Non-Coding RNA

14 pages, 6421 KiB

Open AccessArticle

Long Non-Coding RNAs Associated with Heterochromatin Function in Immune Cells in Psychosis

by Niyati Sudhalkar, Cherise Rosen, Jennifer K. Melbourne, Mi Rae Park, Kayla A. Chase and Rajiv P. Sharma

Non-Coding RNA 2018, 4(4), 43; https://doi.org/10.3390/ncrna4040043 - 18 Dec 2018

Cited by 14 | Viewed by 5481

Abstract

Psychosis is associated with chronic immune dysregulation. Many long non-coding RNAs (lncRNAs) display abnormal expression during activation of immune responses, and play a role in heterochromatic regulation of gene promoters. We have measured lncRNAs MEG3, PINT and GAS5, selected for their previously described [...] Read more.

Psychosis is associated with chronic immune dysregulation. Many long non-coding RNAs (lncRNAs) display abnormal expression during activation of immune responses, and play a role in heterochromatic regulation of gene promoters. We have measured lncRNAs MEG3, PINT and GAS5, selected for their previously described association with heterochromatin. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples collected from 86 participants with a diagnosis of psychosis and 44 control participants. Expression was assessed in relation to diagnosis, illness acuity status, and treatment with antipsychotic medication. We observed diagnostic differences with MEG3, PINT and GAS5, and symptom acuity effect with MEG3 and GAS5. Medication effects were evident in those currently on treatment with antipsychotics when compared to drug-naïve participants. We observed that clinical diagnosis and symptom acuity predict selected lncRNA expression. Particular noteworthy is the differential expression of MEG3 in drug naïve participants compared to those treated with risperidone. Additionally, an in vitro cell model using M2^tol macrophages was used to test the effects of the antipsychotic drug risperidone on the expression of these lncRNAs using quantitative real-time PCR (qRT-PCR). Significant but differential effects of risperidone were observed in M2^tol macrophages indicating a clear ability of antipsychotic medications to modify lncRNA expression. Full article

(This article belongs to the Section Long Non-Coding RNA)

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16 pages, 439 KiB

Open AccessFeature PaperReview

Non-Coding RNAs to Aid in Neurological Prognosis after Cardiac Arrest

by Antonio Salgado-Somoza, Francesca Maria Stefanizzi, Pascal Stammet, David Erlinge, Hans Friberg, Niklas Nielsen and Yvan Devaux

Non-Coding RNA 2018, 4(4), 42; https://doi.org/10.3390/ncrna4040042 - 18 Dec 2018

Cited by 2 | Viewed by 4303

Abstract

Cardiovascular disease in general, and sudden cardiac death in particular, have an enormous socio-economic burden worldwide. Despite significant efforts to improve cardiopulmonary resuscitation, survival rates remain low. Moreover, patients who survive to hospital discharge have a high risk of developing severe physical or [...] Read more.

Cardiovascular disease in general, and sudden cardiac death in particular, have an enormous socio-economic burden worldwide. Despite significant efforts to improve cardiopulmonary resuscitation, survival rates remain low. Moreover, patients who survive to hospital discharge have a high risk of developing severe physical or neurological symptoms. Being able to predict outcomes after resuscitation from cardiac arrest would make it possible to tailor healthcare approaches, thereby maximising efforts for those who would mostly benefit from aggressive therapy. However, the identification of patients at risk of poor recovery after cardiac arrest is still a challenging task which could be facilitated by novel biomarkers. Recent investigations have recognised the potential of non-coding RNAs to aid in outcome prediction after cardiac arrest. In this review, we summarize recent discoveries and propose a handful of novel perspectives for the use of non-coding RNAs to predict outcome after cardiac arrest, discussing their use for precision medicine. Full article

(This article belongs to the Collection Regulatory RNAs in Cardiovascular Development and Disease)

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25 pages, 2042 KiB

Open AccessReview

Non-Coding RNA in Pancreas and β-Cell Development

by Wilson K. M. Wong, Anja E. Sørensen, Mugdha V. Joglekar, Anand A. Hardikar and Louise T. Dalgaard

Non-Coding RNA 2018, 4(4), 41; https://doi.org/10.3390/ncrna4040041 - 13 Dec 2018

Cited by 41 | Viewed by 7226

Abstract

In this review, we provide an overview of the current knowledge on the role of different classes of non-coding RNAs for islet and β-cell development, maturation and function. MicroRNAs (miRNAs), a prominent class of small RNAs, have been investigated for more than two [...] Read more.

In this review, we provide an overview of the current knowledge on the role of different classes of non-coding RNAs for islet and β-cell development, maturation and function. MicroRNAs (miRNAs), a prominent class of small RNAs, have been investigated for more than two decades and patterns of the roles of different miRNAs in pancreatic fetal development, islet and β-cell maturation and function are now emerging. Specific miRNAs are dynamically regulated throughout the period of pancreas development, during islet and β-cell differentiation as well as in the perinatal period, where a burst of β-cell replication takes place. The role of long non-coding RNAs (lncRNA) in islet and β-cells is less investigated than for miRNAs, but knowledge is increasing rapidly. The advent of ultra-deep RNA sequencing has enabled the identification of highly islet- or β-cell-selective lncRNA transcripts expressed at low levels. Their roles in islet cells are currently only characterized for a few of these lncRNAs, and these are often associated with β-cell super-enhancers and regulate neighboring gene activity. Moreover, ncRNAs present in imprinted regions are involved in pancreas development and β-cell function. Altogether, these observations support significant and important actions of ncRNAs in β-cell development and function. Full article

(This article belongs to the Special Issue Non-Coding RNA and Diabetes)

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Figure 1
(A) Diagram of the major morphogenic events during islet development. (B) A cascade of different transcription factors, hormones and cell specific markers are expressed within different stages of pancreatic development that are responsible for the morphogenic events leading to islet formation and cellular differentiation. The diagram was inspired by [<a href="#B6-ncrna-04-00041" class="html-bibr">6</a>,<a href="#B13-ncrna-04-00041" class="html-bibr">13</a>]. DP, Dorsal pancreatic bud; VP, Ventral pancreatic bud; GB, Gall bladder; dpc, days post conception; CS, Cambridge stage. Full article ">Figure 1 Cont.
(A) Diagram of the major morphogenic events during islet development. (B) A cascade of different transcription factors, hormones and cell specific markers are expressed within different stages of pancreatic development that are responsible for the morphogenic events leading to islet formation and cellular differentiation. The diagram was inspired by [<a href="#B6-ncrna-04-00041" class="html-bibr">6</a>,<a href="#B13-ncrna-04-00041" class="html-bibr">13</a>]. DP, Dorsal pancreatic bud; VP, Ventral pancreatic bud; GB, Gall bladder; dpc, days post conception; CS, Cambridge stage. Full article ">Figure 2
Pie charts showing the percentage of (A) the different genes and (B) different RNA types from the human reference genome (GRCh38.p12) following the RefSeq annotation assembly. The annotations are available from the genome database part of the NCBI database, and the data depicted above are from the Annotation Release 109 (<a href="https://www.ncbi.nlm.nih.gov/search/?term=human+genome" target="_blank">https://www.ncbi.nlm.nih.gov/search/?term=human+genome</a> and <a href="https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/109/" target="_blank">https://www.ncbi.nlm.nih.gov/genome/annotation_euk/Homo_sapiens/109/</a>). Full article ">Figure 3
The biogenesis of miRNAs. A schematic depicting the biogenesis and function of a mature miRNA. Primary miRNA (pri-miRNA) is transcribed in the nucleus and processed by Drosha and DiGeorge syndrome critical region 8 (DGCR8). The precursor miRNA (pre-miRNA) is then exported by exportin-5 (EXP-5) out into the cytoplasm. Here, the pre-miRNA is further cleaved by Dicer to yield a double-stranded miRNA duplex. After strand selection, the mature miRNA associates with the RISC. The degree of complementarity between the miRNA and the target mRNA determines whether the mRNA is degraded or the translation process is blocked. Full article ">Figure 4
An overview of the imprinted genomic region on human chromosome 14 between DLK1 and DIO3. Genes marked in green are paternally expressed and genes marked in orange are maternally expressed. DMR: the MEG3 differentially methylated region. Shown are also single miRNAs, the miRNA clusters, SNORD genes and other ncRNAs. Redrawn and updated with inspiration from Benetatos et al. (2013) [<a href="#B157-ncrna-04-00041" class="html-bibr">157</a>]. Full article ">

34 pages, 2858 KiB

Open AccessFeature PaperReview

Non-Coding RNAs in Breast Cancer: Intracellular and Intercellular Communication

by Carolyn M. Klinge

Non-Coding RNA 2018, 4(4), 40; https://doi.org/10.3390/ncrna4040040 - 12 Dec 2018

Cited by 133 | Viewed by 10651

Abstract

Non-coding RNAs (ncRNAs) are regulators of intracellular and intercellular signaling in breast cancer. ncRNAs modulate intracellular signaling to control diverse cellular processes, including levels and activity of estrogen receptor α (ERα), proliferation, invasion, migration, apoptosis, and stemness. In addition, ncRNAs can be packaged [...] Read more.

