Can ncRNA convert to coding RNA/mRNA by mutations?

Can ncRNA convert to coding RNA/mRNA by mutations?

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DNA has transcribed into RNA (Non-coding). Can this RNA mutate and become a Protein-Coding one/mRNA? Have there been any such instances reported by scientists?


  1. Possibly, but with such a low frequency as to be unimportant and undetectable. (Monkeys with typewriters producing Shakespeare's Hamlet comes to mind.)

  2. No. Because it would be extremely difficult to detect, it would seem to be of no importance if it did occur at a very low frequency (you make no suggestion of why it would be of interest) and therefore there would be no reason for anyone to try.

Why would it be difficult to detect?

The difference between mutations that occur in DNA and any that might occur in RNA, is that a mutation in DNA can give rise to a population (e.g. of bacteria, or offspring) all of which carry the mutation. Thus, even though the chance of the particular mutation may be very low (say 1 in a million or less), a situation can arise in which the whole population is producing the RNA transcribed from the mutated gene and the protein encoded by the RNA. If RNA mutation occurs with the same frequency and managed to change a non-coding RNA (perhaps a pseudogene with a frameshift) to a coding RNA, only 1 in a million transcripts would be changed. Therefore if one sequenced the RNA the incidence of such changed RNA would be too low to detect, or if detected would be below the error margin in such experimental work.

Why has nobody looked for it?

Experimental science costs time and money. Scientists explore theories that have an importance and probability of success that attracts competitive funding. If there is no particular reason to look for something and the chances of seeing it are minute, nobody will waste their time on it, and they would certainly not get funded to do so.


The role of RNA was previously considered as being limited to a mere intermediate in the transfer of information from DNA, which encodes genetic information, to its protein products. However, it has demonstrated that over 95% of transcripts consist of non-coding RNA (ncRNA) and that these ncRNAs, including microRNA, piRNA and long ncRNA (lcnRNA), are not merely transcribed but have multiple roles in the control of gene transcription and protein translation. Moreover, more than 1,000 RNA-binding proteins (RBPs) have been identified in human tissues, which suggests that RNA metabolism is more tightly regulated than previously considered, although the functions of numerous RBPs remain undetermined. Overall, the preconceived notion of RNA in the field of molecular biology is rapidly changing on a large scale.

During my career as a neurologist, I have met many patients who were suffered from incurable neurodegenerative diseases, such as Alzheimer&rsquos, Parkinson&rsquos, amyotrophic lateral sclerosis (ALS), and spinocerebellar ataxia. Recent research on these incurable neurodegenerative diseases has developed rapidly and a great deal of knowledge has been acquired. For instance, the RNA-binding proteins TDP-43, FUS and Ataxin-2 are involved in the pathology of ALS, while causative mutations of ALS and spinocerebellar ataxia are identified in non-coding regions and non-coding RNAs. Therefore, it is now believed that aberrant RNA metabolism are profoundly associated with the pathogenesis of many neurodegenerative diseases. On the basis of these backgrounds, I strongly believe that it is required to explore novel approaches for understanding the mechanism of these diseases.

In our laboratory, we aim to clarify how abnormalities of RNA modification or function and dysregulation of RBPs due to mutations are linked to human diseases, especially neurodegenerative diseases, under the new theme of &ldquoRNA pathology&rdquo. It is our ultimate goal that our research will be translated to treatments in the near future. Everyone can choose any disease, any RNA and any RBPs as a research theme in our laboratory. Anyone who aspires to tackle these incurable diseases from a new angle is more than welcome to visit us in our laboratory at any time. If you are interested in our work or available positions, please feel free to contact me.

Common miRNA research applications:

  • miRNA profiling—identify differentially expressed mature miRNAs using qPCR in application areas like oncology, metabolic diseases, and autoimmune diseases
  • miRNA quantitation—specific quantification of mature miRNAs using qPCR
  • miRNA validation—confirm miRNA profiling results using qPCR
  • miRNA functional analysis—miRNA mimics and miRNA inhibitors for functional studies
  • Noncoding RNA (ncRNA) analysis—reliable detection and quantitation of non-coding human, mouse, or rat transcripts longer than 200 nucleotides.
  • Small RNA analysis—detect and measure any small RNA
  • pri-miRNA analysis—detect and measure primary miRNAs from human, mouse, or rat species

Applied Biosystems TaqMan MicroRNA Assays are innovative tools for miRNA research-from isolation through discovery, profiling, quantitation, validation and functional analysis. We have developed two methods for sensitive, precise quantification and profiling of miRNA using real-time PCR TaqMan probe-based analysis.

  • TaqMan Advanced miRNA Assays use ligation-based universal reverse transcription for an easy streamlined workflow.
  • TaqMan MicroRNA Assays— this “classic” version employs target-specific stem-loop reverse transcription primers for 3’ extended templates for highly sensitive detection.


The fundamental importance of RNA not only as a messenger molecule, but as a regulator of genes in its own right is increasingly being recognized. The production of mature messenger RNA (mRNA) is dependent on a plethora of processing and regulatory steps involving a complicated repertoire of sequence elements, RNA binding proteins and other regulatory RNA species. Given the complexity of the regulatory machinery, defects in non-coding regions of genes and regulatory genomic regions are common in genetic disease, being present in up to 50% of cases (Yang et al., 2013 Beaulieu et al., 2014) and are also the most common site of genetic variation conferring susceptibility to common, complex disease (Manolio et al., 2008). There is, however, a silver lining. The complexity that causes errors in gene expression or mRNA processing to be such a common occurrence, also provides multiple and differential points of potential therapeutic intervention. Over the past decade, there have been a number of examples, where the specifics of RNA regulatory machinery have been harnessed to produce novel therapeutics that are now in phase III clinical trials [e.g., Patisiran for Familial amyloid polyneuropathy (Rizk and Tuzmen, 2017), Custirsen for prostate cancer (Edwards et al., 2017) and AGS-003 for renal cell carcinoma (Figlin, 2015)]. This review aims to explore the potential for intervention in mRNA processing or post-transcriptional regulation with selected examples for future therapeutic benefit.


The authors thank many colleagues in the field of RNA biology for exciting discussions that contributed to some of the ideas expressed in this article, and G. Rothschild for proofreading the manuscript. The authors apologize to the many colleagues whose work they were not able to cover in this article owing to limitations of space and scope. This work was supported by grants to U.B. (NIAID 1R01AI099195 and R01AI134988), Leukemia & Lymphoma Society, and the Pershing Square Sohn Cancer Research Alliance. L.N. is supported by an NIH grant (T32 AI106711).

The transcribed part of the mammalian genome

Early estimations of the level of transcription in mammals were based on the hybridization of primary nuclear transcripts to genomic DNA. The major part of the mammalian genome was found to be expressed as nuclear transcripts, from one strand or the other. Hybridization experiments demonstrated that, in rat embryos, primary nuclear transcripts contained both unique and moderately repetitive sequences transcribed from 32.8% and 32.9% of genomic DNA, respectively [8]. The most transcriptionally active rat tissue is the adult brain, where transcribed unique and moderately repetitive DNA represent 46.6% and 13.7% of the whole genome, respectively [8]. Similar results were obtained for mouse brain tissues, where 42% of the genome represented by unique sequences was found to be transcribed [9].

