TY - JOUR
AU - He,, Yanyun
AB - Abstract MicroRNAs (miRNAs) are short (20–23 nt) non-coding RNAs that are involved in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the stability and translation of mRNAs. In recent years, deep sequencing of the transcription is being increasingly utilized with the promise of higher sensitivity for the identification of differential expression patterns as well as the opportunity to discover new transcripts, including new alternative isoforms and miRNAs. In this study, miRNAs from A549 cells treated with/without rapamycin or starvation were subject to genome-wide deep sequencing. A total of 1534 miRNAs were detected from the rapamycin- and starvation-treated A549 cells. Among them, 31 miRNAs were consistently upregulated and 131 miRNAs were downregulated in the treated cells when compared with the untreated cells. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the predicted target genes of the most significantly differentially expressed miRNAs revealed that the autophagy-related miRNAs are involved in cancer pathway. Taken together, our findings indicate that the underlying mechanism responsible for autophagy is associated with dysregulation of miRNAs in rapamycin- or starvation-induced A549 cells. autophagy, miRNA sequencing, A549 cells, rapamycin, starvation Introduction Lung cancer has a high incidence and mortality of all cancers, which is regulated by more and more microRNAs (miRNAs) [1–3]. MiRNAs are small non-coding RNA molecules consisting of 20–23 nt which inhibits gene expression at the post-transcription level by binding to the 3′-untranslated region of target mRNA [4]. MiRNAs have been found to serve as suppressors or activators of autophagy in various types of cancers, and regulate diverse biological processes through downregulation of target protein expression. Autophagy is a highly conserved biological mechanism that is responsible for lysosome-dependent recycling of long-lived, abnormal or misfolded proteins as well as dysfunctional or unnecessary organelles [5,6]. Most recently, dysregulation of autophagy has been implicated in the pathogenesis of a broad spectrum of diseases including neural diseases [7], metabolism defects and cancers [8], and has become a new therapeutic target in cancer treatment. Rapamycin-sensitive pathway regulates mitochondrial membrane potential, autophagy, and survival in irradiated MCF-7 cells [9]. Survival of exo-erythrocytic forms (EEFs) in the autophagosome of the infected hepatocytes also contributes to rapamycin-enhanced development of the malaria liver stage, possibly due to the suppression of autolysosome maturation by EEFs [10]. These findings suggest that autophagy may play a significant role in cancer radiotherapy. However, information on autophagy and lung cancer is currently limited, and available information has shown contradicting results. It has already been certified that autophagy plays principal roles in the radiosensitivity of human lung adenocarcinoma A549 cells [11], and that rapamycin-induced autophagy sensitizes A549 cells to radiation associated with inhibition of DNA damage repair [12]. In this basis, our further research will explore the molecular mechanism of autophagy via rapamycin application to A549 cells. In order to decipher the molecular mechanisms that related to the progression of autophagy in A549 cells, identifying the associated miRNAs is critical. Recent findings have highlighted a relevant role of miRNAs in the regulation of autophagy events [13]. Notably, studies have illustrated that miR-376b controls starvation-related autophagy by targeting ATG4C and BECN1 (Beclin 1) [14]. Based on these premises, we sought to investigate the differential expressed miRNAs in A549 cells during autophagy process, to examine whether miRNAs are the novel and potent modulators of the autophagic activity or not. Our previous work has verified that miR-181a-5p suppresses autophagy but miR-18a-5p promotes autophagy [15,16]. These findings demonstrated that miRNA-regulated autophagy plays important roles in the progression of lung cancer. miRNA-seq technique is widely applied to understand the regulatory relationships of large-scale transcriptomic sequencing data. It has provided an opportunity for profound new discoveries in important areas of cell biology, however, information on the molecular mechanisms of autophagy assoiciated with miRNAs is still limited. In this study, we performed a genome-wide analysis of miRNAs from the rapamycin- and starvation-treated A549 cells utilizing the miRNA-seq technology. Gene ontology (GO) and KEGG pathway analysis revealed that the autophagy-related miRNAs are involved in the cancer development. We also demonstrated that the induction of autophagy via rapamycin application sensitized A549 lung cancer cells to radiation. Materials and Methods Cell culture Human lung epithelial carcinoma cells (A549) were purchased from the American Type Culture Collection (ATCC, Manassas, USA). A549 cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, Gaithersburg, USA) containing 10% fetal bovine serum (FBS; HyClone Laboratories, Logan, USA) and an antibiotic cocktail of 100 U/ml penicillin and 100 μg/ml streptomycin (Gibco). Cells were maintained under a humidified atmosphere of 5% CO2 at 37°C. For autophagy induction, starvation in DMEM without 10% FBS and glucose (9 h) or rapamycin (2.5 μM, 9 h) (Sigma-Aldrich, St Louis, USA) was used. Cells were collected after starvation and rapamycin treatment, respectively. Library preparation and miRNA sequencing Total RNA was extracted by using Trizol reagent (Life Technologies, Carlsbad, USA) according to the manufacturer’s protocol. The quality and quantity of the RNA samples were assessed on a Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, USA) using an RNA 6000 Nano kit (Agilent Technologies). Small RNA libraries were generated according to the protocol of Illumina TruSeq™ Small RNA Sample Preparation kit (Agilent Technologies). RNA from the A549 cells in each group was pooled to generate the two libraries. First, the 3′ adaptor and 5′ adaptor (Agilent Technologies) were ligated