- Research
- Open access
- Published:
Revealing differential expression patterns of piRNA in FACS blood cells of SARS-CoV−2 infected patients
BMC Medical Genomics volume 17, Article number: 212 (2024)
Abstract
Non-coding RNA expression has shown to have cell type-specificity. The regulatory characteristics of these molecules are impacted by changes in their expression levels. We performed next-generation sequencing and examined small RNA-seq data obtained from 6 different types of blood cells separated by fluorescence-activated cell sorting of severe COVID−19 patients and healthy control donors. In addition to examining the behavior of piRNA in the blood cells of severe SARS-CoV−2 infected patients, our aim was to present a distinct piRNA differential expression portrait for each separate cell type. We observed that depending on the type of cell, different sorted control cells (erythrocytes, monocytes, lymphocytes, eosinophils, basophils, and neutrophils) have altering piRNA expression patterns. After analyzing the expression of piRNAs in each set of sorted cells from patients with severe COVID−19, we observed 3 significantly elevated piRNAs - piR−33,123, piR−34,765, piR−43,768 and 9 downregulated piRNAs in erythrocytes. In lymphocytes, all 19 piRNAs were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 5 upregulated (piR−49039 piR−31623, piR−37213, piR−44721, piR−44720) and 35 downregulated. It has been previously shown that piR−31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated.
Introduction
Non-coding RNAs (ncRNA) are functional RNA molecules that are not translated into a protein. There are various types of ncRNAs, including transfer RNAs (tRNAs), microRNAs, small interfering RNAs (siRNAs), PIWI-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs), and long ncRNAs such as Xist and HOTAIR [1]. The functional roles of ncRNAs in gene expression regulation have been widely recognized. They are known to modulate chromatin structure, regulate the assembly and function of nuclear bodies, alter the stability and translation of mRNAs, and interfere with signaling pathways in various ways [2].
The study of ncRNAs is an active area of research, and there is ongoing debate about the functional significance of various ncRNAs [3].
piRNAs are a large subclass of small non-coding RNA molecules [4]. piRNA molecules primarily function in the epigenetic and post-transcriptional silencing of genetic elements in germ line cells by forming complexes with piwi-subfamily Argonaute proteins [5, 6]. They are created from loci that function as transposon traps, providing a kind of RNA-based immune system against transposons, which are DNA sequences that can change their position within the genome [6,7,8].
In addition to their role in transposon silencing, piRNAs are also involved in the regulation of other genetic elements in germ line cells. They are primarily expressed in the testes and ovaries of mammals and are essential for germline development and fertility [9]. The dysregulation of piRNAs has been associated with various diseases [10, 11], including cancer [12], where they can influence gene expression in somatic cells [13]. Nevertheless, the extent to which expression profiles differ in different cell types has been relatively poorly studied. In particular, the composition of piRNAs in different types of blood cells remains insufficiently researched in both normal and pathological conditions.
In a recent study of peripheral leukocytes of rheumatoid arthritis patients it has been shown that two immunoregulation piRNAs (piR-hsa−27620 and piR-hsa−27124) were significantly elevated in patients with rheumatoid arthritis [14]. Latest advancements in sequencing and molecular technologies, along with in silico studies have improved the annotation and functional validation of ncRNAs, enabling a better understanding of their roles in specific cell types and diseases [15]. However, the functional significance of ncRNAs, especially in poorly conserved species, remains a subject of ongoing research.
The interactions of piRNA with SARS-CoV−2 has been an active area of research in recent years. In a study published in 2022 it was observed that piRNAs can interact with the SARS-CoV−2 genome, potentially blocking protein synthesis and controlling the virus’s replication [15]. Another study in 2023 confirmed that piRNAs can interact with the Omicron variant of SARS-CoV−2, highlighting the importance of piRNAs in controlling the virus’s spread [16]. Additionally, there has been research demonstrating that exosomes and microvesicles released by murine neural stem cells contain piRNAs and can target SARS-CoV−2, suppressing its replication [17]. These studies collectively demonstrate the potential of piRNAs in controlling SARS-CoV−2 replication and highlight the importance of further research into the role of piRNAs in antiviral immunity.
