Abstract Protein myristoylation is a key protein modification carried out by N-Myristoyltransferase (NMT) after Methionine aminopeptidase 2 (MetAP2) removes methionine from the amino-terminus of the target protein. Protein myristoylation by NMT augments several signaling pathways involved in a myriad of cellular processes, including developmental pathways and pathways that when dysregulated lead to cancer or immune dysfunction. The emerging evidence pointing to NMT-mediated myristoylation as a major cellular regulator underscores the importance of understanding the framework of this type of signaling event. Various studies have investigated the role that myristoylation plays in signaling dysfunction by examining differential gene or protein expression between normal and diseased states, such as cancers or following HIV-1 infection, however no study exists that addresses the role of microRNAs (miRNAs) in the regulation of myristoylation. By performing a large scale bioinformatics and functional analysis of the miRNAs that target key genes involved in myristoylation (NMT1, NMT2, MetAP2), we have narrowed down a list of promising candidates for further analysis. Our condensed panel of miRNAs identifies 35 miRNAs linked to cancer, 21 miRNAs linked to developmental and immune signaling pathways, and 14 miRNAs linked to infectious disease (primarily HIV). The miRNAs panel that was analyzed revealed several NMT-targeting mRNAs (messenger RNA) that are implicated in diseases associated with NMT signaling alteration, providing a link between the realms of miRNA and myristoylation signaling. These findings verify miRNA as an additional facet of myristoylation signaling that must be considered to gain a full perspective. This study provides the groundwork for future studies concerning NMT-transcript-binding miRNAs, and will potentially lead to the development of new diagnostic/prognostic biomarkers and therapeutic targets for several important diseases. Introduction The onset of carcinogenesis is initiated by mutations to begin with in a normal cell that results in the loss of growth control (hyperplasia). Hyperplasia proceeds with the loss of senescence control, replicative immortality, apoptosis resistance and the ability to evade the immune system, which are the hallmark features of cancer and are intrinsic properties in nature [[30]1]. Later on extrinsic factors get involved where the abnormal cancerous cell begins the process of angiogenesis (acquiring blood supply) in order to thrive, evade the surrounding tissue and colonize at distal sites (metastases) through the epithelial to mesenchyme transition. The culprit for driving such traits in the journey of a normal cell to cancer is primarily dysregulation of the signaling in the abnormal microenvironment in which these cancerous cells exists, which plays an important role in escaping the immune cells responsible for policing them. A healthy microenvironment (extracellular matrix) has the ability to suppress the cancerous growth. In order to create effective and personalized strategies for cancer treatment, it is imperative to understand the many dimensions of signaling dysregulation that characterize different types of cancer and compromised immune cells. Of the signaling molecules implicated in either immune dysfunction or cancer, N-myristoyltransferase (NMT), the enzyme responsible for the covalent attachment of a 14C myristic fatty acid to the N-terminus of target proteins, has been shown to be implicated in both the development of cancer and impaired immune cell function [[31]2–[32]6]. Myristoylation is preceded by the removal of the N-terminal methionine of the target protein by Methionine Aminopeptidase 2 (MetAP2) (and aids in protein trafficking directed to cellular membrane systems ([33]Fig 1) [[34]6]. Prokaryotes lack NMT, whereas lower eukaryotes like protozoans and fungus have a single copy of NMT, and mammals and other vertebrates, NMT has been shown to be present in two major isoforms, NMT1 and NMT2, which catalyze the same reaction, and