Deciphering the Host-Pathogen Interface in COVID-19: The precision molecular insight into epitranscriptomic modifications of high-impact transcripts

Deciphering the Host-Pathogen Interface in COVID-19: The precision molecular insight into epitranscriptomic modifications of high-impact transcripts | 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 Deciphering the Host-Pathogen Interface in COVID-19: The precision molecular insight into epitranscriptomic modifications of high-impact transcripts View ORCID Profile Mateusz Maździarz , View ORCID Profile Katarzyna Krawczyk , View ORCID Profile Ewa Lepiarczyk , View ORCID Profile Łukasz Paukszto , View ORCID Profile Karol G. Makowczenko , View ORCID Profile Beata Moczulska , Piotr Iwanowicz , View ORCID Profile Piotr Kocbach , View ORCID Profile Jakub Sawicki , View ORCID Profile Leszek Gromadziński , View ORCID Profile Marta Majewska doi: https://doi.org/10.1101/2025.01.14.632961 Mateusz Maździarz 1 Department of Botany and Evolutionary Ecology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn , Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mateusz Maździarz Katarzyna Krawczyk 1 Department of Botany and Evolutionary Ecology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn , Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katarzyna Krawczyk Ewa Lepiarczyk 2 Department of Human Physiology and Pathophysiology, School of Medicine, University of Warmia and Mazury in Olsztyn , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ewa Lepiarczyk Łukasz Paukszto 1 Department of Botany and Evolutionary Ecology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn , Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Łukasz Paukszto Karol G. Makowczenko 3 Department of Reproductive Immunology and Pathology, Institute of Animal Reproduction and Food Research of Polish Academy of Sciences , 10-748, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karol G. Makowczenko Beata Moczulska 4 Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury in Olsztyn , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Beata Moczulska Piotr Iwanowicz 5 Diagnostyka Medical Laboratories , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Piotr Kocbach 6 Department of Family Medicine and Infectious Diseases, School of Medicine, University of Warmia and Mazury in Olsztyn , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Piotr Kocbach Jakub Sawicki 1 Department of Botany and Evolutionary Ecology, Faculty of Biology and Biotechnology, University of Warmia and Mazury in Olsztyn , Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jakub Sawicki Leszek Gromadziński 4 Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury in Olsztyn , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leszek Gromadziński Marta Majewska 2 Department of Human Physiology and Pathophysiology, School of Medicine, University of Warmia and Mazury in Olsztyn , 10-082, Olsztyn, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marta Majewska For correspondence: marta.majewska{at}uwm.edu.pl Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a profound global impact since its emergence in late 2019. Characterized by a wide spectrum of clinical manifestations, ranging from asymptomatic infection to severe respiratory distress and death, COVID-19 has necessitated extensive research into the host-pathogen interactions that drive disease progression. Understanding the molecular mechanisms underlying the host response to SARS-CoV-2 infection is crucial for the development of effective therapeutic interventions and preventative strategies. This study employed a multi-omic approach that combined direct RNA sequencing (DRS) and Illumina cDNA sequencing to investigate whole blood transcriptomic profiles in COVID-19 patients. By leveraging the unique capabilities of Nanopore DRS, which provides long-read sequencing data, we were able to capture not only gene expression levels but also crucial RNA modifications, including poly(A) tail length, non-adenine residue (non-A), pseudouridylation (psU), and 5-methylcytosine (m5C) methylation. This comprehensive analysis allowed us to identify differentially expressed genes (DEGs) and explore the impact of these RNA modifications on gene expression and function within the context of COVID-19. Our findings reveal significant alterations in gene expression patterns, poly(A) tail lengths, non-A and the prevalence of psU and m5C modifications in COVID-19 patients compared to healthy controls. These results provide valuable insights into the complex interplay between viral infection, host immune response, and RNA processing, contributing to a deeper understanding of COVID-19 pathogenesis. Introduction The first documented case of COVID-19 emerged in Wuhan, China, in December 2019. Since then, the global case burden has surpassed 775 million, with over 7 million reported deaths ( https://covid19.who.int/ ), which had unprecedented social and economic consequences. Advancements in prophylactic vaccination strategies have significantly mitigated the pandemic’s severity 1 , leading the World Health Organization (WHO) to declare the end of the COVID-19 public health emergency in May 2023 ( https://news.un.org/en/story/2023/05/1136367 ). However, the emergence of novel variants with the potential to trigger surges in cases and mortality remains a concern, especially since the etiological agent responsible for COVID-19 disease, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 2 , unlike other respiratory viruses, does not follow normal seasonal fluctuations, and waves of infection can happen at any time of year 3 , 4 . Its genome encodes a repertoire of viral proteins, categorized into non-structural proteins crucial for viral replication and pathogenesis and structural proteins essential for virion assembly 5 , 6 . Despite the rapid global characterization of clinical symptoms associated with SARS-CoV-2 infection, a comprehensive understanding of the underlying host response and pathogenic mechanisms that govern disease progression toward recovery or fatality remains elusive. Elucidating the molecular foundations of COVID-19 pathogenesis is crucial for developing efficacious preventive and therapeutic strategies, ultimately aiming to reduce mortality and viral transmission. In our previous study, we investigated key genes engaged in SARS-CoV-2 infection using Illumina TruSeq RNA sequencing (RNA-seq) of peripheral blood samples collected from healthy donors and COVID-19 patients 78 . This time, to gain the most extensive insight into the whole blood transcriptomic profiles of the SARS-CoV-2-infected patients, we decided to exploit both RNA-seq short reads and Nanopore long reads. Nanopore sequencing technology enables direct, real-time analysis of long fragments of RNA in fully scalable formats. The transcripts obtained as a result of RNA long reads provide a range of valuable information, which is lost during the RNA-seq technique based on amplification, including RNA methylation, poly(A) tail sequence, and obtaining full-length transcript sequence 8 , 9 . This in-depth understanding of molecular changes induced by the virus is of utmost scientific and clinical importance, as it sheds light on the intricate interplay between viral infection, endothelial dysfunction, and immune responses in COVID-19. Such insights can guide the development of targeted therapeutic strategies to tackle the disease effectively. Results Chest computed tomography The most frequently observed CT findings demonstrated a strong correlation with a COVID-19 diagnosis. These included ground-glass opacities, crazy paving with superimposed interlobular and intralobular septal thickening, consolidations, bronchovascular thickening in lesions, and traction bronchiectasis. The ground-glass opacities and consolidations are usually located bilaterally, they are diffuse and they show peripheral and basal distribution. Peripheral distribution of the lesions, ground-glass opacities, and bronchovascular thickening in the lesions were found to have the highest value in diagnosing COVID-19 patients ( Fig. 1 ). Download figure Open in new tab Fig. 1 Noncontrast axial and coronal plane chest CT images of extensive inflammatory changes characteristic of COVID-19 disease. A CT images of a 67-year-old woman with Covid-19 for 7 days with mild COVID-19 pneumonia. Extensive ground-glass opacities with consolidation, primarily in the lower lobes, were noted, consistent with severe COVID-19 pneumonia. Approximately 50% of the lung parenchyma was affected. B CT images of a 90-year-old woman with