Next Article in Journal
Analysis of lncRNAs in Lupinus mutabilis (Tarwi) and Their Potential Role in Drought Response
Next Article in Special Issue
The Potential microRNA Prognostic Signature in HNSCCs: A Systematic Review
Previous Article in Journal
Methods and Tools in RNA Biology
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Investigation into the Role of Long-Non-Coding RNA MIAT in Leukemia

by
Alessia Ostini
and
Mirna Mourtada-Maarabouni
*
School of Life Sciences, Faculty of Natural Sciences, Keele University, Newcastle-under-Lyme ST5 5BG, UK
*
Author to whom correspondence should be addressed.
Non-Coding RNA 2023, 9(4), 47; https://doi.org/10.3390/ncrna9040047
Submission received: 14 July 2023 / Revised: 3 August 2023 / Accepted: 10 August 2023 / Published: 11 August 2023

Abstract

:
Myocardial Infarction Associated Transcript (MIAT) is a nuclear long non-coding RNA (LncRNA) with four different splicing variants. MIAT dysregulation is associated with carcinogenesis, mainly acting as an oncogene regulating cellular growth, invasion, and metastasis. The aim of the current study is to investigate the role of MIAT in the regulation of T and chronic myeloid leukemic cell survival. To this end, MIAT was silenced using MIAT-specific siRNAs in leukemic cell lines, and functional assays were performed thereafter. This investigation also aims to investigate the effects of MIAT silencing on the expression of core genes involved in cancer. Functional studies and gene expression determination confirm that MIAT knockdown not only affects short- and long-term survival and the apoptosis of leukemic cells but also plays a pivotal role in the alteration of key genes involved in cancer, including c-MYC and HIF-1A. Our observations suggest that MIAT could act as an oncogene and it has the potential to be used not only as a reliable biomarker for leukemia, but also be employed for prognostic and therapeutic purposes.

1. Introduction

Recent advances in RNA-based approaches and Next Generation Technologies have unveiled the existence of non-coding RNAs (ncRNA), transcripts that lack the ability to code for proteins [1,2]. ncRNAs typically lack an open reading frame (ORF) and are subdivided into housekeeping and regulatory transcripts. Housekeeping ncRNAs are usually 50–500 nucleotides (nt) in length [3], while regulatory ncRNAs are further subdivided in two major classes; small non-coding RNA (sncRNA), classified as 200 nt in length, and long non-coding RNA (lncRNA), which are ≤200 nt in length [4]. LncRNAs share similarities with mRNAs; they are both transcribed by the enzyme RNA Polymerase II (RNA Pol II), have the capability to undergo alternative splicing due to their non-coding exons and undergo post-transcriptional modification such as capping at their 5′ end and polyadenylation at their 3′ end [5,6]. LncRNAs are able to regulate various normal cellular and molecular mechanisms, but they have also been associated with the onset and progression of numerous cancer types [7,8].
LncRNAs are further classified based on their localization, which is usually cytoplasmic or nuclear [9]. A major nuclear lncRNA extensively associated with cancer progression and prognosis is Myocardial Infarction Associated Transcript (MIAT), a nuclear retained lncRNA, also known as Gomafu, Retina non-coding (RNCR2) or LINC00066. MIAT was first associated with a higher risk of Myocardial Infarction during a genome-wide association studies [10,11]. MIAT is located on chromosome 22q12.1, has a ~10 kb length, and its transcript contains five exons and is polyadenylated [12,13]. The mature MIAT transcript is retained in the nucleus and displays a punctuated pattern or spots that do not colocalize with any known nuclear domains [14,15].
MIAT was found to regulate post-transcriptional processes, acting mainly as a competitive endogenous RNA (ceRNA) [12]. In fact, increasing evidence shows that MIAT and mRNAs compete for the binding of various microRNAs (miRNA); generally, MIAT interacts with the 3′UTR of a target mRNA, sponging the miRNA of interest. This, in turn, inhibits the translation and generation of end-products, ultimately regulating protein expression [16,17]. Increasing evidence connected the dysregulation of the expression of MIAT with carcinogenesis and the progression of various cancer types, highlighting the pivotal oncogenic or the tumor-suppressive role of this lncRNA [13].
Little is known about the regulatory behavior of MIAT and its potential role in T and chronic myeloid leukemic cells. Recent evidence has displayed that MIAT is upregulated in lymphoid cell lines with mature B cell phenotypes and that MIAT created a regulatory mechanism with the protein OCT4 (octamer-binding transcription factor 4), allowing apoptosis and cell death evasion, ultimately promoting cancer progression [18,19]. In addition, further research highlighted the importance of MIAT in progression of acute myeloid leukemia (AML) as it was reported to sponge miR-495, affecting further downstream target protein production [20]. Nevertheless, little is known regarding the effects of MIAT dysregulation and how the modulation of its expression is potentially relevant to cancer progression and prognosis. To this end, the aim of the current study is to investigate the role of the nuclear lncRNA MIAT in regulating the survival of T and chronic myeloid leukemic cells and the expression of different cancer core genes.

2. Results

2.1. Effects of MIAT Knockdown on the Survival of Leukemic Cells

To investigate the role of MIAT in the regulation of leukemic cell survival, MIAT was silenced in Jurkat T cells by nucleofection with negative siRNA (NC) or one of three MIAT-specific siRNAs (M1, M2, M3). The effects of MIAT silencing upon short- and long-term survival, cell cycle, and apoptosis were assessed. MIAT silencing was confirmed prior to further functional studies (Figure 1A). MIAT silencing resulted in a significant decrease in both viable and total cells for all three MIAT-specific siRNAs (~40% decrease in the number of both total and viable cells in the transfected cells compared to both parental cells and negative control) (Figure 1B). A decrease in short-term survival was further assessed through flow cytometry, which showed a decrease of both viable and total cell numbers in MIAT-transfected cells compared to the control with statistically significant results for M1- and M2-specific siRNAs (Figure 1C). Apoptosis rates were assessed using acridine orange staining (Figure 1E). All three MIAT-specific siRNA transfected cells showed a significant 2-fold increase in apoptotic cells compared to the control at 24 h (Figure 1D). The long-term survival of Jurkat T cells was measured by anchorage-independent clonogenic assay, and MIAT downregulation led to a visible decrease in the colony formation capabilities of transfected cells with statistically significant results for M1-specific siRNA (Figure 1F). To determine whether the decrease in short- and long-term survival of transfected Jurkat T cell was attributed to cell cycle arrest, cell cycle analysis was performed using propidium iodide staining and flow cytometry. The analysis determined that MIAT silencing did not have an effect on cell cycle progression (Results not shown).
The results observed in Jurkat T cells were further confirmed with CEM-C7 cells, confirming the growth inhibitory effects of MIAT silencing. MIAT silencing was confirmed in all MIAT-specific siRNAs (Figure 2A) through real-time PCR. Confirmation of MIAT silencing was followed by short-term survival assessment (Figure 2B,C), resulting in a significant decrease of viable cell numbers for all MIAT-specific siRNAs transfected cells, while no significant changes were observed for the total cell count. siRNA-mediated MIAT knockdown in CEM-C7 cells was associated with a 15–40% increase in basal apoptosis (Figure 2D,E) for all MIAT-specific siRNAs, confirming the results observed for Jurkat T cells. Cell cycle analysis showed that MIAT silencing has no effect on cell cycle progression. This indicates that the increase in the apoptosis rate is responsible for the suppression of growth (Results not shown).
To further confirm the findings of both AML cell lines, chronic myelogenous leukemia cell line K562 was also transfected with MIAT-specific siRNAs. Of the three MIAT-specific siRNAs used, only M1 and M2 led to the downregulation of MIAT, as assessed by qRT-PCR in these cells (Figure 3A), and therefore M3 was excluded from further functional analysis. Consistent with previous Jurkat T cells and CEM-C7 results, a decrease in short-term survival of transfected cells was observed in the cells transfected with MIAT-specific siRNA compared to the control (Figure 3B). Similarly, flow cytometry assessment revealed 60–80% growth inhibition for cells transfected with MIAT-specific siRNAs compared to the negative control (NC) (Figure 3C). Similar to the response in previously assessed AML cell lines, MIAT downregulation significantly increased the level of apoptosis by two-fold (Figure 3D,E). In terms of long-term survival, the number of colonies significantly decreased for cells transfected with both MIAT-specific siRNAs (Figure 3F) (40% for M1 and 60% for M2). In summary, MIAT downregulation decreases the survival of the three leukemic cells lines and triggers a higher rate of apoptosis compared to the controls.