are coded respectively by different genes [[35]7, [36]8]. The biological role of NMT serves as a promising candidate to study with regards to cancer progression and immune function as its dysregulation has been shown to contribute to defective embryo and monocyte development, cell growth, T-cell signaling, and HIV infection. Little is known about the regulation of its expression, signaling, and localization [[37]4, [38]5, [39]8–[40]10]. Fig 1. Schematic representation of protein N-myristoylation. [41]Fig 1 [42]Open in a new tab The methionine residue of a nascent polypeptide is removed co-translationally by MetAP2, which is followed by addition of a myristoyl group to the exposed N-terminal glycine residue by NMT. We are increasingly aware of the multitude of complex and intertwined signaling events occurring within a cell under various stimuli and stresses, and the many forms of regulation that govern and balance them. These events include functions prone to driving cancer, such as cell proliferation, mitosis and migration, and the respective checks in place to limit growth. At the heart of cell signaling and regulation is the myriad of genes that make up the genome, and their respective protein end products that serve to exert specific cellular functions. However, the human genome, once estimated to contain over 100, 000 protein coding genes, has been re-evaluated over the years to be comprised of merely ~20, 000 distinct genes, which in turn code for an estimated 293, 000 non-redundant peptides [[43]11, [44]12]. This revelation has led researchers to seek and shed light on the various forms of genomic regulation that influence gene expression, fine-tune spatio-temporal aspects of cell signaling, and account for the massive repertoire of distinct peptides. To date, several regulatory systems have been identified and well established, including transcriptional regulation through various pleiotropic transcription-factor family proteins, and epigenetic methylation of DNA, as well as post-translational mechanisms of gene regulation, such as increased proteome diversity through mRNA splicing mediated by the spliceosome, and gene downregulation via mRNA silencing by various RNA products [[45]13–[46]16]. Of the various agents responsible for RNA silencing, there has been emerging evidence pointing to the importance of microRNAs (miRNAs) in several facets of cellular functional regulation, including those involved in cancer progression and immune function [[47]17–[48]19]. MicroRNAs (miRNAs) are a family of endogenously expressed small non-coding single stranded RNAs that are generally 21–23 nucleotides in length [[49]20]. The maturation of miRNA in humans is facilitated by two consecutive cleavages mediated by RNAIII enzymes DROSHA and DICER. miRNAs are the final product of larger pri-mRNAs, the latter transcribed by either RNA polymerase II or III. DROSHA complexes along with its partner DiGeorge syndrome critical region 8 (DGCR8) in a stoichiometry of 1:2 respectively form the microprocessor complex. The microprocessor complex reduces the pri-mRNA to an ~85 nucleotide long pre-miRNA containing a hairpin loop [[50]21, [51]22]. Pre-miRNA is transported into the cytoplasm, recruited to the spliceosome, and subsequently cleaved by the DICER enzyme to yield a mature miRNA duplex [[52]23]. One of the strands of the cytoplasmic miRNA, known as guide is used to target miRNA onto a protein named Argonaute to form the RNA-induced silencing complex (RISC). RISC is a multi-protein complex that is guided by the sequence of miRNA transcript to a target (through complementary base pairs) and the protein Argonaute cleaves mRNA (messenger RNA) [[53]24]. Thus, miRNA is capable of causing degradation of the target mRNA if perfect nucleotide complementation is achieved, otherwise translational repression of the target mRNA occurs in the case of imperfect complementarity [[54]25]. miRNA have been shown to have a certain degree of genomic organization, adding an additional layer of complexity to miRNA systems