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Article

MALAT1 Expression Is Deregulated in miR-34a Knockout Cell Lines

by
Andrea Corsi
1,†,
Tonia De Simone
1,†,
Angela Valentino
1,
Elisa Orlandi
1,
Chiara Stefani
1,
Cristina Patuzzo
1,
Stefania Fochi
1,
Maria Giusy Bruno
1,
Elisabetta Trabetti
1,
John Charles Rotondo
2,
Chiara Mazziotta
3,
Maria Teresa Valenti
1,
Alessandra Ruggiero
1,
Donato Zipeto
1,
Cristina Bombieri
1,* and
Maria Grazia Romanelli
1,*
1
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Strada le Grazie 8, 37134 Verona, Italy
2
IRCSS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy
3
Department of Medical Sciences, University of Ferrara, 44121 Ferrara, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Non-Coding RNA 2025, 11(4), 60; https://doi.org/10.3390/ncrna11040060
Submission received: 14 May 2025 / Revised: 17 July 2025 / Accepted: 1 August 2025 / Published: 5 August 2025
(This article belongs to the Section Long Non-Coding RNA)
Browse Figures
Versions Notes

Abstract

Background/Objectives: Non-coding microRNA-34a (miR-34a) regulates the expression of key factors involved in several cellular processes, such as differentiation, apoptosis, proliferation, cell cycle, and senescence. Deregulation of the expression of these factors is implicated in the onset and progression of several human diseases, including cancer, neurodegenerative disorders, and pathologies associated with viral infections and inflammation. Despite numerous studies, the molecular mechanisms regulated by miR-34a remain to be fully understood. The present study aimed to generate miR-34a knockout cell lines to identify novel genes potentially regulated by its expression. Methods: We employed the CRISPR-Cas9 gene editing system to knock out the hsa-miR-34a gene in HeLa and 293T cell lines, two widely used models for studying molecular and cellular mechanisms. We compared proliferation rates and gene expression profiles via RNA-seq and qPCR analyses between the wild-type and miR-34a KO cell lines. Results: Knockout of miR-34a resulted in a decreased proliferation rate in both cell lines. Noteworthy, the ablation of miR-34a resulted in increased expression of the long non-coding RNA MALAT1. Additionally, miR-34a-5p silencing in the A375 melanoma cell line led to MALAT1 overexpression. Conclusions: Our findings support the role of the miR-34a/MALAT1 axis in regulating proliferation processes.

1. Introduction

Micro-RNAs (miRNAs) are small non-coding RNAs that modulate gene expression preferentially by binding the 3′-UTR region of the target messenger RNA. MiRNA-mediated control of gene expression is critical for cellular response to environmental stress, such as starvation, hypoxia, oxidative stress, and DNA damage. A considerable amount of evidence correlates altered miRNA expression to human diseases affecting cell proliferation, survival, and tissue differentiation [1]. A set of miRNAs are functionally classified as oncogenes, referred to as “oncomiRs” or tumor suppressor miRs, whose dysregulation is associated with cancer initiation, progression, and metastasis [2,3]. The miR-34 family, along with the let-7 and miR-200 families, represent the three major families of tumor-suppressive miRNAs [4]. The miR-34 family consists of three members, miR-34a, miR-34b, and miR-34c, which are expressed by two different genes. miR-34a is transcribed from a gene located on chromosome 1p36.22, while a polycistronic transcript from chromosome 11q23.1 expresses miR-34b and c [4]. miR-34a, miR-34b, and miR-34c share a high degree of homology, which may account for overlapping functions in gene expression regulation [5,6]. miR-34a is more highly expressed in human tissues than miR-34b/c, excluding lungs, where miR-34b and c predominate [6]. The miR-34 family has been demonstrated to play a pivotal role in the regulation of apoptosis, cell cycle, and senescence, targeting multiple oncogenic mRNAs, such as CDK4, CDK6, BCL2 apoptosis regulator (BCL2), sirtuin 1 (SIRT1), and cyclin D1 (CCND1) genes [7,8,9]. CpG methylation has been demonstrated to silence the expression of miR-34 family members in various cancers [10]. miR-34a expression is regulated by the tumor suppressor p53 and is frequently downregulated in several cancers [11]. miR-34a may act as a tumor suppressor by negatively regulating the cell cycle [10], epithelial–mesenchymal transition (EMT) suppression [12], and p53-mediated induction of apoptosis [13,14]. In lung cancer, miR-34a has been demonstrated to contribute to increased cell proliferation, metastasis, and invasion [10,15,16].
In this study, we aimed to generate and characterize two independent miR-34a knockout (miR-34a KO) cell lines via CRISPR/Cas9 genetic engineering to identify novel miR-34a targets. Taking advantage of high-throughput transcriptomic analysis, we found that the expression of MALAT1, a long non-coding RNA (lncRNA), is altered in both miR-34a KO cell lines, suggesting a possible interplay in the miR-34a/MALAT1 axis in promoting cellular proliferation.

2. Results

2.1. Generation and Characterization of miR-34a KO HeLa and 293T Cell Lines

A CRISPR/Cas9-mediated gene editing approach was used to generate HeLa and 293T miR-34a-KO cell lines. We selected three gRNAs taking advantage of the bioinformatics tool CHOPCHOP v3 [17]. All three selected guide RNAs recognized regions in the pre-miR-34a (Figure S1). We checked potential off-targets of the three gRNAs and verified that none reside in the coding gene sequences (Table S1). The knockout of the miR-34a gene was validated by Sanger sequencing, end-point PCR (Figure S2), and RT-qPCR using TaqMan probes specific for miR-34a-5p (Figure 1A,B). To ascertain which miR-34 family members were expressed in both cell lines, we also analyzed the expression levels of miR-34b and miR-34c in the wild-type (WT) and KO cell lines. RT-qPCR analyses demonstrated no significant differences in the expression of both miR-34b and miR-34c between 293T WT and KO cell lines, while HeLa cells did not display detectable levels of either transcript (Figure 1C,D). These results suggest that the HeLa miR34-a KO cell model may be applied as an in vitro cell model to investigate the essential role of all members of the miR-34 family.