2.2. Effects of MIAT Knockdown on the Dysregulation of Oncogenes and Tumor Suppressor Gene Expression

RNA sequencing has previously shown that MIAT-specific silencing in SH-SY5Y neuroblastoma cells induced dysregulation of an outstanding number of key genes involved in cancer [14]. These genes include major oncogenes and tumor suppressor genes involved in cancer-related pathways as well as cell cycle, apoptosis, and angiogenesis, and in stress signaling. Figure 4 shows the expression of these genes in SH-SY5Y neuroblastoma cells, as determined by sequencing and analysis of the whole transcriptome [14]. Further experiments were carried out to validate RNA sequencing findings (Figure 4A–I). Following siRNA-mediated MIAT silencing in both Jurkat and CEM-C7 T leukemic cells, total RNA was collected from transfected cells at 24 h post-replating and gene expression was determined using real-time PCR. The relative gene expressions of Cyclin-dependent kinase 6 (CDK6), an important regulatory protein of the cell cycle and also involved in tumor progression; X-Linked Inhibitor of Apoptosis (XIAP), a critical regulator of apoptosis; Growth Arrest and DNA Damage Inducible Alpha (GADD45A), reported to act as either a tumor promotor or suppressor, and the proto-oncogene Casitas B-lineage Lymphoma (CBL) were all significantly lower in MIAT siRNAs transfected cells compared to the negative siRNA control. This was confirmed in both Jurkat T-cells (Figure 5A–D) and CEM-C7 (Figure 6A–D). In both Jurkat (Figure 5E) and CEM-C7 (Figure 6E), the growth factor receptor Fms Related Receptor Tyrosine Kinase 1 (FLT-1) relative gene expression was also lower than the observed control for cells transfected with M1- and M2-specific siRNAs, however no significant changes were observed for M3 siRNA. Similarly, the expression of Hypoxia Inducible Factor 1 Alpha Subunit (HIF-1A), which is associated with tumor metastasis, angiogenesis, and poor prognosis, was significantly lower in the cells transfected with MIAT siRNA in both Jurkat (Figure 5F) and CEM-C7 (Figure 6F) compared to the control. These observations further confirmed the RNA sequencing results and the role of MIAT in the regulation of gene expression. Interestingly, proto-oncogene c-MYC relative expression was also significantly lower in all MIAT-specific siRNAs transfected cells in CEM-C7 (Figure 6G), whereas only Jurkat cells transfected with M3 siRNA showed a decrease in c-MYC gene expression (Figure 5G). Lastly, RNA sequencing revealed the upregulation of both RELA and Nucleotide Binding Oligomerization Domain Containing 1 (NOD1), both of which are reported to regulate apoptosis. These results were confirmed in both Jurkat (Figure 5H,I) and CEM-C7 (Figure 6H,I).

