Influenza A virus (IAV) is a respiratory virus that causes epidemics and pandemics. Knowledge of IAV RNA secondary structure in vivo is crucial for a better understanding of virus biology. Moreover, it is a fundament for the development of new RNA-targeting antivirals. Chemical RNA mapping using selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) coupled with Mutational Profiling (MaP) allows for the thorough examination of secondary structures in low-abundance RNAs in their biological context. So far, the method has been used for analyzing the RNA secondary structures of several viruses including SARS-CoV-2 in virio and in cellulo. Here, we used SHAPE-MaP and dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) for genome-wide secondary structure analysis of viral RNA (vRNA) of the pandemic influenza A/California/04/2009 (H1N1) strain in both in virio and in cellulo environments. Experimental data allowed the prediction of the secondary structures of all eight vRNA segments in virio and, for the first time, the structures of vRNA5, 7, and 8 in cellulo. We conducted a comprehensive structural analysis of the proposed vRNA structures to reveal the motifs predicted with the highest accuracy. We also performed a base-pairs conservation analysis of the predicted vRNA structures and revealed many highly conserved vRNA motifs among the IAVs. The structural motifs presented herein are potential candidates for new IAV antiviral strategies.The online version contains supplementary material available at 10.1007/s00018-023-04764-1. Orthomyxoviridae and Coronaviridae families having been identified many times prior as having pandemic potential . Fo. Fo40]. g) and washed once with 1 × PBS buffer. Purified cellular pellets were suspended in 180 µL of 1 × PBS buffer. For DMS probing, 20 µL of 3% DMS diluted in absolute ethanol was added  and the reaction was incubated at 23 °C for 5 min. The control reaction was treated with the same volume of ethanol. The reaction was quenched with quench buffer  and cells were centrifuged for 3 min at 3000g. The cell pellets were washed twice with quench buffer before suspending them in 225 µL of 1 x PBS. The total RNA was isolated with Trizol LS  according to the manufacturer’s protocol. For NAI probing, 1 M stock of NAI dissolved in anhydrous dimethyl sulfoxide (DMSO)  was prepared just before the reaction. To 180 µL of PBS, 20 µL of 1 M NAI  for the modification reaction (or 20 µL of DMSO for the control reaction) was added to the cell suspension and incubated for 8 min at 37 °C. The reaction was quenched by incubation for 5 min at 37 °C with 30 µL of 1 M dithiothreitol (DTT) and centrifuged . The cell pellets were then washed once with 1 x PBS supplemented with 160 mM DTT and centrifuged. The cell pellets were suspended in 225 µL of 1 x PBS before total RNA isolation with Trizol LS according to the manufacturer’s protocol.The RNA structure in cellulo chemical probing protocol was developed based on published protocols , 27, 29.NAI and DMS chemical reagents were used for the chemical probing in virio. For the NAI probing, 20 µL of 1 M stock of NAI  or anhydrous DMSO (control sample) was added to the viral suspension in the resuspension buffer . After 10 min incubation at 37 °C, the reaction was quenched by adding 20 µL of 1 M DTT, and the reaction was incubated for 5 min at 37 °C before the viral RNA was isolated according to the manufacturer’s protocol with Trizol LS. The same volume of 1 M DTT was added to the control sample as well. For DMS probing, 20 µL of 4% DMS diluted in absolute ethanol  or 20 µL of ethanol (control sample) was added to the viral suspension, and the reaction was incubated for 5 min at 37 °C. The reaction was quenched with 20 µL 1 M DTT and incubated for 5 min before RNA isolation with Trizol LS.2, 1 µL of 10 µM of HFA and HRA primers , and 1 × Phusion Master Mix with HF Buffer. The reaction was initially denatured for 30 s at 98 °C, cycled 30 times , and extended for 10 min at 72 °C. The reaction was cleaned up using 0.5 × Ampure XP Beads  before library preparation with Nextera XT DNA Library Preparation Kit . The final library was size selected using 0.6 × and 0.15 × Ampure XP beads (double-size selection).Total RNA isolated from cells  was treated with DNase I, followed by on-column purification with a QIAGEN RNeasy Mini Kit QIAGEN, 74104). The RNA integrity number (RIN) of the total RNA sample was checked via RNA IQ Assay on Qubit Fluorometer , and only RNA with a RIN > 8.5 was used for the final experiment. Reverse transcription using SuperScript II  was prepared according to the SHAPE-MaP protocol 04. The R. The priFor the additional control sample of chemical mapping in cellulo, 1 µg of IAV-infected total RNA was used for RT reaction with 1 µl of 60 µM random primer mix . The product was purified on the G-25 column, followed by second-strand synthesis using NEBNext Ultra II Non-Directional RNA Second Strand Synthesis Module  according to manufacturer protocol but with an extended time of incubation to 2.5 h at 16 °C. Next, the DNA product was purified with a PureLink PCR Micro Kit , and 1 ng of DNA was intended for library preparation with Nextera XT DNA Library Preparation Kit . The final library was purified with 0.6 × Ampure XP Beads.Segment-specific primers were used to control the presence of all vRNA fragments in PCR products obtainted with HFA/HRA primers (Supplementary E1 Table 1). The PCR was prepared in the same way as described above.For the library preparation from in virio samples, 50–100 ng of viral RNA was reverse transcribed using 1 µl of 60 µM random primer mix (NEB), followed by purification on the G-25 Column. The whole cDNA was used for second-strand synthesis (NEB) and the incubation time at 16 °C was extended to 2.5 h. The DNA product was purified on a PureLink PCR Micro Kit, and 1 ng of DNA was used for library preparation with Nextera XT DNA Library Prep. Kit. The final library was purified using 0.6 × Ampure XP Beads.The libraries were paired-end sequenced (2 × 150 bp) on Mid Output Flow Cell using the NextSeq550 instrument. Bioinformatic analysis was performed with ShapeMapper software version 2.1.5  with thehttps://rnacentral.org/rna/URS00005A14E2/9606). The reactivity data for the control 18S rRNA are available in Supplementary E1 Table 4.The 3D structure of the human ribosome (PDB ID: 4v6x) was analyzed and presented using Chimera software . The secSi) as:i and j are nucleotide indices and Pi,j is the probability of the i-j base-pair. Only valid i-j pairs are included. The pair probabilities are estimated using the partition function in RNAstructure, version 6.4 [Shannon Entropies were calculated from partition function data for local and global structure predictions. To estimate the extent to which one structure dominates in each region of the sequence, we calculated the Shannon Entropy per nucleotide  in virio and in cellulo are gathered in Tables 1 and 2 of Supplementary E6.Data were downloaded from the Influenza Research Database (fludb.org) on August 11th, 2021 using the following parameters: nucleotide data, virus type A, any host, any country/region, segments 1 through 8, any subtype, full-length only, and collapse identical sequences. This resulted in eight segment databases of (in increasing segment order) 46416, 46287, 46821, 70927, 41210, 57097, 36731, and 36411 sequences for a total of 381360 sequences. The retrieved sequences were reverse-complemented to a negative sense and were then aligned to A/California/04/2009 vRNA1-8 sequences. For the alignment, we used the Multiple Alignment using Fast Fourier