Given the fact how significantly COVID−19 alters the biochemical and morphologic features of blood cells [18,19,20], and taking in account the promising research on interrelations between ncRNA immune regulatory features and COVID−19 [21,22,23], it is necessary to state the importance of studying piRNA expression in blood cells of SARS-CoV−2 infected patients.
The aim of our research was to study the repertoire of piRNAs in different blood cells and changes in expression levels of these molecules during severe COVID−19.
Materials and methods
Patients and data collection
Eleven research participants in total were split up into two groups: six healthy donors and five COVID−19 patients. The six healthy donors in our control group had a mean age of 51 ± 22. The five COVID−19 patients, with an average age of 67 ± 11, arrived at our institution between April 1, 2022, and August 23, 2022. Real-time RT-PCR was used in the laboratory to confirm that all patients had SARS-CoV−2 infection. Two patients were transferred to the infectious disease unit and three patients were admitted to the intensive care unit. The following criteria were met by severe patients for ICU admission: body temperature ≥ 39 °C, respiration rate ≥ 30/min, and oxygen saturation (SpO2) ≤ 93%. These criteria are outlined in our national clinical recommendations. Patient’s clinical data which is relevant to the study is presented in Table 1. (inflammatory markers and cell properties) The full clinical data is presented in the supplementary materials section.
Cell sorting and scanning electron microscopy
Sample staining for flow cytometry and subsequent sorting was carried out according to a standardized technique [20].
Erythrocyte sorting was carried out by the following technique:
10 µl whole blood was added to 100 µl PBS, 2 µl anti cd235 (IM2212U) was added, 2 µl cd41 (A07781). 20 min incubated in dark, dissolved to 1 ml phosphate-buffered saline (PBS), next, fluorescent sorting was carried out up to 5 million events on a MoFlo Astrios EQ flow cytometer sorter (equipped with 405, 488, 645). (Fig. 1; Panel A).
Leukocyte sorting was carried out by 2 different methods as follows:
100 µl of heparinized peripheral blood was taken, 1 ml of VersaLyse lysing solution (Beckman Coulter, USA) was added to each tube, stirred and incubated for 10 min. 10 µl of appropriate monoclonal antibodies were added: CD45-APC-AlexaFluor®750+ (A79392 Beckman Coulter, USA), CD16-PC7+(6607118 Beckman Coulter, USA), CD14-PC5.5+(A70204 Beckman Coulter, USA) was used to distinguish lymphocytes and monocytes.
The samples were stirred by vortex mixing and incubated for 15–20 min at room temperature in the dark. Afterwards, 1 mL of phosphate buffer (PBS) was added and centrifuged for 5 min at 1500 rpm, the supernatant was removed and 1 mL of buffer solution was added to each tube. Resuspended precipitant was analyzed on a MoFlo Astrios EQ flow cytometer sorter (equipped with 405, 488, 645). (Fig. 1; Panel C, D)
For isolating subpopulations of granulocytes - eosinophils, basophils and neutrophils the duraclone IM granulocyte antibody panel (B88651) (Beckman Coulter, USA) was used.
Sorting was performed through 70 μm diameter nozzles, 50 000−100 000 events for the leukocyte cell population, into sterile 12 × 75 eppendorfs. (Fig. 1; panel G, H, I, J)
Cell sample purity for all cell populations was > 95% according to flow cytometry data: (Fig. 1; panel B, E, F, L, M, N)
A standardized protocol for sample preparation for scanning electron microscopy (SEM) was followed [24]. Further analysis was performed using a Zeiss Merlin scanning electron microscope in high resolution mode with EHT 0.400 kV and a magnification of 1.00 KX.
RNA separation, library preparation and next generation sequencing
RNA was isolated\extracted using ExtractRNA reagent (Evrogen) according to the provided protocol. After extraction, RNA was dissolved in 10 µl RNAse free water. The quality of leukocyte RNA was checked on a TapeStation instrument (Agillent). Only samples with RIN > 5 were used for the preparation of leukocyte libraries. From each sample we aquired 10 µL of RNA solution (18–43 ng/µL). 15–90 ng of RNA was taken in each reaction. Reversion, preparation of short RNA libraries, and circularization were performed using the Small RNA Library Prep Kit (BGI, 1000006383) according to the manufacturer’s recommendations. Libraries were purified by electrophoresis in a polyacrylamide 6% gel, cutting out the target band corresponding to the length of the target fragment 18−50b (library length 105−140b). Sequencing was performed on a DNBSEQ-G400 (BGI) instrument using the DNBSEQ-G400RS High-throughput Sequencing Set (Small RNA FCL SE50*) (BGI, 1000016998). After sequencing, the amount of information obtained from each sample was 400−1200 MB.