Non-coding RNAs (ncRNAs) are regulators of intracellular and intercellular signaling in breast cancer. ncRNAs modulate intracellular signaling to control diverse cellular processes, including levels and activity of estrogen receptor α (ERα), proliferation, invasion, migration, apoptosis, and stemness. In addition, ncRNAs can be packaged into exosomes to provide intercellular communication by the transmission of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) to cells locally or systemically. This review provides an overview of the biogenesis and roles of ncRNAs: small nucleolar RNA (snRNA), circular RNAs (circRNAs), PIWI-interacting RNAs (piRNAs), miRNAs, and lncRNAs in breast cancer. Since more is known about the miRNAs and lncRNAs that are expressed in breast tumors, their established targets as oncogenic drivers and tumor suppressors will be reviewed. The focus is on miRNAs and lncRNAs identified in breast tumors, since a number of ncRNAs identified in breast cancer cells are not dysregulated in breast tumors. The identity and putative function of selected lncRNAs increased: nuclear paraspeckle assembly transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), steroid receptor RNA activator 1 (SRA1), colon cancer associated transcript 2 (CCAT2), colorectal neoplasia differentially expressed (CRNDE), myocardial infarction associated transcript (MIAT), and long intergenic non-protein coding RNA, Regulator of Reprogramming (LINC-ROR); and decreased levels of maternally-expressed 3 (MEG3) in breast tumors have been observed as well. miRNAs and lncRNAs are considered targets of therapeutic intervention in breast cancer, but further work is needed to bring the promise of regulating their activities to clinical use. Full article

(This article belongs to the Special Issue Non-Coding RNA in Reproductive Organ Cancers)

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miRNAs regulating ERα transcriptional activity. (A) ERα is directly targeted by the indicated miRNAs that are increased in breast tumors (<a href="#ncrna-04-00040-t002" class="html-table">Table 2</a>). E2-liganded ERα recruits coactivators and chromatin remodeling complexes to increase RNA pol II transcription at target genes. E2–ERα increases the transcription of FOXM1, which, in turn as a transcription factor, increases the transcription of ERα, including a number of genes for cell cycle progression [<a href="#B224-ncrna-04-00040" class="html-bibr">224</a>], and UHRF1, which is a key regulator of DNA methylation that is involved in the self-renewal and differentiation of cancer stem cells [<a href="#B225-ncrna-04-00040" class="html-bibr">225</a>]. (B) The selective ER modulator (SERM) tamoxifen is metabolized to 4-hydroxytamoxifen, which binds ERα and alters its conformation, thus inhibiting coactivator recruitment, and instead allowing interaction of the 4-OHT-bound-ERα with corepressors, including NCOR2, which recruits histone deacetylase complex (HDAC) complexes to inhibit target gene transcription in breast tumors. NCOR2 is a target of miR-10a-5p (<a href="#ncrna-04-00040-t002" class="html-table">Table 2</a>). Full article ">Figure 2
Breast cancer dysregulated miRNAs and lncRNAs as ceRNAs in cell signaling, cell cycle, and EMT. Shown are validated targets of some miRNAs dysregulated in human tumors and lncRNAs that at as ceRNAs for the indicated miRNAs. Full article ">Figure 3
Breast cancer dysregulated miRNAs in apoptosis. Shown in abbreviated form are key regulators in the intrinsic and extrinsic pathways of apoptosis and their regulation by miRNAs that are dysregulated in breast tumors (<a href="#ncrna-04-00040-t002" class="html-table">Table 2</a> and <a href="#ncrna-04-00040-t003" class="html-table">Table 3</a>). The lncRNA MIAT is a ceRNA for miR-155-5p (<a href="#ncrna-04-00040-t002" class="html-table">Table 2</a>). Full article ">Figure 4
Exosomal transfer of miRNAs and lncRNAs in breast cancer. Exosomes released from breast cancer cells and cancer-associated fibroblasts into the extracellular compartment contain ncRNAs, mRNAs, mtDNA, proteins, and lipids. Exosomes can deliver their contents to adjacent cells or cells at a distance. Examples of miRNAs and lncRNAs in breast cancer exosomes and their known roles in breast cancer are shown. Full article ">

9 pages, 2251 KiB

Open AccessCommunication

Upregulation of Long Non-Coding RNA DRAIC Correlates with Adverse Features of Breast Cancer

by Dan Zhao and Jin-Tang Dong

Non-Coding RNA 2018, 4(4), 39; https://doi.org/10.3390/ncrna4040039 - 11 Dec 2018

Cited by 22 | Viewed by 4139

Abstract

DRAIC (also known as LOC145837 and RP11-279F6.1), is a long non-coding RNA associated with several types of cancer including prostate cancer, lung cancer, and breast cancer. Its expression is elevated in tumor tissues compared to adjacent benign tissues in breast cancer patients [...] Read more.

DRAIC (also known as LOC145837 and RP11-279F6.1), is a long non-coding RNA associated with several types of cancer including prostate cancer, lung cancer, and breast cancer. Its expression is elevated in tumor tissues compared to adjacent benign tissues in breast cancer patients and is regulated by estrogen treatment in breast cancer cells. In addition, expression analysis of DRAIC in more than 100 cell lines showed that DRAIC expression is high in luminal and basal subtypes compared to claudin low subtype, suggesting a prognostic value of DRAIC expression in breast cancer. In the present study, we analyzed DRAIC expression in 828 invasive breast carcinomas and 105 normal samples of RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and found that DRAIC expression was correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status, and is increased in cancerous tissues. Additionally, higher DRAIC expression was associated with poorer survival of patients, especially in ER positive breast cancer. DRAIC was also investigated in the Oncomine database and we found that DRAIC expression predicted patients’ response to paclitaxel and FEC as well as lapatinib, which are commonly used therapy options for breast cancer. Finally, DRAIC expression in breast cancer was negatively correlated with immune cell infiltration. These results reinforce the importance of DRAIC in breast cancer. Full article

(This article belongs to the Special Issue Non-Coding RNA in Reproductive Organ Cancers)

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DRAIC expression is high in breast cancer compared with normal tissues and its expression correlates with important breast cancer marker genes estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). (A) DRAIC expression was analyzed in 828 breast cancer patients and 105 normal breast tissue samples, the data shown are mean ± SEM (standard error of the mean). (B–D) The breast cancer patients were divided according to their ER, PR, and HER2 status, respectively. Shown is the Whisker plots: Min to max and for each group, the mean value was shown as “+”. Statistical significance was calculated by two-tailed student’s t-test, * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001. DRAIC expression was shown as log2 value. Full article ">Figure 2
High DRAIC expression in breast cancer correlates with certain clinical pathological parameters. (A–D) The 828 breast cancer patients were divided according to their neoplasm disease stages (A), cancer tumor stages (B), neoplasm disease lymph node stages (C), and cancer metastasis stages (D). DRAIC expression in each different group of stages was shown as mean ± SEM (standard error of the mean). Statistical significance was calculated by either one-way ANOVA (for A–C) or two-tailed student’s t-test (for D). (E,F) The 828 breast cancer patients were divided into DRAIC high and DRAIC low group based on the median value of DRAIC expression. In each group, a stacked bar chart was created to show the distribution of patients in different neoplasm disease stages (E) or lymph node stages (F). Chi-square tests were used to test the differences. * indicates p < 0.05; ns, not significant. DRAIC expression was shown as log2 value. Full article ">Figure 3
Overall survival or disease-free survival determined by Kaplan–Meier plots and the log-rank test according to DRAIC expression. The patients were divided as DRAIC high (red) or DRAIC low (blue) according to the median value of DRAIC expression. Kaplan–Meier plots were created using GraphPad Prism 5 software using data for all samples (A) or only ER negative patients (B), ER positive patients (C), and HER2 positive patients (D). Log-rank tests were used to calculate the differences between groups. Full article ">Figure 4
DRAIC expression correlates to chemotherapy treatment response. (A) DRAIC expression in 88 paclitaxel and FEC (fluorouracil/epirubicin/cyclophosphamide) non-responder patients and 27 paclitaxel and FEC responder patients’ samples (Miyake 2012) were shown as mean ± SEM. (B) DRAIC expression in 17 lapatinib resistant cell lines and 5 sensitive cell lines (Barretina 2012) were shown as mean ± SEM. Statistical significance was calculated by two-tailed student’s t-test, * indicates p < 0.05 and ** indicates p < 0.01. Full article ">Figure 5
DRAIC expression negatively correlates with immune cell infiltration levels in breast cancer. The correlation between DRAIC expression and abundance of immune infiltrates is investigated through TIMER [<a href="#B27-ncrna-04-00039" class="html-bibr">27</a>]. Correlation between DRAIC expression and the abundances of six immune infiltrates (B cells, CD4+ T cells, CD8+ T cells, Neutrophils, Macrophages, and Dendritic cells) are displayed. The purity-corrected partial Spearman correlation and statistical significance are shown on the top right corners. Full article ">

20 pages, 2303 KiB

Open AccessFeature PaperArticle

Cell Type-Selective Expression of Circular RNAs in Human Pancreatic Islets

by Simranjeet Kaur, Aashiq H. Mirza and Flemming Pociot

Non-Coding RNA 2018, 4(4), 38; https://doi.org/10.3390/ncrna4040038 - 27 Nov 2018

Cited by 28 | Viewed by 4183

Abstract

Understanding distinct cell-type specific gene expression in human pancreatic islets is important for developing islet regeneration strategies and therapies to improve β-cell function in type 1 diabetes (T1D). While numerous transcriptome-wide studies on human islet cell-types have focused on protein-coding genes, the non-coding [...] Read more.

