The release and uptake of nano-sized extracellular vesicles (EV) is a highly conserved means of intercellular communication. The molecular composition of EV, and thereby their signaling function to target cells, is regulated by cellular activation and differentiation stimuli. EV are regarded as snapshots of cells and are, therefore, in the limelight as biomarkers for disease. Although research on EV-associated RNA has predominantly focused on microRNAs, the transcriptome of EV consists of multiple classes of small non-coding RNAs with potential gene-regulatory functions. It is not known whether environmental cues imposed on cells induce specific changes in a broad range of EV-associated RNA classes. Here, we investigated whether immune-activating or -suppressing stimuli imposed on primary dendritic cells affected the release of various small non-coding RNAs via EV. The small RNA transcriptomes of highly pure EV populations free from ribonucleoprotein particles were analyzed by RNA sequencing and RT-qPCR. Immune stimulus-specific changes were found in the miRNA, snoRNA, and Y-RNA content of EV from dendritic cells, whereas tRNA and snRNA levels were much less affected. Only part of the changes in EV-RNA content reflected changes in cellular RNA, which urges caution in interpreting EV as snapshots of cells. By comprehensive analysis of RNA obtained from highly purified EV, we demonstrate that multiple RNA classes contribute to genetic messages conveyed via EV. The identification of multiple RNA classes that display cell stimulation-dependent association with EV is the prelude to unraveling the function and biomarker potential of these EV-RNAs.The online version of this article (10.1007/s00018-018-2842-8) contains supplementary material, which is available to authorized users. Extracellular vesicles (EV) released by cells are considered as important mediators of intercellular communication , 2. ThesEV are multi-component entities that transfer proteins, lipids, and RNA between cells. The RNA associated with EV mainly consists of small non-coding RNA species (ncRNA), which are thought to be of major importance in altering molecular processes in the recipient cell , 4, 7, 8Although the EV-RNA field receives major attention, with quickly rising numbers of publications on this topic, the technical difficulties to obtain pure EV remain underexposed. It is important to note that a substantial amount of extracellular RNA is not contained in EV, but is associated with ribonucleoprotein particles (RNPs), including argonaute 2 (AGO2), or lipoprotein complexes –15. ThesMiRNAs are the most intensely studied type of small RNA in EV, likely due to the interest in gene-regulatory functions of these RNAs –21. It hDespite the interest in miRNAs, a larger part of EV-associated RNA consists of other ncRNA classes. We and others previously showed that EV released by cultured cells and EV present in a variety of body fluids contain many other small non-coding RNA species 20–300 nt), such as tRNA, snoRNA, snRNA, Y-RNA, Vault RNA, and SRP-RNA  , 30–35. 0 nt, sucHere, we studied how changes in the activation/differentiation status of the parental cell affect the small non-coding RNA content of EV by employing a primary immune cell model, two different types of cell stimulation, and methods yielding highly pure EV populations. For our analysis, we used primary dendritic cells (DC), which are master regulators of immune responses and have been shown to communicate with various other immune cells via EV , 8, 42. g in an SW28 rotor (k-factor 334.2) . The EV-depleted supernatant was carefully pipetted from the tubes, leaving 5 ml in the tubes to prevent disturbance of the pellet. This supernatant was filtered through a 0.22 μm bottle top filter  after which additional IMDM, ultraglutamine, antibiotics, and β-mercaptoethanol were added to prepare complete medium as indicated above. The efficiency of EV depletion from culture medium, as regularly assessed by high-resolution flow cytometric analysis, is on average ~ 90%. Primary bone marrow cells were flushed from the femur and tibia of 8–12-week-old C57bl/6 mice and differentiated to dendritic cells according to Lutz et al.  according to the manufacturer’s instructions. The DNase-treated RNA was pelleted using Pellet Paint  according to the manufacturer’s instructions, reconstituted in milliQ (MQ) and subsequently subjected to ribosomal RNA depletion using RiboZero Gold kit (Human/Mouse/Rat)  according to the manufacturer’s instructions. This is required to deplete residual rRNA from cellular RNA samples and, for comparability, we subjected EV-RNA samples to the same procedure. Hereafter, RNA was pelleted with Pellet Paint and reconstituted into 6 µl MQ. cDNA libraries were prepared using the NebNext smallRNA library prep kit for Illumina , according to the manufacturer’s instructions but with the following adaptations: 3′ adapter-ligation was carried out overnight at 16 °C; PCR amplification was done using Kapa HiFi Readymix 2× PCR mastermix  using barcoded primers and the following PCR programme: 2 min at 95 °C, 15 cycles of 20 s at 98 °C, 30 s at 62 °C, 15 s at 70 °C, and a final elongation step of 5 min at 70 °C. cDNA was purified using magnetic AMPure XP beads  and quantified using Agilent 2100 Bioanalyzer and DNA HiSensitivity chips . Adapter dimers (126 nt in size) were removed by running the cDNA on 6% TBE gels  for 60 min at 145 V, after which cDNA products of 15–300 nt (+ 126 nt adapters) were cut from the gel and purified. Subsequently, all libraries were pooled at equimolar ratios and run on a 4–12% TBE gel . Size fractions of 15–25 nt (includes miRNAs), 25–60 nt, 60–80 nt (includes tRNAs), and 80–275 nt (each + 126 nt adapters) were cut from the gel, and purified and pooled in a volumetric ratio of 2:2:3:3. Sequencing was done using 150 bp paired-end reads on an Illumina HiSeq 4000 machine  at ServiceXS .All data was processed as paired-end reads. Data quality was checked with FastQC and reads were processed with cutadapt (version 1.8) to remove low-quality reads, clip the reads to 100 bp, and remove adapter sequences. Sequences with a minimal length of 17 bp after adapter trimming were retained to ensure high quality reads (cutadapt -q 30 -u -51 -U -51 -n1 --minimum-length 17 -a AGATCGGAAGAGCACACGTCTGAAC -A GATCGTCGGACTGTAGAACTCTGAA). Reads were mapped to the mouse genome mm10 using gsnap . Count tables were prepared using Htseq . For miRNA count tables, miRbase v20 annotation was used. Gene annotation for snoRNA, snRNA, and miscRNA was retrieved from Biomart (Ensembl 84). tRNA and rRNA chromosomal positions were retrieved from the UCSC mm10 repeat mask track. Reproducibility of triplicate experiments (performed on different days) was assessed by evaluating Pearson correlation values .p values were adjusted for multiple testing using Benjamini and Hochberg’s false discovery rate (FDR). Average fold-change over three independent experiments and standard deviation were plotted. Analysis of RNA fragments was done using the UCSC genome browser and Integrated Genome Viewer  in blocking buffer, and washed and incubated for 1–2 h with HRP-coupled secondary antibodies . ECL solution  was used for detection on a Chemidoc imager . Images were analyzed by the Image Lab software .EV purified by density gradient ultracentrifugation as described above were resuspended in 50 µl PBS + 0.4% EV-depleted BSA. Samples were diluted 20 times in PBS  before measurement on a Nanosight NS500 instrument . Data acquisition and processing were performed using NTA software 3.1. Each sample was recorded for 5 times 30 s, 25 frames per second, camera level 14, detection threshold 5.EV and RNP were separated by density gradient centrifugation as described above, and resuspended in 15 µl PBS. 3 µl aliquots of either EV or RNP were added on top of carbon-coated 300 mesh, copper grids, and incubated for 2 min at room temperature. The grids were then washed two times in 50 µl PBS and stained with 2% uranyl acetate. Imaging was performed on a T20 electron microscope (FEI) operated at 200 keV. Images were recorded on a CCD Eagle camera (FEI).32P-labeled DNA oligo probes perfectly complementary to each RNA species were hybridized overnight at 42 °C in PerfectHyb (Sigma) solution. Blots were analyzed by phosphorimaging using a Typhoon Scanner .Cellular or EV-derived small RNA  was denatured in gel loading buffer II (Invitrogen) for 2 min at 70 °C and snap-cooled on ice before loading on a denaturing 15% PAGE gel  as described previously . RNA wasmiR-146a-5pTGA GAA CTG AAT TCC ATG GGTmiR-155-5pGGG TTA ATG CTA ATT GTG ATA GmiR-9-5pGGG TCT TTG GTT ATC TAG CmiR-10a-5pTAC CCT GTA GAT CCG AAT TTG TGmiR-27b-3pTTC ACA GTG GCT AAG TTC TGCmiR-378-5pACT GGA CTT GGA GTC AGA AGmY1GTT ATC TCA ATT GAT TGT TCA CAG TCmY3GGC TGG TCC GAG TGC AGT GGRNU1CCATGATCACGAAGGTGGTTTRNU6CTCGCTTCGGCAGCACAsnord65TAGTGGTGAGCCTATGGTTTTsnord68AGTACTTTTGAACCCTTTTCCAThe following forward primers were used in RT-qPCR (5′ to 3′) in combination with the miScript universal reverse primer.Y1_5pTTG AGA TAA CTC ACT ACC TTC GGA CCA GCCY1_3pGTC AAG TGC AGT AGT GAG AAG5′-tRNA-Glu_CTCCCG AAT CCT AAC CAC TAG ACC ACC AG5′-tRNA-Gly_GTTGCA TTG GTG GTT CAG TGG TAG AAT TCT CGC Ct test. Differences in fold-changes of individual RNAs as measured in RT-qPCR were analyzed by one-way ANOVA. Significance was defined as p < 0.05. Statistical tests on data not derived from RNA sequencing were done in SPSS .Differences between the numbers of EV released by LPS- and VitD3-stimulated DC versus control DC were analyzed by one-way ANOVA with Dunnett’s two-sided post hoc test. Similar statistical testing was performed on differences in RNA yield. Differences in RNA biotypes in EV and cells were analyzed by two-tailed Raw data and count tables are deposited in the GEO database under accession number GSE105151. Written details on experimental procedures have been submitted to the EV-TRACK knowledgebase (EV-TRACK ID: EV170030) [Functionally distinct subsets of mouse bone marrow-derived DCs were generated by exposure of cells to LPS, thereby inducing immune-stimulatory DC (LPS-DC), or to 1α,25-dihydroxyvitaminD3, thereby inducing immune-suppressive DC (VitD3-DC) . Untreatn = 3 biological replicates). Sequencing libraries were prepared using an adapter-ligation-based method routinely used in miRNA profiling [Next, we analyzed how LPS- or VitD3 treatment of DC affected the RNA content of EV released by these cells. It is important to note that extracellular RNA can be associated with either EV or ribonucleoprotein complexes, which may sediment at similar centrifugal force . Using drofiling . Such a rofiling , 32, 61)rofiling –32, 34, n = 3) abundance of a transcript in EV and RNAs with non-adjusted p < 0.05 are highlighted in black. The scatter plots of these data indicated that LPS- and VitD3-treatment affected the levels of miRNA, snoRNA, snRNA, and miscRNA in EV  of individual RNAs in LPS or VitD3 over control conditions were calculated. The scatter plots in Fig. Of the tested RNA classes, tRNA levels in EV showed the lowest rate of change due to LPS or VitD3 stimulation. 