Bioinformatics and statistical analysis
Bioinformatics analysis was performed in miRMaster 2 version 2.0.0 [25], a comprehensive analysis framework for small RNA-seq data. Current version of miRmaster used Ensembl [26], RNACentral piRNA [27], NCBI RefSeq [28], ENA [29], GeneCards [30], PirBase [31] databases for analyzing RNAseq FASTQ data. Statistical analysis was performed in RStudio (version 2023.09.1 + 494.pro2) [32]. Reads per million (RPM) was used as a normalized value of expression. Statistical analysis for the results was executed by applying the Wilcoxon–Mann–Whitney test. A p-value < 0.05 was considered statistically significant. For RNAseq volcano plot the p-value was log transformed to log10 (1.31) for data presentation. Heatmap raw expression data was preliminarily log transformed. Data visualization, images, and charts were made with RStudio ggplot2 [33] and tidyverse [34] open-source collection of packages. All raw data used for statistical analysis and visualization is presented in the supplementary materials.
Results
piRNA expression in sorted blood cells of healthy donors and severe COVID−19 patients
In order to compare piRNA expression in blood cells we sorted them by distinguishing the following cell types: erythrocytes (n = 5), monocytes (n = 5), lymphocytes (n = 4), eosinophils (n = 2), basophils (n = 2), neutrophils (n = 6). The cell sorting procedure is described in materials and methods. For accuracy, we checked the quality of sorting with SEM. As can be seen in the presented images (Fig. 2 panels B, C) morphological features of cells correspond with sorted cell types [19, 20]. We then isolated RNA from sorted cells, prepared small RNA libraries, performed NGS of small RNA and subjected the raw RNAseq data to bioinformatic analysis in miRMaster 2, details are described in materials and methods. All annotated piRNA data is presented in the supplementary materials. We compared the major RNAs overexpressed in different blood cells of healthy control donors. The results are presented in Fig. 2 panel B, C (piechart). Additionally, log transformed raw expression data is presented in the heatmap (Fig. 2 panel A), where 5 (red) indicates maximum expression values, and < 1 (green) minimum. 4 of the most abundant piRNA were expressed relatively equally in all cell types, they included piR−49,145, piR−49,144, piR−49,143 and piR−33,151. We performed an identical analysis on the sorted blood cells of severe COVID−19 patients (Fig. 2), where we observed that the most abundant piRNA does not significantly differ from the expression in control cells.
piRNA differential expression in blood cells of patients with COVID−19
Next, we analyzed the piRNA differential expression in healthy donors and patients with severe COVID−19 in three distinguished sorted cell types erythrocytes (n = 5), monocytes (n = 5) and lymphocytes (n = 4), the results of the comparison are presented as a volcano plot (Fig. 3) where positive increasing log2 fold change corresponds with gene downregulation, and negative values correspond with gene upregulation. All raw data used for analysis is presented in the supplementary materials section.
By reviewing piRNA expression in each group of sorted cells from severe COVID−19 patients we observed 3 upregulated and 10 downregulated piRNAs in erythrocytes (Table 2). Monocytes were presented with a larger amount of statistically significant piRNA, 4 upregulated and 35 downregulated (Table 3). In lymphocytes, each piRNA was upregulated (Table 4).
Discussion
Many ncRNAs exhibit cell type, tissue, and cancer specificity, making them valuable for understanding the molecular basis of various diseases [35, 36].
By presenting piRNA expression profiles of sorted healthy control donor cells we attempted to portray cell type-specific piRNA, clusterization of which could compose a distinct and reliable piRNA expression portrait for healthy erythrocytes, lymphocytes, monocytes, eosinophils, basophils, neutrophils. For accuracy we additionally examined our sorted cells using SEM. The images that we observed confirmed that each sorted sample corresponded with the sought-for cell type. We observed mild cell shrinkage possibly due to the cell sorting procedure. (Fig. 2 panels B, C). Such an approach would potentially allow us to distinguish piRNA peculiarities in sorted blood cells.