Covid-19 for 2 days with mild COVID-19 pneumonia. Extensive bilateral ground-glass opacities with marked interlobular septal thickening, consistent with severe pulmonary involvement due to COVID-19 or pulmonary edema. Approximately 90% of the lung parenchyma was affected. Subsegmental atelectasis or consolidation was present in the lower lobes. Significant pleural effusions were noted, measuring up to 5 cm on the right and 2 cm on the left. Mediastinal lymphadenopathy was present, with nodes measuring up to 16 x 11 mm. C , D CT images of a 63-year-old man with Covid-19 for 7 days with mild COVID-19 pneumonia. The lungs exhibited marked hypoventilation with extensive, confluent ground-glass opacities and consolidations involving all lobes. Patchy, dense consolidations were most prominent in the upper lobe of the left lung, characteristic of severe COVID-19. Approximately 70-75% of the right lung parenchyma and 75-80% of the left lung parenchyma were affected. Comparative transcriptome analysis of COVID-19 patients reveals distinct gene expression signatures Direct RNA sequencing (DRS) provided information on the expression of 16,510 genes, of which 197 were identified as differentially expressed genes (DEGs). Among these molecules, 112 were downregulated and 85 were upregulated. The log2FoldChange ranged from -7.78 to 4.23 for all molecules ( Fig. 2A-D , Supplementary Table 1). Subsequently, a GO (Gene Ontology) analysis of all significant molecules was conducted, which were significantly involved in immune response (GO:0006955), response to virus (GO:0009615), and defense response (GO:0006952) (Supplementary Table 2). In the next stage of bioinformatics, cDNA expression generated by Illumina was analyzed. Sequencing using this method provided information on 36,689 genes. A total of 707 molecules were classified as DEGs, including 362 downregulated genes and 345 upregulated genes. The log2FoldChange ranged from -11.41 to 9.36 for all genes ( Fig. 2A-D , Supplementary Table 3). Subsequently, a GO analysis of all significant molecules was conducted, which were significantly involved in immune response (GO:0006955), response to virus (GO:0009615), and defense response (GO:0006952) (Supplementary Table 4). The next step involved plotting the Pearson correlation between the log2FoldChange of common genes. A total of 52 DEGs common to the cDNA and DRS methods were classified. Among the common genes, NFIX , LAP3 , IGLC3 , SAMD9L , and IFIT3 were identified ( Fig. 2A,C ). The correlation coefficient between their expression log2FC values was 0.96, with a correlation P-value of < 2.2e-16 and a 95% confidence interval of 0.9393051 to 0.9797827. It was found that similar expression signatures were indicated by the results of the correlation coefficients for common genes ( Fig. 2B ). Download figure Open in new tab Fig. 2 Gene expression profiling of controls and COVID patients. A The heatmap displays the normalized expression of 52 common genes for DRS and cDNA sequencing. Red color indicates values greater than 0, while blue indicates values less than 0. B The scatter plot illustrates the relationship between log2FoldChange values for the 52 DEGs identified in both sequencing methods. The x-axis represents log2FoldChange values for cDNA Illumina sequencing, while the y-axis depicts log2FoldChange values for DRS Nanopore sequencing. The red line highlights the Pearson correlation. The R value is displayed in the upper left corner. C The Venn diagram presents DEGs in cDNA Illumina (blue), DEGs in DRS Nanopore (green), and common DEGs (red). D DEGs characteristic of COVID-19 patients. The top panels correspond to DRS Nanopore, while the bottom panels represent cDNA Illumina sequencing. All DEGs are colored according to the Venn diagram. On the left, MA plots show the relationship between log2FoldChange and log2 (Mean of normalized counts), while on the right, Volcano plots show the relationship between -log10(padj) and log2FoldChange. Dissecting the mRNA landscape of COVID-19: A role for poly(A) tail dynamics A total of 2,029,252 poly(A) tails were analyzed. A statistically significant difference in the global distribution of poly(A) tail lengths was found between COVID-19 and control patients (P-value < 2.2e-16). 6,524 transcripts with poly(A) tails were identified ( Fig. 3A , Supplementary Table 5). Lengthening of poly(A) tails was observed in as many as 879 genes in COVID-19 patients, compared to only 8 in the control group ( Fig. 3G ). Subsequently, a GO analysis of all significant transcripts was conducted, which were significantly involved in immune response (GO:0006955), response to virus (GO:0009615), and defense response (GO:0006952) (Supplementary Table 6). Download figure Open in new tab Fig. 3 Poly(A) tail length variation across the genes. A-F The density plots depict the normalized transcript density on the y-axis and the length of poly(A) tails on the x-axis. Panel A shows the global distribution of poly(A) tails, panel B shows the poly(A) distribution of intersected DEGs, while panels C to F present the poly(A) distribution for specific genes: LAP3 , IGLC3 , SAMD9L , and IFIT3 , respectively. G Illustrates genes with distinct poly(A) characteristics. The left panel displays a modified MA plot, depicting the relationship between log2FoldChange (based on poly(A) tail length) and the mean poly(A) tail length. Conversely, the right panel presents a Volcano plot, showcasing the relationship between log2FoldChange (based on poly(A) tail length) and - log10(padj). Significant genes are highlighted in blue, with shortened tails represented by circles and lengthened tails denoted by squares. Furthermore, differences in poly(A) tail lengths in differentially expressed genes (DEGs) common to both methods were examined. A significant result was obtained, indicating a lengthening of poly(A) tails in DEGs of COVID-19 patients (P-value < 2.2e-16) ( Fig. 3B ). Additionally, differences in poly(A) tail lengths were examined for individual genes: LAP3 (P-value = 0.046) ( Fig. 3C ), IGLC3 (P-value < 2.2e-16) ( Fig. 3D ), SAMD9L (P-value = 0.7385) ( Fig. 3E ), and IFIT3 (P-value = 0.026) ( Fig. 3F ). In addition to adenine, other nucleotides such as guanine, uracil, and cytosine were identified within poly(A) tails. A global analysis of the frequency of non-adenine (non-A) residues was conducted, revealing that guanine (22,129 for COVID-19 patients and 15,447 for control patients) was found more frequently poly(A) tails than cytosine (20,300 for COVID-19 patients and 13,165 for control patients) or uracil (19,141 for COVID-19 patients and 12,576 for control patients) ( Fig. 4A , Supplementary Table 7). A shift in this trend was observed in DEGs common to both methods. Cytosine (911) was determined to be the most frequent non-A residue in COVID-19 patients, whereas uracil (556) was found to be the most frequent in the control group. Guanine (700 for COVID-19 patients and 357 for control patients) was identified as the least frequent non-A residue in both groups ( Fig. 4A,B , Supplementary Table 8). Download figure Open in new tab Fig. 4 Characterization of Non-A Residues in COVID Samples and Control. A Non-A occurrence patterns. The left panel visualizes the overall frequency of non-A residues in control and COVID patient samples. The right panel depicts the distribution of non-A residues within differentially expressed genes (DEGs) shared between both groups. B Poly(A) tail illustration. The blue signal represents the adapter, the red signal indicates the poly(A) tail and the black signal corresponds to the transcript. The first panel showcases a canonical poly(A) tail, while subsequent panels demonstrate poly(A) tails with cytosine, guanine, or uracil substitutions, respectively. Impact of pseudouridine modifications on RNA in COVID-19 Pseudouridine (psU) was identified as the most common post-transcriptional modification of RNA. A total of 1,214 significant positions were detected in COVID patients and 664 positions in control patients, with 180 positions being common to both groups ( Fig. 5A , Supplementary Table 10). These positions were in 1,034 transcripts in COVID patients and 590 transcripts in control patients ( Fig. 5B ). Positions within genes were also examined, amounting to 788 genes in COVID patients and 469 genes in controls ( Fig. 5D ). GO analysis of genes with statistically significant psU provided information about the immune response (GO:0006955), response to virus (GO:0009615), and defense response (GO:0006952) as significant processes in the COVID group (Supplementary Table 11). The