2.2. Loss of miR-34a Alters Cellular Proliferation in 293T Cells

CCK-8 proliferation assay was used to measure the proliferative rate of both WT and KO cell lines over a period of 72 h. miR-34a KO HeLa cells showed a reduced proliferation rate effect compared to WT cells, detectable at 48 h. Depletion of miR-34ain 293T cells led to a reduction in the proliferation rate at 48 and 72 h compared to WT cells. These results suggest that the absence of miR-34a affects cell cycle progression and survival (Figure 1E,F).

2.3. RNA-Seq Analysis Identifies Genes Deregulated in HeLa miR-34a KO Cells Compared to WT

Based on the qPCR results showing no detectable expression of miR-34b and miR-34c in the HeLa cell line, we select to perform RNA-seq analysis on miR-34a KO HeLa cells and compare transcripts expression to WT HeLa cells. miR-34a KO HeLa may serve as a suitable model to highlight the essential role of miR-34 family members due to the lack of all three different miR-34s. Transcriptomic analysis revealed 185 genes differentially expressed (logFC > |0.5| and p-value < 0.05), of which 82 were downregulated and 103 were upregulated (Supplementary Figure S3A–C).
Gene Ontology (GO) analysis of the differentially expressed genes identified an enrichment in GO terms regarding biological processes generally associated with neural cells, including axon guidance, synapse assembly, and neuron projection guidance (Supplementary Figure S4A–C). KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis identified pathways related to cancer, specifically small cell lung cancer, and cardiomyopathies, including arrhythmogenic right ventricular cardiomyopathy, cytoskeleton in muscle cells and hypertrophic cardiomyopathy (Supplementary Figure S4D).
We chose to validate the overexpression by qPCR of five of the most significantly upregulated genes identified by RNA-seq: LINC03057, MALAT1 (Metastasis Associated Lung Adenocarcinoma Transcript 1), MAP1B (Microtubule-Associated Protein 1B), ONECUT2 and REL, based on their relevance in proliferative processes and cancer.
LINC03057, also known as lnc-HOXB8-1:2 (ENSG00000272763), is an oncogenic lncRNA associated with colorectal cancer progression [18]. MALAT1, also known as LINC00047 (ENSG00000251562) is a lncRNA involved in triple-negative breast cancer [19], B-cell lymphoma [20], colorectal cancer [21] and multiple myeloma [22]. MAP1B (ENSG00000131711), is a microtubule-associated protein involved in several types of cancer, including non-small cell lung cancer [23], urothelial carcinoma [24], and triple-negative breast cancer [25]. ONECUT2, also known as One Cut Homeobox 2 (ENSG00000119547), is a transcription factor that upregulates cell proliferation, migration, adhesion, and differentiation processes in several tumors, including prostate, colorectal, ovarian, and lung cancer [26]. REL, also known as c-REL (ENSG00000162924), is a subunit of the Nuclear Factor kB (NF-kB) complex involved in inflammation processes, in liquid tumors (including Hodgkin’s lymphoma, Adult T-cell leukemia/lymphoma, marginal zone lymphoma) and solid tumors (including breast cancer, pancreatic cancer, gastric cancer) [27].
Quantitative PCR analysis confirmed the upregulation of LINC03057, MALAT1, MAP1B, ONECUT2, and REL in HeLa KO cells (Figure 2A–E).
Next, we assessed whether these genes were also upregulated in the 293T KO cell line. We found that REL, MALAT1, and LINC03057 were significantly upregulated, while MAP1B and ONECUT2 did not display significant differences in expression compared to WT (Figure 2F–J). These results suggest that LINC03057, MALAT1, and REL could be potential targets of post-transcriptional expression regulation by miR-34a.

2.4. Rescue of miR-34a Expression in 293T miR34-a KO Inhibits MALAT1 Expression

To further investigate the effect of miR-34a expression on LINC03057, MALAT1, and REL transcripts, 293T miR34-a KO cells were transfected with a vector expressing miR-34a (pSG5-miR34a) or the corresponding empty vector as control (pSG5) (Figure 3A).
RT-qPCR analysis showed that miR-34a overexpression led to a statistically significant downregulation of MALAT-1 (Figure 3D), whereas the effect on LINC03057 and REL (Figure 3C,E) was not statistically significant.
These results corroborate the hypothesis that miR-34a may participate in the regulation of MALAT1 gene expression.

2.5. miR-34a Inhibition in A375 Melanoma Cells Affects MALAT1 Expression

It has been previously demonstrated that MALAT1 is able to reduce miR-34a action in melanoma cells by acting as a “sponge” via its direct binding to miR-34a [28]. However, the possibility of miR-34ato counter-regulating MALAT1 expression is currently unexplored. Therefore, we assessed whether the miR-34a expression affected MALAT1 expression by inhibiting miR-34a-5p in A375 melanoma cells.
The result showed that the inhibition of miR-34a-5p induced overexpression of MALAT1 (Figure 4A,B), suggesting that, in the miR-34a/MALAT1 regulatory axis, the microRNA may alter the gene expression of the lncRNA MALAT1.

