Investigating the role of long non-coding RNA in hypertrophic cardiomyopathy

Investigating the role of long non-coding RNA in hypertrophic cardiomyopathy | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Investigating the role of long non-coding RNA in hypertrophic cardiomyopathy View ORCID Profile Graham A. Branscom , View ORCID Profile Michael Morley , View ORCID Profile Jonathan J. Herrera , View ORCID Profile Jaime M. Yob , View ORCID Profile Sharlene M. Day doi: https://doi.org/10.1101/2025.07.26.666851 Graham A. Branscom 1 Cardiology Division, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Graham A. Branscom For correspondence: graham.branscom{at}icahn.mssm.edu Sharlene.Day{at}pennmedicine.upenn.edu Michael Morley 1 Cardiology Division, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Morley Jonathan J. Herrera 2 Department of Molecular and Integrative Physiology, University of Michigan , Ann Arbor, MI USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan J. Herrera Jaime M. Yob 1 Cardiology Division, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jaime M. Yob Sharlene M. Day 1 Cardiology Division, Department of Medicine, University of Pennsylvania , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sharlene M. Day For correspondence: graham.branscom{at}icahn.mssm.edu Sharlene.Day{at}pennmedicine.upenn.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Long non-coding RNA (lncRNA) are transcripts that do not typically code for protein but have essential roles in the regulation of transcription and translation in health and disease. The objective of this study was to identify potential lncRNAs that could play a role in the pathophysiology of hypertrophic cardiomyopathy (HCM). We analyzed RNA-Seq data for lncRNA expression from a mouse model of HCM and cross-referenced transcripts to a published human HCM tissue dataset. We identified a total of 9,140 lncRNA transcripts in the mouse dataset, of which 35 were differentially expressed between transgenic TNNT2 Δ160 mice (TG) and non-transgenic mice (nTG, p- adj < 0.05). Of these, 13 had a human ortholog as predicted by ortho2align. We used the computational tools MiPepid, AlphaFold, and PhyloCSF to predict potential micropeptides that could be coded for by these 13 mouse lncRNAs. We found that predicted micropeptides from 3 of these lncRNAs– G730003C15Rik , 9830004L10Rik, and Gm45012–have higher AlphaFold folding confidence metrics than random peptides or truly non-coding lncRNA negative controls ( p < 0.05). Another 2 of these lncRNAs, 6330403L08Rik and 2900072N19Rik , have positive PhyloCSF scores, also indicating micropeptide coding potential. In summary, we developed a computational workflow to identify differentially expressed lncRNAs in a mouse model of HCM that can be prioritized for future experimental studies based on their cross-species conservation and micropeptide coding potential. NEW & NOTEWORTHY This is the first analysis of RNA-Seq data for lncRNA expression in an HCM mouse model and the first cross-species analysis of HCM lncRNA RNA-Seq data. Additionally, this study demonstrated a novel computational pipeline that combines several tools–RNA-Seq, MiPepid, AlphaFold, and PhyloCSF–to identify potential lncRNAs of interest from RNA-Seq data. INTRODUCTION Long non-coding RNAs (lncRNAs) are transcripts greater than 200 nucleotides long that are typically annotated as not coding for protein. 1 However, lncRNAs have a diverse array of biological roles, including transcriptional regulation of target genes, chromatin remodeling, and nuclear organization, among many others. 2 , 3 Some lncRNAs are subject to similar transcriptional processing as mRNA, such as addition of a 5’ cap, polyadenylation, and splicing. Although lncRNAs were traditionally believed to all be non-coding, many lncRNAs have since been identified to contain small open reading frames (sORFs) that encode for micropeptides less than 100 amino acids long. 4 - 6 Hypertrophic cardiomyopathy (HCM) is an inherited cardiovascular disorder marked by left ventricular (LV) hypertrophy, diastolic dysfunction, and fibrosis. 7 Currently, several lncRNAs have been experimentally linked to cardiac hypertrophy. 8 , 9 For instance, the lncRNA Mhrt is protective against induced pathological hypertrophy by binding to and inhibiting the enzyme Brg1. 10 , 11 Other lncRNAs like Plscr4 regulate pathological hypertrophy by acting as molecular sponges for miRNAs, preventing their binding to downstream mRNA transcript targets. 12 While some lncRNAs have been identified as dysregulated in HCM through methods like RT-qPCR, 13 unbiased high-throughput techniques like total RNA-Seq and lncRNA-based microarrays have identified key lncRNAs that are differentially expressed (DE) in HCM. 14 , 15 GSE130036 is the primary human HCM RNA-Seq dataset that includes lncRNA. 14 These authors identified 150 DE lncRNAs with an absolute fold change (FC) > 2 and a q-value < 0.05, of which 67 were upregulated and 83 downregulated in HCM. Since this initial study was published, several other studies have conducted similar DE analyses of the lncRNA genes in this dataset and constructed predicted lncRNA-miRNA-mRNA networks that could regulate HCM. 16 - 18 Other studies have found gene ontology (GO) terms that are significantly enriched among the DE genes in this dataset, yet none found terms related to non-coding RNA processing. 19 , 20 In addition to traditional DE analysis of lncRNAs in HCM, over 100 lncRNA sORF-encoded micropeptides have been detected in primary cardiomyocytes exposed to hypertrophic agonists using Ribo-Seq which sequences ribosome-protected fragments. 21 Typically, hundreds to thousands of lncRNAs are identified as being differentially expressed in disease compared to control samples. 14 , 15 It is challenging to pare these down to a reasonable number of lncRNA candidates that can be prioritized for experimental studies. Available computational tools use crossspecies conservation metrics to identify evolutionary conserved regions in lncRNAs, 22 which are more likely to serve essential functions in a particular organ. However, non-coding regions of the genome are typically less conserved than protein-coding regions due to weaker evolutionary constraints. 