Loading [Contrib]/a11y/accessibility-menu.js
Research article Special Issues

Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke


  • Background 

    Epigenetic changes, such as DNA methylation and miRNA-target gene mechanisms, have recently emerged as key provokers in Ischemic stroke (IS) onset. However, cellular and molecular events harboring these epigenetic alterations are poorly understood. Therefore, the present study aimed to explore the potential biomarkers and therapeutic targets for IS.

    Methods 

    miRNAs, mRNAs and DNA methylation datasets of IS were derived from the GEO database and normalized by PCA sample analysis. Differentially expressed genes (DEGs) were identified, and GO and KEGG enrichment analyses were performed. The overlapped genes were utilized to construct a protein-protein interaction network (PPI). Meanwhile, differentially expressed mRNAs and miRNAs interaction pairs were obtained from the miRDB, TargetScan, miRanda, miRMap and miTarBase databases. We constructed differential miRNA-target gene regulatory networks based on mRNA-miRNA interactions.

    Results 

    A total of 27 up-regulated and 15 down-regulated differential miRNAs were identified. Dataset analysis identified 1053 and 132 up-regulated and 1294 and 9068 down-regulated differentially expressed genes in the GSE16561 and GSE140275 datasets, respectively. Moreover, 9301 hypermethylated and 3356 hypomethylated differentially methylated sites were also identified. Moreover, DEGs were enriched in terms related to translation, peptide biosynthesis, gene expression, autophagy, Th1 and Th2 cell differentiation, primary immunodeficiency, oxidative phosphorylation and T cell receptor signaling pathway. MRPS9, MRPL22, MRPL32 and RPS15 were identified as hub genes. Finally, a differential miRNA-target gene regulatory network was constructed.

    Conclusions 

    RPS15, along with hsa-miR-363-3p and hsa-miR-320e have been identified in the differential DNA methylation protein interaction network and miRNA-target gene regulatory network, respectively. These findings strongly posit the differentially expressed miRNAs as potential biomarkers to improve ischemic stroke diagnosis and prognosis.

    Citation: Ming-Xi Zhu, Tian-Yang Zhao, Yan Li. Insight into the mechanism of DNA methylation and miRNA-mRNA regulatory network in ischemic stroke[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10264-10283. doi: 10.3934/mbe.2023450

    Related Papers:

    [1] Saboura Haghighi, Hamid Reza Goli . High prevalence of blaVEB, blaGES and blaPER genes in beta-lactam resistant clinical isolates of Pseudomonas aeruginosa. AIMS Microbiology, 2022, 8(2): 153-166. doi: 10.3934/microbiol.2022013
    [2] Chioma Lilian Ozoaduche, Katalin Posta, Balázs Libisch, Ferenc Olasz . Acquired antibiotic resistance of Pseudomonas spp., Escherichia coli and Acinetobacter spp. in the Western Balkans and Hungary with a One Health outlook. AIMS Microbiology, 2025, 11(2): 436-461. doi: 10.3934/microbiol.2025020
    [3] Ogueri Nwaiwu, Chiugo Claret Aduba . An in silico analysis of acquired antimicrobial resistance genes in Aeromonas plasmids. AIMS Microbiology, 2020, 6(1): 75-91. doi: 10.3934/microbiol.2020005
    [4] Alaa Fathalla, Amal Abd el-mageed . Salt tolerance enhancement Of wheat (Triticum Asativium L) genotypes by selected plant growth promoting bacteria. AIMS Microbiology, 2020, 6(3): 250-271. doi: 10.3934/microbiol.2020016
    [5] Mohammad Abu-Sini, Mohammad A. Al-Kafaween, Rania M. Al-Groom, Abu Bakar Mohd Hilmi . Comparative in vitro activity of various antibiotic against planktonic and biofilm and the gene expression profile in Pseudomonas aeruginosa. AIMS Microbiology, 2023, 9(2): 313-331. doi: 10.3934/microbiol.2023017
    [6] Kholoud Baraka, Rania Abozahra, Eman Khalaf, Mahmoud Elsayed Bennaya, Sarah M. Abdelhamid . Repurposing of paroxetine and fluoxetine for their antibacterial effects against clinical Pseudomonas aeruginosa isolates in Egypt. AIMS Microbiology, 2025, 11(1): 126-149. doi: 10.3934/microbiol.2025007
    [7] Rana Abdel Fattah Abdel Fattah, Fatma El zaharaa Youssef Fathy, Tahany Abdel Hamed Mohamed, Marwa Shabban Elsayed . Effect of chitosan nanoparticles on quorum sensing-controlled virulence factors and expression of LasI and RhlI genes among Pseudomonas aeruginosa clinical isolates. AIMS Microbiology, 2021, 7(4): 415-430. doi: 10.3934/microbiol.2021025
    [8] Taish Ramkisson, Diane Rip . Carbapenem resistance in Enterobacterales from agricultural, environmental and clinical origins: South Africa in a global context. AIMS Microbiology, 2023, 9(4): 668-691. doi: 10.3934/microbiol.2023034
    [9] Arsenio M. Fialho, Nuno Bernardes, Ananda M Chakrabarty . Exploring the anticancer potential of the bacterial protein azurin. AIMS Microbiology, 2016, 2(3): 292-303. doi: 10.3934/microbiol.2016.3.292
    [10] Amira ElBaradei, Dalia Ali Maharem, Ola Kader, Mustafa Kareem Ghareeb, Iman S. Naga . Fecal carriage of ESBL-producing Escherichia coli in Egyptian patients admitted to the Medical Research Institute hospital, Alexandria University. AIMS Microbiology, 2020, 6(4): 422-433. doi: 10.3934/microbiol.2020025
  • Background 

    Epigenetic changes, such as DNA methylation and miRNA-target gene mechanisms, have recently emerged as key provokers in Ischemic stroke (IS) onset. However, cellular and molecular events harboring these epigenetic alterations are poorly understood. Therefore, the present study aimed to explore the potential biomarkers and therapeutic targets for IS.