Maximal transcription levels are difficult to measure with hybridization experiments because not all genes may be expressed under particular physiological conditions, and also because of difficulties in the isolation of rare transcripts. Experimental determination of the transcribed part of the well-annotated genomes of Escherichia coli (73% [10]) and Saccharomyces cerevisiae (40% [11]) yielded smaller numbers than calculations based on genomic annotation for the same species (88.6% [12] and 78% [13], respectively see Figure 1). According to a recent detailed analysis of the length of sequence occupied by the annotated genes on several chromosomes in the human genome, primary transcripts cover 42.2%, 46.5%, 43.6%, 42.4% and 51% of chromosomes 6, 7,14, 20 and 22, respectively (reviewed in [14]). But these numbers do not represent the full transcriptional potential of the human genome.

Ratios of the protein-coding, non-coding, and untranscribed sequences in bacterial, yeast, nematode and mammalian genomes. Estimations of the transcribed and protein-coding parts of genomes are based on the sequence length of annotated genes [3, 12, 13, 73]. Estimation of the transcribed portion of the human genome is based on the sequence length occupied by the annotated genes on chromosomes 6, 7, 14, 20, and 22 [5].

The annotation of the human genome mostly comprises data on identified protein-coding genes, while a substantial part of the transcriptome has not yet been identified and annotated. Whole-chromosome analysis with oligonucleotide arrays has demonstrated that the level of transcription from human chromosomes 21 and 22 is significantly higher than can be accounted for by known or predicted sequence annotations [15]. The unmapped part of the mammalian transcriptome may contain numerous non-protein-coding genes, as evidenced by the high proportion of non-protein-coding transcripts in human and mouse cDNA libraries [4, 6]. Estimations of the relative complexity of heterogeneous nuclear (hn) RNA versus mature mRNA, based on analysis of the kinetics of hybridization, suggest that non-protein-coding transcripts could represent half, or more, of all transcriptional output from the genomes of eukaryotic organisms [16, 17]. We might expect that in mammals about half of the genome is transcribed.

The question of how many genes there are in the mammalian genome remains open. Pregenomic estimates of the number of human genes ranged between 30,000 and 120,000 [18–20]. Recent analysis of the mouse transcriptome on the basis of annotation of full-length cDNA collections enabled identification of 33,409 unique full-length transcripts, with an estimated total number of independent transcriptional units in the mouse genome of around 70,000 [4]. Large-scale annotation of the human genome with the UniGene assembly of individual expressed sequence tags (ESTs) and cDNAs revealed 59,500 nonredundant clusters representing putative transcriptional units [21, 22]. Thus, the total number of genes in the mammalian transcriptome could be as high as 60,000-70,000.

Protein-coding genes and their untranslated regions

A detailed inventory of the protein-coding genes was made upon the completion of the human and mouse genome projects [3]. Overall, the mouse proteome is similar to that of the human, and about 99% of the mouse protein-coding genes have a homolog in the human genome [3]. The number of protein-coding genes in the mammalian genome was calculated on the basis of known cDNAs and genes predicted by similarity to protein-coding genes in other organisms, and was extended by computer predictions that are supported by experimental evidence such as ESTs. Catalogs of human and mouse protein-coding genes contain slightly more than 22,000 genes for each species [3]. Recent large-scale sequencing and analysis of the large Japanese collection of human cDNA clones added around 2,000 more new sequences to the human protein catalog [6]. Current approaches to gene identification are likely to miss a substantial number of small genes, such as those encoding neuropeptides, antimicrobial peptides, and small adaptor and regulatory proteins. Taking into account the small genes that have yet to be discovered, the total number of protein-coding genes in the mammalian genome is estimated to be around 30,000 [3]. This upper estimate is still surprisingly close to the number of protein-coding genes in the nematode genome (Table 1). The average size of mammalian protein-coding genes far exceeds the average size of the nematode and yeast protein-coding genes, however, mostly on account of the increased length of introns.

The 5' and 3' untranslated regions

Gene expression in eukaryotic organisms is tightly controlled at various levels, and critical cis-regulatory elements for posttranscriptional control are encoded in the 5' and 3' untranslated regions (UTRs). On average, 5' and 3' UTRs are less conserved than protein-coding sequences across species, but more conserved than untranscribed sequences [23, 24]. Highly conserved nucleotide blocks have been detected in 5' UTRs and, especially, in the 3' UTRs of orthologous genes from different mammalian orders, and even between mammals and birds or fish [25, 26]. For some genes, the conservation of UTRs exceeds the conservation of the corresponding coding regions [27]. Many conserved sequence elements in UTRs have been identified as binding sites for proteins or antisense RNAs, which contribute to the regulation of nucleocytoplasmic transport, subcellular localization, translation and the stability of mRNAs [28–31]. The nucleotide context around the principal functional signals, such as start and stop codons, is also an important determinant of expression level [32, 33].

According to the current, scanning model of translation initiation, the eukaryotic ribosome binds to the 5'-terminal cap of an mRNA and starts scanning the mRNA until it detects the first AUG start codon, where it initiates translation [34, 35]. The 5' UTRs contain binding sites for components of multiprotein transcription complexes and also participate in the recruitment of the 40S ribosomal subunit and translation initiation. The length of 5' UTRs, and the presence of additional upstream transcription start codons, may be important for regulating the basal translation level of an mRNA, It has been shown that transcripts with an optimal start codon context tend to have shorter 5' UTRs, whereas an increased length of 5' UTR correlates with a 'weak' start codon context and with the presence of additional upstream start codons [36]. A reduced level of basal translation also correlates with the presence of minor open reading frames located within 5' UTRs and upstream of the main start codon in some genes. Other sequence elements within 5' UTRs act as internal ribosome entry sites (IRESs) these elements have been found in many cellular mRNAs encoding regulatory proteins [28].

It is widely accepted that 3' UTRs play crucial roles in transcript cleavage, polyadenylation and nuclear export, and in regulating the level of transcription and the stability of transcripts. The 3' UTRs may contain sequence elements that mediate negative posttranscriptional regulation. Increasing numbers of publications describe suppression of mRNA translation by small RNA molecules through base-pairing interactions with complementary sequence motifs within 3' UTRs [37]. It has also been shown that the turnover of mRNA is regulated by cis-acting AU-rich elements that promote mRNA degradation, and such motifs are found in the conserved 3' UTRs of many mRNAs encoding regulatory proteins [38].