3. Discussion

The miRNA-34 family regulates several signaling pathways associated with the immune system, metabolism, cellular structure, and cell cycle progression by targeting specific mRNAs whose expression is modulated at both transcriptional and post-transcriptional levels [9]. Since hundreds of targets are envisaged to be regulated by the miR-34 family, their role in several diseases is not surprising. Members of the miR-34 family have been shown to participate in tumor suppression, such as colorectal, breast, prostate, lung, liver cancer, hematological neoplasm, and osteosarcoma [29], but also in neurological and depressive disorders, and in stress-related psychiatric conditions [30,31,32,33]. miR-34a is one of the miRNAs with the most significant expression regulation mediated by p53-, and displays tumor suppressor properties [10]. In fact, miR-34a can negatively affect cell cycle progression [10], block EMT [12], and induce p53-mediated apoptosis [13,14] through the regulation of its downstream target genes.
Due to the complexity of the molecular mechanisms that may be affected by miR-34a, specific KO cell lines provide a useful in vitro model to interpret the essential role of miR-34a and open the investigation to what processes may be circumvented by other miR-34 members. Here, we characterized two cell lines KO for miR-34a, analyzing proliferation rates and transcript expression.
Gene Ontology analysis of the differentially expressed genes in the miR-34a KO HeLa cells developed in the present study identified enrichment in biological processes associated with neural cells. Interestingly, miR-34a is highly expressed in the human brain, and has been associated with a wide range of neurodevelopmental and neuropathological processes [34]. Moreover, in recent years, Hu and colleagues demonstrated that miR-34a promotes dendritic growth and branching in cultured hippocampal neurons [35]. The KEGG v3 analysis identified pathways related not only to cancer but also to cardiomyopathies. These results are consistent with early reports demonstrating miR-34a role in cardiac functionality, heart and skeletal muscle aging, and cardiomyopathies [36,37,38,39,40], thus proposing miR-34a as a novel therapeutic target for treating cardiovascular diseases [41].
Transcript expression analysis performed on miR-34a KO HeLa cells revealed the deregulation of several genes. In particular, three of them (LINC03057, MALAT1, and REL) resulted upregulated in both HeLa and 293T cells. LINC03057 (also known as lnc-HOXB8-1:2 or RP11–357H14.17) is a lncRNA associated with the progression of different tumors, such as colorectal, gastric and endometrial carcinoma, with shortened overall survival and poor prognosis. Its high expression was associated with invasion depth, increased tumor size, lymphatic metastasis, and TNM (Tumor, Node, Metastasis) stage. LINC03057 acts as an miRNA sponge by negatively regulating hsa-miR-6825-5p, inducing tumor-associated macrophage infiltration, which promotes the progression of neuroendocrine differentiated colorectal cancer [18]. Moreover, LINC03057 plays an oncogene role in activating ATF2 (activating transcription factor 2) signaling and enhancing Treg cells, thus promoting cell proliferation, migration, and invasion in diffused gastric cancer [42,43,44]. This lncRNA, along with a few other lncRNAs, has also been reported to influence immune responses by modulating cellular migration and adhesion, as well as immune cell infiltration in the head and neck squamous cell carcinoma microenvironment [45].
cREL is one of the five members of the NF-κB family, which share a highly conserved DNA binding domain. In particular, c-Rel belongs to the NF-kB class II proteins, containing an additional transcription activation domain responsible for recruiting co-activators. In general, it has been described as a transcriptional activator that is able to determine a permissive chromatin environment at regulated gene promoters [46]. C-Rel regulates different cellular functions, including the expression of the antiapoptotic gene Bcl-xL [47] and a component of the checkpoint kinase Chk1 signaling pathway in an osteosarcoma cell line [48]. The role of the c-Rel subunit has been reported in human diseases, such as cardiac hypertrophy, fibrosis, and inflammatory bowel disease; its dysregulation has been observed in both liquid and solid tumors with an oncogenic or tumor suppressor function [27,49].
MALAT1, also known as LINC00047 (ENSG00000251562), is one of the most studied lncRNAs. It is highly conserved and abundantly expressed in cells and tissues but predominantly located in the nucleus. It is involved in numerous cellular processes, such as cell proliferation, regulation of gene expression, both at the transcriptional and post-transcriptional levels, RNA processing, epigenetic control, and nuclear organization [50,51]. MALAT1 affects various miRNAs, acting as endogenous RNA sponges, consequently increasing the gene expression of several essential genes involved in cancer progression and metastasis [52,53]. Recent research has demonstrated that MALAT-1 plays a role in mediating the EMT, which leads to the acquisition of stem cell-like properties and chemoresistance by interacting with various intracellular signaling pathways, including PI3K/Akt/mTOR and Wnt/β-catenin [53,54]. Overexpression of MALAT1 was initially identified in non-small cell lung cancer (NSCLC) and has been correlated with tumor initiation, progression, distant metastasis, autophagy, drug resistance, and poor outcome in several types of tumor [52], such as glioblastoma [55], breast cancer [50], colorectal cancer [21], acute myeloid leukemia [56]. Accumulating evidence suggests that upregulation of MALAT1 affects various molecular pathways, thus playing an essential role in a wide range of other diseases, including renin–angiotensin–aldosterone system, which is involved in blood pressure regulation and cardiovascular diseases [57], insulin signaling, type 2 diabetes [51], liver [58] and kidney disease [59]. Differential expression of MALAT1 has been reported under various physiological stresses such as serum starvation and hypoxia [51].
Our study provides evidence that miR-34a is required to regulate the expression of MALAT1. Through miR-34a ablation in two cell lines, RNA seq and qPCR, we demonstrated that the absence of miR-34a affects MALAT1 expression. Knockdown of endogenous miR-34a-5p in A375 melanoma cells resulted in an increase in MALAT1 levels, further confirming the regulatory relationship. These findings align with previous studies indicating that miR-34a functions as a tumor suppressor by targeting key oncogenic pathways [4]. The inverse relationship between miR-34a and MALAT1, consistent with a role for MALAT1 as an inhibitor of miR-34a activity, was described in melanoma and osteosarcoma [28,60,61] and was also reported with aging in mouse skeletal muscle [62]. A specific response element for miR-34a in the MALAT1 transcripts has been previously identified by bioinformatics analysis [34]. Furthermore, it has been demonstrated that, by miR-34a sponge activity, MALAT1 regulates cMyc and Met expression [28].
The biological implications of this regulation are particularly relevant in the biology of cancer, as both miR-34a and MALAT1 ncRNAs have been implicated in tumor progression. MiR-34a inhibits cell proliferation and promotes apoptosis, while MALAT1 is linked with enhanced metastatic potential and resistance to therapy. The apparent inverse correlation observed between their expression levels in some cancerous tissues [63,64,65,66,67] supports the thesis that miR-34a may exert its tumor-suppressive function, at least in part, through MALAT1 downregulation. In this respect, MALAT1 upregulation in breast cancer patients and cell lines has been recently correlated to low innate and adaptive immune response due, at least partially, to the interaction with miR-34a target. This interaction alters the expression of miR-34a and miR-17–5p and modulates MICA//MICB (MHC class I-related chain A and B), PDL1 (programmed death ligand-1), and B7-H4 (member of B7 family) expression on triple-negative breast cancer cells [68].
The results of our study are in agreement with previously published results that showed MALAT1 regulation by miRNA interaction. A previous study has provided evidence that miR-9 targets MALAT1, binding two different miRNA binding sites and leading to the degradation of MALAT1 [69]. A similar mechanism may be envisaged for the action of miR-34a, based on a predicted binding site for miR34a in MALAT1 by in silico analyses (Figure 5A). We have schematized the possible mechanisms of reciprocal actions of MALTA1 as a sponge of miR-34a and miR-34a action on the expression of MALAT1 (Figure 5B).
Some limitations of this study must be acknowledged. We have chosen to compare the KO cell lines to parental WT lines as described in the literature. [70,71,72,73,74]. However, to rule out the negative effect by CRSPR/Cas9 procedure, we are aware that additional appropriate negative controls can be represented by additional clonal cell lines selected by puromycin after Cas9 transfection with gRNAs targeting a safe harbor gene, such as AAVS1 (Adeno-Associated Virus Integration Site 1) [75,76,77]. Furthermore, the precise molecular mechanism through which miR-34a may influence MALAT1 expression needs to be fully elucidated. Additionally, our study primarily focused on in vitro cell models using only one clone for both HeLa and 293T cells and further validations in primary cell lines, and in in vivo systems are required to confirm the molecular relevance of these interactions. Finally, given the pleiotropic role of both miR-34a and MALAT1, it will be essential to investigate their cross-talk in different molecular pathways to better understand their functional roles.