    Methods 

    miRNAs, mRNAs and DNA methylation datasets of IS were derived from the GEO database and normalized by PCA sample analysis. Differentially expressed genes (DEGs) were identified, and GO and KEGG enrichment analyses were performed. The overlapped genes were utilized to construct a protein-protein interaction network (PPI). Meanwhile, differentially expressed mRNAs and miRNAs interaction pairs were obtained from the miRDB, TargetScan, miRanda, miRMap and miTarBase databases. We constructed differential miRNA-target gene regulatory networks based on mRNA-miRNA interactions.

    Results 

    A total of 27 up-regulated and 15 down-regulated differential miRNAs were identified. Dataset analysis identified 1053 and 132 up-regulated and 1294 and 9068 down-regulated differentially expressed genes in the GSE16561 and GSE140275 datasets, respectively. Moreover, 9301 hypermethylated and 3356 hypomethylated differentially methylated sites were also identified. Moreover, DEGs were enriched in terms related to translation, peptide biosynthesis, gene expression, autophagy, Th1 and Th2 cell differentiation, primary immunodeficiency, oxidative phosphorylation and T cell receptor signaling pathway. MRPS9, MRPL22, MRPL32 and RPS15 were identified as hub genes. Finally, a differential miRNA-target gene regulatory network was constructed.

    Conclusions 

    RPS15, along with hsa-miR-363-3p and hsa-miR-320e have been identified in the differential DNA methylation protein interaction network and miRNA-target gene regulatory network, respectively. These findings strongly posit the differentially expressed miRNAs as potential biomarkers to improve ischemic stroke diagnosis and prognosis.



    Pseudomonas aeruginosa is a significant microorganism involved in urinary, bloodstream, pulmonary, soft tissue and surgical site infections [1],[2]. Antibiotic resistance is one of the most pressing problems in public health and will remain threatening to modern medicine in the coming decades. Because of the increasing abuse of antibiotics in hospitals and the community, some widely used antibiotics are losing their function, and scientists and doctors must develop new antibiotics to overcome this problem. [3],[4]. Metallo-β-lactamaseproducing (MBL) strains to hydrolyze the β-lactam ring of the drug compound, thereby inactivating them. In contrast to serine β-lactamases, MBLs use at least one but more commonly two Zn2+ ions in their active site to catalyze the hydrolysis of β-lactam rings [5],[6]. There are various methods for identifying MBL and NDM-producing strains, which fall into two phenotypic and genotypic groups. Usually, phenotypic methods have low specificity and low speed, and error results [7],[8]. Therefore, it is necessary to use molecular methods along with phenotypic methods. High-resolution melting (HRMA) analysis is one of the most sensitive and precise molecular methods based on real-time PCR [9],[10].

    HRMA is used to characterize bacterial DNA samples according to their dissociation behavior as they transition from double-strand DNA to single-strand DNA with increasing temperature and fluorescence detection [8],[11]. The HRMA is entirely precise warming of the amplicon DNA from around 50 °C up to around 95 °C. During this process, the amplicon's melting temperature is reached, and the two strands of DNA separate or “melt” apart. The concept of HRMA is to monitor this process happening with real-time PCR [12]. This rationale is achieved by using intercalating dyes. The intercalating dye binds explicitly to double-strand DNA and forms the stable fluorescent, and thus one can monitor the relative quantity of the product during DNA amplification in real-time PCR [13]. However, HRMA takes it one step further in its ability to capture much more detail. It has an increased resolving power, as melting curves from different amplicons may be differentiated based on the melt curve's shape even when the melt temperature values are the same [14],[15].

    HRMA has the potential to be a powerful tool in the clinical microbiology laboratory, providing rapid detection of genetic determinants conferring antibiotic resistance to complement current phenotypic antimicrobial susceptibility testing methods [16],[17]. These methods are labor-intensive, expensive and require unique expertise, and the results are difficult to interpret [18]. An accurate, rapid and cost-effective typing scheme is urgently needed for active surveillance and epidemiological investigations [19]. HRMA typing is a compassionate, rapid and cost-effective option for detection purposes [20],[21]. It can perform highly accurate genotyping to a hefty quantity of samples in a short amount of time [8],[22]. HRMA also is a straightforward technology that can perform both the PCR analysis and HRMA in one instrument [20]. This reduces extra expenses, saves time and creates a simpler workflow [16]. It is only necessary to create the PCR reaction volume for each sample to be analyzed, and it eliminates the need for solvents and electrophoresis gels [22].

    Therefore, we try to extend the application of the HRMA assay to this area to help detect the antibiotic resistance in different strains of NDM producing P. aeruginosa. We modified the multiplex HRMA to amplify both the bacteria NDM producing gene and MBL producing gene and analyze its melting curve.

