez P, Ritland Politz JC, Pederson T. A mRNA and cognate microRNAs localize in the nucleolus. Nucleus. 2014;5(6):636–42.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Politz JC, Hogan EM, Pederson T. MicroRNAs with a nucleolar location. RNA. 2009;15(9):1705–15.

    Article  CAS  PubMed  Google Scholar 

  46. Liu H, Lei C, He Q, Pan Z, Xiao D, Tao Y. Nuclear functions of mammalian MicroRNAs in gene regulation, immunity and cancer. Mol Cancer. 2018;17(1):64.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Porta-de-la-Riva M, Fontrodona L, Villanueva A, Ceron J. Basic Caenorhabditis elegans methods: synchronization and observation. J Vis Exp. 2012;64:e4019.

    Google Scholar 

  48. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. Genome Project Data Processing S: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28(5):511–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. MicroRNA targets in Drosophila. Genome Biol. 2003;5(1):R1.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–44.

    Article  CAS  PubMed  Google Scholar 

  54. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12.

    Article  CAS  PubMed  Google Scholar 

  55. Shen B, Zhang Y, Yu S, Yuan Y, Zhong Y, Lu J, Feng J. MicroRNA-339, an epigenetic modulating target is involved in human gastric carcinogenesis through targeting NOVA1. FEBS Lett. 2015;589(20 Pt B):3205–11.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Some of the strains used in this study were provided by the CGC, funded by the NIH Office of Research Infrastructure Programs (P40 OD010440). We thank Emma Murari and Dalton Meadows for their suggestions and advice during the manuscript preparation. In memory of Amelia.

Funding

This work is supported by NIH grant 1R01GM118796.

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Authors and Affiliations

  1. Molecular and Cellular Biology Graduate Program, Arizona State University, Tempe, AZ, USA

    Anna L. Schorr & Marco Mangone

  2. Virginia G. Piper Center for Personalized Diagnostics, The Biodesign Institute at Arizona State University, 1001 S McAllister Ave, Tempe, AZ, USA

    Anna L. Schorr & Marco Mangone

  3. School of Life Sciences, Arizona State University, 751 E Lemon Mall, Tempe, AZ, 85287, USA

    Anna L. Schorr, Alejandro Felix Mejia & Marco Mangone

  4. Helios Scholars at the Translational Genomics Research Institute, 445 N 5th St 4th Floor, Phoenix, AZ, 85004, USA

    Martina Y. Miranda

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Contributions

ALS and MM designed the experiments. ALS developed and performed the nuclear FACS sorting experiments in collaboration with AFM and MYM, and ALS prepared the sequencing reactions. MM performed the bioinformatic analysis. ALS and MM analyzed the data, led the analysis and interpretation, assembled the figures, and wrote the manuscript. The authors read and approved the final manuscript.

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Correspondence to Marco Mangone.

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Supplementary Information

Additional file 1: Fig. S1.

Nuclei stability overtime. Fig. S2. The C. elegans body muscle dataset. Fig. S3. Muscle fiber diagram showing the main components of the sarcomere. Fig. S4. Promoter region analysis for genes detected in this study. Fig. S5. RT qPCR approach to validate body muscle miRNAs identified from nuclear sequencing. Fig. S6. miRNA validation using a modified RT-qPCR approach.

Additional file 2:

Supplemental Table S1. Comprehensive list of transcripts identified in this study.

Additional file 3:

Supplemental Table S2. Simple Enrichment Analysis of sequence motifs identified in promoters of transcripts identified in this study.

Additional file 4:

Supplemental Table S3. miRNA target prediction of the 16 miRNAs identified in this study using the miRanda algorithm.

Additional file 5:

Supplemental Table S4. Primer list used in this study.

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Schorr, A.L., Mejia, A.F., Miranda, M.Y. et al. An updated C. elegans nuclear body muscle transcriptome for studies in muscle formation and function. Skeletal Muscle 13, 4 (2023). https://doi.org/10.1186/s13395-023-00314-2

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Keywords

Skeletal Muscle

ISSN: 2044-5040

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