97% of tRNA reads mapped to six different tRNA isoacceptors  . Although relatively high tRNA read counts were observed in the EV-associated RNA pool , the levels of these six abundant tRNAs remained stable (log2FC < 1) in response to cellular stimulation . Besides tRNAs, the highly purified EV populations used in this study also contained substantial levels of snRNA, which is an RNA type known to be mainly confined to the nucleus. Although cell stimulation seemed to induce changes in EV-associated snRNA levels Fig. a, of whiIn contrast to tRNAs and snRNAs, the EV-associated levels of three other RNA types tested here, i.e., miRNAs, snoRNAs, and miscRNAs, changed in response to the different stimulate imposed on the DC. We aimed to identify the RNAs differentially present in EV released by LPS- versus VitD3-treated DC, since such RNAs may underlie distinct effects of EV on recipient cells.Within the miRNA group, we observed the highest number of RNAs that differed between the LPS and VitD3 groups with a fold change > 2 and with FDR values < 0.05 . We normalized our qPCR data to the geometric mean of these four genes to minimize errors caused by technical and/or biological variation . Using tBesides changes in miRNA levels, the levels of several EV-associated snoRNAs were found to differ between LPS and VitD3 conditions Fig. c, with tSo far, we compared the abundance of specific RNA classes in EV by assessing the total numbers of reads mapping to non-coding RNA genes. However, also the fragmentation profile of EV-associated RNAs may be functionally important and indicative for the status of the EV-producing cell. Fragmented forms of RNA have been described in EV from a wide range of cells and biological fluids , 78, 81.n = 3) abundance of a transcript in EV, and dot coloring is used to indicate RNAs that were significantly changed in cells, EV, or both. First, we tested whether changes in EV-associated RNAs reflected stimulation-induced changes in cells. RNAs of which the levels in EV significantly changed  upon LPS or VitD3-treatment were categorized as ‘reflective RNAs’ (p < 0.05 in both cells and EV), ‘EV-responsive RNAs’ , or ‘reciprocal RNAs’ (opposite changes in EV and cells with p < 0.05) (Supplementary Table 2). Of all RNAs that significantly changed in EV (set to 100%), a minor percentage of the miRNAs and snoRNAs reflected the up/downregulation in cells or were reciprocally regulated, while most of the RNAs changed only in EV, but not (significantly) in cells  were, therefore, classified as ‘reflective’ (p < 0.05 in both cells and EV), ‘cell-responsive’ , ‘reciprocal’ (opposite changes in EV and cells with p < 0.05), or ‘not in EV’ . Comparable to what we observed in the above analysis of EV-RNA, only a minor percentage of changes in cellular miRNAs and snoRNAs was reflected in EV. The majority of the RNAs that changed in cells did not significantly change in EV  that overlap in size with EV, e.g., ultracentrifugation and precipitation-based methods , 85, 86.Our experimental approach allowed comprehensive analysis of changes in EV-RNA classes induced by diverse immune-relevant stimuli. The most prominent stimulation-induced changes in EV-associated RNA levels were found for miRNA, Y-RNA, and snoRNAs Fig. . AlthougNumerous studies already demonstrated that miRNA levels in EV change upon disease induction or cell stimulation –24. UniqHeligmosomoides polygyrus and in seminal fluid EV for which immunosuppressive effects have been described [Not only immune-suppressive miRNAs but also two types of Y-RNA were found to be specifically excluded from EV released by LPS-stimulated DC Fig. e. This rescribed , 105–107With regard to the biomarker potential of EV-associated RNAs, our data also urge caution in interpreting EV as snapshots of the cell from which they arise. We found that only a subset of changes in EV-RNA reflected changes that occurred at the cellular level. The highest proportion of reflective RNAs was observed within the category of miRNAs and included prominent immune-related RNAs such as miR-155, miR-9, and miR-10-5p Fig. h. For a We observed two classes of RNA, i.e., snRNA and tRNA, of which the EV-associated levels remained stable after differential stimulation of DC. These RNA classes have been commonly detected in small RNAseq studies of EV from multiple cell types and body fluids, but factors impacting their incorporation into EV had until now not been investigated. Although these data need confirmation in different cell types with various external stimuli, our data may be a first indication that tRNAs and snRNAs are constitutive components of EV. Speculatively, these RNAs may be required for basal scaffolding functions in EV biogenesis, or may aid the functional transfer of gene-regulatory RNAs. Based on their stable association with EV, these types of RNAs also have potential to be used as EV-reference RNAs for RT-qPCR normalization, since comparing the EV transcriptome of differentially stimulated cells is complicated by potential differences in the number of EV, RNA yield, and RNA composition. Importantly, the use of well-known stably expressed cellular RNAs is not necessarily valid for normalization of EV qPCR data due to potential differences in RNA shuttling between tested conditions. Therefore, normalization of RT-qPCR data to reference genes which are stable in EV is a reliable way to quantitate differences in individual RNAs. Here, we show how RNA sequencing and RT-qPCR validation can be used to identify candidate EV-reference genes which are constitutively expressed between differently stimulated cells.In conclusion, this study indicates that external stimuli imposed on cells can lead to changes in the levels of some EV-associated RNA classes, while leaving others unchanged. In our DC-model, two different immune-related cell stimuli led to disparate changes in the type and quantity of miRNAs, snoRNAs, and Y-RNAs in EV. Only part of the changes in RNA contents of EV reflected changes in the cellular RNA, which urges caution in interpreting EV as snapshots of cells. These findings show that differences in multiple structurally distinct RNA classes shape the EV transcriptome. These RNA types may contribute to the functional properties of EV in cellular communication, and may be further explored as candidate EV-RNA-based indicators for the immune status of the EV-producing cell.Supplementary material 1 (PDF 2161 kb)Supplementary material 2 (XLSX 116 kb)Below is the link to the electronic supplementary material.


Notably, various recent studies described the presence of exRNA in different biofluids. As biofluids are enriched with RNAses, the exRNA must be efficiently protected. Several studies suggested that, among several RNA protection mechanisms, the RNA could also be encapsulated within EVs5. Cancer cells use EVs as an efficient way of interacting with the tumor microenvironment, as well as with distant tissues. EV-mediated communication have been shown to promote a plethora of cancer-promoting traits, including immunosupression, invasion, and the nourishing of the metastatic niche8. On top of that, as vesicle-encased proteins and RNAs are of tumor-specific origin, they have been suggested as potential biomarkers and therapeutic targets.Recent years have seen the rise of unique cell-to-cell communication mechanisms in the form of extracellular vesicles (EVs). Among various biomolecules shipped between cells, different RNA species have been reported as key messengers. Importantly, although many of the extracellular RNA (exRNA) studies focus on microRNAs (miRNAs), EVs carry various types of RNA species, miRNA being just a small part of this repertoire9 addressed the intriguing question of specific sorting mechanisms of different RNA species into EVs, as a crucial step in understanding the downstream RNA-dependent oncogenic signaling and its biological and pathological role9. The study involved three derivatives of a colorectal cancer (CRC) cell line (DLD-1), differing by their KRAS status. EVs from these cells were previously compared and found to have distinct mutant KRAS-dependent EV cargo. The mutant-derived EVs were shown to be taken up by wild-type (WT) cells, consequently driving tumorigenesis11. In this present study, the researchers aimed to investigate the long-RNA molecules (over 200 nt) encased in the EVs of the three cell derivatives. They have previously determined that the ~40–130 nm EVs contain distinct proteomic and transcriptomic profiles, compared with both their parental cells and with EVs from WT-KRAS cells. In this current study, the authors used RNA-sequencing to analyze the long-RNA content in the same EVs . Unlike miRNAs that were previously shown to be KRAS status dependent, here most of the upregulated long RNAs were similar in all three cell lines. The authors used quantitative PCR to further investigate whether the mRNA profile found in the EVs represents full-length sequences. Several mRNAs known to relate to oncogenic genes, such as trans-well experiments, the KRAS-mutated cells delivered lncRNA molecules to their recipient WT counterparts. Importantly, this effect was KRAS dependent, as such phenomenon was not observed with donor WT-KRAS cells.The last part of the study focused on the EV-mediated transfer of long non-coding RNA (lncRNA). Despite previous reports presenting lncRNA in EVs, the functional role is not yet elucidated. The research group used a CRISPR display system targeting lncRNAs to specific genomic sites accompanied by a reporting luciferase marker. In yet another series of 9 address several key questions in the field of EVs in general, and most specifically in oncogenic mechanisms promoted by such cell-to-cell communication. The authors focused on a major genetic alteration frequently found in CRC. Mutations in KRAS play a central role in driving CRC tumorigenesis and characterizing the involvement of specific RNA cargo of EVs may shed light on such a malignant process. Similar to previous reports, this study suggests specific RNA enrichment in EVs that is selectively sorted. Furthermore, long coding and non-coding RNAs, including pseudogenes transcripts that their regulatory functions are not completely clear, are transferred between cells via EVs and this effect might be governed by mutant KRAS. The simplicity of the in vitro approaches used in this study allows us to decipher and study the RNA composition of each cell type. Taking these conclusions and studying them in a more complex setting, either in the tissue or organism level, might pose a much bigger challenge.The findings summarized in Hinger et al.Nevertheless, these assays allow us to focus on the content of EVs, their specific RNA transcripts, and their ability to be exported and taken up by recipient cells. The fact that many lncRNAs are to be shipped via EVs indicates that these molecules hold significant facilitating roles yet to be acknowledged. This scientific effort is another stride in this direction.