In addition to motifs that have a negative effect on translation, 3' UTRs carry binding sites for factors involved in translation termination and the release of the synthesized polypeptide, processes that are understood much less thoroughly than the initiation of translation [39]. Binding of regulatory proteins to cis-acting elements within a 3' UTR can be either sequence-specific or facilitated by stem-loop structural elements formed within the mRNA. The importance of the secondary structure of the 3' UTR is exemplified by the family of selenoprotein mRNAs. All mammalian selenoproteins identified so far contain a selenocysteine residue encoded by the stop codon UGA. Incorporation of selenocysteine into the growing polypeptide depends on a conserved stem-loop structure within the mRNA formed by the selenocysteine insertion sequence (SECIS), which is necessary for decoding UGA as selenocysteine rather than as a stop signal [40].

Genomic regions corresponding to the UTRs of mRNAs may contain introns, which leads to the formation of alternative UTRs. Introns are more frequently found in 5' UTRs, although 3' UTRs are generally much longer than 5' UTRs. Alternative UTRs can be formed by the use of different transcription start sites, different donor/acceptor splice sites, and different polyadenylation sites. These have been shown to vary with the tissue and the stage of development, and can significantly affect patterns of gene expression [28, 41].


The origin of eukaryotic introns is the subject of much debate. One hypothesis argues that modern nuclear introns are evolutionary descendants of bacterial self-catalytic introns that penetrated into the eukaryotic lineage and gained biological function in eukaryotes in the process of co-evolution with their hosts through their involvement in the splicing of primary RNA transcripts. An alternative notion is that the vast majority of introns arose within multicellular eukaryotes and were randomly inserted into eukaryotic genes (reviewed in [42, 43]). Introns, which are few in unicellular eukaryotes, are greatly increased in numbers and size within the genomes of higher eukaryotes (Table 1). Nematodes contain more DNA in introns than in exons, while in mammalian genomes introns comprise about 95% of the sequence within protein-coding genes [2, 3]. Interspecies sequence conservation studies have demonstrated that introns are generally high in sequence complexity, although they are less conserved than protein-coding sequences introns contain blocks of conserved sequences and a significant number of selectively constrained nucleotides that remain invariant as a result of stabilizing selection. Genomic sequencing of different taxa has allowed large-scale analysis of homologous intron sequences between related species, such as between Caenorhabditis species or Drosophila species, or between human and mouse or rat, and human and whale or seal [44–51]. Using different alignment methods, these studies estimate that the level of selective constraint in introns is between 5% and 28%, as compared to around 60-70% in exons.

One established biological role for introns is their involvement in nucleosome formation and chromatin organization. Introns have higher potential for nucleosome formation than exons or Alu repeats [52]. Other functional elements identified in mammalian introns are scaffold/matrix-attachment regions (S/MARs), which are thought to anchor chromatin loops to the nuclear matrix and to chromosome scaffolds [53, 54]. These elements account for only a small proportion of constrained nucleotides in introns, however.

Alternative splicing is an important source of proteome complexity in higher eukaryotes it amplifies the number of proteins encoded by a single gene by generating isoforms differing in amino-acid sequence. Nevertheless, the dominance of intronic sequences in the protein-coding genes of higher organisms cannot be fully explained by their role in alternative splicing. Although the vast majority of human and mouse protein-coding genes have introns, only about 40% of them show evidence of alternative splicing [4, 55]. As a rule, internal introns within protein-coding regions are not involved in alternative splicing, unlike those in UTRs, and splicing signals located at intron-exon boundaries are relatively short. The significant levels of nucleotide conservation within introns suggest that introns may have other important functional roles, probably at the RNA level. It has been suggested that the products of intron degradation generated during splicing of pre-mRNA transcripts serve as endogenous control molecules of an RNA-based gene-regulatory network [16, 17] but to date, no experimental data confirm or disprove this idea.

In modern eukaryotes, the transcription and processing of mRNA are highly coupled with intron splicing and/or exon recognition. There is an obvious correlation between the number and total length of introns on the one hand and the developmental complexity of organisms on the other, although the reasons for the abundance of intron sequences and their functions in higher organisms are not fully understood. The notion that introns are involved in complex regulation and development in higher eukaryotes is supported by several lines of evidence. For example, there is a negative correlation between the size of introns and the level of transcription of protein-coding genes. Furthermore, introns in highly expressed genes are substantially shorter than those in genes that are expressed at lower levels. This difference is greater in humans, where introns are, on average, 14 times shorter in highly expressed genes than in genes with low expression [5, 56].

The intron sequences of mammalian protein-coding genes have also been shown to harbor independent transcriptional units, such as small RNA genes [57] and repetitive elements [3]. Repeats constitute about 45% of the human and the mouse genomes (Table 1) and can be found in both transcribed (introns and UTRs) and non-transcribed intergenic sequences. It is not obvious whether the proliferation of transposable repetitive elements in mammalian genomes is associated with some biological advantage. There are notable similarities in the genomic distribution of the major repetitive elements, LINEs (long interspersed nucleotide elements) and SINEs (short interspersed nucleotide elements), in the human and mouse genomes. Genome-wide profiling of human gene expression has revealed that SINE elements are mostly associated with highly expressed short-intron genes, while LINE elements are associated with weakly expressed long-intron genes [5]. Furthermore, similar repeats accumulate in orthologous locations in the human and mouse genomes [3, 58].

The expanding world of non-coding RNA genes

Transcripts from non-coding RNA (ncRNA) genes are not translated into proteins and function directly as structural, regulatory or catalytic molecules. It is not clear how many ncRNA genes are present in the mammalian genome. The existing catalog of mammalian genes is strongly biased towards protein-coding genes, because most efforts were made in cloning and sequencing polyadenylated mRNAs, which tend not to be ncRNAs. Analysis of 33,409 full-length mouse cDNAs showed that ncRNA constitutes more than one third of all the identified transcripts [4]. Recently Ota et al. [6] reported the sequencing and characterization of 10,897 novel human full-length cDNA clones, and ncRNAs represent about half of these newly identified transcripts. Nevertheless, it is not known how many real RNA genes have been cloned, and how many clones in fact represent transcriptional artifacts. Surprisingly, a large proportion of ncRNA transcripts have introns, and many ncRNAs demonstrate distinct patterns of splicing [6]. The presence of introns in ncRNAs adds possibilities for regulation, given that the primary transcript might be functionally inactive, with subsequent cleavage and splicing being required to produce an active RNA molecule. Novel ncRNA genes are difficult to recognize and identify on the basis of sequence, and their discovery still depends largely on experimental approaches. The nature of ncRNA genes, which are often small and multicopy, lacking open reading frames and immune to point mutations, makes them difficult targets for genetic screens. Current estimates of the number of independent transcriptional units (around 70,000) and protein-coding genes (around 30,000) in the mouse transcriptome suggest that ncRNA genes may be highly abundant in the mouse genome [4].