4. Materials and Methods

4.1. Cell Cultures

Human cervical cancer cell line HeLa, human embryonic kidney cell line 293T, and human melanoma cell line A375 were cultured in Dulbecco’s Modified Eagle Medium (DMEM), containing 10% of Fetal Bovine Serum (FBS) and 1% of sodium pyruvate, L–Glutamine, penicillin, and streptomycin. All cell cultures were maintained in a humidified incubator at 37 °C with 5% CO2.

4.2. miR-34a Knockout Cell Line Production

To produce HeLa and 293T miR-34a KO cell lines, three guide RNAs (gRNAs) were designed using the online tool CHOPCHOP v3, previously described [17,78,79]. The three gRNAs were selected for their high target specificity (low off-target site recognition) and their location on the target sequence, the pre-miR-34a sequence (Figure S1 and Table S1). The plasmid expressing pSpCas9 and each gRNA was prepared by cloning each gRNA independently into BbsI restriction sites of the pSpCas9(BB)-2A-Puro (PX459) V2.0 vector (#62988, Addgene, Watertown, MA, USA) using the T4 DNA ligase (Promega Corporation, Madison, WI, USA). Sequences of the gRNAs are reported in Table 1. HeLa and 293T cells were transfected using the Trans-IT®-LT1 transfection reagent (MIR2300, Mirus Bio, Madison, WI, USA), following the manufacturer’s protocol. To produce the miR-34a-KO cell lines, HeLa and 293T were seeded at a concentration of 2.5 × 105 cells/well and 4 × 105 cells/well, respectively, in 6-well plates. After 20 h (70–80% confluence), the culture medium was removed and replaced with an antibiotic-deficient medium. Cells were subsequently transfected with the pSpCas9(BB)-2A (PX459) V2.0 plasmid vector (Addgene–Plasmid #62988, Addgene, Watertown, MA, USA), containing the CRISPR/Cas9 system and the gRNAs, using 2 µL of TransIT-LT1 transfection reagent (Mirus Bio, Madison, WI, USA) for each µg of DNA. After the addition of TransIT-LT1 transfection agent, the cells were maintained in an incubator and selected in 0,5 μg/mL puromycin. Clonal cell lines were separated by limiting dilution using discrete Poisson distribution probability.

4.3. miR-34a Knock-Out Validation and miR-34b and miR-34c Expression Analysis

Effectiveness of the knockout approach was evaluated via reverse transcription–quantitative PCR (RT-qPCR) using the TaqMan MicroRNA Assay kit (Applied Biosystems, Waltham, MA, USA) with probes for hsa-miR-34a (Applied Biosystems, Waltham, MA, USA, assay ID 000426), hsa-miR-34b (Applied Biosystems, Waltham, MA, USA, assay ID 00427), hsa-miR-34c (Applied Biosystems, Waltham, MA, USA, assay ID 00428) and the internal reference small RNA U6 (Applied Biosystems, Waltham, MA, USA, assay ID 001973) for HeLa and 293T cells and miR-191-5p (Applied Biosystems, Waltham, MA, USA, assay ID 002299) for A375 melanoma cells, according to manufacturer’s instructions. Expression levels of miR-34a in the KO clones were analyzed using the 2−ΔΔCt method [80] and normalized for U6 or miR-191-5p expression. Additionally, Sanger sequencing of the edited region was used to further confirm the presence of deletions in the desired gene in the miR-34a KO cell lines. The sequences were aligned using Clustal Omega version 1.2.4 [81].