3. Discussion

The nuclear lncRNA MIAT has been shown to be highly dysregulated in a variety of cancer types and also displayed a regulatory role in cell fate decision and survival [13,14]. Nevertheless, despite ongoing efforts to further elucidate the role of MIAT in carcinogenesis and cancer-related processes, little is known about the interconnection between MIAT and leukemia. Due to mounting evidence showing the pivotal role of MIAT throughout cancer processes, the current study aims to elucidate the functions of lncRNA MIAT in T and chronic myeloid leukemic cells. Expression levels of MIAT in leukemic samples have been confirmed to be significantly higher compared to normal samples [18,21]. Thus, introducing the hypothesis that downregulation of MIAT transcript would influence the proliferation and survival of T and chronic myeloid leukemic cells. To this end, MIAT silencing through specific siRNAs indeed caused a significant inhibition of both short- and long-term leukemic cell survival, as well as an increase in apoptotic cell death. These data support a number of independent studies into MIAT function. Wang et al. [20] found that silencing MIAT reduced the viability of human AML cells and increased apoptosis. Similarly, Sattari et al. [18] showed that MIAT knockdown through siRNAs induced an increase in apoptosis in lymphoblastic cell lines and affected caspase 3 and 7 activities. In line with these observations, along with the results obtained by Bountali et al. [14], whereby RNA sequencing revealed that MIAT silencing in SH-SY5Y neuroblastoma cells leads to the dysregulation of a diverse number of genes involved in cancer-related processes, the present study confirmed the role of MIAT in regulating the expression of core cancer genes, including CDK6, XIAP, CBL, FLT, HIF-1A, c-MYC, RELA, and NOD1.
Multiple studies demonstrated the importance of Cyclin-dependent kinase 6 (CDK6) for cell cycle progression in cancer cells, placing this gene in the oncogene category [22,23,24]. CDK6 expression showed dysregulation upon MIAT knockdown, as shown by Bountali et al. [14]. Our results also show that MIAT silencing is associated with downregulation in CDK6 levels of expression. CDK6 was shown to be a key regulator of the G1-S phase cell cycle transition and its inhibition induced apoptosis in T-cell leukemia/lymphoma [25]. Furthermore, downregulation or complete loss of CDK6 in AML leukemic cell lines not only decreased the rate of carcinogenesis, but also was found to be a transcriptional target of fusion protein Nucleoporin 98 (NUP98), which is often associated with poor prognosis in leukemic cancer patients [26]. Similarly, decreased levels of MIAT led to a decrease in the expression levels of both XIAP and GADD45A. XIAP, a well-known inhibitor of apoptosis [27], was found to be overexpressed in various cancer types, including leukemia [28], allowing cancer cells to escape the apoptotic process. MIAT downregulation led to the simultaneous downregulation of XIAP gene expression, preventing apoptotic pathway inhibition and, in turn, increasing the rate of death in leukemic cell lines. Similarly, GADD45A is highly involved in the regulation of stress-related regulation of cell survival and stressful growth arrest conditions, and expression levels were found to be upregulated in breast cancer [29,30]. Nevertheless, the role of GADD45A in leukemogenesis remains unclear as it can act both as an oncogene in response to c-MYC or as a tumor suppressor in response to HRAS [31]. Thus, the observation of GADD45A downregulation upon MIAT silencing requires further investigation.
Our results also confirmed the downregulation of Casitas B-lineage Lymphoma (CBL) gene expression levels upon MIAT silencing. CBL is involved in a variety of cancer-related processes, including angiogenesis [32] and tumorigenesis [33], holding the major role of proto-oncogene. In addition, CBL is found to play an important role in immunosuppression, regulating the function of T cells, and is also capable of inhibiting T cell activation in the absence of CD28 [34,35,36]. For these reasons, CBL has been proposed as a potential biomarker and therapeutic target in immunological diseases. Another gene affected by MIAT silencing is Fms Related Receptor Tyrosine Kinase 1, or FLT-1. FLT-1, which was found to be downregulated upon MIAT silencing, is a gene encoding for proteins of the vascular endothelial growth factor receptor (VEGFR) family. The role of FLT-1 has previously been strongly associated with pre-eclampsia and eclampsia, but recent evidence suggests a role in tumorigenesis and angiogenesis [37]. In fact, FLT-1 was found to be overly expressed in colorectal cancer tissue samples, and its activation was highly correlated to angiogenesis [38]. The oncogenic role of FLT-1 was also assessed in leukemic cell lines, whereby its upregulation led to the release of VEGFR factors, enabling the recruitment of angiogenic factors and stimulating the growth of new blood vessels [39,40].
Similar observations were found for RELA and NOD-1. MIAT silencing was associated with an increased expression of RELA and NOD1. RELA protein, a subunit of NF-κB, plays a regulatory function in immune and inflammatory responses [41,42]. RELA activity has been attributed to the promotion and activation of the NF-κB which holds tumor-promoting roles [43,44,45]. However, other studies reported that overexpression of RELA was associated with a reduction in tumorigenicity and activation of apoptosis in the MCF7 ADR human breast cancer cell line [46]. Equally, NOD-1 protein was found to phosphorylate NF-κB by binding to the protein receptor that interacts with serine/threonine kinase 2 (RIPK2), enhancing the proliferative and invasive properties of ovarian cancer cell lines [46]. On the other hand, NOD-1 expression levels were found to be lower in clear cell renal carcinoma compared to healthy tissue [47], posing the question as to whether this gene acts as a proto-oncogene or has tumor-suppressing capacity [48].
Both HIF-1A and c-MYC were also confirmed to be downregulated upon MIAT silencing in leukemic T cells. The role of HIF-1A in cancer is well reported, and HIF-1A was found to be upregulated in various types of leukemia [49,50,51,52,53]. The upregulation of this gene was also found to activate the NF-κB pathway, and its downregulation led to a decrease in the proliferation and an increase in the inhibition of apoptosis in AML leukemic cell lines [49]. c-MYC has been extensively associated with oncogenic functions, carcinogenesis, angiogenesis, chemoresistance, and many more cancer-promoting roles [54,55]. c-MYC has also been found to be a potential therapeutic target in leukemia, along with other cancer types [56,57,58]. Interestingly, a molecular crosstalk between c-MYC and HIF-1A has been reported in cancer progression [59]. The fact that MIAT downregulation affects HIF-1A and c-MYC expression levels indicates a mechanism yet to be unveiled that has the potential to pose MIAT as a therapeutic tool to target the HIF-1A-c-MYC axis.
Linking the previously described observations, it could be speculated that MIAT exerts its effects by acting as a ceRNA and miRNA sponge. Previous studies have shown that MIAT acts as a sponge for specific miRNAs, sequestering them from their target genes and leading to an increase in the expression of target genes [12]. In gastric cancer (GC), MIAT sponges miR-141, leading to an increased expression of DEAD-box RNA helicase 5 (DDX5) and influencing GC cell proliferation and migration [60]. Similarly, in liver cancer, MIAT interacts with miR-520d-3p and miR-214, affecting EPHA2 and hepatoma-derived growth factor (HDGF) expression, respectively [61,62]. It could be assumed that when MIAT is silenced, its sponging activity is disrupted, leading to the accumulation of target miRNAs within the cell. As a result, these accumulated miRNAs become available to interact with their target mRNAs, ultimately causing a post-transcriptional downregulation of gene expression. Further investigation into the specific miRNA–mRNA interactions affected by MIAT silencing would provide valuable insights into the regulatory role of MIAT in gene expression control.
In conclusion, this study has unveiled novel findings regarding the role of MIAT in the regulation of cell death and survival of leukemic cells and its impact on gene expression. The considerable influence of MIAT on leukemic T and chronic myeloid cells highlights the need for further in-depth investigation into its molecular function and involvement in cancer and other diseases. Additionally, it is crucial to conduct additional research regarding the MIAT-HIF-1A-c-MYC regulatory axis. Notably, the observation that MIAT silencing decreases the expression of the oncogene c-MYC, which is often deregulated in many cancers and is considered ‘undruggable’, provides an exciting opportunity to explore the development of therapeutic materials targeting c-MYC.

4. Materials and Methods

4.1. Cell Culture

Jurkat cell line (Clone E6-1, (ATCC, Manassas, VA, USA), Cat# TIB-152), apoptosis-sensitive cloned CEM-C7 CKM1 cell line (CCRF-CEM, (ATCC, VA, USA), Cat# CCL-119), and K562 cells (ATCC, VA, USA, Cat# CCL 243) were cultured at 37 °C in a 5% CO2 humidified incubator in IMDM medium (GIBCO, Loughborough, UK; #12440053), supplemented with 10% heat inactivated fetal bovine serum (FBS) (Sigma Aldrich, Gillingham, UK; #F9665) and 10 mg/mL gentamicin reagent (GIBCO, UK; #15710-064).
Cells were maintained at a density of 1 × 105–1 × 106 cells/mL. All experiments were carried out using cells in their logarithmic growth phase.

4.2. RNA Interference Using siRNAs

The experiments were performed using nucleofection as a method of transfection. The siRNAs used included FlexiTube Negative control siRNA (QIAGEN, Manchester, UK; #1027418), and three different MIAT-specific siRNAs, (QIAGEN, UK) M1 siRNA (Cat # SI04287423), M2 siRNA (Cat #SI04314919), and M3 siRNA (Cat # SI04344158), targeting different sites of the fifth exon of the full-length MIAT transcript (NR_003491 (10,193 bp)) at a final concentration of 10 µM [14,63,64,65]. Cells were transfected with the Ingenio® kit (Mirus, Geneflow, UK) and the programs T-014 (Jurkat), X-001 (CCRF-CEM) and T-016 (K561-ATCC) were used for the Jurkat, CEM-C7, and K562 cell lines, respectively [63,64]. 5 × 106 cells were transfected and were incubated in complete IMDM medium supplemented with 20% heat inactivated FBS and 10 mg/mL gentamicin reagent for 24 h and re-plated at 4 × 106 for subsequent assessment of long- and short-term cell survival and apoptosis [60,61]. The efficiency of transfection for Jurkat cell line was 64–72% as determined by Cy3 labelling (Invitrogen, Oxford, UK; #1632) [14,63,64].

4.3. RNA Extraction

The Direct-zol™ RNA Miniprep kit (Zymo Research, Orange, CA, USA; #R2050) was utilized to extract total RNA from transfected cells, following the manufacturer’s protocol. To evaluate both the quality and quantity of the extracted RNA, spectrophotometric analysis was performed using the NanoDrop™ 1000 (Thermo Fisher Scientific, Oxford, UK). Samples with a ratio between 1.8 and 2 were considered to be of high purity [14,63,65].