To date it is known that a highly specific ncRNA - mir−451 is abundant in red blood cells [37]. Other specific miRNA include miR−223−3p which is linked to the inhibition of erythrocyte differentiation and is recognized to be crucial in promoting granulocytic differentiation [38]. According to research, miR−223 is a potent granulopoiesis regulator that is mostly expressed in granulocytes [38]. However, we did not observe any similarly specific pattern of piRNA expression in either of the cell populations, therefore, it was not possible to state the prevalence of any distinct piRNA to a specific cell type. It is worth noting that despite the absence of a vivid piRNA expression pattern for erythrocytes, on our heatmap (Fig. 2 panel B) we demonstrate that the top 20 most abundant erythrocyte piRNA show a mildly significant distinguishable expression demarcation when compared to other cell types. This could occur mainly due to the lack of cell nucleus [39] and ribosomes in mature erythrocytes [40]. Thus, the origin of changes in piRNA expression in erythrocytes remains a matter of debate. The overall top 4 expressed piRNA in healthy control donors, as we demonstrate in our heatmap (Fig. 2 panel A) and percentages pie-chart (Fig. 2 panel B), does not significantly differ depending on cell type. We performed identical analysis for the blood cells of severe COVID−19 patients (Fig. 2 panel C) and did not observe any significant differences in piRNA expression. These piRNA include piR−49,144, piR−49,143, piR−33,151 and piR−49,145. Despite having a similar id, their sequence and length differ and each aforementioned piRNA is distinguished separately by RNACentral piRNA [27], ENA [29], GeneCards [30], PirBase [31].
Analyzing COVID−19 patients’ piRNA expression we observed several key features. The absolute majority of lymphocytic piRNA showed to be upregulated, while most erythrocytic and monocytic piRNA demonstrated a significant downregulation. (Fig. 3). In an attempt to interpret these findings we encountered difficulties due to the lack of data on piRNA and its potential roles in immune mediated mechanisms and viral infections as demonstrated with lncRNA [41].
It is important to give an overview on the involvement of piRNA in various biological processes known to date. piRNAs play diverse roles in gene regulation. They are best known for their functions in transposon silencing, fertility, and regulation of germline mRNAs and lncRNAs [6,7,8,9, 42, 43]. Recent studies have also highlighted their involvement in various neuronal processes, including neuronal differentiation and the development of neurodegenerative diseases. In the brain, piRNAs interact with a group of small RNAs and function as a complex to regulate cellular activities. Additionally, piRNAs have been implicated in the self-renewal of stem cells and are abundant in spermatogenic cells [6, 44,45,46]. The precise mechanisms of piRNA-mediated gene regulation in the brain and their associations with neurodegenerative diseases are areas of active research.
We would like to emphasize on piRNAs role in transposon silencing. This process occurs in the cytoplasm, where a PIWI-piRNA complex binds to a piRNA-complementary transposon RNA and cleaves the RNA [6]. There are two main types of silencing: transcriptional silencing - mediated by nuclear PIWI proteins such as PIWI in Drosophila and MIWI2 PIWIL4 in mice and post-transcriptional silencing - mediated by cytoplasmic PIWI proteins such as Aubergine (PIWIL1) and MILI (a.k.a. PIWIL2) in mice [6, 47].
The piRNA pathway guards the germline genome against transposable elements (TEs) by targeting two steps required for all transposons. Regulation of chromatin structure - In the nucleus, piRNAs are implicated in the regulation of chromatin structure, which can affect transcription of transposable elements [9].
Direct targeting and destruction of RNAs - In the cytoplasm, the piRNA pathway directly targets and destroys RNAs of transposons that escaped transcriptional silencing [9, 48]. The piRNA pathway is essential for maintaining genome integrity, as transposable elements can cause DNA breaks, illegitimate recombination, and other genomic damage [9]. Mutations in genes involved in the piRNA pathway, such as Rhino and Armi, can lead to the activation of transposons and female sterility [47].