response to virus (GO:0009615) was not detected as a significant process in the control group, while immune response (GO:0006955) and defense response (GO:0006952) were identified as significant processes (Supplementary Table 12). Download figure Open in new tab Fig. 5 Distribution of pseudouridine and poly(A) tail lengths in control and COVID-19 samples. A,B,D Venn diagrams illustrate the occurrence of psU in the control group (red), COVID-19 group (blue), and the overlap between the two groups (dark red) for A positions, B transcripts, and D genes, respectively. C,F Density plots depict the normalized transcript density on the y-axis and the length of poly(A) tails on the x-axis. Panel C shows the distribution of poly(A) tail lengths in transcripts with psU present in both groups. Panel F corresponds to the distribution of poly(A) tail lengths in transcripts with psU in the COVID-19 group (left, blue) and in the control group (right, red). The next step in our analysis involved examining the length of poly(A) tails in transcripts where psU was detected in both groups. A lengthening of poly(A) tails was observed in transcripts with psU in COVID patients compared to control patients (P-value < 2.2e-16) ( Fig. 5C ). We also decided to examine differences in poly(A) tail lengths between transcripts with psU and uracil in the same genes in both the COVID and control groups. In both cases, a lengthening of the tail was observed in transcripts with psU, with a P-value < 2.2e-16 for both groups ( Fig. 5F ). Furthermore, the relationship between the occurrence of psU and transcript length was investigated. Again, transcripts with psU and uracil in the same genes were compared in both the COVID and control groups. Longer transcripts were observed with detected psU compared to those with uracil, with a P-value < 2.2e-16 for the COVID group and P-value = 1.311e-11 for the control group ( Fig. 5E , Supplementary Table 13,14). Comparative Analysis of m5C Methylation Between Control and COVID-19 Groups The CHEUI program enabled us to compare m5C methylation sites between the COVID-19 and control groups. A total of 153,845 positions were identified and subjected to differential analysis based on pval_U and stoichiometry_diff. The analysis revealed 689 significant positions within 398 transcripts, which were mapped to 161 genes. 366 positions exhibited stoichiometry_diff 0.1 (Higher) ( Fig. 6A , Supplementary Table 15). GO analysis revealed significant processes such as the T cell receptor signaling pathway (GO:0050852) and alpha-beta T cell activation (GO:0046631). Molecules with significant m5C did not reveal immune response (GO:0006955), response to virus (GO:0009615), and defense response (GO:0006952) as significant processes (Supplementary Table 16). The genes involved in the observed replacement of hydrogen atoms with methyl groups within RNA molecules were not found to be associated with immunological functions. Unlike pseudouridylation, this modification was not considered to have a direct impact on the organism’s immediate defense mechanisms. Genes CSF3R , CSDE1 , and KCNAB2 were found to be most enriched with m5C modifications, with 44, 20, and 19 modifications, respectively. Download figure Open in new tab Fig. 6 Distribution of m5C methylation and poly(A) tail lengths in control and COVID-19 samples. A Volcano plot shows -log10(pval_U) on the x-axis and stoichiometry_diff on the y-axis. Statistically significant positions are colored green (Lower) and red (Upper). Gray dots represent statistically insignificant m5C methylation positions. B Boxplots show the relationship between the log2 of transcript length for transcripts with cytosine (light green) and m5C (dark green). C Density plot shows the distribution of poly(A) tail lengths in transcripts with cytosine (light green) and m5C (dark green). The y-axis represents the normalized transcript density, and the x-axis represents the length of poly(A) tails. As a next step in the bioinformatic analyses, the lengths of transcripts and poly(A) tails were examined, similar to the analysis conducted on psU. Transcripts with m5C were observed to be longer than those with cytosine in the same gene (P-value = 3.496e-13), analogous to the findings for psU ( Fig. 6B , Supplementary Table 17). Similarly, the lengths of poly(A) tails were found to be longer in transcripts with detected m5C compared to transcripts with cytosine in the same gene (P-value < 2.2e-16) ( Fig. 6C , Supplementary Table 18). Alternative polyadenylation and its impact on COVID-19 The most frequent alternative polyadenylation (APA) motifs identified were AAUAAA (8 345) and AUUAAA (2 033) ( Fig. 7C,F , Supplementary Table 18). APA motifs were found to be most abundant in the three prime untranslated regions (3’UTR) – 11,104 times, in exons - 426 times, in intergenic regions - 402 times, and in introns - 140 times ( Fig. 7D ). APA sites were observed to occur in various proportions: 1 site was identified in 6 124 genes, 2 sites in 1 613 genes, and 3 sites in 458 genes ( Fig. 7E ). Analysis of APA site use differences in genes identified 27 significant genes with APA sites, of which 23 were found to have a Δusage value greater than 0.3 and 4 were found to have a Δusage value less than 0.3 ( Fig. 7A,B , Supplementary Table 19). Genes exhibiting significantly different APA site usage towards COVID-19 patients included BMI1 , JAZF1 , NIPBL , and DDX46 , while in control patients, RTF1 , PGS1 , SPCS3 , and CCNG2 were identified ( Fig. 7A ). The GO analysis of genes with significant APA revealed the involvement of six significant processes: (GO:1902494), protein-containing complex (GO:0032991), intracellular protein-containing complex (GO:0140535), nucleoplasm (GO:0005654), nuclear lumen (GO:0031981), intracellular membrane-bounded organelle (GO:0043231) (Supplementary Table 20). Download figure Open in new tab Fig. 7 Identification of APA site patterns and associated features. A Heatmap visualizes use of APA sites in genes. Darker purple indicates a value closer to 1. Gene names are listed on the right, and patient names are listed at the bottom of the heatmap. B Volcano plot shows the relationship between -log10(padj) and Δusage. Gray dots represent statistically insignificant APA sites, green dots represent statistically significant APA sites with Δusage 0.3. C Barplots show the frequency of each motif. The x-axis shows the APA motifs and the y-axis shows their count. D Barplots show the location of APA sites and their frequency. The x-axis shows the specific location, and the y-axis shows the number of APA sites at that location. E Barplots show the number of APA sites per gene. The x-axis shows the number of APA sites, and the y-axis shows the frequency of that number of APA sites in the groups. F Barplots show nucleotide frequencies at each position of the detected APA sites. The x-axis shows the position number, and the y-axis the nucleotide frequency. Adenine is represented by green, cytosine by blue, guanine by yellow, and uracil by red. Discussion Differential expression analysis, which involves counting the number of reads per gene in an RNA sequence, has become the primary method for identifying systematic changes across experimental conditions 10 . Studies have shown that combining two sequencing methods can provide additional information through gene expression analysis. Furthermore, it has been shown that these methods could be complementary 11 . The present investigation identified 52 DEGs common to both cDNA and DRS methods. GO analysis of these DEGs categorized them into ‘immune response’, ‘response to virus’, and ‘defense response’ terms. Among the common genes, 2’-5’-Oligoadenylate Synthetase ( OAS1 ), Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3A ( APOBEC3A ) and Interferon Induced Protein 44 ( IFI44 ) were identified. The present study revealed that the SARS-CoV-2 infection was followed by the upregulation of OAS1 which was earlier recognized as a strong antiviral factor against this virus. It has been found that the inactivation of OAS1’s catalytic activity resulted in the loss of its antiviral function by activating RNase L, which degrades both cellular and viral RNA 12 . Investigations have demonstrated that another gene upregulated in COVID-19 patients, namely APOBEC3A , is a critical factor in the inhibition of coronaviruses, by restricting the RNA virus replication 13 . A similar result most probably is exerted by an overexpression of IFI44 in COVID-19 patients. IFI44 belongs to interferon (IFN)-stimulated genes and has been previously found to control