Circulating Y-RNAs, a predicted function controlling the Immune System, Cell Signaling, and DNA Replication in Pediatric Patients with Pilocytic and Diffuse Astrocytoma

doi:10.21203/rs.3.rs-1462683/v1

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Circulating Y-RNAs, a predicted function controlling the Immune System, Cell Signaling, and DNA Replication in Pediatric Patients with Pilocytic and Diffuse Astrocytoma

https://doi.org/10.21203/rs.3.rs-1462683/v1

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Background. Y-RNAs are small noncoding RNAs firstly identified in patients suffering the Sjögren’s syndrome and lupus erythematosus, having different predicted cellular functions. In this work, circulating Y-RNAs were detected in blood serum from pediatric patients with pilocytic or diffuse astrocytoma, by means of the HTA 2.0 arrays. In addition, a bioinformatic approximation of the possible biological functions of Y-RNAs was determined with the “Random Forest” and “Vector Support Machine” algorithms.

Results. Data showed a differential expression of RNY-3, RNY-4, and -5 in tumors relative to the control. Meanwhile, RNY-1 was shown to be highly upregulated in the Diffuse condition versus the Pilocytic one. The bioinformatic analysis showed the interaction of all these Y-RNAs with different proteins, but RNY-4 and RNY-5 had the highest scores of interaction with the Claudin domain-containing protein 1, isoform α and Toll-like receptor 5, and the Hyaluronidase-1 isoform 3. Because Y-RNAs are processed in vivo to produce fragments through RNase processing, a second analysis was performed to determine the Y-RNAs loop domain interactions with these receptors. Results showed the highest score of interaction for RNY-3/B cell receptor CD22 isoform 1, followed by RNY-4/Toll-like receptor 7 and 3.

Conclusions. Altogether, our results showed, for the first time a differential expression of circulating Y-RNAs in pediatric astrocytoma, allowing to distinguish pediatric patients without cancer from patients with pediatric diffuse or pilocytic astrocytoma and their potential involvement in regulating diverse biological processes, such as immune activation or suppression, cell signaling, selective transcription, and cell proliferation through DNA replication activation.

Y-RNAs

Toll-like receptors

Origin Replication Complex

Random Forest

Vector Support Machine.

The RNA-Protein interactions have a critical participation in the cell function regulation [1, 2]. It is estimated nearly 6% from the human proteome could bind to RNA [2], evidence is available of RNA-Proteins network interactions regulating cell signaling and genomic expression [2, 3]. A type of sncRNA are Y-RNAs, which were discovered by forming ribonucleoproteins complexes (RNPs) with the Ro60 (Ro60, Y-RNA Binding Protein) and La (Sjögren syndrome type B antigen (SS-B) proteins, and autoantigens in patients suffering the Sjögren’s syndrome and lupus erythematosus [4]. Y-RNAs, so called due to their identification in the cell cytoplasm “Y for cytoplasmic”) [4], are a family of RNAs transcribed by the RNA polymerase III, defined by their structure and affinity by the Ro60 protein [5]. The four genes are located as a tandem set at the chromosomal locus 7q36 [5], but each Y-RNA has its own promoter and is independently transcribed [6].

Y-RNAs are highly conserved molecules [7–9]. Y-RNAs translocation from the nucleus to the cytoplasm requires their binding to the Ro60 protein and to other snRNAs [10]. Y-RNAs can be secreted into extracellular vesicles (Evs) derived from blood or stem cells [11], immune system cells [12], or in tumor cell exosomes [13].

The first analyses concerning Y-RNA quantification in human cancers were performed with solid tumors from carcinomas and adenocarcinomas of the lung, kidney, bladder, prostate, colon, and cervix. As a general rule, all four Y-RNAs were shown to be overexpressed in these cancers [14]. However, the RNY-5 expression does not generally correlate with that of the other, which could be explained by the fact that its location (cell nucleus) is different from that of the others (cell cytoplasm) [15]. According to this, a recent research has reported the downregulation of all human Y-RNAs in breast cancer, but RNY-1, RNY-3, and RNY-4 showed a high expression correlation, whereas that of RNY-5 was less correlated [16]. Cancer-derived Evs in myelogenous leukemia have a relatively high amount of RNY-5, which is the most abundant RNA after rRNA (ribosomal RNAs) and tRNA (transfer RNAs) [17]. In addition, evidence suggests a role for EVs as regulators for brain tumor progression [18].