4.4. Cell Proliferation Analysis

HeLa and 293T cells were seeded in 96-well plates at a starting concentration of 1 × 103 cells/well and 1 × 104 cells/well, respectively. After 24 h, 10 µL of water-soluble tetrazolium salt reagent (Cell Counting Kit 8, Dojindo Molecular Technologies, Munich, Germany) was added to each well. After two hours, cell proliferation at the starting timepoint (T0) was evaluated by measuring absorbance at 450 nm via a spectrophotometer (Byo-noy 96 plate reader, Enzo Life Sciences, Bruxelles, Belgium). The same procedure was repeated at different timepoints (24, 48 and 72 h). Absorbance from wells containing only the medium and the tetrazolium salt reagents was used as a blank. Proliferation was measured as proliferative rate compared to T0. The cell proliferation analysis was performed at least three times on each cell line, using three technical replicates for each timepoint.

4.5. RNA Extraction for Next-Generation Sequencing (NGS) of HeLa WT and miR-34a KO Cells

Total RNA was extracted from 3 WT samples and 3 miR-34a KO HeLa cell clones by TRIzol (ThermoFisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions and under RNase-free conditions. Quality control of the 6 extracted RNAs included the evaluation of the yield, purity, and integrity. RNA quantity was assessed using both the NanoDrop ND-100 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA) and the Invitrogen Qubit 2.0 fluorometer (ThermoFisher Scientific, Waltham, MA, USA).
RNA purity was determined by measuring the 260/280 and 260/230 nm absorbance ratios (higher than 1.8 and between 2.0 and 2.2, respectively), as evaluated using the NanoDrop ND-100 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA). RNA integrity was determined by capillary electrophoresis using Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA), yielding an RQN (RNA Quality Number) greater than 7.9 for each RNA sample.

4.6. Library Preparation and RNA-Sq of HeLa WT and miR-34a KO Cells

Total RNA samples were sent to BMR Genomics,(Padua, Italy), for the Transcriptomic analyses. According to the Company’s procedures, RNA samples were paired-end sequenced on NovaSeq 6000 (Illumina, San Diego, CA, USA) with a read length of 2 × 100 bp and a mean read depth of 2 × 20 million reads per library. Briefly, starting with 1 µg of total RNA per sample, mRNA was polyA-selected and fragmented. The TruSeq Stranded mRNA Library Preparation kit (Illumina, San Diego, CA, USA) was used to prepare first-strand cDNAs by random hexamer priming and to synthesize second-strand cDNAs ready for library construction. The cDNAs were end-repaired and adenylated before being ligated with multiple indexed adapters and were then PCR-enriched and purified to create the final 6 cDNA libraries. After a quality check of the libraries using Fragment Analyzer (Agilent Technologies, Santa Clara, CA, USA), the libraries were normalized, pooled, and sequenced by the NovaSeq 6000 flow cell.
The RNAseq raw data have been made available through the European Nucleotide Archive (ENA) portal (accession PRJEB91112).

4.7. Data Processing for RNA-Seq of HeLa WT and miR-34a KO Cells

The bioinformatic analysis of the data obtained by RNA-seq was performed by BMR Genomics, Padua, Italy. Data processing protocols applied by the company are described below.
Raw paired-end sequencing reads obtained by the Illumina system were pre-processed using fastp v0.20.0 [82], applying parameters to remove residual adapter sequences and keep only high-quality data.
Passing filter reads were mapped and aligned to the genome reference (Homo sapiens) using STAR v2.7.9.a [83] with standard parameters, except for the sjdbOverhang option set on read length. Genome and transcript annotations provided as input were downloaded from v105 of the Ensembl repository. Alignments were then elaborated by RSEM v1.3.3 [84] to estimate transcript and gene abundances.
Differential expression was computed by edgeR [85] from raw counts in each comparison. Multiple testing controlling procedure was applied, and genes with a p-value ≤ 0.05 and logFC > |0.5| were considered differentially expressed. Annotation of differentially expressed genes was performed using the BioMart package [86] inR 4.3, querying available Ensembl Gene IDs and retrieving Gene Names and Entrez gene IDs.
The heatmap represents a selection of the 50 most differentially expressed genes based on the logFC value. A Volcano Plot representative of the RNA seq was produced using SRplot [87]. Gene ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG v3) enrichment analyses were performed using the GO pathway enrichment bubble plot tool from SRplot [87], accessed on 17 April 2025.

4.8. miR-34a Phenotypic Rescue in 293T KO Cell Line

293T miR-34a KO cells were seeded in 6-well plates at a starting concentration of 4 × 105 cells/well. After 24 h, cells were transfected with pSG5-miR-34a plasmid or pSG5 empty vector as control, kindly gifted from Martin Hart’s Laboratory [88], using the Trans-IT®-LT1 transfection reagent (MIR2300, Mirus Bio, Madison, WI, USA) and according to the manufacturer’s protocol. RNA was extracted and analyzed as described in Section 4.10. Efficient miR-34a overexpression was evaluated as described in Section 4.3.