respiratory syncytial virus (RSV) infection by exerting antiviral and antiproliferative properties 14 . The primary objective of the present study, however, was not to focus on the differential expression of genes detected in the SARS-CoV-2 infected patients, as we have already described them in our previous study 7 , but to leverage the advantages of RNA long-read sequencing. As Nanopore DRS is based on single-molecule RNA detection, it offers a unique opportunity to examine multiple epitranscriptomic features of individual RNA molecules such as poly(A) tail length, and occurrence of alternative polyadenylation sites, m5C sites and pseudouridine sites, which are often lost during amplification-based RNA-seq techniques and have not been comprehensively explored in COVID-19 patients. Furthermore, the understanding of the functional consequences of RNA modifications has enabled the development of highly effective mRNA-based vaccines for COVID-19 15 . Poly(A) tails were found to be dynamic elements of transcripts, rather than static units simply marking the 3’ end of mRNA, which significantly influence the post-transcriptional regulation of the fragile balance of mRNA survival–degradation 16 . The analysis of these structures may provide novel insights into the post-transcriptional regulation of gene expression in the contexts of development, differentiation, and various disease states 17 . In our study, lengthening of poly(A) tails was observed in as many as 879 genes in COVID-19 patients, compared to only 8 in the control group. The GO analysis of all transcripts with significant poly(A) length changes revealed their significant contribution to terms such as immune response, response to virus and defense response again. This intriguing observation raises a question: why do post-transcriptionally modified transcripts exhibit increased length? As the dynamic part of the poly(A) tail is also longer in these transcripts possibly this elongation may enhance the recruitment of ribosomes and thus facilitate translation initiation. Investigations conducted on Xenopus laevis oocytes demonstrated no discernible disparity in the translation of mRNAs with poly(A) tails of 32 or 150 nucleotides in length 18 , 19 . Moreover, it was found that transcripts with poly(A) tails shorter than 16 nucleotides were not translated. Furthermore, the frequency of molecules possessing fewer than 30 adenines at the 3’ end of the poly(A) tail was significantly lower within the cellular environment. During current research, we identified a change of poly(A) tails median length between the COVID-19 (81 nt) and control patients (62 nt). Moreover, our investigations revealed a significant increase in the lengths of exons, introns, and poly(A) tails within transcripts bearing m5C and psU modifications. The shorter poly(A) tail length observed in modified RNAs suggests a link between the modification and the regulation of the 3’ end 20 . The length of mRNA can influence the density of ribosomes bound to it, which in turn may affect the efficiency of protein synthesis. Shorter gene isoforms are less likely to be targeted by ribosomes compared to longer isoforms, leading to a direct impact on the protein synthesis 21 . Longer genes tend to be associated with functions that are important in the early embryonic stages, while smaller genes tend to play a role in functions that are important throughout the whole life, like the actions of the immune system, which require fast responses 22 . Conversely, extended poly(A) tails may safeguard genes from rapid enzymatic degradation 23 , 24 . In the present study, both sequencing methods detected significant poly(A) tail elongation in DEGs identified in COVID-19 patients for instance in the Interferon-inducible protein with tetrapeptide repeats 3 ( IFIT3 ), Immunoglobulin Lambda Constant 3 ( IGLC3 ), Leucine Aminopeptidase 3 ( LAP3 ) or Sterile Alpha Motif Domain Containing 9 Like ( SAMD9L ). Genetic or epigenetic variations that promote IFIT3 expression in response to viral infection may contribute to protection against SARS-CoV-2. IFIT3 restricts viral spread by binding to viral RNA and inhibiting translation 25 . Moreover, IFIT3 amplifies the interferon response by stabilizing the IFIT1 and IFIT2 protein complexes, further enhancing their antiviral effects and upregulating the expression of ISGs, including the previously described OAS1 26 – 29 . Type I and type II interferons are crucial components of the innate immune response to viral infections, including SARS-CoV-2 30 . A significant upregulation of the IFN-stimulated genes (ISG) has been detected during COVID–19 disease. Overexpression of IFIT3 has been detected in uninfected or asymptomatic females who were repeatedly exposed to their symptomatic COVID-19 male partners 31 . Given its potential role in antiviral immunity, the IFIT3 offers a promising avenue for understanding the mechanisms underlying protection from SARS-CoV-2 infection. Elucidating these mechanisms may have significant implications for developing new therapeutic strategies. NSP16 is essential to the SARS-CoV-2 replication cycle because it is essential to coronavirus’ immune evasion 32 . NSP16 is a 2′-O-methyltransferase (2′-O-MTase) that forms part of the replication-transcription complex 33 . Inhibition of NSP16 enhances SARS-CoV-2 susceptibility to IFN-I-induced antiviral effectors, such as IFIT1 and IFIT3 34 . Patients with severe COVID-19 displayed increased B cell activation and upregulation of IGLC3, a marker of antibody processing. These findings suggest a robust antibody response to enhance the host protection and enhanced interferon signaling in these individuals 35 . Single-cell RNA sequencing revealed IGLC3 among the other 15 differentially expressed genes between patients who survived COVID-19 infection 36 . Accurate identification of prognostic factors in critical COVID-19 patients can aid in risk assessment and guide tailored therapeutic interventions. SARS-CoV-2 infection alters the host gene expression profile, leading to the upregulation of interferon-stimulated genes, including LAP3 . The interferon IFN-stimulated ISG15 had the largest increase in serum of COVID-19 patients, followed by several other IFN-induced proteins, such as LAP3 37 . Similarly to SAMD9L was among the differentially regulated interferon-stimulated genes in mild and severe disease cohorts, suggesting that it may play a critical role in SARS-CoV-2 pathogenesis 38 . Previous studies demonstrated that the SAMD9L pathway acts as a crucial host defense mechanism, which poxviruses actively suppress to establish infection 39 . Notably, this pathway was identified among the interferon-stimulated genes (ISGs) exhibiting significantly reduced expression in patients with severe COVID-19 compared to those with mild cases. The identification of SAMD9L as a downregulated gene in severe COVID-19 highlights its potential role as a critical host restriction factor that SARS-CoV-2 must overcome to establish infection 38 . Moreover in people with severe COVID-19 the infection reveals a diminished antiviral response marked by the downregulation of antiviral genes such as OAS1 , SAMD9L and IFIT2 , and suppression of antiviral immune response pathways 40 . Shortening the 3’UTR through alternative polyadenylation (APA) may be a key mechanism contributing to COVID-19 pathogenesis. APA-mediated reduction of 3’UTR length can increase gene expression by evading miRNA-mediated silencing during SARS-CoV-2 infection 41 . Moreover, global 3′UTR shortening affects protein abundance, and the impact of the 3′UTR on protein production may depend on the gene. However, the APA of the genes confers different functions and needs further investigation. It was observed that the expression of 3′ processing factors was down-regulated when cells were infected by vesicular stomatitis virus, which might be one of the reasons underlying genome-wide APA when cells were infected with viruses 41 . It has been found that the expression level of 3′ processing factors is also altered in COVID-19 patients. SARS-CoV-2 proteins can bind to APA factors affecting the gene expression level of APA factors to regulate this process 41 . Additionally, alternative polyadenylation has been suggested to impair antigen presentation by MHC molecules in infected cells. Disrupting alternative polyadenylation and splicing could further enhance the ability of SARS-CoV-2 to evade the host immune response 42 . The poly(A) tail of mRNA has been conventionally considered a homogenous stretch of adenosine nucleotides, devoid of significant information content beyond its length. However, the non-canonical poly(A) polymerases, TENT4A and TENT4B, have been identified as enzymes capable of incorporating non-A nucleotides, such as guanine, uracil, and cytosine, into the poly(A) tail 43 . While the function of this mechanism remains unclear, it is hypothesized that the presence of these non-adenine nucleotides (non-A mutations) may impede the activity of deadenylase enzymes, slowing the rate of poly(A) tail shortening and increasing the mRNA stability 16 , 43 However, Poly(A)-binding protein can stimulate the removal of adenine residues from the poly(A) tail, a process known as deadenylation. This contrasts with the expectation that stable, highly translated mRNAs would possess longer poly(A) tails 23 . It has to be mentioned that the present study revealed also the non-A residues in both COVID and control samples, and SARS-CoV-2-infected patients exhibited increased cytosine content and decreased guanine content in non-A residues. The methylation of cytosine residue position 5 (m5C), is a widespread RNA modification catalyzed by members of the NOL1/NOP2/SUN domain (NSUN) family (NSUN1-NSUN7) particularly NSUN2 which has the broadest substrate specificity. 