Our understanding of the cellular function of ncRNAs has expanded far beyond the initial notion of their being intermediates and accessories in protein biosynthesis. The size of ncRNA molecules ranges from 20 nucleotides (microRNAs) to thousands of nucleotides (ncRNAs involved in gene silencing) [59]. Furthermore, ncRNAs are involved in many processes, including transcriptional and posttranscriptional regulation, chromosome replication, genomic imprinting, RNA processing, modification and alternative splicing, mRNA stability and translation, and even protein degradation and translocation [59–62]. Within the genome, ncRNA genes are found in extended stretches of conservation within orthologous regions of related genomes in intergenic and intronic sequences that have elevated GC content. Important noncanonical RNA species include families of translational repressors, such as microRNAs and small temporary RNAs (stRNAs) that inhibit translation of target mRNAs, small nuclear RNAs (snRNAs) that function as components of spliceosomes, and small nucleolar RNAs (snoRNAs) that are involved in the chemical modification of structural RNAs. Another important class of ncRNA molecules comprises those with catalytic activity, such as ribonuclease P. The functional importance of ncRNA genes is emphasized by the recent discoveries that link human genetic disorders with non-protein-coding genes [63, 64].

The inhibition and silencing of genes by RNA molecules exploits the highly specific complementarity of nucleic acid interactions. There are two types of naturally occurring regulatory ncRNAs. First, cis-antisense transcripts originate from the same genomic region as the target gene, but have the opposite orientation, and can form long perfect duplexes with their targets such cis-antisense transcripts may be expressed from imprinted regions of vertebrate chromosomes and play roles in chromatin structure. Second, trans-antisense RNAs are short molecules that are transcribed from loci distinct from their mRNA targets and form imperfect duplexes with complementary regions within their targets examples of trans-antisense RNAs are microRNAs and small interfering (si) RNAs [59, 62, 65]. It seems that the increased complexity of gene expression and regulation in higher organisms has promoted the increased use (during evolution) of modular systems, whereby substrate recognition is delegated to diverse small RNA molecules that share a common protein catalytic subunit to exert their effects. An example of such a mechanism is the site-specific methylation of structural RNAs (rRNAs, tRNAs and snRNAs), in which numerous different snoRNAs provide specificity for methylation and pseudouridylation of target bases on the structural RNAs by complementarity, while catalytic activity is conferred by a protein methylase or pseudo-U synthetase associated with the snoRNA [61].

Another class of sequence elements that contributes to the mammalian transcriptome comprises Alu and other repeats. The observed evolutionary selection against change in Alu repeat sequences in the human genome has led to the hypothesis that they are functionally important. In a few cases, Alu elements have been shown to serve as regulators of transcription of adjacent genes [66] and in nucleosome positioning within chromatin [67]. More recent studies indicate that Alu repeats serve as templates for non-coding RNAs that can be involved in the regulation of gene activity and posttranscriptional gene silencing through repression of expression of other genes that contain similar repeats. A strong increase in the level of Alu transcripts in the cell is observed under stress conditions and after viral infection [61].

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Role of regulatory ncRNAs in plant stress responses

Regulatory roles of ncRNAs in various stress episodes also have been well-studied in plants. Activation of different regulatory ncRNAs by biotic and abiotic stress elicitors leads to the regulation of crucial stress-responsive pathways through target transcripts (Fig. 5). As stated in the earlier section, among different ncRNAs, miRNAs are the extensively investigated class followed by siRNAs and lncRNAs (Khraiwesh et al. 2012 Song et al. 2019 Yu et al. 2019). Regulation of gene expression mediated by miRNAs during different stress responses (drought, heat, salinity, cold, nutrient, and pathogen) has been exemplified in a different model and crop plants such as Arabidopsis, wheat, rice, maize, and barley (Barciszewska-Pacak et al. 2015 Ferdous et al. 2017 He et al. 2019 Hua et al. 2019 Mangrauthia et al. 2017a Sailaja et al. 2014). In addition, there are comprehensive reviews that delineated the expression and regulation of different conserved miRNAs during various environmental stress episodes (Ferdous et al. 2015 Megha et al. 2018 Song et al. 2019 Zhao et al. 2016).

General model of stress-responsive regulation by regulatory non-coding RNAs in abiotic and biotic stresses. Abiotic and biotic stresses elicit the production of reactive oxygen species (ROS) and pattern-associated molecular pattern (PAMP) recognition in plants through signal perception. Synthesis of different classes of non-coding RNAs (ncRNAs) in response to ROS and PAMP is one of the defensive mechanisms in plants. miRNAs and other small ncRNAs thus produced in response to stress, bind to their respective target genes with the aid of Argonaute (AGO) proteins by forming RNA-induced silencing complex (RISC), and thus regulate the expression of target genes. Long non-coding RNAs (lncRNAs) regulate the gene expression by mimicking the miRNA targets or through alternative splicing or by chromatin modification. Circular RNAs (circRNAs) regulate the gene expression by acting as miRNA sponges. Successively, thus generated ncRNAs regulate the different metabolic pathways that primes in the stress-responsive regulatory mechanism. BE- bacterial effector, SS- silencing suppressor, dsRNA- double-stranded RNA, PRRs- pathogen recognition receptors, R proteins- resistance protein, RISC- RNA-induced silencing complex

The wide range of miRNAs expression in each stress response has been witnessed in many plant species. However, few miRNA-target modules can show definite expression patterns against specific stress by regulating target genes, and their pattern of expression can be conserved across different plant species (Song et al. 2019). In addition to plant growth and development, the role of conserved miRNA-target modules is also crucial in conferring stress tolerance by integrating with metabolic pathways. Well-known conserved miRNA-target modules such as miR156-SPL, miR159-MYB, miR160-ARF, miR164-NAC (NAM, ATAF, and CUC), miR167-ARF, miR169-NUCLEAR TRANSCRIPTION FACTOR-Y (NFY), miR319-TEOSINTE BRANCHED/CYCLOIDEA/PROLIFERATING CELL FACTORS (PCF) (TCP), miR394-LEAF CURLING RESPONSIVENESS (LCR), miR396-GROWTH REGULATING FACTOR (GRF), and miR398- COPPER/ZINC SUPEROXIDE DISMUTASE (CSD) are known to play an important regulatory role in different stress environments to mitigate the detrimental effects (Fig. 4). For instance, different miRNAs are known to target TFs in phytohormone regulation, such as ABA, GA, ethylene signaling, and auxin signaling under drought conditions (Ferdous et al. 2017). The miR167-ARF module regulates the auxin signaling pathway during drought stress. ARF6 and ARF8, the targets of miR167, negatively regulates auxin signaling pathway through GRETCHEN HAGEN 3 (GH3). During drought, miR167 was upregulated in Arabidopsis, wheat, and maize, while it was downregulated in rice (Song et al. 2019). Similarly, miR169-NFY module also plays a significant role during water-deficit conditions. In Arabidopsis, tomato, and Medicago, downregulation of miR169 enhances the expression of its target, NF-Y (Li et al. 2008 Megha et al. 2018 Zhang et al. 2011a). The increased expression of NF-Y in stomatal guard cells enhances the drought tolerance by controlling the aperture of the guard cell in plants (Li et al. 2008). Besides, miR160-ARF, miR156-SPL, miR159-MYB33, miR164-NAC, miR172-AP2 etc. modules are also shown to be involved in the regulation of drought stress response (Song et al. 2019). Similarly, several miRNAs were also identified in the regulation of plant’s heat stress response (Mangrauthia et al. 2017a Ravichandran et al. 2019 Sailaja et al. 2014 Wang et al. 2011). In Arabidopsis, Brassica, and Populus, one of the important and most conserved miRNA-target modules as a part of the heat stress response is miR398-CSD (Guan et al. 2013 Yu et al. 2012). In Arabidopsis, increased expression of miR398 enhanced the heat tolerance in plants by negatively regulating the expression of its targets — CSD1, CSD2, and COPPER CHAPERONE OF CSD (CCD) (Guan et al. 2013 Lu et al. 2013). Decreased levels of CSD1, CSD2, and CCD aids in the accumulation of heat shock transcription factors (HSFs) and heat shock proteins (HSPs). Furthermore, other conserved modules, viz., miR156-SPL, miR172-AP2 also contribute to heat stress response in plants (Song et al. 2019 Zhao et al. 2016). In addition, the highly conserved miR394-LCR module participates in the cold stress response of plants. In Arabidopsis, overexpressed miR394a plants exhibit cold tolerance by negatively regulating the LCR gene (Song et al. 2016). Furthermore, the increased expression of genes encoding C-REPEAT BINDING FACTORS (CBFs) or DEHYDRATION-RESPONSIVE ELEMENT-BINDING FACTORS 1 (DREB1) in overexpressed miR394 and lcr mutant plants exhibits cold stress tolerance, which infers the positive regulation of miR394 through CBF-dependent pathway in acquiring cold stress tolerance (Song et al. 2016). Furthermore, the regulatory role of conserved miR319-TCP module conferring salinity tolerance in plants evidenced through overexpression studies of osa-miR319a in bentgrass (Agrostis stolonifera) (Zhou et al. 2013). In addition to the abovementioned studies, the involvement of miRNAs in nutrient uptake and nutrient homeostasis also has been shown. For instance, participation of miR399-PHOSPHATE OVER ACCUMULATOR 2 (PHO2) module during phosphate deficiency, miR827-NITROGEN LIMITATION ADAPTATION (NLA) and miR169-NF-Y modules in nitrogen deficiency, and miR395-SULFATE TRANSPORTER21 (SULTR2) in sulfur assimilation and transportation were also studied (Song et al. 2019). Besides miRNAs, studies also suggested the role of different isomiRs in plant stress responses. For instance, the differential expression of various isomiRs of the conserved miR156 family was identified during drought stress in maize (miR156a, b, c, d, e, h, i, and l) and rice (miR156d-5p.2, miR156f-5p.2, miR156h-5p.2, and miR156j-5p.2) (Balyan et al. 2020 Zheng et al. 2019a). Also, during heat stress, the highly differential expression of miR156 isoform than its canonical miRNA has been witnessed in Arabidopsis, which elucidates the important regulatory role of isomiRs (Baev et al. 2014).

Furthermore, the regulatory role of miRNA-target modules during biotic stresses caused by bacteria, fungi, viruses, and insects has also been established (Brant and Budak 2018 Khraiwesh et al. 2012 Song et al. 2019) (Fig. 4). In Arabidopsis, the regulatory role of miR393-TRANSPORT INHIBITOR RESPONSE1(TIR1), AUXIN SIGNALING F-BOX1 (AFB2), and AFB3 was the first identified module as a defensive response against Pseudomonas syringae pv. tomato DC3000, a bacterial pathogen. Here, increased miR393 expression levels due to bacterial PATHOGEN-ASSOCIATED MOLECULAR PATTERNS (PAMP) flagellin (flg22) downregulate TIR1, AFB2, and AFB3, which results in increased bacterial resistance. Similarly, pathogen-associated triggered immunity in response to fungal pathogens, miR773-METHYLTRANSFERASE 2 (MET2) module, displayed enhanced resistance (Salvador-Guirao et al. 2018). Also, in rice, the miR528-ASCORBATE OXIDASE (AO) module contributes towards the enhancement of viral defense by accumulating reactive oxygen species (ROS). Upon the rice stripe virus (RSV) infection, miR528 masked by AGO 18 leads to elevated AO activity and in turn helps in the accumulation of basal reactive oxygen species (ROS) to enhance antiviral defense. In addition to the above discussed prominent regulatory roles of miRNA modules in both abiotic and biotic stress responses, there are many other modules (reviewed in Song et al. 2019) and are not further discussed here.

In addition, other sncRNAs like tasiRNAs are also shown to be involved in plant stress responses. For instance, HEAT-INDUCED TAS1 TARGET 1 (HTT 1) and HTT 2 mRNA targets of TAS1 (trans-acting siRNA precursor 1)-derived tasiRNAs form miR173 contribute to thermotolerance in Arabidopsis (Li et al. 2014a). Plants with elevated levels of TAS1-siRNAs and decreased levels of the HTT genes are sensitive to heat stress, while the plants overexpressing HTT1 and HTT2 genes exhibited enhanced thermotolerance (Li et al. 2014a). Furthermore, during phosphate homeostasis, positive regulation of protein derived from PHOSPHATE12 (PHO12) gene and its cis-NAT (cis-NATPHO12) in Arabidopsis has been confirmed. Downregulation of cis-NATPHO12 through RNAi revealed the impaired allocation of phosphate from root to shoot, which ultimately led to reduced seed yield by reduction of PHO12 proteins (Jabnoune et al. 2013). Similarly, the regulatory role of natsiRNAs during salt stress was demonstrated in Arabidopsis. natsiRNA (24 nt) generated from SIMILAR-TO-RCD-ONE 5 (SRO5) mRNA, targets D1-PYRROLINE-5-CARBOXYLATE DEHYDROGENASE (P5CDH) results in the subsequent formation of 21 nt natsiRNAs. The generated natsiRNAs further participates in the cleavage of P5CDH mRNA. During salt stress, induction of SRO5 protein results in the declined expression of P5CDH activity leading to proline and reactive oxygen species (ROS) accumulation. Thus, the role of natsiRNAs of SRO5 on P5CDH genes, together with their respective proteins in osmoprotection and oxidative stress during salt stress has been confirmed (Borsani et al. 2005 Khraiwesh et al. 2012). Similarly, the role of phasiRNAs derived from miR482, miR828, and miR6455 during drought stress was studied in populus, where populus-specific miR6455 derived 22-nt phasiRNA targeted NAC genes, that are known to play a crucial role in drought stress (Shuai et al. 2016). Furthermore, during biotic stress, the first plant-endogenous siRNA nat-siRNAATGB2 regulates R-gene-mediated ETI (effector-triggered immunity) towards bacterial pathogen Pseudomonas syringae (Ps) infection (Navarro et al. 2006). Induction of this siRNA inhibits the expression of antisense target PENTATRICOPEPTIDE REPEAT PROTEIN-LIKE (PPRL), a negative regulator of RPS2-mediated ETI in response to Ps. Generated endogenous siRNA, nat-siRNAATGB2, aids in R-gene, RPS2-mediated race-specific disease resistance by inhibiting the expression of predicted negative regulator PPRL gene (Katiyar-Agarwal and Jin 2010). Furthermore, in Arabidopsis, phasiRNAs derived from PPR genes confers a defensive response against the Phytophthora capsici infection (Hou et al. 2019). In tomato, transgenic lines expressing short tandem target mimic (STTM) RNAs of miR482/2118 confirm the role of derived phasiRNAs in the regulation of nucleotide-binding site leucine-rich repeat (NLR) genes and the important role of NLR proteins in conferring disease resistance against bacterial and oomycete pathogens (Canto-Pastor et al. 2019). Similarly, overexpression of two tasiRNAs derived from TAS1 and TAS2 loci resulted in reduced virulence against the fungal pathogen Botrytis cinerea (Cai et al. 2018). Also, a study by Wu et al. (2020) reported the crucial role of 22-nt siRNAs derived from nitrate reductase (NIA1 and NIA2) genes helps in plant adaptation to different environmental stress responses by inducing gene silencing and translational repression. In addition to the mechanistic theme of regulation by sncRNAs, the emerging lncRNAs also have considerable attention for their regulatory role in plant stress responses.