to the total RNA by T4 RNA ligase (Thermo Fisher Scientific, Waltham, USA). Ligated RNA was reverse-transcribed by SuperScriptII reverse transcriptase (Invitrogen, Carlsbad, USA) to generate cDNA. Then, the cDNAs were amplified with a common primer and a primer containing one of 48 index sequences to generate the small RNA library. DNA size and the purity of the cDNA library were checked by using a high sensitivity DNA 1000 kit (Agilent Technologies) on the Bioanalyzer 2100 system, and quantification of the cDNA libraries were performed with a Qubit™ dsDNA HS kit (Life Technologies) on a Qubit 2.0 Fluorometer (Life Technologies). The cDNA libraries were subject to single-end sequencing on Illumina Genome AnalyzerIIx (GAIIx) utilizing the proprietary Solexa sequencing-by-synthesis method at the Shanghai Biotechnology Corporation (Shanghai, China) according to the manufacturer’s guide. Image analysis and base calling were performed by Illumina built-in SCS2.8/RTA1.8 software. Reads mapping and annotation To obtain clean and unique small RNA reads, the fastx_toolkit (v0.0.13.2, downloadable at http://hannonlab.cshl.edu/fastx_toolkit/) was used to filter off (1) low quality reads, (2) reads with 5′ primer contaminants, (3) reads without a 3′ primer, (4) reads without the insert tag, (5) reads with poly(A) or simple repeats, and (6) reads shorter than 18 nt. The clean reads were screened against and mapped to the latest human genome assembly (Oar_v3.1, released September 20, 2012) using the SOAP program (SOAP aligner v2.21, http://soap.genomics.org.cn/) and were annotated by the CLC Genomics Workbench 5.5. Reads from mRNA, rRNA, tRNA, snRNA, and snoRNA were excluded for further analysis. The remaining sequences were subsequently aligned to known mature miRNAs in miRBase (Release 20) to identify homologs of known miRNAs. Small RNAs that perfectly matched to known human miRNAs were considered conserved miRNAs. Sequences that were identical to or related (i.e. no more than one mismatch in the seed sequence and a few end nucleotides fluctuation in the entire length) to the reference mature miRNAs were annotated as miRNA candidates. The miRNA candidates were then clustered into categories according to sequence similarity, and the sequences varying only in length and/or a limit end nucleotides were gathered under the same miRNA identifier. Determination of miRNA differential expression profile The differentially expressed miRNAs were identified by the R package ‘EBSeq’. Raw small RNA counts were quantile normalized to variable library sizes. EBSeq is an empirical Bayesian approach that models a number of features observed in the RNA-seq data. A list of the differentially expressed genes with a 2-fold difference was generated and controlled with a false discovery rate (FDR) at 0.05 in an experiment comparing two biological conditions without replicates. When replicates are not available, EBSeq estimates the variance by pooling similar genes into a certain number of bins, and the best function of this approach works effectively when there are no more than 50% differentially expressed genes in the dataset. Target gene prediction of differentially expressed miRNAs miRanda (downloadable at http://www.microrna.org/microrna/getDownloads.do) was used to identify potential miRNA-target genes of the differentially expressed miRNAs which were analyzed by using the miRanda program. Next, by integration of the mRNA and miRNA data, only those mRNAs that were negatively correlated with the specific miRNA were considered as potential targets for further analysis. Annotations of negatively associated genes with differentially expressed miRNAs In order to extract biological significance from the potential target genes associated with the differentially expressed miRNAs, target genes were enriched by GO and Kyoto Encyclopedia of Genes and Genomes databases (KEGG, http://www.genome.jp/kegg). Quantitative real-time PCR assay Total RNA was extracted from A549 cells by using Trizol according to the manufacturer’s instructions. Reverse transcription was performed using the PrimeScript™ RT reagent Kit (Takara, Dalian, China). U6 snRNA was used as the endogenous control for miRNA. All the samples were treated under the same condition and analyzed by quantitative real-time PCR (qRT-PCR) using SYBR Premix Ex Taq™ (Takara) according to the manufacturer’s protocol. PCR conditions were following: 95°C for 5 min, 95°C for 30 s, 53°C for 10 s, 55∼85°C for 30 s, 5 s +0.5°C/cycle Plate read, 40 cycles. Results were expressed using the relative quantification (2−ΔΔCt) method. Primer sequences are shown in Table 1. Table 1. Sequence of primers used for qRT-PCR Name Usage Sequence (5′→3′) U6 snRNA qPCR forward CTCGCTTCGGCAGCACA qPCR reverse AACGCTTCACGAATTTGCGT miR-4454 qRT-PCR GGATCCGAGTCACGGCACCA miR-1307-5p qRT-PCR TCGACCGGACCTCGACCGGCT miR-30e-3p qRT-PCR CTTTCAGTCGGATGTTTACAGC miR-26a-5p qRT-PCR TTCAAGTAATCCAGGATAGGCT miR-424-3p qRT-PCR CAAAACGTGAGGCGCTGCTAT miR-21-5p qRT-PCR TAGCTTATCAGACTGATGTTGA miR-574 qRT-PCR TGAGTGTGTGTGTGTGAGTGTGT miR-99b-5p qRT-PCR CACCCGTAGAACCGACCTTGCG miR-454-3p qRT-PCR TAGTGCAATATTGCTTATAGGGT miR-181a-2-3p qRT-PCR ACCACTGACCGTTGACTGTACC miR-10b-5p qRT-PCR TACCCTGTAGAACCGAATTTGTG miR-149-5p qRT-PCR TCTGGCTCCGTGTCTTCACTCCC miR-31-3p qRT-PCR TGCTATGCCAACATATTGCCAT miR-374-3p qRT-PCR GGTTGTATTATCATTGTCCGAG miR-193a-5p qRT-PCR TGGGTCTTTGCGGGCAAGATGA miR-100-5p qRT-PCR AACCCGTAGATCCGAACTTGTG miR-744-5p qRT-PCR TGCGGGGCTAGGGCTAACAGCA miR-4455 qRT-PCR AGGGTGTGTGTGTTTTT miR-501-3p qRT-PCR AATGCACCCGGGCAAGGATTCT Name Usage Sequence (5′→3′) U6 snRNA qPCR forward CTCGCTTCGGCAGCACA qPCR reverse AACGCTTCACGAATTTGCGT miR-4454 qRT-PCR GGATCCGAGTCACGGCACCA miR-1307-5p qRT-PCR TCGACCGGACCTCGACCGGCT miR-30e-3p qRT-PCR CTTTCAGTCGGATGTTTACAGC miR-26a-5p qRT-PCR TTCAAGTAATCCAGGATAGGCT miR-424-3p qRT-PCR CAAAACGTGAGGCGCTGCTAT miR-21-5p qRT-PCR TAGCTTATCAGACTGATGTTGA miR-574 qRT-PCR TGAGTGTGTGTGTGTGAGTGTGT miR-99b-5p qRT-PCR CACCCGTAGAACCGACCTTGCG miR-454-3p qRT-PCR TAGTGCAATATTGCTTATAGGGT miR-181a-2-3p qRT-PCR ACCACTGACCGTTGACTGTACC miR-10b-5p qRT-PCR TACCCTGTAGAACCGAATTTGTG miR-149-5p qRT-PCR