Pediatric Ast (p-Ast) are the leading cause of childhood solid tumor deaths [19] and although p-Ast and adult Ast (a-Ast) share signaling pathways that are altered, the molecular components are distinct [20]. Based on this, p-Ast and a-Ast are different clinical entities with specific genetic alterations [21]. In 2016, the WHO proposed a new CNS tumor classification based on molecular and epigenetic markers, as well as in the clinical behavior of adult patients; however, more studies are needed to provide more molecular tools to this new classification with the aim of having a better Ast classification, impacting the diagnosis, prognosis, patient’s survival, and drug therapy. In the present work, the circulating Y-RNAs expression was determined by means of the HTA 2.0 Arrays from blood serum samples from pediatric patients with Ast. Bioinformatical analysis showed their interaction with proteins, suggesting their involvement mainly in the control of immune system, cell signaling, and DNA replication.

GeneChip HTA 2.0 Array

The comparison between the Pilocytic and Control conditions showed subtle changes in the expression of RNY-3 (upregulated in PAst), RNY-4 and RNY-5 (both downregulated) (Table 1; Fig. 1A). By contrast, RNY-1, -3, and − 5 were highly upregulated in the Diffuse condition compared to the Pilocytic and Control conditions (Table 1; Fig. 1B-C).

Table 1

Y-RNA relative levels detected in exosomal samples of pediatric astrocytoma and controls.
Y-RNA	Diffuse	Pilocytic	Fold change
RNY-1	15.95	9.37	95.69
RNY-5	10.18	5.5	25.63
RNY-3	18.7	14.19	22.68
RNY-4	8.76	10.68	-3.77
Y-RNA	Diffuse	Control	Fold change
RNY-3	18.74	11.58	143.58
RNY-1	16.15	9.53	98.05
RNY-5	9.81	8.09	3.29
RNY-4	7.67	12.7	-32.63
Y-RNA	Pilocytic	Control	Fold change
RNY-3	14.43	11.58	7.21
RNY-5	5.22	8.09	-7.3
RNY-4	10.19	12.7	-5.68

Predicted human Y-RNAs interactions with proteins

To gain insight in Y-RNAs knowledge, the interaction of Y-RNAs with cell receptors was bioinformatically studied in most cases, the RF algorithm showed the highest scores of interactions for RNY-4 and RNY-5, followed by RNY-1 and RNY-3 (Table 2). RNY-4 and RNY-5 showed most interaction with: Claudin domain-containing protein, G-protein coupled receptor 37, cation channel subfamily M, Hyaluronidase-1, and Sorting nexin-4, among others. Meanwhile RNY-1 with G-protein coupled receptor 37 and Linker for activation of T-cells for example. RNY-3 has the potential to bind with Lysosome membrane protein 2 and Fibroblast growth factor 23.