Spinach2 unfolds in four discrete steps as force is increased to 7 pN and refolds in reciprocal steps upon force relaxation. In contrast, GQ-core unfolding in iMangoIII and MangoIV occurs in one discrete step at forces >10 pN and refolding occurred at lower forces showing hysteresis. Co-transcriptional folding using superhelicases shows reduced misfolding propensity and allowed a folding pathway different from refolding. Under physiologically relevant pico-Newton levels of force, these aptamers may unfold in vivo and subsequently misfold. Understanding of the dynamics of RNA aptamers will aid engineering of improved fluorogenic modules for cellular applications.Recent advances in fluorogen-binding “light-up” RNA aptamers have enabled protein-free detection of RNA in cells. Detailed biophysical characterization of folding of G-Quadruplex (GQ)-based light-up aptamers such as Spinach, Mango and Corn is still lacking despite the potential implications on their folding and function. In this work we employ single-molecule fluorescence-force spectroscopy to examine mechanical responses of Spinach2,  iMangoIII and MangoIV, which is of interest for the development of improved fluorogenic modules for imaging applications.Light-up aptamers are widely used for fluorescence visualization of non-coding RNA in vivo. Here the authors employ single-molecule fluorescence-force spectroscopy to characterize the mechanical responses of the G-Quadruplex based light-up aptamers Spinach2,  Because RNA is inherently nonfluorescent, fluorescent reporter molecules must be added for RNA visualization in live cells. An RNA binding protein fused with a fluorescent protein can be directed to a modified RNA that contains a protein binding motif, and incorporation of dozens of repeats into a gene of interest allows researchers to visualize single messenger RNA molecules with high spatiotemporal resolution in cells2. However, this method requires large modifications to the gene and the RNA that may potentially perturb function and can cause high fluorescent background coming from unbound proteins3. Since the discovery that an RNA aptamer can specifically interact with a fluorogen to generate a module that makes the fluorogen much brighter than its counterpart free in solution4, many RNA-fluorogen complexes have been developed and used for fluorescent detection of RNA in cells12. Such an RNA aptamer that can specifically interact with a fluorogen to elicit fluorescence is known as a “light-up” aptamer14.Non-coding RNAs (ncRNAs), including microRNAs, circular RNAs, long intergenic ncRNAs, and small nucleolar RNAs, regulate many cellular processes underlying gene expression, cell fate determination, morphogenesis, and polarization5. Although spinach variants such as Spinach215 have been engineered with improved fluorogenic activities, because of their large size and complex geometry, they are known to misfold and hence hinder imaging applications6. A recent addition to the repertoire of light-up aptamers is mango, which specifically binds to a functionalized thiazole orange (TO1)9. Mango and its variants constitute the smallest light-up aptamers developed to date. A common structural feature of spinach and mango is the presence of a fluorogen-binding G-Quadruplex (GQ) core with mixed parallel and antiparallel connectivity, flanked by A-form duplex stem(s)18. In addition, non-GQ light-up aptamers that can selectively bind to malachite green20 or cyanine dyes with submicromolar affinity have also been developed22.The first such light-up aptamer shown to be effective for live-cell RNA imaging is spinach. Spinach spectrally emulates green fluorescent protein (GFP) upon binding to DFHBI, an analog of the GFP chromophore24, and have been studied in vitro, in bulk solution28 and also at the single-molecule level29. Although putative GQ forming sequences have been identified in the transcriptome, the folding propensity of RNA GQs in cells is still under debate31. The light-up aptamers integrate the distinct structural characteristics of RNA GQs with their ligand-binding ability. Although folding of RNA aptamers found in riboswitches has been studied extensively35, biophysical characterizations of light-up aptamers regarding their folding properties are sparse37. For example, in order to function efficiently in cells, an RNA aptamer must fold correctly and stably and resist inadvertent unfolding induced by mechanical forces exerted by the fluctuating environments or by RNA binding or RNA-translocating proteins.RNA GQs have been proposed to play regulatory functions in transcriptional and posttranscriptional processesiMangoIII and MangoIV, at the single-molecule level38 either in refolding format where folding of a fully synthesized RNA is induced by changes in ionic conditions or in vectorial folding format where nascent RNA is allowed to fold during synthesis. Furthermore, in order to characterize their nanomechanical properties, we performed fluorescence-force spectroscopy using a hybrid instrument combining single-molecule fluorescence resonance energy transfer (smFRET) and optical tweezers39. Our results illustrate mechanical superiority of mangos, with respect to Spinach2, making them better adept to physiological levels of tension.In this study, we investigated GQ formation in Spinach2 and mango variants, E between the donor (Cy3) and acceptor (Cy5) reflects the end-to-end distance between A24 and A75 of the Spinach2 sequence17 and is expected to be higher in the folded state  and K+ (100 mM), CD spectroscopy showed GQ formation   Fig. , suggest43. Approximately 39% of the Spinach2 molecules tethered on the single-molecule surface manifested GFP-like fluorescence upon addition of DFHBI-1T , and the apparent free energy of activation ΔG‡47. DFHBI-1T-binding caused changes in Δxu‡s to the E1  and E3 states , but not the E2 state   but the E ~ 0.86 state becomes dominant at 5 mM Mg2+  in 100 mM K+ alone, which evolve into a stably folded form (E ~ 0.86) upon addition of 5 mM Mg2+  and antiparallel connectivity Fig. 18. Howev.55 Fig. 9,41. IntU26 Fig. 51. This +, iMangoIII typically underwent gradual decreases in E from ~0.55 to ~0.4, followed by an abrupt transition to E ~ 0.05  and refolding (frefold) forces were defined as those corresponding to the midpoint of transitions between E ~ 0.4 and ~ 0.05 (funfold) and vice versa (frefold). Such transitions are suggestive of cooperative unfolding, similar to that of canonical GQs52. In all, unfolding occurred at a range of force values with a peak at ~12 pN, while refolding occurred at ~3.5 pN showing hysteresis  and was similar to the Δxu‡ value of 4.2 ± 0.5 nm from the single unfolding transition observed in 100 mM K+ without Mg2+, suggesting that both share a common GQ core. The addition of Mg2+ induces an auxiliary base triple to stabilize the overall structure. The base triple unfolds at ~2.5 pN, with a corresponding Δxu‡ of 7.2 ± 1.8 nm , likely because the GQ core is necessary for the aptamer to bind the fluorogen -functionalized biotinylated thiazole orange (henceforth referred to as TO1), iMangoIII unraveled in two discrete steps also in the presence of TO1 . However, refolding occurred at ~4 pN, which is lower than ~7.5 pN in the absence of TO1  when compared with iMangoIII (frefold ~ 12 pN) may be attributed to an additional G-quartet51. Furthermore, unlike iMangoIII (and Spinach2), MangoIV folding is insensitive to Mg2+, likely because of the absence of a base triplet junction at the interface of MangoIV GQ and the flanking duplex stem48.10, whereas mango aptamers consist of an invariant ligand-binding core spanning only 23 nucleotides9. Our fluorescence-force measurements demonstrate higher mechanical stability of mango and hence less susceptibility to mechanical forces an RNA molecule may experience in vivo during its synthesis, processing, trafficking, and function.The class of mango aptamers is smaller in size than the spinach aptamers. For example, a minimal spinach construct known as baby spinach comprises of 51 nucleotides2+. Mg2+ promotes formation of a base triple underneath the G-quartets in iMangoIII without globally affecting the fluorogen-binding GQ core. In contrast, Mg2+ directly impacts the GQ core in Spinach2 by stabilizing the connecting stem loop, P3 54.In general, stable folding of GQ core in mango was independent of Mg P3 Fig.  at its i8. In cells, transcription may prompt inadvertent GQ misfolding from the 5′ end55, inhibiting subsequent fluorogen binding37. In order to gain insight into co-transcriptional folding of “light-up” aptamers, we used a highly processive superhelicase Rep-X to mimic the vectorial nature of co-transcriptional RNA folding57. To this end, Rep-X was first loaded on to the surface-immobilized aptamer-heteroduplex where the aptamer portion is annealed to a DNA strand that has a dT20 3′ overhang. Subsequent unwinding of the heteroduplex and RNA folding was initiated by adding Mg2+ and ATP  and zipping up of the adjacent stem after ~0.15 s :1.4 ratio respectively in a buffer containing 50 mM NaCl and 10 mM Tris-HCl, pH 7.5, at 95 °C for 5 min, followed by slow cooling for 3 h and quenching on ice for 5 min. The λ-bridge was further added to the above mixture in the ratio of 2:1 to the Cy5-labeled strand and incubated with rotation at room temperature for an hour. The G4-construct(s) thus generated were used for single-molecule experiments by total internal reflection fluorescence (TIRF) microscopy−1, respectively. The mixture was incubated/rotated at room temperature for 2–3 h. Finally, the dig-strand (5′/dig/CCCGCCGCTGGA/dig/3′) was added to a concentration of 200 nM and then incubated with rotation at room temperature for 1 h.For integrated smFRET-optical tweezers assay, the construct generated above was annealed to λ-DNA (New England Biolabs) and the dig-labeled strand. For annealing, λ-DNA (16 nM) was first heated in the presence of 120 mM NaCl at 80 °C for 10 min and then placed on ice for 5 min. The RNA aptamer construct(s) and BSA were added to the λ-DNA at a final concentration of 8 nM and 0.1 mg mL38. For TIRF experiments, 30 pM RNA aptamer construct(s) were immobilized on the surface via biotin–neutravidin interaction. Finally, imaging buffer was added for data acquisition. Unless otherwise mentioned, the imaging buffer comprised of 50 mM Tris-HCl pH 7.5, 0.8% w/v D-glucose [Sigma], 165 U mL−1 glucose oxidase [Sigma], 2170 U mL−1 catalase [Roche], 3 mM Trolox [Sigma], and predetermined amount of KCl/MgCl2.The quartz slides and coverslips, passivated with PEG  were assembled to form imaging chambers−1 BSA [NEB], 1 mg mL−1 tRNA [Ambion]) for 1 h. The aptamer constructs were then diluted to 10 pM and immobilized on the surface via biotin–neutravidin interaction. Subsequently, 1 μM anti-digoxigenin coated polystyrene beads (Polysciences), diluted in a buffer containing 10 mM Tris-HCl pH 7.5 and 50 mM NaCl, were added to the imaging chamber and incubated for 30 min. Finally, data were acquired in the imaging buffer.For integrated fluorescence-force measurements, the imaging chamber was incubated in blocking buffer  through the back port of the microscope (Olympus) on the sample plane with a 100× immersion objective (Olympus). A piezo stage with the microscope slide was translated at a speed of 455 nm s−1, between 14 and 16.8–17 μm, over ~6.5 s, in order to apply ~0.3–28 pN force on the sample tethers. The applied force was read out via position detection of the tethered beads using a quadrant photodiode (Thorlabs)62. A confocal excitation laser  was focused on the sample through the side port of the microscope. A piezo-controlled steering mirror (Physik Instrument) raster scanned the sample with the excitation laser. The fluorescence emission was filtered from infrared laser by a band pass filter  and excitation by a dichroic mirror  and subsequently detected by two avalanche photodiodes (Excelitas Technologies).An integrated fluorescence-optical trap instrument was recently developed in our lab to study conformational changes of biomolecular systems under tension38. The smFRET efficiency, E, was estimated using IA/(IA + ID), where IA and ID are the donor and acceptor intensities, respectively, after background subtraction and crosstalk correction. FRET efficiency histograms were constructed by averaging the first ten data points of each molecule’s time trace, acquired at an integration time of 30 ms.For smFRET imaging in the absence of force, we used prism-type TIRF microscopy, with 532 nm laser excitation and back-illuminated electron-multiplying charge-coupled device camera 6, TO1 (ABM Inc.): 50 nM9) were added to the imaging buffer for understanding the conformational dynamics of fluorogen-bound aptamers. As TO1 is functionalized with biotin, the free neutravidin on the single-molecule surface was concealed with saturated concentrations of biotin (Sigma-Aldrich), prior to addition of the fluorogen. The Spinach2-DFHBI-1T binding was quantified by incubation of 5 μM DFHBI-1T for 30 min followed by imaging with a 488 nm excitation laser (Coherent) and band pass emission filter . For the mango aptamers, TO1 was pulled down via biotin–neutravidin interaction and Cy3-labeled mango binding was quantified with a 532 nm laser (Cobolt), following the protocol of Dologoshiena et al.8.Saturated concentrations of cognate fluorogens (DFHBI-1T (Lucerna): 5 μM62. In summary, a tethered bead was trapped and its origin was determined by stretching the tether in opposite directions along x- and y-axes. The trapped bead was then moved from its origin by 14 μm and a confocal laser was used to scan and locate the fluorescence spot on the tether. Fluorescence emission from the tether molecule was detected concurrent to the application of force, 20 ms after each step in the stage movement.A detailed data acquisition procedure for single-molecule fluorescence-force spectroscopy has been described previously by Hohng et al.ν = 1/2)47. These derived parameters , etc.) were then used to reconstruct the force profiles.The unfolding force histograms were analyzed and the free energy parameters such as the transition distances to unfolding and refolding, etc., were predicted using the Dudko–Szabo models  for 2 min. The unbound Rep-X was washed off simultaneously and the unwinding reaction was initiated by adding the unwinding buffer . The unwinding reaction was quenched after a minute to capture the conformation of maiden folded RNA aptamers. For the real-time measurements, imaging was started a few seconds before addition of the unwinding buffer. A detailed protocol has been described by Hua et al.57. The single-molecule time trajectories were aligned based on the PIFE peak centers and the GQ formation and dwell times were determined as shown in Supplementary Fig. The RNA aptamer construct(s) engineered for smFRET studies was annealed to its complementary sequence with a dT+/Mg2+ ions. The CD spectra was averaged from three scans, recorded between 220 and 320 nm at room temperature and were corrected for baseline and signal contributions from the buffer.CD spectroscopy of the RNA aptamers were performed on an Aviv-420 spectropolarimeter , using a quartz cell of 1 mm optical path length. The oligonucleotides were diluted to 5 μM in a buffer containing 50 mM Tris pH 7.5 and appropriate concentration of Kex = 482 nm6 and (2) 100 nM mango and 200 nM TO1, λex = 506 nm (iMangoIII)51 and 510 nm (MangoIV)9. All measurements were done in a buffer containing 50 mM Tris pH 7.5, 100 mM K+ and 5 mM MgCl2 (1 mM MgCl2 for MangoIV) at room temperature.Fluorescence measurements of the RNA aptamer and fluorogen complex were performed with a fluorometer . Fluorescence spectra were collected under the following conditions: (1) 100 nM Spinach2 and 1 μM DFHBI-1T, λFurther information on research design is available in the Supporting InformationPeer Review FileReporting SummarySource Data


At the beginning of this century, the Human Genome Project produced the firstdrafts of the human genome sequence. Following this, large-scale functionalgenomics studies were initiated to understand the molecular basis underlying thetranslation of the instructions encoded in the genome into the biological traitsof organisms. Instrumental in the ensuing revolution in functional genomics werethe rapid advances in massively parallel sequencing technologies as well as thedevelopment of a wide diversity of protocols that make use of these technologiesto understand cellular behavior at the molecular level. Here, we review recentadvances in functional genomic methods, discuss some of their currentcapabilities and limitations, and briefly sketch future directions within thefield. Progress in genomics during the last decade has been largely dependent on thedevelopment of methods for massively parallel sequencing of large numbers (up to afew hundred million per experiment in some cases) of short DNA sequences, where asingle sequence read is about 50 to 150 base pairs. These methods—oftenreferred to as next-generation sequencing (NGS)—are used for genomesequencing, but their high throughput also makes them very attractive for functionalgenomics assays, where the primary goal is to annotate genome regions with theirbiological function. This often requires quantitative estimates, which typically arederived from read counts. A myriad of experimental protocols and the accompanyingbioinformatic pipelines have been designed that leverage the high-throughputcapabilities of NGS to monitor a wide variety of molecular phenotypes reflectingvery diverse genomic functions.1, have largely been substituted by sequencing-based methods. Our review thusfocuses on these. We also discuss recently developed methodologies for large-scaleperturbation experiments that can probe the functionality of genomic loci in amassively parallel fashion. We discuss the limitations of short read length, effortstoward developing long read sequencing technologies, and novel approaches to analyzethe behavior of single cells and cells in the context of their tissue of origin. Weconclude by discussing the role of large-scale international consortia and futuredirections in functional genomics.In this review, we summarize the protocols that are most frequently applied infunctional genomics. Although oligonucleotide array-based methods for genotyping andgene expression are still widely used for large population screens because of thelow cost and straightforward bioinformatics, genome tiling arrays, which wereinstrumental in the emergence of functional genomics 2. The number of cells from which the DNA is extracted is important, as too fewcells may cause excessive digestion, leading to reads mapping to closed chromatinregions, whereas an excess of cells can cause insufficient digestion, creating longfragments that may be difficult to sequence 3. The methylation status of DNA can be assayed by using endonuclease digestionby methylation-dependent restriction enzymes, by using bisulfite treatment toconvert unmethylated cytosine to uracil (which is recognized as thymine duringsequencing), or by using methylation-dependent antibodies to immunoprecipitatemethylated chromatin followed by sequencing (ChIP-seq) 4. False positives may occur if unmethylated cytosines fail to convert touracil and are interpreted as methylated cytosines. It is essential to minimize DNAdegradation during bisulfite treatment, as DNA fragmentation will hamper polymerasechain reaction (PCR) amplification. Tet-assisted bisulfite sequencing 5 can be used to resolve 5-methylcytosine and 5-hydroxymethylcytosine, both ofwhich are found in mammalian genomes 7 but are indistinguishable in traditional bisulfite sequencing. ChIP-seq-basedapproaches do not give single-nucleotide resolution of DNA methylation and areaffected by genomic variations in CpG density.Chromatin can be divided into the transcriptionally active open euchromatin and thetranscriptionally silent heterochromatin or closed chromatin. The ATAC-seq (assayfor transposase-accessible chromatin using sequencing) protocol uses transposasesacting on accessible chromatin to create DNA fragments, which then are sequenced toidentify open genomic regions in a particular cell type or condition 8 or the binding affinities of transcription factors to genomic regions 10. Improvements to the ChIP-seq protocol have allowed greater resolution whilereducing the number of cells required 12. The use of a sensitive but specific antibody is essential for obtaininghigh-quality ChIP-seq data. Genomic assays typically produce an “epigenomicprofile” along the genome (by mapping reads obtained by the assay to thegenome). To combine and integrate epigenomic profiles produced by different assays, segmentationalgorithms are employed , in contrast, specifically sequences the5′ end of transcripts to pinpoint the transcription start site and thereforethe promoter region of each transcript 18. CAGE uses a random oligonucleotide as the primer for the reversetranscription reaction, instead of an oligo-dT primer against the poly(A)-tail,allowing both poly(A)+ and poly(A)− transcripts—such as certain longnon-coding RNA (lncRNAs)—to be profiled. Specific RNA fractions can beselected for inclusion in the sequencing libraries depending on their size, thechemical group at their 5′ end, the presence or absence of a poly(A) tail, orsubcellular localization in the cytoplasm, nucleoplasm, or chromatin fraction 21. These fractions may correspond to different classes of RNA or differentstages in the pathway of RNA synthesis and post-processing. Protocols to assess theRNA-interacting partners include ribosome profiling 22 to identify mRNAs undergoing translation as well as various protocols basedon high-throughput sequencing of RNA after cross-linking and immunoprecipitation(CLIP-seq and its variants) 23 to find transcripts bound to a specific protein. RNA modifications 24 such as methylation of adenosine nucleotides 25 can be identified by methylation-dependent immunoprecipitation of RNAfollowed by sequencing 27.Quantitative profiling of the transcriptional output of the genome relies onsequencing libraries of cDNA fragments derived from RNA (RNA-seq) 28. Performing ligation of the 5′ adapter after reverse transcriptionallows ligation to the 5′ end of the cDNA, avoiding biases due to thepresence of any modifications at the 5′ end of the short RNA molecule 29. To avoid biases introduced by oligonucleotide adapter ligation to the3′ end, polyadenylation of the short RNA may be used instead. However, adisadvantage of this approach is that it does not allow the identification of theexact 3′ end of the short RNA, which is unfortunate, as addition and removalof nucleotides—in particular, adenosines—to the 3′ end ofmiRNAs are prevalent and may play a regulatory role in miRNA stabilization anddegradation 32.Profiling of short non-coding RNAs—in particular, microRNAs(miRNAs)—typically relies on the ligation of oligonucleotide adapters to the3′ and 5′ end of the short RNA followed by reverse transcription andPCR amplification making use of primer sequences in the adapters 33, which are produced by chemical cross-linking, DNA fragmentation, proximityligation, and high-throughput sequencing of ligated fragments, have shown thatchromatin is organized in topologically associating domains, each consisting ofchromosome regions that frequently interact with each other, and chromatin loopingin which distal regulatory regions such as enhancers and promoters come in closephysical proximity to each other. ChIA-PET  34 combines proximity ligation of DNA fragments with chromatinimmunoprecipitation of specific proteins to globally detect the chromatininteractions in which they participate.The 3D structure of the genome in the nucleus provides the physical architecture forexpression regulation. Hi-C libraries 36 may mediate distal interactions 37, identifying the chromatin binding sites of regulatory RNAs is of centralimportance. Chromatin isolation by RNA purification (ChIRP-seq) 38 uses tiling oligonucleotides to pull down a specific lncRNA together with theprotein and DNA bound to it, followed by sequencing of the DNA component todetermine the exact binding positions of the lncRNA to chromatin. For unbiasedmapping of RNA–chromatin interactions, on a genome-wide scale, severalprotocols related to Hi-C have been developed that rely on proximity ligation of DNAto RNA using a bivalent oligonucleotide linker molecule: MARGI (mappingRNA–genome interactions) 39, global RNA interactions with DNA by deep sequencing (GRID-seq) 40, and chromatin-associated RNA sequencing (ChAR-seq) 41.As transcripts produced at regulatory sites Although chromatin features and transcriptional activity at a genomic region can beindicative of a biological role, ultimately the function of a genomic region isdefined by its contribution to the phenotype. Perturbation studies, in whichactivation or inhibition of specific genomic regions or their products is followedby phenotypic or molecular assays of the cellular response, can be used to elucidatethe functional role of genomic loci.42 to edit or otherwise modify the genome at specific sites. Whereas programmingzinc finger nucleases 43 and transcription activator-like effector nucleases (TALENs) 44 to target specific genomic sites required extensive protein engineering thatlimited the scalability of these approaches, CRISPR/Cas9 recognizes and cleaves aparticular DNA site using only a single guide RNA, which can easily bemass-produced. Nucleotide insertions and deletions introduced near the double-strandbreak by non-homologous end-joining can abrogate the expression or function ofprotein-coding genes but may not be sufficient to fully abolish the function oflncRNAs. Therefore, dual-CRISPR systems have been developed that use a pair of guideRNAs to generate double-strand breaks on both sides of a gene of interest, followedby non-homologous end-joining to repair the lesion, resulting in a full deletion ofthe gene 46.Recent developments in perturbation technologies now allow genomic perturbationexperiments to be highly multiplexed, with thousands of knockdowns or activationsoccurring in parallel in a population of cells. High-throughput perturbationexperiments have become feasible in particular because of the development of theCRISPR/Cas9 technology 47, tight binding of CRISPR-dCas9 to DNA prevents binding of the transcriptionalmachinery, effectively shutting down transcript production at the targeted site.Improvements to the knockdown efficiency were achieved by fusing aKrüppel-associated box (KRAB) repressor domain to Cas9 48, inducing histone modification changes associated with repression ofexpression. Similarly, dCas9 fused to a transactivating domain can be used to inducegene expression at specific sites 48. Epigenome editing can be performed by fusing dCas9 with epigenome writers orerasers  49.The applicability of this technology can be extended beyond genome editing byreplacing the endonuclease Cas9 with catalytically inactive Cas9 , which binds to specific genomic sites without inducing cleavage. In CRISPRinterference (CRISPRi) 50.Second-generation CRISPR editing tools allow the conversion of specific nucleotidesat the genomic target site without inducing a double-strand break  51. CRISPRi-mediated knockdowns followed by single-cell genome-wide expressionprofiling (Perturb-seq) 53 can be employed to obtain detailed information on the global transcriptomeresponse to the perturbation and to observe cell state changes that are not easilyassessed in a straightforward cellular assay.Large-scale CRISPR-based screening relies on designing guide RNAs in large quantitiesand introducing them, usually using a lentiviral vector, into a population of cellsat a sufficiently low multiplicity of infection, such that most cells will receiveeither zero or one guide RNA; puromycin selection is applied to exclude cells thatwere not infected. Typically, up to 10 guide RNAs are used for a single locus,allowing checks for reproducibility and off-target effects due to inadvertentbinding of guide RNAs elsewhere in the genome. During cell culture, the proportionof cells containing guide RNAs that inhibit the expression of genes essential forcell growth will decrease. Therefore, genes involved in cell growth can beidentified by quantifying, using NGS, the relative enrichment of each guide RNAafter culturing 54.For functional genomics, perturbation technologies that target DNA have one inherentdrawback: it is unclear whether any observed phenotype is due to the inhibition ofregulatory DNA elements at the targeted locus or to any RNA product originatingthere. This ambiguity can be avoided by targeting specific transcripts directlyusing locked nucleotide antisense oligonucleotides (ASO gapmers) or by using smallinterfering RNAs (siRNAs) or small hairpin RNAs (shRNAs). Target RNAs bound by anASO gapmer are recognized and cleaved by endonuclease RNase H, followed bydegradation of the RNA. In contrast, the RNA interference (RNAi) pathway relies onprocessing of siRNAs and shRNAs by Dicer and uptake into the RNA-induced silencing(RISC) complex, which binds to the target RNA, causing its cleavage by Argonaute anddegradation by cellular exonucleases. The relative performance of RNAi or RNaseH-mediated knockdown depends in part on the cellular localization of the targetedtranscripts in either the nucleus or the cytoplasm 55. The second makes use of the ability to detect changes in an electrical fieldthat are induced when a single nucleotide in the DNA molecule passes through a porestructure 56. Long read technologies have been used for “de novo” genomesequencing 57, resequencing of the human genome 58, and phased diploid genome assembly 59. They have also been used for transcript characterization 60, since short reads are unable to fully resolve the exonic structure ofalternative transcript isoforms, which usually share a substantial fraction of thesequence. One advantage of long read technologies is that they are able to directlysequence RNA molecules 61, eliminating the biases induced during the generation of the cDNA librariesinherent to short read NGS.Reconstruction of the full-length sequence of long DNA or RNA molecules from NGSsequence reads is challenging because of their short read length. Two maintechnologies are being developed for long read sequencing . The first relies on the abilityto detect the incorporation of a single nucleotide in an elongating DNA chain, whichis synthesized by a DNA polymerase attached to the template DNA molecule to besequenced 62 and long 63 read sequencing.Because of the large dynamic range of genome activity , unbiased sequencing of samples to interrogate function genome-wide maybe ineffective in uncovering sites within the genome with weak activity. Therefore,depending on the purpose, NGS analysis may be restricted to targeted genomic locicaptured using oligonucleotide probes. Capture has been performed, for instance, toidentify and characterize the exonic structure of lowly abundant transcripts using both short 65. Traced back to single-molecule fluorescence in situhybridization (FISH) 66, these methods have dramatically increased throughput by using massivelyparallel sequencing. For instance, histological sections have been positioned onarrayed reverse transcription primers with unique positional barcodes to maintainthe two-dimensional positional information in subsequent RNA-seq analysis 67.Protocols to prepare libraries for NGS typically require a fair amount of inputmaterial (RNA or chromatin), necessitating large numbers of cells to produce asingle library. If the samples are obtained from heterogeneous biological sources,such as tissues and organs, they are likely to contain multiple distinct cell types,which interact in a highly structured anatomical environment. Bulk sequencing ofthese samples reflects an average across the populations of cells used and is unableto resolve the underlying cell types and their position within the tissue sample.Recently, spatially resolved omics methods have been developed with the aim ofperforming functional genomics assays while retaining positional information 68. Genome sequencing of single cells has been used to identify genomic changesin tumor samples and to track their evolution 74.Population averages, such as obtained from bulk tissue sequencing, may furtherobscure the biological reality of cellular heterogeneity even among cells of thesame type. For example, genes that appear to be lowly expressed on the basis oftheir population average may be highly expressed in a few cells in the populationand exert their biological role only there. To capture the heterogeneity among cellsin a population, newer protocols have been developed that dramatically lower theamount of input material required, allowing profiling of individual cells 4 cells in parallel perexperiment, depending on the protocol used; typical coverages are 1,000 to 6,000genes per cell in case of primary cells and 5,000 to 10,000 genes for cell lines 75. In plate-based methods such as STRT (single-cell tagged reversetranscription) 76, Smart-seq 77, and Smart-seq2 78, 50 to 500 single cells are placed in individual wells in 96-well plates, andcell lysis and reverse transcription occur in each well. Alternatively, amicrofluidic chip can be used for cell placement and reactions 79.Single-cell RNA profiling can assay 50 to 10 81, a unique barcode is applied to individual cells in the initial stages in theprotocol. As the barcode is incorporated in the cDNA during reverse transcription,the cDNAs originating from different cells are distinguishable and can be pooled forPCR amplification. To further increase throughput and reproducibility, in massivelyparallel single-cell RNA-seq, cells are sorted by fluorescence-activated cellsorting (FACS) into more homogeneous subpopulations in a 384-well plate beforesingle-cell sequencing 82. As a poly-T primer is typically used for reverse transcription toselectively include poly(A)-tailed transcripts into the library while avoidingribosomal RNA, other untailed transcripts such as some lncRNAs may be missed.Alternative strategies to reduce the ribosomal RNA component include duplex-specificnuclease treatment 83 and using a template-switching reaction to enrich for 5′-cappedtranscripts 84. Droplet-based isolation of single cells 86 enabled massive parallelization with numbers of cells in the range of 10,000or more, albeit at lower sensitivity.Cell selection is biased by the cellular size and condition, which affects theprobability of a cell to be placed successfully in a well. In pooled approaches suchas CEL-seq 87.As an alternative to single-cell analysis, deconvolution methods can be employed toestimate the proportions of constituent cell types in bulk tissue transcriptomicsamples on the basis of the expression of known cell type marker genes 88 and therefore genome comparisons are, in general, less helpful to annotatethem. For instance, whereas 80% of human protein-coding genes can be detected inmouse, fewer than 10% of lncRNAs are conserved between the two species 89. Comparative transcriptomic studies help to further understand to what extentthe patterns of gene expression and the regulatory networks are conserved acrossspecies 92. Particular attention has been paid to comparisons between human and mousegiven the unique role of the mouse as a model of human biology 94. While gene expression levels averaged over RNA samples are correlatedbetween human and mouse 89, substantial differences are found when comparing the transcriptome ofhomologous cell types 97. Overall, conservation of the patterns of expression between human and mouseseems to largely depend on the gene. The expression of some genes variesconsiderably across species and little across organs and that of other genes variesmore across organs than across species. The mouse is a particularly good model forthe biology of these genes in humans 98.Comparative genomics methods have also been widely used to identify and cataloguefunctional regions in genomes. The underlying assumption is that functional regionsare more conserved through evolution than genomic regions devoid of function. Genomesequence comparisons are widely used, in particular, to annotate protein-codinggenes in newly sequenced genomes. However, while protein coding genes are understrong selective pressure, other functional regions, such as lncRNA genes andregulatory regions, have levels of conservation that are often barelydistinguishable from background conservation 103. These projects have not only generated data resources that are widely usedby the scientific community but also defined “de facto” communitystandards for experimental protocols and bioinformatic pipelines, placing genomicsin general and functional genomics in particular within the reach of individuallaboratories. As a result of advances in experimental and computational methods,functional genomics assays are rapidly expanding beyond cell lines and tissuesamples, which were mostly profiled in these projects, to complex organs,conditions, primary cells, and even single cells. Indeed, the Human Cell Atlasconsortium has formed to characterize each individual cell type in the human body 104. Functional genomics assays are also being increasingly performed acrossmultiple individuals within a species, allowing investigators to specifically linkgenetic variants to molecular phenotypes, as well as across different species. Mostof these analyses to date have focused on the identification of single-nucleotidepolymorphisms (SNPs) that affect gene expression (expression quantitative traitloci) 105 but other molecular phenotypes, such as methylation and histonemodifications, have also been monitored 106. Moving toward personalized genomics, the EN-TEx pilot project is currentlycarrying out multiple functional genomics assays in multiple tissues extracted froma few donors 107.Massively parallel sequencing has been at the core of most large-scale functionalgenomic projects to date 108 and neuropathology 109. The GTEx project 103 has produced high-resolution histological images for 20,000 human tissuesamples, together with matching genome and transcriptome sequencing data. Methods toidentify SNPs associated with image-derived organ phenotypes such as neuroanatomicaltraits  have been developed, and an upcoming challenge is to relate molecularphenotypes, as monitored by functional genomics, to organ phenotypes, as monitoredby imaging technologies.How these molecular phenotypes propagate in turn through intermediate levels oforganizational complexity  determines how geneticvariation ultimately impacts the phenotypic traits of organisms. Thanks to advancesin digital imaging, data on intermediate phenotypes, such as tissues and organs,have recently been made available with associated genomics and molecular data inareas as diverse as cancer invivo multi-omics characterization of all of the cells within amulticellular organism—without interfering with the system. In the pathtoward this ideal, less-invasive protocols and protocols to probe the multiplefacets of genome function at single-cell resolution  will certainly be developed,as will methods able to simultaneously integrate data monitoring the behavior ofmillions of cells. The computational challenges associated with the exponentialgrowth of data, driven by genomic technologies, will be enormous. New algorithms aswell as new developments in hardware that overcome the limitations of currenttechnological solutions may need to be developed.We believe that we are still in the infancy of genomic technologies and there isample opportunity for progress. The ultimate goal is the real-time


Heart failure is a worldwide leading cause of death. Diet and obesity are particularly of high concern in heart disease etiology. Gravely, altered nutrition during developmental windows of vulnerability can have long-term impact on heart health; however, the underlying mechanisms are poorly understood. In the understanding of the initiation of chronic diseases related to developmental exposure to environmental challenges, deregulations in epigenetic mechanisms including micro-RNAs have been proposed as key events. In this context, we aimed at delineating the role of micro-RNAs in the programming of cardiac alterations induced by early developmental exposure to nutritional imbalance. To reach our aim, we developed a human relevant model of developmental exposure to nutritional imbalance by maternally exposing rat to high-fat diet during gestation and lactation. In this model, offspring exposed to maternal high-fat diet developed cardiac hypertrophy and increased extracellular matrix depot compared to those exposed to chow diet. Microarray approach performed on cardiac tissue allowed the identification of a micro-RNA subset which was down-regulated in high-fat diet-exposed animals and which were predicted to regulate transforming growth factor-beta (TGFβ)-mediated remodeling. As indicated by in vitro approaches and gene expression measurement in the heart of our animals, decrease in DiGeorge critical region 8 (DGCR8) expression, involved in micro-RNA biogenesis, seems to be a critical point in the alterations of the micro-RNA profile and the TGFβ-mediated remodeling induced by maternal exposure to high-fat diet. Finally, increasing DGCR8 activity and/or expression through hemin treatment in vitro revealed its potential in the rescue of the pro-fibrotic phenotype in cardiomyocytes driven by DGCR8 decrease. These findings suggest that cardiac alterations induced by maternal exposure to high-fat diet is related to abnormalities in TGFβ pathway and associated with down-regulated micro-RNA processing. Our study highlighted DGCR8 as a potential therapeutic target for heart diseases related to early exposure to dietary challenge. Importantly, these factors have even more serious impact on health when the exposure occurs during developmental windows of vulnerability such as in utero and early post-natal life. As such, maternal overweight and obesity, the incidence of which are increasing  and which are often attributed to excessive intake of calorie-dense food, in particular high-fat diets, and reduced physical activity1, not only have consequences in the short term with pregnancy complications, but also impact offspring health later during life. Maternal obesity and dietary imbalances are particularly of high concern in the early origins of heart failure3. Since human cardiomyocyte have limited proliferative capacity, the exposure of the developing heart to environmental challenges such as nutritional excess may have deleterious effects on lifelong cardiomyocyte functions4. Indeed, maternal exposure to high-fat diet and maternal obesity increases the risk for impaired cardiac function in adulthood by modifying heart rate and inducing left ventricular wall thickness, hypertrophy5 and fibrosis7. In the early origins of heart diseases, epigenetic mechanisms are likely key players. In particular, micro-RNAs, which belong to small non-coding RNAs (around 22 nucleotides in length) involved in each step of heart development, seem to play a crucial role in the unfavorable programming of heart diseases8. By controlling about 60% of all protein-coding genes in mammals, micro-RNAs are considered as significant post-transcriptional regulators of gene expression9, and regulate almost all biological processes including development, differentiation, proliferation, apoptosis, metabolism and tissue remodeling. The critical role of maintaining micro-RNA balance in the heart has been revealed notably in models of mice where cardiomyopathies were induced by inhibiting the expression of proteins involved in micro-RNA biogenesis in cardiomyocytes12. Micro-RNA biogenesis is a regulated multistep process starting in the cell nucleus where primary micro-RNAs (pri-micro-RNAs) are transcribed mainly by RNA polymerase II. Through the action of a microprocessor complex including nuclear ribonuclease III (DROSHA) and its cofactor DiGeorge critical region 8 (DGCR8), pri-micro-RNAs are trimmed into 70 nucleotide hairpins called precursor micro-RNAs (pre-micro-RNAs)13. Then, pre-micro-RNAs assembles into a complex constituted by Exportin-5 (XPO5) and RanGTP and translocate into the cytoplasm14 where they continue their maturation, through a splicing realized by a complex including DICER and TAR RNA-binding protein 2 (TRBP). This results in RNA duplexes comprising the mature micro-RNAs and the passenger strand, micro-RNAs*15. Finally, each mature single strand micro-RNA strand is incorporated into the RNA-induced silencing complex (RISC) comprising an Argonaute (AGO) protein to direct the silencing of the targeted mRNA16. Interestingly, the micro-RNA biogenesis machinery is sensitive to hormonal regulation and dietary changes19. In this context, micro-RNAs and their regulation represent a potential candidate in the understanding of the long-term effects on cardiac functions induced by exposure to nutritional challenges early during development. Thus, based on a human relevant model of cardiac alterations programmed by exposure to maternal high-fat diet, we aimed at delineating the role of micro-RNAs, identifying the defects in the micro-RNA biogenesis machinery in such programming and at highlighting potential targets and approaches for the phenotype reversal.A worldwide upward trend in the burden of non-communicable diseases such as cancers, stroke, heart and chronic lung diseases is currently observed. These diseases share common risks factors that are related to lifestyle including smoking, physical inactivity, alcohol and unhealthy diet consumption ; At post-natal day 77 (PND77), whereas no difference was detected in the body weight of animals exposed to chow diet (CTRL) or to high-fat diet (HFD)  software. Notably, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the altered micro-RNAs are predicted to target extracellular matrix–receptor interaction, fatty acid degradation and metabolism. The DIANA-miRPath software calculates the significance for all the miRNA–mRNA pairs in a pathway, and then combines them into a merged els Fig. . A closeels Fig. . In the els Fig. . As suchTo verify whether the micro-RNA down-regulation was associated with alterations in the TGFβ pathway in the heart of animals exposed to maternal high-fat diet, we measured the expression levels of the predicted targets of these micro-RNAs, involved in TGFβ-mediated fibrosis and hypertrophy. Effectively, in the heart of animals exposed to high-fat diet compared to chow diet, increased transforming growth factor-beta receptor type III (TGFβR3) mRNA levels were detected Fig. . This waPrompting a search for underlying mechanisms in micro-RNA down-regulation induced by maternal exposure to high-fat diet, we investigated the different steps of micro-RNA biogenesis by measuring the expression levels of key components of the micro-RNA biogenesis machinery: DROSHA, DGCR8, DICER, XPO5 and AGO2 Fig. . Among tDgcr8 (gap DGCR8) induced a 74% decrease in Dgcr8 mRNA levels compared to the cells transfected with a control gapmeR   Fig. . DiffereRL) Fig. . We thenway Fig. . We founβr3 Fig. . The mRNsed Fig. . Similarted Fig. . These d25 and in vivo26, has been described as an efficient approach in the reversal of fibrosis in a model of hypertension27, and as a negative regulator of TGFβ pathway28. Therefore, we wondered if these beneficial effects of hemin on fibrosis and TGFβ pathway described in other models could be also found in cardiomyocytes, and if those effects are mediated by DGCR8 and micro-RNAs. To answer this question, cardiomyocytes were treated with 1 μM hemin. These cells exhibited increased Dgcr8 mRNA levels compared to control cells (treated with vehicle)    of heme and which has been shown to stimulate the microprocessor activity in vitrole) Fig. . FurtherRL) Fig. . FinallyRL) Fig. . Overall29. Our current study showed that maternal exposure to high-fat diet induced long-term histological and molecular changes in the heart prior to the onset of overt obesity. Notably, the present work highlighted the decrease in a micro-RNA subset as a potential mechanistic basis linking maternal nutritional imbalance early during development and mortality related to cardiac diseases. Here, the down-regulation of a subset of cardiac micro-RNAs induced by maternal exposure to high-fat diet for 35 days, during pregnancy and lactation, was detected later during life, in adulthood. Importantly, this observation indicates that a transient exposure to dietary distress during developmental sensitive periods induces sustained down-regulation of a micro-RNA subset. This observation is reminiscent of the professor Barker hypothesis related to the concept of the developmental origins of health and disease. In this context, it is noteworthy that altered micro-RNA patterns may be associated with disturbed developmental processes during fetal or neonatal life. Here, our work further highlighted micro-RNA alterations as a possible cause for TGFβ-mediated cardiac fibrosis and hypertrophy induced by early developmental exposure to dietary challenge. Various intracellular signaling pathways are thought to play a critical role in the pathological remodeling of the heart and, importantly, are activated both independently and downstream of TGF-β. Among the targets we identified in this work, Tgfβr3 was up-regulated. TGFβRIII is a widely expressed accessory receptor30 for multiple classes of growth factors , inhibin, activin, bone morphogenetic protein (BMP)-2, BMP-4 and BMP-7), and as such, TGFβRIII has the potential to influence diverse cellular processes. TGFβRIII enhances TGFβ signaling32 and also activates RAS-ERK and p38 MAPK in a ligand-independent manner, both involved in fibrosis33 and hypertrophy, in a SMAD-independent manner. Importantly, we newly identified alterations in DGCR8 expression as a potential cause for long-term cardiac micro-RNA down-regulation in this model. DGCR8 importance in micro-RNA biogenesis and in pathophysiology has been revealed in human 22q11.2 deletion syndrome (known as DiGeorge syndrome)34 and in animal model where the Dgcr8 gene was heterozygously deleted35. The haploinsufficiency of Dgcr8 reduces the abundance of DGCR8 protein, resulting in lower pri-micro-RNA processing