Transform (MAFFT) web server and the Fast Fourier Transform progressive method (FFT-NS-2) with low memory mode. A custom Python script was then used to count the base pairs observed in each sequence based on the experimental secondary structures. All canonical pairings (including GU wobble pairs) were considered to be conserved. All custom Python scripts can be found at Plus sense sequence databases were aligned using MAFFT, as previously described. After identifying regions of interest, a custom Python script was used to count codon frequencies in the major reading frame of each vRNA segment. Any region spanning a start codon was aligned with the reading frame, regardless of actual translational capacity. Two vRNA7 regions  were aligned with the spliced matrix protein 2 (M2) reading frame, as they were beyond the stop codon for the larger, un-spliced matrix protein 1 (M1). Motif alignments to codon frequency data can be found in Supplementary E7.r) ranges from − 1 to 1, where r ≥ 0.9 means very high positive correlation, 0.7 ≤ r < 0.9 means high positive correlation, 0.5 ≤ r < 0.7 means moderate positive correlation, and < 0.5 means low positive correlation. All additional statistical analyses were performed with the OriginPro2021 program. We found our data to be highly reproducible between independent biological experiments. Pearson coefficients for reactivities of individual vRNAs ranged from r = 0.77 to 0.99 indicating a high correlation between replicates. The lowest correlation was observed in the case of vRNA segments from the in cellulo experiment for which we obtained an incomplete subset of data.Before calculating the average reactivity values from both SHAPE and DMS probing, we examined the Pearson Correlation Coefficient (PCC) between all biological replicates. The PCC for each pair of reactivity datasets was calculated with OriginPro2021 (OriginLab). The correlation coefficients are presented in Supplementary F1 Fig. S1. PCC between reactivity datasets in virio and in cellulo was calculated using the MovCoef function over a sliding 50 nucleotide window . The Pearson coefficient  . For theWe used two algorithms for the secondary structure prediction: MFE and MEA. Additionally, both folding algorithms were calculated using two approaches: the first without constraining the base-pairing distance, and the second limited to a distance equal to 150 nt. Next, the in virio and in cellulo global and local MFE/MEA structures were compared using the CircleCompare tool (RNAstructure) . A comparison of the average reactivity profiles between in cellulo and in virio showed that average nucleotide reactivities in those environments were similar in both NAI and DMS probing experiments . The PCC calculated for reactivities of segments vRNA5, 7, and 8 showed a high positive correlation between in virio and in cellulo experiments . Calculation of the reactivity difference ΔNAI and ΔDMS at the single-nucleotide level showed that the vRNA nucleotides were mostly more reactive in cellulo .We used human 18S ribosomal RNA (rRNA) as a control for in cellulo chemical mapping experiments using NAI and DMS chemical reagents (Supplementary E1 Table 4). 18S rRNA is a component of the small ribosomal subunit, and it has a known structure . We corrWe found that more single-stranded regions were predicted in vRNA MEA structures when compared to MFE Fig. . In contThe structures of vRNA5, 7, and 8 in virio and in cellulo showed varying levels of similarity depending on the particular segment and global/local context. The CircleCompare tool (RNAstructure) was used to estimate the similarity and variability of vRNA structures in virio and in cellulo for segments 5, 7, and 8 Fig. . We obseNext, we checked for the presence of long-range interactions common between in virio and in cellulo global MEA vRNA structures. In the case of vRNA5, we did not observe such interactions. For vRNA7, we observed common long-range interactions in regions: 26–49/974–993 nt (without 44/979 pair), 396–401/620–614 nt, and 732–741/954–945 nt. In the case of vRNA8, one long-range interaction in region 210–222/405–416 nt (without 407/220 pair) was observed in both environments  and local  contexts. Average vRNA structure conservation in cellulo and in virio was very similar between nearly all generated structures  structural motifs in virio (Supplementary E6). Other vRNA segments had multiple conserved motifs predicted within all generated structures, both MEA and MFE, in the local and global context (Supplementary E6). Interestingly, some motifs of high conservation were found in local or global, MFE or MEA structures exclusively. For example, in the case of segment 7 vRNA in virio, a hairpin in region 34–41/61–54 nt (99.86% of conservation) was predicted in both local structures, but was not found in global contexts. In contrast, a hairpin motif in region 115–119/128–124 nt was predicted in global MFE and MEA structures only. Another example is a hairpin formed in region 788–796/809–801 nt (99.77% of conservation) which was predicted in all generated structures. Other highly conserved short hairpins in regions 96–99/128–124 nt (99.89%) and 323–336/408–393 nt (96.46%) were predicted in the local MEA structure only. In the case of in cellulo vRNA structure prediction, we also found some differences in conserved motifs between versions of the predicted structures of particular vRNA (Supplementary E6). For example, in segment 7 vRNA, one hairpin in region 788–796/809–801 nt (99.33%) was predicted in all generated structures, while a hairpin in region 64–69/78–73 nt (97.81%) was found in nearly all predictions, except for local MFE structure. Accordingly, a highly conserved (99.96%) hairpin in region 935–942/954–946 nt was predicted in local structures only. Apart from locally folded structural motifs, we noted highly conserved long-range interactions predicted in global structures, such as the pairing in region 40–44/1405–1401 nt (96.67% of conservation) predicted in MFE and MEA global vRNA5 structures in cellulo.Thereafter, we performed a detailed analysis to find particular RNA motifs in virio and in cellulo with the highest base-pairing conservation (≥95%) (Supplementary E6). The analysis confirmed the conservation of long-range intramolecular interactions, including the , S19–20 in cellulo). In such regions, it is most probable that single well-defined structural motif is formed [For the identification of the most stable, well-determined local RNA structural motifs, we identified low-Shannon entropy, low-DMS, and low-SHAPE regions  and DMS. The major benefit is a more accurate structure prediction via the RNAstructure program , 69. ThiThe interpretation of the RNA secondary structure in its biological context is challenging both experimentally and computationally and demands a different methodology for structure prediction compared to in vitro studies. We utilized an expanded global/local base-pairing approach, as the vast majority of identified viral RNA structures were predicted using a limited maximum base-pairing distance , 70, 71.We decided to use different strategies while predicting vRNA structures in the RNAstructure program using experimental constraints. First, we used parameters based on nearest neighbor thermodynamic calculation for determining MFE structures with and without adding additional base-pair limitations < 150 nt). This allowed us to detect both local structural motifs and long-distance interactions. Next, we performed partition function calculations, which were used to predict MEA structures in both local and global contexts  among