Table 2

Top 10 interactions of human Y-RNAs.
Higher average scores for each human Y-RNAs with different families of receptor proteins.
Protein	Y-RNAs Subtypes
Protein	NP_ or XP_	1	3	4	5	A
Transient receptor potential cation channel subfamily M	001353082.1	0.7	0.75	0.85	0.85	0.7875
G-protein coupled receptor 37-like 1	004758.3	0.75	0.65	0.85	0.9	0.7875
Claudin domain-containing protein 1 isoform a	001035271.1	0.75	0.7	0.95	0.7	0.775
Lysosome membrane protein 2 isoform 1	005497.1	0.7	0.85	0.85	0.7	0.775
Hyaluronidase-1 isoform 3	695015.1	0.6	0.7	0.85	0.95	0.775
Sorting nexin-4	003785.1	0.75	0.6	0.85	0.85	0.7625
Linker for activation of T-cells family member 1 isoform a	055202.1	0.75	0.6	0.85	0.85	0.7625
Fibroblast growth factor 23	065689.1	0.65	0.8	0.85	0.75	0.7625
Claudin domain-containing protein 1 isoform d	001035290.1	0.65	0.7	0.9	0.8	0.7625
Tumor necrosis factor receptor superfamily member 19	061117.2	0.75	0.7	0.85	0.75	0.7625
Higher average score for each of the human Y-RNAs with Toll-like proteins.
B-cell receptor CD22 isoform 1	001762.2	0.7	0.65	0.9	0.75	0.75
Protein unc-93 homolog B1	112192.2	0.65	0.7	0.75	0.85	0.7375
Toll-like receptor 8 isoform 2	619542.1	0.6	0.8	0.9	0.65	0.7375
Toll-like receptor 7	057646.1	0.55	0.65	0.9	0.8	0.725
Toll-like receptor 10 isoform a	001017388.1	0.55	0.65	0.8	0.9	0.725
Toll-like receptor 8 isoform 1	057694.2	0.65	0.7	0.9	0.65	0.725
Toll-like receptor 10 isoform a	112218.2	0.55	0.65	0.8	0.9	0.725
Toll-like receptor 5	003259.2	0.55	0.6	0.95	0.8	0.725
MHC class I-related gene protein isoform 1	001522.1	0.65	0.7	0.85	0.65	0.7125
Interleukin-1 receptor type 1 isoform 4	001307913.1	0.75	0.55	0.85	0.7	0.7125
Higher average score for each of the human Y-RNAs Loop domains with Toll-like proteins.
B-cell receptor CD22 isoform 1	001762.2	0.7	0.9	0.8	0.75	0.7875
B-cell receptor CD22 isoform 3	001172029.1	0.7	0.85	0.8	0.7	0.7625
Toll-like receptor 7	057646.1	0.6	0.75	0.85	0.8	0.75
Toll-like receptor 3	003256.1	0.65	0.7	0.85	0.7	0.725
Interleukin-1 receptor type 1 isoform 1	000868.1	0.6	0.8	0.8	0.65	0.7125
B-cell receptor CD22 isoform 4	001172030.1	0.6	0.8	0.75	0.7	0.7125
Interleukin-1 receptor type 1 isoform 4	001307913.1	0.7	0.7	0.7	0.7	0.7
Protein unc-93 homolog B1	112192.2	0.65	0.7	0.7	0.75	0.7
Toll-like receptor 10 isoform a	001017388.1	0.6	0.75	0.7	0.7	0.6875
Single Ig IL-1-related receptor		0.55	0.75	0.7	0.75	0.6875
Higher average score for each of the human Y-RNAs against wnt cell rceptors
Alpha-tubulin N-acetyltransferase 1 isoform 1	001026892.1	0.6	0.5	0.65	0.6	0.5875
Glycogen synthase kinase-3 beta isoform 2	001139628.1	0.55	0.55	0.6	0.4	0.525
Alpha-tubulin N-acetyltransferase 1 isoform 3	001177653.1	0.55	0.55	0.7	0.5	0.575
Metastasis-associated protein MTA1 isoform MTA1s	001190187.1	0.6	0.6	0.75	0.55	0.625
CCN family member 4 isoform 3	001191798.1	0.4	0.5	0.65	0.6	0.5375
CCN family member 4 isoform 4	001191799.1	0.4	0.45	0.6	0.55	0.5
Alpha-tubulin N-acetyltransferase 1 isoform 4	001241881.1	0.6	0.55	0.65	0.55	0.5875
Protein Wnt-8a isoform 1 precursor	001287867.1	0.5	0.55	0.8	0.75	0.65
Protein Wnt-8a isoform 2 precursor	001287868.1	0.45	0.55	0.7	0.7	0.6
Alpha-tubulin N-acetyltransferase 1 isoform 5	001305691.1	0.6	0.55	0.65	0.55	0.5875

Cell receptors

Results generated by the RF algorithm showed high scores (7, 8.5, or 9) for the Y-RNAs and generalized cell receptors interactions. The most significant interactions corresponded to RNY-4 and RNY-5, followed by RNY-1 and RNY-3 (Table 2; Additional file 2). RNY-4 downregulation in the Pilocytic and Diffuse conditions compared to the control might result in aberrant TLRs activation or OCR control. This might be affecting cell division and response to stimulus, such as comunication of imunne related cells with the local environment (Fig. 2A-C).

Toll-like receptors

Due to experimental evidence indicating the interaction of many Y-RNAs with the TLR3 (Toll Like Receptor 3) and TLR7 (Toll Like Receptor 7) receptors³⁰, Y-RNAs versus Toll Like Receptors interactions were predicted. Like the above results, the RNY-4 and RNY-5 had the most significant score values of interaction (according to RF), with a 0.9 score for the RNY-4 and RNY-5 interaction with CD22 (CD22 molecule) and TLR7 and TLR8 (Table 2). The RNY-4 interaction with TLR5 (Toll Like Receptor 5) showed an interaction score of 0.95 and that of the RNY-5 with TLR10 (Toll Like 10 Receptor) and the unc-93 protein were 0.9 and 0.85, respectively (Table 2). When we considered the average score for the four Y-RNAs, the highest score was with CD22, followed by unc-93, and TLRs 7 and 8. Interestingly, RNY-4 had the most significant score of interaction with TLRs (Additional file 3).

Wnt receptors

To the best of our knowledge, Y-RNAs interactions with Wnt receptors have not been previously reported; therefore, these interactions were tested. Y-RNAs versus Wnt receptors interactions showed very low score values, suggesting weak probabilities of interaction of these molecules in vivo (Table 2; Additional file 4).

Y-RNAs “loop domain”

Recent evidence shows the in vivo RNase 1 processing of Y-RNAs, producing different fragments of different size of these RNAs³³.Based on this, the interaction of the “loop domain” of the Y-RNAs with TLRs was tested. Data showed high scores of interactions, which were very similar to that obtained when the whole Y-RNAs (with their four domains) were analyzed (Table 2; Additional file 5).

Y-RNAs interactions with ORC-associated proteins

Predictions showed some of the highest scores for Y-RNAs interactions with ORC-associated proteins, particularly for RNY-4 and RNY-5 (Table 3; Additional files 6–9); this reinforces previous evidence indicating the presence of Y-RNAs in ORC³². Interactions with the highest scores involved diverse proteins related to DNA replication, i.e., DNA polymerases, transcription factors, helicases, and others. According to the RF classifier, RNY-4 seems to be particularly important since it showed a lot of predicted interactions with this type of proteins. These interactions might have a biological impact on DNA replication and cell proliferation.

Table 3

Top 10 interactions with the highest average scores for the interactions of Y-RNAs with the ORC associated proteins
Top 10 interactions for RNY-1 with ORC related proteins
	NP_ or XP_	RF	SVM
DNA polymerase eta isoform X2	024302234.1	0.8	0.887
Domain-containing protein 1	060386.1	0.8	0.606
General transcription factor 3C polypeptide 2 isoform b	001030598.1	0.8	0.901
Proline-rich nuclear receptor coactivator 1	006804.1	0.8	0.88
DNA polymerase lambda isoform X6	011537958.1	0.75	0.689
DNA polymerase lambda isoform X10	016871575.1	0.75	0.665
TATA box-binding protein-associated factor RNA polymerase I subunit A	647603.1	0.75	0.787
General transcription and DNA repair factor IIH helicase subunit XPD	000391.1	0.75	0.787
ATPase WRNIP1 isoform 2	569079.1	0.75	0.756
General transcription factor 3C polypeptide 2 isoform a	001305838.1	0.75	0.92
Top 10 interactions for RNY-3 with ORC related proteins
DNA-directed RNA polymerase III subunit RPC5 isoform 5	001244965.	0.8	0.968
General transcription factor 3C polypeptide 2 isoform b	001030598.1	0.8	0.984
General transcription factor 3C polypeptide 2 isoform b	001512.1	0.8	0.984
F-box only protein 7 isoform 1	036311.3	0.8	0.961
E3 ubiquitin-protein ligase Jade-2 isoform X3	024301775.1	0.8	0.96
DNA polymerase eta isoform X4	024302235.1	0.75	0.965
DNA polymerase lambda isoform X10	016871575.1	0.75	0.925
DNA-directed RNA polymerase III subunit RPC5 isoform 1	060589.1	0.75	0.97
DNA-directed RNA polymerase III subunit RPC5 isoform 3	001244964.1	0.75	0.98
DNA-directed RNA polymerase III subunit RPC5 isoform 2	001244962.1	0.75	0.968
Top 10 interactions for RNY-4 with ORC related proteins
DNA polymerase delta subunit 3 isoform 2	001350526.1	0.95	0.987
DNA polymerase eta isoform X2	024302234.1	0.95	0.984
DNA polymerase delta subunit 3 isoform X1	011543036.1	0.95	0.99
DNA-directed RNA polymerase I subunit RPA2 isoform 5	001269705.1	0.95	0.989
Synaptopodin 2-like protein isoform b	079151.2	0.95	0.992
DNA-directed RNA polymerase I subunit RPA2 isoform 6	001269708.1	0.9	0.99
DNA replication licensing factor MCM3 isoform 6	001353301.1	0.9	0.98
Poly [ADP-ribose] polymerase 2 isoform X1	005267304.1	0.9	0.974
ATP-dependent RNA helicase SUPV3L1, Mitochondrial isoform 1	003162.2	0.9	0.989
SMU1 DNA replication regulator and spliceosomal factor	060695.2	0.9	0.988
Top 10 interactions for RNY-5 with ORC related proteins
DNA polymerase eta isoform X2	024302234.1	0.9	0.948
DNA polymerase eta isoform X1	005249243.1	0.9	0.94
APC membrane recruitment protein 3	689911.2	0.9	0.944
DNA ligase 3 isoform beta precursor	002302.2	0.85	0.939
DNA repair protein REV1 isoform 6	001308389.1	0.85	0.955
DNA repair protein REV1 isoform 5	001308387.1	0.85	0.956
DNA-directed RNA polymerase III subunit RPC5 isoform 1	060589.1	0.85	0.932
RNA polymerase I-specific transcription initiation factor RRN3 isoform 2	001287993.1	0.85	0.973
Mediator of RNA polymerase II transcription subunit 26	004822.2	0.85	0.929
DNA repair protein REV1 isoform X7	024308728.1	0.85	0.955