    Subcultures of P. aeruginosa ATCC 15442, P. aeruginosa PAO-1, and P. aeruginosa ATCC 27853 were used from Hamadan Medical University, Microbiology Department microbial bank. Reference strains were cultivated at 37 °C for 24 h in cetrimide agar (Merck, Germany). All strains were kept at −20 °C in TSB containing 20% glycerol. The Ethics Committee approved this study of Hamadan University of Medical Sciences (Code No: IR.UMSHA.REC.1398.573).

    P. aeruginosa DNA extraction was performed using a DNA extraction kit (Qiagen, Germany); the steps were followed according to the kit protocol. DNA concentration was determined using a spectrophotometer (Nanodrop-200, Hangzhou Allsheng Instruments Co., Ltd., China). In this study, the sequencing method used was the dideoxy chain-terminating Sanger method [23].

    Primer sequences were initially set up as outlined in previous studies [24][27] (Table 1). For sensitivity and specificity of primers, nine-fold serial dilutions of 0.5 McFarland DNA (1.5 × 108 CFU/mL) were made (1:1–1, 1:1–2, 1:1–3, 1:1–4, 1:1–5, 1:1–6, 1:1–7, 1:1–8). Serial dilution real-time PCR tested primers' efficiencies for both target genes and reference genes. Standard curves were constructed by the Ct (y-axis) versus log DNA dilution (x-axis). The primer efficiency (E) of one cycle in the exponential phase was calculated according to the equation: E = 10−(1/slope)−1 × 100 [28].

    Briefly, 2 µL (0.5 µM) of each primer, 2 µL of DNA template, 4 µL of EvaGreen, and made up to a final volume of 20 µL using ddH2O. Real-time PCR reactions were performed on the ABI real-time machine (ABI StepOnePlus, USA). The thermal cycles were set for reverse transcription steps (55 °C for 5 min, 95 °C for 10 min, 95 °C for 20 s) followed by PCR steps: 95 °C for 15 s, 59 °C for 30 s, repeated for 40 cycles. The slope value was calculated from serial dilutions for each gene, which was then used to determine the reaction's efficiency [12].

    Table 1.  Oligonucleotide sequences used in this study.
    Target Primer Name Sequence of Primers Melting Tm (±0.5 °C) Accession Number Primer Location Product Size (bp) Ref
    NDM-1 N-1 F: GACCGCCCAGATCCTCAA
    R: CGCGACCGGCAGGTT
    89.57 MN193055.1 71–122 55 [24]
    N-2 F: TTGGCCTTGCTGTCCTTG
    R: ACACCAGTGACAATATCACCG
    76.92 MH168506.2 630–586 85 [25]
    N-3 F: GCGCAACACAGCCTGACTTT
    R: CAGCCACCAAAAGCGATGTC
    82.97 MK371546.1 298–452 155 [23]

    MBL blaSIM F: TACAAGGGATTCGGCATCG
    R: TAATGGCCTGTTCCCATGTG
    85.35 KX452682.1 1–570 570 [27]
    blaVIM F: TCTCCACGCACTTTCATGAC
    R: GTGGGAATCTCGTTCCCCTC
    84.56 NG_068039.1 332–455 124 [26]
    blaSPM F: AAAATCTGGGTACGCAAACG
    R: ACATTATCCGCTGGAACAGG
    86.62 KX452683.1 1–271 271 [27]

     | Show Table
    DownLoad: CSV

    The efficiency and the analytical sensitivity of the HRMA-PCR were evaluated by triplicate testing of 9-fold serial dilution series of each of the three reference strains. The Applied Biosystems StepOnePlus real-time PCR system was used to amplify and detect products. The reaction mix was prepared using the following components for each of the samples: 4 µL of Master Mix HRMA (HOT FIREPol EvaGreen HRMA Mix), 1 µM of each respective primer and 12 µL of DMSO (Sigma-Aldrich, USA). The following cycle parameters were used: 2 min at 50 °C, 10 min at 95 °C. Moreover, 40 cycles with denaturing for 15 s at 95 °C and by annealing/elongation for 1 min at 60 °C. Melting curves were generated after each run to confirm a single PCR product (from 60 °C to 95 °C, increasing 1 °C/3 s).

    Data analysis was performed using ABI Thermo Fisher software (release 2018, version 3.0.2) and BioEdit 7.4 software (Caredata, Inc., USA). Normalized and difference plots were generated.

    After 9-fold dilutions, a high CT was observed in the 100 CFU/mL and low CT in the 108 CFU/mL. Moreover, increasing CT values were identified as the inhibitor binding to the DNA, as such binding will reduce the amount of template available for amplification. More comprehensive CT value ranges indicated more binding; likewise, smaller ranges corresponded to less interaction between DNA and inhibitor. The CT values for these cell densities were within the 9 to 40 cycle range in the amplification process, while higher DNA concentrations appeared within 9 to 31 cycles. As seen in Figures 1 and 2, relative to a linear range of each standard curve, melting peaks can be seen for many lower DNA concentrations; however, the concentrations could not be quantified. Therefore, the actual quantitative, linear portions of the calibration curves did not extend as low as the detection limit. Melting curves displayed a single melting Tm: 89.57 °C for the N-1 gene, 76.92 °C for the N-2 gene, 82.97 °C for the N-3 gene, 84.56 °C for the blaVIM gene, 86.62 °C for the blaSIM gene, and 85.35 °C for the blaSPM gene (Figures 1 and 2). Samples containing DNA exhibited positive real-time PCR amplification, and negative controls failed to show amplification. Also, serial dilutions of positive-control DNA amplification curves showed Ct values inversely related to template DNA concentration (Figure 3).