23 , 24 While there are an estimated 30,000 to 60,000 lncRNA genes expressed in human, only 1,731 are computationally predicted to be conserved across human and mouse. 25 - 27 This study aims to identify lncRNAs that may be important in the pathophysiology of HCM. We analyze an RNA-Seq data from a mouse model of HCM ( TNNT2 Δ160) and cross reference our findings to a human HCM dataset. After we identified lncRNAs that are DE in mice and possess a human ortholog, we employed various computational tools to identify micropeptide coding potential among these lncRNAs. These lncRNAs can be prioritized for future experimental investigation. MATERIALS AND METHODS The overall methodological workflow is shown in Fig. 1A . Download figure Open in new tab Figure 1. Scientific methodology A. The methodology workflow for this study. Mouse RNA-Seq data includes left ventricular tissue from 64 mice (n=31 transgenic TNNT2 Δ160 (TG), n=33 non-transgenic (nTG)). Within TG, there are 17 males and 14 females. Within nTG, there are 15 males and 18 females. Within the TG group, there were 14 HIIT and 17 Sed mice, and 18 HIIT and 15 Sed within the nTG group. Human RNA-Seq data includes myocardial tissue from 37 patients (n=28 HCM, n=9 non-failing). Among HCM samples, 19 were from males and 9 were from females. Among NF samples, 8 were from males and 1 was from a female. B. The filtering of genes used when analyzing the mouse RNA-Seq data. p -values were corrected under the Benjamini-Hochberg procedure. TG: transgenic, nTG: non-transgenic, p -adj: adjusted p-value, DE: differentially expressed. Mouse model C57BL/6J mice (n=64) were obtained from Dr. Jil Tardiff’s laboratory. 28 , 29 This included 33 non-transgenic (nTG) and 31 transgenic (TG) mice which had a deletion of Glu160 (Δ160E) in TNNT2 driven by the α-MHC promoter. Mutant cTnT replaced 70% of endogenous cTnT. This model recapitulates key features of human HCM including diastolic dysfunction and hypercontractility. There were 17 TG males and 14 TG females and 15 nTG males and 18 nTG females. In a concurrent study, mice were run under a high intensity interval training (HIIT) and sedentary (Sed) protocol (unpublished). Within the TG group, there were 14 HIIT and 17 Sed mice, and 18 HIIT and 15 Sed within the nTG group. The HIIT group was run on a treadmill with high intensity intervals for a total of 30 minutes, 3 days per week, for 6 weeks. We investigated overall differences in TG vs nTG mice with exercise and sex as covariates. RNA-Seq data collection Total RNA was extracted from the LVs of mice using random hexamer primers. RNA was subject to ribosomal depletion and bulk and paired RNA-Seq reaching a high depth. The dataset is made publicly available under GSE297707. Human RNA-Seq data was downloaded from a published dataset (GSE130036). In this study, myocardial tissue was obtained from 37 patients (28 patients with HCM and 9 non-failing (NF) donor controls). There were 19 men and 9 women within HCM, 8 NF men, and 1 NF woman. This RNA was subject to total RNA extraction and strand-specific library preparation. 14 Differential expression analysis lncRNA transcripts were identified using a full bioinformatics pipeline ( Fig. 1A ). The FASTQ files were first subject to FASTQC (v0.12.0) and MultiQC (v1.23) to assess read quality. The adapter sequences were cut using Cutadapt (v4.9) and then aligned to the GENCODE primary assembly mouse genome (GRCm38/mm10) with STAR (v2.7.11b). 30 Cufflinks and Cuffmerge (v2.2.1) were used for transcript assembly. 31 The Cuffmerge output GTF file was compared with 60,411 known lncRNA genes from GENCODE, which found 9,126 known unique lncRNA genes across all our mouse samples. FEELnc (v0.2.1) found 14 novel lncRNA genes, 32 for a total of 9,140 lncRNA genes. RSEM (v1.3.3) was used to generate count matrices. 33 These matrices were imported into R (v4.4.2) using tximport and analyzed with DESeq2 in R via RStudio (v2024.12.1). 34 , 35 We used ortho2align (v1.0.5) to find known and predicted human orthologs of mouse lncRNA genes. 36 Human RNA-Seq abundance matrices were downloaded from GSE130036. We used the author’s published spreadsheet of genes identified by RNA-Seq to identify lncRNA genes and analyzed the data using DESeq2. 14 , 35 A variance stabilization transformation was used on the data for count heatmaps and principal component analyses (PCA). DESeq2 analysis used independent filtering with Benjamini-Hochberg (BH)-corrected p -values ( p -adj) with an alpha of 0.05 and a FC of one. Genes were DE if they had a p -adj < 0.05, regardless of their FC value since FC values were very low (mean log 2 FC among mouse genes with p -adj < 0.05 was -0.35 (SD 0.73); -0.228 (SD 1.23) for human). Other R packages used included pheatmap, EnhancedVolcano, ggVennDiagram, tidyverse, and dplyr. All computation except the analysis in R was done on the Penn Medicine Academic Computing Services High Performance Computing cluster. Code and other resources can be found in Supplementary Code C1. We searched each DE lncRNA on the RNAcentral database for known tissue-specific expression data. 37 , 38 Gene ontology analysis GO analysis was run on both mouse and human RNA-Seq data using all ontologies (biological process, molecular function, and cellular component). For over representation/enrichment analysis (ORA), we input only genes (protein-coding and non-coding) with a p -adj < 0.05 (n=1,052 for mouse, n=7,630 for human). For gene set enrichment analysis (GSEA), we input all genes (protein-coding and non-coding) regardless of p -adj (n=16,892, n=16,741 for mouse and human, respectively). We used the R packages clusterProfiler and enrichPlot. Enriched terms had a BH-adjusted p -values < 0.05. Computational prediction of micropeptide coding potential 13 lncRNA genes that had p -adjusted < 0.05 and possessed human orthologs were assessed for micropeptide coding potential ( Fig. 1B ). The contiguous exon DNA sequence of their canonical isoforms was run through MiPepid , a machine-learning based tool for identifying sORFs that may encode for micropeptides. 