5-methylcytosine (m5C) modification was found to play a crucial role in many aspects of RNA processing, tRNA stability, rRNA assembly, and mRNA translation. The effect of viral infection on m5C process modifications is observed. Using bisulfite sequencing, infection with Simian retrovirus was found to affect a total of 2475 m5C sites located on 517 genes related to viral infection 44 . The functional significance of 5-methylcytosine (m5C) modification in viral biology remains largely unknown. SARS-CoV-2 gRNAs carry abundant m5C modifications that affect the virus’s life cycle 45 , 46 Low levels of m5C modification in viral RNAs caused by NSUN2 depletion were beneficial to viral replication and infection. Moreover, NSUN2-mediated m5C modification in different regions of viral transcripts reduced the stability and levels of the corresponding mRNA. NSUN2 was found to be a critical host factor in limiting SARS-CoV-2 infection, as NSUN2-deficient mice exhibited more severe infection and lung tissue damage compared to control mice 47 . Certainly, m5C modifications in SARS-CoV-2 transcripts are crucial to viral replication and pathogenicity. In the current investigation, we discovered 689 m5C methylation sites, assigned to the T cell receptor signaling pathway and alpha-beta T cell activation. Jiang et al. 48 used methylated RNA immunoprecipitation sequencing to identify m5C modifications in lncRNAs of H1N1-infected and uninfected A549 cells. These authors recognized 2984 differentially modified (DM) m5C sites on lncRNAs in influenza A virus-infected cells, including 1317 upregulated and 1667 downregulated sites, compared to the uninfected group. Moreover, the target genes of methylated lncRNAs were enriched in virus infection-related pathways (e.g. Epstein-Barr virus infection, Measles and Herpes simplex virus 1 infection). In our data, among the analyzed genes, CSF3R , CSDE1 , and KCNAB2 were found to be most enriched with m5C modifications. CSF3R is assumed to be an inflammation-related gene that participates in the immune response to SARS-CoV-2 infection and was significantly upregulated in the blood immune cells of COVID-19 patients 49 . Moreover, it acts as a receptor to promote the activation of granulocytes and macrophages, particularly, which can subsequently upregulate the production of inflammatory cytokines 50 . It has been revealed that CSDE1 is required for translation in human rhinovirus and poliovirus 51 . SARS-CoV-2’s success relies on its ability to repurpose host RNA-binding proteins (RBPs) and to evade antiviral RBPs. Transcriptome analyses identified CSDE1 as a proviral RBP that influences multiple stages of the mRNA lifecycle 52 . CSDE1 knockdown inhibits SARS-CoV-2 replication by suppressing viral RNA levels 52 . Moreover, SARS-CoV-2 may recruit CSDE1 to regulate IRES-dependent translation initiation 53 of SARS-CoV-2 gRNA and sgRNAs. KCNAB2, a regulatory beta subunit of the potassium voltage-gated channels was recognized as an interferon-stimulated gene upregulated, specific to type I IFN-mediated antiviral signaling upon SARS-CoV-2 infection 54 . The present study focused also on the pseudouridylation, the modification of uridine to pseudouridine (psU), which is one of the most abundant post-transcriptional modifications of RNA 55 . The current research revealed a total of 1,214 significant psU modification sites in COVID-19 patients and 664 in control patients, with 180 sites shared between the two groups. The GO analysis of genes with statistically significant P-values revealed that the COVID-19 group was significantly enriched for biological processes related to immune response, response to virus and defense response once again. Additionally, a lengthening of poly(A) tails was observed in transcripts with psU in COVID patients compared to control patients. Pseudouridylation has been implicated in regulating both translational processes and cellular stress responses. Alterations in the dynamic pseudouridylation landscape have been previously associated with the pathogenesis of cancer and genetic disorders 56 . The elevated translation rates observed for pseudouridylated mRNA can be attributed to the increased tendency of unmodified mRNA to undergo activation upon binding to RNA-dependent protein kinase 56 , 57 . Interestingly, present data concerning significant psU modification sites in SARS-CoV-2 infected patients revealed results comparable to these obtained in human HeLa cells 58 . It has been also found, that the strategic incorporation of N1-methyl-pseudouridine (m1Ψ) modifications into mRNA vaccines has been crucial for mitigating their innate immunogenicity and facilitating their efficacy against COVID-19 56 , 57 . Izadpanah et al. 59 suggested that SARS-CoV-2 is able to escape the host immune response through the viral RNA pseudouridylation process. According to the current results, the poly(A) tails of pseudouridylated transcripts were statistically longer in COVID-19 (median of 85 nt) compared to the control group (median of 69 nt). We may assume that such a protection mechanism is utilized by viruses to protect against, i.e., rapid degradation of viral transcripts by the host’s enzymes. Conclusions To the best of our knowledge, this is the first study to decipher in such a deep extent both Nanopore long reads and RNA-seq datasets to investigate the whole blood transcriptomic profiles of the SARS-CoV-2 infected patients by providing comprehensive insights into the epitranscriptome features and post-transcriptional modifications. The identification of DEGs such as OAS1 , APOBEC3A , and IFI44 , as well as genes associated with immune responses, highlights the robust activation of antiviral pathways during COVID-19. Notably, the investigation revealed extensive poly(A) tail lengthening in COVID-19 patients, particularly among immune-related transcripts, suggesting an adaptive mechanism to enhance transcript stability and translation efficiency. Epitranscriptome modifications, including m5C and pseudouridylation, were significantly associated with altered transcript length and immune responses. The identification of specific genes enriched in m5C and psU modifications, such as CSF3R , CSDE1 , and KCNAB2 , underscores the potential regulatory roles of these modifications in SARS-CoV-2 infection. Exploring further host–SARS-CoV-2 interactions at a deep molecular level may be a fascinating focus for research for future therapeutic treatment. Material and Methods Patients and sample collection Peripheral blood samples were collected from nine healthy donors, coded as 1-9, and six patients diagnosed with COVID-19, coded as 10-15. The subjects with confirmed cases of COVID-19 were enrolled at the Clinical Department of Communicable Diseases in Ostróda, Poland. The patients in question ranged in age from 54 to 70 years. They fulfilled the requisite criteria for a viral diagnosis of SARS-CoV-2, with viral genes confirmed by RT-PCR analysis of nasopharyngeal swabs. The RT-PCR reactions were conducted using the commercially available COVID-19 Real-Time Multiplex RT-PCR Kit (Labsystems Diagnostics OY, Vantaa, Finland). The kit is designed to detect the ORF1ab, N, and E genes of the SARS-CoV-2 genome in a single reaction. The RT-PCR reactions were conducted following the manufacturer’s recommended protocol, and the results were analysed using a QuantStudio™ 5 Real-Time PCR System