the IAV for each vRNA MEA and MFE structure, in global and local contexts (Supplementary E2-E5). In general, a similar average conservation of the whole structures  is observed within each RNS segment, with some distinct highly conserved motifs Fig. .Independent research concerning the analysis of at least 35,000 influenza virus sequences for each vRNA showed that sequence conservation is different within particular vRNA segments . SeveralWe determined many structural motifs of vRNA 1–3 and 5–8, which have ≥ 95% conservation between influenza strains (Supplementary E6 Tables 1 and 2). In the case of all predicted structures  we found numerous highly conserved short-range interactions. Furthermore, we found a few long-range interactions in global predictions, for example helixes in global vRNA3 in virio structures (MEA and MFE) in regions 609–622/1754–1732 nt (conservation: 96.87%), 96–102/1784–1778 nt (conservation: 95.54%) and in global vRNA5 in cellulo structure (MEA and MFE) in region 40–44/1405–1401 nt (conservation: 96.96%). Interestingly, some highly conserved structural motifs were predicted in all structures , while some were exclusive to global, local, or MFE, MEA structures only. For example, in vRNA1 in virio  in vRNA4 in virio in local folding prediction (Supplementary E6). Similarly, one short hairpin was observed in vRNA6 in virio in local folding prediction in region 19–28 nt (99.15%) (without a 21–26 pair in the MEA structure).In the case of vRNA4 and 6, we observed a limited number of highly conserved motifs (≥ 95%). Apart from a The comparison of the median low-SHAPE and low-Shannon entropy regions Figs. A, B. The. Additionally, the first genome-wide analysis of in vitro, ex virio, and in virio structures of A/WSN/33 (H1N1) vRNA segments was published [Our previous research concerning vRNA5, 7, and 8 in vitro and vRNA8 in cell lysate suggested multiple conserved structural RNA motifs that may play roles during the viral replication cycle and that might be preserved in virio and in cellulo –35, 37. ublished , and we ublished , 34, 36.Detailed analysis of segments 5, 7, and 8 Tables , 7 confiThe least studied influenza vRNA structures are within segments 1, 2, 3, 4, and 6, with only data of A/WSN/33 being available . We obseMultiple studies suggest that biologically functional RNA motifs exist in regions of strong primary sequence conservation and that suppression of synonymous codon usage may be a result of maintaining RNA structure –84. AddiThe exact relationship between the vRNA secondary structure and the role it plays in intersegmental interactions in virions and cells remains a mystery. Independent research has confirmed the existence of intersegmental vRNA-vRNA interactions in virio for IAV and reported that these interactions are strain-dependent. It is worth mentioning that so far no such studies have been published on the strain A/California/04/2009. Le Sage et al.  revealedIn A/WSN/1933 vRNA6, the “hot spot” region 825–890 nt is responsible for interacting with vRNA1, vRNA2, and vRNA3 . In thisPrevious research has shown that vRNA7 and vRNA8 are involved in intersegmental interactions but to a lower extent than vRNA3, vRNA5, and vRNA6 , 18. Dadpanhandle interaction between the 5ʹ- and 3ʹ-end of vRNAs is not the only intrasegmental interaction and many others can take place, especially for segments 1, 2, 3, 5, 6, and 7 [Our predictions of the global in virio and in cellulo vRNA structures show many long-range intrasegmental base-pairing. Such long-range interactions, at least in virio, are in agreement with the previously established intrasegmental interaction network proposed in crosslinking experiments , 18. Pre6, and 7 , 18. Int6, and 7 , 18 This6, and 7 , 18.Lee et al. demonstrated that NP binding with vRNA of both A/California/04/2009 and A/WSN/1933 strains in virio is generally uneven . In addiInteractions between vRNA segments can occur in cells . This isIn summary, RNA secondary structure of the IAV genome of strain A/California/04/2009 (H1N1) was determined in virio and in cellulo. Our research showed that despite differences between the vRNA secondary structures of individual IAV strains, some structural motifs are common and highly conserved across distant strains. Notably, for the first time, we showed a vRNA secondary structure in living cells. Moreover, we highlighted potentially functional structural motifs that exist in virio and in cellulo environments. These motifs are of interest for further investigation, as they may play key roles in the viral replication cycle. The knowledge gained in this research may also be used to design more specific RNA-targeting inhibitors, focusing on vRNA motifs universal to all IAV strains.Supplementary file1 (XLSX 2673 KB)Supplementary file2 (XLSX 453 KB)Supplementary file3 (XLSX 565 KB)Supplementary file4 (XLSX 450 KB)Supplementary file5 (XLSX 534 KB)Supplementary file6 (XLSX 32 KB)Supplementary file7 (XLSX 147 KB)Supplementary file8 (PDF 4784 KB)Supplementary file9 (PDF 17363 KB)Below is the link to the electronic supplementary material.


Recent exciting studies have uncovered how membrane‐less organelles, also known as biocondensates, are providing cells with rapid response pathways, allowing them to re‐organize their cellular contents and adapt to stressful conditions. Their assembly is driven by the phase separation of their RNAs and intrinsically disordered protein components into condensed foci. Among these, stress granules (SGs) are dynamic cytoplasmic biocondensates that form in response to many stresses, including activation of the integrated stress response or viral infections. SGs sit at the crossroads between antiviral signaling and translation because they concentrate signaling proteins and components of the innate immune response, in addition to translation machinery and stalled mRNAs. Consequently, they have been proposed to contribute to antiviral activities, and therefore are targeted by viral countermeasures. Equally, SGs components can be commandeered by viruses for their own efficient replication. Phase separation processes are an important component of the viral life cycle, for example, driving the assembly of replication factories or inclusion bodies. Therefore, in this review, we will outline the recent understanding of this complex interplay and tug of war between viruses, SGs, and their components.RNA in Disease and Development > RNA in DiseaseTranslation > RegulationRNA Interactions with Proteins and Other Molecules > RNA‐Protein ComplexesThis article is categorized under: Viruses depend entirely on host machinery and therefore aim to conquer the host and defeat its defenses to co‐opt its resources for its own replication. Improved understanding of how viruses counteract host immune responses such as stress granule assembly, detailed here, will inform future antiviral therapeutic strategies. This can be achieved by surrounding them with lipid membranes in organelles such as the nucleus, lysosomes, or mitochondria. However, in recent years, the assembly of membraneless compartments, known as biocondensates or membraneless organelles, has been increasingly recognized as an alternative way to organize cellular components. These biocondensates are maintained through a combination of protein–protein, protein–RNA, and RNA–RNA interactions which result in increased local concentrations of client RNAs and proteins  or shut‐off global translation to prevent the production of viral proteins while favoring the production of essential genes (host‐led). These mechanisms have all been reviewed extensively in recent