that can be manipulated to drive evolution and specialization [[55]26]. Some miRNAs have been shown to form polycistronic clusters that in some cases co-express several miRNAs that target different mRNAs responsible for proteins within the same protein complex [[56]27, [57]28]. These findings demonstrate the ability of miRNA to influence protein-protein interactions. The repertoire of miRNA that can be expressed by a cell constitutes an essential layer of post-transcriptional gene control for many cellular processes. miRNAs are relatively young on the evolutionary timescale, being expressed only in animals, plants, and some viruses [[58]29–[59]31]. Around 30% of the Homo sapiens protein-coding genes are regulated by miRNAs, which control the genes at the post-transcriptional level [[60]32]. There are two modes by which miRNAs regulates the expression of genes; first, miRNA-mediated transcript degradation and second, inhibition of protein translation [[61]25, [62]33]. For target degradation model, miRNA binds predominantly to the target sequence found within the 3’ untranslated region (UTR) of the target mRNA with perfect complementarity, leading the mRNA to be cleaved [[63]34, [64]35]. Similarly to inhibit the translation of target genes, miRNA binds with imperfect complementarity with the target. However, recent studies suggest that even with the imperfect complementarity between miRNA and target mRNA sequences, miRNAs are capable of carrying out target recognition and subsequent translational inhibition and/or transcriptional decay [[65]36]. In addition to its functions in post-transcriptional gene regulation, miRNAs are also known for regulating protein complexes and acting as a key-determining molecule in protein-protein interaction [[66]27, [67]28]. The role of miRNA has been suggested to be more of a fine-tuning mechanism of gene regulation, rather than as a master regulator; however, increasing evidence has shown that miRNAs are heavily dysregulated in many diseases, including cancer [[68]37–[69]40]. There are several studies linking microRNA as a driving factor in the progression of some cancers, such as the promotion of colorectal cancer proliferation and invasion by miR-320b [[70]41]. In some cases miRNA can act as a tumor suppressor, such as miR-29c, which is correlated with breast cancer survival and downregulates B7-H3 protein which is associated with metastasis and poor prognosis in breast cancer patients [[71]42]. Beyond the potential of miRNAs to play a positive or negative role in disease, they may serve to act as novel biomarkers [[72]43]. Increasing evidence is revealing that specific circulating miRNAs may be used as non-invasive biomarkers for neoplastic diseases, such as breast cancer (miR-29c, 199a, 424) [[73]44], colorectal cancer (miR-24, 320a, 423-5p) [[74]45], and liver cancer (miR-200 family) as well as its regression (miR-199a-3p) [[75]46, [76]47]. In cancer biology, miRNAs may be playing a critical role by modulating key signaling pathways, as they have been shown to affect the sensitivity of a cell to signal transduction by signaling molecules such as epidermal growth factor, Notch, TGF- β, and WNT [[77]48, [78]49]. We suspect that the role of critical signaling modulation by miRNAs can be extended to their interactions with NMT translation and influence over protein-protein interaction related to NMT signaling. Dysregulation of NMT1 activity is implicated in cancer and stem cell differentiation [[79]8, [80]50]. The plasticity of transition from normal to cancerous cells as well as stem cell differentiation and proliferation to T lymphocytes depend on the miRNA target gene regulation [[81]4, [82]51]. In this study we predicted the miRNAs that target NMT1/NMT2/MetAP2 transcripts, and the possibility of these interactions to inhibit NMT1/2 expression, and their function in relation to cancer, stem cell, T-cell/B-cell signaling and infectious diseases, using the bioinformatics techniques TargetScan 7 and DIANA. Materials and methods Sequence selection for computational