instrument. The inclusion criteria comprised a positive PCR test for SARS-CoV-2, as well as a clinical diagnosis of COVID-19 requiring hospitalisation. The exclusion criteria encompassed patients with neoplasms, autoimmune disease, a pressive or immunodeficient state, and human immunodeficiency virus (HIV) infection. The control group was constituted by volunteers who had tested negative for SARS-CoV-2 infection and showed no signs of respiratory tract infections or lung pathologies, as confirmed by a physician. The control group was constituted in compliance with the following inclusion criteria (verified by a screening questionnaire): absence of a history of travel to high-risk areas, lack of admission to the vaccine, lack of known exposure to a proven or suspected case of SARS-CoV-2 in the previous 14 days, absence of upper or lower respiratory tract infection or any other active illness at the time of blood collection, and lack of past or current history of serious chronic disease such as immune disease. Whole blood samples (3 mL) were collected from all patients (1-15) and placed into Tempus™ Blood RNA Tubes (Applied Biosystems, Waltham, Massachusetts, USA). These samples were stored at -80°C until analysis time. The study was conducted following the ethical standards outlined in the Declaration of Helsinki and received approval from the Bioethics Committee of the Warmia-Mazury Medical Chamber (OIL.3/2021/Bioet) in Olsztyn, Poland. All participants provided written informed consent, as evidenced by their signature, to take part in the study. Chest Computed Tomography Chest computed tomography (CT) was employed as the primary diagnostic tool for managing patients during the initial stages of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. A peripheral arterial oxygen saturation level ≤ 93% was one of the criteria used for hospital admission, according to the institutional protocol. All patients underwent a chest CT scan without intravenous contrast in the supine position (Toshiba Medical System, Aquilion Prime type TSX-303A/BK; a tube kilovoltage (kV), 120-135 kV, tube current 530-600 mA, 160 layers, 80 rows). The scans were analyzed using the Osirix MD 11.0™ software (Pixmeo Company, Bernex, Suiça) by two radiologists with experience in chest CT, without previous knowledge of the RT-PCR results of the individual patients. Chest CT scans were qualitatively assessed to identify opacity types, specifying their morphology, distribution, and percentage of involvement of the lung parenchyma. Total RNA extraction from peripheral blood The total RNA was isolated from the whole blood of both the experimental and control groups using the Tempus™ Spin RNA Isolation Kit (Applied Biosystems, Waltham, Massachusetts, USA). Before extraction, the Tempus tubes containing the patient’s blood were thawed and transferred into a 50 mL tube. Subsequently, 3 mL of PBS (Ca²⁺/Mg²⁺-free) was added to reach a total volume of 12 mL. The tubes were vigorously vortexed for a minimum of 30 s and subsequently centrifuged at 4°C at 3000×g for 30 min. Following this, the supernatant was carefully poured off, and the RNA pellet was purified under the manufacturer’s instructions. Finally, total RNA quantity and quality were evaluated utilising an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Nanopore direct RNA sequencing (DRS) Total RNA isolates were enriched for mRNA using NEBNext® Poly(A) mRNA Magnetic Isolation Module (New England Biolabs) which removed ribosomal RNA (rRNA). Long read libraries were then prepared from 50 ng of poly(A)-tailed mRNA per sample using the Direct RNA Sequencing Kit SQK-RNA002 (Oxford Nanopore Technologies) following the manufacturer’s protocol. SuperScript III Reverse Transcriptase (Thermo Fisher Scientific) was used in the first step of library preparation, which is the synthesis of the complementary strand to the RNA, creating an RNA-cDNA hybrid. Next, sequencing adapters were attached using T4 DNA Ligase (2M U/ml, New England Biolabs) in combination with NEBNext® Quick Ligation Reaction Buffer. The libraries were quantified with the Qubit dsDNA HS Assay Kit (ThermoFisher) and sequenced on a MinION MK1C sequencing device (ONT) using FLO-MIN 106 Flow Cells R.9.4.1 (ONT). The Flow Cells were prepared for sequencing with the Flow Cell Priming Kit EXP-FLP002 (ONT). Long-read digital MinION signals were first converted from POD5 to FAST5 format using the pod5-file-format program ( https://github.com/nanoporetech/pod5-file-format ). Next, transcriptomic sequences were basecalled by Guppy v.6.0.0 ( https://community.nanoporetech.com/docs/prepare/library_prep_protocols/Guppy-protocol/v/gpb_2003_v1_revax_14dec2018/guppy-software-overview ). Short-read RNA sequencing RNA sequencing generating short reads was performed by an external service provider, Macrogen (Amsterdam, The Netherlands), utilizing the Illumina technology (Illumina, San Diego, CA, USA). Briefly, the quality (RIN) and quantity of isolated RNA were assessed using the Tapestation 2200 (Agilent Technologies, Santa Clara, CA, USA). Samples with a RIN value greater than 7.0 were selected for further processing. Sequencing libraries were constructed using the Illumina TruSeq Stranded Total RNA with Ribo-Zero Plus rRNA Depletion kit (Illumina, San Diego, CA, USA), following the manufacturer’s protocol outlined in the TruSeq Stranded mRNA Reference Guide (#1000000092426 v01). Generated libraries were quantified using both qPCR and the KAPA Library Quantification Kit (Roche, Pleasanton, CA, USA). Subsequently, libraries were normalized, pooled in equimolar concentrations, and sequenced on the Illumina NovaSeq 6000 platform in a paired-end configuration (2 x 150 bp). Binary base call (BCL) files were converted into FASTQ format using the Illumina bcl2fastq v.2.19 package ( https://github.com/brwnj/bcl2fastq ). Following conversion, the sequencing data was directed for further analysis. Expression profiling based on nanopore DRS The FASTQ raw reads were then quality-checked and subjected to mapping steps against a reference Homo sapiens genome v.GRCh38. This mapping was performed using minimap2 v.2.26 software with -ax splice option (10.1093/bioinformatics/bty191). Gene expression profiles were subsequently estimated using featureCounts v.2.0.6 (10.1093/bioinformatics/btt656), based on a GTF file (GRCh38.p14) from which information about hemoglobin coding genes had been removed. A statistical test based on a negative binomial model and shrink, implemented in DESeq2 v.1.42.0 60 was additionally employed for analysis. The statistical significance of differentially expressed genes (DEGs) was determined using the following parameters: adjusted P-value 1. Expression profiling based on cDNA Illumina sequencing Raw reads were trimmed using Trimmomatic v.0.39 ( https://github.com/usadellab/Trimmomatic ) 61 with the following parameters: crop: 140, leading: 20, trailing: 20, minlen: 140, avgqual: 20 . Next, STAR v.2.7.11b ( https://github.com/alexdobin/STAR ) 62 was used to map FASTQ files against H. sapiens reference genome v.GRCh38 utilizing ENCODE standard parameters. Gene count information was obtained using featureCounts with the reference GTF file (without hemoglobin genes). Then, similar to DRS, DESeq2 was used to assess the significance of differential gene expression. The fluctuation of gene expression with adjusted P-value 1 was considered statistically significant. The Pearson correlation was plotted between the log2(Fold Change) values from cDNA Illumina sequencing and DRS for molecules that showed statistical significance. Differential adenylation The FASTQ files were remapped into H. sapiens transcriptome (GRCh38.p14). Tail information for each transcript was then extracted using the nanopolish v.0.14.1 tool ( https://github.com/jts/nanopolish ). Subsequently, a statistical method based on the Wilcoxon test was applied by the nanotail v.0.1.0 package ( https://github.com/smaegol/nanotail ) to determine the differences in tail length between molecules transcribed in both conditions. Only reads tagged by nanopolish as ‘pass’ or ‘suffclip’ were considered in the following analyses. mRNA tails shorter than 10 bp and transcripts with fewer than 10 counts were excluded from the analysis. Poly(A) tails of genes with an adjusted P-value less than 0.05 were considered statistically significant. Statistical significance of overall tail length difference between COVID-19 and control patients was assessed using the Wilcoxon test. Non-adenine residue analysis Previously generated nanopolish outcomes, sequencing summary generated by the Guppy basecaller, and FAST5 files were used to identify non-adenine (non-A) sites in the poly(A) tail. This identification was performed by the ninetails v.1.0.0 software ( https://github.com/MystPi/ninetails ). Alternative polyadenylation sites detection The BAM files generated from mapping long reads to a reference genome were utilized to identify and analyze variations in alternative polyadenylation sites (APA). The LAPA program 63 was used to predict statistically significant APA sites, which considered the following parameters adjusted P-value 0.3. 