To date there are several significant reports of viral impact on transposable element activity [49, 50]. It is explicitly stated in a study by Macchietto et al. that early up-regulated transposons are a component of the first wave response during virus infection and that transposon up-regulation is a common phenomena during virus infection in humans and mice [51]. According to Ivancevic et al. [52] immune cells show the highest enrichment of transposable element-derived enhancers. This allows us to propose that the expression of transposable elements could possibly correlate with the expression of certain piRNA, and elucidate our findings of the piRNA upregulation tendency in lymphocytes of COVID−19 patients. It has also been shown on a A549 cell culture that SARS-CoV−2 induces a great number of differentially expressed transposable element loci, which additionally supports our assumptions [53].
To better understand the nature of piRNA upregulation in lymphocytes during COVID−19 it is necessary to consider some individual piRNAs: piR−35,462, piR−36,074, piR−36,038 have been found to be associated with eosinophil count and total serum IgE in large, independent childhood asthma cohorts [54].
Other piRNAs expressed in lymphocytes are mentioned by different authors in papers on germ cells [55], oocytes, embryos [56], human neuroblastoma [57], differentiation of human mesenchymal stromal cells [58], bladder cancer [59] and gastric cancer, where piR−31,355, another piRNA upregulated in COVID−19 patients’ lymphocytes was pointed out as a potential biomarker of gastric cancer [60]. The function and role of aforementioned piRNAs remains a matter of debate, as the present piRNA dataset is not available for KEGG mapping or functional enrichment analysis [61].
When analyzing differential expression in COVID−19 patients’ monocytes our attention was drawn by 1 of 4 highly upregulated piRNAs’ - piR−31,623. In a research dedicated to studying exosomes derived from lung basal epithelial cells infected by respiratory syncytial virus (RSV), Chahar et al. presented a table of top 15 highly upregulated exosome-derived piRNA, where piR−31,626 demonstrates an average read count of 48.19 in RSV exosomes, and 0.62 in mock exosomes [62]. Additionally, in our research, we observed downregulation of piR−36,169, whereas the same piRNA in RSV infected exosomes was highly upregulated [62]. Both RSV and SARS-CoV−2 can induce syncytia formation in infected cells, but the mechanisms and consequences of syncytia formation may differ between the two viruses [63]. In summary, RSV and SARS-CoV−2 have different genome sizes, polarities, protein encoding, and cellular receptors, however, both viruses can induce syncytia formation, but the mechanisms and consequences of syncytia formation may differ between the two viruses. Furthermore, exosomes isolated from RSV-infected cells were able to trigger the release of chemokines from A549 alveolar basal epithelial cells and human monocytes, according to Chahar et al. This suggests that exosomes released during infection may change cellular responses, either activating or suppressing the innate immune system [62]. Given the fact that both viruses cause syncytia formation, it is possible that piR−31,626 may be circumstantial evidence of syncytium remodeling.
In our study we attempted to portray distinguishable expression profiles for erythrocytes of healthy control donors and COVID−19 patients. Erythrocytes express their genetic material and proteins during their development in the bone marrow [64]. There is currently limited information available on how piRNAs are expressed in erythrocytes, especially given the fact that they lack mitochondria, ribosomes, nuclei, and other organelles [40]. Supposedly, the piRNA content in red blood cells that we observed remains from proerythroblast stages.
Conclusion
Overall piRNA expression differs depending on cell type. Four of the most abundant piRNA were expressed relatively equally in all cell types. By reviewing piRNA expression in each group of sorted cells from severe COVID−19 patients we observed 3 upregulated and 10 downregulated piRNAs in erythrocytes. In lymphocytes, each of 19 piRNA were upregulated. Monocytes were presented with a larger amount of statistically significant piRNA, 4 upregulated and 35 downregulated. piRNA was upregulated in lymphocytes of all COVID−19 patients. It has been previously shown that piR−31,623 has been associated with respiratory syncytial virus infection, and taking in account the major role of piRNA in transposon silencing, we presume that the differential expression patterns which we observed could be a signal of indirect antiviral activity or a specific antiviral cell state. Additionally, in lymphocytes, all 19 piRNAs were upregulated.
Data availability
No datasets were generated or analysed during the current study.
References
Hombach S, Kretz M. Non-coding RNAs: classification, Biology and Functioning. Adv Exp Med Biol. 2016;937:3–17.