4.4. Reverse Transcription and Real-Time Polymerase Chain Reaction (RT-qPCR)

Effects of the RNA interference through siRNAs upon gene expression levels were assessed by Real-Time PCR (RT-qPCR). Extracted RNA from transfected cells (as per described in Section 4.3) was reverse-transcribed into cDNA using the Omniscript® RT kit (QIAGEN, Manchester, UK), 10 μM random primers (Thermo Fisher Scientific, Oxford, UK; #SO142), and 10 units/μL of RNaseOUT recombinant ribonuclease inhibitor (Invitrogen; 10777019), following the manufacturer’s instructions [14,61,62]. Real-time PCR was subsequently performed utilizing SensiFastTM Probe Hi-ROX kit (Bioline, TN, USA; #BIO-92020) and TaqMan® Gene Expression Assays (Thermo Fisher, Waltham, MA, USA), (Assay code: MIAT, Hs00402814_m1; eukaryotic 18S rRNA, Hs99999901; CDK6, Hs01026371_m1; XIAP, Hs00745222_s1; GADD45A, Hs00169255_m1; CBL, Hs01011446_m1; FLT1, Hs01052961_m1; HIF-1A, Hs00153153_m1; c-MYC, Hs00153408_m1; RELA, Hs01042014_m1; NOD1, Hs01036720_m1; as described previously [14,62]. The AriaMx (Agilent Technologies, Manchester, UK) was used for the measurement of real-time fluorescence and the AriaMX software was used to perform the data analysis. Expression comparisons were made relative to the negative siRNA (NC) of transfected cells, using the 2−ΔΔCt method [14,63].

4.5. Cell Survival and Apoptosis Determination

At 24 h post-transfection, cells were seeded at a concentration of 4 × 106 into a 6 well plate, and cells were thereafter assessed to determine cell viability and apoptosis. Cell viability was determined by counting trypan blue solution 0.4% w/v (Sigma-Aldrich, UK, # T8154) stained samples using a hemocytometer and light microscopy. Cell viability was also determined using a commercial Muse® Count and Viability Kit (Luminex, TX, USA; #MCH100104) and the Muse® Cell Analyzer (Merck Millipore, Darmstadt, Germany), following manufacturer’s protocol [14,63,64,65].
Apoptosis was assessed by examining nuclear morphology under fluorescence microscopy following staining with acridine orange (25 μg/mL) (Sigma-Aldrich, UK; # 235474). Cells with condensed or fragmented chromatin were considered apoptotic [14,63,64].

4.6. Cell Cycle Analysis

Cell cycle analysis was performed using the Muse cell cycle kit (Luminex, Austin, TX, USA #MCH1001060) and flow cytometry, as described previously [14,63,64]. Transfected cells were plated at a density of 4 × 106 cells/well in 6-well plates containing 5 mL of fresh cell culture medium. Cells were collected following 24 h incubation and washed with 500 μL of phosphate-buffered saline (PBS) before being fixed in 1 mL of ice-cold 70% ethanol mixed with 30% PBS. The fixed cells were incubated at −20 °C for 3 h. Subsequently, the cells were re-suspended in 200 μL of Muse™ Cell Cycle Reagent and incubated in the dark for 30 min. The data were then acquired using the Muse Cell analyser.

4.7. Long-Term Survival Assessment

Long-term survival was assessed by the cells ability to form colonies on soft agar [63,64]. An equal cell number proportion of cells was diluted in 3 mL of IMDM containing 20% heat-inactivated FBS, 10% cell-specific filtered conditioned medium, and 10% noble agar solution 0.5% w/v (Difco, Omagh, UK; #214230). The newly formed mix containing cells was placed in 6-well plates. Plates were left at RT °C under sterile conditions, allowing the agar to solidify. Lastly, an overlay consisting of 2 mL IMDM complete medium supplemented with 10% cell-conditioned medium was added to the plates. Plates were cultured at 37 °C in a 5% CO2 humidified incubator, and the number of colonies formed were counted after 2–3 weeks.

4.8. Statistical Analysis

Statistical analyses were conducted using GraphPad Prism 9 (GraphPad Software, Boston, MA, USA). Data are presented as the mean ± SEM, and the “n” represents the number of observations, with each transfected sample obtained from separate experiments. For comparisons, unpaired Student’s t-test, One-Way ANOVA, or Two-Way ANOVA with Dunnett’s multiple comparison test (MCT) were applied as appropriate. A significance level of 0.05 was set to determine statistical significance. Differences were considered statistically significant when the p-value was <0.05 at a 95% confidence level.

5. Conclusions

In summary, the current investigation showed that downregulation of the lncRNA MIAT negatively affects the short- and long-term survival of T and chronic myeloid leukemic cells while simultaneously increasing the apoptotic rate. In addition, gene expression analysis showed the expression of a variety of core cancer genes as being affected by the silencing of MIAT, highlighting the crucial role of MIAT in carcinogenesis and cancer progression. Nevertheless, further investigations are required to elucidate and establish the major pathways affected by MIAT and their interactions with components within the pathways highlighted.