years and will only be introduced here in the context of the factors involved in the downstream assembly of SG  by eIF4F, which recognizes the 5′‐cap  at residue Ser‐51, plays a key role in translational control during stress. This is achieved by one of four eIF2α serine/threonine kinases Figure , which i2.2.2Viruses can antagonize the interactions underpinning the eIF4F complex assembly and its interaction with 43S particles by cleavage of eIF4G, separating the eIF4E‐interacting domain from the eIF4A‐ and eIF3‐binding segment, thereby abolishing eIF4F assembly and therefore cap‐dependent translation. PABP is also targeted by viral proteases inhibiting its interaction with the 3′‐polyadenylated tail and association with eIF4G thereby inhibiting translation  infection, or the nuclear retention of eIF4E and PABP seen during infection with rotavirus and Rift Valley fever virus  coalesce into a concentrated state  are different from classical proteins in that they do not fold into a defined stable 3D structure and exist as a highly dynamic and heterogeneous conformational ensemble . These Negri bodies were found to be inclusion bodies (IBs)  nucleoprotein has been shown to interact with viral RNA and phosphatidylinositol  bisphosphate P2) at the plasma membrane during the packaging of the eight viral genome segments, which must be compacted together in single copies during replication  that can cross‐link and accelerate SG assembly. Bridges can form two connections (v = 2) and caps form one (v = 1), therefore these slow or prevent SG growth, respectively  experiments have demonstrated the rapid exchange of many SG components, some with half‐lives of less than 30 s, despite this, other components were more immobile  treatment, osmotic stress, and heat shock takes from 60 to 120 min  family including RIG‐I and MDA5. RLRs signal via the mitochondrial‐associated adaptor protein MAVS. The endosomal TLRs signal via TRIF and MyD88, and cytosolic DNA sensors (CDSs) such as Z‐DNA binding protein 1 (ZBP‐1), cyclic GMP‐AMP synthase (cGAS), DDX41, and interferon‐gamma inducible protein 1 (IFI16) signal via the ER adaptor molecule stimulator of interferon response CGAMP interactor (STING) , adenovirus, and SINV, and in addition, when SG were knocked out by depletion of SG components, IFN production was greatly reduced, and viral replication was increased. These results prompted Onomoto et al. to propose the term avSGs as they serve as a platform where innate immune sensors can be activated by viral RNA to initiate the IFN response and an antiviral state  degradation , the adenoviral RNA‐I (VAI) binds a single monomer of PKR preventing its dimerization and activation by viral dsRNA, therefore keeping it in its inactive state  inhibits SG formation by an unknown mechanism, and without the cleavage of G3BP1 unlike other picornaviruses  encoded accessory protein (AcP10) of the recently identified beluga whale coronavirus, SW1  3C , Moloney murine leukemia virus (MoMLV), and mouse mammary tumor virus  all cleave eIF4GI and SIV, MoMLV, and MMTV also cleave eIF4GII. Similarly, to picornaviruses and caliciviruses, HIV‐1, HIV‐2, and MMTV also cleave PABP abrogating cellular cap‐mediated translation while allowing IRES‐mediated viral translation ; formal analysis (lead); investigation (lead); writing – original draft (lead); writing – review and editing (lead). Nicolas Locker: Conceptualization ; formal analysis (supporting); funding acquisition (lead); project administration (lead); supervision (lead); writing – original draft (supporting); writing – review and editing (supporting).Work in N.L.'s laboratory is supported by Biotechnology and Biological Sciences Research Council research grants .The authors declare no competing interests.Regulation of stress granules and P‐bodies during RNA virus infection


Procuring high-grade RNA from mature coconut tissues is a tricky and labor-intensive process due to the intricate scaffold of polysaccharides, polyphenols, lipids, and proteins that form firm complexes with nucleic acids. However, we have effectively developed a novel method for the first time, letting the retrieval of high-grade RNA from the roots, endosperm, and mesocarp of mature coconut trees take place. In this method, we exploited dichloromethane as a replacement to phenol/chloroform for RNA recovery from mature coconut tissues. The amount of high-grade RNA acquired from the roots of mature coconut trees was 120.7 µg/g, with an A260/280 ratio of 1.95. Similarly, the mature coconut mesocarp yielded 134.6 µg/g FW of quality RNA with A260/280 ratio of 1.98, whereas the mature coconut endosperm produced 120.4 µg/g FW of quality RNA with A260/280 ratio of 2.01. Furthermore, the RNA isolation using the dichloromethane method exhibited excellent performance in downstream experiments, particularly in RT-PCR for cDNA production and amplification. On the contrary, the RNA plant kit, TRIZOL, and Cetyl Trimethyl Ammonium Bromide (CTAB) methods were unsuccessful in isolating substantial quantities of RNA with exceptional purities from the mentioned coconut tissues. In view of these findings, we conclude that the newly developed method will be pivotal in effectively extracting RNA with high purity from mature coconut tissues. Brassica napus [Arabidopsis thaliana [Oryza sativa [Plant tissues predominantly comprise polysaccharides, proteins, lipids, and polyphenols, which have a tendency to bind with RNA and disrupt its segregation process. These macro-biomolecules precipitate alongside RNA, leading to significant impacts on the integrity and amount of the isolated RNA ,2,3. It ca napus , Arabidothaliana , and Orya sativa , commerca sativa . Despitea sativa . LikewisIn order to overcome these challenges, the Coconut Research Institute (CRI) has undertaken significant endeavors to isolate substantial quantities of premium-quality RNA from young coconut tissues. These tissues are rich in primary cell wall components, lipids, and other complex biomolecules. To date, the CRI has successfully established methods for isolating high-quality RNA from diverse sources, including palm leaves , coconutTriplicated samples of mature coconut tree roots and mesocarp were collected to ensure robust statistical analysis. The study employed four different RNA extraction methods. The dichloromethane method served as the primary experimental group for extracting RNA from mature coconut tree roots and mesocarp. Additionally, the RNA plant kit was used following the manufacturer’s instructions, the TRIZOL reagent was used according to the standard protocol, and the CTAB method was applied as previously described by Wang and Stegemann  to extraTM II 1st strand cDNA synthesis kit was utilized, and a Real Time PCR kit was employed for amplifying the cDNA in the PCR process. Numerous laboratory reagents and equipment were used in the study, including agarose gel, isopropanol, sodium acetate, guanidine thiocynate, β-merceptoethanol, glycerol, Polyvinylpyrrolidone-40 (PVP-40), pipette, mortar and pestle, Ribonuclease-free tubes, plastic bags, spatula, cutting tools, freezer, DPEC water, ethidium bromide, Tris borate EDTA (TBE) buffer, and DNA ladder. The reagents employed in this study were of superior quality and acquired from Chemical Reagents and Real Times, China. For reverse transcription, a TaKaRa PrimeScriptCautions!Ethidium bromide is a perilous chemical and must be managed with utmost caution in a fume hood. The other reagents used may also have certain effects, so it is important to avoid eye and skin contact while working with them. Safety precautions should be