38 These sORFs were translated using EMBOSS transeq into micropeptides and then run through AlphaFold2 via the ColabFold interface (v1.5.5). 40 , 41 We used mean predicted local distance difference test and predicted aligned error to assess coding potential. We compared these predictions to micropeptides coded by sORFs from true non-coding (TNC) lncRNAs, false non-coding (FNC) lncRNA (i.e. lncRNAs that encode micropeptides), and randomly generated peptide sequences. To find TNC and FNC sequences, the entire mouse lncRNA transcriptome (n=155,417 transcripts) was run through MiPepid. The TNC and FNC sORFs were the top 120 sORFs with the highest non-coding and coding probabilities from the MiPepid output, respectively. For the randomly generated micropeptides, we generated 120 peptides using the Python (v3.9.19) random module. We stratified the results by 10 amino acid length increments since sORF length is known to be a strong predictor of micropeptide coding potential by MiPepid. 39 Independent two sample t-tests compared scoring differences between micropeptide types. AlphaFold structures were rendered via UCSF ChimeraX (v1.9.0). 42 The number of predicted sORFs for each gene ran through AlphaFold was compared to the number for all lncRNA genes in mouse, and significance was determined with a permutation test. These 13 lncRNA genes were also run through PhyloCSF, a tool that uses a multi-species alignment to identify protein-coding regions among lncRNA sequences. 22 We employed the methodology described in a previous study to load PhyloCSF as a track hub on the UCSC genome browser and analyzed the smoothed, approximate coding regions, candidate coding regions, and relative branch lengths tracks. 43 The power track is a score from 0 to 1, with higher scores representing higher statistical confidence. Figures were generated with ggplot in R and Plotly in Python and edited with Adobe Illustrator (v29.5.1). RESULTS Mouse lncRNA DE analysis Using the computational pipeline outlined in Fig. 1A , we identified 9,140 unique lncRNA genes in mice. There were 77.87E6 (SD 9.45E6) reads per sample. A heatmap of the top 40 lncRNA genes shows that there is sample clustering primarily driven by genotype ( Fig. 2A ). PCA confirms that there is clustering present when samples are colored by their genotype ( Fig. 2B ), but clustering is not present when colored by sex ( Fig. 2C ). The outlier in the upper-left corner of both PCA plots had the highest percentage of unmapped reads among all samples (8.6%, average among all samples: 5.8%). TG vs nTG analysis resulted in the most DE genes (n=25) ( Fig. 2D ) compared to sex (n=4) ( Fig. 2E ) or exercise group (n=0). However, we also included analyses stratified by sex and exercise group (Supplemental Fig. S1) to minimize covariate effects and maximize the number of DE genes captured. 35 unique lncRNA genes were found among all the TG vs nTG comparisons. 11 were found only in TG vs nTG, 8 among TG HIIT vs nTG HIIT, 1 among TG males vs nTG males, and 15 were found across multiple comparison groups ( Fig. 2F , Supplemental Fig. S1). Among these 35, 8 are upregulated in TG and 27 are downregulated in TG. Download figure Open in new tab Figure 2. DE analysis of mouse RNA-Seq data A. Heatmap of the 40 lncRNA genes with the highest counts, annotated by genotype and sex. Both rows (genes, n=40) and columns (samples, n=64) were clustered. Count data was subject to a variance stabilizing transformation prior to clustering and scaled gene-wise. B. PCA of the top 500 genes with the highest variance after variance stabilizing transformation, colored by genotype. C. Same PCA as panel B but colored by sex. D. Volcano plot from the DE analysis of all the lncRNA genes for the TNNT2 Δ160 transgenic (TG) vs nontransgenic (nTG) comparison. y -axis denotes p -values adjusted with the Benjamini-Hochberg procedure. The horizontal line above the x-axis indicates a p -adjusted cutoff of 0.05. E. Similar to panel D but DE for male vs female. F. Bar chart depicting the number of unique lncRNAs that have p -adjusted < 0.05 across five different comparisons: TG vs nTG (among all samples, n=64), TG M vs nTG M (among males only, n=32), TG F vs nTG F (among females only, n=32), TG HIIT vs nTG HIIT (among HIIT mice only, n=32), TG Sed vs nTG Sed (among sedentary mice only, n=32). Sex and exercise were treated as covariates. The bottom panel indicates which comparisons are included in each bar. PCA: principal component analysis, DE: differential expression, TG: transgenic, nTG: non-transgenic, M: male, F: female, HIIT: high intensity interval training, Sed: sedentary These 35 genes in total, along with their human orthologs (if any) and known functions are in Supplemental Table T1. Only four of these genes have a known role. Two are known to be DE following irradiation, 44 - 46 three are DE in cancer, 47 - 50 and one called ANRIL regulates myocardial infarction and coronary artery disease. 51 - 53 A screen of the RNAcentral database showed that 14 of the 35 lncRNAs have known tissue-specific expression, with 9 of these having known expression in the heart. Over-representation analysis (ORA) analysis among all DE genes, both protein-coding and non-coding (n=1,052), revealed that “regulation of non-coding RNA (ncRNA) transcription” was an enriched term in the mouse dataset. Human lncRNA DE analysis Previous studies have already conducted DE analysis on a human HCM dataset specifically for lncRNA genes. 