    Figure 1.  Analytical sensitivity of real-time PCR and examples of optimization of primer pairs based on melting curve analysis for NDM primers to detect NDM producing P. aeruginosa strains. The melting curves for each primer pair were investigated: (left) N-1 gene with a melting point of 89.53 ± 0.5 °C, (middle) N-2 gene with a melting point of 76.92 ± 0.5 °C, (right) N-3 gene with a melting point 82.97 ± 0.5 °C. The mean of a: 108; b: 107; c: 106; d: 105; e: 104; f: 103; g: 102; h: 101 and i: 100 CFU/mL of DNA dilutions. Bold black horizontal lines represent the cycle threshold of real-time PCR. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step.
    Figure 2.  Analytical sensitivity of real-time PCR and examples of optimization of primer pairs based on melting curve analysis for MBL primers to detect NDM producing P. aeruginosa strains. The melting curves for each primer pair were investigated: (left) blaVIM gene with a melting point of 84.56 ± 0.5 °C, (middle) blaSPM gene with a melting point of 85.35 ± 0.5 °C, (right) blaSIM gene a melting point of 86.62 ± 0.5 °C. The mean of a: 108; b: 107; c: 106; d: 105; e: 104; f: 103; g: 102; h: 101 and i: 100 CFU/mL of DNA dilutions. Bold black horizontal lines represent the cycle threshold of real-time PCR. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step.
    Figure 3.  Melting curve analysis and analytical specificity of real-time PCR for NDM primers (A) and MBL primers (B) used to detect NDM producing P. aeruginosa strains: (a) blank tube, (b) P. aeruginosa PAO-1 and (c) P. aeruginosa ATCC 27853. One peak with a shoulder corresponds to genomic DNA amplification; no peak corresponds to no amplification. EvaGreen color and single-tube reactions were used in this test. Also, real-time PCR was performed as a single step. A 0.5-McFarland concentration (1.5 x 108 CFU/mL of DNA) was used to determine primer specificity.

    Reaction efficiencies were found to be within the range of 3 to 3.5 when calculated from the standard curves using the ABI Thermo Fisher analysis software (Version 2.3.2) with a formula of E = 10(−1/slope)−1. For the N-1, N-2 and blaVIM primer set, the reaction efficiency reached a value slightly greater than 3, at 3.2, which would suggest the efficiency of 101%. Efficiencies greater than 100% can be obtained. All the investigated dilutions showed low efficiencies: N-3, E = 98.8%; blaSMP, E = 95.588%; blaSIM, E = 96.493 (Figures 1 and 2).

    For the N-1 and blaVIM primer set, the linear range was determined to extend as low as 100 CFU/mL, N-2 was 103 CFU/mL, N-3 was 104 CFU/mL, blaSMP was 102 CFU/mL, and blaSIM was 105 CFU/mL, as indicated by the lowest DNA concentration value on each of the standard curves. Points that caused the curves to deviate from linearity (mostly those with lower concentrations) were excluded (Figures 1 and 2).

    Fluorescence data were analyzed using the tools for HRMA incorporated in the ABI Thermo Fisher analysis software. HRMA PCR amplification curves of samples analyzed for the presence of NDM producing P. aeruginosa are shown in Figures 3, 4, and 5. Difference plots of normalized data show the differences in fluorescence between each sample of DNA. Derivative plots display the rate of fluorescence change; the peak indicates the melting temperature of a sample. All plots displayed a single melting domain, typically between 87.07 °C–87.57 °C for the N-1 gene, 76.42 °C–76.92 °C for the N-2 gene, 82.47 °C–82.97 °C for the N-3 gene, 84.06 °C–84.56 °C for the blaVIM gene, 86.12 °C–86.62 °C for the blaSIM gene and 85.30 °C–85.80 °C for the blaSPM gene, following different product sizes.

    The results of this representative experiment show that all samples containing P. aeruginosa DNA had measurable amplification, as detected by exponential fluorescence (Figure 3), and all the DNA dilutions of NDM producing P. aeruginosa were identified (dilution of 108 to 100 CFU/mL). The N-1 and blaVIM genes in NDM producing P. aeruginosa were detected in all dilutions of DNA. Moreover, N-2, N-3, blaSPM and blaSIM primers can be able to detect bacterial DNA in dilutions of 103 CFU/mL, 104 CFU/mL, 102 CFU/mL and 105 CFU/mL, respectively (Figures 4 and 5).

    Figure 4.  HRMA graphs corresponding to one high-resolution melting analysis of a subset of NDM producing P. aeruginosa strains by N-1 (A), N-2 (B) and N-3 (C) genes. DNA samples from all the dilutions involved in this study were prepared and amplified successfully using the EvaGreen dye-based method in the ABI instrument. Primers' specific melting peaks (Tm) were obtained via HRMA, allowing the differentiation of all investigated β-lactamase enzymes. Due to the positively saturating EvaGreen dye and the HRMA, the resolution accuracy was ±0.1–0.5 °C.
    Figure 5.  HRMA graphs corresponding to one high-resolution melting analysis of a subset of NDM producing P. aeruginosa strain by blaVIM (A), blaSIM (B) and blaSPM (C) genes. DNA samples from all the dilutions involved in this study were prepared and amplified successfully using the EvaGreen dye-based method in the ABI instrument. Primers' specific melting peaks (Tm) were obtained via HRMA, allowing the differentiation of all investigated β-lactamase enzymes. Due to the positively saturating EvaGreen dye and the HRMA, the resolution accuracy was ±0.1 °C–0.5 °C.