Author Contributions

M.M.-M. conceived the study and supervised the project. A.O. performed experiments, interpreted results, undertook statistical analyses and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Salzberg, S. Open questions: How many genes do we have? BMC Biol. 2018, 16, 94. [Google Scholar]
  2. The Human Genome Project—Genome.gov. Available online: https://www.genome.gov/human-genome-project (accessed on 11 June 2023).
  3. Zhang, P.; Wu, W.; Chen, Q.; Chen, M. Non-Coding RNAs and their Integrated Networks. J. Integr. Bioinform. 2019, 16, 0027. [Google Scholar]
  4. Su, Y.; Wu, H.; Pavlosky, A.; Zou, L.L.; Deng, X.; Zhang, Z.X.; Jevnikar, A.M. Regulatory non-coding RNA: New instruments in the orchestration of cell death. Cell Death Dis. 2016, 7, e2333. [Google Scholar]
  5. Chi, Y.; Wang, D.; Wang, J.; Yu, W.; Yang, J. Long non-coding RNA in the pathogenesis of cancers. Cells 2019, 8, 1015. [Google Scholar] [CrossRef] [Green Version]
  6. Derrien, T.; Johnson, R.; Bussotti, G.; Tanzer, A.; Djebali, S.; Tilgner, H.; Guernec, G.; Martin, D.; Merkel, A.; Knowles, D.; et al. The GENCODE v7 catalogue of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression. Genome Res. 2012, 22, 1775–1789. [Google Scholar]
  7. Slack, F.J.; Chinnaiyan, A.M. The role of non-coding RNAS in oncology. Cell 2019, 179, 1033–1055. [Google Scholar]
  8. Hulshoff, M.S.; del Monte-Nieto, G.; Kovacic, J.; Krenning, G. Non-coding RNA in endothelial-to-mesenchymal transition. Cardiovasc. Res. 2019, 115, 1716–1731. [Google Scholar]
  9. Gudenas, B.L.; Wang, L. Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features. Sci. Rep. 2018, 8, 16385. [Google Scholar] [CrossRef] [Green Version]
  10. Liao, J.; He, Q.; Li, M.; Chen, Y.; Liu, Y.; Wang, J. LncRNA MIAT: Myocardial infarction associated and more. Gene 2016, 578, 158–161. [Google Scholar]
  11. Boon, R.A.; Ja, N.; Holdt, L.; Dimmeler, S. Long Noncoding RNAs from Clinical Genetics to Therapeutic Targets. J. Am. Coll. Cardiol. 2016, 67, 1214–1226. [Google Scholar] [CrossRef]
  12. Ghafouri-Fard, S.; Azimi, T.; Taheri, M. Myocardial Infarction Associated Transcript (MIAT): Review of its impact in the tumorigenesis. Biomed. Pharmacother. 2020, 133, 111040. [Google Scholar]
  13. Da, C.; Gong, C.; Nan, W.; Zhou, K.; Wu, Z.; Zhang, H. The role of long non-coding RNA MIAT in cancers. Biomed. Pharmacother. 2020, 129, 110359. [Google Scholar] [CrossRef]
  14. Bountali, A.; Tonge, D.; Mourtada-Maarabouni, M. RNA sequencing reveals a key role for the long non-coding RNA MIAT in regulating neuroblastoma and glioblastoma cell fate. Int. J. Biol. Macromol. 2019, 130, 878–891. [Google Scholar] [CrossRef]
  15. GeneCard Database. Available online: https://www.genecards.org/cgi-bin/carddisp.pl?gene=MIAT#summaries (accessed on 11 June 2023).
  16. Hao, J.; Jin, X.; Chai, W.; Yin, Z.; Li, Y.; Dong, F.; Wang, W. Long non-coding RNA MIAT competitively binds miR-150-5p to regulate ZEB1 expression in osteosarcoma. Oncol. Lett. 2019, 17, 1229–1236. [Google Scholar]
  17. Jin, S.; Yang, X.; Li, J.; Yang, W.; Ma, H.; Zhang, Z. p53-targeted lincRNA-p21 acts as a tumor suppressor by inhibiting JAK2/STAT3 signaling pathways in head and neck squamous cell carcinoma. Mol. Cancer 2019, 18, 38. [Google Scholar] [CrossRef] [Green Version]
  18. Sattari, A.; Siddiqui, H.; Moshiri, F.; Ngankeu, A.; Nakamura, T.; Kipps, T.J.; Croce, C.M. Upregulation of long noncoding RNA MIAT in aggressive form of chronic lymphocytic leukemias. Oncotarget 2016, 7, 54174–54182. [Google Scholar] [CrossRef] [Green Version]
  19. Fabris, L.; Juracek, J.; Calin, G. Non-Coding RNAs as Cancer Hallmarks in Chronic Lymphocytic Leukemia. Int. J. Mol. Sci. 2020, 21, 6720. [Google Scholar] [CrossRef]
  20. Wang, G.; Li, X.; Song, L.; Pan, H.; Jiang, J.; Sun, L. Long noncoding RNA MIAT promotes the progression of acute myeloid leukemia by negatively regulating mir-495. Leuk. Res. 2019, 87, 106265. [Google Scholar] [CrossRef]
  21. Gao, J.; Wang, F.; Wu, P.; Chen, Y.; Jia, Y. Aberrant lncrna expression in leukemia. J. Cancer 2020, 11, 4284–4296. [Google Scholar] [CrossRef]
  22. Sherr, C.J.; Beach, D.; Shapiro, G.I. Targeting CDK4 and CDK6: From discovery to therapy. Cancer Discov. 2016, 6, 353–367. [Google Scholar] [CrossRef] [Green Version]
  23. Fassl, A.; Geng, Y.; Sicinski, P. Cdk4 and CDK6 kinases: From basic science to cancer therapy. Science 2022, 375, abc1495. [Google Scholar]
  24. Nebenfuehr, S.; Kollmann, K.; Sexl, V. The role CDK6 in cancer. Int. J. Cancer 2020, 147, 2988–2995. [Google Scholar] [CrossRef] [PubMed]
  25. Ishio, T.; Kumar, S.; Shimono, J.; Daenthanasanmak, A.; Dubois, S.; Lin, Y.; Bryant, B.; Petrus, M.N.; Bachy, E.; Huang, D.W.; et al. Genome-wide CRISPR screen identifies CDK6 as a therapeutic target in adult T-cell leukemia/lymphoma. Blood 2022, 139, 1541–1556. [Google Scholar] [CrossRef] [PubMed]
  26. Schmoellerl, J.; Barbosa IA, M.; Eder, T.; Brandstoetter, T.; Schmidt, L.; Maurer, B.; Troester, S.; Pham, H.T.T.; Sagarajit, M.; Ebner, J.; et al. CDK6 is an essential direct target of NUP98 fusion proteins in acute myeloid leukemia. Blood 2020, 136, 387–400. [Google Scholar] [CrossRef]
  27. Tu, H.; Costa, M. Xiap’s profile in human cancer. Biomolecules 2020, 10, 1493. [Google Scholar]
  28. Abbas, R.; Larisch, S. Targeting xiap for promoting cancer cell death—The story of arts and SMAC. Cells 2020, 9, 663. [Google Scholar] [CrossRef] [Green Version]
  29. Wang, J.; Wang, Y.; Long, F.; Yan, F.; Wang, N.; Wang, Y. The expression and clinical significance of GADD45A in breast cancer patients. PeerJ 2018, 6, e5344. [Google Scholar] [CrossRef] [Green Version]
  30. Pietrasik, S.; Zajac, G.; Morawiec, J.; Soszynski, M.; Fila, M.; Blasiak, J. Interplay between BRCA1 and GADD45A and its potential for nucleotide excision repair in breast cancer pathogenesis. Int. J. Mol. Sci. 2020, 21, 870. [Google Scholar] [CrossRef] [Green Version]
  31. Liebermann, D.; Tront, J.S.; Sha, X.; Mukherjee, K.; Mohamed-Hadley, A.; Hoffman, B. Gadd45 stress sensors in malignancy and Leukemia. Crit. Rev. Oncog. 2011, 16, 129–140. [Google Scholar] [CrossRef] [Green Version]
  32. Chen, S.; Sun, Q.; Sun, D.; Willette-Brown, J.; Anderson, M.J.; Gu, Q.; Lewandoski, M.; Hu, Y.; Zhu, F.; Wei, F.; et al. C-CBL is required for inhibition of angiogenesis through modulating JAK2/STAT3 activity in ROP development. Biomed. Pharmacother. 2020, 132, 110856. [Google Scholar] [CrossRef]
  33. Lyle, C.L.; Belghasem, M.; Chitalia, V.C. C-CBL: An important regulator and a target in angiogenesis and tumorigenesis. Cells 2019, 8, 498. [Google Scholar] [PubMed] [Green Version]