14 , 16 , 18 We reran the DE analysis to harmonize criteria with the mouse dataset ( p -adj < 0.05) to enable valid comparisons. We found that of the 2,398 total unique lncRNA genes captured by RNA-Seq, 340 were DE ( p -adj < 0.05) across all HCM vs NF samples and the sex-stratified comparisons. Specifically, 64 were DE among all HCM vs NF, 31 among HCM vs NF males, 24 among HCM vs NF females, and 221 were DE across multiple comparison groups. Of these 340 total genes, 139 were higher in HCM than NF and 198 were lower in HCM than NF. (Three have conflicting results, e.g. higher in all HCM vs all NF, but lower in HCM vs NF males). ORA among all DE genes, both protein-coding and non-coding (n=7,630), revealed that “ncRNA processing,” “ncRNA 5’-end processing” and “ncRNA transcription” were enriched in the human dataset ( p -adj < 0.05). Additionally, gene set enrichment analysis (GSEA) revealed that there was one term (“ncRNA processing”) that had a significant enrichment score ( p -adj < 0.05). The running enrichment score, a measure of the cumulative enrichment, for ncRNA processing was higher for coding and non-coding genes that had both higher (higher among HCM) and lower (higher among NF) log 2 FC values, with a slightly skewed enrichment toward NF. This indicates that “ncRNA processing” consists of genes that are both upregulated and downregulated in HCM, with a slight bias toward downregulation ( Fig. 3A ). Download figure Open in new tab Figure 3. GO analysis and comparison between mouse and human RNA-Seq datasets A. Gene set enrichment analysis (GSEA) gene ontology (GO) analysis among all genes identified in the human dataset, both coding and non-coding (n=16,741) total. The non-coding RNA (ncRNA) processing GSEA term had a p -adjusted < 0.05. The x -axis denotes the rank of all the genes in the dataset using descending log 2 FC, from higher in HCM (left/lower rank) to higher in non-failing samples (right/higher rank). B. Overlap between all unique lncRNA genes identified by RNA-Seq in both the mouse and human datasets. No filtering was applied. C. Overlap between all unique lncRNA genes among the mouse and human datasets, but only for genes with a p -adjusted < 0.05, regardless of log 2 FC value. P -values are adjusted by the Benjamini-Hochberg procedure. GO: gene ontology, GSEA: gene set enrichment analysis, FC: fold change, HCM: hypertrophic cardiomyopathy, NF: non-failing Cross-species lncRNA comparison Among all the lncRNA genes identified by RNA-Seq in mouse (n=9,140) and human (n=2,398), only 100 (0.87%) were shared across both species ( Fig. 3B ). Among the DE genes ( p -adj < 0.05) in mouse (n=35) and human (n=340), 0 were shared across both species ( Fig. 3C ). Even using an unadjusted p -value < 0.05 as the DE criterion instead of the BH-adjusted p -value, there were still no shared DE genes between mouse and human. Micropeptide coding prediction We ran micropeptide coding prediction analysis on the 13 lncRNA genes that were DE in mouse and had human orthologs. We chose these genes because they are potentially involved with HCM pathophysiology and are evolutionarily conserved. We first used MiPepid to predict small open reading frames (sORFs) in the lncRNA gene sequences. 9/13 of these transcripts had a significantly greater number of small open reading frames (sORFs) than the average of all 155,417 mouse lncRNA transcripts, making them more likely to encode for micropeptides ( p < 0.05, Supplemental Fig. S2A). We then ran the peptide sequences coded by these predicted sORFs using AlphaFold. We followed a similar methodology to published studies that used the mean per-residue predicted local distance difference test (pLDDT) value from AlphaFold, which measures folding confidence, as a proxy for micropeptide coding potential. 54 , 55 We found that the lncRNA G730003C15Rik has predicted micropeptides in the 1-10 amino acid length range that have higher mean pLDDT scores than the randomly generated peptides negative control, indicating greater confidence in these being true micropeptides ( p < 0.01, Fig. 4A ). 9830004L10Rik and Gm45012 have micropeptides within two and one different length ranges, respectively, with higher mean pLDDT scores than random peptides (p < 0.05, Fig. 4A ). Download figure Open in new tab Figure 4. Predicting micropeptide coding potential A. Measures of folding confidence for micropeptides coded by the 13 DE ( p -adj < 0.05) mouse lncRNAs with human orthologs, stratified by micropeptide length. Only lncRNA genes with significant results are shown. Mean per-residue predicted local distance difference test (pLDDT) scores are for micropeptides falling into buckets of length 10 amino acids long from 1 to 80. Higher pLDDT scores indicate lower folding confidence and higher micropeptide coding potential. There are n=70 micropetides for G730003C15Rik , n=233 for 9830004L10Rik , and n=106 for Gm45012 . There are 120 micropeptides each for false non-coding (FNC, i.e. true coding), random, and true non-coding (TNC), 15 for each length range. Error bars depict standard deviation. p -values were determined with independent two sample t-tests. p -values are denoted by asterisks sitting above and having the color of the two groups that they are comparing within a given length range subset. For simplicity, p -values are only shown when any of the three lncRNAs have a greater pLDDT than random or TNC micropeptides. B. Representative predicted micropeptides structures outputted by AlphaFold and rendered with UCSF ChimeraX. Two micropeptide-encoded sORFs from the 10 with the highest pLDDT scores are shown for G730003C15Rik, 9830004L10Rik, Gm45012 , and FNC. Two from the 10 median scoring sORFs are shown for the random micropeptides. Two from the 10 with the lowest pLDDT scores are shown for TNC. Numbers in each box are listed as: micropeptide length (number of amino acids), pLDDT. Each residue is colored based on its pLDDT value according to the color gradient. C. PhyloCSF tracks in the UCSC Genome Browser for the mouse lncRNA 6330403L08Rik in genome build GRCm38/mm10. The topmost track is the PhyloCSF novel track, indicating novel coding regions (green is on plus strand, red is on minus strand). Below that is the Smoothed PhyloCSF track for each of the six possible reading frames, which depicts the scores given by PhyloCSF’s hidden Markov model. Next is the PhyloCSF regions track for all reading frames, indicating the Smoothed track’s predicted coding regions. Next is the PhyloCSF Power track, which indicates a score from 0 to 1 indicating statistical confidence, with a higher score indicating higher confidence. Next are the genes located within this region. 