    The software automatically analyzed the raw melting curve data and set the starting (pre-melt) and ending (post-melt) fluorescence signals of all data to actual values to aid interpretation and analysis (Figures 4 and 5). The cursors for these two points have defaulted to the ends of the curve. However, these regions were manually adjusted to encompass a representative baseline for the pre-melt and post-melt phases. Widening the normalization regions into the melt phase was avoided to ensure that curves normalize effectively. Moreover, we performed a melt curve analysis of HRMA PCR samples to assess the amplicon's specificity. The results of the HRMA showed a very similar melt peak for all serial dilutions of P. aeruginosa.

    Based on the current study, the efficiency of genes was at least 99.99%, the r2 was >0.99.99, and melt curves yielded single peaks. Interestingly, the Ct vs. DNA relationship's slope varied little across the nine-fold dilutions tested, ranging from −3.589 to −3.955. According to previous studies, this can be justified because short fragments bind less fluorescent and are compensated by a higher primer concentration [29],[30]. However, sometimes the peak height of the short amplicon increases in a different replicate. This problem gets worse in the MCA, such that sometimes we lost the long amplicon even at the primer ratio of 1:1. Furthermore, these results agree with Mentasti et al. [5].

    Moreover, three blaNDM-1 primers with different amplicon length and MBL primers were used to detect NDM producing P. aeruginosa strains. These results indicated that the primers' specificity, with a 0.5 °C error range, could detect NDM producing P. aeruginosa. Andini et al. showed that an accurate analysis of the melting curve could play a significant role in the diagnosis [31]. Ashrafi et al. found that, to obtain the best performance in sophisticated methods such as HRMA, the DNA's melting temperature must be monitored in various dilutions to obtain accurate sensitivity and specificity [21]. Tahmasebi et al. also confirmed that efficiency is probably due to the shorter length of primers' products, which enabled better amplification in PCR [9].

    Based on the current study, Figures 1 and 2 showed that the highest analytical sensitivity and specificity were reported for the detection of antibiotic-resistant strains, as indicated by the lowest DNA concentration value on each of the standard curves. Smiljanic et al. also illustrated that identifying Gram-negative NDM and MBL producing strains is difficult because the resistance to carbapenems in these bacteria is encoded by similar sequences [32]. Thus, using a sensitive and precise method such as HRMA and specific primers could identify strains such as MBL and NDM producing strains. In a study, Ding et al. proposed the PASGNDM699 strain resistance to a wide range of antibiotics. They also confirmed the clinical importance of PASGNDM strains in causing resistant infections [33].

    Based on Figure 4 and Figure 5, DNA dilutions of NDM producing P. aeruginosa strains were identified (dilution of 108 to 100 CFU/mL). The results were different from those obtained by Naas et al. and Smiljanic et al. [32],[34]. Identification of MBL and NDM producing strains has been performed in various studies in Sweden [17], USA [35], Australia [25] and Italy [36] in Gram-negative bacteria by the HRMA method.

    Nevertheless, one of the most important benefits of the multiplex HRMA method is the simultaneous identification of different NDM varieties. Identification of these variants using phenotypic methods has low accuracy and speed. Those methods also require spending much time optimizing. Makena et al. found that the identification of NDM variants by phenotypic methods requires protein stability and is not practical due to the evolution of NDM-producing strains [37].

    According to our results, primers with a short length had the best sensitivity and specificity in the HRMA assay. Słomka et al. demonstrated that when the DNA quality is low, DNA degradation or long DNA breaks during extraction makes the long template harder to amplify [20]. Though, the capacity to monitor PCRs in real-time has revolutionized how PCR is used in the clinical microbiology field. HRMA assay is used to amplify and concurrently quantify a targeted DNA molecule and enables both detection and quantification of DNA. HRMA PCR needs a fluorescent reporter that binds to the finished product and reports its presence by fluorescence. The Eischeid study confirms these results [38].

    In this study, we optimized the HRMA method to identify antibiotic resistance gene variants. We did not use designed primers in this study. The design and optimization of C + G values ​​ in designed primers provide the sensitivity and specificity of HRMA in the simultaneous identification of drug resistance [39]. Thus, the length of the selected primers should be considered to identify bacterial sub-strains. Another limitation of our study was the lack of use of different fluorescent dyes in identifying subtypes. Based on previous studies, the type of dye used significantly affects the sensitivity and specificity of HRMA [40]. Therefore, in different master mixes from different suppliers, calibration is necessary to establish the new Tm data on the reference and clinical strains.