Understanding distinct cell-type specific gene expression in human pancreatic islets is important for developing islet regeneration strategies and therapies to improve β-cell function in type 1 diabetes (T1D). While numerous transcriptome-wide studies on human islet cell-types have focused on protein-coding genes, the non-coding repertoire, such as long non-coding RNA, including circular RNAs, remains mostly unexplored. Here, we explored transcriptional landscape of human α-, β-, and exocrine cells from published total RNA sequencing (RNA-seq) datasets to identify circular RNAs (circRNAs). Our analysis revealed that circRNAs are highly abundant in both α- and β-cells. We identified 10,830 high-confidence circRNAs expressed in human α-, β-, and exocrine cells. The most highly expressed candidates were MAN1A2, RMST, and HIPK3 across the three cell-types. Alternate circular isoforms were observed for circRNAs in the three cell-types, indicative of potential distinct functions. Highly selective α- and β-cell circRNAs were identified, which is suggestive of their potential role in regulating β-cell function. Full article

(This article belongs to the Special Issue Non-Coding RNA and Diabetes)

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Circular RNA (circRNA) distribution in α-, β-, and exocrine cells. The figure shows (A) the total number of circRNAs expressed per sample, (B) the overlap between the high confidence circRNAs in α-cell (n = 7667), β-cell (n = 8396) and exocrine-cell (n = 456) and (C) the overlap with known circRNAs from CircNet and circBase. Full article ">Figure 2
Genomic features of circRNAs in α- and β-cells. The figure shows (A) the chromosomal distribution of circRNAs, (B) total number of back-spliced exons in circRNAs, and (C) number of alternate circularization events per gene in α- (blue) and β- (red) cells. Full article ">Figure 3
Exon lengths of circRNAs in α- and β-cells. circRNAs with single exons had longer exon lengths in both (A) α-cells and (B) β-cells as compared to circRNAs with multiple exons. Full article ">Figure 4
Distribution of junction spanning reads and smear plot of differentially expressed circRNAs. (A) Total circRNA junction spanning reads per sample for the high confidence circRNA candidates in α- and β-cells; (B) A smear plot highlighting differentially expressed candidates (as red dots) in β-cells when compared to α-cells. Full article ">Figure 5
Expression profiles of differentially expressed candidates. Heatmap of normalized expression (log2 RC) of differentially expressed circRNAs (A) and their host mRNAs (B) in α- and β-cells. Full article ">Figure 6
Enriched gene ontology (GO) terms for α- and β-cell selective circRNA host genes. GO terms (biological process) associated with Cluster 1 (β-cell) circRNA host genes are shown in red, Cluster 2 (α-cell) circRNA host genes in blue, and common terms for both clusters are shown in grey. Full article ">Figure 7
circ-TGFBR3.25 with sponge effect on miRNAs as predicted by CircNet. Yellow nodes represent miRNAs, blue nodes represent genes, and pink nodes represent circRNA. Red edges represent negative regulation of miRNA to the target gene; blue edges represent negative regulation of circRNA to miRNAs. Full article ">

17 pages, 863 KiB

Open AccessArticle

Serum Levels of miR-148a and miR-21-5p Are Increased in Type 1 Diabetic Patients and Correlated with Markers of Bone Strength and Metabolism

by Giuseppina E. Grieco, Dorica Cataldo, Elena Ceccarelli, Laura Nigi, Giovanna Catalano, Noemi Brusco, Francesca Mancarella, Giuliana Ventriglia, Cecilia Fondelli, Elisa Guarino, Isabella Crisci, Guido Sebastiani and Francesco Dotta

Non-Coding RNA 2018, 4(4), 37; https://doi.org/10.3390/ncrna4040037 - 27 Nov 2018

Cited by 42 | Viewed by 3864

Abstract

Type 1 diabetes (T1D) is characterized by bone loss and altered bone remodeling, resulting into reduction of bone mineral density (BMD) and increased risk of fractures. Identification of specific biomarkers and/or causative factors of diabetic bone fragility is of fundamental importance for an [...] Read more.

Type 1 diabetes (T1D) is characterized by bone loss and altered bone remodeling, resulting into reduction of bone mineral density (BMD) and increased risk of fractures. Identification of specific biomarkers and/or causative factors of diabetic bone fragility is of fundamental importance for an early detection of such alterations and to envisage appropriate therapeutic interventions. MicroRNAs (miRNAs) are small non-coding RNAs which negatively regulate genes expression. Of note, miRNAs can be secreted in biological fluids through their association with different cellular components and, in such context, they may represent both candidate biomarkers and/or mediators of bone metabolism alterations. Here, we aimed at identifying miRNAs differentially expressed in serum of T1D patients and potentially involved in bone loss in type 1 diabetes. We selected six miRNAs previously associated with T1D and bone metabolism: miR-21; miR-24; miR-27a; miR-148a; miR-214; and miR-375. Selected miRNAs were analyzed in sera of 15 T1D patients (age: 33.57 ± 8.17; BMI: 21.4 ± 1.65) and 14 non-diabetic subjects (age: 31.7 ± 8.2; BMI: 24.6 ± 4.34). Calcium, osteocalcin, parathormone (PTH), bone ALkaline Phoshatase (bALP), and Vitamin D (VitD) as well as main parameters of bone health were measured in each patient. We observed an increased expression of miR-148a (p = 0.012) and miR-21-5p (p = 0.034) in sera of T1D patients vs. non-diabetic subjects. The correlation analysis between miRNAs expression and the main parameters of bone metabolism, showed a correlation between miR-148a and Bone Mineral Density (BMD) total body (TB) values (p = 0.042) and PTH circulating levels (p = 0.033) and the association of miR-21-5p to Bone Mineral Content-Femur (BMC-FEM). Finally, miR-148a and miR-21-5p target genes prediction analysis revealed several factors involved in bone development and remodeling, such as MAFB, WNT1, TGFB2, STAT3, or PDCD4, and the co-modulation of common pathways involved in bone homeostasis thus potentially assigning a role to both miR-148a and miR-21-5p in bone metabolism alterations. In conclusion, these results lead us to hypothesize a potential role for miR-148a and miR-21-5p in bone remodeling, thus representing potential biomarkers of bone fragility in T1D. Full article

(This article belongs to the Special Issue Non-Coding RNA and Diabetes)

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The expression of hsa-miR-148a and miR-21-5p is increased in the serum of patients with T1D. Single assay RT-qPCR validation of miR-21-5p (a), miR-24 (b), miR-27a (c), miR-148a (d), miR-214 (e), and miR-375 (f) in n = 14 non-diabetic and n = 15 T1D patients. Data are reported as mean ± SD of normalized 2−ΔCT values. Statistics using Mann–Whitney U test, p < 0.05. Full article ">Figure 2
The expression of hsa-miR-148a and miR-21-5p levels are correlated with bone metabolism parameters in T1D patients and non-diabetic control subjects. Correlation analysis between miR-148a serum expression levels, reported as normalized 2−ΔCT values, and BMD total body (TB) reported as g/cm2 (a) and circulating levels of parathormone (PTH) reported as pg/mL (b). Correlation analysis between miR-21-5p serum expression levels and BMC-FEM reported as g/cm2 (c). Spearman R test was performed to evaluate r-values and p-values (p < 0.05). Full article ">Figure 3
miR-148a and miR-21-5p regulate common pathways involved in bone metabolism and remodeling. Hierarchical Clustering Heatmap bioinformatic analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of hsa-miR-148a-3p and hsa-miR-21-5p target genes, showed FoxO and TGF-β signaling pathway as main pathways involved in bone metabolism and remodeling. Of note, FoxO signaling pathway is commonly identified both in miR-148a-3p and miR-21-5p analysis. Color key indicates Log p values from the less significant pathway (light yellow) to the most significant pathway (dark red). Full article ">

21 pages, 1438 KiB

Open AccessFeature PaperReview

Herpes Simplex Virus 1 Deregulation of Host MicroRNAs

by Maja Cokarić Brdovčak, Andreja Zubković and Igor Jurak

Non-Coding RNA 2018, 4(4), 36; https://doi.org/10.3390/ncrna4040036 - 23 Nov 2018

Cited by 32 | Viewed by 6942

Abstract

Viruses utilize microRNAs (miRNAs) in a vast variety of possible interactions and mechanisms, apparently far beyond the classical understanding of gene repression in humans. Likewise, herpes simplex virus 1 (HSV-1) expresses numerous miRNAs and deregulates the expression of host miRNAs. Several HSV-1 miRNAs [...] Read more.