lncRNAs that are responsive to different abiotic and biotic stresses also have been identified in different plant species. For instance, drought-responsive lncRNAs have been identified in Arabidopsis, populus, maize, rice, etc., (Chung et al. 2016 Di et al. 2014 Pang et al. 2019 Qin et al. 2017 Shuai et al. 2014). During stress periods, it is evident that lncRNAs regulate the expression of multiple genes through possible mechanisms and act as potential gene regulators in different plant biological processes. For instance, in Arabidopsis, the lncRNA, DROUGHT INDUCED LNCRNA (DIR) is responsive to drought and salinity stress and acts as a positive regulator by modifying the expression of a series of genes. The overexpressed DIR plants exhibited enhanced drought and salinity tolerance (Qin et al. 2017). In rice, genes encoding for zinc-finger proteins of drought QTL region, qSDT2-1, were found to be the predicted targets of identified lncRNAs, which signifies their regulatory role in drought stress (Weidong et al. 2020). Similarly, heat stress–responsive lncRNAs were also identified in brassica, cassava, rice, etc., (Ding et al. 2019 Luo et al. 2018 Wang et al. 2019b). In Brassica rapa, two heat stress–responsive lncRNAs identified as endogenous target mimics for miR164a and contrasting expression of both miRNA and lncRNA define their important role in heat stress response (Wang et al. 2019b). Furthermore, different abiotic stress–responsive lncRNAs act as target mimics for miR156, miR159, and miR172, thus involves in the regulation of various stress-responsive genes ABA, ethylene signaling, HSPs, and HSFs pathways (Ding et al. 2019 Wang et al. 2019b). Similarly, cold and salinity–responsive lncRNAs were identified in several plant species (Karlik and Gozukirmizi 2018 Qin et al. 2017 Wang et al. 2015b, 2019c). Two lncRNAs, COOLAIR and COLD ASSISTED INTRONIC NON-CODING RNA (COLDAIR), promote flowering in plants during cold conditions (Whittaker and Dean 2017). Similarly, signatures of lncRNA regulation in biotic stress responses were evident from different studies (Nejat and Mantri 2017 Yu et al. 2019). For instance, in tomato during Phytophthora infestans infection, the lncRNA16397 induces the expression of GLUTAREDOXIN 22 gene by acting in cis and resulted in the enhanced resistance (Cui et al. 2017). Collectively, these results demonstrate the complex regulatory function of lncRNAs in defensive pathways by modulating the expression of defense responsive genes.

Similarly, the research on stress-responsive circRNAs and derived ncRNAs is in the course of its way. The expression of stress-responsive circRNAs using high-throughput sequencing technologies has been identified. In wheat, Wang et al. (2016a) identified 62 circRNAs in response to dehydration stress. Similarly, in pear fruits, 23 circRNAs showed increased expression during drought stress (Wang et al. 2018b). Furthermore, the expression of circRNAs in response to bacterial pathogen infection by Pseudomonas syringae pv. actinidiae (PSA) in kiwi fruits and by Pectobacterium carotovorum subsp. Brasiliense (PCB) infection in potato delineates their role in biotic stress (Wang et al. 2017a Zhou et al. 2018). A recent study by Fan et al. (2020) in rice showed the contribution of circRNAs in response to Magnaporthe oryzae, a fungal pathogen. The high diversity of circRNAs with tolerant genotype (IR25) during M. oryzae infection is due to more 3′ and 5′ alternative back-splicing and complex splice sites. Furthermore, the role of circR5g05160 in enhancing immunity against M. oryzae has been reported (Fan et al. 2020). Besides circRNAs, accumulation of different derived ncRNAs such as tRFs (tRNA-Val-CAC, tRNA-Thr-UGU, tRNA-Tyr-GUA, and tRNA-Ser-UG) has been reported during heat and osmotic stress in wheat and phosphate stress in Arabidopsis and barley (Hackenberg et al. 2013 Hsieh et al. 2010 Wang et al. 2016b). Furthermore, activation of TE-derived lncRNA11195 after various abiotic stress treatments in Arabidopsis revealed the important role of transposon-derived lncRNAs in stress responses (Wang et al. 2017b). Though sequencing technologies expedite our understanding on the circular and derived ncRNAs in plants, still their functional characterization and in-depth investigation are prerequisite to assign the exact role of these emerging regulatory non-coding RNAs. We have summarized the stress-responsive regulatory non-coding RNAs and their expression which are valuable molecular resources in Tables S1 and S2, to understand their regulatory patterns associated with stress tolerance and plant defense mechanisms.

Epitranscriptomic technologies and analyses

RNA can interact with RNA-binding proteins (RBPs), mRNA, or other non-coding RNAs (ncRNAs) to form complex regulatory networks. High-throughput CLIP-seq, degradome-seq, and RNA-RNA interactome sequencing methods represent powerful approaches to identify biologically relevant ncRNA-target and protein-ncRNA interactions. However, assigning ncRNAs to their regulatory target genes or interacting RNA-binding proteins (RBPs) remains technically challenging. Chemical modifications to mRNA also play important roles in regulating gene expression. Investigation of the functional roles of these modifications relies highly on the detection methods used. RNA structure is also critical at nearly every step of the RNA life cycle. In this review, we summarize recent advances and limitations in CLIP technologies and discuss the computational challenges of and bioinformatics tools used for decoding the functions and regulatory networks of ncRNAs. We also summarize methods used to detect RNA modifications and to probe RNA structure.

Keywords: CLIP-seq RNA modification quantification and locus-specific detection methods RNA structure probing methods RNA structuromes bioinformatics ncRNA transcriptome-wide sequencing technologies.

Lioliou E, Romilly C, Romby P, Fechter P. RNA-mediated regulation in bacteria: from natural to artificial systems. Nat Biotechnol. 201027:222–35.

Wagner E, Flardh K. Antisense RNAs everywhere? Trends Genet. 200218:223–6.