analysis For NMT1 and NMT2 reference sequences were retrieved from the GenBank. NMT1 and NMT2 reference numbers were [83]NM_021079.4 and [84]NM_004808, respectively. For MetAP2, the reference sequence used was [85]NM_001317182.1. Prediction of miRNA targets The putative miRNA targets were predicted using two annotation programs as described previously [[86]52]. Prediction of miRNAs was done in the subtractive library. The sequences were submitted for in silico annotation of ncRNAs. During the annotation process, we searched for the RNA structures by using Infernal (INFERence of RNA ALignment) software as described in other study [[87]53]. The BLAST program was used to search similar miRNA sequences in the National Center for Biotechnology Information (NCBI) database as described elsewhere [[88]54]. Finally, miRNAs with the best p-value (≤0.05) were selected for further analysis and their details are presented in tabulated form ([89]S1 Table). Identification and functional annotation of miRNAs regulating NMT and MetAP2 genes using TargetScan and DIANA Tools mirPath Sequences for human NMT1/2 and MetAP2 genes (18 and 11 transcripts respectively) were downloaded from the ENSEMBL genome browser (NMT: ENSG00000136448, MetAP2: ENSG00000111142). Sequences of human mature miRNAs (2,588) were downloaded from miRBase version 21 ([90]http://mirdb.org) [[91]55]. Normalized mRNA and miRNA expression values were downloaded from Gene Expression Omnibus (GEO) repository (accession ID: [92]GSE62030). miRNAs targeting NMT1/2 and MetAP2 genes were predicted by TargetScan7 (v7.0; [93]targetscan.org) [[94]56]. To validate the predicted miRNA: target interactions, Pearson correlation coefficient (PCC) was calculated using the normalized expression values of miRNA and target genes. All the miRNA:target interactions having significantly high inverse/negative PCC (r < -0.5, p ≤ 0.05) were considered as true miRNA:target interactions. The p-value of PCC was calculated by Student’s t-test using “R” software. Biological pathways influenced by miRNAs targeting NMT and METAP2 genes were identified using DIANA Tools mirPath version 3 (v3.0) ([95]http://www.microrna.gr/miRPathv3) [[96]57]. Functional enrichment analysis of miRNA targets To determine or predict the function(s) of miRNAs which targets these genes, pathway enrichment analysis was performed using DIANA Tools mirPath (v3.0) as described previously [[97]57]. This tool provides information on experimentally-supported miRNA functional annotation using Gene Ontology (GO) or GOSlim terms [[98]58], combined with statistically-enriched pathways, mainly Kyoto Encyclopedia of Genes and Genomes (KEGG) molecular pathways, and based on target genes which query miRNAs targets [[99]57]. Literature search for miRNAs associated with cancer and signaling pathways The chosen set of miRNAs from bioinformatics analyses were thoroughly researched in the literature for their association, functions and expression changes in cancer and signaling pathways. PubMed, PubMed Central and Google Scholar search engines were utilized to perform the literature survey. MiRNAs that showed at least one publication record were considered for further analysis in the study. MiRNA clustering analysis MiRNA expression values were extracted from “[100]GSE62037” GEO dataset [[101]59]. The miRNAs that were predicted to target NMT1/2 transcripts were filtered out and their expression values were specifically extracted from the above dataset. In order to recognize the normalized expression patterns of these miRNAs, an unsupervised hierarchical cluster analysis were carried out using Cluster v3.0 as described elsewhere [[102]60]. TreeView software was used to generate and visualize the heatmaps. Green color shows positive PCC values (0.5 ≤ r ≤ 1) and red color shows inverse/negative PCC values (-0.5 ≤ r ≤ -1). Thereafter, it was determined whether the miRNA data fit into known post-transcriptional ‘RNA regulon (operon) model’ which describes how RNA molecules are organized at a higher-organization level