Statello L, Guo C-J, Chen L-L, Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat Rev Mol Cell Biol. 2021;22:96–118.
Palazzo AF, Lee ES. Non-coding RNA: what is functional and what is junk? Front Genet. 2015;6.
Huang S, Yoshitake K, Asakawa S. A review of Discovery profiling of PIWI-Interacting RNAs and their diverse functions in Metazoans. Int J Mol Sci. 2021;22:11166.
Darricarrère N, Liu N, Watanabe T, Lin H. Function of Piwi, a nuclear Piwi/Argonaute protein, is independent of its slicer activity. Proc Natl Acad Sci U S A. 2013;110:1297–302.
Wang C, Lin H. Roles of piRNAs in transposon and pseudogene regulation of germline mRNAs and lncRNAs. Genome Biol. 2021;22:27.
Litwack G. Chapter 10 - nucleic acids and Molecular Genetics. In: Litwack G, editor. Human biochemistry. Boston: Academic; 2018. pp. 257–317.
Xiao Y, Ke A. PIWI takes a giant step. Cell. 2016;167:310–2.
Tóth KF, Pezic D, Stuwe E, Webster A. The piRNA Pathway guards the germline genome against transposable elements. Adv Exp Med Biol. 2016;886:51–77.
Zhang T, Wong G. Dysregulation of human somatic piRNA expression in Parkinson’s Disease subtypes and stages. Int J Mol Sci. 2022;23:2469.
Rayford KJ, Cooley A, Strode AW, Osi I, Arun A, Lima MF et al. Trypanosoma Cruzi dysregulates expression profile of piRNAs in primary human cardiac fibroblasts during early infection phase. Front Cell Infect Microbiol. 2023;13.
Chattopadhyay T, Gupta P, Nayak R, Mallick B. Genome-wide profiling of dysregulated piRNAs and their target genes implicated in oncogenicity of tongue squamous cell carcinoma. Gene. 2023;849:146919.
Cheng Y, Wang Q, Jiang W, Bian Y, zhou Y, Gou A, et al. Emerging roles of piRNAs in cancer: challenges and prospects. Aging. 2019;11:9932–46.
Ren R, Tan H, Huang Z, Wang Y, Yang B. Differential expression and correlation of immunoregulation related piRNA in rheumatoid arthritis. Front Immunol. 2023;14.
Akimniyazova A, Yurikova O, Pyrkova A, Rakhmetullina A, Niyazova T, Ryskulova A-G, et al. In Silico Study of piRNA interactions with the SARS-CoV–2 genome. Int J Mol Sci. 2022;23:9919.
Rakhmetullina A, Akimniyazova A, Niyazova T, Pyrkova A, Kamenova S, Kondybayeva A, et al. Endogenous piRNAs can interact with the Omicron variant of the SARS-CoV–2 genome. Curr Issues Mol Biol. 2023;45:2950–64.
Ikhlas S, Usman A, Kim D, Cai D. Exosomes/microvesicles target SARS-CoV–2 via innate and RNA-induced immunity with PIWI-piRNA system. Life Sci Alliance. 2022;5.
Lucas C, Wong P, Klein J, Castro TBR, Silva J, Sundaram M, et al. Longitudinal analyses reveal immunological misfiring in severe COVID–19. Nature. 2020;584:463–9.
Kondratov KA, Artamonov AA, Mikhailovskii VY, Velmiskina AA, Mosenko SV, Grigoryev EA, et al. SARS-CoV–2 impact on red blood cell morphology. Biomedicines. 2023;11:2902.
Velmiskina AA, Nikitin YV, Mikhailovskii VYu, Mosenko SV, Anisenkova AYu, Apalko SV, et al. Analysis of the morphology of monocytes and lymphocytes from COVID–19 patients using low-voltage scanning Electronic Microscopy. Bull Exp Biol Med. 2023;176:297–302.
Lin Y, Sun Q, Zhang B, Zhao W, Shen C. The regulation of lncRNAs and miRNAs in SARS-CoV–2 infection. Front Cell Dev Biol. 2023;11:1229393.
Ding J, Chen J, Yin X, zhou J. Current understanding on long non-coding RNAs in immune response to COVID–19. Virus Res. 2022;323:198956.