efficiency and abnormal expression of a subset of micro-RNAs36. In humans, the DiGeorge syndrome is associated with increased risk for congenital heart defects37. In mice, cardiomyocyte-specific deletion of Dgcr8 induces cardiac fibrosis with left ventricular malfunction progressing to a dilated cardiomyopathy and premature lethality12. Our data highlighted a new potential pathway through which DGCR8 alterations could lead to cardiac hypertrophy and fibrosis involving at least in part TGFβ pathway. Although modulation of TGF-β signaling with different approaches has proven effective pre-clinically and clinically in the treatment of multiple pathologies40, the rescue of the phenotype remains partial. In the light of our findings, DGCR8 appears as an attractive candidate in the reversal of the cardiac phenotype induced by early exposure to dietary challenge since its modulation might impact the TGFβ signaling pathway at multiple levels. In this sense, studies have demonstrated the possibility of regulating DGCR8 expression and activity in several ways including by cofactor binding, phosphorylation, acetylation and proteolytic cleavage43. Among them, hemin, a ferric form (Fe3+) of heme, has proven its efficiency in the up-regulation of DGCR8 activity. Hemin is incorporated in a DGCR8 dimer and stimulates the microprocessor activity in vitro25 and in vivo26. Mechanistically, DGCR8 was shown to depend on hemin to recognize the apical loop of primary micro-RNA44. Furthermore, the hemin level in human cells can impact the expression of several micro-RNAs26 and decrease cardiac oxidative stress and fibrosis in a rat model of systemic hypertension27. Here, we showed that hemin can restore micro-RNA levels in cardiomyocytes while Dgcr8 expression was decreased, supporting the physiological function of hemin in stimulating micro-RNA biogenesis and benefits for heart functions. Similarly, through the stable binding to DGCR8, Cobalt (III) protoporphyrin has been shown to compensate micro-RNA processing deficiency and restore micro-RNA levels in models where DGCR8 expression was decreased in vivo45. Thus, it is of high interest to test the potential of hemin treatment in the prevention and/or reversal of cardiac alterations in animals exposed to dietary challenge early during development.Our study addresses critical health problems (obesity and heart diseases) associated with modern lifestyle diet (high-fat diet). Maternal obesity and high-fat diet have been associated with increased risk for heart diseases, notably through the induction of cardiac remodeling. Indeed, maternal exposure to high-fat diet induces cardiac hypertrophy and fibrosis that are observed early during development (at PND1 and PND10)46, and also in aging47 and in a number of human diseases including cancer48, schizophrenia49, kidney50 and auto-immune diseases51. To restore micro-RNA processing by enhancing the activity of DGCR8 protein using small molecule agents represents an interesting candidate. Thus, the targeting of DGCR8 offers opportunities for intervention and may emerge as an exciting and important arena for drug development. Also, micro-RNAs are strikingly stable and can be detected in body fluids, including blood. This observation has raised the possibility that micro-RNAs may be probed in the circulation and can serve as novel diagnostic markers53. Thus, this is of high interest to evaluate the potential of the micro-RNAs identified in our study as early blood-based biomarkers of cardiac alterations induced by exposure to dietary challenges. Our micro-RNA profile could provide a novel strategy for diagnosis in chronic cardiac dysfunctions programmed by environmental stressors early in life.More widely, alterations in primary micro-RNA processing have been reported in other models of exposure to environmental challenges such as cigarette smokeProtease inhibitor cocktail, Tween-20, Hemin, High Pure miRNA Isolation Kit and other reagents were purchased from Sigma Aldrich. Antibodies raised against DGCR8, β-actin, anti-rabbit and anti-mouse IgG HRP-linked antibody; BCA Protein Assay Kit; RIPA buffer; phosphatase inhibitor cocktail; chemiluminescent western pico plus chemiluminescent substrate, Power up SybrGreen Master Mix, High Capacity Reverse Transcriptions Kit, TaqMan Advanced miRNA cDNA Synthesis Kit and miRNA Advanced Taqman assays were purchased from Thermo Fisher Scientific, Inc. The polyvinylidene difluoride (PVDF) membranes were obtained from Amersham. DICER, DROSHA, XPO5, CTGF, SPARC and GAPDH antibodies were obtained from Santa Cruz Biotechnology. Antibodies raised against phospho-p70 S6K and p70 S6K were purchased from Cell Signaling Technology, Inc. GapmeRs were purchased from Qiagen.n = 5) or high-fat diet . At birth, pups were sexed by anogenital distance and litters were culled to 5 pups per dam by removing or adding female pups as necessary. The remaining pups were killed by CO2 inhalation. At PND21, all male rats were given a chow (R03). Animals were killed by CO2 inhalation on PND77 (young adults). Each group contains 18 male rats.The animal experiments were performed in accordance with EU legislation (Directive 2010/63/EU) and approved by a local animal care and use committee . Pregnant Sprague Dawley rats at gestational day 7 (GD7)  were individually housed in temperature-controlled rooms with 12 h light/12 h dark cycles and ad libitum access to water and feed. Upon arrival, pregnant females were either fed with chow diet  were stained with Masson’s trichrome. Five animals per group were analyzed. Image acquisition was performed on a Nikon Eclipse Ti microscope. Fibrotic area quantification was performed as described by Chen et al.2-controlled (5%) incubator. Cells were transfected with 20 pmol gapmeR targeting Dgcr8 (gap DGCR8) or a negative control using Lipofectamine RNAimax Transfection reagent, according to the manufacturer’s protocol. At 48 h following transfection, cells were harvested for RNAs and protein extraction. For rescue experiments, 24 h after transfection, cells were incubated with 1 μM hemin, or with the vehicle (water) for 48 h.H9C2 cardiomyocytes cell line was maintained in medium supplemented with 10% heat-inactivated fetal bovine serum at 37 °C in a humidified, COCells were seeded on 96-well plate and transfected with gapmeRs. Cells were washed with phosphate buffer saline, fixed in ice-cold methanol for 10 min and stained with 0.5% crystal violet solution at room temperature for 10 min. Optical density was measured on a plate reader Infinite M200Pro (Nanoquant).Aliquots (~20 mg) of heart tissue powder were homogenized in RIPA buffer (supplemented with 1% proteases and phosphatases inhibitor cocktail). Protein concentration was determined with the bicinchoninic acid assay. In total, 10 to 30 μg were used for western blot analysis.Lysates were separated using sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to PVDF membranes. The membranes were blocked with 1% bovine serum albumin in phosphate-buffered saline (pH 7.4) containing 0.05% Tween-20 and were incubated with primary antibodies and horseradish peroxidase (HRP)-conjugated secondary anti-rabbit or -mouse antibodies. The proteins of interest were visualized using SuperSignal West Pico PLUS Chemiluminescent Substrate. Membranes were scanned with a luminescent image analyzer camera G:Box (Syngene) and quantified with Gene Tools software (Syngene).Total RNA was isolated from frozen heart powder using 1 ml TRIzol reagent and 200 μl chloroform. The aqueous phase was precipitated with 1.5 vol of ethanol 100% at 4 °C overnight followed by 75% ethanol. Total RNAs were further purified on a column. The RNA purity was checked by optical density measurement at 230, 260 and 280 nm. The quantities of total RNAs and micro-RNAs were evaluated with fluorescent-based methods on a Qubit apparatus . RNA integrity was evaluated with a Fragment Analyzer (Agilent).Sample quality was tested and was ranging from RQN  6.7 to RQN 8.0.http://www.r-project.org). Invariant-based normalization was performed as previously described55. After normalization, micro-RNA not detected in any sample were filtered, leaving 240 miRNAs for analysis. For statistical analyses, we performed linear model using Bioconductor package Limma with the two conditions (chow diet and high-fat diet) as factors, extraction of contrasts of interest, moderated t-test for comparisons were applied, adjustment of P values by the Benjamini–Hochberg method and controlling for false discovery rate were realized.To assess the effect of maternal exposure to high-fat diet on cardiac miRNA levels, RNAs from four biological replicates of high-fat diet vs. control diet were hybridized on the microarrays . Each sample was prepared according to the Agilent’s micro-RNA Microarray System protocol. The arrays were scanned with an Agilent microarray scanner using high dynamic range settings as specified by the manufacturer. Agilent Feature Extraction Software Version 10.7.3.1 was used to extract the data. Normalization was performed on the total Gene Signal from Agilent “GeneView” data files in R language, version 3.4, an open source statistical scripting language  software. Potential target RNAs for down-regulated micro-RNAs from each cluster were identified using the prediction algorithm DIANA-microT-CDS (v5.0). The potential mRNA targets for the down-regulated micro-RNAs were then subjected to KEGG pathway analysis. The DIANA-miRPath software calculates the significance for all the miRNA–mRNA pairs in a pathway, and then combines them into a merged  β-actin or to Gapdh using the Ct method.In total, 200 ng of RNAs was reverse transcribed using High Capacity Reverse Transcription Kit according to the manufacturer’s protocols in a SimplyAmp Thermal Cycler . RT-qPCR was performed with 2 μl of diluted cDNA (1:20) using the I-Taq Universal SybrGreen Master Mix and both sense and antisense primers in a final volume of 10 μl in a Quantstudio 3 apparatus . The primers that were used are listed in supplemental table In total, 10 ng of RNAs was reverse transcribed with advanced miRNA RT kit on a SimplyAmp Thermal Cycler . RT product was diluted by 10-fold, and 2.5 µl was used for qPCR analyses. qPCR was performed with TaqMan Advanced Master Mix according to the manufacturer’s protocol on a Quantstudio 3 apparatus . The relative expression levels of miRNAs were calculated using the comparative ΔΔCt method by normalizing to miR-16 levels.t-test was performed to determine whether there were differences between two groups, and analysis of variance when multiple groups were compared. P < 0.05 was considered statistically significant.The data from the different experiments were analyzed with GraphPad Prism software version 6.05 . The values were expressed as mean ± SEM to account for sample and animal variation within a dataset. Student’s supplementary information