TCTGGCTCCGTGTCTTCACTCCC miR-31-3p qRT-PCR TGCTATGCCAACATATTGCCAT miR-374-3p qRT-PCR GGTTGTATTATCATTGTCCGAG miR-193a-5p qRT-PCR TGGGTCTTTGCGGGCAAGATGA miR-100-5p qRT-PCR AACCCGTAGATCCGAACTTGTG miR-744-5p qRT-PCR TGCGGGGCTAGGGCTAACAGCA miR-4455 qRT-PCR AGGGTGTGTGTGTTTTT miR-501-3p qRT-PCR AATGCACCCGGGCAAGGATTCT Table 1. Sequence of primers used for qRT-PCR Name Usage Sequence (5′→3′) U6 snRNA qPCR forward CTCGCTTCGGCAGCACA qPCR reverse AACGCTTCACGAATTTGCGT miR-4454 qRT-PCR GGATCCGAGTCACGGCACCA miR-1307-5p qRT-PCR TCGACCGGACCTCGACCGGCT miR-30e-3p qRT-PCR CTTTCAGTCGGATGTTTACAGC miR-26a-5p qRT-PCR TTCAAGTAATCCAGGATAGGCT miR-424-3p qRT-PCR CAAAACGTGAGGCGCTGCTAT miR-21-5p qRT-PCR TAGCTTATCAGACTGATGTTGA miR-574 qRT-PCR TGAGTGTGTGTGTGTGAGTGTGT miR-99b-5p qRT-PCR CACCCGTAGAACCGACCTTGCG miR-454-3p qRT-PCR TAGTGCAATATTGCTTATAGGGT miR-181a-2-3p qRT-PCR ACCACTGACCGTTGACTGTACC miR-10b-5p qRT-PCR TACCCTGTAGAACCGAATTTGTG miR-149-5p qRT-PCR TCTGGCTCCGTGTCTTCACTCCC miR-31-3p qRT-PCR TGCTATGCCAACATATTGCCAT miR-374-3p qRT-PCR GGTTGTATTATCATTGTCCGAG miR-193a-5p qRT-PCR TGGGTCTTTGCGGGCAAGATGA miR-100-5p qRT-PCR AACCCGTAGATCCGAACTTGTG miR-744-5p qRT-PCR TGCGGGGCTAGGGCTAACAGCA miR-4455 qRT-PCR AGGGTGTGTGTGTTTTT miR-501-3p qRT-PCR AATGCACCCGGGCAAGGATTCT Name Usage Sequence (5′→3′) U6 snRNA qPCR forward CTCGCTTCGGCAGCACA qPCR reverse AACGCTTCACGAATTTGCGT miR-4454 qRT-PCR GGATCCGAGTCACGGCACCA miR-1307-5p qRT-PCR TCGACCGGACCTCGACCGGCT miR-30e-3p qRT-PCR CTTTCAGTCGGATGTTTACAGC miR-26a-5p qRT-PCR TTCAAGTAATCCAGGATAGGCT miR-424-3p qRT-PCR CAAAACGTGAGGCGCTGCTAT miR-21-5p qRT-PCR TAGCTTATCAGACTGATGTTGA miR-574 qRT-PCR TGAGTGTGTGTGTGTGAGTGTGT miR-99b-5p qRT-PCR CACCCGTAGAACCGACCTTGCG miR-454-3p qRT-PCR TAGTGCAATATTGCTTATAGGGT miR-181a-2-3p qRT-PCR ACCACTGACCGTTGACTGTACC miR-10b-5p qRT-PCR TACCCTGTAGAACCGAATTTGTG miR-149-5p qRT-PCR TCTGGCTCCGTGTCTTCACTCCC miR-31-3p qRT-PCR TGCTATGCCAACATATTGCCAT miR-374-3p qRT-PCR GGTTGTATTATCATTGTCCGAG miR-193a-5p qRT-PCR TGGGTCTTTGCGGGCAAGATGA miR-100-5p qRT-PCR AACCCGTAGATCCGAACTTGTG miR-744-5p qRT-PCR TGCGGGGCTAGGGCTAACAGCA miR-4455 qRT-PCR AGGGTGTGTGTGTTTTT miR-501-3p qRT-PCR AATGCACCCGGGCAAGGATTCT Protein extraction and western blot analysis Western blot analysis was performed as previously described [17,18]. Total protein was extracted from the cells using Radio Immunoprecipitation Assay (RIPA) lysis buffer (CWBIO, Beijing, China) and quantified with a Protein BCA Assay Kit (Bio-Rad, Hercules, USA). Protein samples were subject to SDS-PAGE (10%) and then transferred to polyvinylidene fluoride (PVDF) membrane. The membrane was blocked in Tris-buffered saline-Tween-20 (20%) buffer containing 5% low-fat milk for 50 min with gentle shaking. After incubated with primary and secondary antibodies, membranes were developed using the chemiluminescence reagent kit (Millipore, Billerica, USA). Protein bands were quantitated by densitometric analysis using Image Lab analysis software and normalized to GAPDH. Rabbit anti-LC3 (1:1000), anti-mTOR (1:1000), anti-p62 (1:500), anti-phospho-S6K1 (1:1000), anti-S6K1 (1:1000), anti-BECN1 (1:1000), anti-ATG5 (1:500), and rabbit anti-GAPDH (1:1000) antibodies were used as primary antibodies. All these antibodies were purchased from Cell Signaling Technology (Danvers, USA). Horseradish peroxidase-conjugated goat-anti rabbit IgG was used as the secondary antibody (1:10,000; Santa Cruz Biotechnology, Santa Cruz, USA). Statistical analysis Data are expressed as the mean ± SEM and analyzed using GraphPad Prism 5 software, using Student's t-test for two-group comparisons and one-way ANOVA for three or more group comparisons. A P < 0.05 was considered to indicate a statistically significant result. Results Autophagy is induced by rapamycin or starvation Rapamycin is a common reagent used to induce autophagy [19]. Therefore, we assessed whether rapamycin treatment induces autophagy in A549 cells. And in the starvation condition, autophagy could also be induced. We detected autophagy after 9 h of treatment with rapamycin or starvation. LC3 was the most frequent autophagic marker [20]. Proteins were extracted after rapamycin or starvation treatment, and western blot analysis was performed to measure the expression level of LC3 protein. Our results revealed that treatment with rapamycin/starvation significantly increased the LC3B-II level (Fig. 1A). Next, we explored the expression level of mTOR protein, and no obvious change was found. We also explored the expression level of S6K1 protein, and the results showed that treatment with rapamycin/starvation elevated the expression of S6K1 protein, but decreased the expressions of p-S6K1 and p62 proteins (Fig. 1B). To further validate the activation of autophagy under rapamycin/starvation, we examined the expression levels of BECN1 and ATG5 proteins, and found a higher level of BECN1 and ATG5 expression compared with the control (Fig. 1C). These results indicated that both rapamycin and starvation could induce cell autophagy. Figure 1. View largeDownload slide Rapamycin and starvation-induced autophagy in A549 cells (A) Western blot analysis was performed to identify the expression level of LC3. GAPDH was used as an internal control. Autophagy was assessed following DMSO, starvation in DMEM without 10% FBS and glucose (9 h) or rapamycin treatment (2.5 μM, 9 h). Image Lab software was used to analyze the expression level of the blots. (B) The expression levels of mTOR, p-S6K1, and S6K1 and p62 detected by western blot analysis. Starvation-induced and autophagy-related degradation of P62 was decreased. GAPDH was used as an internal control. (C) The expression levels of BECN1 and ATG5 detected by western blot analysis. All experiments were performed at least in triplicate with similar results. *P < 0.05, **P < 0.01, ***P < 0.001. Figure 1. View largeDownload slide Rapamycin and starvation-induced autophagy in A549 cells (A) Western blot analysis was performed to identify the expression level of LC3. GAPDH was used as an internal control. Autophagy was assessed following DMSO, starvation in DMEM without 10% FBS and glucose (9 h) or rapamycin treatment (2.5 μM, 9 h). Image Lab software was used to analyze