Arman K, Dalloul Z, Bozgeyik E. Emerging role of microRNAs and long non-coding RNAs in COVID–19 with implications to therapeutics. Gene. 2023;861:147232.
Fedorov A, Kondratov K, Kishenko V, Mikhailovskii V, Kudryavtsev I, Belyakova M, et al. Application of high-sensitivity flow cytometry in combination with low-voltage scanning electron microscopy for characterization of nanosized objects during platelet concentrate storage. Platelets. 2020;31:226–35.
Fehlmann T, Kern F, Laham O, Backes C, Solomon J, Hirsch P, et al. miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale. Nucleic Acids Res. 2021;49:W397–408.
Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, Barnes I, et al. Ensembl 2023. Nucleic Acids Res. 2023;51:D933–41.
The RNAcentral Consortium. RNAcentral: a hub of information for non-coding RNA sequences. Nucleic Acids Res. 2019;47:D221–9.
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–745.
Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, et al. The European Nucleotide Archive. Nucleic Acids Res. 2011;39:28–31. Database issue:D.
Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from Gene Data Mining to Disease Genome sequence analyses. Curr Protocols Bioinf. 2016;54:1301–13033.
Wang J, Shi Y, Zhou H, Zhang P, Song T, Ying Z, et al. piRBase: integrating piRNA annotation in all aspects. Nucleic Acids Res. 2022;50:D265–72.
RStudio Team. RStudio Team. (2020). RStudio: Integrated Development for R. 2020.
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-; 2016.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.
Grillone K, Riillo C, Scionti F, Rocca R, Tradigo G, Guzzi PH, et al. Non-coding RNAs in cancer: platforms and strategies for investigating the genomic dark matter. J Experimental Clin Cancer Res. 2020;39:117.
Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435:834–8.
Sun L, Yu Y, Niu B, Wang D. Red blood cells as potential repositories of MicroRNAs in the Circulatory System. Front Genet. 2020;11:442.
Yuan S, Wu Q, Wang Z, Che Y, Zheng S, Chen Y, et al. miR–223: an Immune Regulator in Infectious disorders. Front Immunol. 2021;12:781815.
Zhang Z-W, Cheng J, Xu F, Chen Y-E, Du J-B, Yuan M, et al. Red blood cell extrudes nucleus and mitochondria against oxidative stress. IUBMB Life. 2011;63:560–5.
Pretini V, Koenen MH, Kaestner L, Fens MHAM, Schiffelers RM, Bartels M, et al. Red blood cells: chasing interactions. Front Physiol. 2019;10:945.
Li Z, Gao J, Xiang X, Deng J, Gao D, Sheng X. Viral long non-coding RNA regulates virus life-cycle and pathogenicity. Mol Biol Rep. 2022;49:6693–700.
Kawase M, Ichiyanagi K. The expression dynamics of piRNAs Derived from male germline piRNA clusters and retrotransposons. Front Cell Dev Biology. 2022;10.
Gainetdinov IV, Skvortsova YV, Kondratieva SA, Klimov A, Tryakin AA, Azhikina TL. Assessment of piRNA biogenesis and function in testicular germ cell tumors and their precursor germ cell neoplasia in situ. BMC Cancer. 2018;18:20.
Sato K, Takayama K, Inoue S. Role of piRNA biogenesis and its neuronal function in the development of neurodegenerative diseases. Front Aging Neurosci. 2023;15.
Zuo L, Wang Z, Tan Y, Chen X, Luo X. piRNAs and their functions in the brain. Int J Hum Genet. 2016;16:53–60.
Ali SD, Tayara H, Chong KT. Identification of piRNA disease associations using deep learning. Comput Struct Biotechnol J. 2022;20:1208–17.
Halic M, Moazed D. Transposon silencing by piRNAs. Cell. 2009;138:1058–60.
Luo S, Lu J. Silencing of transposable elements by piRNAs in Drosophila: an evolutionary perspective. Genom Proteom Bioinform. 2017;15:164–76.
Roy M, Viginier B, Saint-Michel É, Arnaud F, Ratinier M, Fablet M. Viral infection impacts transposable element transcript amounts in Drosophila. Proc Natl Acad Sci U S A. 2020;117:12249–57.