  34. Lutz-Nicoladoni, C.; Wolf, D.; Sopper, S. Modulation of immune cell functions by the E3 ligase cbl-b. Front. Oncol. 2015, 5, 58. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Chiang, Y.J.; Kole, H.K.; Brown, K.; Naramura, M.; Fukuhara, S.; Hu, R.J.; Jang, I.K.; Gutkind, J.S.; Shevach, E.; Gu, H. CBL-B regulates the CD28 dependence of T-cell activation. Nature 2000, 403, 216–220. [Google Scholar] [CrossRef]
  36. Augustin, R.C.; Bao, R.; Luke, J.J. Targeting CBL-B in cancer immunotherapy. J. ImmunoTherapy Cancer 2023, 11, 006007. [Google Scholar] [CrossRef]
  37. Lacal, P.M.; Graziani, G. Therapeutic implication of vascular endothelial growth factor receptor-1 (VEGFR-1) targeting in cancer cells and tumor microenvironment by competitive and non-competitive inhibitors. Pharmacol. Res. 2018, 136, 97–107. [Google Scholar]
  38. Mohammad Rezaei, F.; Hashemzadeh, S.; Ravanbakhsh Gavgani, R.; Hosseinpour Feizi, M.; Pouladi, N.; Samadi Kafil, H.; Rostamizadeh, L.; Kholghi Oskooei, V.; Taheri, M.; Sakhinia, E. Dysregulated KDR and FLT1 Gene Expression in Colorectal Cancer Patients. Rep. Biochem. Mol. Biol. 2019, 8, 244–252. [Google Scholar] [PubMed]
  39. Casalou, C.; Fragoso, R.; Nunes, J. VEGF/PLGF induces leukemia cell migration via P38/ERK1/2 kinase pathway, resulting in Rho GTPases activation and caveolae formation. Leukemia 2007, 21, 1590–1594. [Google Scholar]
  40. Fragoso, R.; Pereira, T.; Wu, Y.; Zhu, Z.; Cabeçadas, J.; Dias, S. VEGFR-1 (FLT-1) activation modulates acute lymphoblastic leukemia localization and survival within the bone marrow, determining the onset of extramedullary disease. Blood 2005, 107, 1608–1616. [Google Scholar]
  41. Giridharan, S.; Srinivasan, M. Mechanisms of NF-κB p65 and strategies for therapeutic manipulation. J. Inflamm. Res. 2018, 11, 407–419. [Google Scholar]
  42. Yu, H.; Lin, L.; Zhang, Z.; Zhang, H.; Hu, H. Targeting NF-κB pathway for the therapy of diseases: Mechanism and clinical study. Signal Transduct. Target. Ther. 2020, 5, 209. [Google Scholar]
  43. Geismann, C.; Hauser, C.; Grohmann, F.; Schneeweis, C.; Bölter, N.; Gundlach, J.P.; Schneider, G.; Röcken, C.; Meinhardt, C.; Schäfer, H.; et al. NF-κB/RELA controlled A20 limits trail-induced apoptosis in pancreatic cancer. Cell Death Dis. 2023, 14, 3. [Google Scholar] [CrossRef] [PubMed]
  44. Yu, S.; Li, P.; Li, B.; Miao, D.; Deng, Q. RELA promotes proliferation but inhibits osteogenic and chondrogenic differentiation of mesenchymal stem cells. FEBS Lett. 2020, 594, 1368–1378. [Google Scholar] [CrossRef] [PubMed]
  45. Xia, Y.; Shen, S.; Verma, I. NF-κB, an active player in human cancers. Cancer Immunol Res. 2014, 2, 823–830. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Ricca, A.; Biroccio, A.; Trisciuoglio, D.; Cippitelli, M.; Zupi, G. relA over-expression reduces tumorigenicity and activates apoptosis in human cancer cells. Br. J. Cancer 2001, 85, 1914–1921. [Google Scholar]
  47. Zhang, W.; Wang, Y. Activation of RIPK2-mediated NOD1 signaling promotes proliferation and invasion of ovarian cancer cells via NF-κB pathway. Histochem. Cell Biol. 2021, 157, 173–182. [Google Scholar] [CrossRef]
  48. Mey, L.; Jung, M.; Roos, F.; Blaheta, R.; Hegele, A.; Kinscherf, R.; Urbschat, A. NOD1 and nod2 of the innate immune system is differently expressed in human clear cell renal cell carcinoma, corresponding healthy renal tissue, its vasculature and primary isolated renal tubular epithelial cells. J. Cancer Res. Clin. Oncol. 2019, 145, 1405–1416. [Google Scholar] [CrossRef]
  49. Wang, D. NOD1 and NOD2 are potential therapeutic targets for cancer immunotherapy. Comput. Intell. Neurosci. 2022, 2022, 2271788. [Google Scholar]
  50. Zhu, Y.; Liu, Z.; Wan, Y.; Zou, L.; Liu, L.; Ding, S.; Lu, C.; Qiu, F. PARP14 promotes the growth and glycolysis of acute myeloid leukemia cells by regulating hif-1α expression. Clin. Immunol. 2022, 242, 109094. [Google Scholar]
  51. Griggio, V.; Vitale, C.; Todaro, M.; Riganti, C.; Kopecka, J.; Salvetti, C.; Bomben, R.; Bo, M.D.; Magliulo, D.; Rossi, D.; et al. HIF-1α is over-expressed in leukemic cells from tp53-disrupted patients and is a promising therapeutic target in chronic lymphocytic leukemia. Haematologica 2019, 105, 1042–1054. [Google Scholar]
  52. Frolova, O.; Samudio, I.; Benito, J.M.; Jacamo, R.; Kornblau, S.M.; Markovic, A.; Schober, W.; Lu, H.; Qiu, Y.H.; Buglio, D.; et al. Regulation of hif-1α signaling and chemoresistance in acute lymphocytic leukemia under hypoxic conditions of the bone marrow microenvironment. Cancer Biol. Ther. 2012, 13, 858–870. [Google Scholar]
  53. Kontos, C.K.; Papageorgiou, S.G.; Diamantopoulos, M.A.; Scorilas, A.; Bazani, E.; Vasilatou, D.; Gkontopoulos, K.; Glezou, E.; Stavroulaki, G.; Dimitriadis, G.; et al. MRNA overexpression of the hypoxia inducible factor 1 alpha subunit gene (HIF1A): An independent predictor of poor overall survival in chronic lymphocytic leukemia. Leuk. Res. 2017, 53, 65–73. [Google Scholar]
  54. Zhou, H.; Jiang, Y.; Huang, Y.; Zhong, M.; Qin, D.; Xie, C.; Pan, G.; Tan, J.; Deng, M.; Zhao, H.; et al. Therapeutic inhibition of PPARA-HIF1A-PGK1 signaling targets leukemia stem and progenitor cells in acute myeloid leukemia. Cancer Lett. 2023, 554, 215997. [Google Scholar] [CrossRef]
  55. Fatma, H.; Maurya, S.K.; Siddique, H.R. Epigenetic modifications of c-myc: Role in cancer cell reprogramming, progression and chemoresistance. Semin. Cancer Biol. 2022, 83, 166–176. [Google Scholar]
  56. Dhanasekaran, R.; Deutzmann, A.; Mahauad-Fernandez, W.D.; Hansen, A.S.; Gouw, A.M.; Felsher, D.W. The MYC oncogene—The grand orchestrator of cancer growth and immune evasion. Nat. Rev. Clin. Oncol. 2021, 19, 23–36. [Google Scholar] [CrossRef]
  57. Goswami, S.; Mani, R.; Nunes, J.; Chiang, C.-L.; Zapolnik, K.; Hu, E.; Frissora, F.; Mo, X.; Walker, L.A.; Yan, P.; et al. PP2A is a therapeutically targetable driver of cell fate decisions via a c-myc/p21 axis in human and murine acute myeloid leukemia. Blood 2022, 139, 1340–1358. [Google Scholar] [CrossRef]
  58. Ma, L.; Wang, J.; Zhang, Y.; Fang, F.; Ling, J.; Chu, X.; Zhang, Z.; Tao, Y.; Li, X.; Tian, Y.; et al. BRD4 PROTAC degrader MZ1 exerts anticancer effects in acute myeloid leukemia by targeting C-Myc and ANP32B genes. Cancer Biol. Ther. 2022, 23, 1–15. [Google Scholar] [CrossRef]
  59. Li, Y.; Sun, X.X.; Qian, D.Z.; Dai, M.S. The Molecular Crosstalk Between MYC and HIF in Cancer. Front. Cell Dev. Biol. 2020, 8, 590576. [Google Scholar] [CrossRef]
  60. Gotwals, P.; Cameron, S.; Cipolletta, D.; Cremasco, V.; Crystal, A.; Hewes, B.; Mueller, B.; Quarantino, S.; Sabatos-Peyton, C.; Petruzzelli, L.; et al. Prospects for combining targeted and conventional cancer therapy with immunotherapy. Nat. Rev. Cancer 2017, 17, 286–301. [Google Scholar] [CrossRef]
  61. Huang, X.; Gao, Y.; Qin, J.; Lu, S. LncRNA MIAT promotes proliferation and invasion of HCC cells via sponging mir-214. Am. J. Physiol.-Gastrointest. Liver Physiol. 2018, 314, G559–G565. [Google Scholar]