    We demonstrated that the HRMA assay is a rapid and sensitive pre-sequence screening tool that allows the detection of low DNA concentration. Further, our study results showed that NDM and MBL genes' co-existence could be detected using the HRMA method reference strains. Moreover, the HRMA method for identifying NDM and MBL producing strains has high sensitivity and specificity. The present study also confirmed that primer product length and fluorescent dye play critical roles in increasing the sensitivity and specificity of the HRMA assay. However, the selection of the melting temperature range is essential to the analysis, as there needs to be sufficient data both before and following the melting transition to allow reliable normalization of the melting curves.



    [1] D. Mozaffarian, E. J. Benjamin, A. S. Go, D. K. Arnett, M. J. Blaha, M. Cushman, et al., Heart disease and stroke statistics-2016 update: A report from the American heart association, Circulation, 133 (2016), e38–e360. https://doi.org/10.1161/CIR.0000000000000409 doi: 10.1161/CIR.0000000000000409
    [2] S. S. Virani, A. Alonso, E. J. Benjamin, M. S. Bittencourt, C. W. Callaway, A. P. Carson, et al., Heart disease and stroke statistics-2020 update: A report from the American heart association, Circulation, 141 (2020), e139–e596. https://doi.org/10.1161/CIR.0000000000000757 doi: 10.1161/CIR.0000000000000757
    [3] A. H. Barfejani, M. Jafarvand, S. M. Seyedsaadat, R. T. Rasekhi, Donepezil in the treatment of ischemic stroke: Review and future perspective, Life Sci., 263 (2020), 118575. https://doi.org/10.1016/j.lfs.2020.118575 doi: 10.1016/j.lfs.2020.118575
    [4] Y. Qian, M. Chopp, J. Chen, Emerging role of microRNAs in ischemic stroke with comorbidities, Exp. Neurol., 331 (2020), 113382. https://doi.org/10.1016/j.expneurol.2020.113382 doi: 10.1016/j.expneurol.2020.113382
    [5] G. S. Silva, R. G. Nogueira, Endovascular treatment of acute ischemic stroke, Continuum (Minneap Minn), 26 (2020), 310–331. https://doi.org/10.1212/CON.0000000000000852 doi: 10.1212/CON.0000000000000852
    [6] A. K. Boehme, C. Esenwa, M. S. Elkind, Stroke risk factors, genetics, and prevention, Circ. Res, 120 (2017), 472–495. https://doi.org/10.1161/CIRCRESAHA.116.308398 doi: 10.1161/CIRCRESAHA.116.308398
    [7] J. W. Doria, P. B. Forgacs, Incidence, implications, and management of seizures following ischemic and hemorrhagic stroke, Curr. Neurol. Neurosci. Rep., 19 (2019), 37. https://doi.org/10.1007/s11910-019-0957-4 doi: 10.1007/s11910-019-0957-4
    [8] H. Xu, J. Zhang, Y. Ma, J. Gu, X. Jing, S. Lu, et al., The identification and verification of key long noncoding RNAs in ischemic stroke, Biomed. Res. Int., 2020 (2020), 2094320. https://doi.org/10.1155/2020/2094320 doi: 10.1155/2020/2094320
    [9] H. Wang, L. Shen, Y. Li, J. Lv, Integrated characterisation of cancer genes identifies key molecular biomarkers in stomach adenocarcinoma, J. Clin. Pathol., 73 (2020), 579–586. https://doi.org/10.1136/jclinpath-2019-206400 doi: 10.1136/jclinpath-2019-206400
    [10] G. J. Hankey, Stroke, Lancet, 389 (2017), 641–654. https://doi.org/10.1016/S0140-6736(16)30962-X
    [11] Z. Qi, Y. Zhao, Y. Su, B. Cao, J. J. Yang, Q. Xing, Serum extracellular vesicle-derived miR-124-3p as a diagnostic and predictive marker for early-stage acute ischemic stroke, Front. Mol. Biosci., 8 (2021), 685088. https://doi.org/10.3389/fmolb.2021.685088 doi: 10.3389/fmolb.2021.685088
    [12] The cochrane database of systematic reviews, J. Evid. Based Med., 3 (2010), 130–131. https://doi.org/10.1111/j.1756-5391.2010.01079.x
    [13] H. Saber, B. B. Navi, J. C. Grotta, H. Kamel, A. Bambhroliya, F. S. Vahidy, et al., Real-world treatment trends in endovascular stroke therapy, Stroke, 50 (2019), 683–689. https://doi.org/10.1161/STROKEAHA.118.023967 doi: 10.1161/STROKEAHA.118.023967
    [14] S. A. Sheth, S. Lee, S. J. Warach, J. Gralla, R. Jahan, M. Goyal, et al., Sex differences in outcome after endovascular stroke therapy for acute ischemic stroke, Stroke, 50 (2019), 2420–2427. https://doi.org/10.1161/STROKEAHA.118.023867 doi: 10.1161/STROKEAHA.118.023867
    [15] C. V. Borlongan, Concise review: Stem cell therapy for stroke patients: Are we there yet?, Stem Cells Transl. Med., 8 (2019), 983–988. https://doi.org/10.1002/sctm.19-0076 doi: 10.1002/sctm.19-0076