and how their functional dynamics are connected to post-transcriptional regulatory events such as stability and translation [[103]61, [104]62]. Results Identification of miRNAs targeting NMT and METAP2 genes Mature miRNAs targeting 18 transcripts and 11 transcripts of NMT1/2 and MetAP2 respectively, were identified using TargetScan7 [[105]63]. Using the stringent cutoff, a total of 13,798 miRNA-target interactions were predicted by TargetScan for the NMT1/2 genes. In contrast, 7,708 interactions were predicted for the MetAP2 gene. In order to filter out false positive miRNA: target interactions, Pearson correlation coefficient (PCC) was calculated between targeting miRNA and target gene expression. In general, miRNAs down-regulates the expression of a target gene. Based on previous reports, the threshold PCC for determining the true positive miRNA: target interaction was set at 0.5 [[106]64, [107]65]. Thus, a true interaction will be indicated by significantly high inverse PCC (r ≤ -0.5, p ≤ -1). Out of 13,798 putative miRNAs: target interactions predicted for the NMT1/2 genes, only 221 miRNAs: target interactions showed r ≤ -0.5 ([108]S1 Table). The top five miRNAs targeting the NMT1/2 genes with the highest inverse PCC were miR-421, miR-4317, miR-606, miR-140-5p and miR-941. Interestingly, these miRNAs were also found to be regulating multiple NMT1/2 transcripts ([109]S1 Table). Similarly, for the METAP2 gene, out of 7,708 interactions predicted, 165 miRNAs: target interactions showed PCC values above our cutoff threshold (r ≤ -0.5) ([110]S2 Table). Based on the PCC values the top five miRNAs targeting the MetAP2 gene turned out to be miR-330-3p, miR-421, miR-409-3p, miR-139-3p, and miR-1246 ([111]S2 Table). Next, to determine whether same miRNA targets NMT1/2 and MetAP2 genes, we compared the targeting miRNAs lists ([112]S1 and [113]S2 Tables). The results revealed that 7 miRNAs are common which targets NMT1/2 and MetAP2 genes while 20 miRNAs were found to be exclusively targeting either NMT1/2 or MetAP2 genes ([114]Table 1; [115]Fig 2). From the list of commonly targeting miRNAs, miR-421 has the highest inverse co-expression with the target genes and appeared in the top five miRNAs. Making this miRNA a top candidate for further evaluation. Table 1. MicroRNAs (miRNAs) that are specific and common to N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. miRNAs targeting NMT1/2 miRNAs targeting MetAP2 Common miRNAs 1 miR-1205 miR-107 miR-330-3p 2 miR-1265 miR-139-3p miR-1246 3 miR-140-5p miR-1972 miR-409-3p 4 miR-943 miR-199a-3p miR-421 5 miR-1193 miR-199b-3p miR-543 6 miR-1278 miR-299-3p miR-665 7 miR-137 miR-362-5p miR-671-5p 8 miR-3174 miR-4306 9 miR-3197 miR-485-5p 10 miR-4307 miR-501-5p 11 miR-4317 miR-532-3p 12 miR-512-3p miR-650 13 miR-548p miR-654-3p 14 miR-599 miR-1244 15 miR-127-3p miR-140-3p 16 miR-652-5p miR-299-5p 17 miR-941 miR-324-5p 18 miR-346 miR-4319 19 miR-4264 miR-520d-5p 20 miR-606 miR-628-3p [116]Open in a new tab Fig 2. The distribution of microRNAs (miRNAs) that target N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. [117]Fig 2 [118]Open in a new tab The miRNAs that target either NMT or MetAP2, or both genes are illustrated in the Venn diagram. Seven miRNAs target both NMT1/2 and MetAP2 genes. Functional enrichment analysis of miRNA targets To identify the biological pathways under regulation of the miRNAs identified in the previous section, functional enrichment was done using DIANA Tools mirPath version 3 [[119]57]. The statistically significant KEGG pathways enriched from the analyses are summarized in [120]Table 2. The analysis revealed 88 pathways that belong under the KEGG pathways. It was found that the most significantly enriched pathway regulated by miRNAs that target both NMT1/2 genes and the MetAP2 gene was proteoglycans in cancer. Interestingly, one pathway regulated by the selected miRNAs is the ErbB signaling pathway. This pathway plays an important role in regulating cancer [[121]66]. Furthermore, Wnt, mTOR, and VEGF signaling pathways were found to be regulated by miRNAs that target NMT gene. Additionally, we also predicted KEGG pathways that are associated with the miRNAs that exclusively targets NMT1/2 or MetAP2 genes. From these pathways, the most significant ones were filtered for further exploration in terms of their relevance in cancer. Table 2. KEGG pathways enrichment annotation of the microRNAs (miRNAs) that target N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. KEGG pathway p-value #genes #miRNAs 1 ErbB signalling pathway 3.60E-29 48 17 2 Prostate cancer 8.54E-27 46 17 3 Colorectal cancer 8.44E-23 36 17 4 Wnt signalling pathway 1.88E-21 71 17 5 mTOR signalling pathway 6.21E-21 36 14 6 Long-term potentiation 1.03E-20 36 12 7 VEGF signalling pathway 1.38E-19 34 16 8 Pancreatic cancer 5.86E-19 36 17 9 Focal adhesion 1.04E-18 83 16 10 Endometrial cancer 1.20E-17 29 16 11 Non-small cell lung cancer 2.04E-16 28 14 12 Neurotrophin signalling pathway 4.14E-16 54 17 13 MAPK signalling pathway 6.31E-16 100 17 14 Insulin signalling pathway 1.79E-15 58 17 15 B cell receptor signalling pathway 9.73E-15 36 17 16 TGF-beta signalling pathway 2.47E-14 39 15 17 Acute myeloid leukemia 7.30E-14 29 14 18 Axon guidance 2.30E-13 57 16 19 Dopaminergic synapse 2.60E-12 54 16 20 PI3K-Akt signalling pathway 3.19E-12 118 17 21 Glioma 3.82E-12 35 14 22 Long-term depression 3.85E-12 34 12 23 Pathways in cancer 4.64E-12 125 17 24 Chronic myeloid leukemia 7.05E-11 34 16 25 Melanoma 9.58E-11 32 14 26 Gap junction 1.33E-10 38 15 27 Aldosterone-regulated sodium reabsorption 6.90E-10 19 11 28 Renal cell carcinoma 3.82E-09 34 16 29 Regulation of actin cytoskeleton 4.15E-09 82 16 30 HIF-1 signalling pathway 4.40E-09 44 15 31 mRNA surveillance pathway 5.38E-09 38 16 32 T cell receptor signalling pathway 6.87E-09 43 18 33 Circadian rhythm 3.95E-08 15 9 34 GnRH signalling pathway 1.24E-07 36 14 35 Prion diseases 3.83E-07 11 7 36 RNA degradation 3.83E-07 30 11 37 Retrograde endocannabinoid signalling 3.99E-07 46 16 38 Protein processing in endoplasmic reticulum 8.13E-07 64 15 39 Thyroid cancer 1.10E-06 14 11 40 Cholinergic synapse 2.16E-06 46 17 41 Fc gamma R-mediated phagocytosis 3.37E-06 36 16 42 Progesterone-mediated oocyte maturation 4.67E-06 33 17 43 Shigellosis 5.15E-06 27 12 44 Hedgehog signalling pathway 6.01E-06 21 15 45 Small cell lung cancer 6.63E-06 32 14 46 Hepatitis B 1.10E-05 55 17 47 Melanogenesis 1.15E-05 38 16 48 Fc epsilon RI signalling pathway 1.47E-05 28 16 49 Adherens junction 1.68E-05 32 15 50 Glutamatergic synapse 2.85E-05 45 16 51 Ubiquitin mediated proteolysis 3.98E-05 48 14 52 Bacterial invasion of epithelial cells 6.28E-05 29 12 53 Phosphatidylinositol signalling system 7.57E-05 32 14 54 Viral myocarditis 9.57E-05 26 14 55 Calcium signalling pathway 1.46E-04 60 17 56 Type II diabetes mellitus 1.89E-04 19 13 57 HTLV-I infection 1.96E-04 86 18 58 Tight junction 2.10E-04 48 17 59 p53 signalling pathway 2.17E-04 27 14 60 Hepatitis C 2.50E-04 45 17 61 Chemokine signalling pathway 3.14E-04 61 17 62 Transcriptional misregulation in cancer 4.00E-04 61 17 63 Hypertrophic cardiomyopathy (HCM) 4.82E-04 30 14 64 Osteoclast differentiation 7.03E-04 44 16 65 Vascular smooth muscle contraction 8.81E-04 42 15 66 Endocrine and other factor-regulated calcium reabsorption 2.18E-03 20 10 67 Dilated cardiomyopathy 2.56E-03 31 16 68 Protein digestion and absorption 2.65E-03 30 13 69 Epithelial cell signalling in Helicobacter pylori infection 2.90E-03 24 12 70 Basal cell carcinoma 2.90E-03 20 16 71 RNA transport 4.84E-03 50 17 72 Nicotine addiction 4.87E-03 18 11 73 Amoebiasis 7.13E-03 36 14 74 Chagas disease (American trypanosomiasis) 7.19E-03 36 17 75 Jak-STAT signalling pathway 7.39E-03 48 16 76 Arrhythmogenic right ventricular cardiomyopathy (ARVC) 9.00E-03 29 14 77 Serotonergic synapse 9.00E-03 37 15 78 Apoptosis 1.24E-02 32 15 79 Adipocytokine signalling pathway 1.31E-02 23 11 80 Inositol phosphate metabolism 1.92E-02 21 14 81 Gastric acid secretion 2.73E-02 25 12 82 Oocyte meiosis 3.01E-02 42 15 83 ABC transporters 3.47E-02 15 10 85 NOD-like receptor signalling pathway 3.82E-02 19 12 86 Salivary secretion 4.10E-02 28 13 87 Fanconi anemia pathway 4.91E-02 18 12 88 Endocytosis 4.91E-02 62 16 [122]Open in a new tab The DIANA Tools mirPath analysis predicted that most significantly enriched KEGG pathway regulated by miRNAs targeting both NMT and MetAP2 genes was ‘ErbB signaling pathway’ (p = 3.60E-29) which involved 48 genes and 17 miRNAs ([123]Table 2; [124]Fig 3). ErbB receptor molecules regulate cell proliferation, differentiation, cell motility, and cell survival. Therefore, ErbB receptor mutations or overexpression have been associated with cancer cell migration, development, invasion and progression of cancers such as non-small cell lung cancer [[125]67], breast cancer [[126]68], ovarian cancer and bladder cancer [[127]69]. This is mainly due to the role of this pathway in phosphorylation of many important kinases involved in cancer pathology. Fig 3. ErbB signaling pathway that is enriched with target genes of microRNAs (miRNAs) which negatively regulate N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. [128]Fig 3 [129]Open in a new tab The figure illustrates ErbB signaling pathway that contain genes that are targeted by miRNAs which regulate NMT gene. (EGF, epidermal growth factor; TGF, transforming growth factor; BTC, betacellulin; HB-EGF, heparin-binding epidermal growth factor (EGF)-like growth factor; EREG, epiregulin; NRG1, neuregulin-1; NRG2, neuregulin-2; NRG3, neuregulin-3; NRG4, neuregulin-4; PLCγ, phospholipase C type gamma; CAMK2B, calcium/calmodulin dependent protein kinase; PRKCB, Protein kinase C-beta; STAT5, Signal transducer and activator of transcription 5; src, Rous sarcoma virus gene; CRK, C T10 regulator of a tyrosine kinase; NCL, NCK Adaptor Protein 2; PTK2, PTK2 protein tyrosine kinase 2; ABL2, V-Abl Abelson Murine Leukemia Viral Oncogene Homolog 2; PAK2, P21 (RAC1) Activated Kinase 2; MAP2K4, Mitogen-Activated Protein Kinase Kinase 4; MAPK10, Mitogen-Activated Protein Kinase 10; SOS1, SOS Ras/Rac Guanine Nucleotide Exchange Factor 1; Grb2, Growth Factor Receptor Bound Protein 2; SHC4, Src Homology 2 Domain-Containing-Transforming Protein C4; PIK3C4, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit; AKT3, KT Serine/Threonine Kinase 3; mTOR, Mechanistic Target Of Rapamycin Kinase; BCL2, BCL2 Associated Agonist Of Cell Death; GSK3B, Glycogen Synthase Kinase 3 Beta; CDKN1A, Cyclin Dependent Kinase Inhibitor 1A; EIF4EBP1, Eukaryotic Translation Initiation Factor 4E Binding Protein 1; BRAF, B-Raf Proto-Oncogene, Serine/Threonine Kinase; RPS6KB1, Ribosomal Protein S6 Kinase B1; KRAS, KRAS Proto-Oncogene, GTPase; JUN, Jun Proto-Oncogene, AP-1 Transcription Factor Subunit; ELK, ETS Transcription Factor; Myc, MYC Proto-Oncogene, BHLH Transcription Factor; ER, endoplasmic reticulum. DNA, deoxyribonucleic acid). When analyzing pathways associated with colorectal ([130]Fig 4A) and prostate cancers ([131]Fig 4B), we observed that miRNAs targeting both NMT1/2 and MetAP2 genes also regulate the expression of key genes involved in these pathways, including AKT1, GSK3B, BRAF, MAPK and many others ([132]Fig 4). Fig 4. Colorectal cancer and prostate cancer pathways that are enriched with target genes of microRNAs (miRNAs) which negatively regulate N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. [133]Fig 4 [134]Open in a new tab The figure illustrates A) colorectal cancer pathway and B) prostate cancer pathway that contain genes that are targeted by miRNAs which regulate NMT gene. (Rac1, Rac family small GTPase 1; Wnt, Wingless-related integration site; FOS, Fos Proto-Oncogene, AP-1 Transcription Factor Subunit; APC, Adenomatosis Polyposis Coli Tumor Suppressor; AXIN2, Axis Inhibition Protein 2; CTNNB1, Catenin Beta 1; CASP3, Caspase 3; APPL, Adaptor Protein, Phosphotyrosine Interacting With PH Domain And Leucine Zipper 1; CCND1, Cyclin D1; RALGDS, Ral Guanine nucleotide dissociation stimulator; MSH6, MutS homolog 6; BCL2, B-Cell CLL/Lymphoma 2; TGFBR2, Transforming Growth Factor Beta Receptor 