  62. Xiang, Y.; Huang, Y.; Sun, H.; Pan, Y.; Wu, M.; Zhang, J. (2019) Deregulation of mir-520d-3p promotes hepatocellular carcinoma development via lncRNA MIAT regulation and EPHA2 signaling activation. Biomed. Pharmacother. 2018, 109, 1630–1639. [Google Scholar] [CrossRef]
  63. Mourtada-Maarabouni, M.; Williams, G.T. Protein phosphatase 4 regulates apoptosis, proliferation and mutation rate of human cells. Biochim. Biophys. Acta Mol. Cell Res. 2008, 1783, 1490–1502. [Google Scholar] [CrossRef] [Green Version]
  64. Kavousi, N.; Tonge, D.P.; Mourtada-Maarabouni, M. New insights into the functional role of protein phosphatase 4 regulatory subunit PP4R3A/SMEK1 in the regulation of Leukemic Cell Fate. Int. J. Biol. Macromol. 2023, 233, 123467. [Google Scholar] [CrossRef]
  65. Almnaseer, Z.A.; Mourtada-Maarabouni, M. Long noncoding RNA MIAT regulates apoptosis and the apoptotic response to chemotherapeutic agents in breast cancer cell lines. Biosci. Rep. 2018, 38, BSR20180704. [Google Scholar] [CrossRef] [Green Version]
Figure 1. MIAT-specific silencing inhibits short- and long-term survival and increases the apoptosis of Jurkat cells. Jurkat cells were transfected with the negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2, M3) using Nucleofection, and were assessed 24 h/48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT gene for all three siRNAs (A). Total and viable cell counts, as determined by vital blue staining, were reduced in Jurkat cells at 48 h post-transfection (B). The results were further confirmed utilizing flow cytometry, showing a decrease in total and viable cell count at 48 h (C). The rate of apoptosis, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at both 24 h (D). Representations of cells undergoing apoptosis are depicted by white arrows (E). Long-term survival of Jurkat cells was reduced upon MIAT-specific silencing, as demonstrated by clonogenic assay (F). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicate a p-value < 0.01; ***/**** indicate a p-value < 0.001, as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Figure 1. MIAT-specific silencing inhibits short- and long-term survival and increases the apoptosis of Jurkat cells. Jurkat cells were transfected with the negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2, M3) using Nucleofection, and were assessed 24 h/48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT gene for all three siRNAs (A). Total and viable cell counts, as determined by vital blue staining, were reduced in Jurkat cells at 48 h post-transfection (B). The results were further confirmed utilizing flow cytometry, showing a decrease in total and viable cell count at 48 h (C). The rate of apoptosis, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at both 24 h (D). Representations of cells undergoing apoptosis are depicted by white arrows (E). Long-term survival of Jurkat cells was reduced upon MIAT-specific silencing, as demonstrated by clonogenic assay (F). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicate a p-value < 0.01; ***/**** indicate a p-value < 0.001, as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Ncrna 09 00047 g001
Figure 2. MIAT-specific silencing inhibits short-term survival and increases the apoptosis of CEM-C7 cells. CEM-C7 cells were transfected with the negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2, M3) using Nucleofection, and were assessed 24 h and 48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT for all three siRNAs (A). Total and viable cell count, as determined by vital blue staining, was reduced in CEM-C7 cells at 24 h post-transfection (B). Further confirmation by flow cytometry showed a decrease in total and viable cell count at 48 h (C). The rate of apoptosis, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at 24 h (D). Representation of cells undergoing apoptosis are depicted by white arrows (E). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; **** indicates a p-value < 0.001 as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Figure 2. MIAT-specific silencing inhibits short-term survival and increases the apoptosis of CEM-C7 cells. CEM-C7 cells were transfected with the negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2, M3) using Nucleofection, and were assessed 24 h and 48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT for all three siRNAs (A). Total and viable cell count, as determined by vital blue staining, was reduced in CEM-C7 cells at 24 h post-transfection (B). Further confirmation by flow cytometry showed a decrease in total and viable cell count at 48 h (C). The rate of apoptosis, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at 24 h (D). Representation of cells undergoing apoptosis are depicted by white arrows (E). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; **** indicates a p-value < 0.001 as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Ncrna 09 00047 g002
Figure 3. MIAT-specific silencing inhibits short- and long-term survival and increases the apoptosis of K562 cells. K562 cells were transfected with the negative siRNA (NC) or one of the two MIAT-specific siRNAs (M1, M2) using Nucleofection, and were assessed 24 h and 48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT gene for M1 and M2 siRNAs (A). Total and viable cell counts, as determined by vital blue staining, were reduced in K562 cells at 48 h post-transfection (B). The rate of apoptotic cells, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at 24 h (C). Representation of cells undergoing apoptosis is depicted by white arrows (D). Long-term survival of K562 cells was reduced upon MIAT-specific silencing as demonstrated by clonogenic assay (E). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicate a p-value < 0.01; ***/**** indicate a p-value < 0.001 as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Figure 3. MIAT-specific silencing inhibits short- and long-term survival and increases the apoptosis of K562 cells. K562 cells were transfected with the negative siRNA (NC) or one of the two MIAT-specific siRNAs (M1, M2) using Nucleofection, and were assessed 24 h and 48 h post-replating. Relative gene expression of MIAT was measured by Real-Time PCR 24 h post-transfection and confirmed the silencing of MIAT gene for M1 and M2 siRNAs (A). Total and viable cell counts, as determined by vital blue staining, were reduced in K562 cells at 48 h post-transfection (B). The rate of apoptotic cells, determined by acridine orange staining, is increased in MIAT-specific siRNAs transfected cells at 24 h (C). Representation of cells undergoing apoptosis is depicted by white arrows (D). Long-term survival of K562 cells was reduced upon MIAT-specific silencing as demonstrated by clonogenic assay (E). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicate a p-value < 0.01; ***/**** indicate a p-value < 0.001 as measured by One-way and Two-Way ANOVA tests and Dunnett’s multiple comparison test (MCT).