Viruses utilize microRNAs (miRNAs) in a vast variety of possible interactions and mechanisms, apparently far beyond the classical understanding of gene repression in humans. Likewise, herpes simplex virus 1 (HSV-1) expresses numerous miRNAs and deregulates the expression of host miRNAs. Several HSV-1 miRNAs are abundantly expressed in latency, some of which are encoded antisense to transcripts of important productive infection genes, indicating their roles in repressing the productive cycle and/or in maintenance/reactivation from latency. In addition, HSV-1 also exploits host miRNAs to advance its replication or repress its genes to facilitate latency. Here, we discuss what is known about the functional interplay between HSV-1 and the host miRNA machinery, potential targets, and the molecular mechanisms leading to an efficient virus replication and spread. Full article

(This article belongs to the Special Issue Non-Coding RNAs in Viral Infections)

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Figure 1
Host miR-138 targets herpes simplex virus 1 (HSV-1) ICP0 messenger RNA (mRNA) and promotes latency. A schematic representation of host-neuron-specific miR-138 regulating an important productive replication viral protein ICP0. miR-138 in association with RNA-induced silencing complex (RISC) binds to two microRNA (miRNA) target sites within the three prime untranslated region (3′UTR) of the HSV-1 ICP0 transcript, leading to decreased protein levels of ICP0, and reduced overall lytic gene expression to facilitate latency. Figure adapted from Pan et al. [<a href="#B19-ncrna-04-00036" class="html-bibr">19</a>]. Full article ">Figure 2
HSV-1 deregulates host miRNAs. A schematic representation of reported deregulated host miRNAs in HSV-1 infection and miRNAs with pro/antiviral functions. HSV-1 infects cells and triggers massive changes in host cell miRNAome. Many miRNAs have been found to be upregulated (arrow up within the nucleus) or downregulated (arrow down in the nucleus). These miRNAs regulate different host (blue boxes) or viral transcripts (light blue box) with functions in (a) regulation of apoptosis, (b) antiviral immunity, (c) inhibition of viral replication, and (d) miRNAs with targets not known depicted in separate boxes. During early infection (EI), miR-23a is downregulated, while later in the infection (LI) miR-23a is upregulated. The exact targets of miR-155 and miR-146a have not been identified; however, they regulate host immune response (yellow boxes) by regulating T-cell differentiation and the arachidonic acid cascade (AA cascade) pathway, respectively. Upregulated miR-132 activates Ras by removing Ras-GAP, leading to corneal neovascularization (CV). Neuron-specific miR-138 regulates virus protein ICP0. Arrows indicate positive regulation. PD-1: Programmed cell death protein 1; PDCD-4: Programmed cell death protein 4; IRF1: Interferon regulatory factor 1; CFH: Complement factor H; TNFα: Tumor necrosis factor alpha; IFNβ/γ: Interferon beta/gamma; MALT1: Mucosa-associated lymphoid tissue lymphoma translocation gene 1; NF-κB: Nuclear factor kappa-light-chain-enhancer of activated B cells; Ras-GAP: Ras–glyceraldehyde-3-phosphate; ICP0: Infected cell polypeptide 0; ATP5B: ATP synthase subunit beta; GRSF1: G-rich sequence factor 1; ARHGAP21: Rho GTPase-Activating Protein 21; Cdc42: Cell division control protein 42 homolog. Full article ">Figure 3
The molecular mechanism of the miR-183/96/182 cluster upregulation in HSV-1 infection. HSV-1 expresses its genes in a controlled cascade of gene expression, first immediate early (IE), early (E), and then late (L) genes. IE protein ICP0 directs host protein Zinc Finger E-Box Binding Homeobox (ZEB) for ubiquitin-dependent proteasomal degradation and de-represses the miR-183/96/182 cluster [<a href="#B86-ncrna-04-00036" class="html-bibr">86</a>]. The targets of the co-expressed miR-183, miR-96, and miR-182 are unknown. Adapted from Lutz et al. [<a href="#B86-ncrna-04-00036" class="html-bibr">86</a>]. Full article ">

11 pages, 652 KiB

Open AccessArticle

Influence of Disease Duration on Circulating Levels of miRNAs in Children and Adolescents with New Onset Type 1 Diabetes

by Nasim Samandari, Aashiq H. Mirza, Simranjeet Kaur, Philip Hougaard, Lotte B. Nielsen, Siri Fredheim, Henrik B. Mortensen and Flemming Pociot

Non-Coding RNA 2018, 4(4), 35; https://doi.org/10.3390/ncrna4040035 - 21 Nov 2018

Cited by 24 | Viewed by 3491

Abstract

Circulating microRNAs (miRNAs) have been implicated in several pathologies including type 1 diabetes. In the present study, we aimed to identify circulating miRNAs affected by disease duration in children with recent onset type 1 diabetes. Forty children and adolescents from the Danish Remission [...] Read more.

Circulating microRNAs (miRNAs) have been implicated in several pathologies including type 1 diabetes. In the present study, we aimed to identify circulating miRNAs affected by disease duration in children with recent onset type 1 diabetes. Forty children and adolescents from the Danish Remission Phase Cohort were followed with blood samples drawn at 1, 3, 6, 12, and 60 months after diagnosis. Pancreatic autoantibodies were measured at each visit. Cytokines were measured only the first year. miRNA expression profiling was performed by RT-qPCR. The effect of disease duration was analyzed by mixed models for repeated measurements adjusted for sex and age. Eight miRNAs (hsa-miR-10b-5p, hsa-miR-17-5p, hsa-miR-30e-5p, hsa-miR-93-5p, hsa-miR-99a-5p, hsa-miR-125b-5p, hsa-miR-423-3p, and hsa-miR-497-5p) were found to significantly change in expression (adjusted p-value < 0.05) with disease progression. Three pancreatic autoantibodies, ICA, IA-2A, and GAD65A, and four cytokines, IL-4, IL-10, IL-21, and IL-22, were associated with the miRNAs at different time points. Pathway analysis revealed associations with various immune-mediated signaling pathways. Eight miRNAs that were involved in immunological pathways changed expression levels during the first five years after diagnosis and were associated with variations in cytokine and pancreatic antibodies, suggesting a possible effect on the immunological processes in the early phase of the disease. Full article

(This article belongs to the Special Issue Non-Coding RNA and Diabetes)

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16 pages, 2740 KiB

Open AccessArticle

Long Non-Coding RNA Modulation of VEGF-A during Hypoxia

by Tiina Nieminen, Tristan A. Scott, Feng-Mao Lin, Zhen Chen, Seppo Yla-Herttuala and Kevin V. Morris

Non-Coding RNA 2018, 4(4), 34; https://doi.org/10.3390/ncrna4040034 - 20 Nov 2018

Cited by 14 | Viewed by 4520

Abstract

The role and function of long non-coding RNAs (lncRNAs) in modulating gene expression is becoming apparent. Vascular endothelial growth factor A (VEGF-A) is a key regulator of blood vessel formation and maintenance making it a promising therapeutic target for activation in ischemic diseases. [...] Read more.

The role and function of long non-coding RNAs (lncRNAs) in modulating gene expression is becoming apparent. Vascular endothelial growth factor A (VEGF-A) is a key regulator of blood vessel formation and maintenance making it a promising therapeutic target for activation in ischemic diseases. In this study, we uncover a functional role for two antisense VEGF-A lncRNAs, RP1-261G23.7 and EST AV731492, in transcriptional regulation of VEGF-A during hypoxia. We find here that both lncRNAs are polyadenylated, concordantly upregulated with VEGF-A, localize to the VEGF-A promoter and upstream elements in a hypoxia dependent manner either as a single-stranded RNA or DNA bound RNA, and are associated with enhancer marks H3K27ac and H3K9ac. Collectively, these data suggest that VEGF-A antisense lncRNAs, RP1-261G23.7 and EST AV731492, function as VEGF-A promoter enhancer-like elements, possibly by acting as a local scaffolding for proteins and also small RNAs to tether. Full article

(This article belongs to the Section Long Non-Coding RNA)

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19 pages, 5408 KiB

Open AccessArticle

lncRNA Expression after Irradiation and Chemoexposure of HNSCC Cell Lines

by Kacper Guglas, Tomasz Kolenda, Anna Teresiak, Magda Kopczyńska, Izabela Łasińska, Jacek Mackiewicz, Andrzej Mackiewicz and Katarzyna Lamperska

Non-Coding RNA 2018, 4(4), 33; https://doi.org/10.3390/ncrna4040033 - 14 Nov 2018

Cited by 18 | Viewed by 3209

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world. To improve the quality of diagnostics and patients’ treatment, new and effective biomarkers are needed. Recent studies have shown that the expression level of [...] Read more.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cause of cancer mortality in the world. To improve the quality of diagnostics and patients’ treatment, new and effective biomarkers are needed. Recent studies have shown that the expression level of different types of long non-coding RNAs (lncRNAs) is dysregulated in HNSCC and correlates with many biological processes. In this study, the response of lncRNAs in HNSCC cell lines after exposure to irradiation and cytotoxic drugs was examined. The SCC-040, SCC-25, FaDu, and Cal27 cell lines were treated with different radiation doses as well as exposed to cisplatin and doxorubicin. The expression changes of lncRNAs after exposure to these agents were checked by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Target prediction was performed using available online tools and classified into specific biological processes and cellular pathways. The results indicated that the irradiation, as well as chemoexposure, causes changes in lncRNA expression and the effect depends on the cell line, type of agents as well as their dose. After irradiation using the dose of 5 Gy significant dysregulation of 4 lncRNAs, 10 Gy-5 lncRNAs, and 20 Gy-3 lncRNAs, respectively, were observed in all cell lines. Only lncRNAs Zfhx2as was down-regulated in all cell lines independently of the dose used. After cisplatin exposure, 14 lncRNAs showed lower and only two higher expressions. Doxorubicin resulted in lower expressions of eight and increased four of lncRNAs. Common effects of cytotoxic drugs were observed in the case of antiPEG11, BACE1AS, PCGEM1, and ST7OT. Analysis of the predicted targets for dysregulated lncRNAs indicated that they are involved in important biological processes, regulating cellular pathways connected with direct response to irradiation or chemoexposure, cellular phenotype, cancer initiating cells, and angiogenesis. Both irradiation and chemoexposure caused specific changes in lncRNAs expression. However, the common effect is potentially important for cellular response to the stress and survival. Further study will show if lncRNAs are useful tools in patients’ treatment monitoring. Full article

(This article belongs to the Section Long Non-Coding RNA)

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Heat map and clustering of 96 lncRNAs after irradiation of head and neck squamous cell carcinoma (HNSCC) cell lines using dose of 5 Gy, 10 Gy, and 20 Gy. Data shown as 2−ΔΔCt (compared to non-irradiated control). Full article ">Figure 2
Differences in lncRNAs expression: HOTAIR, HOXA3as, SNHG5, and Zfhx2as in HNSCC cell lines after exposure to 5 Gy irradiation. Paired t-test; the graphs show relative expression, mean value with standard error of mean (SEM); * p < 0.05. Full article ">Figure 3
Differences in lncRNAs expression: CAR Intergenic 10, Dio3os (family), HAR1A, Zfhx2as, and HAR1B in HNSCC cell lines after exposure to 10 Gy irradiation and in non-irradiated controls. Paired t-test; the graphs show relative expression, mean value with SEM; * p < 0.05; ** p < 0.01. Full article ">Figure 4
Differences in lncRNAs expression: HOXA6as, PTENP1, and Zfhx2as in HNSCC cell lines after exposure to 20 Gy irradiation. Paired t-test; the graphs show relative expression, mean value with SEM; * p < 0.05. Full article ">Figure 5
Heat map and clustering of 96 lncRNAs after cisplatin and doxorubicin exposure of HNSCC cell lines. Data shown as 2−ΔΔCt (compared to non-treated control). Full article ">Figure 6
Differences in lncRNAs expression: AIR, antiPEG11, BACE1AS (family), CAR Intergenic 10, DISC2 (family), IPW, MEG3 (family), lincRNA-ROR, ncR-uPAR, PCGEM1, PRINS, PSF Inhibiting RNA, PTENP1, SNHG6, SRA, and ST7OT in HNSCC cell lines after exposure to cisplatin. Paired t-test; the graphs show relative expression, mean value with SEM; * p < 0.05, ** p < 0.01. Full article ">Figure 7
Differences in lncRNAs expression: antiPEG11, BACE1AS (family), EgoA, Evf1 and EVF2, lincRNA-p21, lincRNA-SFMBT2, Malat1, Nespas, PCGEM1, UM9-5, Zfas1, and ST7OT in HNSCC cell lines after exposure to doxorubicin and controls non-treated. Paired t-test; the graphs show relative expression, mean value with SEM; * p < 0.05, ** p < 0.01, *** p < 0.001. Full article ">

25 pages, 312 KiB

Open AccessReview

Diabetes in Pregnancy and MicroRNAs: Promises and Limitations in Their Clinical Application

by Adriana Ibarra, Begoña Vega-Guedes, Yeray Brito-Casillas and Ana M. Wägner

Non-Coding RNA 2018, 4(4), 32; https://doi.org/10.3390/ncrna4040032 - 12 Nov 2018

Cited by 36 | Viewed by 8200

Abstract

Maternal diabetes is associated with an increased risk of complications for the mother and her offspring. The latter have an increased risk of foetal macrosomia, hypoglycaemia, respiratory distress syndrome, preterm delivery, malformations and mortality but also of life-long development of obesity and diabetes. [...] Read more.

Maternal diabetes is associated with an increased risk of complications for the mother and her offspring. The latter have an increased risk of foetal macrosomia, hypoglycaemia, respiratory distress syndrome, preterm delivery, malformations and mortality but also of life-long development of obesity and diabetes. Epigenetics have been proposed as an explanation for this long-term risk, and microRNAs (miRNAs) may play a role, both in short- and long-term outcomes. Gestation is associated with increasing maternal insulin resistance, as well as β-cell expansion, to account for the increased insulin needs and studies performed in pregnant rats support a role of miRNAs in this expansion. Furthermore, several miRNAs are involved in pancreatic embryonic development. On the other hand, maternal diabetes is associated with changes in miRNA both in maternal and in foetal tissues. This review aims to summarise the existing knowledge on miRNAs in gestational and pre-gestational diabetes, both as diagnostic biomarkers and as mechanistic players, in the development of gestational diabetes itself and also of short- and long-term complications for the mother and her offspring. Full article

(This article belongs to the Special Issue Non-Coding RNA and Diabetes)

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12 pages, 1051 KiB

Open AccessFeature PaperReview

Contemporary Ribonomics Methods for Viral microRNA Target Analysis

by Lauren A. Gay, Peter C. Turner and Rolf Renne

Non-Coding RNA 2018, 4(4), 31; https://doi.org/10.3390/ncrna4040031 - 9 Nov 2018

Cited by 5 | Viewed by 6007

Abstract

Numerous cellular processes are regulated by microRNAs (miRNAs), both cellular and viral. Elucidating the targets of miRNAs has become an active area of research. An important method in this field is cross-linking and immunoprecipitation (CLIP), where cultured cells or tissues are UV-irradiated to [...] Read more.

Numerous cellular processes are regulated by microRNAs (miRNAs), both cellular and viral. Elucidating the targets of miRNAs has become an active area of research. An important method in this field is cross-linking and immunoprecipitation (CLIP), where cultured cells or tissues are UV-irradiated to cross-link protein and nucleic acid, the RNA binding protein of interest is immunoprecipitated, and the RNAs pulled down with the protein are isolated, reverse-transcribed, and analyzed by sequencing. CLIP using antibody against Argonaute (Ago), which binds to both miRNA and mRNA as they interact in RISC, has allowed researchers to uncover a large number of miRNA targets. Coupled with high-throughput sequencing, CLIP has been useful for revealing miRNA targetomes for the γ-herpesviruses Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV). Variants on the CLIP protocol are described, with the benefits and drawbacks of each. In particular, the most recent methods involving RNA–RNA ligation to join the miRNA and its RNA target have aided in target identification. Lastly, data supporting biologically meaningful interactions between miRNAs and long non-coding RNAs (lncRNAs) are reviewed. In summary, ribonomics-based miRNA targetome analysis has expanded our understanding of miRNA targeting and has provided a rich resource for EBV and KSHV research with respect to pathogenesis and tumorigenesis. Full article

(This article belongs to the Special Issue Non-Coding RNAs in Viral Infections)