6330403L08Rik is marked with a red arrow. Lastly, there is the Conservation track of 100 vertebrates, including mouse, for reference. * p < 0.05, ** p < 0.01 sORF: small open reading frame, pLDDT: predicted local distance difference test, FNC: false non-coding (i.e. true coding), TNC: truly non-coding We used another AlphaFold metric called predicted aligned error (PAE), where lower scores indicate higher confidence in micropeptide folding. We found that at the 1-10 range, G730003C15Rik had a lower mean PAE than both the random micropeptides and micropeptides from truly non-coding (TNC) lncRNAs, which served as another negative control ( p < 0.05). At the 11-20 amino acids range, 9830004L10Rik had a lower mean PAE value than the random peptides ( p < 0.05, Supplemental Fig. S2B). Representative renderings of predicted micropeptides from the three lncRNAs with significant pLDDT and PAE results are shown in Fig. 4B . Higher scoring predicted micropeptides like those shown for the three lncRNA genes and the falsely non-coding (FNC) lncRNAs positive control tend to be alpha helices, whereas those for lower scoring micropeptides like TNC and random tended to be beta sheets or peptides with little secondary structure. Two of the 13 mouse lncRNAs had positive PhyloCSF scores. PhyloCSF predicted that 6330403L08Rik had 17 predicted sORF, with the longest being 141 base pairs long. Although that sORF had a low power score (0.255), five shorter sORFs had high power scores (0.598-0.956). Another lncRNA, 2900072N19Rik , had one small nine base pair sORF with a positive smoother PhyloCSF score in 2/6 reading frames (Supplemental Fig. S3). DISCUSSION In summary, we found 35 mouse lncRNAs that are DE in an HCM mouse model. 13 of these genes have a human ortholog, indicating that they are evolutionarily conserved. 24 , 56 We found that 5/13 (38.5%) of these lncRNAs have predicted micropeptides with significant AlphaFold folding confidence metrics or PhyloCSF results. By conducting GO analysis among all DE mouse and human genes, we also found that there is one enriched term related to lncRNA processing in mouse and three in human, consisting of genes that are both upregulated and downregulated in the HCM model. Although studies have analyzed RNA-Seq data for lncRNA expression among human HCM samples, 14 a cross-species comparator study has not been previously reported. Compared to their protein-coding counterparts, lncRNA genes are known to be less conserved due to lower evolutionary pressures. 23 , 24 Accordingly, a prior analysis showed that there is only a 3.4% overlap between lncRNAs expressed in mouse and human among all tissues. 25 Similarly, we found that only 0.88% of all lncRNAs were shared across both species. We found a smaller overlap than the prior study perhaps because we are focused only on lncRNAs expressed in the heart. None of the lncRNAs that were DE in TG compared to nTG mouse hearts were also DE in human HCM hearts compared to NF ( Fig. 3B ). Among the 35 lncRNA genes that are DE in mice and possess a human ortholog, several of them have been shown by prior studies to be involved in cardiac physiology and pathophysiology. For instance, ANRIL acts as a transcriptional regular and epigenetic modifier in cardiovascular pathologies. 51 - 53 ANRIL is antisense to CDKN2B , which codes for a cyclin-dependent kinase inhibitor, and thus can inhibit its expression. Accordingly, ANRIL KD has been shown to increase CDKN2B expression. 52 , 53 Additionally, the human lncRNA MIR181A1HG has been shown to be a positive regulator of inflammation in atherosclerosis. 57 , 58 However, no studies have linked ANRIL or MIR181HG specifically to HCM or cardiac hypertrophy. ANRIL and MIR181HG also have roles outside of cardiovascular biology such as cancer. 48 - 50 Two of the other 35 lncRNAs, E230013L22Rik and PARTICL , have been studied before in the context of irradiation therapy and cancer, 44 - 47 although they have no known cardiovascular function. The GO analysis reveals that coding and non-coding gene sets related to non-coding RNA processing are significantly enriched. This suggests that non-coding RNA processing may be dysregulated in HCM pathophysiology. More specifically, the GSEA results for the human dataset show that genes involved in ncRNA processing are both upregulated and downregulated in HCM ( Fig. 3C ). Many lncRNAs were falsely classified as non-coding by initial algorithms that arbitrarily set an open reading frame (ORF) threshold of <300 nucleotides in length to be considered non-coding. Recent work has identified that many ncRNAs contain sORFs that encode for micropeptides. 5 , 59 , 60 We used the AlphaFold folding confidence metric pLDDT to assess for micropeptide coding potential since prior work has shown that sORFs from misannotated lncRNAs (i.e. lncRNAs that code for micropeptides) have high mean pLDDT values compared to random peptides and micropeptides for which there is high confidence that they are truly non-coding. 54 We found this to be the case for three lncRNAs ( G730003C15Rik, 9830004L10Rik , and Gm45012 ), indicating that they likely code for micropeptides. We found that two of those lncRNAs ( G730003C15Rik, 9830004L10Rik ) also had significantly lower mean PAE scores than controls. We observed alpha helical structures for the micropeptides coded G730003C15Rik, 9830004L10Rik , and Gm4501 , which is a pattern also observed for misannotated lncRNAs in prior work. 54 Lastly, positive PhyloCSF scores for two lncRNAs ( 6330403L08Rik and 2900072N19Rik ) indicate that they contain small (9-141 base pair) regions with phylogenetic patterns that are more likely in a coding model than a non-coding model. 22 Indications of micropeptide coding potential for these lncRNAs provides a rationale for future work on HCM samples, which could identify sORFs from lncRNAs that are being actively translated into micropeptides. 