2; SMAD, SMAD Family Member 3 (mothers against decapentaplegic); Myc, MYC Proto-Oncogene; BAX, BCL2 Associated X, Apoptosis Regulator; CYCS, Cytochrome C, DCC, Deleted In Colorectal Carcinoma; LEF, lymphoid enhancer binding factor 1; P53, Phosphoprotein-53. AR, androgen receptor; FOXO1, Forkhead Box O1; PTEN, Phosphatase And Tensin Homolog; GSTP1, Glutathione S-Transferase Pi 1; CDK2, Cyclin Dependent Kinase 2; Rb1, Retinoblastoma 1; E2F3, E2F Transcription Factor 3; EGF, Epidermal Growth Factor; SRD5A2, Steroid 5 Alpha-Reductase 2; PSA, Kallikrein 3; MDM2, RING-Type E3 Ubiquitin Transferase Mdm2; PDK1, Pyruvate Dehydrogenase Kinase 1; CDKN1A, Cyclin Dependent Kinase Inhibitor 1A; CREBBP, CREB Binding Protein; CREB3L1, AMP Responsive Element Binding Protein 3 Like 1). Apart from cancer pathways, miRNAs were also shown to regulate genes in immune cells. Therefore, we further analyzed KEGG pathways that are associated with immune responses. For this, we performed pathway enrichment analysis using miRNAs that target only NMT transcripts. Interestingly, T cell receptor signaling pathway (p = 6.87E-09) involving 43 genes and 18 miRNAs ([135]Table 2; [136]Fig 5A); and B cell receptor signaling pathway (p = 9.73E-15) involving 36 genes and 17 miRNAs were predicted by the analysis ([137]Table 2; [138]Fig 5B). A total of 11 and 8 miRNAs that target NMT1 and NMT2, respectively were identified to be associated with T cell and B cell receptor pathways ([139]Table 3). Among these miRNAs, miR-654 displayed an interesting relationship with NMT1/2 and MetAP2 genes where miR-654-5p targeted NMT1/2 while miR-654-3p targeted MetAP2. Similarly, miRNA-199b-5p was found to be targeting NMT1/2 while miRNA-199b-3p targeted MetAP2. This 5p and 3p pattern was not limited to these two miRNAs. Same pattern was observed with miR-628, and miRNA-139. These observations suggest that the miRNA species deriving from the 5' arm (5p) and 3' arm (3p) of the same pre-miRNA can regulate both NMT1/2 and MetAP2 genes depending upon which mature miRNA is loaded on to Argonaute protein. Additionally, miR-1246 was also common between NMT1/2 and MetAP2 genes. Fig 5. T-cell and B-cell receptor pathways that are enriched with target genes of microRNAs (miRNAs) which negatively regulate N-myristoyltransferase (NMT1/2) and methionine aminopeptidase 2 (MetAP2) genes. [140]Fig 5 [141]Open in a new tab The figure illustrates two cellular pathways A) T cell receptor signaling and B) B cell receptor signaling that contains genes that are targeted by miRNAs, which regulate NMT1/2 gene. (PD1, programmed cell death-1; ZAP70, Zeta Chain Of T-Cell Receptor Associated Protein Kinase 70; LAT, Linker for Activation Of T-Cells; ICOS, Inducible T-Cell Co-stimulator; DLG1, Discs Large MAGUK Scaffold Protein 1; NCK2, NCK Adaptor Protein 2; LCP2, Lymphocyte Cytosolic Protein 2; GM-CSF, Granulocyte-macrophage colony-stimulating factor; IFN-γ, Interferon gamma; TNFα, Tumor Necrosis Factor alpha; IL, Interleukin; NFKB1, Nuclear Factor Kappa B Subunit 1; CDK4, Cyclin Dependent Kinase 4; PPP3R2, Protein Phosphatase 3 Regulatory Subunit B, Beta; CTLA4, Cytotoxic T-lymphocyte Associated Protein; PKC8, protein kinase C-8; FYN, FYN Proto-Oncogene, Src Family Tyrosine Kinase; Raf-1, Raf-1 Proto-Oncogene, Serine/Threonine Kinase. CD, cluster of differentiation; BCR, break-point cluster region; BTK, Bruton Tyrosine Kinase; DAPP1, Dual Adaptor Of Phosphotyrosine And 3-Phosphoinositides 1; VAV3, Vav Guanine Nucleotide Exchange Factor 3; SHIP, SH2 Domain-Containing Inositol 5-Phosphatase; Rac1, Rac Family Small GTPase 1; SYK, Spleen Associated Tyrosine Kinase; NFATC1, Nuclear Factor of activated T-cells 1; PPP3R2, Protein Phosphatase 3 Regulatory Subunit B, Beta; RASGRP3, RAS Guanyl Releasing Protein 3; MALT1, Mucosa Associated Lymphoid Tissue Lymphoma Translocation; Lyn, LYN Proto-oncogene src family tyrosine kinase). Table 3. MicroRNAs (miRNAs) that target T cell and B cell signaling pathways and stem cell signaling. NMT1 targeting miRNAs Specific Up/Down Regulated References