5-methylcytosine sites detection The FAST5 files were combined to enable the re-execution of basecalling for both the COVID-19 and control groups. The resulting FASTQ files were re-mapped to the transcriptome sequence using the minimap2 v.2.26 software with the -ax map-ont parameter. The resulting BAM files were then sorted using samtools v.1.16.1 64 . Signal data was rescaled to the aligned sequences using nanopolish. The CHEUI tool ( https://github.com/comprna/CHEUI ) 65 was used to extract 5-methylcytosine (m5C) differential information. Preprocessing involved predictions of stoichiometry values and modification probabilities at transcriptomic sites using two models. Next, sites that occurred in both control and COVID patients were subjected to m5C differential analysis, in which only sites with P-value 0.1 were deemed statistically significant. Pseudouridine sites detection The combined FASTQ files from both conditions (COVID-19 and healthy patients) were divided into separate datasets, after which pseudouridine site identification was conducted using the NanoSPA tool ( https://github.com/sihaohuanguc/NanoSPA ). The reference transcriptome mapped by long reads was analyzed with the following parameters: remove_intron , extract_features , and prediction_psU functions with default options. Pseudouridine sites with a probability > 0.95 were considered statistically significant. Functional annotations All statistically significant molecules were then scanned for enrichment analysis in Gene Ontology (GO) annotations 66 , 67 using the g:profiler v.0.2.2 R package 68 . For the essential genes, biological processes (BP), cellular components (CC) and molecular functions (MF) terms were assigned as ontological annotations. Enrichment analysis was subsequently employed to identify GO terms regulated by significant molecules, using an adjusted P-value < 0.05 cut-off. Transcript length fluctuation Transcript length information was obtained from the reference GTF ( H. sapiens v.GRCh38). Differences in transcript lengths between transcripts with detected significant RNA modifications and the other transcripts of the same gene were compared using the Wilcoxon test. A significance level of P-value < 0.05 was set. Visualization The visualizations were generated using the R environment and the following packages: ggplot2 v.3.5.1 69 , ComplexHeatmap v.2.18.0 70 , and ggvenn v.0.1.10 ( https://github.com/yanlinlin82/ggvenn ). Furthermore, visualizations offered by the previously employed software were leveraged. Data availability The data underlying this article are available in the European Nucleotide Archive repository, under accession numbers PRJEB84380, PRJEB74103 ( https://www.ebi.ac.uk/ena/browser/view/PRJEB84380 , https://www.ebi.ac.uk/ena/browser/view/PRJEB74103 ). Contributions Conceptualization, M.A.M., and M.M.; Formal Analysis, M.A.M.; Software, M.A.M., E.L., K.G.M., and M.M.; Methodology, M.A.M., K.K., J.S., and M.M.; Investigation, M.A.M., K.K., E.L., Ł.P., K.G.M., P.I., J.S., L.G., and M.M.; Resources, B.M., P.I., P.K., L.G., and M.M.; Writing - original draft: M.A.M., K.K., E.L., K.G.M., P.I., J.S., L.G., and M.M.; Supervision M.M.; Project administration M.M.; Funding acquisition J.S. and L.G.; All authors read and approved this manuscript. Competing interests The authors declare no competing interests. Footnotes https://www.ebi.ac.uk/ena/browser/view/PRJEB84380 https://www.ebi.ac.uk/ena/browser/view/PRJEB74103 References 1. ↵ Rahmani , W. et al. Attenuation of SARS-CoV-2 infection by losartan in human kidney organoids . iScience 25 , 103818 ( 2022 ). OpenUrl CrossRef PubMed 2. ↵ Zhu , N. et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019 . N Engl J Med 382 , 727 – 733 ( 2020 ). OpenUrl CrossRef PubMed 3. ↵ Silk , B. J. et al. COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration ― United States, May 11, 2023 . MMWR Morb Mortal Wkly Rep 72 , 523 – 528 ( 2023 ). OpenUrl CrossRef PubMed 4. ↵ Zhang , H. ping et al. Recent developments in the immunopathology of COVID-19 . Allergy: European Journal of Allergy and Clinical Immunology vol. 78 369 – 388 Preprint at doi: 10.1111/all.15593 ( 2023 ). OpenUrl CrossRef 5. ↵ Astuti , I. & Ysrafil . Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): An overview of viral structure and host response . Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14 , 407 – 412 ( 2020 ). OpenUrl CrossRef 6. ↵ Uddin , M. et al. SARS-CoV-2/COVID-19: Viral Genomics, Epidemiology, Vaccines, and Therapeutic Interventions . Viruses 12 , 526 ( 2020 ). OpenUrl CrossRef PubMed 7. ↵ Majewska , M. et al. SARS-CoV-2 disrupts host gene networks: Unveiling key hub genes as potential therapeutic targets for COVID-19 management . Comput Biol Med 183 , 109343 ( 2024 ). OpenUrl CrossRef PubMed 8. ↵ Wang , Y. , Zhao , Y. , Bollas , A. , Wang , Y. & Au , K. F . Nanopore sequencing technology, bioinformatics and applications . Nat Biotechnol 39 , 1348 – 1365 ( 2021 ). OpenUrl CrossRef PubMed 9. ↵ MacKenzie , M. & Argyropoulos , C . An Introduction to Nanopore Sequencing: Past, Present, and Future Considerations . Micromachines (Basel) 14 , 459 ( 2023 ). OpenUrl CrossRef PubMed 10. ↵ Love , M. I. , Anders , S. , Kim , V. & Huber , W . RNA-Seq workflow: gene-level exploratory analysis and differential expression . F1000Research 2016 4:1070 4 , 1070 ( 2016 ). OpenUrl 11. ↵ Maździarz , M. et al. Epitranscriptome insights into Riccia fluitans L. (Marchantiophyta) aquatic transition using nanopore direct RNA sequencing . BMC Plant Biol 24 , 1 – 16 ( 2024 ). OpenUrl CrossRef PubMed 12. ↵ Danziger , O. , Patel , R. S. , DeGrace , E. J. , Rosen , M. R. & Rosenberg , B. R . Inducible CRISPR Activation Screen for Interferon-Stimulated Genes Identifies OAS1 as a SARS-CoV-2 Restriction Factor . PLoS Pathogens vol. 18 ( 2022 ). 13. ↵ Milewska , A. et al. APOBEC3-mediated restriction of RNA virus replication . Sci Rep 8 , 1 – 12 ( 2018 ). OpenUrl CrossRef PubMed 14. ↵ Busse , D. C. et al. Interferon-Induced Protein 44 and Interferon-Induced Protein 44-Like Restrict Replication of Respiratory Syncytial Virus . J Virol 94 , e00297 – 20 ( 2020 ). OpenUrl PubMed 15. ↵ Nance , K. D. & Meier , J. L . Modifications in an Emergency: The Role of N1-Methylpseudouridine in COVID-19 Vaccines . ACS Cent Sci 7 , 748 – 756 ( 2021 ). OpenUrl CrossRef PubMed 16. ↵ Liu , Y. , Nie , H. , Zhang , Y. , Lu , F. & Wang , J . Comprehensive analysis of mRNA poly(A) tail reveals complex and conserved regulation . bioRxiv 2021.08.29.458068 ( 2021 ) doi: 10.1101/2021.08.29.458068 . OpenUrl Abstract / FREE Full Text 17. ↵ Jalkanen , A. L. , Coleman , S. J. & Wilusz , J . Determinants and implications of mRNA poly(A) tail size--does this protein make my tail look big? Semin Cell Dev Biol 34 , 24 – 32 ( 2014 ). OpenUrl CrossRef PubMed 18. ↵ Nudel , U. , et al. Globin mRNA Species Containing Poly(A) Segments of Different Lengths . Eur J Biochem 64 , 115 – 121 ( 1976 ). OpenUrl CrossRef PubMed 19. ↵ Mercer , J. F. B. & Wake , S. A . An analysis of the rate of metallothionein mRNA POLY(A)-shortening using RNA blot hybridization . Nucleic Acids Res 13 , 7929 – 7943 ( 1985 ). OpenUrl CrossRef PubMed Web of Science 20. ↵ Kim , D. et al. The Architecture of SARS-CoV-2 Transcriptome . Cell 181 , 914 – 921 .e10 ( 2020 ). OpenUrl CrossRef PubMed 21. ↵ Fernandes , L. D. , Moura , A. P. S. D. & Ciandrini , L . Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights . Scientific Reports 2017 7:1 7 , 1 – 11 ( 2017 ). OpenUrl CrossRef PubMed 22. ↵ Lopes , I. , Altab , G. , Raina , P. & de Magalhães , J. P . Gene Size Matters: An Analysis of Gene Length in the Human Genome . Front Genet 12 , 559998 ( 2021 ). OpenUrl CrossRef PubMed 23. ↵ Passmore , L. A. & Coller , J . Roles of mRNA poly(A) tails in regulation of eukaryotic gene expression . Nature Reviews Molecular Cell Biology 2021 23:2 23 , 93 – 106 ( 2021 ). OpenUrl PubMed 24. ↵ Alles , J. , Legnini , I. , Pacelli , M. & Rajewsky , N . Rapid nuclear deadenylation of mammalian messenger RNA . iScience 26 , 105878 ( 2023 ). OpenUrl CrossRef PubMed 25. ↵ Hsu , J. C.