    [16] Z. G. Zhang, B. Buller, M. Chopp, Exosomes-beyond stem cells for restorative therapy in stroke and neurological injury, Nat. Rev. Neurol., 15 (2019), 193–203. https://doi.org/10.1038/s41582-018-0126-4 doi: 10.1038/s41582-018-0126-4
    [17] C. Feschotte, N. Jiang, S. R. Wessler, Plant transposable elements: where genetics meets genomics, Nat. Rev. Genet., 3 (2002), 329–341. https://doi.org/10.1038/nrg793 doi: 10.1038/nrg793
    [18] R. Sunkar, J. K. Zhu, Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis, Plant Cell, 16 (2004), 2001–2019. https://doi.org/10.1105/tpc.104.022830 doi: 10.1105/tpc.104.022830
    [19] A. M. Cheng, M. W. Byrom, J. Shelton, L. P. Ford, Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis, Nucleic Acids Res., 33 (2005), 1290–1297. https://doi.org/10.1093/nar/gki200 doi: 10.1093/nar/gki200
    [20] S. Crippa, M. Cassano, M. Sampaolesi, Role of miRNAs in muscle stem cell biology: proliferation, differentiation and death, Curr. Pharm. Des., 18 (2012), 1718–1729. https://doi.org/10.2174/138161212799859620 doi: 10.2174/138161212799859620
    [21] P. Kumar, H. Wu, J. L. McBride, K. E. Jung, M. H. Kim, et al., Transvascular delivery of small interfering RNA to the central nervous system, Nature, 448 (2007), 39–43. https://doi.org/10.1038/nature05901 doi: 10.1038/nature05901
    [22] Z. D. Smith, A. Meissner, DNA methylation: roles in mammalian development, Nat. Rev. Genet., 14 (2013), 204–220. https://doi.org/10.1038/nrg3354 doi: 10.1038/nrg3354
    [23] P. A. Jones, Functions of DNA methylation: islands, start sites, gene bodies and beyond, Nat. Rev. Genet., 13 (2012), 484–492. https://doi.org/10.1038/nrg3230 doi: 10.1038/nrg3230
    [24] S. Seisenberger, C. Popp, W. Reik, Retrotransposons and germ cells: reproduction, death, and diversity, F1000 Biol. Rep., 16 (2010), 2. https://doi.org/10.3410/B2-44 doi: 10.3410/B2-44
    [25] S. Wernig-Zorc, M. P. Yadav, P. K. Kopparapu, M. Bemark, H. L. Kristjansdottir, P. O. Andersson, et al., Global distribution of DNA hydroxymethylation and DNA methylation in chronic lymphocytic leukemia, Epigenet. Chromatin, 12 (2019), 4. https://doi.org/10.1186/s13072-018-0252-7 doi: 10.1186/s13072-018-0252-7
    [26] L. Miao, R. X. Yin, Q. H. Zhang, X. J. Hu, F. Huang, W. X. Chen, et al., Integrated DNA methylation and gene expression analysis in the pathogenesis of coronary artery disease, Aging (Albany NY), 11 (2019), 1486–1500. https://doi.org/10.18632/aging.101847 doi: 10.18632/aging.101847
    [27] M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, et al., limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43 (2015), e47. https://doi.org/10.1093/nar/gkv007 doi: 10.1093/nar/gkv007
    [28] R. A. Irizarry, B. Hobbs, F. Collin, Y. D. Beazer-Barclay, K. J. Antonellis, U. Scherf, et al., Exploration, normalization, and summaries of high density oligonucleotide array probe level data, Biostatistics, 4 (2003), 249–264. https://doi.org/10.1093/biostatistics/4.2.249 doi: 10.1093/biostatistics/4.2.249
    [29] Y. Chen, X. Wang, miRDB: an online database for prediction of functional microRNA targets, Nucleic Acids Res., 48 (2020), D127–d131. https://doi.org/10.1093/nar/gkz757 doi: 10.1093/nar/gkz757
    [30] B. P. Lewis, C. B. Burge, D. P. Bartel, Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets, Cell, 120 (2005), 15–20. https://doi.org/10.1016/j.cell.2004.12.035 doi: 10.1016/j.cell.2004.12.035
    [31] B. John, A. J. Enright, A. Aravin, T. Tuschl, C. Sander, D. S. Marks, Human MicroRNA targets, PLoS Biol., 2 (2004), e363. https://doi.org/10.1371/journal.pbio.0020363 doi: 10.1371/journal.pbio.0020363
    [32] E. A. C. Goossens, M. R. de Vries, K. H. Simons, H. Putter, P. H. A. Quax, A. Y. Nossent, miRMap: profiling 14q32 microRNA expression and DNA methylation throughout the human vasculature, Front. Cardiovasc. Med., 6 (2019), 113. https://doi.org/10.3389/fcvm.2019.00113 doi: 10.3389/fcvm.2019.00113
    [33] H. Y. Huang, Y. C. Lin, J. Li, K. Y. Huang, S. Shrestha, H. C. Hong, et al., miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database, Nucleic Acids Res., 48 (2020), D148–d154. https://doi.org/10.1093/nar/gkz896 doi: 10.1093/nar/gkz896
    [34] P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498–2504. https://doi.org/10.1101/gr.1239303 doi: 10.1101/gr.1239303
    [35] C. von Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, B. Snel, STRING: a database of predicted functional associations between proteins, Nucleic Acids Res., 31 (2003), 258–261. https://doi.org/10.1093/nar/gkg034 doi: 10.1093/nar/gkg034