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Outline of high-throughput sequencing cross-linking and immunoprecipitation (HITS-CLIP), photoactivatable-ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) and cross-linking and sequencing of hybrids (CLASH) ribonomics protocols, with significant differences indicated. The steps from UV irradiation of cells through to sequencing library construction are shown. 4SU: 4-thiouridine; Ago: Argonaute. The black circles indicate mutations introduced by cross-linking damage, and the squares the nucleotide incorporated following reverse transcription. For PAR-CLIP, reverse transcription of RNA containing a crosslinked 4SU results in the misincorporation of a G in the opposite strand instead of A. The 3′ and 5′ adaptors are shown in red, and in orange following conversion to cDNA. Reproduced with permission from Sethuraman et al., Nucleic Acids Research; published by Oxford University Press, 2018, [<a href="#B12-ncrna-04-00031" class="html-bibr">12</a>]. Full article ">Figure 2
(A) The quick CLASH (qCLASH) method. The shaded box indicates the portion of the method that takes place on beads; (B) The two possible ways in which microRNAs (miRNAs) and mRNAs can join during intermolecular ligation. From [<a href="#B27-ncrna-04-00031" class="html-bibr">27</a>], Copyright © 2018 American Society for Microbiology, Journal of Virology, 92, e02138-17, 2018. Full article ">Figure 3
Patterns of binding for selected individual Kaposi’s sarcoma-associated herpesvirus (KSHV) miRNAs. The status of each nucleotide (bound or unbound) along the length of the miRNA portion of each hybrid was determined, based on the Vienna diagrams generated through Hyb. BR1, BR2 and BR3 indicates biological replicates 1, 2 and 3. (A) KSHV miR-K12-1; (B) KSHV miR-K12-6-5p; (C) KSHV miR-K12-3. Adapted from [<a href="#B27-ncrna-04-00031" class="html-bibr">27</a>], Copyright © 2018 American Society for Microbiology, Journal of Virology, 92, e02138-17, 2018. Full article ">

13 pages, 2043 KiB

Open AccessFeature PaperReview

Biogenesis, Stabilization, and Transport of microRNAs in Kidney Health and Disease

by Melissa J. Thomas, Donald J. Fraser and Timothy Bowen

Non-Coding RNA 2018, 4(4), 30; https://doi.org/10.3390/ncrna4040030 - 3 Nov 2018

Cited by 10 | Viewed by 5177

Abstract

The kidneys play key roles in the maintenance of homeostasis, including fluid balance, blood filtration, erythropoiesis and hormone production. Disease-driven perturbation of renal function therefore has profound pathological effects, and chronic kidney disease is a leading cause of morbidity and mortality worldwide. Successive [...] Read more.

The kidneys play key roles in the maintenance of homeostasis, including fluid balance, blood filtration, erythropoiesis and hormone production. Disease-driven perturbation of renal function therefore has profound pathological effects, and chronic kidney disease is a leading cause of morbidity and mortality worldwide. Successive annual increases in global chronic kidney disease patient numbers in part reflect upward trends for predisposing factors, including diabetes, obesity, hypertension, cardiovascular disease and population age. Each kidney typically possesses more than one million functional units called nephrons, and each nephron is divided into several discrete domains with distinct cellular and functional characteristics. A number of recent analyses have suggested that signaling between these nephron regions may be mediated by microRNAs. For this to be the case, several conditions must be fulfilled: (i) microRNAs must be released by upstream cells into the ultrafiltrate; (ii) these microRNAs must be packaged protectively to reach downstream cells intact; (iii) these packaged microRNAs must be taken up by downstream recipient cells without functional inhibition. This review will examine the evidence for each of these hypotheses and discuss the possibility that this signaling process might mediate pathological effects. Full article

(This article belongs to the Special Issue Non-Coding RNA, Fibrogenesis, and Fibrotic Disease)

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19 pages, 1508 KiB

Open AccessFeature PaperReview

HCMV miRNA Targets Reveal Important Cellular Pathways for Viral Replication, Latency, and Reactivation

by Nicole L. Diggins and Meaghan H. Hancock

Non-Coding RNA 2018, 4(4), 29; https://doi.org/10.3390/ncrna4040029 - 22 Oct 2018

Cited by 31 | Viewed by 4964

Abstract

It is now well appreciated that microRNAs (miRNAs) play a critical role in the lifecycles of many herpes viruses. The human cytomegalovirus (HCMV) replication cycle varies significantly depending on the cell type infected, with lytic replication occurring in fully-differentiated cells such as fibroblasts, [...] Read more.

It is now well appreciated that microRNAs (miRNAs) play a critical role in the lifecycles of many herpes viruses. The human cytomegalovirus (HCMV) replication cycle varies significantly depending on the cell type infected, with lytic replication occurring in fully-differentiated cells such as fibroblasts, endothelial cells, or macrophages, and latent infection occurring in less-differentiated CD14+ monocytes and CD34+ hematopoietic progenitor cells where viral gene expression is severely diminished and progeny virus is not produced. Given their non-immunogenic nature and their capacity to target numerous cellular and viral transcripts, miRNAs represent a particularly advantageous means for HCMV to manipulate viral gene expression and cellular signaling pathways during lytic and latent infection. This review will focus on our current knowledge of HCMV miRNA viral and cellular targets, and discuss their importance in lytic and latent infection, highlight the challenges of studying HCMV miRNAs, and describe how viral miRNAs can help us to better understand the cellular processes involved in HCMV latency. Full article

(This article belongs to the Special Issue Non-Coding RNAs in Viral Infections)

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11 pages, 966 KiB

Open AccessReview

Local Tandem Repeat Expansion in Xist RNA as a Model for the Functionalisation of ncRNA

by Neil Brockdorff

Non-Coding RNA 2018, 4(4), 28; https://doi.org/10.3390/ncrna4040028 - 19 Oct 2018

Cited by 23 | Viewed by 5717

Abstract

Xist, the master regulator of the X chromosome inactivation in mammals, is a 17 kb lncRNA that acts in cis to silence the majority of genes along the chromosome from which it is transcribed. The two key processes required for Xist RNA [...] Read more.

Xist, the master regulator of the X chromosome inactivation in mammals, is a 17 kb lncRNA that acts in cis to silence the majority of genes along the chromosome from which it is transcribed. The two key processes required for Xist RNA function, localisation in cis and recruitment of silencing factors, are genetically separable, at least in part. Recent studies have identified Xist RNA sequences and associated RNA-binding proteins (RBPs) that are important for these processes. Notably, several of the key Xist RNA elements correspond to local tandem repeats. In this review, I use examples to illustrate different modes whereby tandem repeat amplification has been exploited to allow orthodox RBPs to confer new functions for Xist-mediated chromosome inactivation. I further discuss the potential generality of tandem repeat expansion in the evolution of functional long non-coding RNAs (lncRNAs). Full article

(This article belongs to the Special Issue Non-Coding RNAs, from an Evolutionary Perspective)

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Xist RNA in the interphase nucleus. The schematic represents cross-sections of the nucleus illustrating the deduced relationship of Xist RNA (green) relative to the inactive X chromosome (Xi), interphase chromatin (blue), and interchromatin channels at different scales. The green circles represent single Xist RNA molecules. PM and NM denote the plasma- and nuclear-membrane respectively. The black lines represent the nuclear matrix proteins. Coloured shapes indicate the Xist RNA-associated proteins linked to tethering Xist RNA to the nuclear matrix (lilac diamond) or Xist-mediated chromatin silencing (red triangles). Arrows indicate the activity of the silencing factors towards proximal chromatin sites. Full article ">Figure 2
Local tandem repeats in Xist RNA. The schematic illustrates the intron/exon structure of human and mouse Xist genes with conserved tandem repeat blocks indicated in different colours. The key indicates the label, approximate copy number, and monomer length for each repeat block based on the mouse and human Xist/XIST RNA sequence. Full article ">Figure 3
Xist ribonucleoprotein particles (RNPs) generate local concentrations of silencing factors within interchromatin channels. The schematic illustrating a model for how Xist RNA bound RNA binding proteins (RBPs) (red triangles) can function to generate a local concentration of chromatin-modifying factors (grey shape). Multiple RBP molecules that are strongly bound to a tandemly repeated element on the anchored Xist RNA molecule (green) interact weakly/transiently with the chromatin-modifying factor such that the local concentration of unbound molecules increases within the interchromatin channel. The unbound chromatin-modifying factor can then act at the available chromatin sites (circular arrows) indiscriminately within a zone surrounding the Xist RNP, resulting in a widespread deposition or removal of specific chromatin modifications (red flags). Full article ">Figure 4
The amplification of RBP binding sites as a driver for Xist RNP anchoring. A schematic illustrating a model for how the amplification of RBP-binding sites on Xist RNA facilitates RNA anchoring. For a typical messenger RNA (mRNA) (grey circle), an RBP (lilac diamond) that interacts transiently with nuclear matrix proteins (black lines) immobilises the mRNA in interchromatin channels for a short time (hypothetical dwell time <1 s). For Xist RNA (green circle), amplification of the number of binding sites for the RBP increases the dwell time hypothetically up to minutes or even hours in a manner proportional to the local concentration of the RBP, the interaction strength with Xist RNA, and the number of binding sites. Full article ">

21 pages, 2474 KiB

Open AccessArticle

Copaifera langsdorffii Novel Putative Long Non-Coding RNAs: Interspecies Conservation Analysis in Adaptive Response to Different Biomes

by Monica F. Danilevicz, Kanhu C. Moharana, Thiago M. Venancio, Luciana O. Franco, Sérgio R. S. Cardoso, Mônica Cardoso, Flávia Thiebaut, Adriana S. Hemerly, Francisco Prosdocimi and Paulo C. G. Ferreira

Non-Coding RNA 2018, 4(4), 27; https://doi.org/10.3390/ncrna4040027 - 8 Oct 2018

Cited by 3 | Viewed by 3913

Abstract

Long non-coding RNAs (lncRNAs) are involved in multiple regulatory pathways and its versatile form of action has disclosed a new layer in gene regulation. LncRNAs have their expression levels modulated during plant development, and in response to stresses with tissue-specific functions. In this [...] Read more.