In the present work, the circulating Y-RNAs expression was determined in the blood serum of pediatric patients with PAst or DAst, since these molecules have a relatively high concentration in extracellular vesicles, including exosomes. As YRNAs showed relative high expression changes, their interactions with proteins were bioinformatically predicted. This type of RNAs is involved in diverse biological processes, such as cell communication, immune activation [12], and DNA replication [24, 14]. Evidence has demonstrated individual interactions of Y-RNAs with specific .proteins as TLRs [22]; however, to our knowledge, Y-RNAs interactions with different protein families have not been tested. Therefore, the present study showed a predictive approach to identify Y-RNAs interactions with different types of proteins. Interactions with TLRs were predicted to validate our bioinformatics model. Data indicate strong interactions of Y-RNAs with protein families not previously described.

The enigma regarding the relative abundance of Y-RNAs and their fragments in serum, as well as their function as circulating molecules, is still unknown. In healthy individuals, circulating Y-RNAs have been described as both free circulating molecules and inside Evs, and their overexpression was observed in cancer [25, 26]. The predictions of interaction probabilities presented here support the notion that Y-RNAs could be effectively multitasking molecules, regulating key biological processes by controlling metabolism pathways and by affecting cellular processes in cancer or other pathologies. Thus, as an aftermath of molecular interactions with cell receptors or other type of proteins this might result in the immune signaling regulation by activating cellular processes as: apoptosis, abnormal cell proliferation, cell cycle alterations, and others. Our predictions showed the interaction of Y-RNAs with diverse cell receptors, which could change cell responses to environment. The biological processes affected were cellular process, metabolic process, and biological regulation. The protein interaction network exhibit interaction among proteins being candidates of Y-RNAs interaction.

The fact that specific subtypes of Y-RNAs showed higher interaction scores with specific proteins could indicate specific functions for each of these RNAs. Therefore, this type of studies allows us to predict specific interactions from the whole Y-RNAs molecules or from their fragments with cell receptors, which in turn might trigger signaling cascades depending on the cell receptor repertoire and the relative abundance of the distinct Y-RNAs subtypes in a tissue. The highest score values were observed for RNY-4 and RNY-5 by interacting with different types of proteins. It is of interest to note the high scores for RNY-4 and RNY-5 interactions with TLR10 and TLR5. TLR10, unlike other TLRs, do not activate the immune system and instead they suppresses inflammatory signaling [27]. Importantly, TLR10 ligand has not been identified yet and these Y-RNAs could be natural ligands for this receptor. Meanwhile, TLR5 is an immune sensor and a receptor recognizing flagellated bacteria [28] and it is involved in the control of inflammatory responses and adaptive immunity [29]. In innate immunity, TLRs induce cell activation, leading to cytokine release; particularly, the activation of TLR3, TLR7, TLR8, and TLR9 induce the expression of inflammatory-cytokines and type I interferons to counteract viral infections [30]. TLRs are ancient elements of the immune system and, similarly to Y-RNAs, they are evolutionarily conserved [31]. Since HEK 293 cells express either TLR-3, TLR-7, or TLR-8, these cells were used to determine the TLR-inducing activities of Y-RNAs. This study showed that human and murine RNY-3 induce the activity of TLR-3, but not that of TLR7, indicating YRNAs specificity for TLRs. For the first time was demonstrated differential activation of TLRs by distinct Y-RNA subtypes [22]. According to this study, the authors conclude that Y-RNAs are selective for specific TLRs. Our bioinformatic data indicated that the most likely targets for YRNAS are TLRs. Y-RNAs interactions with TLRs might be related to innate immune system regulation and consequent feedback by cytokines and/or chemokines secretion by target cells or tissues.