4 , 5 , 60 , 61 A limitation of our approach is that the human dataset comprised a smaller number of samples and a lower read depth (mean 57.32E6 (SD 7.03E6)) reads per sample) 14 compared to the mouse dataset (77.87E6 (SD 9.45E6)). This likely limited the power to identify DE lncRNA genes across both species. Addressing this limitation could be prioritized for future studies on the contribution of lncRNAs to HCM pathophysiology. Additionally, a small amount of variance in the RNA-Seq data can be explained by genotype, as evidenced by PC1 and PC2 only capturing 8.95% of variance ( Fig. 2B ). This could indicate that a large amount of the variance is driven by unknown factors. Moreover, sex and exercise are covariates that we had to control for in order to capture as many DE lncRNAs as possible. Lastly, the control peptide sequences did not always have the expected pattern for pLDDT and PAE values. For instance, at range 1-10, the FNC positive control lncRNAs had a lower score than the TNC and random negative controls. This is the opposite of the expected behavior of the controls and thus the high score for G730003C15Rik at this range may not actually indicate coding potential. In conclusion, we established a computational pipeline that identifies DE lncRNAs in a murine disease model, predicts human orthologs, merges analyses between mouse and human RNA-Seq data sets, and quantifies micropeptide coding potential. Using this pipeline, we identified lncRNAs that were both downregulated and upregulated in a mouse model of HCM, several of which had human orthologs and can be prioritized for future experimental studies to probe their role in HCM pathophysiology. DATA AVAILABILITY Mouse and human RNA-Seq data is from GSE297707 and GSE130036, respectively. SUPPLEMENTAL MATERIAL Supplemental Figures S1-3, Table T1, and Code C1 can be found at: https://github.com/gbranscom/lncrna_hcm/tree/main (DOI: https://doi.org/10.6084/m9.figshare.29604356.v1 ). GRANTS SMD is supported by NHLBI R33-HL-164376, R01-HL-168841, and R01-HL-167524, as well as a philanthropic gift and a Presidential Professorship that funded this project. DISCLOSURES The authors have no disclosures to make. AUTHOR CONTRIBUTIONS G.A.B. performed experiments, analyzed the data, interpreted the results of experiments, prepared figures, and drafted the manuscript. S.M.D. conceived and designed the study, along with editing, revising, and approving the final version of the manuscript. J.J.H. conceived the initial mouse study and performed the RNA isolation and sequencing. M.M. provided input into the bioinformatics approaches. J.M.Y., M.M., and J.J.H. provided technical support and critical review of the manuscript. ACKNOWLEDGMENTS We would like to acknowledge Taehyong Kim, Jingjing Li, Yifan Yang, Marcus Wagner, Christopher McAllister, Hyung Chul Kim, Emmitt Jolly, Dmitry Mylarshchikov, and the Penn Medicine Academic Computing Services staff for their technical assistance. Funder Information Declared NHLBI , R33-HL-164376 , R01-HL-168841 , R01- HL-167524 Presidential Professorship Philanthropic gift Footnotes https://doi.org/10.6084/m9.figshare.29604356.v1 REFERENCES 1. ↵ Mattick JS , Amaral PP , Carninci P , Carpenter S , Chang HY , Chen L-L , et al. Long non-coding RNAS: Definitions, functions, challenges and recommendations . Nature Reviews Molecular Cell Biology . 2023 Jan 3; 24 ( 6 ): 430 – 47 . doi: 10.1038/s41580-022-00566-8 OpenUrl CrossRef PubMed 2. ↵ Statello L , Guo C-J , Chen L-L , Huarte M. Gene regulation by long non-coding RNAS and its biological functions . Nature Reviews Molecular Cell Biology . 2020 Dec 22; 22 ( 2 ): 96 – 118 . doi: 10.1038/s41580-020-00315-9 OpenUrl CrossRef PubMed 3. ↵ Hombach S , Kretz M. Non-coding RNAS: Classification, biology and functioning . Advances in Experimental Medicine and Biology . 2016 ; 3 – 17 . doi: 10.1007/978-3-319-42059-2_1 OpenUrl CrossRef PubMed 4. ↵ Xiao Y , Ren Y , Hu W , Paliouras AR , Zhang W , Zhong L , et al. Long non-coding RNA-encoded micropeptides: Functions, mechanisms and implications . Cell Death Discovery . 2024 Oct 23; 10 ( 1 ). doi: 10.1038/s41420-024-02175-0 OpenUrl CrossRef 5. ↵ Makarewich CA , Olson EN . Mining for Micropeptides . Trends in Cell Biology . 2017 Sept ; 27 ( 9 ): 685 – 96 . doi: 10.1016/j.tcb.2017.04.006 OpenUrl CrossRef PubMed 6. ↵ Leong AZ-X , Lee PY , Mohtar MA , Syafruddin SE , Pung Y-F , Low TY . Short open reading frames (sorfs) and microproteins: An update on their identification and validation measures . Journal of Biomedical Science . 2022 Mar 17; 29 ( 1 ). doi: 10.1186/s12929-022-00802-5 OpenUrl CrossRef PubMed 7. ↵ Ho CY , Day SM , Ashley EA , Michels M , Pereira AC , Jacoby D , et al. Genotype and lifetime burden of disease in hypertrophic cardiomyopathy . Circulation . 2018 Oct 2; 138 ( 14 ): 1387 – 98 . doi: 10.1161/circulationaha.117.033200 OpenUrl CrossRef PubMed 8. ↵ Shahzadi SK , Naidoo N , Alsheikh-Ali A , Rizzo M , Rizvi AA , Santos RD , et al. Reconnoitering the role of long-noncoding RNAS in hypertrophic cardiomyopathy: A Descriptive Review . International Journal of Molecular Sciences . 2021 Aug 29; 22 ( 17 ): 9378 . doi: 10.3390/ijms22179378 OpenUrl CrossRef PubMed 9. ↵ Abbas M , Gaye A. Emerging roles of noncoding RNAS in cardiovascular pathophysiology . American Journal of Physiology-Heart and Circulatory Physiology . 2025 Mar 1; 328 ( 3 ). doi: 10.1152/ajpheart.00681.2024 OpenUrl CrossRef 10. ↵ Cunha WR , Martin de la Vega M , Rodrigues de Barros P , Espinosa-Diez C. LncRNAs in vascular senescence and microvascular remodeling . American Journal of Physiology-Heart and Circulatory Physiology . 2025 Jun 1; 328 ( 6 ). doi: 10.1152/ajpheart.00750.2024 OpenUrl CrossRef 11. ↵ Han P , Li W , Lin C-H , Yang J , Shang C , Nurnberg ST , et al. A long noncoding RNA protects the heart from pathological hypertrophy . Nature . 2014 Aug 10; 514 ( 7520 ): 102 – 6 . doi: 10.1038/nature13596 OpenUrl CrossRef PubMed Web of Science 12. ↵ Lv L , Li T , Li X , Xu C , Liu Q , Jiang H , et al. The lncrna PLSCR4 controls cardiac hypertrophy by regulating mir-214 . Molecular Therapy - Nucleic Acids . 2018 Mar ; 10 : 387 – 97 . doi: 10.1016/j.omtn.2017.12.018 OpenUrl CrossRef PubMed 13. ↵ Kitow J , Derda AA , Beermann J , Kumarswarmy R , Pfanne A , Fendrich J , et al. Mitochondrial long noncoding RNAS as blood based biomarkers for cardiac remodeling in patients with hypertrophic cardiomyopathy . American Journal of Physiology-Heart and Circulatory Physiology . 