Long non-coding RNAs (lncRNAs) are involved in multiple regulatory pathways and its versatile form of action has disclosed a new layer in gene regulation. LncRNAs have their expression levels modulated during plant development, and in response to stresses with tissue-specific functions. In this study, we analyzed lncRNA from leaf samples collected from the legume Copaifera langsdorffii Desf. (copaíba) present in two divergent ecosystems: Cerrado (CER; Ecological Station of Botanical Garden in Brasília, Brazil) and Atlantic Rain Forest (ARF; Rio de Janeiro, Brazil). We identified 8020 novel lncRNAs, and they were compared to seven Fabaceae genomes and transcriptomes, to which 1747 and 2194 copaíba lncRNAs were mapped, respectively, to at least one species. The secondary structures of the lncRNAs that were conserved and differentially expressed between the populations were predicted using in silico methods. A few selected lncRNA were confirmed by RT-qPCR in the samples from both biomes; Additionally, the analysis of the lncRNA sequences predicted that some might act as microRNA (miRNA) targets or decoys. The emerging studies involving lncRNAs function and conservation have shown their involvement in several types of biotic and abiotic stresses. Thus, the conservation of lncRNAs among Fabaceae species considering their rapid turnover, suggests they are likely to have been under functional conservation pressure. Our results indicate the potential involvement of lncRNAs in the adaptation of C. langsdorffii in two different biomes. Full article

(This article belongs to the Section Long Non-Coding RNA)

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Putative lncRNAs identified in CER and ARF samples and its fold change regulation in comparison to each other: There were 2893 differentially regulated transcripts identified from a total of 8020 copaíba lncRNAs. The majority of the transcripts were 2 to 5 times differently expressed on either sample, yet there were 565 transcripts regulated above 5-fold on either sample. Full article ">Figure 2
lncRNA interspecies conservation and genome alignment analysis: the graph shows the total number of genomes versus the total number of transcripts aligned to Fabaceae genomes (Vicia faba, Glycine max, Medicago truncatula, Phaseolus vulgaris, Lotus japonica, Vigna unguiculata and Cicer reticulatum). The blue line represents the lncRNA gathered from the ARF samples and the red line represents the lncRNA gathered from the CER sample, their alignment profile is very similar as expected, around 1800 transcripts aligned to at least one of the genomes used. Full article ">Figure 3
Subset of transcripts which aligned to all Fabaceae genomes presenting differential expression: From 156 transcripts which aligned to all genomes analyzed, there were 45 copaíba lncRNAs upregulated in either condition represented in this graph. Each transcript is represented by a single bar. In red are indicated the lncRNAs upregulated in CER samples in relation to ARF. In blue are indicated the lncRNAs upregulated in ARF samples in relation to CER. The x axis indicates the fold change regulation of the transcripts. Full article ">Figure 4
Count of copaíba lncRNAs aligned to each Fabaceae transcriptome with BLASTN (50% identity): This diagram shows the amount of copaíba lncRNA aligned to the transcriptome of each species, segregated by a color pattern indicated on the legend. In the colored overlapped area are the transcripts which were aligned to more than one species transcriptome, and its respective amount. There are only five species shown in this diagram, for illustration purposes we left out P. vulgaris. Full article ">Figure 5
Count of copaíba lncRNAs aligned to each Fabaceae transcriptome with BLASTN (90% identity): This diagram shows the amount of copaíba lncRNA aligned to the transcriptome of each species, segregated by a color pattern indicated on the legend. In the colored overlapped area are the transcripts which were aligned to more than one species transcriptome, and its respective amount. There are only five species shown in this diagram, for illustration purposes we left out P. vulgaris. Full article ">Figure 6
BLASTN comparison of copaíba lncRNA aligned to each species genome and complementary DNA (cDNA): In the venn diagram is compared the amount of copaíba transcripts aligned to the genome (blue circle), to the transcriptome (red square) or to both (overlapped area). (A) is the V. unguiculata comparison, (B) is M. truncatula, (C) is P. vulgaris, (D) is L. japonicus, (E) is G. max and (F) is C. reticulatum. It is possible to observe that G. max, L japonicus and M. truncatula presented a higher number of overall aligned transcripts and also overlapped ones. Full article ">Figure 7
Total transcripts aligned to Fabaceae genomes and transcriptome: This diagram shows the amount of copaíba lncRNA aligned to the both genome and transcriptome of each species segregated by a color pattern indicated in the legend. In the colored overlapped area are the transcripts which were aligned to more than one species genome and transcriptome, and its respective amount. Full article ">Figure 8
log2fold change comparison of copaíba lncRNA conserved in Fabaceae species: A total of 1141 lncRNA aligned to multiple Fabaceae genomes and transcriptomes. Comparing the expression of the ARF against CER samples, we found 256 transcripts that presented log2fc above 1. The graph displays the regulated transcripts of ARF samples compared to CER, each bar corresponds to a single transcript. It is possible to notice that 24 transcripts are regulated above 3-fold. Full article ">Figure 9
Predicted MFE value and lncRNA relative expression RPKM: The regulated 186 conserved lncRNAs, which were folded to predict their secondary structure stability and presented MFE below −80 kcal/mol. The red bars represent the expression regulation (log2FC) of each lncRNA from ARF samples in comparison to CER. The green bars are the transcripts respective MFE value. Full article ">

12 pages, 733 KiB

Open AccessReview

LncRNAs in TGF-β-Driven Tissue Fibrosis

by Patrick Ming-Kuen Tang, Ying-Ying Zhang and Hui-Yao Lan

Non-Coding RNA 2018, 4(4), 26; https://doi.org/10.3390/ncrna4040026 - 4 Oct 2018

Cited by 32 | Viewed by 5099

Abstract

Transforming growth factor-β (TGF-β) is a crucial mediator in tissue fibrosis that promotes accumulation of extracellular matrix (ECM), myofibroblasts to epithelial–mesenchymal transition (EMT), endothelial-mesenchymal transition (EndoMT), and apoptosis via canonical and noncanonical signaling pathways. In the past decades, a number of microRNAs have [...] Read more.

Transforming growth factor-β (TGF-β) is a crucial mediator in tissue fibrosis that promotes accumulation of extracellular matrix (ECM), myofibroblasts to epithelial–mesenchymal transition (EMT), endothelial-mesenchymal transition (EndoMT), and apoptosis via canonical and noncanonical signaling pathways. In the past decades, a number of microRNAs have been reported to participate in TGF-β-mediated tissue scarring; however, the roles of long noncoding RNAs (lncRNAs) in fibrogenesis remain largely unknown. Recently, emerging evidence has shown that lncRNAs are involved in the development of different diseases, including cancer, autoimmune diseases, cardiovascular diseases, and fibrotic diseases. In this review, we summarize the current updates of lncRNAs in TGF-β1-driven tissue fibrosis and discuss their therapeutic potential for the treatment of chronic fibrotic diseases. Full article

(This article belongs to the Special Issue Non-Coding RNA, Fibrogenesis, and Fibrotic Disease)