the expression level of the blots. (B) The expression levels of mTOR, p-S6K1, and S6K1 and p62 detected by western blot analysis. Starvation-induced and autophagy-related degradation of P62 was decreased. GAPDH was used as an internal control. (C) The expression levels of BECN1 and ATG5 detected by western blot analysis. All experiments were performed at least in triplicate with similar results. *P < 0.05, **P < 0.01, ***P < 0.001. Summary of the raw sequence reads To profile miRNA expression in the process of autophagy, small RNA sequencing was performed in A549 cells treated with rapamycin or starvation. With a Q-score (quality score) >20, 21.4, 21.9, and 22.3 million raw reads per sample were obtained respectively (Table 2). And 97.16%, 94.92%, and 96.23% of them were clean reads, respectively, after removal of low quality reads as described in methods (Table 3). These reads were compared with Sanger miRBase, ncRNA, piRNA, and Rfam database. As shown in Fig. 2, 934,415 small RNAs were identified, including 337,701 annotated and 597,344 unannotated. In starvation- and rapamycin-treated A549 cells, 36.5% and 32.3% were annotated, respectively (Fig. 2). Table 2. Quality control of the small RNA sequencing Sample name Seq type Clean reads Q20 value (%) Result A549 control 9 h miRNA-seq 21,410,400 98.08 Pass A549 starve 9 h miRNA-seq 21,953,240 98.06 Pass A549 rapa 9 h miRNA-seq 22,376,041 97.96 Pass Sample name Seq type Clean reads Q20 value (%) Result A549 control 9 h miRNA-seq 21,410,400 98.08 Pass A549 starve 9 h miRNA-seq 21,953,240 98.06 Pass A549 rapa 9 h miRNA-seq 22,376,041 97.96 Pass Table 2. Quality control of the small RNA sequencing Sample name Seq type Clean reads Q20 value (%) Result A549 control 9 h miRNA-seq 21,410,400 98.08 Pass A549 starve 9 h miRNA-seq 21,953,240 98.06 Pass A549 rapa 9 h miRNA-seq 22,376,041 97.96 Pass Sample name Seq type Clean reads Q20 value (%) Result A549 control 9 h miRNA-seq 21,410,400 98.08 Pass A549 starve 9 h miRNA-seq 21,953,240 98.06 Pass A549 rapa 9 h miRNA-seq 22,376,041 97.96 Pass Table 3. Effective ratio of raw reads Sample name Clean reads Effective reads Effective ratio (%) A549 control 9 h 21,410,400 20,801,535 97.16 A549 starve 9 h 21,953,240 20,838,710 94.92 A549 rapa 9 h 22,376,041 21,532,404 96.23 Sample name Clean reads Effective reads Effective ratio (%) A549 control 9 h 21,410,400 20,801,535 97.16 A549 starve 9 h 21,953,240 20,838,710 94.92 A549 rapa 9 h 22,376,041 21,532,404 96.23 Table 3. Effective ratio of raw reads Sample name Clean reads Effective reads Effective ratio (%) A549 control 9 h 21,410,400 20,801,535 97.16 A549 starve 9 h 21,953,240 20,838,710 94.92 A549 rapa 9 h 22,376,041 21,532,404 96.23 Sample name Clean reads Effective reads Effective ratio (%) A549 control 9 h 21,410,400 20,801,535 97.16 A549 starve 9 h 21,953,240 20,838,710 94.92 A549 rapa 9 h 22,376,041 21,532,404 96.23 Figure 2. View largeDownload slide Clean reads annotated ratio of small RNA Sequences that were identical to or related to the reference mature miRNAs, were annotated as miRNA candidates. About 36.5% and 32.3% were annotated, respectively, for starvation- and rapamycin-treated A549 cells. Figure 2. View largeDownload slide Clean reads annotated ratio of small RNA Sequences that were identical to or related to the reference mature miRNAs, were annotated as miRNA candidates. About 36.5% and 32.3% were annotated, respectively, for starvation- and rapamycin-treated A549 cells. Differential expression profile of miRNAs We detected 1534 miRNAs with one or more sequencing reads in the miRBase from the A549 cell line, of which ~47% were miRNAs with at least 10 reads. The correlation between control and starvation or rapamycin-treated A549 cells was analyzed. The results showed well correlation, and the R values are 0.800 and 0.803, respectively (Fig. 3A,B). The differentially expressed miRNAs were not significant when the cut-off was set at 2 folds (Fig. 3C,D). In order to reduce the sequencing data noise, we focused on 585 miRNAs with at least 20 counts in the control samples for further analysis. Among them, 270 (171 downregulated and 99 upregulated) differentially expressed miRNAs (46% of 585 miRNAs) were identified with FDR adjusted P value < 0.05 in starvation group compared with control group (Supplementary Table S1). In rapamycin, 270 differentially expressed miRNAs were identified compared with the control group (Supplementary Table S1). Among all the differentially expressed miRNAs, 53 were upregulated (Fig. 4A) and 131 were downregulated (Fig. 4B) in both treatments, which may play important roles in the process of autophagy. The heatmap (Fig. 4C) showed the differentially expressed miRNAs in rapamycin-treated and starvation-treated A549 cells when compared with the control. Figure 3. View largeDownload slide The correlation and differential expression between control and starvation- and rapamycin-treated A549 cells (A,B) The correlation between control and starvation or rapamycin was assayed. (C,D) Scatter plot of the normalized sequencing read counts from the two samples showing statistically differentially expressed miRNAs. Figure 3. View largeDownload slide The correlation and differential expression between control and starvation- and rapamycin-treated A549 cells (A,B) The correlation between control and starvation or rapamycin was assayed. (C,D) Scatter plot of the normalized sequencing read counts from the two samples showing statistically differentially expressed miRNAs. Figure 4. View largeDownload slide Differentially expressed miRNAs were analyzed in deep-sequence data of the rapamycin- and starvation-treated A549 cells (A,B) Venn diagram of differentially expressed miRNA: (A) upregulated miRNAs in rapamycin-treated and starvation-treated A549 cells, (B) downregulated miRNAs in rapamycin-treated and starvation-treated A549 cells. (C) The heatmap showed the differentially expressed miRNAs in rapamycin-treated and starvation-treated A549 cells when compared with the control cells. The fold change of downregulation was set at <0.8, and the fold change of upregulation was set at >1.2, FDR < 0.5. Figure 