2016 Sept 1; 311 ( 3 ). doi: 10.1152/ajpheart.00194.2016 OpenUrl CrossRef PubMed 14. ↵ Liu X , Ma Y , Yin K , Li W , Chen W , Zhang Y , et al. Long non-coding and coding RNA profiling using strand-specific RNA-seq in human hypertrophic cardiomyopathy . Scientific Data . 2019 Jun 13; 6 ( 1 ). doi: 10.1038/s41597-019-0094-6 OpenUrl CrossRef 15. ↵ Yang W , Li Y , He F , Wu H. Microarray profiling of long non-coding RNA (lncrna) associated with hypertrophic cardiomyopathy . BMC Cardiovascular Disorders . 2015 Jul 4; 15 ( 1 ). doi: 10.1186/s12872-015-0056-7 OpenUrl CrossRef 16. ↵ Sun Y , Xiao Z , Chen Y , Xu D , Chen S. Susceptibility modules and genes in hypertrophic cardiomyopathy by WGCNA and cerna network analysis . Frontiers in Cell and Developmental Biology . 2022 Feb 1; 9 . doi: 10.3389/fcell.2021.822465 OpenUrl CrossRef 17. Zhang Y , Zhao J , Jin Q , Zhuang L. Transcriptomic analyses and experimental validation identified immune-related lncrna–mrna pair MIR210HG–BPIFC regulating the progression of hypertrophic cardiomyopathy . International Journal of Molecular Sciences . 2024 Feb 29; 25 ( 5 ): 2816 . doi: 10.3390/ijms25052816 OpenUrl CrossRef PubMed 18. ↵ Cao J , Yuan L. Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncrna and gene expression . Frontiers in Cardiovascular Medicine . 2022 Aug 4; 9 . doi: 10.3389/fcvm.2022.946229 OpenUrl CrossRef 19. ↵ Cui Y , Liu C , Luo J , Liang J. Dysfunctional network and mutation genes of hypertrophic cardiomyopathy . Journal of Healthcare Engineering . 2022 Jan 28; 2022 : 1 – 11 . doi: 10.1155/2022/8680178 OpenUrl CrossRef 20. ↵ Yu T , Huang Z , Pu Z. Identification of potential diagnostic biomarkers and biological pathways in hypertrophic cardiomyopathy based on bioinformatics analysis . Genes . 2022 Mar 17; 13 ( 3 ): 530 . doi: 10.3390/genes13030530 OpenUrl CrossRef 21. ↵ Yan Y , Tang R , Li B , Cheng L , Ye S , Yang T , et al. The cardiac translational landscape reveals that micropeptides are new players involved in cardiomyocyte hypertrophy . Molecular Therapy . 2021 Jul ; 29 ( 7 ): 2253 – 67 . doi: 10.1016/j.ymthe.2021.03.004 OpenUrl CrossRef PubMed 22. ↵ Lin MF , Jungreis I , Kellis M. PhyloCSF: A comparative genomics method to distinguish protein coding and non-coding regions . Bioinformatics . 2011 Jun 14; 27 ( 13 ): i275 – 82 . doi: 10.1093/bioinformatics/btr209 OpenUrl CrossRef PubMed Web of Science 23. ↵ Ponjavic J , Ponting CP , Lunter G. Functionality or transcriptional noise? evidence for selection within long noncoding RNAS . Genome Research . 2007 Mar 26; 17 ( 5 ): 556 – 65 . doi: 10.1101/gr.6036807 OpenUrl Abstract / FREE Full Text 24. ↵ Johnsson P , Lipovich L , Grandér D , Morris KV . Evolutionary conservation of long non-coding RNAS; sequence, structure, function . Biochimica et Biophysica Acta (BBA) - General Subjects . 2014 Mar ; 1840 ( 3 ): 1063 – 71 . doi: 10.1016/j.bbagen.2013.10.035 OpenUrl CrossRef PubMed 25. ↵ Zhou Q , Jiang Y , Cai C , Li W , Leow MK-S , Yang Y , et al. Multidimensional Conservation Analysis decodes the expression of conserved long noncoding RNAS . Life Science Alliance . 2023 Apr 5; 6 ( 6 ). doi: 10.26508/lsa.202302002 OpenUrl Abstract / FREE Full Text 26. Ruan X , Li P , Chen Y , Shi Y , Pirooznia M , Seifuddin F , et al. In vivo functional analysis of non-conserved human lncrnas associated with cardiometabolic traits . Nature Communications . 2020 Jan 2; 11 ( 1 ). doi: 10.1038/s41467-019-13688-z OpenUrl CrossRef PubMed 27. ↵ Iyer MK , Niknafs YS , Malik R , Singhal U , Sahu A , Hosono Y , et al. The landscape of long noncoding RNAS in the human transcriptome . Nature Genetics . 2015 Jan 19; 47 ( 3 ): 199 – 208 . doi: 10.1038/ng.3192 OpenUrl CrossRef PubMed 28. ↵ Chandra M , Tschirgi ML , Tardiff JC . Increase in tension-dependent ATP consumption induced by cardiac troponin T mutation . American Journal of Physiology-Heart and Circulatory Physiology . 2005 Nov ; 289 ( 5 ). doi: 10.1152/ajpheart.00571.2005 OpenUrl CrossRef PubMed Web of Science 29. ↵ Haim TE , Dowell C , Diamanti T , Scheuer J , Tardiff JC . Independent FHC-related cardiac troponin T mutations exhibit specific alterations in myocellular contractility and calcium kinetics . Journal of Molecular and Cellular Cardiology . 2007 Jun ; 42 ( 6 ): 1098 – 110 . doi: 10.1016/j.yjmcc.2007.03.906 OpenUrl CrossRef PubMed Web of Science 30. ↵ Dobin A , Davis CA , Schlesinger F , Drenkow J , Zaleski C , Jha S , et al. Star: Ultrafast universal RNA-seq aligner . Bioinformatics . 2012 Oct 25; 29 ( 1 ): 15 – 21 . doi: 10.1093/bioinformatics/bts635 OpenUrl CrossRef PubMed Web of Science 31. ↵ Trapnell C , Roberts A , Goff L , Pertea G , Kim D , Kelley DR , et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks . Nature Protocols . 2012 Mar ; 7 ( 3 ): 562 – 78 . doi: 10.1038/nprot.2012.016 Branscom 11 OpenUrl CrossRef PubMed 32. ↵ Wucher V , Legeai F , Hédan B , Rizk G , Lagoutte L , Leeb T , et al. FEELNC: A tool for long non-coding RNA annotation and its application to the dog transcriptome . Nucleic Acids Research . 2017 Jan 3; doi: 10.1093/nar/gkw1306 OpenUrl CrossRef PubMed 33. ↵ Li B , Dewey CN . RSEM: Accurate transcript quantification from RNA-seq data with or without a reference genome . BMC Bioinformatics . 2011 Aug 4; 12 ( 1 ). doi: 10.1186/1471-2105-12-323 OpenUrl CrossRef PubMed 34. ↵ Soneson C , Love MI , Robinson MD . Differential analyses for RNA-seq: Transcript-level estimates improve gene-level inferences . F1000Research . 2016 Feb 29; 4 : 1521 . doi: 10.12688/f1000research.7563.2 OpenUrl CrossRef 35. ↵ Love MI , Huber W , Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with deseq2 . Genome Biology . 2014 Dec 5; 15 ( 12 ). doi: 10.1186/s13059-014-0550-8 OpenUrl CrossRef PubMed 36. ↵ Mylarshchikov DE , Mironov AA . Ortho2align: A sensitive approach for searching for orthologues of novel lncrnas . BMC Bioinformatics . 2022 Sept 19; 23 ( 1 ). doi: 10.1186/s12859-022-04929-y OpenUrl CrossRef PubMed 37. ↵ Petrov AI , Kay SJ , Kalvari I , Howe KL , Gray KA , Bruford EA , et al. RNAcentral: A comprehensive database of non-coding RNA sequences . Nucleic Acids Research . 