YRNAs interactions with the ORC

On average, all four Y-RNAs are significantly overexpressed in solid tumors by 4- and 13-fold compared to non-neoplastic tissues. The RNY-1 and RNY-4 expressions were demonstrated [14]. The downregulation of either RNY-1 or RNY-3, or both by siRNAs, leads to a significant reduction of the replicating cells in the S phase, indicating their participation in the ORC formation. In agreement to this, our data showed a strong interaction of RNY-4 with MCM3 (Minichromosome Maintenance Complex Component 3), which is involved in the initiation of the eukaryotic genome replication. This protein, together with other MCM proteins, conform the pre-replication complex (pre-RC) and may be involved in the replication fork formation and in the recruitment of other DNA replication of related proteins [32]. In pediatric patients with Ast, the circulating RNY-4 expression was considerably diminished suggesting a loss of its regulatory function in comparison to controls; however, it is important to know if the concentration of free or packed RNY-4 in other Evs also decreases. By contrast, the RNY-5 expression was upregulated, but its interaction scores were not so significant. The identification of the target cells of the exosomes of pediatric patients with Ast is crucial since this will make it possible to know if RNY-4 is integrated to the ORC of these cells or have different functions.

Y-RNAs loop domain

An interesting result was the fact that the Y-RNAs “loop domain” by itself interacted with TLRs, supporting the notion that processed Y-RNA fragments could be functional [33]. This domain is the least conserved in Y-RNAs and, according to this, it is speculated that the “loop domain” gives a specific function to each Y-RNA among species [33]. In fact, this domain could be participating in specific cellular functions [33], either as activator or modulator of their function and has been experimentally used for several protein binding sites for the Y-RNAs “loop domain” of proteins such as NCL (nucleolin), PTBP1 (polypyrimidine-tract-binding protein 1), and ZBP1 (Z-DNA-binding protein 1).

Y-RNAs in diagnosis

In formalin paraffin embedded bladder cancer cells, a decrease in the Y-RNAs expression was demonstrated ¹⁸. By contrast, the circulating expression of hY1 and hY4, as well as that of hY1, hY4, and hY5 was respectively overexpressed in blood cancer [35] and glioma [36]. Similarly, the hY1, hY4 and hY5 expression was overexpressed in breast cancer [37] and that of hY1, hY3 and hY5 in lung and prostate cancer [14]. The molecular characterization of plasma EVs for melanoma skin cancer showed an increase of the hY1, hY3 and hY4 expression [38]. Based on all the above, Y- RNAs seem to fulfill very particular functions in cancer that do not carry out in a healthy individual.

Our results showed expression changes in Y-RNAs, in patients with pediatric astrocytoma, which might be clinically used for diagnosis. The proteins with which Y-RNAs interact, in other tissues and cell types, could be therapeutic targets for the treatment of these tumors.

Samples

Blood serum samples from pediatric patients with Ast (Past (Pilocytic astrocytoma), n = 10 (four boys and six girls; DAst (Diffuse astrocytoma), n = 13 (five boys and eight girls)) were collected from 2017 to 2020 (Table 4), according to the approved protocol by the Ethics Committee of the CMNSXXI-IMSS. The samples were taken as part of the routine tests, with a vacutainer. The control children were boys and girls referred to the hospital for a cause other than cancer. All children included in this study were patients from the Hospital de Pediatría “Silvestre Frenk Freund”, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social (IMSS). The patient´s age range was 0–15 years old; they had no history of relatives with cancer. All children’s relatives or tutors signed an informed consent.

Ethics

The project had the approval of the Hospital de Pediatría “Silvestre Frenk Freund”, CMNSXXI-IMSS, Ethics in Security Research (R-2018-3603-048) and all the experimental procedures were performed in accordance to the Declaration of Helsinki (1964) and its later amendments or comparable ethical standards.