    [36] X. He, J. Zhang, Why do hubs tend to be essential in protein networks? PLoS Genet., 2 (2006), e88. https://doi.org/10.1371/journal.pgen.0020088 doi: 10.1371/journal.pgen.0020088
    [37] M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, et al., Gene ontology: Tool for the unification of biology, Nat. Genet., 25 (2000), 25–29. https://doi.org/10.1038/75556 doi: 10.1038/75556
    [38] M. Kanehisa, S. Goto, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28 (2000), 27–30. https://doi.org/10.1093/nar/28.1.27 doi: 10.1093/nar/28.1.27
    [39] M. E. Glickman, S. R. Rao, M. R. Schultz, False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies, J. Clin. Epidemiol., 67 (2014), 850–857. https://doi.org/10.1016/j.jclinepi.2014.03.012 doi: 10.1016/j.jclinepi.2014.03.012
    [40] A. Abukaresh, R. Al-Abadlah, B. Böttcher, K. El-Essi, Ischaemic stroke management at Al-Shifa Hospital in the Gaza Strip: a clinical audit, Lancet, 391 (2018), S7. https://doi.org/10.1016/S0140-6736(18)30373-8 doi: 10.1016/S0140-6736(18)30373-8
    [41] Y. Shen, C. Peng, Q. Bai, Y. Ding, X. Yi, H. Du, et al., Epigenome-wide association study indicates hypomethylation of MTRNR2L8 in large-artery atherosclerosis stroke, Stroke, 50 (2019), 1330–1338. https://doi.org/10.1161/STROKEAHA.118.023436 doi: 10.1161/STROKEAHA.118.023436
    [42] R. Fujii, H. Yamada, E. Munetsuna, M. Yamazaki, G. Mizuno, Y. Tsuboi, et al., Dietary vegetable intake is inversely associated with ATP-binding cassette protein A1 (ABCA1) DNA methylation levels among Japanese women, Nutrition, 65 (2019), 1–5. https://doi.org/10.1016/j.nut.2019.02.010 doi: 10.1016/j.nut.2019.02.010
    [43] C. Soriano-Tárraga, J. Jiménez-Conde, E. Giralt-Steinhauer, M. Mola, A. Ois, A. Rodríguez-Campello, et al., Global DNA methylation of ischemic stroke subtypes, PLoS One, 9 (2014), e96543. https://doi.org/10.1371/journal.pone.0096543 doi: 10.1371/journal.pone.0096543
    [44] Z. Wu, W. Wei, H. Fan, Y. Gu, L. Li, H. Wang, Integrated analysis of competitive endogenous RNA networks in acute ischemic stroke, Front. Genet., 13 (2022), 833545. https://doi.org/10.3389/fgene.2022.833545 doi: 10.3389/fgene.2022.833545
    [45] G. X. Deng, N. Xu, Q. Huang, J. Y. Tan, Z. Zhang, X. F. Li, et al., Association between promoter DNA methylation and gene expression in the pathogenesis of ischemic stroke, Aging (Albany NY), 11 (2019), 7663–7677. https://doi.org/10.18632/aging.102278 doi: 10.18632/aging.102278
    [46] X. Liu, T. Yamashita, J. Shang, X. Shi, R. Morihara, Y. Huang, et al., Molecular switching from ubiquitin-proteasome to autophagy pathways in mice stroke model, J. Cereb. Blood Flow Metab., 40 (2020), 214–224. https://doi.org/10.1177/0271678X18810617 doi: 10.1177/0271678X18810617
    [47] L. Chen, M. He, M. Zhang, Q. Sun, S. Zeng, H. Zhao, et al., The Role of noncoding RNAs in colorectal cancer, with a focus on its autophagy, Pharmacol. Ther., 226 (2021), 107868. https://doi.org/10.1016/j.pharmthera.2021.107868 doi: 10.1016/j.pharmthera.2021.107868
    [48] H. Ren, Q. Wang, Noncoding RNA and diabetic kidney disease, DNA Cell Biol., 40 (2021), 553–567. https://doi.org/10.1089/dna.2020.5973 doi: 10.1089/dna.2020.5973
    [49] Z. Zhang, F. Cui, C. Cao, Q. Wang, Q. Zou, Single-cell RNA analysis reveals the potential risk of organ-specific cell types vulnerable to SARS-CoV-2 infections, Comput. Biol. Med., 140 (2021), 105092. https://doi.org/10.1016/j.compbiomed.2021.105092 doi: 10.1016/j.compbiomed.2021.105092
    [50] G. C. Jickling, B. P. Ander, X. H. Zhan, D. Noblett, B. Stamova, D. Z. Liu, microRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets, PloS One, 9 (2014), 51. https://doi.org/10.1371/journal.pone.0099283 doi: 10.1371/journal.pone.0099283
    [51] A. R. Sharma, U. Shashikiran, A. R. Uk, R. Shetty, K. Satyamoorthy, P. S. Rai, Aberrant DNA methylation and miRNAs in coronary artery diseases and stroke: a systematic review, Brief. Funct. Genomics, 19 (2020), 259–285. https://doi.org/10.1093/bfgp/elz043 doi: 10.1093/bfgp/elz043
  • This article has been cited by:

    1. Xiaoyu Xu, Nan Wang, Liguo Feng, Jianwen Wang, Simple Sequence Repeat Fingerprint Identification of Essential-Oil-Bearing Rosa rugosa via High-Resolution Melting (HRM) Analysis, 2023, 13, 2218-273X, 1468, 10.3390/biom13101468
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3203) PDF downloads(202) Cited by(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog