In the mammary gland transcriptome of lactating dairy cows genes encoding milk proteins are highly abundant, which can impair the detection of lowly expressed transcripts and can bias the outcome in global transcriptome analyses. Therefore, the aim of this study was to develop and evaluate a method to deplete extremely highly expressed transcripts in mRNA from lactating mammary gland tissue.CSN1S1, CSN1S2, CSN2 and CSN3) and whey proteins (LALBA and PAEP) within total RNA followed by RNase H-mediated elimination of the respective transcripts. The effect of the RNA depletion procedure was monitored by RNA sequencing analysis comparing depleted and non-depleted RNA samples from Escherichia coli (E. coli) challenged and non-challenged udder tissue of lactating cows in a proof of principle experiment. Using RNase H-mediated RNA depletion, the ratio of highly abundant milk protein gene transcripts was reduced in all depleted samples by an average of more than 50% compared to the non-depleted samples. Furthermore, the sensitivity for discovering transcripts with marginal expression levels and transcripts not yet annotated was improved. Finally, the sensitivity to detect significantly differentially expressed transcripts between non-challenged and challenged udder tissue was increased without leading to an inadvertent bias in the pathogen challenge-associated biological signaling pathway patterns.Selective RNA depletion was performed by hybridization of antisense oligonucleotides targeting genes encoding the caseins  contains supplementary material, which is available to authorized users. RNA sequencing (RNA-Seq) has revolutionized the study of whole transcriptomes in cells and tissues and has opened a new horizon for the understanding of global gene expression by providing fundamental new insights into the structural organization and functional regulation of the genomes at transcriptional level. Molecular cataloguing of cell- and tissue-specific transcriptome elements is essential for the identification and functional annotation of regulatory features. These are important to understand physiological changes and molecular mechanisms in an organism in response to environmental challenges, during development and under disease-associated conditions.CSN2, CSN3, CSN1S1, CSN1S2) and whey protein genes, progestogen-associated endometrial protein gene (PAEP) encoding ß lactoglobulin and α lactalbumin (LALBA) [Bovine mastitis is an inflammatory disease of the mammary gland mainly due to bacterial infection , 2. It a (LALBA) , 11. Tra (LALBA) . As a co (LALBA) –15, willIn order to be able to identify even rare transcripts accurately and reliably in the lactating mammary gland transcriptome, a high level of sequencing depth must be achieved, but this ultimately leads to higher sequencing costs. The aim of our study was therefore to develop a reliable, cost-efficient method that reduces the proportion of high-frequency transcripts in mRNA from bovine mammary gland tissue in order to be able to establish a comprehensive catalogue of mammary gland transcripts. Using this method, an improvement of detection sensitivity of transcripts with marginal expression levels should be achieved while simultaneously the costs for deep transcriptome profiling of mammary gland from lactating cows via RNA-Seq are reduced.There are two predominant techniques underlying the different RNA depletion procedures and commercially available kits applied to reduce the prevalence of highly abundant genes, such as globin RNA in blood and ribosomal and mitochondrial RNA fractions in total RNA from various tissues. In the first step, both strategies rely on hybridization of gene-specific complementary oligonucleotides to the targeted gene sequences in the total RNA. The difference between these techniques is in eliminating the unwanted targeted genes from the pool of total RNA sequences. In the second step, therefore one strategy is based on capturing of the RNA:DNA duplexes using the paramagnetic bead technology. In the alternative strategy, the RNA:DNA hybrids are degraded by RNAse H so that the targeted sequences are no longer available for subsequent applications such as polyA+ selection. A cross-site comparative study with commercially available rRNA depletion kits including kits based on the capture of rRNA by complementary oligonucleotides coupled to beads and also kits based on the hybridization of rRNA with antisense DNA oligonucleotides followed by degradation of RNA:DNA hybrids with RNase H, showed that although there were differences between the underlying rRNA depletion chemistries, all tested kits were able to successfully remove a significant amount of rRNA in library preparations . All kitE. coli challenged and non-challenged mammary gland tissue of three lactating Holstein-Friesian dairy cows. To monitor the performance and efficiency of this targeted RNA depletion protocol, a comparative RNA-Seq analysis was carried out on depleted and non-depleted mammary gland RNA samples.In our study we developed an RNase H-mediated RNA depletion approach targeting highly expressed milk protein genes, which was validated experimentally on total RNA isolated from Targeted RNA removal of highly abundant transcripts prior to RNA-Seq has been successfully introduced into commercial library preparation kits to deplete globin RNA in blood and ribosomal and mitochondrial RNA fractions in total RNA from various tissue types –21.Focusing on the removal of highly abundant milk protein transcripts in the total RNA pool extracted from mammary gland samples of lactating cows, we have developed an RNase H-mediated RNA depletion method based on sequence-specific anti-sense oligonucleotides targeting milk protein genes. The final methodological procedure is illustrated in Fig. Prior to applying this RNA depletion strategy in a proof of principle experiment on RNA samples from pathogen-challenged and non-challenged udder tissue of lactating cows, several technical optimization steps were performed and monitored by comparative RT-qPCR analysis of depleted and non-depleted RNA samples. Total RNA extracted from udder samples of two lactating cows was used to analyze the effect of modifications in the RNA depletion protocol.LALBA, CSN1S1 and PAEP compared to CSN3, CSN2 and CSN1S2, we modified the hybridization assay conditions and replaced the antisense oligonucleotide sets for LALBA, CSN1S1 and PAEP with others. These modifications established in RNA depletion variant A improved the RNA depletion efficiency only slightly. In addition, the effect of the RNA depletion procedure itself (RNA treatment with buffer instead of antisense oligonucleotides) was also monitored.In the initial experiment we adjusted the ratio of antisense oligonucleotides in the oligonucleotide depletion mix according to the abundance of the targeted milk protein genes in lactating mammary gland as retrieved from literature data . Since RFinally, the concentration of the respective oligonucleotides in the depletion mix was modified and adjusted to the same equimolar level for all oligonucleotides for RNA depletion variant B. In the end, the results of RT-qPCR analysis of depleted and non-depleted samples revealed that the mRNA abundance of targeted milk protein genes was reduced by 30 to 90% in the depleted samples depending on the specific gene targeted and the experimental conditions applied. Examples of RT-qPCR evaluation for the two methodological variants A and B used in our follow-up study on RNA samples from pathogen-challenged and non-challenged udder tissue of lactating cows are shown in Fig. Exemplarily, test RNA-Seq libraries were prepared from depleted and non-depleted RNA samples from the same tissue sample and subjected to paired-end RNA-Seq. Monitoring of the RNase H-mediated depletion effect by RNA-Seq analysis revealed a decreased proportion of reads mapping to the targeted milk protein genes, declining from about 60% in the transcriptome for the non-depleted sample to 30% in the RNA depleted sample in non-challenged tissue samples (data not shown). Thus, the results of the transcriptome analysis essentially confirmed those obtained by RT-qPCR indicating a substantial reduction of the prevalence of milk protein genes in the transcriptome of the mammary gland from lactating cows. Nevertheless, we were still able to detect milk protein gene transcripts by both RT-qPCR and RNA-Seq.However, the use of an RNA depletion approach, which reduces the total milk protein gene content by about 50% in total, enables sequencing costs to be reduced. We can assume that usually about 60% of the genes expressed in the transcriptome of the lactating mammary gland are milk protein genes see Fig. . For desE. coli. Another udder quarter of the same cow served as a non-challenged control.In a proof-of-principle study on RNA samples from pathogen-challenged and non-challenged udder tissue of lactating cows, transcriptome sequencing analysis has been carried out with a total of 18 RNA-Seq libraries prepared from the mammary gland of three lactating cows. One udder quarter of each cow had been experimentally challenged with RNase H-mediated RNA depletion targeting highly abundant milk protein gene transcripts was carried out on total RNA isolated from challenged and non-challenged mammary gland samples prior to library preparation. RIN values of the non-depleted RNA samples were in a range from 7.7 to 9.6; the scores have declined by about 2 units after RNA depletion treatment, as we had also seen in initial investigations, which may indicate partial RNA degradation. However, the RIN values were not associated to the RNA depletion differences for individual target genes (see below).E. coli was performed to compare the effects of the targeted milk protein gene depletion procedure in case of lower milk protein synthesis/milk production and to evaluate a possible bias in the samples caused by applying the RNA depletion method.Challenge of udder quarters with the pathogen Bos taurus reference genome revealed an average mapping rate of 98% across all 18 samples  accounting for a total of 77% of reads. The study by Cánovas et al. [Previous transcriptome studies , 22, 23 s et al.  has showE. coli challenged udder quarters 30% (0.6–57%) of fragments mapped to transcripts coding for milk proteins  of the total number of mapped fragments to milk protein genes, whereas in  studies , 25 and E. coli caused massive tissue damage as observed during sample collection and in turn led to a decrease in milk production in the cows investigated. Mastitis-associated reduction of milk production and decrease of the milk protein gene expression levels following infection with E. coli have been reported in other studies [E. coli-challenges compared to non-challenged samples   or 24% (21–30%)  of the fragments from the non-challenged udder quarters were assigned to the milk protein genes , independently of the challenge status of the udder tissue and resulted in a reduction of milk protein transcripts by more than 50% compared to the non-depleted sample.CSN2 transcripts were the most abundant milk protein gene transcripts  in depleted samples compared to non-depleted samples (Table CSN2 transcripts with a reduction from an average proportion of 16% of the counted fragments in non-depleted samples to 4% (variant A and B) after RNA depletion. Transcript depletion of CSN1S1, PAEP and LALBA was less comprehensive . However, it is conceivable that it would be more advantageous to select the capture oligonucleotides as close as possible to the 3′ end of the gene to be depleted, which was not always possible due to the inherent sequence properties of individual genes .Visual inspection of targeted milk protein gene sequences revealed that there are no internal polyA stretches with a number of consecutive A nucleotides > 7 present, which could have compromised the subsequent polyA selection step. Differences in the distance between the selected oligonucleotide position and the 3′ end of the target gene can also be excluded as a possible general cause for variable RNA depletion efficiency, since the CSN3. As CSN3 transcripts were almost completely depleted, obviously this sequence variation did not affect the depletion efficiency.Another reason for insufficient depletion of target transcripts might be genetic variants localized in the gene section covered by the oligonucleotide used for RNA depletion. However, transcript sequence analysis of the individuals included in this study revealed that SNPs in capture oligonucleotide sequences could only be detected for CSN1S1 can be due to the existence of a high number of alternative splice variants, which were not all addressed by the capture oligonucleotides designed. The new bovine genome annotation ARS-UCD1.2 at NCBI that has been published very recently displays 25 transcript variants predicted based on now available RNA-Seq data (https://www.ncbi.nlm.nih.gov/gene/282208), not all of which were known at the time of primer design. Both CSN1S1 target oligonucleotides are located in different splice sites, where an intron may be inserted, so that some highly expressed transcript variants cannot be covered. In this case, adding additional capture oligonucleotides in the RNA depletion assay, which cover all CSN1S1 transcript variants may be beneficial in future iterations of the depletion protocol. For the other genes with incomplete RNA depletion efficiency, PAEP and LALBA, neither alternative splice variants nor genetic variation in oligonucleotide sequences were detected.However, the RNA depletion approach may have limitations, if the animals to be analyzed show not yet detected alternative splice sites in the targeted gene. The visual inspection of read alignments using the Integrative Genomics Viewer (IGV) revealed that the lower RNA depletion efficiency observed for PAEP and LALBA read alignments using the IGV showed that their reads in depleted RNA samples were biased toward increased coverage at the 3′ end of the respective gene. In these samples, a clear decline of read coverage is visible behind the last nucleotide of the capture oligonucleotide in 5′ direction of the gene as expected. Respective non-depleted samples showed no analogous decline in read coverage,  showed 1079 significantly differentially expressed loci  showed that RNA depletion improved the sensitivity to detect such well-known effects on gene expression. We observed that significantly differential expression of some cytokine encoding genes in response to E. coli challenge was only detectable in the depleted samples A and B in contrast to the non-depleted samples.Further, we tested for potential differential expression of individual genes well described in literature to be affected by ry gland . We had IL6 gene [CCL16 gene. The CCL20 gene was found to be significantly differentially expressed only in samples processed with RNA depletion variant B. These cytokines and chemokines are known to be regulated after challenge with E. coli [TREM1 (triggering receptor expressed on myeloid cells 1) gene, a critical regulator of diverse cellular functions including amplification of inflammation [CCR2 and CCR7 genes, both encoding G-protein coupled receptors, which are essential for leukocyte recruitment [IL6 and CCL20 genes between pathogen-challenged and non-challenged tissue samples confirmed a higher sensitivity for detecting a response to pathogens in depletion variant B , the total number of expressed loci found in the transcriptomes of each group was determined. Non-depleted samples showed a lower number of expressed loci than samples depleted with variant A or B Table . Even afThe results of our study suggest that the sensitivity of the RNA-Seq analysis is improved when removing highly abundant milk protein gene transcripts from the total RNA pool of the mammary gland from lactating cows prior to RNA-Seq library preparation (negative selection). The implementation of a selective RNase H-mediated RNA depletion of milk protein gene transcripts from the transcriptome of the mammary gland tissue of lactating cows will help to achieve a more comprehensive transcript catalogue of the mammary gland transcriptome, which better reflects its complexity. At the same time, our data confirm that targeted milk protein transcript depletion does not introduce a bias in the outcome of transcriptome analyses within mammary gland pathogen challenge experiments.E. coli challenge  of parenchymal tissue from the challenged and from a control  udder quarter were pulverized in liquid nitrogen, and total RNA was extracted with TRIzol reagent  followed by an on-column-purification using the NucleoSpin RNA II kit  with modifications of the DNase digestion step according to Weikard et al. . After tCSN1S1, CSN1S2, CSN2 and CSN3), α lactalbumin gene (LALBA) and progestagen associated endometrial protein gene (PAEP) in lactating mammary gland tissue is based on hybridization of antisense oligonucleotides specific for these target transcripts to total RNA prior to library preparation used for RNA-sequencing (RNA-Seq). The targeted RNA depletion procedure is presented in Fig. Bos taurus reference genome assembly UMD3.1  using tease 105 .Table 6AThe effect of concentrations of the antisense oligonucleotides in the hybridization assay was tested in two different experimental conditions Table . In the Duplicates of 1 μg of total RNA from each sample were denatured by incubation for 2 min at 95 °C in hybridization buffer  and hybridized for 5 min at 65 °C with antisense oligonucleotides pooled in a total volume of 10 μl. The reactions were carried out in a thermocycler and completed with cooling down to 4 °C.2, 0.02 mM DTT) was prepared on ice. The hybridized RNA-oligonucleotide hybrids were added to 10 μl of RNAse H reaction mixture, mixed, incubated at 37 °C for 10 min and cooled down to 4 °C. To stop the reaction, 1 μl 0.5 M EDTA was added immediately. After shortly spinning down the reaction mixtures the respective duplicates of the samples were pooled, and finally the pooled samples were purified with the RNeasy MinElute Cleanup kit (Qiagen). The RNA quality was controlled with the Agilent Bioanalyzer 2100 and the RNA quantification was carried out using the Qubit Fluorometer.Immediately after hybridization of the RNA samples with the antisense oligonucleotides, a selective digestion of the RNA strand of the RNA-oligonucleotide hybrids was carried out with RNase H . The RNase H reaction mixture consisting of 2 U RNase H and 20 U SUPERase-In  in RNase H-buffer  according to the manufacturer’s instructions. RT-qPCR analysis was performed as described  on a LightCycler qPCR platform (Roche). Milk protein gene copy numbers were normalized against those of ease 105 ) using tease 105 . SequencThe effect of the RNA depletion on the transcript composition of the respective samples was monitored by comparative transcriptome analysis of depleted (variant A and B) and non-depleted RNA samples (0), from the challenged and an non-challenged control udder quarter using RNA-Seq. Stranded indexed poly(A+) selected libraries were prepared from 250 ng depleted or non-depleted RNA of mammary gland samples using the TruSeq RNA Library Preparation Kit v2 set A (Illumina). The standard procedures for Illumina’s mRNA-Seq were applied, and the libraries were subjected to paired-end (2 × 100 bp) mRNA sequencing in two lanes on the HiSeq 2500 Sequencing System (Illumina) .After removal of adapters with Cutadapt (version 1.13) , qualityBos taurus genome annotation (Ensembl-Annotation release 87). First, all sequencing fragments of a sample were summed up (=100%) using R, version 3.4.3 [CSN1S1, CSN1S2, CSN2, CSN3, LALBA and PAEP) were counted, and the respective fraction across all target genes was calculated for each sample. Finally, averages of all three animals for control and depleted samples were calculated from these values and compared. In addition to average values across all genes in depleted and non-depleted, challenged and non-challenged samples, fragments for single milk protein genes of each udder quarter were analyzed and compared for a more detailed evaluation.To assess the success of the targeted RNA depletion of the milk protein gene transcripts, the proportion of these transcripts in the transcriptomes of depleted (variant A and B) and non-depleted control samples of challenged and control udder tissue was determined. FeatureCounts  was usedon 3.4.3 . AfterwaTo determine if/how the general expression patterns of genes of the mammary transcriptome were affected by the RNA-depletion procedure, the transcripts present in the mammary transcriptome of lactating udder tissue were grouped into expression categories based on their transcript expression levels.Fragments per kilobase per million fragments mapped (FPKM) were calculated for all loci based on the fragment counts determined by featureCounts. To define the limits of the expressions categories, an average FPKM for each locus across all non-depleted samples  was calculated. All loci with an average FPKM value lower than 1.0 and the six milk protein genes targeted by RNA depletion were removed from the dataset.From this final dataset, 25, 50 and 75%- quartiles were calculated. The limits of expression categories were defined accordingly. The expression quartiles comprised transcripts with FPKM values ranging from 1 to 3.55 FPKM (“very low”), 3.55 to 7.75 FPKM (“low”), 7.75 to 17.19 FPKM (“medium”) and 17.19 to 21,068.1 FPKM (“high”).In the next step, average FPKM values of all transcripts of each defined expression quartile were calculated. This was performed for the non-depleted as well as the depleted samples (variants A and B) separately, but with challenged and non-challenged samples together.Read counts per gene as obtained by featureCounts from the Subread package  based onE. coli challenge and whether this is affected by the targeted RNA depletion of highly abundant transcripts was performed using Cuffdiff (v.2.2.1) [Bos taurus UMD 3.1 Ensembl v87 genome annotation. The differential gene expression of challenged versus non-challenged udder tissue samples was compared in both experimental RNA depletion variants (A and B) and the control group separately (0), respectively. Thereafter, using R-scripts [Investigation of differential gene expression in response to v.2.2.1)  with def-scripts  the totaThe Cuffdiff output of each group  was also used for biologically functional pathway analyses performed with the Ingenuity Pathway Analysis software (IPA) . IPA ana, were normalized against that of the PPP1CC gene serving as reference gene. Differential expression between challenged and non-challenged samples was analyzed by t-tests within non-depleted and RNA-depleted samples.Depleted and non-depleted RNA samples were reverse transcribed to cDNA and RT-qPCR analysis was performed essentially as described above. Primer sequences of transcripts subjected to quantification are provided in Table To evaluate the RNA depletion effect on the capability to detect yet unknown loci, we used StringTie  to perfoBased on these annotations the total number of loci expressed in each group was determined with featureCounts. To this end, an average FPKM value for each locus was calculated. To improve the specificity of the data and remove potential background noise those loci with an FPKM < 1 were removed from the dataset.In addition, the group-specific assemblies were evaluated with regard to unknown/new loci with GffCompare . The detAdditional file 1:PAEP gene for the E. coli challenged udder sample from cow 2. O, A and B represent the non-depleted or the depleted RNA samples (protocol variant A and B), respectively. Exon 6 is zoomed in for better demonstration of the position of the antisense capture oligonucleotide (see red line). (PDF 235 kb)IGV screen shot of the read distribution across the Additional file 2:E. coli challenged and non-challenged udder samples of each cow investigated. 0, A, and B represent the non-depleted or the depleted RNA samples (protocol variant A and B), respectively. (PDF 1551 kb)Correlation plot of gene expression level (in log10 FPKM) between depleted and non-depleted RNA samples within Additional file 3:Distribution of gene expression level (FPKM) for the depleted and non-depleted RNA samples from non-challenged udder samples. 0, A, and B represent the non-depleted or the depleted RNA samples (protocol variant A and B), respectively. (PDF 283 kb)Additional file 4:Number and overlap of expressed genes with FPKM > 1 in depleted (protocol variants A and B) and non-depleted (0) RNA samples. (JPG 645 kb)Additional file 5:Lists of differentially expressed genes comparing pathogen-challenged and non-challenged udder quarters for non-depleted (0) and depleted RNA samples (protocol variants A and B) at q < 0.05 used for upload into IPA pathway analysis. (XLSX 70 kb)Additional file 6:Lists of IPA canonical pathways significantly enriched for differentially expressed genes in non-depleted (0) and depleted RNA samples (protocol variants A and B) from pathogen-challenged and non-challenged udder quarters. Pathways are classified for being observed across all protocols  or shared between two or all three protocols. (PDF 214 kb)


Gene expression is typically quantified as RNA abundance, which is influenced by both synthesis (transcription) and decay. Cytoplasmic decay typically initiates by deadenylation, after which decay can occur through any of three cytoplasmic decay pathways. Recent advances reveal several mechanisms by which RNA decay is regulated to control RNA abundance. mRNA can be post-transcriptionally modified, either indirectly through secondary structure or through direct modifications to the transcript itself, sometimes resulting in subsequent changes in mRNA decay rates. mRNA abundances can also be modified by tapping into pathways normally used for RNA quality control. Regulated mRNA decay can also come about through post-translational modification of decapping complex subunits. Likewise, mRNAs can undergo changes in subcellular localization . Additionally, specialized functions of mRNA decay pathways were implicated in a genome-wide mRNA decay analysis in Arabidopsis. Advances made using plants are emphasized in this review, but relevant studies from other model systems that highlight RNA decay mechanisms that may also be conserved in plants are discussed. This review examines cytoplasmic mRNA decay with a focus on how mRNA decay regulates transcript abundance. Typically, changes in RNA abundances are attributed to transcription; however, considerable evidence supports important contributions from mRNA decay and this review focuses on recent advances in this area. Failing to account for changes in mRNA abundance that arise from altered decay rates can compromise molecular strategies for improving agriculture and ignores interesting biological phenomena.7G) cap. mRNA decay is initiated by the removal of the 3′ poly(A) tail in a process called deadenylation1 , which is also known as DIS3-like 3′-5′ exoribonuclease 2 (DIS3L2) in fungi and metazoans. To initiate 5′→3′ decay, the m7G cap is removed by the decapping complex, resulting in a 5′ monophosphorylated mRNA that is vulnerable to digestion by the cytoplasmic eXoRiboNuclease 4 . All three of these RNA decay pathways are highly conserved in eukaryotic model systems, with the exception ofSaccharomyces cerevisiae, which lacks homologs of the decapping complex scaffold, VARICOSE (VCS), and SOV/DIS3L2. Thus, advances in any model system are of potential importance to the field.Two stability determinants protect mRNA from untimely degradation: (1) the 3′ polyadenosine (poly(A)) tail and (2) the 5′ 7-methylguanosine -specific ribonuclease (PARN), and the poly(A) nuclease (PAN). CAF1 (of the CCr4-NOT complex) is a major deadenylase in plants, as loss-of-function mutants in this protein result in severely impaired mRNA decay3. However, despite their importance for mRNA metabolism, the specificity of these three modes of deadenylation and their regulation are not well understood.Removal of the poly(A) tail of mRNA by deadenylases is the first and rate-limiting step in mRNA degradation2. Eukaryotic exosomes resemble the bacterial RNase PH and polynucleotide phosphorylase (PNPase) RNA decay complexes in that that they are large protein complexes with a barrel-like configuration4. However, for bacterial PNPase, the barrel’s interior is the site of active RNA decay. This is in contrast to metazoan and fungal exosome core proteins, which lack catalytic activity, even though their sequences show some conservation6. In addition to the catalytically inactive central core (known as Exo9 for its nine subunits), the eukaryotic exosome contains peripheral subunits that carry out RNA processing and decay . A recent study showed that the Arabidopsis RRP41, an Exo9 subunit of the central barrel, retains catalytic activity8. This activity appears to extend through the entire plant lineage.The RNA exosome is a large multi-subunit complex with both nuclear and cytoplasmic functionsvcs mutants9. It is a broadly conserved RNase II domain protein with highly conserved homologs in metazoans and fungi13. In these systems, SOV/DIS3L2 substrates include non-coding RNAs (ncRNAs), long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and their precursors, and mRNAs17.Drosophila sov/dis3l2 knock-down lines show over-growth phenotypes18, and mutations in theSOV/DIS3L2 gene in humans result in an embryonic-lethal cellular over-growth condition known as Perlman syndrome20. These severe phenotypes are in contrast to Arabidopsis, as several phenotypically normal accessions lack a functional SOV, including the Col-0 reference strain10.The other cytoplasmic 3′→5′ exonuclease, SOV/DIS3L2, is an exosome-independent enzyme that was first identified in Arabidopsis as an accession-specific suppressor of7G cap of mRNAs is catalysed by DECAPPING2 (DCP2), which forms a heterodimer with its activator, DECAPPING1 (DCP1). InS. cerevisiae, dimerization of these two proteins is sufficient for decapping21, but higher eukaryotes also require a scaffold protein, known as VCS 25. A notable feature of the mRNA decapping complex is that it can localize to cytoplasmic foci called processing bodies (P-bodies)26.Removal of the 5′ m27. Although plants do not possess an XRN1 ortholog, they do have an ortholog of XRN2, which is known as XRN4 and localizes to the cytoplasm and functions like XRN1 in other model systems28.Decapped mRNAs are further degraded by XRN proteins, which also function in nuclear RNA metabolism . XRN1 and XRN2 are the major 5′→3′ exonucleases in the fungal and metazoan cytoplasm and nucleus, respectively29. Although endonucleolytic cleavage, which can be specified by small RNAs (including miRNAs), is an important regulatory process, space limitations prevent its inclusion here and so we refer readers to several excellent reviews33. We consider five emerging areas that reveal either regulation by decay or its potential: (1) RNA structure , (2) RNA quality control (RQC), (3) regulation of mRNA decapping , (4) mRNA localization , and (5) decay pathway interplay and the potential for an RNA to switch decay pathways.The focus of this review is on regulation, a term we use in its strict sense: a tunable parameter that alters decay rates of specific mRNAs and results in changes in their abundanceMany RNA fates are determined by the activity of their binding proteins; different sets of proteins bind to RNAs as they progress from nascent transcripts through their eventual degradation. These proteins promote functions such as post-transcriptional processing, translation initiation, and targeting for decay. Recent progress in understanding the roles of covalent RNA modifications and their subsequent effects on the affinities of RNA-binding proteins and RNA secondary structure highlights their importance in regulating mRNA stability.6A) is the most prevalent reversible covalent mark on eukaryotic RNA and plays important roles in many steps of RNA metabolism, including mRNA decay34. Dynamic m6A modification has been demonstrated to be vital for development, most notably cell differentiation38. Enzymes that catalyse the addition and removal of this modification  have been characterized. Regulatory outcomes arise through reader proteins, which consist of YT521-B homology (YTH) domain proteins that bind m6A-modified RNAs.N6-methyladenosine (m6A modification of eukaryotic mRNA occurs adjacent to stop codons and transcription start sites and within 3′ untranslated regions (UTRs)39. Transcriptome-wide studies of m6A modifications in Arabidopsis suggest that plants additionally have m6A sites adjacent to start codons40. Accordingly, there are plant-specific modification motifs (such as URUAY) that are methylated along with the general eukaryotic RRACH and RAC consensus sequences41. In fungi and metazoans, the YTHDF (reader) proteins remove the bound m6A-modified transcripts from the translational pool and initiate their decay by recruiting the CCR4-NOT deadenylase complex43. A similar modification—2′-O-dimethyladenosine (m6Am)—at, or adjacent to, the 5′ cap also impacts decay rates by inhibiting the action of the decapping enzyme (DCP2), thereby slowing decay rates44.m6A readers in plants that share homology with human YTHDF proteins46. Roles for plant m6A readers have been implicated by developmental defects in mutants: plants mutant for the m6A readers ECT2 and ECT3 have defects in leaf and trichome development46. Plants mutant for components of the m6A methyltransferase (writer) complex have hypomethylated transcripts and display enlarged shoot apical meristems and organogenesis defects due to increased stability of their target transcripts38. Mutants with defects in the m6A eraserALKB HOMOLOG 10B (ALKBH10B) have suppressed vegetative growth and a delayed transition to flowering because of global hypermethylation47, which was associated with stabilization of transcripts encoding FLOWERING LOCUS T (FT) and SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 3 and 9 (SPL3/9). Thus, contrary to animals, m6A modification in plants appears to stabilize target transcripts. This is emphasized by the observation that faster RNA decay also occurred inect2 reader mutants, indicating that the binding of ECT2 to m6A-modified RNAs generally led to their stabilization41. Whether this pattern of stabilization by m6A modification extends to all modified plant RNAs, and the mechanisms that bring about the differing responses in plants and other systems, will be an important topic of future exploration.EVOLUTIONARILY CONSERVED C-TERMINAL REGION 2 and 3 (ECT2 and ECT3) are cytoplasmic m6A modification in many additional cellular responses, including viral responses in both plants and metazoans, have also been reported49. Teasing apart how cells integrate these reversible modifications and distinguish between selectively altered mRNA stability and other m6A functions is likely to become a very interesting story.Roles of m52. Both URT1 and HESO1 uridylate miRNAs. In metazoan and fungal systems, miRNA uridylation leads to destabilization via SOV/DIS3L253 but whether this is also the case in Arabidopsis is unknown.RNAs are also modified on their 3′ ends, and poly(A) tail addition is the best-known example. Recent studies highlight the importance of another 3′ modification, uridylation, which is catalysed by TERMINAL URIDYLYLTRANSFERASES (TUTases). UTP:RNA URIDYLTRANSERASE (URT1) and HEN1 SUPPRESSOR 1 (HESO1) are the two major TUTases in Arabidopsis54. In other systems, mRNA uridylation has been linked to degradation by SOV/DIS3L2, including formation of SOV/DIS3L2-TUTase complexes55. However, whether mRNA uridylation in plants also leads to transcript destabilization is not known, perhaps because most Arabidopsis uridylation studies have used the Col-0 accession, which is ansov mutant10. Finally, uridylation also tags the 5′ cleavage fragments of mRNAs that result from miRNA-induced cleavage. This feature promotes decay via RISC-INTERACTING CLEARING 3′-5′ EXORIBONUCLEASES1 and 2 56, which not only targets these fragments for decay but also appears to be important for allowing fast cycling of RISC complexes. In addition, non-stop decay  can eliminate 5′ cleavage products if miRNA or small interfering RNA (siRNA)-induced cleavage occurs in mRNA coding regions57.mRNAs are also uridylated, which in fungal and metazoan systems is associated with destabilization. In Arabidopsis, URT1 is the major mRNA TUTase. It prevents trimming of the poly(A) tail and is also necessary to repair deadenylated RNAs60. The impact of RNA structure and protein binding on RNA decay was recently explored in the context of root epidermal development. RNA secondary structures that were specific for root-hair or non-root-hair fates were found, and proteins that bound to these cell type–specific folds were identified61. Interestingly, one of these interactions was with SERRATE, a zinc finger domain protein with functions in miRNA biogenesis, splicing, and epigenetic silencing62, and appears to contribute to root-hair fate selection by miRNA-independent stabilization of specific mRNAs. Folded structures can also be impacted by m6A modification, which can act as a structural switch by disrupting local secondary structures to promote interaction with RNA-binding proteins. These m6A switches are enriched in 3′ UTRs and near stop codons, and switches located in introns were shown to play a role in alternative splicing39. The refinement of methods for determiningin vivo RNA structure promises many more insights ahead63.The complex folded structures of RNAs can also have important implications for stability. Analyses of RNA secondary structure in Arabidopsis found patterns of structure distributions in mRNAs, including less structure in the UTRs than in coding regions, and the observation that more structure generally resulted in lower transcript stabilitymRNAs that potentially encode aberrant protein products, such as truncated proteins caused by premature termination codons (PTCs) or by physical impediments to translation, or that disrupt ribosome homeostasis (due to lack of a start codon) are potentially deleterious for normal cellular function. Cells avoid these problems by identifying and degrading the offending mRNAs in a series of reactions known generally as RQC. These pathways include nonsense-mediated decay (NMD), which degrades RNAs with an abnormally positioned PTC; non-stop decay, which degrades mRNAs that lack a stop codon; and no-go decay, which degrades mRNAs with stalled ribosomes.64. For example, alternative splicing can lead to mRNA isoforms, including ones with PTCs. In plants, environmental stresses lead to enhanced production of mRNA variants containing PTCs, and their subsequent degradation allows adaptive responses to the initial stress67. Similarly, NMD selectively regulates transcript abundance of isoforms arising from alternative transcription start sites68 and can also function in transcript autoregulation69.These pathways, however, have the potential to go well beyond a protective function and can contribute to regulation of mRNA abundance70. The accumulation of siRNAs has been observed in Arabidopsis mRNA decapping mutants and has been shown to elicit the severe phenotypes of decapping mutants because mutations in RNA-DEPENDENT RNA POLYMERASE 6 (rdr6), which is required for siRNA amplification, suppress this severe phenotype71. This result highlights the importance of mRNA decapping in modulating RNA abundances. Similarly, siRNA accumulates in double mutants that lack both XRN4 and SKI2 (a component of the RNA exosome), and the resulting severe phenotype was similarly alleviated by loss of RDR6 function72. Thus, in addition to potential specialized functions, the major cytoplasmic mRNA decay pathways prevent generation of siRNAs.Another mechanism of RQC is the production of siRNAs that direct ARGONAUT-induced cleavage and decay of corresponding mRNAs74. DCP1 phosphorylation by MITOGEN-ACTIVATED PROTEIN KINASE 6 (MPK6) was shown to arise in response to dehydration. This modification is thought to slow mRNA decay, as transgenic lines with a non-phosphorylatable version of DCP1 showed slower decay ofEXPL1 RNA. Furthermore, rapid phosphorylations of VCS and DCP2 were found in a phosphoproteomic analysis of rapid responses to osmotic stress74. The major site of VCS phosphorylation is in its S-rich linker domain. Because DCP2 requires DCP1 for activation and VCS serves as their interaction scaffold, phosphorylation of the S-rich linker could alter DCP2 activation. Salt stress similarly leads to VCS phosphorylation but via the SNF1-RELATED KINASE, SnRK2G75. VCS and SnSRKG2 show constitutive physical interaction, and salt stress led the SnSRK2G-VCS complex to relocate to P-bodies75. VCS phosphorylation was also correlated with changes in RNA decay rates, as a set of VCS-dependent RNAs decayed faster in wild-type (WT) (Col-0) plants following exposure to salt stress. This correlation suggests that VCS phosphorylation led to faster decay of these RNAs. Important questions for the future include determining how phosphorylation of the mRNA decapping complex subunits causes changes in mRNA decay; for example, is substrate recruitment affected or are mRNA decapping kinetics altered?Each of the subunits of the mRNA decapping complex are subject to phosphorylation and these post-translational modification events have been implicated in regulating mRNA abundance78. The cytoplasmic complex binds to the 3′ termini of oligoadenylated and deadenylated mRNA and recruits the mRNA decapping complex. Genetic analysis of the LSM complex of Arabidopsis revealed that this complex functions in decay and is necessary for normal responses to abiotic stresses, including high salinity and cold temperatures, because it regulates the targeting of mRNAs to the decapping complex80. A functionally related protein, protein associated with topoisomerase II 1 (PAT1), also has diverse functions in post-transcriptional regulation of RNA, including decay81. In Arabidopsis, PAT1 has been implicated in pathogen response–based changes in gene expression. It is phosphorylated by MPK4, which causes its localization to P-bodies in response to challenge by bacteria and where it activates decay of specific mRNAs82.The SM-LIKE (LSM) complex consists of seven RNA-binding subunits and produces distinct nuclear and cytoplasmic complexes84. These concentrated collections of decay enzymes and mRNAs have been generally considered to expedite RNA decay. However, in yeast anddrosophila, mRNA decapping does not require formation of these bodies86. Recent studies using Arabidopsis have advanced our understanding of how specific mRNAs come to be localized in P-bodies. One participating protein is SPIRRIG (SPI), a BEACH-domain protein that interacts with DCP187. SPI is required for localization of specific salt-response RNAs to P-bodies and for their stabilization. This compelling story is complicated by the multi-faceted functions of SPI, which also localizes to endosomes and is required for normal endosomal transport and vacuole morphology88. The LSM proteins also have roles in localizing RNAs to P-bodies. These proteins interact to form heptameric rings, activate decapping, and associate with both mRNAs and the decapping complex in yeast89. In Arabidopsis, the LSM proteins were shown to associate with stress-specific mRNAs and drive their localization into P-bodies, leading to faster transcript decay90. Although both SPI and LSM complexes move RNAs to P-bodies, their P-body localization has opposing effects on RNA stability. This raises many questions, including how P-bodies can be both stabilizing and destabilizing in an RNA-specific manner.P-bodies form through phase separation of intrinsically disordered regions of proteins, such as the decapping complex subunits, and RNA92. This analysis confirmed P-body localization of mRNA decapping proteins, but there was no evidence that the localized mRNAs were undergoing decay. Instead, the results implicated P-bodies as an important site for translational arrest. However, whether P-bodies function similarly in all tissue types, and the extent to which human P-bodies can serve as a model for plants, needs to be determined. The stress-inducible P-bodies of yeast might be a more relevant model, even thoughS. cerevisiae lacks a homolog of the decapping complex scaffold, VCS. P-bodies of yeast have been shown to be sites of both decay and sequestration, and sequestered mRNAs can be restored to the translational pool93. Understanding P-body functions and sorting out how P-body localization can lead to different RNA fates are important directions for future research.P-body studies using non-plant systems might offer some insight into these questions. A purification method was recently developed that allowed both proteomic and transcriptomic analyses of P-bodies isolated from human epithelial cellser SOV transgene),vcs andsov single mutants, and avcs sov double mutant94. Contributions of decapping (VCS) and SOV to the decay of mRNAs followed the assumption that mRNA substrates of decapping (VCS) and SOV would decay more slowly invcs andsov mutants, respectively. We found that most RNAs decay by combined contributions of two or more pathways. While decapping (VCS) is required to sustain normal decay for 67% of the 17,293 analyzed RNAs, few were solely dependent on decapping for their decay. In addition, VCS-dependent RNAs tend to decay quickly, have abundances that are responsive to stress or developmental signals, and/or encode transcription factors94. Decay of 22% of the analyzed transcripts was not attributable to either VCS (decapping) or SOV, suggesting a large role for the RNA exosome. In contrast to decapping (VCS)-dependent RNAs, putative exosome substrates were generally slow-decaying RNAs that encode proteins with housekeeping functions. Thus, both mRNA decapping and the RNA exosome are specialized in terms of mRNA substrate functions and decay rates.An under-explored aspect of mRNA decay is whether the three major cytoplasmic decay pathways  have unisov mutants initially suggested that SOV/DIS3 contributes to decay of only about 9% of the analyzed transcripts94. Curiously, 33% of these RNAs decay much faster insov mutants than in WT. This faster decay comes from compensatory activity of the mRNA decapping complex, as indicated by slower decay of these same transcripts invcs sov double mutants .RNA decay pathways are highly conserved across eukaryotes, and research using Arabidopsis continues to contribute strongly to this field, as the genetic resources for studying mRNA decay in Arabidopsis make it an outstanding choice. However, many mRNA decay studies appear to be technically and computationally challenged. Selection of time points can have enormous implications on outcomes and accordingly should be selected on the basis of mRNA half-life range. Similarly, quantitative approaches to data analysis can compromise data outcomes. Pools of RNA change over time and can lead to the false impression that the abundance of stable RNAs increase. Furthermore, slight differences in measured decay rates can be difficult to assess. We have generated an R package in Bioconductor (RNAdecay) that, among other things, assists with normalization and allows statistical comparisons between treatments. This resource is freely available through Bioconductor (Recent discoveries have led to the identification of novel regulatory mechanisms for mRNA decay, including uridylation, methylation, and the potential for mRNAs to switch between decay pathways. Similarly, recent discoveries have led to the reconsideration of some past concepts, including P-bodies and the functional consequences of localized mRNAs. However, most studies address the behavior of only a few mRNAs, cell types, or a single condition, limiting the generality of outcomes. As costs for genome-wide approaches continue to decline, we can look forward to a clearer picture of the flexibility of mRNA stability, mechanisms of stability control, and positioning decay in the overall control of mRNA abundance.


This study investigated the association between intergenerational transfer and self-rated health (SRH) using data collected from 323 older Chinese immigrants in Honolulu and 752 Chinese older adults in Wuhan. We also explored the mediating role of resilience and the moderating role of gender in linking the associations. Findings show, for both study sites, receiving greater emotional support was associated with increased levels of SRH and this association was partially explained by resilience for females but not for males. For country differences, receiving economic support was negatively associated with SRH whereas providing economic support was positively associated with SRH for males but not for females in Honolulu. In Wuhan, receiving housework support was negatively associated with SRH for females but not for males. These findings indicate that future studies need to consider country differences as well as the impact of acculturation when examining intergenerational transfers and health among Chinese older adults.


Non-coding RNAs (ncRNAs) have emerged as critical components of gene regulatory networks across a plethora of plant species. In particular, the 20–30 nucleotide small ncRNAs (sRNAs) play important roles in mediating both developmental processes and responses to biotic stresses. Based on variation in their biogenesis pathways, a number of different sRNA classes have been identified, and their specific functions have begun to be characterized. Here, we review the current knowledge of the biogenesis of the primary sRNA classes, microRNA (miRNA) and small nuclear RNA (snRNA), and their respective secondary classes, and discuss the roles of sRNAs in plant–pathogen interactions. sRNA mobility between species is also discussed along with potential applications of sRNA–plant–pathogen interactions in crop improvement technologies. Over the past decade, non-coding RNAs (ncRNAs) have taken the scientific community by storm. Defined as RNAs that are transcribed from DNA but not translated into proteins, ncRNAs have been implicated in a host of different epigenetic regulatory mechanisms and have been shown to exert regulatory effects primarily through complementary base pair matching with messenger RNAs (mRNAs) . HoweverIn the time since their initial discovery, ncRNAs have been segregated based on their biogenesis pathways into a number of different classes, many of which have distinct, yet overlapping, functions . The mosTriticum aestivum) and rice . A particular focus is placed on the roles of sRNAs in plant–pathogen interactions, including sRNA mobility between species and the applications of HIGS. Finally, future perspectives are explored.This review article provides an overview of the biogenesis and specific modes of action of four classes of sRNAs: siRNA, miRNA, small nuclear RNA , small nucleolar RNA (snoRNA), and tasiRNA  as for all coding mRNAs, sRNA transcription is induced by double-stranded RNA (dsRNA), and transcription of the corresponding gene is driven by an RNA polymerase (RNAP); (2) dsRNA is then processed to a length of 18–25 nucleotides; (3) methylation occurs at the 3′ end of the sRNAs; and (4) the sRNAs are incorporated into effector complexes that allow them to recognize, and interact with, their target sites .RNAP II (RNAPII)  duplex from the stem  . Once tr(RNAPII) . This DCthe stem , 2013. Tthe stem . Previouthe stem . HoweverTAS transcripts (TAS1 and TAS2 (miR173), TAS3 (miR390), and TAS4 (miR828) families, respectively (SUPPRESSOR OF GENE SILENCING 3 (SGS3), which facilitates dsRNA formation by RNAPII. The dsRNAs are then processed by DCL4, yielding phased siRNAs that are 21 nucleotides long, which, like miRNAs, are methylated by HEN1 and loaded into AGO-containing RISCs  via miRNA-guided cleavage, which creates nscripts . Three mectively . These png RISCs . tasiRNAng RISCs .However, in general, biogenesis of siRNAs is much more diverse than miRNAs, and can differ greatly between siRNA classes. Initially, most siRNAs are conceived from pre-formed dsRNAs. This includes, but is not limited to, those transcribed by RNA-dependent RNAPs (RDRs) . siRNA fIn comparison, little is known about the specifics of snRNA biogenesis in plants, as most research has been conducted in mammalian cells and the process varies depending on the specific snRNA being transcribed . GeneralAnother major group of snRNAs is involved in regulating RNA metabolism. Within this group, one sub-class, known as scaRNAs , directs a group of snRNPs known as the scaRNPs , which have been implicated in pre-ribosomal RNA (pre-rRNA) modification and processing in the Cajal bodies. A second sub-class, the snoRNAs, is mainly located in the nucleolus and form snoRNPs, which are involved in regulating and modifying other spliceosomal snRNAs . The curPseudomonas aeruginosa  , and cotton  specifically, members of the miR482 family have been shown to be downregulated upon infection with the fungal pathogen Verticillium dahliae -triggered immunity (PTI) and effector-triggered immunity (ETI) . In mostruginosa . This paruginosa . HoweverR-genes) . It is rven time . Furtherrade-off . In part dahliae . Further dahliae .R-gene expression by targeting genes related to co-expression of R-genes, and thereby contributing to negative-feedback loops  have elevated levels of SNC1 transcript, whose overexpression triggers downregulation of R-genes in the RPP5 locus via co-suppression  from their target mRNA cleavage site  in tobacco and induce resistance. Additionally, technology has been developed to help analyze the overall expression patterns of sRNAs in the genome, known as sRNA high-throughput sequencing. As demonstrated by Rice stripe virus (RSV) rice plants, this technology can help pinpoint which sRNAs play dominant roles when a host is infected, along with a multitude of other stress-inducing conditions  a PHLOEM SMALL-RNA BINDING PROTEIN 1 (PSRP1) acts as a major component in the formation of a sRNA ribonucleoprotein complex (sRNPC), which allows movement of sRNAs into sink organs. In speculation, this mechanism could represent a method of RNA silencing-mediated resistance, as it gives a plant the ability to recover from viral infection. This may result in the upper regions of a plant exhibiting secondary resistance to a viral infection, appearing phenotypically disease free compared to the infected lower regions  which, when investigated, highlighted 43 miRNAs, 13 of which were novel, that were differentially expressed upon infection with yellow dwarf disease phytoplasmas. In particular, mul-miR482a-5p was shown to hold the ability to move from scions to rootstock, and was upregulated upon infection . However, although this mechanism is efficient in sucking insects and nematodes, some species of insect, including phloem sap-feeding hemipterans such as the global crop pest Bemisia tabaci (Leptinotarsa decemlineata (Colorado potato beetle), Helicoverpa armigera (cotton bollworm), and Diabrotica virgifera (Western corn rootworm)  .Agrobacterium tumefaciens , eliciting both public and regulatory concerns. To address these issues, alternative technologies have also been trialed to explore other possible pathways for induction of RNAi . One sucAll three of the methods above, HIGS, VIGS, and naked dsRNA application, have important implications for crop breeding efforts, as they not only highlight novel techniques for potentially improving crop resistance to pathogens, but also help to shed light on possible mechanisms and genes that may already be at play, and efficient ways of investigating them . HoweverEB wrote the paper. HB conceived the idea and edited the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The GC skew in vertebrate mitochondrial genomes results in synthesis of RNAs that are prone to form G-quadruplexes (G4s). Such RNAs, although mostly non-coding, are transcribed at high rates and are degraded by an unknown mechanism. Here we describe a dedicated mechanism of degradation of G4-containing RNAs, which is based on cooperation between mitochondrial degradosome and quasi-RNA recognition motif (qRRM) protein GRSF1. This cooperation prevents accumulation of G4-containing transcripts in human mitochondria. In vitro reconstitution experiments show that GRSF1 promotes G4 melting that facilitates degradosome-mediated decay. Among degradosome and GRSF1 regulated transcripts we identified one that undergoes post-transcriptional modification. We show that GRSF1 proteins form a distinct qRRM group found only in vertebrates. The appearance of GRSF1 coincided with changes in the mitochondrial genome, which allows the emergence of G4-containing RNAs. We propose that GRSF1 appearance is an evolutionary adaptation enabling control of G4 RNA. G-rich RNAs encoded in mitochondrial DNA are prone to form four-stranded structures called G-quadruplexes (G4s). Here the authors show using in vitro and in vivo approaches that GRSF1 promotes melting of G4 RNA structures in mtRNAs, thus leading to their decay by the hSuv3–PNPase complex. All other proteins, including those needed for mitochondrial genome expression and RNA decay, are nuclear-encoded, synthesized in the cytoplasm, and imported into mitochondria2.Human mitochondrial genome (mtDNA) is a circular DNA molecule encoding 2 rRNAs, 22 tRNAs, and 13 protein-coding genes3. As a result, long polycistronic transcripts are synthesized that undergo endonucleolytic processing that releases immature tRNAs, rRNAs, mRNAs, and non-coding RNAs (ncRNA)6. Subsequently, immature functional RNAs are modified. Messenger RNAs are oligo- and polyadenylated7, whereas rRNAs are subject to methylation and pseudouridylation8 and tRNAs undergo several types of nucleotide modifications and addition of CCA at the 3′ end9.Transcription of both mtDNA strands starts in the D-loop region and tRNAPhe gene, and continues through almost the entire genome1. In contrast, RNAs derived from L-strand transcription are mostly non-coding/non-functional. Around 90% and 9% of L-strand and H-strand transcripts, respectively, constitute non-coding RNAs, which are antisense to functional RNAs transcribed from the opposite strand. Overall, steady-state levels of non-coding mtRNAs are extremely low, although transcription of the L-strand, which is the major source of mt-ncRNA, is more frequent than that of the H-strand10. Interestingly, G-rich L-strand transcripts are theoretically prone to form non-canonical four-stranded structures called G-quadruplexes (G4s)11. G4s present in nuclear-encoded RNAs play important roles in regulation of splicing, polyadenylation, RNA turnover, translation, and mRNA targeting12. The role of G4s in mtRNA metabolism is less known, although there is an evidence indicating that G4 formation during transcription of the CSBII region terminates RNA synthesis13.A peculiar feature of many mitochondrial genomes, including that of humans, is an unequal distribution of guanines between DNA strands. Due to this asymmetry, the strands can be separated using buoyant density ultracentrifugation to isolate the heavy (H-strand) and light strand (L-strand). In humans, most mitochondrial genes are encoded by the G-poor strand (L-strand), thus the complementary H-strand is used as a template for transcription16  and the ribonuclease PNPase19  is the best-described mtRNA degradation machinery in human cells21. This degradosome complex is an exoribonuclease that degrades RNA from the 3′ to 5′ end20. In vivo, the degradosome forms in distinct areas of mitochondria named D-foci, which co-localize with mtRNA granules (MRG) and mitochondrial nucleoids21. The activity of the degradosome was shown to be required for mt-mRNA turnover22, 16S rRNA decay23, and exonucleolytic processing of the 3′-end of ND6 mRNA24. Many mt-ncRNA species can be detected only when degradosome activity is inhibited21. This rapid disappearance of non-coding antisense mtRNAs appears to be physiologically important given their potential to interfere with the functions or expression of sense transcripts. However, the mechanism by which this highly efficient degradation is achieved is not fully understood, especially that many of mt-ncRNAs could form G4s, which can hamper transcripts degradation.The mechanism by which human mitochondrial RNA (mtRNA) is degraded was unclear for many years. To date, the mitochondrial degradosome, a complex of the RNA helicase hSuv325. One mitochondrial RBP, GRSF1, was originally described as a cytoplasmic poly(A) + mRNA binding protein26, but was later found to localize to mitochondria28. GRSF1 belongs to a protein family characterized by the presence of a quasi-RNA recognition motif (qRRM)29 that seems to be absent in bacteria from which mitochondria originated. This is in contrast to proteins which contain classical RRM domain represented in mitochondria by SLIRP22.Many RNA-related mechanisms, including RNA decay, are dependent on RNA-binding proteins (RBPs). Several mitochondrial RBPs involved in different RNA-related activities have been identifiedHere we show that GRSF1 positively regulates degradosome-dependent decay of non-coding mitochondrial transcripts that form G4 structures. Comprehensive molecular analyses revealed that GRSF1 melts G4 RNA structures in mtRNAs, which facilitates their degradation by the hSuv3–PNPase complex both in vitro and in vivo. Based on phylogenetic analyses, we propose that the appearance of GRSF1 in mitochondria is an evolutionary adaptation that occurred when mitochondrial genomes underwent a transition from G4-poor to G4-rich and enabled control of mitochondrial G4 RNA levels.16. Mitochondria were isolated from the cells, lysed, and the protein extracts were subjected to affinity chromatography. Co-purified proteins were identified by mass spectrometry and their amounts were quantified by a label-free approach  approachzed Fig. , c. Intezed Fig. . The obt16, whereas PNPase or GRSF1 levels were depleted by siRNA transient transfections , which produces an effect equivalent to that of hSuv3 silencingons Fig. .Fig. 2DyTo cover a wide range of RNA classes, we generated two types of strand-specific RNA-seq libraries. A ligation-based approach was used to analyze short RNAs (from ~20 to ~200 nucleotides), whereas longer RNAs (>~100 nucleotides) were analyzed after random fragmentation followed by reverse transcription primed with random oligomers.16, whereas reduction of PNPase levels achieved by transient siRNA transfection can vary from cell to cell. Importantly, changes in the mitochondrial transcriptome following PNPase silencing were rescued by expression of its transgenic siRNA-insensitive counterpart  and ELAC2, resulted in accumulation of tRNA-like , which suggested that GRSF1 could be required for degradation of G4-containing RNAs. Our search for G4 consensus in mitochondrial genome by means of regular expression revealed multiple sequences that had G4 forming potential  located in the D-loop region. This sequence was experimentally demonstrated to form G438 and can recognize non-canonical RNA G4s39. We observed the expected signal increase in ThT fluorescence for two known RNAs forming G4s, as well as signals that were above that for negative controls  for our targets , and monitor changes in absorbance at 260 and 295 nm wavelengths within a temperature gradient  which differentially affect G4 structures . Increasing amounts of wild type, but not mutated, GRSF1 protein showed reductions in ThT fluorescence, indicating the disruption of G4 structures Fig. . BecauseTo unequivocally test whether the lack of the effect of the GRSF1 mutant on G4 unwinding was due to a decrease in affinity, we performed electrophoretic mobility shift assay (EMSA) experiments with tRNA-like and Mito3 substrates  by all three  to examine 13 organisms containing the qRRMs we analyzed above. Both methods showed that G4 consensus sequences are overrepresented by an order of magnitude in mitochondrial genomes of GRSF1-positive organisms  that resulted in a substantial increase in sequences with G4-forming potential.RNA molecules can form many different structures, many of which involve non-canonical base pairing. Four-stranded structures known as G4s have been shown to be important in various aspects of RNA biology. The extraordinary GC skew of vertebrate mitochondrial genomes results in synthesis of G-rich RNAs that are prone to form G4s. Such RNAs, which are mostly antisense to functional RNAs, are transcribed at high rates but their steady-state levels are extremely low. In this study, we found that the mitochondrial degradosome associates and cooperates with GRSF1 to control G4-containing mtRNAs. We revealed a crucial mechanism that prevents accumulation of antisense transcripts in human mitochondria, which is of great importance for unperturbed expression of mitochondrial genes essential for humans.Results from biochemical assays allowed us to propose a molecular mechanism by which GRSF1 facilitates RNA degradation by the mitochondrial degradosome. In this mechanism, GRSF1 binds G4 and melts the G4 structure, but the RNA remains tightly bound to GRSF1, which presents an obstacle for PNPase activity Fig. . Thus, h22, whereas the FASTK protein was shown to control exonucleolytic, degradosome-mediated processing of the 3′ UTR of ND6 mRNA24. In the case of LRPPRC–SLIRP, recent study based on RNase footprinting and PAR-CLIP showed its preference for binding of mRNAs which affects their secondary structure and translation ability47. Analysis of LRPPRC-binding sites revealed a highly degenerate consensus recognition sequence47. Importantly, G residues were strongly underrepresented in this 40 nucleotide long sequence. Such results are in accord with our findings, which indicate that GRSF1-augmented degradosome-mediated decay has specificity for G-rich regions. Thus, we can speculate that G-poor sequences, which are enriched in mRNAs are protected from degradation by LRPPRC-SLIRP complex while G-rich non-coding RNA species are degraded with the help of GRSF1.In contrast to activators, two antagonists of degradosome-mediated RNA decay were previously identified. The LRPPRC– SLIRP complex was suggested to regulate degradosome-dependent mtRNA turnover49 as well as by an increasing number of identified pathogenic mutations in humans51. Indeed, Suomalainen and co-workers directly linked Leigh syndrome to impairments in degradosome-dependent mtRNA degradation51, thus confirming the physiological significance of this process. Results of our studies can be of great value in complete understanding of diseases’ mechanisms. Interestingly, in bacteria, degradosome activity involves an endoribonuclease that provides an entry site for exonucleolytic activity. Whether this situation also occurs in human mitochondria remains to be established. If so, initial endonucleolytic cleavage of long L-strand transcripts could improve their degradation by the hSuv3–PNPase. Two candidates for such a mitochondrial endoribonuclease are of particular interest: LACTB252 and YBEY53. Their functional interplay with the hSuv3–PNPase complex awaits investigation.RNA-seq analysis of degradosome-impaired cells provided the first picture of the mitochondrial transcriptome in the presence of RNA decay inhibition. The extent to which non-coding RNAs accumulate, especially in hSuv3-G207V-expressing cells, implied that the hSuv3–PNPase complex plays a primary role in regulating non-coding mtRNAs, which mostly are produced by L-strand transcription. The importance of PNPase- and Suv3-dependent processes is underscored by the embryonic lethality of mice with knockouts of these genes54. tRNA-like is post-transcriptionally modified by the addition of non-templated CCA at the 3′ end. This modification was already observed by others for mitochondrial non-tRNA molecules55. Importantly, one of the previously identified sites of addition of non-templated CCA55 is consistent with the 3′ end of the tRNA-like. The function of this modification for non-tRNA species is currently unclear. Nuclear-encoded, structurally unstable tRNAs and tRNA-like species undergo quality control that involves the addition of CCACCA sequences56. However, this type of modification was not observed either by us or others55 in mitochondria.Analysis of short RNAs that accumulate upon GRSF1 or degradosome dysfunction showed strong accumulation of the tRNA-like. Whether this transcript plays any role in mitochondrial function or is merely a by-product of the processing of primary RNA requires further studies. When the activity of nuclear RNA surveillance pathways is altered, a significant number of transcripts, which are not detected upon normal conditions, accumulate57 recently showed that the majority of predicted G4s are in fact unfolded in the cell57. Based on this finding, they propose that either an organism does not have G4 sequences  or it developed countermeasures to preserve the majority of these G4 RNAs in an unfolded state (eukaryotes). The acquisition of G4 sequences seems to correlate with the evolution of biological complexity. Our work showed a similar phenomenon in mitochondrial genomes. Only the mitochondrial genomes from complex life forms underwent a transition from G4-poor to G4-rich. Our data suggest that the evolutionary response to the increasing number of G4s was acquisition of a protein (GRSF1) that can melt such structures.The data presented here have some interesting evolutionary implications since we show that GRSF1 is the only qRRM protein present in mitochondria. We propose that changes to the mitochondrial genome resulted in the increased presence of G4 sequences and that acquisition of GRSF1 was a response to this change that allowed maintenance of most G4 RNAs in an unwound state. Guo and Bartel57. This finding, together with the fact that G4 structures are under surveillance of cellular machinery, suggests that G4s pose a problem for the cell rather than an advantage. Indeed, the presence of G4 structures in mtDNA was linked with the occurrence of deletions58. Guo and Bartel proposed that maintenance of G4 melting machinery in prokaryotes is costly, and the minimal genome would decrease the likelihood of the appearance of G4s. In contrast, in eukaryotes the presence of G4s would provide opportunities for regulation. Which scenario would apply to mitochondrial genomes is currently unclear. It was shown that GTPase activity of NOA1, which functions in mitochondrial ribosome assembly, is influenced in vitro by G4 RNAs59. Moreover, TFAM, a major mitochondrial nucleoid protein that is essential for transcription and replication, was shown to have versatile G4 binding ability60. The exact role of these features is unknown, but it seems likely that G4s RNA can influence the activity of mitochondrial proteins. Thus, not surprisingly dysfunctions of the degradosome components or GRSF1 are manifested by pleiotropic mitochondrial phenotypes including abnormal mitochondrial translation, affected steady-state levels of functional RNAs, and impeded maintenance of mitochondrial genome61.The question remains: why did G4 sequences appear in the first place? Expression of G4 sequences in bacteria impairs their growth and produces multiple defects such as perturbed translation, increased stop codon read-through and frame shifting16. 293 Flp-In T-REx cells expressing an siRNA-insensitive form of PNPase were described elsewhere21. Cells were cultured at 37 °C under 5% CO2 in Dulbecco's modified Eagle's medium (DMEM) medium (Gibco) supplemented with 10% fetal bovine serum (Gibco). Transgenes were induced with tetracycline (25 ng/ml). The identity of parental 293 cells and HeLa cells was confirmed using STR profiling by DSMZ (Germany). Cells were tested for mycoplasma contamination.Immunofluorescence experiments were performed using HeLa Flp-In T-Rex (a kind gift from Matthias Hentze). Other experiments were performed using 293 Flp-In T-REx cells  or their stably transfected derivatives. 293 Flp-In T-REx cells expressing hSuv3-TAP, PNP-TAP, mt-TAP, or a catalytically inactive version of hSuv3 (G207V) were described previouslyCells were transfected using RNAiMAX (Thermo Fisher Scientific) in the forward manner according to the manufacturer’s instructions and collected after 3 days. The final concentration of siRNA was 20 nM. All siRNAs were purchased from Invitrogen Table .Table 1s16. Eluted proteins were identified using MS/MS analysis. MS analysis was performed by LC-MS in the Laboratory of Mass Spectrometry . Samples were processed and subjected to label-free quantification as described elsewhere62. Protein abundance was defined as the mean signal intensity calculated by MaxQuant software for a protein divided by its molecular weight. Enrichment was defined as the ratio of mean protein intensity measured in three bait purifications relative to background level . Putative degradosome-associated proteins met the following criteria: (1) were present in all purifications of hSuv3-TAP and PNP-TAP, (2) were enriched by at least three-fold for both baits, (3) were previously suggested to be mitochondrial proteins  were performed as described previously16. All other northern blots were performed as follows: 3 µg of total RNA was mixed with an equal volume of denaturing loading dye , heat denatured for 3 min at 85 °C, run on a 10% polyacrylamide/8 M urea gel in 1× TBE using a Mini-Protean II apparatus (BioRad) and blotted onto Hybond-NX membranes  by wet electrotransfer in 0.5× TBE buffer (14–16 h at constant amperage of 20 mA) using a Mini Trans-Blot Cell apparatus (BioRad). After transfer of nucleic acids, membranes were stained with methylene blue solution . All hybridizations were performed overnight in PerfectHyb Plus buffer (Sigma) at 37 °C. The following strand-specific PNK-radiolabeled oligoprobes were used: RSZ654 GTAGGAGAGTGATATTTGATCAGGAGAACGT , RSZ655 ACGTTCTCCTGATCAAATATCACTCTCCTAC , anti-hSuv3 , anti-MRPP1 , and anti-ELAC2 . Appropriate horseradish peroxidase-conjugated secondary antibodies  were detected by enhanced chemiluminescence  according to the manufacturer’s instructions. Uncropped scans of blots are shown as Supplementary Fig. Total protein cell extracts were prepared in lysis solution  Triton X-100, 1% (w/v) deoxycholate, 0.1% (w/v) SDS). Protein concentration was determined by the Bradford method. Protein extracts (30 μg per lane) were separated by SDS-PAGE and transferred to a nitrocellulose membrane . Western blotting was performed according to standard protocols using the following primary antibodies: anti-FLAG , anti-PNPase , anti-GRSF1  active degradosome—pRS409 (hSuv3WT pBiFC-VC155) and pRS411 (PNPaseWT pBiFC-VN173), (2) inactive degradosome—pRS415 (hSuv3-G207V pBiFC-VC155) and pRS505 (PNPaseR445446E_pBiFC-VN173). TransIT2020 Reagent (Mirus) was used for transfection according to the manufacturer’s recommendations. At 24 h after transfection the cells were plated on glass coverslips and cultured for an additional 24 h. The cells were then washed with PBS twice and treated for 30 min with 5% (v/v) formaldehyde and 0.25% (w/v) Triton X-100 solution in PBS. Cells were washed with PBS three times and incubated for 30 min with 3% (w/v) BSA in PBS before an overnight incubation at 4 °C with the primary antibody anti-GRSF1  diluted 1:200 in 3% BSA in PBS. After washing with PBS, relevant secondary antibodies conjugated with Alexa555 were applied for 1 h at room temperature. Mitochondria were stained by addition of MitoTracker DeepRed (200 nM) to the culture medium 1 h prior to fixation. Cells were imaged with a FluoView1000 confocal microscope (Olympus) and a PLANAPO 60.0 × 1.40 oil objective. Images were collected with 215 nm resolution. Object-based colocalization was performed with Imaris 7.2.3 software. Objects with centers separated by <215 nm were considered to be colocalized.HeLa cells were transiently co-transfected with the following BiFC DNA constructs63 except that a Ribo-Zero rRNA Removal Kit  was used for rRNA removal. Libraries for analysis of small RNAs were prepared as described by Labno et al.64 except that total RNA, which was not subjected to rRNA depletion, was used for library preparation. Sequencing, whose results are presented in Figs RNA-seq experiments were performed in triplicate. Libraries of long RNAs were prepared as described in detail elsewhere65. Reads from short RNA-seq libraries were additionally merged within pairs and only ones which overlapped by at least five bases were further aligned to the genome using GSNAP short read aligner. The quality control, read processing and filtering, visualization of the results, and counting of reads for the annotation was performed using custom scripts incorporating elements of the RSeQC (version 2.6.1), BEDtools (version 2.23.0), and SAMtools packages. The reference transcript annotation used was the GENECODE v26. To prepare figures tracks of mapped reads were displayed using the Integrative Genomics Viewer (IGV) and assembled using graphical software (CorelDraw).Sequences of all RNA-seq reads were striped of adapters and trimmed from poor quality bases using cutadapt (version 1.9.2.dev0). Long RNA libraries were mapped using the GSNAP short read aligner (version 2016-05-01) allowing for spliced read alignment and default settings. The assemblies were prepared with reference human genome GRCh38 facilitating the alignment of known splice junctions of the basic GENECODE v26 annotation and treating the mitochondrial genome as a circular DNA molecule to allow for mapping of all reads in the D-loop regionUniquely mapped reads were used to prepare pileups of expression corrected to the total number of reads mapping to both strands of the mitochondrial genome. The coverage over the non-coding bases of the mitochondrial genome, additionally excluding regions antisense to rRNA and tRNA to avoid bleed-trough from the other strand, was summarized separately for the L-strand and H-strand utilizing custom scripts incorporating elements of the RSeQC and bwtool (version 1.0) package and visualized in R.We used deeptools bamCoverage software to transform alignment of reads to genome coverage. Subsequently, computeMatrix software was used to calculate average scores for designated genome regions. Results were normalized and visualized using R.https://www.encodeproject.org/search/?type=Experiment&ass) and mapped utilizing our pipeline as described for long RNAseq libraries.Publicly available GRSF1 eCLIP datasets with their corresponding mock controls  have been downloaded from the ENCODE data repository , sliding window calculations, sequence generation, and data visualization were performed using R, Circos, Python, and Biopython. Additionally, QGRS MapperRNA oligomers in annealing buffer  were heated to 85 °C for 5 min and over the course of 3 h were cooled slowly to room temperature. The samples were then incubated at 4 °C overnight.TERRA RNA oligomer labeled with fluorophore and quencher  and an Infinite M1000 plate reader (Tecan) with excitation and emission wavelengths of 430 nm and 490 nm, respectively, and a 10 nm bandwidth.RNA oligos (1 μM) were annealed in 10 mM LiCacodylate pH 7.0 and 5 mM KCl buffer. Measurements were made using a Jasco J-810 CD spectrometer equipped with a thermostat-controlled cell holder (JASCO Corporation) and a 1 cm path-length quartz cuvette with a magnetic stirrer. Three scans at 25 °C between 220 and 300 nm were recorded, averaged, and buffer-corrected. For thermal denaturation studies, absorbance spectra and CD spectra were recorded between 220 and 320 nm, from 3 °C to 98 °C with 5 °C steps, and a 2 min equilibrium time. Data were analyzed with Spectra Manager v2 (JASCO Corporation) and R.32P] adenosine triphosphate  with T4 polynucleotide kinase (NEB). After labeling, oligonucleotides were subjected to phenol–chloroform extraction, precipitated, and purified by denaturing polyacrylamide gel electrophoresis.5′-end labeling of substrates was performed using [γ-EMSAs were performed using a constant RNA concentration of 5 nM and increasing protein concentrations . Binding proceeded for 30 min at 30 °C in 15 μl reaction volumes containing 10 mM Tris-HCl pH 8.0, 2.5 mM KCl, 30 mM NaCl, 1 mM EDTA, and 0.2 mM DTT enriched with 75 μg/ml heparin. Samples were mixed with 5 μl Gel Loading Dye (Thermo Fisher Scientific) and separated on a 7% polyacrylamide gel (19:1) run at constant 10 W. The gels were exposed in Phosphorimager cassettes overnight and the results were recorded with a Typhoon FLA 9000 scanner . Quantification of the results was performed using ImageJ software.2PO4, 2 mM MgCl2, 1 mM ATP, 1 mM DTT. Reactions were started by the addition of annealed RNA enriched with ATP. Measurements were collected at 0, 15, 30, and 60 min. For the control samples (without protein), measurements were taken at 0 and 60 min. Reactions were stopped at the indicated time points by addition of an equal volume of RNA loading dye  xylene cyanol, and 0.01% (w/v) bromophenol blue) and then flash-frozen in liquid nitrogen. When all samples were collected, they were heated for 7 min at 85 °C. Subsequently, 10 μg proteinase K was added to each sample and they were incubated for 10 min in RT. The samples were then separated on a denaturing gel  run at constant 20 W. The gels were exposed in Phosphorimager cassettes overnight and the results were recorded with a Typhoon FLA 9000 scanner . Quantification of the results was performed using ImageJ software.Degradation assays were carried out at 37 °C in a 30 μl reaction volume containing 1 pmol RNA and 3 pmol of the indicated protein/s in 20 mM Tris-HCl pH 8.0, 50 mM NaCl, 10 mM NaH44, MultiLoc245, and MitoProt43. Protein sequence alignments were compiled using the T-coffee server41 and Simple Phylogeny (EMBL-EBI) with neighbor-joining clustering42.We used three localization prediction servers, each using a different algorithm: TargetP29. hSuv3 and PNPase were truncated at the N-terminus in agreement with previous reports67. For proteins overproduction Escherichia coli BL21 strain was transformed with appropriate plasmids. Bacteria were cultured in autoinduction Super Broth base including trace elements medium (Formedium), supplemented with 2% glycerol and kanamycin (50 μg/ml) for 48 h at 18 °C. Bacteria were pelleted and homogenized using EmulsiFlex and protein extracts were subjected to purification. Depending on proteins’ stability, several various methods were used in purification procedures. 6xHis-SUMO-tagged proteins purification included following steps: Ni affinity chromatography on the 5 ml column filled with Ni-NTA Superflow resin (Qiagen), followed by SUMO protease on-column cleavage, desalting, a second round of Ni affinity chromatography with collection of unbound material, and gel filtration Hiload 16/60 Superdex S200 column . The 6xHis tagged proteins purification was simpler and included Ni affinity chromatography followed by gel filtration. The purification procedure was performed using an ÄKTA express apparatus. Purified proteins were analyzed by standard SDS-PAGE and Nanodrop (Thermo Fisher Scientific). When necessary additional ion exchange chromatography purification step was performed using 6 ml Resource Q column .hSuv3 (47-786 aa) and GRSF1  were expressed as N-terminal 6xHis-SUMO-tagged proteins whereas PNPase (38-783 aa) was expressed as C-terminal 6xHis fusion protein. The truncation of GRSF1 was designed based on prediction of the mitochondrial targeting sequence using MitoProt II v1.101. The point mutations in GRSF1  were designed based on structural studies of the qRRM domain of the hnRNP F proteinGSE106368. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE68 partner repository with the dataset identifier PXD009826. Other relevant data are included in the paper and accompanying Supplementary Information or are available from the corresponding author upon reasonable request.RNA-seq data obtained in this study are available in the GEO repository: All scripts used in bioinformatics analyses are available for researchers upon reasonable request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1