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Transforming growth factor-β 1 (TGF-β1) mediates a signaling pathway in tissue fibrosis. The latent TGF-β binding proteins (LTBP) complex is cleaved by proteases to release the active TGF-β1 that binds to the extracellular domain of TGF-β receptor type II (TβRII). The activated TβRII then phosphorylates TGF-β receptor type I (TβRI) kinase, thus triggering downstream signaling via either or both of the canonical (Smads-dependent) and noncanonical (Smads-independent) pathways. In the canonical pathway, TβRI phosphorylates Smad2 and Smad3, and then these Smads bind with Smad4 and this complex translocates into the nucleus. Meanwhile, TGF-β1 also activates Smad ubiquitin regulatory factor (Smurf) to degrade Smad7 to further enhance signaling. On the other hand, TGF-β1 can also induce profibrotic responses via a noncanonical pathway in a Smads-independent manner. TGF-β1 activates extracellular signal-regulated kinase (ERK) activation (Ras recruits Raf to the plasma membrane and leads to activation of ERK through mitogen-activated protein kinase (MEK)); c-Jun amino terminal kinase (JNK)/p38 activation JNK and p38 are at the tertiary layer of the mitogen-activated protein kinase (MAPK) pathway, in which they are activated by the MAP kinase kinases (MKKs), MKK4 and MKK3/6, respectively); Rho-like GTPases activation (the Rho-like GTPases include RhoA, Rac, and Cdc42); Phosphoinositide3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) activation (AKT is activated via PI3K, which then controls translational responses through mammalian target of rapamycin (mTOR)); induction of reactive oxygen species (ROS) (hypoxia-responsive element activity and hypoxia-inducible factor-1α expression by TGF-β1, then the p53 tumor suppressor can be induced). In addition, crosstalks may occur between TGF-β1/Smad and other pathways during tissue fibrosis. Full article ">

10 pages, 791 KiB

Open AccessFeature PaperReview

MicroRNA-Attenuated Virus Vaccines

by Elizabeth J. Fay and Ryan A. Langlois

Non-Coding RNA 2018, 4(4), 25; https://doi.org/10.3390/ncrna4040025 - 2 Oct 2018

Cited by 17 | Viewed by 6094

Abstract

Live-attenuated vaccines are the most effective way to establish robust, long-lasting immunity against viruses. However, the possibility of reversion to wild type replication and pathogenicity raises concerns over the safety of these vaccines. The use of host-derived microRNAs (miRNAs) to attenuate viruses has [...] Read more.

Live-attenuated vaccines are the most effective way to establish robust, long-lasting immunity against viruses. However, the possibility of reversion to wild type replication and pathogenicity raises concerns over the safety of these vaccines. The use of host-derived microRNAs (miRNAs) to attenuate viruses has been accomplished in an array of biological contexts. The broad assortment of effective tissue- and species-specific miRNAs, and the ability to target a virus with multiple miRNAs, allow for targeting to be tailored to the virus of interest. While escape is always a concern, effective strategies have been developed to improve the safety and stability of miRNA-attenuated viruses. In this review, we discuss the various approaches that have been used to engineer miRNA-attenuated viruses, the steps that have been taken to improve their safety, and the potential use of these viruses as vaccines. Full article

(This article belongs to the Special Issue Non-Coding RNAs in Viral Infections)

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20 pages, 3083 KiB

Open AccessFeature PaperReview

The Structure-To-Function Relationships of Gammaherpesvirus-Encoded Long Non-Coding RNAs and Their Contributions to Viral Pathogenesis

by Gabriela Chavez-Calvillo, Sarah Martin, Chad Hamm and Joanna Sztuba-Solinska

Non-Coding RNA 2018, 4(4), 24; https://doi.org/10.3390/ncrna4040024 - 26 Sep 2018

Cited by 13 | Viewed by 5793

Abstract

Advances in next-generation sequencing have facilitated the discovery of a multitude of long non-coding RNAs (lncRNAs) with pleiotropic functions in cellular processes, disease, and viral pathogenesis. It came as no surprise when viruses were also revealed to transcribe their own lncRNAs. Among them, [...] Read more.

Advances in next-generation sequencing have facilitated the discovery of a multitude of long non-coding RNAs (lncRNAs) with pleiotropic functions in cellular processes, disease, and viral pathogenesis. It came as no surprise when viruses were also revealed to transcribe their own lncRNAs. Among them, gammaherpesviruses, one of the three subfamilies of the Herpesviridae, code their largest number. These structurally and functionally intricate non-coding (nc) transcripts modulate cellular and viral gene expression to maintain viral latency or prompt lytic reactivation. These lncRNAs allow for the virus to escape cytosolic surveillance, sequester, and re-localize essential cellular factors and modulate the cell cycle and proliferation. Some viral lncRNAs act as “messenger molecules”, transferring information about viral infection to neighboring cells. This broad range of lncRNA functions is achieved through lncRNA structure-mediated interactions with effector molecules of viral and host origin, including other RNAs, proteins and DNAs. In this review, we discuss examples of gammaherpesvirus-encoded lncRNAs, emphasize their unique structural attributes, and link them to viral life cycle, pathogenesis, and disease progression. We will address their potential as novel targets for drug discovery and propose future directions to explore lncRNA structure and function relationship. Full article

(This article belongs to the Special Issue Non-Coding RNAs in Viral Infections)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Structure-mediated multifunctionality of PAN RNA. The secondary structure of Kaposi’s sarcoma-associated herpesvirus polyadenylated nuclear RNA (KSHV PAN RNA) is represented in the middle with color-coded domains: I (yellow), II (pink), and III (light blue). The position of two cis-acting motifs that are involved in PAN RNA stability and functionality, Mta-responsive element (MRE) and triple helix, are indicated. (A) PAN RNA interaction with PRC2 components: EZH2 (blue), SUZ12 (orange), EDD (yellow), leads to histone methylation and gene repression; (B) The interaction of PAN RNA with UTX (lime green)/MLL2 (purple)/JMJD3 (light grey) targets histones for demethylation to increase gene expression; (C) PAN RNA interacts with viral proteins ORF29 (blue), ORF57 (purple), ORF59 (green) and LANA (grey); and, (D) The interaction of PAN RNA with LANA is partially responsible for the LANA-episome dissociation leading to KSHV lytic reactivation. Full article ">Figure 2
The structure-to-function relationship of EBER 1 and 2. (A) EBER 1 stem-loops I (violet), III (pink), and IV (green) create a scaffold for interaction with L22 (orange) resulting in the re-localization of EBER1:L22 into the nucleoplasm. EBER1 binds to the La protein (green) via the 3′ polyuridylate stretch (purple), shielding EBERs from recognition by host proteins; (B) EBER2 participates in the formation of a ternary complex with PAX5 (red), which involves host proteins (orange and green) and a nascent transcript (blue) expressed from the terminal repeats (TR) of the EBV genome. This interaction influences genome packaging and induces lytic gene expression, resulting in EBV reactivation. Full article ">Figure 3
Maturation pathway of TMERs. Each TMER transcript contains a viral tRNA non-coding region (vtRNA, orange) and microRNA (miRNA) hairpins (blue). Through the RNaseZL pathway, the vtRNA is separated from the hairpins that are then processed by Dicer into miRNAs. TMERs are essential for the establishment of latency and viral dissemination, however, due to their close structural relationship their individual functions are not well defined. Full article ">Figure 4
Putative functions of HSUR RNAs (A, HSUR1; B, HSUR2). HSURs have been found to have highly conserved regions responsible for binding of host miRNAs, i.e., miR-16 (green), miR-27 (pink), miR-142 (blue) and host proteins, i.e., Ago2 (orange), involved in RISC complex formation, spliceosomal Sm proteins (blue). While the exact mechanism and function of HSURs are not yet understood the recruitment of host miRNAs and proteins likely regulates gene expression of the target messenger RNA. Full article ">Figure 5
Novel molecular approaches to address the structure-to-function relationship of gammaherepsvirus-encoded lncRNAs. (A) RNA secondary structure analysis by selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) takes advantage of electrophilic chemical probes that target single-stranded or structurally unconstrained nucleotides and modify them at the 2′-hydroxyl group. When coupled with mutational profiling (MaP), the modified nucleotides are detected as internal miscoding nucleotides during reverse transcription followed by massively parallel sequencing; (B) RNA antisense purification (RAP) is used to purify a target lncRNA in complex with other RNAs, proteins and DNA. Biotinylated probes (light green) designed to associate with the target lncRNA (brown) are hybridized to the transcript that has been specifically cross-linked to interacting partners. Streptavidin magnetic beads (dark green) capture the biotinylated probes (light green) to pull-down the lncRNA and associated molecules, i.e., other RNAs, proteins, and DNA (blue) for further analysis by mass spectrometry, DNA-seq or RNA-seq; (C) Examples of deep-sequencing based mapping techniques that are used to address the four most common epitranscriptomic modifications in RNA. The N6-methyladenosine (m6A, red) is detected with individual-nucleotide-resolution cross-linking and immunoprecipitation (miCLIP) methodology. Here, immunoprecipitation (IP) and UV crosslinking with m6A-specific antibodies is coupled with reverse transcription and deep-sequencing, and the sites of modification are detected as either misincorporation of base-pairs or truncation. Pseudouridine (Ψ, yellow) is detected by CMC-derivatization, where sodium carbonate removes the CMC derivative from non-pseudouridine modifications. The 5-methylcytosine (m5C, purple) uses bisulfite conversion that causes non-methylated cytosines to be converted to guanine. The N1-methyladenosine (m1A, green), similar to miCLIP, relies on using m1A-specific antibodies. Full article ">

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