4. View largeDownload slide Differentially expressed miRNAs were analyzed in deep-sequence data of the rapamycin- and starvation-treated A549 cells (A,B) Venn diagram of differentially expressed miRNA: (A) upregulated miRNAs in rapamycin-treated and starvation-treated A549 cells, (B) downregulated miRNAs in rapamycin-treated and starvation-treated A549 cells. (C) The heatmap showed the differentially expressed miRNAs in rapamycin-treated and starvation-treated A549 cells when compared with the control cells. The fold change of downregulation was set at <0.8, and the fold change of upregulation was set at >1.2, FDR < 0.5. Target gene prediction of the differential miRNAs A total of 922 genes were predicted to be potential targets of eight differentially expressed miRNAs (miR-4455, miR-26a-2-3p, miR-148a-5p, miR-148a-3p, miR-33b-3p, miR-6087, miR-4791, miR-3182; Supplementary Table S2). Based on the relationship between the miRNAs and the targets, only those miRNA-target pairs with opposite upregulations or downregulations are biologically meaningful. GO analysis showed that the significantly enriched biological processes (P < 0.01 and FDR < 0.2) were related to G-protein coupled receptor (Fig. 5A). KEGG pathway analysis indicated the significant pathway was associated with cancer (Fig. 5B). Figure 5. View largeDownload slide Gene ontology and KEGG pathway enrichment analysis Gene ontology (A) and KEGG pathway (B) enrichment analyses were performed using 922 differentially expressed genes. Those miRNA-target pairs with opposite upregulations or downregulations are biologically meaningful. Figure 5. View largeDownload slide Gene ontology and KEGG pathway enrichment analysis Gene ontology (A) and KEGG pathway (B) enrichment analyses were performed using 922 differentially expressed genes. Those miRNA-target pairs with opposite upregulations or downregulations are biologically meaningful. Validation of differential miRNA expression Totally, 19 differential expressed miRNAs were selected for the stem-loop qPCR validation. The fold changes detected by qPCR were similar to those observed the miRNA-seq analysis in A549 cells (Fig. 6). The expressions of 19 differential miRNAs were also examined after starvation or rapamycin treatment in H1299 cells, which showed similar results (Supplementary Fig. S1). These consistencies validate the miRNA-seq data. In addition, we generated and analyzed survival signatures of lung adenocarcinoma patients with low or high miRNA expression using the OncoMiR online database (http://www.oncomir.org/). We selected the miRNAs which have obvious effect in the survival of patients. Among the cases, we found that lung adenocarcinoma patients with low miRNA (miR-26a-2-3p, miR-148a-5p, miR-148a-3p, miR-30e-3p, miR-21-5p) or high miRNA (miR-31-3p) expression had lower survival rates (Supplementary Fig. S2). This implied that miRNA may be an oncogene (miR-31-3p) or a tumor suppressor (miR-26a-2-3p, miR-148a-5p, miR-148a-3p, miR-30e-3p, miR-21-5p). Figure 6. View largeDownload slide qPCR validation of 19 miRNAs in A549 cells All experiments were repeated at least in triplicate with similar results. U6 was used an internal control for qRT-PCR assay. *P < 0.05, **P < 0.01. Figure 6. View largeDownload slide qPCR validation of 19 miRNAs in A549 cells All experiments were repeated at least in triplicate with similar results. U6 was used an internal control for qRT-PCR assay. *P < 0.05, **P < 0.01. Discussion Autophagy is an intracellular self-digestion progression [21]. Human diseases exhibiting regulated autophagy may be interdependent with defects in miRNA-mediated regulation of gene networks [22]. As a very important physiological process in cancer cells, autophagy is associated with apoptosis, cell growth, cell invasion, and metastasis [5,23–25]. Moreover, autophagy and miRNA are currently related with information, and they regulate each other. For example, Ma et al. [26] demonstrated that miR-143 mediates oxidative stress-induced autophagy to improve the survival of c-kit+ CPCs by targeting Atg7 for improving CPC-based heart repair. Sheng et al. [27] found that miR-224 is regulated by autophagy-preferred degradation in hepatocellular carcinoma tumorigenesis. Accumulating evidence has uncovered the significant role of miRNAs in the modulation of autophagy (Table 4). In addition, miRNAs could induce autophagy with starvation or rapamycin treatment. miR-181a regulates starvation-induced and rapamycin-induced autophagy, and ATG5 is a rate-limiting miRNA target in this effect [28]. Yong et al. [12] demonstrated that rapamycin-induced autophagy is more sensitive to A549 cells. In RAW 264.7 cells, both starvation and rapamycin treatments can induce BECN1-dependent autophagy [29]. On the other hand, studies have demonstrated that miRNAs also control starvation- and mTOR inhibition-related autophagy [14]. Here, we revealed that rapamycin- or starvation-induced autophagy is related to differential expression of miRNA. Table 4. The miRNA candidates associated with autophagy miRNAs Carcinomas Target gene Autophagy Reference miR-143 Cardiac progenitor ATG7 Suppress [26] miR-22 Renal tubulointerstitial fibrosis PTEN Suppress [30] miR-1 NSCLC ATG3 Suppress [31] miR-23a Inflammatory ATG12 Promote [32] miR-489 Breast cancer LAPTM4B Suppress [33] miR-26a Swine Sertoli ULK2 Suppress [34] miR-21 Nucleus pulposus PTEN Suppress [35] miR-665-3p Intestinal ischemia/reperfusion breast cancer ATG4B Suppress [36] miR-155-5p Cervical cancer Promote [37] miR-34a-5p Steatotic hepatocytes Suppress [38] miR-143 Crohn’s disease ATG2B Suppress [39] miR-124 Cholangiocarcinoma EZH2, STAT3 Promote [40] miR-375 Breast cancer ATG7 Suppress [41] miR-103 PC12 Suppress [37] miR-140-5p/miR-149 Osteoarthritis FUT1 Promote [42] miR-181 NSCLC Suppress [43] miR-142-3p Hepatocellular carcinoma ATG5/ATG16L1 Suppress [44] miR-214 Colorectal cancer ATG12 Suppress [45] miR-18a NSCLC IRF2 Promote [16] miR-107/103 Epidermis Suppress [46] miR-221 Colorectal cancer TP53INP1 Suppress [47] miR-93-5p Glaucoma PTEN Suppress [48] miRNAs Carcinomas Target gene Autophagy Reference miR-143 Cardiac progenitor