2016 Oct 28; 45 ( D1 ). doi: 10.1093/nar/gkw1008 OpenUrl CrossRef PubMed 38. ↵ Papatheodorou I , Moreno P , Manning J , Fuentes AM-P , George N , Fexova S , et al. Expression atlas update: From tissues to single cells . Nucleic Acids Research . 2019 Oct 30; doi: 10.1093/nar/gkz947 OpenUrl CrossRef PubMed 39. ↵ Zhu M , Gribskov M. MIPEPID: Micropeptide identification tool using machine learning . BMC Bioinformatics . 2019 Nov 8; 20 ( 1 ). doi: 10.1186/s12859-019-3033-9 OpenUrl CrossRef 40. ↵ Jumper J , Evans R , Pritzel A , Green T , Figurnov M , Ronneberger O , et al. Highly accurate protein structure prediction with alphafold . Nature . 2021 Jul 15; 596 ( 7873 ): 583 – 9 . doi: 10.1038/s41586-021-03819-2 OpenUrl CrossRef PubMed 41. ↵ Mirdita M , Schütze K , Moriwaki Y , Heo L , Ovchinnikov S , Steinegger M. Colabfold: Making protein folding accessible to all . Nature Methods . 2022 May 30; 19 ( 6 ): 679 – 82 . doi: 10.1038/s41592-022-01488-1 OpenUrl CrossRef PubMed 42. ↵ Meng EC , Goddard TD , Pettersen EF , Couch GS , Pearson ZJ , Morris JH , et al. UCSF ChimeraX: Tools for structure building and analysis . Protein Science . 2023 Oct 20; 32 ( 11 ). doi: 10.1002/pro.4792 OpenUrl CrossRef PubMed 43. ↵ Brito-Estrada O , Hassel KR , Makarewich CA . An integrated approach for microprotein identification and sequence analysis . Journal of Visualized Experiments . 2022 Jul 12;(185). doi: 10.3791/63841 OpenUrl CrossRef 44. ↵ Yu Q , Duan S , Xing X , Fan X , Zhang N , Song G , et al. Generation of a competing endogenous RNA network and validation of BNIP1 expression in the lung of irradiated mice . Translational Oncology . 2024 Sept ; 47 : 102007 . doi: 10.1016/j.tranon.2024.102007 OpenUrl CrossRef PubMed 45. O’Leary VB , Hain S , Maugg D , Smida J , Azimzadeh O , Tapio S , et al. Long non-coding RNA particle bridges histone and DNA methylation . Scientific Reports . 2017 May 11; 7 ( 1 ). doi: 10.1038/s41598-017-01875-1 OpenUrl CrossRef PubMed 46. ↵ O’Leary VB , Ovsepian SV , Smida J , Atkinson MJ . particle ™ the RNA podium for genomic silencers . Journal of Cellular Physiology . 2019 May 6; 234 ( 11 ): 19464 – 70 . doi: 10.1002/jcp.28739 OpenUrl CrossRef PubMed 47. ↵ O’Leary VB , Smida J , Buske FA , Carrascosa LG , Azimzadeh O , Maugg D , et al. Particle triplexes cluster in the tumor suppressor WWOX and may extend throughout the human genome . Scientific Reports . 2017 Aug 2; 7 ( 1 ). doi: 10.1038/s41598-017-07295-5 OpenUrl CrossRef PubMed 48. ↵ Akrami S , Tahmasebi A , Moghadam A , Ramezani A , Niazi A. Integration of mrna and protein expression data for the identification of potential biomarkers associated with pancreatic ductal adenocarcinoma . Computers in Biology and Medicine . 2023 May ; 157 : 106529 . doi: 10.1016/j.compbiomed.2022.106529 OpenUrl CrossRef PubMed 49. Xu H , Lu M , Liu Y , Ren F , Zhu L. Identification of a pyroptosis-related long non-coding RNA signature for prognosis and its related Cerna Regulatory Network of Ovarian Cancer . Journal of Cancer . 2023 ; 14 ( 16 ): 3151 – 68 . doi: 10.7150/jca.88485 OpenUrl CrossRef PubMed 50. ↵ Sanchez A , Lhuillier J , Grosjean G , Ayadi L , Maenner S. The long non-coding RNA anril in cancers . Cancers . 2023 Aug 17; 15 ( 16 ): 4160 . doi: 10.3390/cancers15164160 OpenUrl CrossRef PubMed 51. ↵ Moglad E , Kaur P , Menon SV , Abida, Ali H , Kaur M , et al. Anril’s epigenetic regulation and its implications for cardiovascular disorders . Journal of Biochemical and Molecular Toxicology . 2024 Dec ; 38 ( 12 ). doi: 10.1002/jbt.70076 OpenUrl CrossRef 52. ↵ da Cunha Agostini L , Almeida TC , da Silva GN . Anril, H19 and TUG1: A review about critical long non-coding RNAS in cardiovascular diseases . Molecular Biology Reports . 2023 Dec 28; 51 ( 1 ). doi: 10.1007/s11033-023-09007-x OpenUrl CrossRef 53. ↵ Congrains A , Kamide K , Oguro R , Yasuda O , Miyata K , Yamamoto E , et al. Genetic variants at the 9p21 locus contribute to atherosclerosis through modulation of Anril and CDKN2A/B . Atherosclerosis . 2012 Feb ; 220 ( 2 ): 449 – 55 . doi: 10.1016/j.atherosclerosis.2011.11.017 OpenUrl CrossRef PubMed Web of Science 54. ↵ Nabi A , Dilekoglu B , Adebali O , Tastan O. Discovering misannotated lncrnas using Deep Learning Training Dynamics . Bioinformatics . 2022 Dec 26; 39 ( 1 ). doi: 10.1093/bioinformatics/btac821 OpenUrl CrossRef 55. ↵ Monzon V , Haft DH , Bateman A. Folding the unfoldable: Using alphafold to explore spurious proteins . Bioinformatics Advances . 2022 Jan 1; 2 ( 1 ). doi: 10.1093/bioadv/vbab043 OpenUrl CrossRef 56. ↵ Szczesniak MW , Kubiak MR , Wanowska E , Makałowska I. Comparative genomics in the search for conserved long noncoding RNAS . Essays in Biochemistry . 2021 Oct ; 65 ( 4 ): 741 – 9 . doi: 10.1042/ebc20200069 OpenUrl CrossRef PubMed 57. ↵ Ni H , Ge Y , Zhuge Y , Liu X , Chen H , Liu J , et al. LncRNA MIR181A1HG deficiency attenuates vascular inflammation and atherosclerosis . Circulation Research . 2025 Apr 11; 136 ( 8 ): 862 – 83 . doi: 10.1161/circresaha.124.325196 OpenUrl CrossRef PubMed 58. ↵ Oo JA , Warwick T , Leisegang MS . Long noncoding RNA MIR181A1HG takes a proinflammatory driver’s seat in atherosclerosis by hijacking FOXP1 . Circulation Research . 2025 Apr 11; 136 ( 8 ): 884 – 6 . doi: 10.1161/circresaha.125.326366 OpenUrl CrossRef PubMed 59. ↵ Anderson DM , Anderson KM , Chang C-L , Makarewich CA , Nelson BR , McAnally JR , et al. A micropeptide encoded by a putative long noncoding RNA regulates muscle performance . Cell . 2015 Feb ; 160 ( 4 ): 595 – 606 . doi: 10.1016/j.cell.2015.01.009 OpenUrl CrossRef PubMed 60. ↵ Patraquim P , Magny EG , Pueyo JI , Platero AI , Couso JP . Translation and natural selection of micropeptides from long non-canonical RNAS . Nature Communications . 2022 Oct 31; 13 ( 1 ). doi: 10.1038/s41467-022-34094-y OpenUrl CrossRef PubMed 61. ↵ Pan J , Wang R , Shang F , Ma R , Rong Y , Zhang Y. Functional micropeptides encoded by long non-coding RNAS: A comprehensive review . Frontiers in Molecular Biosciences . 2022 Jun 13; 9 . doi: 10.3389/fmolb.2022.817517 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted July 31, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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