While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at  As the number of available genome sequences has rapidly expanded in databases, numerous tools have been developed that can detect genomic features of interest based on the genome sequence. Prominent representatives are Glimmer to identify open reading frames (ORFs) , tRNAscaRecent developments in high-throughput sequencing offer solutions to this problem. RNA sequencing (RNA-seq) has revolutionized how differential gene expression can be measured and is widely used for this purpose . In addi8Here we present ANNOgesic, a modular, command-line tool that can integrate data from different RNA-seq protocols such as dRNA-seq as well as conventional RNA-seq performed after transcript fragmentation and generate high-quality genome annotations that include features missing in most bacterial annotations . The central approach is to detect transcript boundaries and then subsequently attach additional information about type as well as function to the predicted features and also to infer interactions between them. Several of ANNOgesic’s core functions represent new implementations that are not found in other programs. Third-party tools embedded into ANNOgesic are accessible via a consistent command-line interface. Furthermore, their results are improved, e.g., by dynamic parameter optimizations or by removing false positives. Numerous visualizations and statistics help the user to quickly evaluate the feature predictions. The tool is modular and has been intensively tested with several RNA-seq datasets from bacterial as well as from archaeal species.ANNOgesic consists of the following modules, their names indicate their functions: Sequence modification, Annotation transfer, SNP/Mutation, Transcript, TSS, Terminator, UTR, Processing site (PS), Promoter, Operon, sRNA, sRNA target, small ORF (sORF), Gene Ontology (GO) term, Protein-protein interaction network, Subcellular localization, Riboswitch, RNA thermometer, Circular RNA, and Clustered regularly interspaced short palindromic repeat (CRISPR). Several potential workflows connecting these modules are displayed in Supplementary Fig. S1. An overview of the novelties and improvements of the modules in ANNOgesic are listed in Supplementary Table S1, and all the dependencies of ANNOgesic are shown in Supplementary Table S2.Depending on the task, ANNOgesic requires a specific set of input information, either as coverage information in wiggle format or alignments in binary alignment map (BAM) format. This can be generated by short-read aligners such as STAR , segemehhttps://pypi.org/project/ANNOgesic/. The comprehensive documentation can be found at http://annogesic.readthedocs.io/, and releases are automatically submitted to Zenodo (https://zenodo.org/) to guarantee long-term availability. It can be easily installed using pip (https://pip.pypa.io). In order to provide a frictionless installation including non-Python dependencies, we additionally offer a Docker image at (https://hub.docker.com/r/silasysh/annogesic/) [ANNOgesic’s source code is implemented in Python 3 and hosted at ogesic/) .TPm is the number of manually detected TSSs. TPc/TPRc represents the true positive/true positive rate of the current parameters. TPb/TPRb represents the true positive/true positive rate of the best parameters. FPc/FPRc represents the false- positive/false-positive rate of the current parameters. FPb/FPRb represents the false- positive/false-positive rate of the best parameters. If one of these six situations is true, it will replace the best parameters with current parameters.For several parts of ANNOgesic, the selection of parameters has a strong impact on the final results. Especially the TSS prediction, building on TSSpredator , require\documentHelicobacter pylori 26695 [Campylobacter jejuni 81116 [H. pylori conventional RNA-seq data, i.e., without TEX treatment (which degrades transcripts without a 5’-triphosphate) and with fragmentation of the transcript before the library preparation, was also retrieved from NCBI SRA (accession number SRR031126). Moreover, for assessing the performance of ANNOgesic, dRNA-seq, and conventional RNA-seq datasets of Escherichia coli, K-12 MG1655 were downloaded from NCBI GEO  [E. coli K-12 MG1655 were compared to the databases RegulonDB, EcoCyc, and DOOR2 [In order to test ANNOgesic’s performance, we applied it to RNA-seq datasets originating from ri 26695 ,13 and Cni 81116 . The dRNtrieved) ,27. The nd DOOR2  –33H. pylori 26695 and C. jejuni 81116 and discuss the prediction results as examples in the following sections.All genomic features that can be detected by ANNOgesic are listed in Table Conventionally, differences in the genome sequence of a strain of interest and the reference strain are determined by DNA sequencing. However, RNA-seq reads can also be repurposed to detect such single nucleotide polymorphisms (SNPs) or mutations that occur in transcribed regions, which can help to save the resources required for dedicated DNA sequencing or DNA SNP microarray measurements. The two drawbacks of this method are that only locations that are expressed can be analyzed and that, due to RNA editing, changes could be present only in the RNA level and are not found in the genome. On the other hand, it has been shown to be a valid approach for eukaryotic species and that the majority of SNPs are found in the expressed transcripts ,35. SuchH. pylori 26695 and C. jejuni 81116 annotation files in GFF3 format were obtained from NCBI RefSeq. Because of this, there was no need to transfer the annotation from a closely related strain.ANNOgesic integrates the Rapid Annotation Transfer Tool , which cKnowing the exact boundaries and sequence of a transcript is crucial for a comprehensive understanding of its behavior and function. For example, UTRs can be the target of regulation by sRNAs or small molecules  ,40 or evThere are numerous tools available for the detection of transcripts , but moH. pylori 26695 and 1,147 transcripts in C. jejuni 81116. These transcripts cover 1,520 and 1,568 genes, which shows that 97% and 93% of the known genes are expressed in at least one condition, respectively.By running ANNOgesic’s subcommand for transcript prediction, we detected 1,716 transcripts in For the prediction of TSSs, ANNOgesic builds on TSSpredator , which tH. pylori 26695 and C. jejuni 81116 (Supplementary Tables S5 and S6). This set was used to perform the predictions of TSSpredator with default settings as well with the parameters optimized by ANNOgesic. For the test set of the benchmarking, we manually annotated TSSs from first 200 kb to 400 kb in the genome of H. pylori 26695 and C. jejuni 81116 (Supplementary Tables S5 and S6). As displayed in Table H. pylori 26695 (from 96.8% to 99.6%); it strongly increased the sensitivity for the TSS prediction for C. jejuni 81116 (67.1% to 98.7%) while keeping the same level of specificity. To underpin those findings, we looked at the overlap of the predicted TSS and predicted transcripts. This was nearly the same for H. pylori 26695 (82% for default and 83% for optimized setting) but also increased significantly for C. jejuni 81116 from 81% for default parameters to 96% with optimized parameters.In order to test the performance of ANNOgesic, we manually annotated TSSs in the first 200 kb of the genome of Moreover, TSSs are classified depending on their relative positions to genes by TSSpredator. Based on these classifications, Venn diagrams representing the different TSS classes are automatically generated .H. pylori 26695 and C. jejuni 81116, respectively.Several transcripts undergo processing, which influences their biological activity ,43. In oWhile the TSSs are in general clearly defined borders, the 3’-end of a transcript is often not very sharp. A commonly used tool for the prediction of the 3’-end of a transcript is TransTermHP , which dBased on the CDS locations and the above-described detection of TSSs, terminators, and transcripts, 5’ UTR and 3’ UTR can be annotated by ANNOgesic. Additionally, it visualizes the distribution of UTR lengths in a histogram .ANNOgesic integrates MEME , which dBased on the TSS and transcript prediction, ANNOgesic can generate statements regarding the organization of genes in operons and suboperons as well as report the number of monocistronic operons and polycistronic operons Fig. .The detection of sRNAs based on RNA-seq data is a nontrivial task. While numerous sRNAs are found in intergenic regions, several cases of 5’ and 3’ UTR-derived sRNAs are reported , 48,49. In order to classify newly detected intergenic transcripts as sRNAs, ANNOgesic tests several of their features. If a Basic Local Alignment Search Tool +  search obona fide sRNA, the above-described quality filters  are applied to judge the potential of a candidate . For the validation of sRNA candidates in our test case, the described sRNAs of two publications were chosen. Sharma et al. [C. jejuni 81116 set, 31 sRNAs were described by Dugar et al. [For sRNAs that share a transcript with CDSs—5’ UTR, inter-CDS, or 3’ UTR located sRNAs—we implemented several detection heuristics Fig. . The 5’ r et al. , and ANNIn order to deduce potential regulatory functions of newly predicted sRNAs, ANNOgesic performs prediction of interaction between them and mRNAs using RNAplex ,53, RNAubona fide sORF.All newly detected transcripts that do not contain a previously described CDS as well all 5’ UTRs and 3’ UTRs are scanned for potential sORFs  Fig. 6)6). For tIn order to facilitate a better understanding of the biological function of known and newly detected transcripts, ANNOgesic predicts several attributes for these features.This includes the allocation of GO as well as GOslim  terms toANNOgesic integrates the tool ”testrealign.x” from the segemehl package for the detection of circular RNAs  and addsCRISPR/Cas systems represent a bacterial defense system against phages and consist of repeat units and spacer sequences as well as Cas proteins . The CRIRiboswitches and RNA thermometers are regulatory sequences that are part of transcripts and influence the translation based on the concentration of selected small molecules and temperature change, respectively. For the prediction of these riboswitches and RNA thermometers, ANNOgesic searches  the sequE. coli K-12 MG1655 by Thomason et al. [In order to assess the performance of ANNOgesic, we compared its predictions based on a dRNA-seq dataset and conventional RNA-seq of n et al.  and McCln et al.  with then et al. 33. Most n et al. ,68) to en et al.  for commn et al. , which iWhile RNA-seq has become a powerful method for annotating genomes, the integration of its data is usually very laborious and time consuming. It requires bioinformatic expertise and involves the application of different programs to perform the different required steps. Here, we present ANNOgesic, a modular, user-friendly annotation tool for the analysis of bacterial RNA-seq data that integrates several tools, optimizes their parameters, and includes novel prediction methods for several genomic features. With the help of this command-line tool, RNA-seq data can be efficiently used to generate high-resolution annotations of bacterial genomes with very little manual effort. In addition to the annotation files in standard formats, it also returns numerous statistics and visualizations that help the user to explore and to evaluate the results. While it ideally integrates conventional RNA-seq  as well as dRNA-seq  as input together , it can also perform sufficient predictions with only one class of data for the majority of the genomic features (Supplementary Table S3).H. pylori 26695 and C. jejuni 81116, respectively. The sRNAs missed by ANNOgesic can be explained by low coverage, not being associated with TSSs or lack of expression in the assayed conditions .Here, we demonstrated the performance of ANNOgesic by applying it on two published datasets and comparing the results to manually conducted annotations. ANNOgesic could detect 90% and 83% of the manually annotated sRNAs H. pylori 26695 and C. jejuni 81116), ANNOgesic was successfully applied for detecting transcripts, sRNAs, and TSSs in additional annotation projects  and eukaryotic genomes that have no introns .In addition to the analyses presented as examples in this study (aeroides ). DespitANNOgesic is freely available under the OSI-compliant ISCL open source license (some of the dependencies are available under other FLOSS licenses), and extensive documentation has been produced to guide novice and advanced users.ANNOgesic is a powerful tool for annotating genome features based on RNA-seq data from multiple protocols. ANNOgesic not only integrates several available tools but also improves their performance by optimizing parameters and removing false positives. For the genomic features that cannot be detected using available tools, several novel methods have been developed and implemented as part of ANNOgesic. Comprehensive documentation and useful statistics as well as visualizations are also provided by ANNOgesic.Project name: ANNNOgesichttps://github.com/Sung-Huan/ANNOgesic.Project home page: GitHub - https://pypi.org/project/ANNOgesic/.PyPI - https://hub.docker.com/r/silasysh/annogesic/.DockerHub - SciCrunch RRID: SCR_016326Operating system(s): Linux, Mac OSProgramming language: Pythonhttp://annogesic.readthedocs.io/en/latest/required.html).Other requirements: Please check the documentation .GIGA-D-18-00029-Original_Submission.pdfClick here for additional data file.GIGA-D-18-00029_Revision_1.pdfClick here for additional data file.GIGA-D-18-00029_Revision_2.pdfClick here for additional data file.Response_to_Reviewer_Comments_Original_Submission.pdfClick here for additional data file.Response_to_Reviewer_Comments_Revision1.pdfClick here for additional data file.Reviewer_1_Report_ -- A. Rebecca Wattam02-26-2018 ReviewedClick here for additional data file.Reviewer_2_Report_.pdf -- Raquel Tobes, Ph.D04-14-2018 ReviewedClick here for additional data file.Reviewer_3_Report_.pdf -- Roy Chaudhuri07-17-2018 ReviewedClick here for additional data file.Reviewer_3_Report_Revision_1.pdf -- Roy Chaudhuri07-06-2018 ReviewedClick here for additional data file.Supplement_Files.zipClick here for additional data file.