ATG7 Suppress [26] miR-22 Renal tubulointerstitial fibrosis PTEN Suppress [30] miR-1 NSCLC ATG3 Suppress [31] miR-23a Inflammatory ATG12 Promote [32] miR-489 Breast cancer LAPTM4B Suppress [33] miR-26a Swine Sertoli ULK2 Suppress [34] miR-21 Nucleus pulposus PTEN Suppress [35] miR-665-3p Intestinal ischemia/reperfusion breast cancer ATG4B Suppress [36] miR-155-5p Cervical cancer Promote [37] miR-34a-5p Steatotic hepatocytes Suppress [38] miR-143 Crohn’s disease ATG2B Suppress [39] miR-124 Cholangiocarcinoma EZH2, STAT3 Promote [40] miR-375 Breast cancer ATG7 Suppress [41] miR-103 PC12 Suppress [37] miR-140-5p/miR-149 Osteoarthritis FUT1 Promote [42] miR-181 NSCLC Suppress [43] miR-142-3p Hepatocellular carcinoma ATG5/ATG16L1 Suppress [44] miR-214 Colorectal cancer ATG12 Suppress [45] miR-18a NSCLC IRF2 Promote [16] miR-107/103 Epidermis Suppress [46] miR-221 Colorectal cancer TP53INP1 Suppress [47] miR-93-5p Glaucoma PTEN Suppress [48] Table 4. The miRNA candidates associated with autophagy miRNAs Carcinomas Target gene Autophagy Reference miR-143 Cardiac progenitor ATG7 Suppress [26] miR-22 Renal tubulointerstitial fibrosis PTEN Suppress [30] miR-1 NSCLC ATG3 Suppress [31] miR-23a Inflammatory ATG12 Promote [32] miR-489 Breast cancer LAPTM4B Suppress [33] miR-26a Swine Sertoli ULK2 Suppress [34] miR-21 Nucleus pulposus PTEN Suppress [35] miR-665-3p Intestinal ischemia/reperfusion breast cancer ATG4B Suppress [36] miR-155-5p Cervical cancer Promote [37] miR-34a-5p Steatotic hepatocytes Suppress [38] miR-143 Crohn’s disease ATG2B Suppress [39] miR-124 Cholangiocarcinoma EZH2, STAT3 Promote [40] miR-375 Breast cancer ATG7 Suppress [41] miR-103 PC12 Suppress [37] miR-140-5p/miR-149 Osteoarthritis FUT1 Promote [42] miR-181 NSCLC Suppress [43] miR-142-3p Hepatocellular carcinoma ATG5/ATG16L1 Suppress [44] miR-214 Colorectal cancer ATG12 Suppress [45] miR-18a NSCLC IRF2 Promote [16] miR-107/103 Epidermis Suppress [46] miR-221 Colorectal cancer TP53INP1 Suppress [47] miR-93-5p Glaucoma PTEN Suppress [48] miRNAs Carcinomas Target gene Autophagy Reference miR-143 Cardiac progenitor ATG7 Suppress [26] miR-22 Renal tubulointerstitial fibrosis PTEN Suppress [30] miR-1 NSCLC ATG3 Suppress [31] miR-23a Inflammatory ATG12 Promote [32] miR-489 Breast cancer LAPTM4B Suppress [33] miR-26a Swine Sertoli ULK2 Suppress [34] miR-21 Nucleus pulposus PTEN Suppress [35] miR-665-3p Intestinal ischemia/reperfusion breast cancer ATG4B Suppress [36] miR-155-5p Cervical cancer Promote [37] miR-34a-5p Steatotic hepatocytes Suppress [38] miR-143 Crohn’s disease ATG2B Suppress [39] miR-124 Cholangiocarcinoma EZH2, STAT3 Promote [40] miR-375 Breast cancer ATG7 Suppress [41] miR-103 PC12 Suppress [37] miR-140-5p/miR-149 Osteoarthritis FUT1 Promote [42] miR-181 NSCLC Suppress [43] miR-142-3p Hepatocellular carcinoma ATG5/ATG16L1 Suppress [44] miR-214 Colorectal cancer ATG12 Suppress [45] miR-18a NSCLC IRF2 Promote [16] miR-107/103 Epidermis Suppress [46] miR-221 Colorectal cancer TP53INP1 Suppress [47] miR-93-5p Glaucoma PTEN Suppress [48] In recent years, deep sequencing has been applied to establish a database for human disease and genes regulation [49], as well as for autophagy [50]. Alessandro et al. [51] established a database for human lysosomal genes and their regulation, as well as cellular homeostasis such as membrane repair, autophagy, and protein metabolism. Giltae et al. [52] established a new alternative reference strain genome sequences which can be used in autophagy studies. Notably, Tang et al. [53] found that the cleavage and polyadenylation (CPA) complex acts as a downstream effector of TORC1 signaling to regulate alternative splicing (AS) and alternative polyadenylation (APA), in which APA and AS are synchronized to regulate autophagy, and reveal an additional layer of complexity in the regulation of starvation responses. Multiple aspects of autophagy need to be modulated in order to adapt to the multiple environments of cancer and maintain their distinct biological features. In this study, we perform a genome-wide analysis of miRNAs from rapamycin- and starvation-treated A549 cells using the miRNA sequencing technology, trying to find clues that might explain the autophagy differences between the different conditions. We found that both rapamycin and starvation could induce autophagy. miRNA-seq analysis revealed 1534 miRNAs from A549 cells treated with rapamycin or starvation. The differentially expressed miRNAs may play vital roles in the autophagy process. Through deep sequencing of the transcriptome, we screened the differential expression patterns, which may help to discover new transcripts, including new alternative isoforms and miRNAs related to autophagy. These findings can help understand the underlying mechanisms responsible for autophagy differences between medicine treatment and starvation. As those miRNAs were identified by RNA sequencing, their potential roles in the responses to starvation or rapamycin still remain unknown and need to be examined in the future. Therefore, the mechanisms of autophagy regulation still need to be further explored. Acknowledgement We acknowledge Ms Fatemeh Alsadat Jafari Sheshtamad (Mashhad University of Medical Science) for critical reading of the manuscript. All lab members are acknowledged for stimulating discussions. Funding This work was supported by the grants from the National Natural Science Foundation of China (Nos. 91543123 and 81601887). References 1 Torre LA , Bray F , Siegel RL , Ferlay J , Lortet-Tieulent J , Jemal A . Global cancer statistics, 2012 . CA Cancer J Clin 2015 , 65 : 87 – 108 . Google Scholar Crossref Search ADS PubMed 2 Jemal A , Bray F , Center MM , Ferlay J , Ward E , Forman D . Global cancer statistics . CA Cancer J Clin 2011 , 61 : 69 – 90 . Google Scholar Crossref Search ADS PubMed 3 Guz M , Rivero-Muller A , Okon E , Stenzel-Bembenek A , Polberg K , Slomka M , Stepulak A . MicroRNAs-role in lung cancer . Dis Markers 2014 , 2014 : 218169 . Google Scholar Crossref Search ADS PubMed 4 Bartel DP . MicroRNAs: genomics, biogenesis, mechanism, and function . Cell 2004 , 116 : 281 – 297 . Google Scholar Crossref