4.9. miR-34a Inhibition in A375 Cell Line

A375 cells were seeded in 6-well plates at a concentration of 3 × 105 cells/well and transfected with miRNA Inhibitor Negative Control (Cohesion Biosciences, Cat. CIH0000-5NMOL, London, UK) and hsa-miR-34a-5p miRNA Inhibitor (Cohesion Biosciences, Cat. CIH0060-10NMOL, London, UK), using the Lipofectamine™ 3000 Transfection Reagent (Invitrogen, Cat L3000001, Waltham, MA, USA), following the protocol’s instructions. After 48 h, RNA was extracted and analyzed as described in Section 4.10. Efficient miR-34a-5p inhibition was evaluated as described in Section 4.3.

4.10. RNA Isolation and RT-qPCR

Total RNA was extracted from cells using Qiazol reagent (Qiagen, Venlo, NL, USA) followed by phase separation employing chloroform. Following RNA precipitation by isopropanol/ethanol, the pellet was resuspended using nuclease-free water and quantified by Nanodrop 1000 V3.7.1 Spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA). One microgram of total RNA was subjected to first-strand cDNA synthesis using QuantiTect Reverse Transcription kit (Qiagen, Venlo, NL). The cDNA was used to assess gene expression via RT-qPCR using the SensiFASTTM SYBR® No-Rox kit (Meridian Biosciences, Cincinnati, OH, USA). The primers, relative to the sequences of REL (REL proto-oncogene, NF-κB subunit), MAP1B (Microtubule-Associated Protein 1B), ONECUT2 (One Cut Homeobox 2), MALAT1 (Metastasis Associated Lung Adenocarcinoma Transcript 1), LINC03057 (Long Intergenic Non-Protein Coding RNA 3057), and RPLP0 (Ribosomal Protein Lateral Stalk Subunit P0) genes used for the quantitative analyses, are reported in Table 2. RPLP0 was used as an internal reference gene. Gene expression was analyzed using the 2−ΔΔCt method [80] and normalized for RPLP0 expression.

4.11. Bioinformatic Prediction of miR-34a/MALAT1 Interaction

Structural prediction of the possible interaction between MALAT1 and miR-34a-3p or miR-34a-5p was performed using RNAhybrid version 2.2 [89]. For MALAT1, a reference sequence of 8779 nucleotides of length was used as input (NR_002819.4), while sequences for miR-34a-5p and miR-34a-3p were retrieved from miRbase (accession MIMAT0000255 and MIMAT0004557, respectively). RNAhybrid was set to produce only the best possible interaction between the lncRNA and the miRNA, retrieving only the structure showing the lowest minimum free energy.

4.12. Statistical Analysis

Data are expressed as mean ± SD. Statistical differences were calculated by two-tailed Student’s t-test or one-way ANOVA test using GraphPad Prism v8 (GraphPad Inc., San Diego, CA, USA), where p ≤ 0.05 was considered statistically significant.

5. Conclusions

This study shows that knockout or inhibition of miR-34a non-coding RNA deregulates the expression of LINC03057, REL, and MALAT1 genes. Specifically, the absence or reduced expression of miR-34a leads to increased expression of MALAT1 in three different cell types. Our findings provide novel insights into the regulatory network modulating MALAT1 expression and suggest that targeting the miR-34a/MALAT1 axis could represent a potential therapeutic approach in diseases where MALAT1 is dysregulated. Future studies should explore these interactions and their effect on pathway-specific cellular responses.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ncrna11040060/s1, Figure S1—gRNA design and strategy for miR-34a KO generation; Figure S2—Alignment of Sanger sequences from WT and miR-34a KO cells and PCR verification of the presence of deletion in the KO cells; Figure S3—NGS RNA-seq analyses in HeLa WT and miR-34a KO cells; Figure S4—Gene Ontology and KEGG analysis of RNA-seq data; Table S1—CHOPCHOP prediction of off-target effects for the three guide RNAs used.

Author Contributions

Conceptualization, M.G.R., A.C. and T.D.S.; methodology, A.C., T.D.S., S.F., C.S., A.V., M.G.B., E.O., C.M., M.T.V., M.G.R., D.Z., E.T., C.B. and J.C.R.; validation, A.C., T.D.S., S.F., C.S., E.O., D.Z., E.T., C.B., J.C.R. and M.G.R.; formal analysis, A.C., A.V., C.M., M.T.V., M.G.B., E.O., T.D.S. and M.G.R.; investigation, A.C., T.D.S., A.V., M.G.B., E.O., C.P. and M.G.R.; resources, M.G.R., E.T., C.B., D.Z. and A.R.; data curation, M.G.R., A.C., T.D.S., A.V., M.G.B., E.O. and C.S.; writing—original draft preparation, M.G.R., A.C., A.V. and C.S.; writing—review and editing, M.G.R., E.T., C.B., A.C., D.Z., M.T.V., J.C.R. and C.M.; visualization, A.C., A.V. and M.G.B.; supervision, M.G.R.; funding acquisition, M.G.R., E.T., C.B., D.Z. and M.T.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by MUR as part of the Excellence Project 2023-2027 of the Department of Neuroscience, Biomedicine and Movement Sciences of the University of Verona. J.C.R. is supported by Associazione Italiana per la Ricerca sul Cancro (AIRC) (ID:21956).

Data Availability Statement

Raw reads produced from RNA-seq experiments are available at the European Nucleotide Archive (ENA) browser under the accession number PRJEB91112. Excel datasheets containing raw and elaborated data from Figure 1, Figure 2, Figure 3 and Figure 4 and statistically significant differentially expressed genes from Figure S3 are available on Zenodo (https://doi.org/10.5281/zenodo.15913985).