Shen SS, Nanda H, Aliferis C, Langlois RA. Characterization of influenza a virus induced transposons reveals a subgroup of transposons likely possessing the regulatory role as eRNAs. Sci Rep. 2022;12:2188.
Macchietto MG, Langlois RA, Shen SS. Virus-induced transposable element expression up-regulation in human and mouse host cells. Life Sci Alliance. 2020;3:e201900536.
Ivancevic A, Chuong EB. Transposable elements teach T cells new tricks. Proceedings of the National Academy of Sciences. 2020;117:9145–7.
Marston JL, Greenig M, Singh M, Bendall ML, Duarte RRR, Feschotte C et al. SARS-CoV–2 infection mediates differential expression of human endogenous retroviruses and long interspersed nuclear elements. JCI Insight 6:e147170.
Li J, Hong X, Jiang M, Kho AT, Tiwari A, Wang AL et al. A novel piwi-interacting RNA associates with type 2–high asthma phenotypes. J Allergy Clin Immunol. 2023.
Girard A, Sachidanandam R, Hannon GJ, Carmell MA. A germline-specific class of small RNAs binds mammalian piwi proteins. Nature. 2006;442:199–202.
Roovers EF, Rosenkranz D, Mahdipour M, Han C-T, He N, Chuva de Sousa Lopes SM, et al. Piwi proteins and piRNAs in mammalian oocytes and early embryos. Cell Rep. 2015;10:2069–82.
Roy J, Mallick B. Investigating piwi-interacting RNA regulome in human neuroblastoma. Genes Chromosomes Cancer. 2018;57:339–49.
Della Bella E, Menzel U, Basoli V, Tourbier C, Alini M, Stoddart MJ. Differential Regulation of circRNA, miRNA, and piRNA during early osteogenic and chondrogenic differentiation of human mesenchymal stromal cells. Cells. 2020;9:398.
Chu H, Hui G, Yuan L, Shi D, Wang Y, Du M, et al. Identification of novel piRNAs in bladder cancer. Cancer Lett. 2015;356(2 Pt B):561–7.
Vinasco-Sandoval T, Moreira FC, Vidal F, Pinto A, Ribeiro-dos-Santos P, Cruz AM. Global analyses of expressed piwi-interacting RNAs in gastric Cancer. Int J Mol Sci. 2020;21:7656.
Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50:W216–21.
Chahar HS, Corsello T, Kudlicki AS, Komaravelli N, Casola A. Respiratory syncytial virus infection changes Cargo Composition of Exosome released from Airway Epithelial cells. Sci Rep. 2018;8:387.
Lin L, Li Q, Wang Y, Shi Y. Syncytia formation during SARS-CoV–2 lung infection: a disastrous unity to eliminate lymphocytes. Cell Death Differ. 2021;28:2019–21.
Moras M, Lefevre SD, Ostuni MA. From erythroblasts to mature red blood cells: Organelle Clearance in mammals. Front Physiol. 2017;8:1076.
Acknowledgements
Not applicable.
Funding
This work was supported by St. Petersburg State University, project ID: 95412780. The investigation of the structure by means of low-voltage scanning electron microscopy was carried out at the IRC for Nanotechnology of the Science Park of St. Petersburg State University within the framework of project No. AAAA-A19−119091190094-6.
Author information
Authors and Affiliations
Contributions
K.A.K. investigation, visualization, writing, supervision, softwareA.A.A.visualization, writin, softwareY.V.N. investigationA.A.V. investigationV.Y.M. investigation, visualizationS.V.M. project administrationI.A.P. investigationA.Y.A. project administrationS.V.A. project administrationN.N.S. project administrationA.M.I. project administrationS.G.S. funding acquisition.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines of the declaration of Helsinki and approved by the ethics committee. The study was approved by the expert ethics council of St. Petersburg City Hospital No. 40 (protocol No. 171 of 18 May 2020).Informed consent was obtained from all subjects involved in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Kondratov, K.A., Artamonov, A.A., Nikitin, Y.V. et al. Revealing differential expression patterns of piRNA in FACS blood cells of SARS-CoV−2 infected patients. BMC Med Genomics 17, 212 (2024). https://doi.org/10.1186/s12920-024-01982-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12920-024-01982-9