Khorkova O, Myers A, Hsiao J, Wahlestedt C. Natural antisense transcripts. Hum Mol Gen. 201423:54–63.

Hindley J. Fractionation of 32P-labelled ribonucleic acids on polyacrylamide gels and their characterization by fingerprinting. J Mol Biol. 196730:125–36.

Vanhée-Brossollet C, Vaquero C. Do natural antisense transcripts make sense in eukaryotes? Gene. 1998211:1–9.

Lybecker M, Bilusic I, Raghavan R. Pervasive transcription: detecting functional RNAs in bacteria. Transcription. 20145:e944039.

Numata K, Okada Y, Saito R, Kiyosawa H, Kanai A, Tomita M. Comparative analysis of cis-encoded antisense RNAs in eukaryotes. Gene. 2007392:134–41.

Goh S, Stach J, Good L. Antisense effects of PNAs in bacteria. Methods Mol Biol. 20141050:223–36.

Cho KH, Kim JH. Cis-encoded non-coding antisense RNAs in streptococci and other low GC Gram (+) bacterial pathogens. Front Genet. 20156:110–5.

Rasmussen LCV, Sperling-Petersen HU, Mortensen KK. Hitting bacteria at the heart of the central dogma: sequence-specific inhibition. Microb Cell Fact. 20076:24–8.

Lapidot M, Pilpel Y. Genome-wide natural antisense transcription: coupling its regulation to its different regulatory mechanisms. EMB Rep. 20067:1216–22.

Isaacs FJ, Dwyer DJ, Collins JJ. RNA synthetic biology. Nat Biotechnol. 200624:545–54.

Caldelari I, Chao Y, Romby P, Vogel J. RNA-mediated regulation in pathogenic bacteria. Cold Spring Harbor perspect in medicine 201319:123–9

Thomason MK, Bischler T, Eisenbart SK, Forstner KU, Zhang A, Herbig A, et al. Global transcriptional start site mapping using differential RNA sequencing reveals novel antisense RNAs in Escherichia coli. J Bacteriol. 2015197:18–28.

Duygu B, Poels EM, Da Costa Martins PA. Genetics and epigenetics of arrhythmia and heart failure. Front Genet. 201330:202–4.

Wagner EG. Kill the messenger: bacterial antisense RNA promotes mRNA decay. Nat Struct Mol Biol. 200916:804–6.

Georg J, Hess WR. cis-antisense RNA, another level of gene regulation in bacteria. Microbiol Mol Biol Rev. 201175:286–300.

Stazic D, Lindell D, Steglich C. Antisense RNA protects mRNA from RNase E degradation by RNA–RNA duplex formation during phage infection. Nuc Acids Res. 201139:4890–9.

Green PJ, Pines O, Inouye M. The role of antisense RNA in gene regulation. Annu Rev Biochem. 198655:569–97.

Guillier M, Gottesman S, Storz G. Modulating the outer membrane with small RNAs. Genes Dev. 200620:2338–48.

Delihas N, Forst S. MicF: an antisense RNA gene involved in response of Escherichia coli to global stress factors. J Mol Biol. 2001313:1–12.

Faghihi MA, Wahlestedt C. Regulatory roles of natural antisense transcripts. Nat Rev Mol Cell Biol. 200910:637–43.

Waters LS, Storz G. Regulatory RNAs in bacteria. Cell. 2009136:615–28.

Kopf M, Klähn S, Scholz I, Hess WR, Voß B. Variations in the non-coding transcriptome as a driver of inter-strain divergence and physiological adaptation in bacteria. Sci Rep. 20155:9560–7.

Mok WW, McManus SA, Li Y. Small Size, Big Impact: Bacterial Functional Nucleic Acids and Their Applications.Chemical Biology of Nucleic Acids: Springer. 201440:309–323.

Sharma CM, Vogel J. Differential RNA-seq: the approach behind and the biological insight gained. Curr Opin Microbiol. 201419:97–105.

Thomason MK, Storz G. Bacterial antisense RNAs: how many are there and what are they doing? Annu Rev Genet. 201044:167–88.

Liu SR, Hu CG, Zhang JZ. Regulatory effects of cotranscriptional RNA structure formation and transitions. Wiley interdiscip rev RNA. 201647:503–27.

Lasa I, Toledo-Arana A, Dobin A, Villanueva M, De Los Mozos IR, Vergara-Irigaray M, et al. Genome-wide antisense transcription drives mRNA processing in bacteria. Proc Natl Acad Sci U S A. 2011108:20172–7.

Yang Z, Jin X, Rao X, Hu FA. Natural antisense transcript regulates mucD gene expression and biofilm biosynthesis in Pseudomonas aeruginosa. Microbiol. 201180:768–74.

Wagner EGH, Simons RW. Antisense RNA control in bacteria, phages, and plasmids. Annu Rev Microbiol. 199448:713–42.

Chen S, Zhang A, Blyn LB, Storz G. MicC, a second small-RNA regulator of Omp protein expression in Escherichia coli. J Bacteriol. 2004186:6689–97.

Sillers R, Al-Hinai MA, Papoutsakis ET. Aldehyde-alcohol dehydrogenase and/or thiolase overexpression coupled with CoA transferase downregulation lead to higher alcohol titers and selectivity in Clostridium acetobutylicum fermentations. Biotechnol Bioeng. 2009102:38–49.

Wagner EGH, Altuvia S, Romby P. 12 Antisense RNAs in bacteria and their genetic elements. Adv Genet. 200246:361–98.

Faridani OR, Nikravesh A, Pandey DP, Gerdes K, Good L. Competitive inhibition of natural antisense Sok-RNA interactions activates Hok-mediated cell killing in Escherichia coli. Nucleic Acids Res. 200634:5915–22.

Kawano M, Aravind L, Storz G. An antisense RNA controls synthesis of an SOS-induced toxin evolved from an antitoxin. Mol Microbiol. 200764:738–54.

Kawano M. Divergently overlapping cis-encoded antisense RNA regulating toxin-antitoxin systems from E. coli: hok/sok, ldr/rdl, symE/symR. RNA Biol. 20129:1520–7.

Moghaddam MM, Khodi S, Mirhosseini A. Quorum Sensing in Bacteria and a Glance on Pseudomonas aeruginosa. Clin Microbial. 20143:1–10.

Aghamollaei H, Moghaddam MM, Kooshki H, Heiat M, Mirnejad R, Barzi NS. Detection of Pseudomonas aeruginosa by a triplex polymerase chain reaction assay based on lasI/R and gyrB genes. J Infec Public Health. 20158:314–22.

Romby P, Vandenesch F, Wagner EGH. The role of RNAs in the regulation of virulence-gene expression. Curr Opin Microbiol. 20069:229–36.

Liu JM, Camilli A. A broadening world of bacterial small RNAs. Curr Opin Microbiol. 201013:18–23.

Courtney C, Chatterjee A. cis-Antisense RNA and Transcriptional Interference: Coupled Layers of Gene Regulation. J Gene Ther. 20142:9–15.