Table 4

Clinical data of pediatric patients
Histopathological grade	Age	Gender	Location	Outcome
Pilocytic astrocytoma
PAst 1	2	F	Temporal lobe	Alive
PAst 2	13	F	Posterior fossa	Alive
PAst 3	5	F	Posterior fossa	Alive
PAst 4	5	M	Visual pathway optical	Alive
PAst 5	6	F	Posterior fossa	Alive
PAst 6	14	F	Infratentorial	Alive
PAst 7	13	M	Temporal lobe	Alive
PAst 8	8	F	Posterior fossa	Alive
PAst 9	5	M	Temporal lobe	Alive
PAst 10	13	M	Visual pathway optical	Dead
Diffuse astrocytoma
DAst 1	8	F	Temporal lobe	Alive
DAst 2	9	M	Posterior fossa	Alive
DAst 3	3	M	Infratentorial	Alive
DAst 4	13	M	Temporal lobe	Dead
DAst 5	4	F	Thalamus	Alive
DAst 6	2	F	Posterior fossa	Dead
DAst 7	16	F	Posterior fossa	Alive
DAst 8	5	F	Temporal lobe	Dead
DAst 9	9	M	Posterior fossa	Dead
DAst 10	7	F	Visual pathway optical	Alive
DAst 11	1	M	Posterior fossa	Dead
DAst 12	5	F	Temporal lobe	Alive
DAst 13	12	F	Posterior fossa	Alive

Exosome and RNA Isolation

Exosomes were isolated with the Total Isolation Exosome kit (from serum) (ThermoScientific) and the presence and integrity of exosomes was visualized by electron microscopy. For RNA isolation, exosomes were incubated for 15 h (4ºC) with TRIZOL® (this was to optimally resuspend exosomes) and later with 40 µL chloroform for 15 minutes at 4ºC. The aqueous phase (RNA) was transferred to a new tube and 500 µL isopropanol were added for RNA precipitation. Finally, RNA was washed three times with 75% ethanol and resuspended in 5 µL RNase-free water. RNA quantification was performed by spectrophotometry (Nanodrop 1000, Thermo Fisher Scientific).

GeneChip® Human Transcriptome Array 2.0

Three conditions (The Control Pull (n = 50), The Pilocytic Pull (n = 10), and The Diffuse Pull (n = 13) were used for the expression analysis. The HTA 2.0 arrays (ThermoScientific) determined the differential expression among arrays of distinct experimental conditions. The expression analysis was done by groups (Pulls) and not individually, therefore differences in the transcript expression for each sample could not be detected. With this array, it was possible to evaluate > 285,000 full-length covered transcripts, > 245,000 coding transcripts, > 40,000 non-coding transcripts, and > 339,000 probe sets covering exon-exon junctions. Median expression levels (all p < 0.001) were significant.

Bioinformatic analyses

To perform bioinformatic analyses, the web server interface (free access), belonging to the Iowa State University, was used (RNA-Protein Interaction Prediction Server “PRIDB: “Protein-RNA Interaction Database RPI-Seq Version” Dobbs Lab) [39, 40]. Sequences of the four human Y-RNAs were downloaded from the NCBI (National Center for Biotechnology Information) nucleotide databases.

Protein sequences of cell receptors from diverse protein families were obtained by using key words: Cell, Receptor, Brain, Human and "Homo sapiens"[txid9606], being in total 1319 sequences. Filtered applied: 0 TO 1200 A.A. length. Protein sequences of Toll-like cell receptors were downloaded from the NCBI protein database by using key words: Toll, Cell, Receptors, and Human and "Homo sapiens" [txid9606], being in total 119 not filtered sequence. The removal of the terminal segments in the 5´- and 3´- terminals, to generate transcripts only with the loop domain from the four human Y-RNAs, was performed as shown in Additional file 1.

Protein sequences of the Wnt cell receptors were downloaded by using key words: Wnt, Receptors, Human and "Homo sapiens" [txid9606], being in total 37 sequences. In the same way, protein sequences related to the Origin of Replication Complex (ORC) were retrieved using key words: (DNA, Polymerase, and Human) and "Homo sapiens" [txid9606], being in total 1286. Filtered applied: from 0 to 1200 A.A. length. All the sequences were checked manually to avoid redundant annotations or duplicated sequences and formatted before being entered into the web server: http://pridb.gdcb.iastate.edu/RPISeq.