Search ADS PubMed 5 Mowers EE , Sharifi MN , Macleod KF . Autophagy in cancer metastasis . Oncogene 2017 , 36 : 1619 – 1630 . Google Scholar Crossref Search ADS PubMed 6 Weckman A , Rotondo F , Di Ieva A , Syro LV , Butz H , Cusimano MD , Kovacs K . Autophagy in endocrine tumors . Endocr Relat Cancer 2015 , 22 : R205 – R218 . Google Scholar Crossref Search ADS PubMed 7 Umezawa K , Kojima I , Simizu S , Lin Y , Fukatsu H , Koide N , Nakade Y , et al. Therapeutic activity of plant-derived alkaloid conophylline on metabolic syndrome and neurodegenerative disease models . Hum Cell 2018 , 31 : 95 – 101 . Google Scholar Crossref Search ADS PubMed 8 Huang T , Kim CK , Alvarez AA , Pangeni RP , Wan X , Song X , Shi T , et al. MST4 phosphorylation of ATG4B regulates autophagic activity, tumorigenicity, and radioresistance in glioblastoma . Cancer Cell 2017 , 32 : 840 – 855 e848 . Google Scholar Crossref Search ADS PubMed 9 Paglin S , Lee NY , Nakar C , Fitzgerald M , Plotkin J , Deuel B , Hackett N , et al. Rapamycin-sensitive pathway regulates mitochondrial membrane potential, autophagy, and survival in irradiated MCF-7 cells . Cancer Res 2005 , 65 : 11061 – 11070 . Google Scholar Crossref Search ADS PubMed 10 Zhao C , Liu T , Zhou T , Fu Y , Zheng H , Ding Y , Zhang K , et al. The rodent malaria liver stage survives in the rapamycin-induced autophagosome of infected Hepa1-6 cells . Sci. Rep 2016 , 6 : 38170 . Google Scholar Crossref Search ADS PubMed 11 Xu L , Wang Y , Liu Q , Luo H , Zhong X , Li Y . Role of autophagy in the radiosensitivity of human lung adenocarcinoma A549 cells . Zhongguo Fei Ai Za Zhi 2016 , 19 : 799 – 804 . Google Scholar PubMed 12 Li Y , Liu F , Wang Y , Li D , Guo F , Xu L , Zeng Z , et al. Rapamycin-induced autophagy sensitizes A549 cells to radiation associated with DNA damage repair inhibition . Thorac Cancer 2016 , 7 : 379 – 386 . Google Scholar Crossref Search ADS PubMed 13 Yao D , Jiang Y , Gao S , Shang L , Zhao Y , Huang J , Wang J , et al. Deconvoluting the complexity of microRNAs in autophagy to improve potential cancer therapy . Cell Prolif 2016 , 49 : 541 – 553 . Google Scholar Crossref Search ADS PubMed 14 Korkmaz G , le Sage C , Tekirdag KA , Agami R , Gozuacik D . miR-376b controls starvation and mTOR inhibition-related autophagy by targeting ATG4C and BECN1 . Autophagy 2012 , 8 : 165 – 176 . Google Scholar Crossref Search ADS PubMed 15 Wei JL , Li YC , Ma ZL , Jin YX . MiR-181a-5p promotes anoikis by suppressing autophagy during detachment induction in the mammary epithelial cell line MCF10A . Protein Cell 2016 , 7 : 305 – 309 . Google Scholar Crossref Search ADS PubMed 16 Liang C , Zhang X , Wang HM , Liu XM , Zhang XJ , Zheng B , Qian GR , et al. MicroRNA-18a-5p functions as an oncogene by directly targeting IRF2 in lung cancer . Cell Death Dis 2017 , 8 : e2764 . Google Scholar Crossref Search ADS PubMed 17 Li YL , Liu XM , Zhang CY , Zhou JB , Shao Y , Liang C , Wang HM , et al. MicroRNA-34a/EGFR axis plays pivotal roles in lung tumorigenesis . Oncogenesis 2017 , 6 : e372 . Google Scholar Crossref Search ADS PubMed 18 Ma ZL , Hou PP , Li YL , Wang DT , Yuan TW , Wei JL , Zhao BT , et al. MicroRNA-34a inhibits the proliferation and promotes the apoptosis of non-small cell lung cancer H1299 cell line by targeting TGFbetaR2 . Tumor Biol 2015 , 36 : 2481 – 2490 . Google Scholar Crossref Search ADS 19 Spang N , Feldmann A , Huesmann H , Bekbulat F , Schmitt V , Hiebel C , Koziollek-Drechsler I , et al. RAB3GAP1 and RAB3GAP2 modulate basal and rapamycin-induced autophagy . Autophagy 2014 , 10 : 2297 – 2309 . Google Scholar Crossref Search ADS PubMed 20 Daniels BH , McComb RD , Mobley BC , Gultekin SH , Lee HS , Margeta M . LC3 and p62 as diagnostic markers of drug-induced autophagic vacuolar cardiomyopathy: a study of 3 cases . Am J Surg Pathol 2013 , 37 : 1014 – 1021 . Google Scholar Crossref Search ADS PubMed 21 Levine B , Kroemer G . Autophagy in the pathogenesis of disease . Cell 2008 , 132 : 27 – 42 . Google Scholar Crossref Search ADS PubMed 22 Gibbings D , Mostowy S , Voinnet O . Autophagy selectively regulates miRNA homeostasis . Autophagy 2013 , 9 : 781 – 783 . Google Scholar Crossref Search ADS PubMed 23 Guo F , Jiao D , Sui GQ , Sun LN , Gao YJ , Fu QF , Jin CX . Anticancer effect of YWHAZ silencing via inducing apoptosis and autophagy in gastric cancer cells . Neoplasma 2018 , 65 : 693 – 700 . Google Scholar Crossref Search ADS PubMed 24 Ye Z , Fang B , Pan J , Zhang N , Huang J , Xie C , Lou T , et al. miR-138 suppresses the proliferation, metastasis and autophagy of non-small cell lung cancer by targeting Sirt1 . Oncol Rep 2017 , 37 : 3244 – 3252 . Google Scholar Crossref Search ADS PubMed 25 Tong Y , Zhang G , Li Y , Xu J , Yuan J , Zhang B , Hu T , et al. Corilagin inhibits breast cancer growth via reactive oxygen species-dependent apoptosis and autophagy . J Cell Mol Med 2018 , 8 : 3795 – 3807 . Google Scholar Crossref Search ADS 26 Ma W , Ding F , Wang X , Huang Q , Zhang L , Bi C , Hua B , et al. By targeting Atg7 microRNA-143 mediates oxidative stress-induced autophagy of c-Kit(+) mouse cardiac progenitor cells . EBioMedicine 2018 , 32 : 182 – 191 . Google Scholar Crossref Search ADS PubMed 27 Lan SH , Wu SY , Zuchini R , Lin XZ , Su IJ , Tsai TF , Lin YJ , et al. Autophagy-preferential degradation of MIR224 participates in hepatocellular carcinoma tumorigenesis . Autophagy 2014 , 10 : 1687 – 1689 . Google Scholar Crossref Search ADS PubMed 28 Tekirdag KA , Korkmaz G , Ozturk DG , Agami R , Gozuacik D . MIR181A regulates starvation- and rapamycin-induced autophagy through targeting of ATG5 . Autophagy 2013 , 9 : 374 – 385 . Google Scholar Crossref Search ADS PubMed 29 Li X , Prescott M , Adler B , Boyce JD , Devenish RJ . Beclin 1 is required for starvation-enhanced, but not rapamycin-enhanced, LC3-associated phagocytosis of Burkholderia pseudomallei in RAW 264.7 cells . Infect Immun 2013 , 81 : 271 – 277 . Google Scholar Crossref Search ADS PubMed 30 Zhang Y , Zhao S , Wu D , Liu X , Shi M , Wang Y , Zhang F , et al. MicroRNA-22 promotes renal tubulointerstitial fibrosis by targeting PTEN and suppressing