Acknowledgments

We kindly thank Martin Hart’s laboratory for gifting the plasmids pSG5-miR-34a and pSG5 used in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
3’-UTR3’-Untranslated Region
AktAKT Serine/Threonine kinase
ANOVAAnalysis of Variance
ATF2Activating Transcription Factor 2
BCL2B-cell lymphoma 2
Bcl-xLB-cell lymphoma extra large
CCK8Cell Counting Kit 8
CCND1Cyclin D1
CDK4Cyclin Dependent Kinase 4
CDK6Cyclin Dependent Kinase 6
cDNAComplementary DNA
Chk1Checkpoint Kinase 1
c-MycMYC proto-oncogene
CRISPR-Cas9Clustered Regularly Interspaced Short Palindromic Repeats Cas9 associated
DMEMDulbecco’s Modified Eagle Medium
EMTEpithelial–Mesenchymal transition
FBSFetal Bovine Serum
FCFold Change
GOGene Ontology
gRNAGuide RNA
KEGGKyoto Encyclopedia of Genes and Genomes
KOKnock–out
LINC03057Long Intergenic Non-Protein Coding RNA 3057
lncRNALong non-coding RNA
MALAT1Metastasis Associated Lung Adenocarcinoma Transcript 1
MAP1BMicrotubule-Associated Protein 1B
MetMET proto-oncogene
miRNAMicro-RNA
mfeMinimum Free Energy
mTORMechanistic Target Of Rapamycin Kinase
NGSNext-Generation Sequencing
NSCLCNon-small cell lung cancer
ONECUT2One Cut Homeobox 2
PI3KPhosphatidylinositol 3-kinase
qPCRQuantitative PCR
RELREL proto-oncogene, NF-κB subunit
RNA-seqRNA sequencing
RPLP0Ribosomal Protein Lateral Stalk Subunit P0
RQNRNA quality number
RT-qPCRReverse transcription–quantitative PCR
SDStandard Deviation
SIRT1Sirtuin 1
TNMTumor, Node, Metastasis
WntWnt Family Member 1
WTWild-Type