In this work, data obtained with the RF algorithm were mainly discussed, since its predictions are the most validated at the experimental level. Only in the interactions of Y-RNAs with the proteins related to the ORC, the scores obtained with RF and SVM were shown. This was with the purpose of comparing the results obtained with both classifiers. Although values greater than 0.5 were taken as significant, those closest to 1 have the highest probability of occurring in vivo. Essentially, RPIseq exploits the aminoacidic composition of proteins sequences and the ribonucleotides composition from the RNA sequences to predict the probability of in vivo interactions of a pair (RNA-Protein). This web server RPIseq implements the RPIseq method developed by Muppirala [39, 40]. This algorithm takes a data pair of sequence belonging to RNA and protein as an input, and computes probabilities of interaction through the RF and SVM trained classifiers using the datasets from the RPI2241. This interface could accept many proteins against a specific RNA molecule or vice versa users could introduce a maximum of 100 sequences.

String Protein Network

Cytoscape is an open source software platform for visualizing molecular interaction networks and biological pathways and integrate these networks with annotations, gene expression profiles, and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization [41]. The String Networks were constructed with Cytoscape 3.8.2 for windows, using String Protein Query database.

Enrichment analyses

We used PANTHER GO-slim", which uses a selected set of terms from the Gene Ontology TM for classifications by molecular function, biological process, and cellular component. The PANTHER Protein Class ontology was adapted from the PANTHER/X molecular function ontology and includes commonly used classes of protein functions, many of which are not covered by GO molecular function. Download the classes and relationship information [42].

RNPs. Ribonucleoproteins complexes.

Ro60. Ro60, Y-RNA Binding Protein.

La. (Sjögren síndrome type B antigen (SS-B).

EVs. Extracellular vesicles.

rRNA. Ribosomal RNAs.

tRNA. Transfer RNAs.

p-Ast. Pediatric Ast.

a-Ast. Adult Ast.

TLRs. Toll-like receptors.

TLR3. Toll Like Receptor 3.

TLR7. Toll Like Receptor 7.

CD22. CD22 molecule.

TLR5. Toll Like Receptor 5.

TLR10. Toll Like 10 Receptor.

PAst. Pilocytic astrocytoma.

DAst. Diffuse astrocytoma.

MCM3. Minichromosome Maintenance Complex Component 3.

Pre-RC. Pre-replication complex.

NCL. Nucleolin.

PTBP1. Polypyrimidine-tract-binding protein 1.

ZBP1. Z-DNA-binding protein 1.

Competing interests

"The authors declare that they have no competing interests"

Acknowledgements

We thank Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Victoria, 87010, Cd Victoria, Tamaulipas, México (Email: [email protected]). JMRC was supported by the Consejo Nacional de Ciencias y Tecnología with a doctoral grant scholarship: CVU: 101863, Support: 472835. In addition, we thank Sintagma Translations for the English proofreading and editing.

Funding

The authors did not receive any type of funding.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the OSF repository, [osf.io/8n3tg].

Ethics approval and consent to participate

The project had the approval of the Hospital de Pediatría “Silvestre Frenk Freund”, CMNSXXI-IMSS, Ethics in Security Research (R-2018-3603-048). All children’s relatives or tutors signed an informed consent.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

All authors have read and agreed to the published version of the manuscript.

Author´s contributions

Writing — original draft preparation, JMRC. Writing — review and editing: MAVF, RRE-G and JVHV.

Author´s details

¹Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Victoria, 87010, Cd Victoria, Tamaulipas, México. Email: [email protected]

²Catedrática CONACyT, Laboratorio de RNAs No Codificantes, Unidad de Investigación Médica en Genética Humana del Hospital de Pediatría “Silvestre Frenk Freund”, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social (IMSS), 06720, CDMX, México. Email: [email protected]

³Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Victoria, 87010, Cd Victoria, Tamaulipas, México. Email: [email protected]

⁴Laboratorio de RNAs No Codificantes, Unidad de Investigación Médica en Genética Humana del Hospital de Pediatría “Silvestre Frenk Freund”, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social (IMSS), 06720, CDMX, México. Email: [email protected]

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Circulating Y-RNAs, a predicted function controlling the Immune System, Cell Signaling, and DNA Replication in Pediatric Patients with Pilocytic and Diffuse Astrocytoma

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Abstract

Figures

Background

Results

Cell receptors

Toll-like receptors

Wnt receptors

Discussion

YRNAs interactions with the ORC

Y-RNAs loop domain

Y-RNAs in diagnosis

Conclusions

Methods

Abbreviations

Declarations

References

Competing Interests

Supplementary Files

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