taken when handling all chemicals to ensure a safe laboratory environment.Prior to the experiment, all glassware, spatulas, and mortars and pestles must be disinfected at 121 °C for 20 min. After autoclaving, they must be in folded in aluminum foil and be kept in a hot air oven until the time of use to prevent contamination from extrinsic RNase. Taking these precautions ensures a clean and sterile environment for the experiment.Vortex ;Bench centrifuge (Eppendorf Centrifuge 5804 R);PCR machine/thermocycler ;Nanodrop ;Agrose gel electrophoresis system;Gel Doc system .Root tissues were obtained from a mature coconut tree, whereas mesocarp and endosperm samples were collected from mature coconuts harvested from a tree at the CRI, China. Additionally, samples from various tissues of coconut  were collected from newly planted coconuts at CRI. Upon collection, every specimen was delicately chopped into tiny sections using sanitized shears or a sharp blade and moved into zipper bags. The bags containing the samples were finally rapidly immersed in liquid nitrogen and subsequently stored at −80 °C until subsequent examination. To prepare a 100 mL buffer solution, addGuanidine thiocyanate             : 23 g3M sodium acetate (pH 5)       : 3 mLGlycerol                                     : 4 mL2O or DEPC water            : up to 100 mLddHPVP-40                                      : 1.6%β-marceptoethanol                   : 1.9%Take DEPC water (60 mL) and sequentially blend the ingredients with it, making sure to reach a total volume of 100 mL through the addition of DEPC water. The resulting buffer should be stored in the refrigerator. 1.Before homogenizing the tissue, the sample-filled zipper bags from the refrigerator were moved to liquid nitrogen to prevent defrosting.Critical step Defrosting could lead to RNA degradation by RNase enzymes.2.The sterile mortar and pestle, previously autoclaved, were cooled down by the addition of liquid nitrogen.3.Approximately 0.1 g of refrigerated sample was taken from the zipper bag and moved into the liquid nitrogen and PVP-40-holding mortar. The sample was then finely powdered using the pestle, with the mortar being periodically refilled with liquid nitrogen in the course of milling process to avoid defrosting.Critical step (a)To elude sample loss, it is essential to add liquid nitrogen slowly to the mortar, taking care to prevent splashing during the process.(b)Preventing sample defrosting is crucial since it can cause the liberation of RNA from disrupted cells, rendering it susceptible to degradation by RNase enzymes.(c)To ensure the prevention of sample cross-contamination with RNase, it is vital to carry out this step in a fume hood.Due to its significance and sensitivity, the homogenization process required careful and meticulous handling:4.Using a frozen spatula, the ground sample was rapidly shifted into an eppendorf tube holding 700 μL of the extraction buffer. After vortexing the contents of the tube for 15 s, we introduced 500 μL of dichloromethane, vortexed it again for 15 s, and, finally, centrifuged the tube for 5 min at 16,639 rcf and 4 °C.Critical step It was crucial to ensure that whole sample was shifted to the Ribonuclease-free tube having the extraction buffer. Neglecting this step may cause substantial loss of RNA.5.We gently moved 500 µL of top aqueous fraction of the solution to a new tube, ensuring that the tube was Ribonuclease-free. Subsequently, we introduced the same amount of chilled isopropanol into the tube, inverted the tubes to mix the solution, and set it aside at ambient temperature for 5 min.Critical step With utmost care, we moved the supernatant to a new tube that was free from RNase to prevent any potential contamination. The utilization of chilled isopropanol expedited the process by rapidly precipitating RNA within a short period of time.6.After the incubation, we centrifuged the contents of the tube at 16,639 rcf and 4 °C for 5 min and subsequently disposed of the supernatant. The nucleic acids aggregated at the base of the tube.Critical step 7.The pellet was rinsed with 1 mL of 75% ethanol, accompanied by centrifugation at 4 °C and 11,000 rcf for 5 min. The washing step was repeated once more in the same manner.8.The mixture was supplemented with 50 μL of DPEC and 1 μL of DNAse (to collect RNA) and then incubated at 37 °C for 30 min.9.We added the same amount of chilled isopropanol into the tube, inverted the tubes to mix the solution, and set it aside at ambient temperature for 5 min. After incubation we centrifuged the contents of the tube at 16,639 rcf and 4 °C for 5 min and subsequently disposed of the supernatant. The RNA aggregated at the base of the tube.10.The pellet was rinsed with 1 mL of 75% ethanol, accompanied by centrifugation at 4 °C and 11,000 rcf for 5 min. The washing step was repeated once more in the same manner.11.After drying the pellet for 5 min, we carefully re-dissolved the RNA in 20 μL of Ribonuclease-free (DEPC) water using a pipette until it was fully dissolved.Prior to drying the pellet, it was essential to mark its location on the exterior of the tube. Drying could often render the pellet translucent, which could make it challenging to locate afterward. Furthermore, thorough drying of the pellet could pose challenges when attempting to dissolve it later on.10.The RNA abundance and integrity were evaluated using the NanoDrop, whereas the purity was validated through agarose gel electrophoresis.(a)The amount of RNA was assayed spectrophotometrically using NanoDrop 2000 analysis. A sample (1 μL) was loaded onto the NanoDrop 2000, and the optical density was measured at 260 and 280 nm. The quantity of RNA was calculated using the absorbance at A260, whereas the purity of the RNA was assessed based on the A260/280 ratio.(b)w/v) agarose gel. The conical flask was subjected to microwave heating until the agarose was fully dispersed in TBE buffer. Following that, ethidium bromide (2 μL) was introduced to the contents and stirred to achieve thorough mixing. The mixture was deposited inside the gel tray alongside a comb and allowed to solidify for about 20 min. Once the gel had solidified, RNA-loading buffer (2 μL) was combined with the RNA sample (3 μL), and the resulting mixture was introduced to the gel. The gel was subsequently subjected to electrophoresis at 120 V and 240 mA for 12 min. Once the electrophoresis process was complete, the gel bands were spotted, we recorded the Gel Doc system to capture images, and saved the files. Microsoft Office Picture Manager was utilized for cropping the gel images.To set up the agarose gel for electrophoresis, agarose (0.2 g) was dissolved in 25 mL of 0.5x TBE buffer in a Ribonuclease-free conical flask, resulting in a 0.8%  was blended with 1 μL of oligo dT primer (50 μM) + dNTP mixture (10 mM), and the quantity was set to 10 μL, employing DPEC water in a Microtube. After incubating the tube at 65 °C for 5 min, it was cooled on ice. Afterward, 4 μL of 5x PrimeScriptTM II buffer, 0.5 μL of ribonuclease inhibitor, and 1 μL of PrimeScriptTM II RTase were incorporated, and the overall volume was brought to 20 μL with DPEC water. The mixture was placed in an incubator at 42 °C for 60 min to facilitate cDNA synthesis. To render the enzymes inactive, the mixture was subjected to an additional incubation of 5 min at 95 °C, and then it was cooled on ice.2O (39.5 μL) in a micro-centrifuge tube. Afterward, the tube was moved to a thermocycler with the designated conditions: 94 °C for 3 min; followed by 35 cycles of 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 1 min; and a