autophagy in diabetic nephropathy . J Diabetes Res 2018 , 2018 : 4728645 . Google Scholar PubMed 31 Hua L , Zhu G , Wei J . MicroRNA-1 overexpression increases chemosensitivity of non-small cell lung cancer cells by inhibiting autophagy related 3-mediated autophagy . Cell Biol Int 2018 , 42 : 1240 – 1249 . Google Scholar Crossref Search ADS 32 Si X , Cao D , Chen J , Nie Y , Jiang Z , Chen MY , Wu JF , et al. miR23a downregulation modulates the inflammatory response by targeting ATG12mediated autophagy . Mol Med Rep 2018 , 18 : 1524 – 1530 . Google Scholar PubMed 33 Soni M , Patel Y , Markoutsa E , Jie C , Liu S , Xu P , Chen H . Autophagy, cell viability and chemo-resistance are regulated by miR-489 in Breast cancer . Mol Cancer Res 2018 , 16 : 1348 – 1360 . Google Scholar Crossref Search ADS PubMed 34 Ran M , Li Z , Cao R , Weng B , Peng F , He C , Chen B . miR-26a suppresses autophagy in swine Sertoli cells by targeting ULK2 . Reprod Domest Anim 2018 , 53 : 864 – 871 . Google Scholar Crossref Search ADS PubMed 35 Wang WJ , Yang W , Ouyang ZH , Xue JB , Li XL , Zhang J , He WS , et al. MiR-21 promotes ECM degradation through inhibiting autophagy via the PTEN/akt/mTOR signaling pathway in human degenerated NP cells . Biomed Pharmacother 2018 , 99 : 725 – 734 . Google Scholar Crossref Search ADS PubMed 36 Li Z , Wang G , Feng D , Zu G , Li Y , Shi X , Zhao Y , et al. Targeting the miR-665-3p-ATG4B-autophagy axis relieves inflammation and apoptosis in intestinal ischemia/reperfusion . Cell Death Dis 2018 , 9 : 483 . Google Scholar Crossref Search ADS PubMed 37 Wang F , Shan S , Huo Y , Xie Z , Fang Y , Qi Z , Chen F , et al. MiR-155-5p inhibits PDK1 and promotes autophagy via the mTOR pathway in cervical cancer . Int J Biochem Cell Biol 2018 , 99 : 91 – 99 . Google Scholar Crossref Search ADS PubMed 38 Li C , Wang K , Guo L , Sun H , Huang H , Lin X , Li Q . Inhibition of miR-34a-5p alleviates hypoxia-reoxygenation injury by enhancing autophagy in steatotic hepatocytes . Biol Open 2018 , 7 : bio033290 . Google Scholar Crossref Search ADS PubMed 39 Lin XT , Zheng XB , Fan DJ , Yao QQ , Hu JC , Lian L , Wu XJ , et al. MicroRNA-143 targets ATG2B to inhibit autophagy and increase inflammatory responses in Crohn’s disease . Inflamm Bowel Dis 2018 , 24 : 781 – 791 . Google Scholar Crossref Search ADS PubMed 40 Ma J , Weng L , Wang Z , Jia Y , Liu B , Wu S , Cao Y , et al. MiR-124 induces autophagy-related cell death in cholangiocarcinoma cells through direct targeting of the EZH2-STAT3 signaling axis . Exp Cell Res 2018 , 366 : 103 – 113 . Google Scholar Crossref Search ADS PubMed 41 Liu L , Shen W , Zhu Z , Lin J , Fang Q , Ruan Y , Zhao H . Combined inhibition of EGFR and c-ABL suppresses the growth of fulvestrant-resistant breast cancer cells through miR-375-autophagy axis . Biochem Biophys Res Commun 2018 , 498 : 559 – 565 . Google Scholar Crossref Search ADS PubMed 42 Wang Z , Hu J , Pan Y , Shan Y , Jiang L , Qi X , Jia L . miR-140-5p/miR-149 affects chondrocyte proliferation, apoptosis, and autophagy by targeting FUT1 in osteoarthritis . Inflammation 2018 , 41 : 959 – 971 . Google Scholar Crossref Search ADS PubMed 43 Liu J , Xing Y , Rong L . miR-181 regulates cisplatin-resistant non-small cell lung cancer via downregulation of autophagy through the PTEN/PI3K/AKT pathway . Oncol Rep 2018 , 39 : 1631 – 1639 . Google Scholar PubMed 44 Zhang K , Chen J , Zhou H , Chen Y , Zhi Y , Zhang B , Chen L , et al. PU.1/microRNA-142-3p targets ATG5/ATG16L1 to inactivate autophagy and sensitize hepatocellular carcinoma cells to sorafenib . Cell Death Dis 2018 , 9 : 312 . Google Scholar Crossref Search ADS PubMed 45 Hu JL , He GY , Lan XL , Zeng ZC , Guan J , Ding Y , Qian XL , et al. Inhibition of ATG12-mediated autophagy by miR-214 enhances radiosensitivity in colorectal cancer . Oncogenesis 2018 , 7 : 16 . Google Scholar Crossref Search ADS PubMed 46 Wang S , Kobeissi A , Dong Y , Kaplan N , Yang W , He C , Zeng K , et al. MicroRNAs-103/107 regulate autophagy in the epidermis . J Invest Dermatol 2018 , 138 : 1481 – 1490 . Google Scholar Crossref Search ADS PubMed 47 Liao D , Li T , Ye C , Zeng L , Li H , Pu X , Ding C , et al. miR-221 inhibits autophagy and targets TP53INP1 in colorectal cancer cells . Exp Ther Med 2018 , 15 : 1712 – 1717 . Google Scholar PubMed 48 Li R , Jin Y , Li Q , Sun X , Zhu H , Cui H . MiR-93-5p targeting PTEN regulates the NMDA-induced autophagy of retinal ganglion cells via AKT/mTOR pathway in glaucoma . Biomed Pharmacother 2018 , 100 : 1 – 7 . Google Scholar Crossref Search ADS PubMed 49 Daugaard I , Hansen TB . Biogenesis and function of ago-associated RNAs . Trends Genet 2017 , 33 : 208 – 219 . Google Scholar Crossref Search ADS PubMed 50 Feuermann M , Gaudet P , Mi H , Lewis SE , Thomas PD . Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes . Database (Oxford) 2016 , 2016 , 1 – 11 . Google Scholar Crossref Search ADS 51 Brozzi A , Urbanelli L , Germain PL , Magini A , Emiliani C . hLGDB: a database of human lysosomal genes and their regulation . Database (Oxford) 2013 , 2013 : bat024 . Google Scholar Crossref Search ADS PubMed 52 Song G , Balakrishnan R , Binkley G , Costanzo MC , Dalusag K , Demeter J , Engel S , et al. Integration of new alternative reference strain genome sequences into the Saccharomyces genome database . Database (Oxford) 2016 , 2016 , 1 – 7 . Google Scholar Crossref Search ADS 53 Tang HW , Hu Y , Chen CL , Xia B , Zirin J , Yuan M , Asara JM , et al. The TORC1-regulated CPA complex rewires an RNA processing network to drive autophagy and metabolic reprogramming . Cell Metab 2018 , 27 : 1040 – 1054 e1048 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2019. Published by Oxford University Press on behalf of the Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Rapamycin- and starvation-induced autophagy are associated with miRNA dysregulation in A549 cells
JO - Acta Biochimica et Biophysica Sinica
DO - 10.1093/abbs/gmz022
DA - 2019-04-01
UR - https://www.deepdyve.com/lp/oxford-university-press/rapamycin-and-starvation-induced-autophagy-are-associated-with-mirna-4OOQGXFlRR
SP - 393
VL - 51
IS - 4
DP - DeepDyve
ER -