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Figure 1. miR-34a expression and cell proliferation evaluation in HeLa and 293T WT and KO cells. qPCR analysis of miR-34a-5p gene expressions in HeLa (A) and 293T (B) WT and KO cell lines. qPCR analysis of miR-34b (C) and miR-34c (D) expression in HeLa and 293T WT and KO cell lines. Proliferation curves of WT versus KO in HeLa (E) and 293T (F) cells measured as a fold change relative to timepoint 0 using CCK-8 assay. * = p-value ≤ 0.05; ** = p-value ≤ 0.01; **** = p-value ≤ 0.0001; n.d. = not detectable; WT = wild-type for miR-34a; KO = knockout for miR-34a; n = 3 for each experimental group, except 293T miR-34a KO cells in the proliferation analysis (n = 4).
Figure 1. miR-34a expression and cell proliferation evaluation in HeLa and 293T WT and KO cells. qPCR analysis of miR-34a-5p gene expressions in HeLa (A) and 293T (B) WT and KO cell lines. qPCR analysis of miR-34b (C) and miR-34c (D) expression in HeLa and 293T WT and KO cell lines. Proliferation curves of WT versus KO in HeLa (E) and 293T (F) cells measured as a fold change relative to timepoint 0 using CCK-8 assay. * = p-value ≤ 0.05; ** = p-value ≤ 0.01; **** = p-value ≤ 0.0001; n.d. = not detectable; WT = wild-type for miR-34a; KO = knockout for miR-34a; n = 3 for each experimental group, except 293T miR-34a KO cells in the proliferation analysis (n = 4).
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Figure 2. RT-qPCR analysis of LINC03057, MALAT1, MAP1B, ONECUT2, and REL mRNA expression in miR-34a KO compared to WT cells. mRNAs expression levels in HeLa WT vs. miR-34a KO (A–E); mRNAs expression in 293T WT vs. miR-34a KO (F–J). * = p-value ≤ 0.05; ** = p-value ≤ 0.01; *** = p-value ≤ 0.001; **** = p-value ≤ 0.0001; n.s. = not statistically significant; n = 3 for each experimental group.
Figure 2. RT-qPCR analysis of LINC03057, MALAT1, MAP1B, ONECUT2, and REL mRNA expression in miR-34a KO compared to WT cells. mRNAs expression levels in HeLa WT vs. miR-34a KO (A–E); mRNAs expression in 293T WT vs. miR-34a KO (F–J). * = p-value ≤ 0.05; ** = p-value ≤ 0.01; *** = p-value ≤ 0.001; **** = p-value ≤ 0.0001; n.s. = not statistically significant; n = 3 for each experimental group.
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Figure 3. Analysis of LINC03057, MALAT1 and REL mRNA expression in 293T miR-34a KO cells rescued for miR-34a expression. (A) Graphical scheme summarizing the experimental design for miR-34a rescue in 293T miR-34a KO cells. (B) qPCR analysis of miR-34a-5p gene expression in 293T WT, 293T miR-34a KO and 293T miR-34a KO cells transfected with an miR-34a overexpressing vector; (C–E) RT-qPCR analysis of LINC03057 (C), MALAT1 (D) and REL (E) mRNA expression in miR-34a KO cells compared to miR-34a KO cells rescued for miR-34a expression. * = p-value ≤ 0.05; n.s. = not statistically significant; n = 3 for each experimental group.
Figure 3. Analysis of LINC03057, MALAT1 and REL mRNA expression in 293T miR-34a KO cells rescued for miR-34a expression. (A) Graphical scheme summarizing the experimental design for miR-34a rescue in 293T miR-34a KO cells. (B) qPCR analysis of miR-34a-5p gene expression in 293T WT, 293T miR-34a KO and 293T miR-34a KO cells transfected with an miR-34a overexpressing vector; (C–E) RT-qPCR analysis of LINC03057 (C), MALAT1 (D) and REL (E) mRNA expression in miR-34a KO cells compared to miR-34a KO cells rescued for miR-34a expression. * = p-value ≤ 0.05; n.s. = not statistically significant; n = 3 for each experimental group.
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Figure 4. Analysis of MALAT1 expression levels in melanoma A375 cells in presence of miR-34a-5p inhibition. (A) qPCR analysis of relative miR-34a-5p gene expression in A375 melanoma cells transfected with scramble (NC) or miR-34a-5p inhibitor, showing reduced expression of miR-34a-5p after inhibition. (B) RT-qPCR analysis of MALAT1 in A375 melanoma cells transfected with scramble (NC) or miR-34a-5p inhibitor. * = p-value ≤ 0.05; n = 3 for each experimental group.
Figure 4. Analysis of MALAT1 expression levels in melanoma A375 cells in presence of miR-34a-5p inhibition. (A) qPCR analysis of relative miR-34a-5p gene expression in A375 melanoma cells transfected with scramble (NC) or miR-34a-5p inhibitor, showing reduced expression of miR-34a-5p after inhibition. (B) RT-qPCR analysis of MALAT1 in A375 melanoma cells transfected with scramble (NC) or miR-34a-5p inhibitor. * = p-value ≤ 0.05; n = 3 for each experimental group.
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Figure 5. Proposed mechanism of miR-34a/MALAT1 interaction. (A) Structural prediction realized with RNAhybrid 2.2 of the possible interaction between miR-34a-3p (upper part, in green) and miR-34a-5p (lower part, in green) with MALAT1 (in red). The minimum free energy (mfe) structure for both interactions is shown and the mfe value is reported. (B) Proposed molecular model in which miR-34a is normally sponged by MALAT1 lncRNA (on the left), while in miR-34a KO cell lines the absence of miR-34a leads to increased MALAT1 expression (on the right, red cross).
Figure 5. Proposed mechanism of miR-34a/MALAT1 interaction. (A) Structural prediction realized with RNAhybrid 2.2 of the possible interaction between miR-34a-3p (upper part, in green) and miR-34a-5p (lower part, in green) with MALAT1 (in red). The minimum free energy (mfe) structure for both interactions is shown and the mfe value is reported. (B) Proposed molecular model in which miR-34a is normally sponged by MALAT1 lncRNA (on the left), while in miR-34a KO cell lines the absence of miR-34a leads to increased MALAT1 expression (on the right, red cross).
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Table 1. Target and gRNA sequences.
Table 1. Target and gRNA sequences.
gRNATarget SequencegRNA Sequence (5′-3′)
gRNA 1TTCTTTGGCAGTGTCTTAGCTGGFw: CACCGTTCTTTGGCAGTGTCTTAGC
Rv: AAACGCTAAGACACTGCCAAAGAAC
gRNA 2GCCAGCTGTGAGTGTTTCTTTGGFw: CACCGCCAGCTGTGAGTGTTTCTT
Rv: AAACAAGAAACACTCACAGCTGGC
gRNA 3TAGAAGTGCTGCACGTTGTGGGGFw: CACCGCACAACGTGCAGCACTTCTA
Rv: AAACTAGAAGTGCTGCACGTTGTGC
Table 2. Primers used for RT-qPCR.
Table 2. Primers used for RT-qPCR.
TargetPrimer Sequence (5’-3’)
RELFw: ATTTGACGACTGCTCTTCCTC
Rv: TCCTCTGACACTTCCACAATTC
MAP1BFw: CTCCTTCCAGAACTTCATAGAGATT
Rv: TTCAGGACAGAACAGGGTTAAG
ONECUT2Fw: GGAATCCAAAACCGTGGAGTAA
Rv: CTCTTTGCGTTTGCACGCTG
MALAT1Fw: ATGCGAGTTGTTCTCCGTCT
Rv: TATCTGCGGTTTCCTCAAGC
LINC03057Fw: TGTTCTGCGTCTGTGTCTAC
Rv: CCACTCCCTTTCTTCCTTGAA
RPLP0Fw: ACATGTTGCTGGCCAATAAGGT
Rv: CCTAAAGCCTGGAAAAAGGAGG
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Corsi, A.; De Simone, T.; Valentino, A.; Orlandi, E.; Stefani, C.; Patuzzo, C.; Fochi, S.; Bruno, M.G.; Trabetti, E.; Rotondo, J.C.; et al. MALAT1 Expression Is Deregulated in miR-34a Knockout Cell Lines. Non-Coding RNA 2025, 11, 60. https://doi.org/10.3390/ncrna11040060

AMA Style

Corsi A, De Simone T, Valentino A, Orlandi E, Stefani C, Patuzzo C, Fochi S, Bruno MG, Trabetti E, Rotondo JC, et al. MALAT1 Expression Is Deregulated in miR-34a Knockout Cell Lines. Non-Coding RNA. 2025; 11(4):60. https://doi.org/10.3390/ncrna11040060

Chicago/Turabian Style

Corsi, Andrea, Tonia De Simone, Angela Valentino, Elisa Orlandi, Chiara Stefani, Cristina Patuzzo, Stefania Fochi, Maria Giusy Bruno, Elisabetta Trabetti, John Charles Rotondo, and et al. 2025. "MALAT1 Expression Is Deregulated in miR-34a Knockout Cell Lines" Non-Coding RNA 11, no. 4: 60. https://doi.org/10.3390/ncrna11040060

APA Style

Corsi, A., De Simone, T., Valentino, A., Orlandi, E., Stefani, C., Patuzzo, C., Fochi, S., Bruno, M. G., Trabetti, E., Rotondo, J. C., Mazziotta, C., Valenti, M. T., Ruggiero, A., Zipeto, D., Bombieri, C., & Romanelli, M. G. (2025). MALAT1 Expression Is Deregulated in miR-34a Knockout Cell Lines. Non-Coding RNA, 11(4), 60. https://doi.org/10.3390/ncrna11040060

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