final extension step at 72 °C for 5 min. To verify the absence of chromosomal DNA contamination, a control experiment was conducted using RNA without the presence of reverse transcriptase.Subsequent to the production of first-strand cDNA, RT-PCR was conducted exploiting a Real Time kit to augment the cDNA. In coconut tree root and mature mesocarp samples, a housekeeping gene Actin was examined, utilizing the following primers designed by the Biotechnology lab at CRI, CATAS: CnACT-For (ATAAAGTATGGCTGATGCTGAGG) and CnACT-Re (CAACAATGCTTGGGAACACA). The cDNAs were amplified by blending cDNA (2 μL), reverse primer (1 μL of 10 μM), forward primer (1 µL of 10 μM), 10x TAQ buffer (5 μL), dNTP mix (1 μL of 10 mM), TAQ (0.5 µL of 5U/ µL) from TaKaRa, and ddHThe use of dichloromethane in the extraction buffer successfully isolated RNA with exceptional purity from the roots of mature coconut tree A,B and mSimilarly, the A260/280 ratios of the RNA extracted from the mature coconut tree roots and mature coconut mesocarp using RNA plant (0.76 ± 0.165 and 1.02 ± 0.002) and CTAB (0.83 ± 0.052 and 0.92 ± 0.094) were also very low. Moreover, the quantities of extracted RNA from the mentioned tissues using the RNA plant (5.1 ± 1.86 µg/g FW and 47.8 ± 15.78 µg/g FW) and CTAB (18.9 ± 1.15 µg/g FW and 11.4 ± 1.33 µg/g FW) were minimal.In contrast, the dichloromethane method successfully isolated RNA from mature tree roots (120.7 ± 3.18 µg/g FW) and the mature mesocarp of coconut (134.6 ± 0.61 µg/g FW) in substantial quantities. The purities of RNA by the dichloromethane method from mature tree roots (1.95 ± 0.012) and mature mesocarp (1.98 ± 0.028) were also high . FurtherThe electrophoretogram results showed the existence of three RNA bands, namely, 28S, 18S, and 5S rRNA, in the root samples from mature coconut tree and mesocarp samples from mature coconut after extraction with dichloromethane A. SimilaTo confirm the purity of RNA extracted with dichloromethane from the root of mature coconut tree and mesocarp of mature coconut, RT-PCR was performed to synthesize and amplify cDNA. The RNA extracted through the dichloromethane method successfully produced amplified RT-PCR products B. ConverFor RNA sequencing or constructing a cDNA library, it is crucial to have pure RNA. Additionally, when profiling gene expression, the significance of pure RNA cannot be overlooked ,19,20. OThe presence of both macro- and micro-biomolecules in varying amounts within plant species underscores the need for the appropriate method to isolate pure RNA. In fact, the extraction of pure RNA faces obstacles from RNA-binding proteins impeding complete RNA retrieval , ribonucIn conclusion, the dichloromethane-based method demonstrated its effectiveness in isolating high-quality RNA from mature coconut tree roots and mesocarp, as well as from various other coconut tissues. This method proved to be a valuable alternative to other conventional extraction protocols that showed low yields and poor RNA quality from these lignified tissues. Furthermore, the success of this method opens up possibilities for its application in extracting pure RNA from other plant and animal species, providing a valuable tool for molecular studies and gene expression profiling.


In the amphipods, in addition to the nearly 150 putativeDNA adducts characterized earlier, we detected 60 putative RNA adducts.For the structural identification of the detected RNA adducts, theMODOMICS database was used. The identified RNA adducts included simplemono- and dimethylation and other larger functional groups on differentribonucleosides and deaminated product inosine. However, 54 of theseRNA adducts are not yet structurally identified, and further workon their characterization may uncover new layers of information relatedto the transcriptome and help understand their biological significance.Considering the susceptibility of nucleic acids to environmental factors,including pollutants, the developed multi-adductomics methodologywith further advancement has the potential to provide biomarkers fordiagnostics of pollution effects in biota.Adductomics studies are used for the detection and characterizationof various chemical modifications (adducts) of nucleic acids and proteins.The advancements in liquid chromatography coupled with high-resolutiontandem mass spectrometry (HRMS/MS) have resulted in efficient methodsfor qualitative and quantitative adductomics. We developed an HRMS-basedmethod for the simultaneous analysis of RNA and DNA adducts in a singlerun and demonstrated its application using Baltic amphipods, usefulsentinels of environmental disturbances, as test organisms. The noveltyof this method is screening for RNA and DNA adducts by a single injectionon an Orbitrap HRMS instrument using full scan and data-independentacquisition. The MS raw files were processed with an open-source program,  These nucleicacids are covalently adducted with various chemical groups, collectivelycalled the adductome. As RNA and DNA have different roles in storinggenetic information and transcript processing, it is essential tounderstand adduct formation and the mechanisms involved in both nucleicacids. Moreover, adduct formation and dysregulation on the single-strandedRNA may be more responsive to environmental stimuli than the rigiddouble-stranded DNA molecule and, thus, provide more information onthe acute stressors and biological adaptions to handle them.5 While only a few methylated DNA adducts are identified, nearly 70types of RNA methylation are known for all RNA species, includingtransfer RNA, ribosomal RNA, and messenger RNA.6 Themethylated adducts can be further oxidized, including ten-eleven translocation(TET) mediated oxidation of 5-methyl-2′-deoxycytidine to 5-hydroxymethyl-2′-deoxycytidine.8Understanding the formation and functions of all nucleic acid modificationsand their measurement is essential for deciphering the full adductome.Methylation 1 is one N- and O-atoms; see Motwani et al. 20189 for the nucleophilic sites] can also result frombinding to genotoxic (electrophilic) reactive compounds or metabolitesby a nucleophilic substitution mechanism. These types of adducts areprimarily studied in DNA , but a few studies16 have shown such nucleophilic substitution binding of electrophiliccompounds to RNA as well.When considering the global adductome, in additionto the epigeneticallymodified DNA or post-transcriptionally modified RNA, chemical modificationson the nucleophilic sites of the nucleobases ,DNA from calf thymus (ctDNA) sodium salt, nuclease P1 from Penicillium citrinum (NP1), phosphodiesterase I from Crotalus adamanteus (snake) venom (SVPDE), alkalinephosphatase from Escherichia coli (AKP),ammonium acetate, ammonium bicarbonate, tris(hydroxymethyl)aminomethane, zinc chloride, and formic acid were obtainedfrom Sigma-Aldrich . Chelex-100 resin was purchasedfrom Bio-Rad . All the solvents used were of HPLC grade.Experiments containing nucleic acids were carried out in DNA LoBindtubes, 1.5 mL (Eppendorf).Guanosine(Guo), cytidine (Cyt), adenosine (Ado), uridine (Urd), 5-methylcytidine(5-me-Cyt), 5-methyluridine (5-me-Urd), 2.2M. affinis , were collected from the NorthernBaltic Proper within the Swedish National Marine Monitoring Program;the sampling details and contamination status are described elsewhere.21 Nucleic acids were extracted from individual animals by heatingwith Chelex-100 ion-exchange resin and enzymatically digested as describedin Gorokhova et al. 2020.20 Further, aliquotsfrom the digested 12 amphipods20 were pooledin a septum-sealed vial to a final volume of 300 μL and storedat −20 °C till analysis by LC-HRMS as described in Sousaet al. 2021.22 Blank samples were obtainedwith the procedure being repeated in triplicates but without any DNA/RNA.Amphipods, 2.3m/z 10 and ranging from m/z 200 to 350. The full MS scan was conductedat a resolution of 120 000, an automatic gain control (AGC)target of 3e6, a maximum ion injection time (IT) of 200ms, and a scan range from 110 to 650 m/z. The DIA sequential precursor window method was set to a mass resolutionof 60 000, an AGC target of 5e5, a maximum ion ITof 120 ms, a loop count of 16, and a scan range from 195 to 355 m/z, which was divided into 16 discrete m/z ranges with an isolation window of10 m/z .All conditions, including mobile phase, column, gradient, and MS parametersfor MS1 acquisition and MS2 DIA, are described in our previous work.22A Dionex UltiMate 3000 LC devicecoupled to an Orbitrap Q Exactive HF mass spectrometer  with a heated electrospray ionization source in positivemode was used. The MS was run in FS/DIA mode, with a DIA window widenessof 2.4m/z of a nucleoside adduct’s parent ion isknown, e.g., calculated or observed for a known adduct, the accuratemass of the corresponding specific fragment ion (or nucleobase adduction) can be calculated using the following equationsIf the nLossFinder is described in ourearlier work.22 For data processing ofRNA adducts, a list of 163 RNA modifications with accurate m/z of each molecular ion, [M + H]+, was exported from MODOMICS (http://genesilico.pl/modomics/modifications) to a .csv file. When obtaining the RNA modification list in MODOMICS,originating base was selected as “all bases”, and theselected chemical type of modification was All. Theexported adduct list consisted of Name, ShortName, Formula, Monoisotopic mass, and Protonated mass; the list is shown in Supporting Information Table S1. The .csv filewas then exported to TraceFinder software , which was used to screen for the ribonucleosideadducts. The nucleoside adducts originating from RNA were consideredas detected if the precursor ions were found in TraceFinder with amass accuracy within 5 ppm of the theoretical mass and the presenceof the ribose fragment ion (m/z 133.04954± 5 ppm) in the corresponding MS2 window.Data processing for detecting DNA adductsusing the program nLossFinder in MATLAB for a nontargeted screeningof RNA adducts with a mass range m/z of 200–350 of the nucleosides. This software was programmed22 to be coupled with an HRMS detection methodthat collects MS1 with an FS and MS2 with a DIA scan mode. For screeningof DNA adducts, the deoxyribose neutral loss  wasused,22 and in the present work for thescreening of RNA adducts, the ribose (R) neutral loss correspondingto 132.0423 Da was employed. nLossFinder generateda list with putative nucleoside adducts from RNA based on peak matchingbetween the MS1 ion and the MS2 ion generated from a neutral lossof 132.0423 Da. The mass tolerance was set to 5 ppm, and the minimumtrack points and the track missing points were 5 and 3, respectively.All other program settings were the same as in the software description.22 The adduct list from nLossFinder was converted to a .csv file and exported to TraceFinder. The adductswere again screened with Tracefinder for a second level of confirmationand considered as detected if the precursor ions were found in TraceFinderwith a mass accuracy within 5 ppm of the mass imported from nLossFinder and the presence of the ribose fragment ion(m/z 133.04954 ± 5 ppm) inthe corresponding MS2 window. The lists from the targeted approach(MODOMICS) and the nontargeted approach (nLossFinder) were combined to obtain each molecular ion’s peak area fromEIC for all the detected nucleoside adducts, which were normalizedto the peak area of Guo (peak area-adduct *100/peak area-Guo).Further, theHRMS data were processed by 2.5m/z [M + H]+ ion of the nucleoside adductbetween that obtained from the high mass accuracy data of MS1 andthe corresponding theoretical values calculated from the chemicalformula obtained from MODOMICS using a 5 ppm accuracy window. Thepresence of MS2 m/z [(M –R)+H]+, nucleobase adduct fragment ion (±5 ppm), inthe corresponding DIA window within the same chromatographic windowof the parent nucleoside adduct ion in MS1, was used for confirmation.Further, the chemical structures of 5-me-Cyt, 5-me-Urd, N6-me-Ado, Ino, and 7-me-Guo were confirmedusing authentic reference standards. Standard solutions of 5-me-Cyt,5-me-Urd, and N6-me-Ado(each 100 fmol/μL) were prepared in deionized water, and Inoand 7-me-Guo  were diluted in deionized water in a range from 5to 50 fmol/μL. The solutions were analyzed by LC-Orbitrap MSemploying the same settings described above. The resulting HRMS dataand retention time on the chromatogram were compared with the correspondingdata obtained from the amphipod samples. Spiking of the amphipod sampleswith the respective standard solutions and comparison of LC-HRMS datawith the non-spiked amphipod samples were performed to further confirmthe identification of Cyt, Guo, Ado, Urd, Ino, 5-me-Cyt, 7-me-Guo, N6-me-Ado, and 5-me-Urd.Preliminary identificationof each RNA modification was based on comparison of the 33.1Figure S1). The specific fragmentions from each precursor nucleoside were detected in the respectiveMS2 DIA window  and ribonucleosides Guo, Ado, Cyt, and Urd(RNA). The detection was based on respective molecular and fragmentions by high mass accuracy and retention time 1 and by A window 1.m/z 268.1040, two peaks were detected forthe EICin MS1 (m/z 152.0567) and adenine ion (m/z 136.0618), respectively. Using this differencein MS2 and searching for the respective fragment ions (guanine andadenine) in MS2 under an experiment with the DIA window range m/z 250–270, dG was shown to eluteat 6.5 min (inset in m/z 136.0618 (adenine ion), two peaks were detected, one for Ado (4.4min) and the other for dA (5.2 min). Both peaks were detected becausetheir respective precursors  arein the same DIA window range, m/z 250–270. On the other hand, in the MS2 insert for m/z 152.0567 (guanine ion), only dG andnot Guo was detected because the precursor for Guo (m/z 284.0989) was outside the selected DIA windowrange. All four nucleosides of DNA and the four nucleosides of RNAwere detected in the amphipod samples and characterized using ourmethod . Using TraceFinder,these adducts were searched for in the MS raw files based on highmass accuracy in MS1 spectra and the presence of the ribose fragmention, m/z 133.04954 ± 5 ppm,within the same chromatographic retention in the corresponding MS2DIA window (for adducts within the employed mass range m/z 200–350). As a result, nine putative RNAadducts were detected with the observed m/z [M + H]+ accuracy of the respective ribonucleosideadducts within 5 ppm  moiety, which is a common characteristic of allRNA, resulting in a corresponding specific nucleobase adduct fragmention [(M – R)+H]+. The search for ribonucleosideadducts with nLossFinder using the ribose neutralloss , resulting from the difference between the precursorion in MS1 and its corresponding specific fragment ion in the MS2relevant DIA window, yielded both the precursor and fragment ionspresent within the same chromatographic retention time.The untargeted screening approach used here consideredthat thefragmentation by MS/MS of ionized ribonucleoside adducts , [M + H]nLossFinder, within the mass range m/z 200–350 of the nucleosides,based on the neutral loss of the ribose moiety (132.0423 Da ±5 ppm). nLossFinder was developed and evaluated forthe nontargeted detection of 2′-deoxyribonucleoside adductsusing the corresponding deoxyribose (dR) as neutral loss,22 and here, we adapted it for RNA adductomics.In total, 60 putative RNA adducts were detected in the amphipods . The m/z of the protonated ribonucleoside adducts forthe three RNA adducts not detected by nLossFinder were 398.1493 (ms2io6A), 426.2057 (nm5ges2U), and 413.1415 (t6A).All these adducts were beyond m/z 350, explaining that they were not detectable by nLossFinder with the DIA window set to max m/z 350. However, all adducts in the range m/z 200—350 detected using the targeted approach weredetected by nLossFinder, which, at least, partlyvalidates the use of this software for the nontargeted detection ofRNA adducts. No nucleosides were detected in the blank samples preparedby the same procedure but in the absence of DNA/RNA.We appliedthe untargeted screening approach for RNA adductomicsusing 3.3Table S2) based on their respective observedaccurate mass of the ribonucleoside adduct ion and its correspondingnucleobase adduct ion and the retention time under the employed LCconditions. For the six adducts found in the MODOMICS library (Table S2), elemental composition and chemicalstructures were proposed . Afterspiking with the reference standard 5-me-Urd, the peak at 4.9 minrelatively increased in intensity, implying this being the 5-me-Urdadduct. Further, the corresponding nucleobase adduct fragment ion, m/z 127.052, was observed under each ofthe five chromatographic peaks in the correct DIA window of m/z 250–260 (m/z 250–260) would constitute the precursor m/z 259.0925 of 5-me-Urd. This findingsuggests that the peaks other than at 4.9 min are possible structuralisomers of 5-me-Urd, which could be the methylation on positions otherthan C-5 on Urd. EICs for 5-me-Cyt, N6-me-Ado, and 7-me-Guo showed minor peaks .22 Among them were the epigenetic markers, 5-me-dC and N6-me-dA, and the oxidativestress marker, 8-oxo-dG. See Sousa et al. 202122 for a detailed description of the approach for DNA adductomicsand a complete list of the identified DNA adducts.In the pooled mixture of nucleosides from the amphipodsamples,nearly 150 putative DNA adducts were detected by employing m/z 269.0873). This findingis supported by the fact that inosine is one of the most abundantpost-transcriptional RNA modifications.33 Moreover, adenosine to inosine conversion is catalyzed by the adenosinedeaminase action on RNA enzyme, and the aberrations of the enzyme’sactivity have been associated with, e.g., metabolic disorders andcancer,37 indicating the potential use of inosine as a biomarker in (eco)toxicologicalstudies.In additionto these DNA adducts, while reanalyzing the HRMS-rawfiles, 60 putative RNA adducts were detected in the amphipods, and9 were structurally identified based on high mass accuracy 2. Howeve15 used the detection of RNA adducts in benzo[a]pyrene-exposed human hepatoma cell line to confirm putativeDNA adducts detected by MS-based adductomics. DNA and RNA were extractedseparately using different extraction kits, followed by digestion.Benzo[a]pyrene-diolepoxide nucleoside adducts fromboth DNA and RNA were detected using HRMS and manual monitoring ofthe neutral loss, deoxyribose for protonated 2′-deoxynucleosideadducts and ribose for protonated ribonucleoside adducts.Screening for unknown RNA adducts is less studied thanscreeningfor DNA adducts. Recently, Takeshita and Kanaly (2019)40 mainly involve small chemical groups added to any of the four ribonucleosides.All the adducts identified in our study  molecularion (m/z) of the protonated ribonucleosideadduct from MS1 with high mass accuracy; (ii) retention time underthe employed LC conditions providing relative hydrophilicity; (iii)MS-fragmentation pattern including m/z of the nucleobase adduct fragment ion, obtained after neutral lossof the ribose moiety, with high mass accuracy; and (iv) relative abundancebased on MS1 peak areas assuming equal MS response. There is scopefor improving the detection limit, e.g., by employing nano-LC;54 apart from their cost and maintenance requirement;a disadvantage with nano-LC is the requirement of longer chromatographicruns. Further, as the possibility of false positives, including isotopesand ESI adducts, assigned as individual adducts cannot be ruled out,future work is required toward structural identification of theseadducts to aid in characterization of the RNA adductome. Improvingthe sensitivity for adductome analysis along with knowing the chemicalstructure of the modifications and the extent of their dysregulationby various environmental stressors will facilitate the developmentof adductome-based biomarkers and their use for environmental monitoringof biological effects.To our knowledge, this is the first study on nontargeted detectionof modified ribonucleosides over the mass range of Using the multi-adductomics methodologydeveloped in the currentwork, there are still many adducts that have not been structurallyidentified in the amphipods. A main reason for this is that we useda neutral loss approach combined with high mass accuracy for adductomicsand not relied only on the comparison with a library (such as MODOMICS).This, in fact, is a significant aspect of our work as we have notonly performed adduct screening based on earlier known adducts butalso have developed a state-of-the-art untargeted approach that allowsus to detect earlier unknown adducts. Further work is required tostructurally identify the putative novel adducts detected in the amphipodsand use experimental studies to identify potential association ofchemical exposure with these nucleic acid modifications.5m/z range sequentially. In the MS analysis, a challengeof the same m/z for the protonateddG and Ado was overcome (i) by using MS2 fragment data of the specificnucleobases, G and A, which have different masses and (ii) by thedifference in their relative chromatographic retention. For the DNAadductome, nLossFinder was employed to detect nucleosideadducts by tracking a neutral loss under MS fragmentation, with thestructural identification primarily based on literature knowledgeof possible modifications on the nucleobases. For the RNA adductome, nLossFinder screening was combined with the MODOMICS databaseof RNA modifications, which aided the structural identification ofseveral adducts. In the amphipods, we structurally identified simplemethylated adducts on DNA (5-me-dC and N6-me-dA) and RNA , genotoxicadducts (8-oxo-dG), and more complex post-transcriptional modifications. Information on m/z of molecular and fragment ions related to thechemical formula and chromatographic retention related to hydrophilicitywas provided for 54 RNA modifications detected in the amphipods. Morework, including MS3 experiments and exposure studies with model organism,is needed to facilitate structural characterization and validationof these adducts. The discovery of new RNA modifications opens newvenues in analytical validation and understanding their biologicalsignificance on epitranscriptome and the impact of contaminants onthe adductome, which can support future applications of specific adductsas molecular biomarkers in environmental monitoring.We introduce an approachfor simultaneous RNA and DNA adductomicsusing a single injection on LC-Orbitrap HRMS instrumentation. RNAand DNA adducts can be detected by measuring modified ribonucleosidesand 2′-deoxyribonucleosides, respectively. The workflow formulti-adductomics was demonstrated by analyzing amphipod RNA and DNAadducts as a proof-of-principle study. Our MS approach with FS providedHRAM data in the MS1, and DIA resulted in comprehensive fragmentation,providing MS2 data for all ions across a broad selected