Papenfort K, Vogel J. Regulatory RNA in bacterial pathogens. Cell Host Microbe. 20108:116–27.

Delihas N. Regulation of gene expression by trans-encoded antisense RNAs. Mol Microbiol. 199515:411–4.

Gottesman S. The small RNA regulators of Escherichia coli: roles and mechanisms. Annu Rev Microbiol. 200458:303–28.

Qin D, Xu C. Study strategies for long non-coding RNAs and their roles in regulating gene expression. Cell Mol Biol Letters. 201520:323–49.

Wahlestedt C. Natural antisense and noncoding RNA transcripts as potential drug targets. Drug Discov Today. 200611:503–8.

Wall ME, Hlavacek WS, Savageau MA. Design of gene circuits: lessons from bacteria. Nat Rev Genet. 20045:34–42.

Kemmer C, Neubauer P. Antisense RNA based down-regulation of RNaseE in E. coli. Microb Cell Fact. 20065:38–44.

Tummala SB, Junne SG, Papoutsakis ET. Antisense RNA Downregulation of Coenzyme A Transferase Combined with Alcohol-Aldehyde Dehydrogenase Overexpression Leads to Predominantly Alcohologenic Clostridium acetobutylicum Fermentations. J Bacteriol. 2003185:3644–53.

Desai RP, Papoutsakis ET. Antisense RNA strategies for metabolic engineering of Clostridium acetobutylicum. Appl Environ Microbiol. 199965:936–45.

Bouazzaoui K, LaPointe G. Use of antisense RNA to modulate glycosyltransferase gene expression and exopolysaccharide molecular mass in Lactobacillus rhamnosus. J Microbiol Methods. 200665:216–25.

Sturino JM, Klaenhammer TR. Engineered bacteriophage-defence systems in bioprocessing. Nat Rev Microbiol. 20064:395–404.

Ghafourian S, Raftari M, Sadeghifard N, Sekawi Z. Toxin-antitoxin systems: classification, biological function and application in biotechnology. Curr Issues Mol Biol. 20146:9–14.

Alessandra S, Flavio S. BMB reports: Artificial antisense RNAs silence LacZ in E. coli by decreasing target mRNA concentration. Biochem Mol Biol Rep. 200841:568–84.

Singh SB, Phillips JW, Wang J. Highly sensitive target-based whole-cell antibacterial discovery strategy by antisense RNA silencing. Curr Opin Drug Discov Dev. 200710:160–5.

Genilloud O. The re-emerging role of microbial natural products in antibiotic discovery. Antonie Van Leeuwenhoek. 2014106:173–88.

Nikravesh A, Dryselius R, Faridani OR, Goh S, Sadeghizadeh M, Behmanesh M, et al. Antisense PNA accumulates in Escherichia coli and mediates a long post-antibiotic effect. Mol Ther. 200715:1537–42.

Ji Y, Yin D, Fox B, Holmes DJ, Payne D, Rosenberg M. Validation of antibacterial mechanism of action using regulated antisense RNA expression in Staphylococcus aureus. FEMS Microbiol Lett. 2004231:177–84.

Good L, Stach JE. Synthetic RNA silencing in bacteria – antimicrobial discovery and resistance breaking. Front Microbiol. 20112:185–94.

Liang JC, Bloom RJ, Smolke CD. Engineering biological systems with synthetic RNA molecules. Mol Cell. 201143:915–26.

Hong P, Li W, Li J. Applications of aptasensors in clinical diagnostics. Sensors. 201212:1181–93.

Said N, Rieder R, Hurwitz R, Deckert J, Urlaub H, Vogel J. In vivo expression and purification of aptamer-tagged small RNA regulators. Nucleic Acids Res. 200937:e133.

Wu Z, Liu X, Liu L, Deng H, Zhang J, Xu Q, et al. Regulation of lncRNA expression. Cell Mol Biol Lett. 201419:561–75.

Qi L, Lucks JB, Liu CC, Mutalik VK, Arkin AP. Engineering naturally occurring trans-acting non-coding RNAs to sense molecular signals. Nucleic Acids Res. 201240:5775–86.

Neupert J, Karcher D, Bock R. Design of simple synthetic RNA thermometers for temperature-controlled gene expression in Escherichia coli. Nucleic Acids Res. 200836:e124.

Lavelle C, Busi F, Arluison V. Multiple Approaches for the Investigation of Bacterial Small Regulatory RNAs Self- assembly. RNA Nanotechnology and Therapeutics: Springer. 201510:21–42.

Saragliadis A, Krajewski SS, Rehm C, Narberhaus F, Hartig JS. Thermozymes: Synthetic RNA thermometers based on ribozyme activity. RNA Biol. 201310:1009–16.

Neupert J, Bock R. Designing and using synthetic RNA thermometers for temperature-controlled gene expression in bacteria. Nat Protoc. 200949:1262–73.

Waldminghaus T, Kortmann J, Gesing S, Narberhaus F. Generation of synthetic RNA-based thermosensors. Biol Chem. 2008389:1319–26.

Hebert CG, Valdes JJ, Bentley WE. Beyond silencing--engineering applications of RNA interference and antisense technology for altering cellular phenotype. Curr Opin Biotechnol. 200819:500–5.

Chang TH, Wu LC, Lin JH, Huang HD, Liu BJ, Cheng KF, et al. Prediction of small non-coding RNA in bacterial genomes using support vector machines. Expert Syst Appl. 201037:5549–57.

Song Y, Hahn T, Thompson IP, Mason TJ, Preston GM, Li G, et al. Ultrasound-mediated DNA transfer for bacteria. Nucleic Acids Res. 200735:e129.

Luo D, Saltzman WM. Synthetic DNA delivery systems. Nat Biotechnol. 200018:33–7.

Järver P, Coursindel T, Andaloussi SE, Godfrey C, Wood MJ, Gait MJ. Peptide-mediated cell and in vivo delivery of antisense oligonucleotides and siRNA. Mol Ther Nucleic Acids. 20121:e27.

Saei AA, Barzegari A, Majd MH, Asgari D, Omidi Y. Fe3O4 nanoparticles engineered for plasmid DNA delivery to Escherichia coli. J Nanopart Res. 201416:1–11.

Heiat M, Aghamollaei H, Moghaddam MM, Kooshki H. Using CM11 peptide as a cell Permeable agent for the Improvement of conventional plasmid transformation methods in Escherichia coli and Bacillus subtilis. Minerva Biotecnol. 201426:149–57.

Shepherd J, Ibba M. Bacterial transfer RNAs. FEMS Microbiol Rev. 201539:280–300.

Availability of data and materials

All data supporting the findings of this study are available within the manuscript except for the raw sequence data. Any data providing genotype information is considered to be personal property by the Chinese law, hence the submission to public achieves is prohibited. The raw sequence data can be acquired upon reasonable request from the authors ([email protected]) if approval could be granted from the Ethics Committee of Guang’anmen Hospital, China Academy of Chinese Medical Sciences.