-C. , Laurent-Rolle , M. & Cresswell , P . Translational regulation of viral RNA in the type I interferon response . Curr Res Virol Sci 2 , 100012 ( 2021 ). OpenUrl CrossRef 26. ↵ Chai , B. et al. Murine ifit3 restricts the replication of rabies virus both in vitro and in vivo . Journal of General Virology 102 , 001619 ( 2021 ). OpenUrl 27. Chikhalya , A. et al. Human IFIT3 Protein Induces Interferon Signaling and Inhibits Adenovirus Immediate Early Gene Expression . mBio 12 , ( 2021 ). 28. Imaizumi , T. , et al. IFIT Proteins Are Involved in CXCL10 Expression in Human Glomerular Endothelial Cells Treated with a Toll-Like Receptor 3 Agonist . Kidney Blood Press Res 46 , 74 – 83 ( 2021 ). OpenUrl CrossRef PubMed 29. ↵ Xu , S. et al. IFIT3 Is Increased in Serum from Patients with Chronic Hepatitis B Virus (HBV) Infection and Promotes the Anti-HBV Effect of Interferon Alpha via JAK-STAT2 In Vitro . Microbiol Spectr 10 , ( 2022 ). 30. ↵ Watson , A. et al. Dynamics of IFN-β responses during respiratory viral infection insights for therapeutic strategies . Am J Respir Crit Care Med 201 , 83 – 94 ( 2020 ). OpenUrl CrossRef PubMed 31. ↵ de Castro , M. V. et al. Potential protective role of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) in COVID-19 . Front Cell Infect Microbiol 14 , 1464581 ( 2024 ). OpenUrl CrossRef PubMed 32. ↵ Daffis , S. et al. 2′-O methylation of the viral mRNA cap evades host restriction by IFIT family members . Nature 2010 468:7322 468 , 452 – 456 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 33. ↵ Vithani , N. et al. SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential . Biophys J 120 , 2880 – 2889 ( 2021 ). OpenUrl CrossRef PubMed 34. ↵ Schindewolf , C. et al. SARS-CoV-2 Uses Nonstructural Protein 16 To Evade Restriction by IFIT1 and IFIT3 . J Virol 97 , ( 2023 ). 35. ↵ Garduno , A. et al. Parallel Dysregulated Immune Response in Severe Forms of COVID-19 and Bacterial Sepsis via Single-Cell Transcriptome Sequencing . Biomedicines 11 , 778 ( 2023 ). OpenUrl CrossRef PubMed 36. ↵ Amrute , J. M. et al. Cell specific peripheral immune responses predict survival in critical COVID-19 patients . Nature Communications 2022 13:1 13 , 1 – 11 ( 2022 ). OpenUrl CrossRef PubMed 37. ↵ Babačić , H. et al. Comprehensive proteomics and meta-analysis of COVID-19 host response . Nature Communications 2023 14:1 14 , 1 – 18 ( 2023 ). OpenUrl CrossRef PubMed 38. ↵ Sandi , J. D. et al. Upper Airway Epithelial Tissue Transcriptome Analysis Reveals Immune Signatures Associated with COVID-19 Severity in Ghanaians . J Immunol Res 2024 , 6668017 ( 2024 ). OpenUrl CrossRef PubMed 39. ↵ Meng , X. et al. A paralogous pair of mammalian host restriction factors form a critical host barrier against poxvirus infection . PLoS Pathog 14 , e1006884 ( 2018 ). OpenUrl CrossRef PubMed 40. ↵ Goodnow , C. C . COVID-19, varying genetic resistance to viral disease and immune tolerance checkpoints . Immunol Cell Biol 99 , 177 – 191 ( 2021 ). OpenUrl CrossRef 41. ↵ Jia , X. et al. The role of alternative polyadenylation in the antiviral innate immune response . Nature Communications 2017 8:1 8 , 1 – 12 ( 2017 ). OpenUrl CrossRef PubMed 42. ↵ An , S. et al. Genome-Wide Profiling Reveals Alternative Polyadenylation of Innate Immune-Related mRNA in Patients With COVID-19 . Front Immunol 12 , 756288 ( 2021 ). OpenUrl CrossRef PubMed 43. ↵ Lim , J. et al. Mixed tailing by TENT4A and TENT4B shields mRNA from rapid deadenylation . Science (1979) 361 , 701 – 704 ( 2018 ). OpenUrl Abstract / FREE Full Text 44. ↵ Wei , Z. et al. Topological Characterization of Human and Mouse m5C Epitranscriptome Revealed by Bisulfite Sequencing . Int J Genomics 2018 , 1351964 ( 2018 ). OpenUrl PubMed 45. ↵ Wang , H. et al. Epitranscriptomic m5C methylation of SARS-CoV-2 RNA regulates viral replication and the virulence of progeny viruses in the new infection . Sci Adv 10 , 9519 ( 2024 ). OpenUrl CrossRef 46. ↵ Lin , X. et al. Azacytidine targeting SARS-CoV-2 viral RNA as a potential treatment for COVID-19 . Sci Bull (Beijing) 67 , 1022 – 1025 ( 2022 ). OpenUrl CrossRef PubMed 47. ↵ Zou , Y. , Guo , Z. , Ge , X. Y. & Qiu , Y . RNA Modifications in Pathogenic Viruses: Existence, Mechanism, and Impacts . Microorganisms 2024, Vol. 12, Page 2373 12 , 2373 ( 2024 ). OpenUrl CrossRef 48. ↵ Jiang , S. et al. Transcriptome-wide 5-methylcytosine modification profiling of long non-coding RNAs in A549 cells infected with H1N1 influenza A virus . BMC Genomics 24 , 1 – 14 ( 2023 ). OpenUrl CrossRef PubMed 49. ↵ Qin , S. et al. Novel insight into the underlying dysregulation mechanisms of immune cell-to-cell communication by analyzing multitissue single-cell atlas of two COVID-19 patients . Cell Death & Disease 2023 14:4 14 , 1 – 12 ( 2023 ). OpenUrl CrossRef PubMed 50. ↵ Jaillon , S. et al. Neutrophil diversity and plasticity in tumour progression and therapy . Nature Reviews Cancer 2020 20:9 20 , 485 – 503 ( 2020 ). OpenUrl CrossRef PubMed 51. ↵ Jiang , L. , et al. High-sensitivity profiling of SARS-CoV-2 noncoding region–host protein interactome reveals the potential regulatory role of negative-sense viral RNA . mSystems 8 , ( 2023 ). 52. ↵ Lee , S. et al. The SARS-CoV-2 RNA interactome . Mol Cell 81 , 2838 – 2850 .e6 ( 2021 ). OpenUrl CrossRef PubMed 53. ↵ Lee , K. M. , Chen , C. J. & Shih , S. R . Regulation Mechanisms of Viral IRES-Driven Translation . Trends Microbiol 25 , 546 – 561 ( 2017 ). OpenUrl CrossRef PubMed 54. ↵ Vanderboom , P. M. et al. Proteomic Signature of Host Response to SARS-CoV-2 Infection in the Nasopharynx . Molecular & Cellular Proteomics 20 , 100134 ( 2021 ). OpenUrl CrossRef PubMed 55. ↵ De Zoysa , M. D. & Yu , Y. T. Posttranscriptional RNA Pseudouridylation . Enzymes (Essen) 41 , 151 – 167 ( 2017 ). OpenUrl CrossRef 56. ↵ Morais , P. , Adachi , H. & Yu , Y. T . The Critical Contribution of Pseudouridine to mRNA COVID-19 Vaccines . Front Cell Dev Biol 9 , 789427 ( 2021 ). OpenUrl CrossRef PubMed 57. ↵ Karikó , K. et al. Incorporation of Pseudouridine Into mRNA Yields Superior Nonimmunogenic Vector With Increased Translational Capacity and Biological Stability . Molecular Therapy 16 , 1833 – 1840 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 58. ↵ Tavakoli , S. et al. Semi-quantitative detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct long-read sequencing . Nature Communications 2023 14:1 14 , 1 – 12 ( 2023 ). OpenUrl CrossRef PubMed 59. ↵ Izadpanah , A. , Rappaport , J. & Datta , P. K . Epitranscriptomics of SARS-CoV-2 Infection . Front Cell Dev Biol 10 , 1 – 12 ( 2022 ). OpenUrl 60. ↵ Love , M. I. , Huber , W. & Anders , S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol 15 , 1 – 21 ( 2014 ). OpenUrl CrossRef PubMed 61. ↵ Bolger , A. M. , Lohse , M. & Usadel , B . Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 30 , 2114 – 2120 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 62. ↵ Dobin , A. et al. STAR: ultrafast universal RNA-seq aligner . Bioinformatics 29 , 15 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 63. ↵ Çelik , M. H. & Mortazavi , A . Analysis of alternative polyadenylation from long-read or short-read RNA-seq with LAPA . bioRxiv 2022.11.08.515683 ( 2022 ) doi: 10.1101/2022.11.08.515683 . OpenUrl Abstract / FREE Full Text 64. ↵ Li , H. et al. The Sequence Alignment/Map format and SAMtools . Bioinformatics 25 , 2078 – 2079 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 65. ↵ Acera Mateos , P. , et al. Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications . Nat Commun 15 , 1 – 17 ( 2024 ). OpenUrl CrossRef PubMed 66. ↵ Ashburner , M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium . Nat Genet 25 , 25 – 9 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 67. ↵ Carbon , S. et al. Expansion of the gene ontology knowledgebase and resources: The gene ontology consortium . Nucleic Acids Res 45 , D331 – D338 ( 2017 ). OpenUrl CrossRef PubMed 68. ↵ Kolberg , L. et al. g:Profiler—interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update) . Nucleic Acids Res 51 , W207 – W212 ( 2023 ). OpenUrl CrossRef PubMed 69. ↵ Villanueva , R. A. M. & Chen , Z. J . ggplot2: Elegant Graphics for Data Analysis (2nd ed.) . Measurement (Mahwah N J) 17 , 160 – 167 ( 2019 ). OpenUrl 70. ↵ Gu , Z. , Eils , R. & Schlesner , M . Complex heatmaps reveal patterns and correlations in multidimensional genomic data . Bioinformatics 32 , 2847 – 2849 ( 2016 ). OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 15, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. 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