Ncrna 09 00047 g003
Figure 4. MIAT silencing induced dysregulation of several genes in SH-SY5Y neuroblastoma cells. RNA sequencing previously performed showed differentially expressed (D,E) genes upon MIAT silencing [14]. Data are the difference in expression between SH-SY5Y neuroblastoma cells transfected with negative siRNA and cells transfected with MIAT-specific siRNA (M2), expressed as a normalized log2 fold change (log2FC). Blue bars represent downregulated genes, red bars represent upregulated genes. (A) CDK6: Cyclin-dependent kinase 6; (B) XIAP: X-Linked Inhibitor of Apoptosis; (C) GADD45A: Growth Arrest and DNA Damage Inducible Alpha; (D) CBL: Casitas B-lineage Lymphoma; (E) FLT1: Fms Related Receptor Tyrosine Kinase 1; (F) HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; (G) c-MYC: MYC Proto-Oncogene; (H) RELA: RELA Proto-Oncogene, NF-KB Subunit; (I) NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Figure 4. MIAT silencing induced dysregulation of several genes in SH-SY5Y neuroblastoma cells. RNA sequencing previously performed showed differentially expressed (D,E) genes upon MIAT silencing [14]. Data are the difference in expression between SH-SY5Y neuroblastoma cells transfected with negative siRNA and cells transfected with MIAT-specific siRNA (M2), expressed as a normalized log2 fold change (log2FC). Blue bars represent downregulated genes, red bars represent upregulated genes. (A) CDK6: Cyclin-dependent kinase 6; (B) XIAP: X-Linked Inhibitor of Apoptosis; (C) GADD45A: Growth Arrest and DNA Damage Inducible Alpha; (D) CBL: Casitas B-lineage Lymphoma; (E) FLT1: Fms Related Receptor Tyrosine Kinase 1; (F) HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; (G) c-MYC: MYC Proto-Oncogene; (H) RELA: RELA Proto-Oncogene, NF-KB Subunit; (I) NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Ncrna 09 00047 g004
Figure 5. MIAT knockdown induces changes in the relative expression of leukemic-related genes. Jurkat T cells were transfected with negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2 or M3) using nucleofection. The relative gene expression was measured by Real-Time PCR 24 h post-transfection CDK6 (A); XIAP (B); GADD45A (C); CBL (D); FLT-1 (E); HIF-1A (F); c-MYC (G); RELA (H); NOD1 (I). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; ***/**** indicates a p-value < 0.001 as measured by One-Way ANOVA tests and Dunnett’s multiple comparison test (MCT). CDK6: Cyclin-dependent kinase 6; XIAP: X-Linked Inhibitor of Apoptosis; GADD45A: Growth Arrest and DNA Damage Inducible Alpha; CBL: Casitas B-lineage Lymphoma; FLT1: Fms Related Receptor Tyrosine Kinase 1; HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; c-MYC: MYC Proto-Oncogene; RELA: RELA Proto-Oncogene, NF-KB Subunit; NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Figure 5. MIAT knockdown induces changes in the relative expression of leukemic-related genes. Jurkat T cells were transfected with negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2 or M3) using nucleofection. The relative gene expression was measured by Real-Time PCR 24 h post-transfection CDK6 (A); XIAP (B); GADD45A (C); CBL (D); FLT-1 (E); HIF-1A (F); c-MYC (G); RELA (H); NOD1 (I). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; ***/**** indicates a p-value < 0.001 as measured by One-Way ANOVA tests and Dunnett’s multiple comparison test (MCT). CDK6: Cyclin-dependent kinase 6; XIAP: X-Linked Inhibitor of Apoptosis; GADD45A: Growth Arrest and DNA Damage Inducible Alpha; CBL: Casitas B-lineage Lymphoma; FLT1: Fms Related Receptor Tyrosine Kinase 1; HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; c-MYC: MYC Proto-Oncogene; RELA: RELA Proto-Oncogene, NF-KB Subunit; NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Ncrna 09 00047 g005
Figure 6. MIAT knockdown induces changes in the relative expression of leukemic-related genes. CEM-C7 cells were transfected with negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2 or M3) using nucleofection. The relative gene expression was measured by Real-Time PCR 24 h post-transfection CDK6 (A); XIAP (B); GADD45A (C); CBL (D); FLT-1 (E); HIF-1A (F); c-MYC (G); RELA (H); NOD1 (I). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; ***/**** indicates a p-value < 0.001 as measured by One-way ANOVA tests and Dunnett’s multiple comparison test (MCT). CDK6: Cyclin-dependent kinase 6; XIAP: X-Linked Inhibitor of Apoptosis; GADD45A: Growth Arrest and DNA Damage Inducible Alpha; CBL: Casitas B-lineage Lymphoma; FLT1: Fms Related Receptor Tyro-sine Kinase 1; HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; c-MYC: MYC Proto-Oncogene; RELA: RELA Proto-Oncogene, NF-KB Subunit; NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Figure 6. MIAT knockdown induces changes in the relative expression of leukemic-related genes. CEM-C7 cells were transfected with negative siRNA (NC) or one of the three MIAT-specific siRNAs (M1, M2 or M3) using nucleofection. The relative gene expression was measured by Real-Time PCR 24 h post-transfection CDK6 (A); XIAP (B); GADD45A (C); CBL (D); FLT-1 (E); HIF-1A (F); c-MYC (G); RELA (H); NOD1 (I). Data are represented with bar graphs depicting the means ± SEM from independent experiments. * Indicates a p-value < 0.05; ** indicates a p-value < 0.01; ***/**** indicates a p-value < 0.001 as measured by One-way ANOVA tests and Dunnett’s multiple comparison test (MCT). CDK6: Cyclin-dependent kinase 6; XIAP: X-Linked Inhibitor of Apoptosis; GADD45A: Growth Arrest and DNA Damage Inducible Alpha; CBL: Casitas B-lineage Lymphoma; FLT1: Fms Related Receptor Tyro-sine Kinase 1; HIF-1A: Hypoxia Inducible Factor 1 Alpha Subunit; c-MYC: MYC Proto-Oncogene; RELA: RELA Proto-Oncogene, NF-KB Subunit; NOD1: Nucleotide Binding Oligomerization Domain Containing 1.
Ncrna 09 00047 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ostini, A.; Mourtada-Maarabouni, M. Investigation into the Role of Long-Non-Coding RNA MIAT in Leukemia. Non-Coding RNA 2023, 9, 47. https://doi.org/10.3390/ncrna9040047

AMA Style

Ostini A, Mourtada-Maarabouni M. Investigation into the Role of Long-Non-Coding RNA MIAT in Leukemia. Non-Coding RNA. 2023; 9(4):47. https://doi.org/10.3390/ncrna9040047

Chicago/Turabian Style

Ostini, Alessia, and Mirna Mourtada-Maarabouni. 2023. "Investigation into the Role of Long-Non-Coding RNA MIAT in Leukemia" Non-Coding RNA 9, no. 4: 47. https://doi.org/10.3390/ncrna9040047

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop