Alternative splicing, translational control and mRNA stability have been shown to be tightly regulated during the innate immune response and participate in modulating gene expression in a global or gene specific manner. More recently, microRNAs assisting RBPs and post-transcriptional modification of RNA bases are also emerging as essential players of the innate immune process. In this review, we highlight the numerous roles played by specific RNA-binding effectors in mediating post-transcriptional control of gene expression to shape innate immunity.Innate immunity is the frontline of defense against infections and tissue damage. It is a fast and semi-specific response involving a myriad of processes essential for protecting the organism. These reactions promote the clearance of danger by activating, among others, an inflammatory response, the complement cascade and by recruiting the adaptive immunity. Any disequilibrium in this functional balance can lead to either inflammation-mediated tissue damage or defense inefficiency. A dynamic and coordinated gene expression program lies at the heart of the innate immune response. This expression program varies depending on the cell-type and the specific danger signal encountered by the cell and involves multiple layers of regulation. While these are achieved mainly  In that regard, innate immune responses are crucial for the generation of a robust, antigen-specific adaptive response, and the maintenance of memory , local cells are prone to rapidly sense any tissue dysregulation and to send signals of danger that initiate the innate arm of immune responses. Local cellular sensors include epithelial cells, stromal cells and fibroblasts, whose role will be essential when being the initial target of infection (in case of strictly intracellular pathogens) or injury –4. In adf memory , 7.e.g. double-stranded (ds) or single-stranded (ss) RNA, dsDNA, peptides…), or their location . Upon ligand binding, each family of PRRs recruits a specific adaptor such as myeloid differentiation primary response 88 (MyD88), mitochondrial antiviral signaling protein (MAVS) or stimulator of interferon genes (STING) for TLRs, RLRs or cGAS, respectively , or anti-inflammatory  , each being specific for classes of molecules known as Pathogen Associated Molecular Patterns (PAMPs) or Damage Associated Molecular Pattern (DAMPs) . Essenti III IFN , 11 and/ III IFN , 13, or α, etc…) , 15, eveα, etc…) .e.g. intolerance, auto-immunity), whereas their under-expression leads to an inefficient defense strategy . Altogether, RBPs act at all steps of the life of RNAs: pre-RNA processing , mRNA transport, ribosome biogenesis, translation, and finally RNA decay (RNA-Binding Proteins (RBPs) are key effectors of post-transcriptional regulatory processes –26 by taNA decay –29. In lNA decay , 31 and NA decay . More reNA decay , 34.Here, we review the multiple roles played by RBPs in shaping the innate immune response, from pathogen and danger detection to the regulation of signal transduction and effector functions.via RNA-centric or protein-centric methods  mRNAs (corresponding to protein coding RNAs) , total Rin vitro kinetics of binding and dissociation of the RBP DAZL to a thousands of RNA targets (e.g. Thbs1 transcript) in a transcriptome-wide manner  followed by RNA-sequencing in order to identify the RNA species bound to the RBP of interest. One can access to the precise RNA sequence where the RBP is binding by coupling immuno-purification with a RNase treatment and small-RNA sequencing . Severale manner . Once apAltogether, RNA-centric and protein-centric protocols have greatly participated in our understanding of RBPs and their functional role in a myriad of different cellular processes including the innate immune response.e.g. Influenza, SARS, Hepatitis, Measles, etc.). Although sensing of foreign RNA leads to a substantial and rapid antiviral response, it must be tightly controlled in order to avoid hazardous effects , with pectively , 114. Thectively . Similarectively .κB signaling pathway . La has been recently shown to directly bind RIG-I once bound to a viral dsRNA , resulti pathway . Therefo pathway .κB)  , 128 FFκB  . Ro60 caα domain of ADAR1 (via Receptor Interacting Protein Kinase 3 (RIPK3)  mostly located in their 3’UTRs . AIRs caof ADAR1  and Z-DNof ADAR1  with dif (RIPK3) –85. More (RIPK3) . By cont (RIPK3) . A-to-I  (RIPK3) . Similar (RIPK3) . Those re.g. herpes simplex virus 1 (HSV-1), Epstein-Barr virus (EBV) and influenza A virus (IAV)] have been shown to disrupt the nucleus membrane and to induce a global downregulation of host proteins’ synthesis. This leads to increased availability of RNA5SP141 in the cytoplasm and its unmasking from the RBPs  is present in the nucleus. When RNA5SP141 translocates into the cytoplasm, it recruits different RBPs such as ribosomal protein L5 (RPL5) and mitochondrial ribosomal protein L18 (MRPL18), avoiding unwanted activation of the immune system by PRRs. Some DNA viruses . Assembated see Figure 4 mTORC1) , 218. Th complex , 219. Onand eIF3 , 220. uLand eIF3 .γ. These include transcripts coding for chemokines, chemokine receptors  mRNA after 8 and 24 hours. However, while VEGF-A protein levels are increased 8 hours post IFN-γ treatment, its level returns to baseline at 24 hours. Indeed, VEGF-A translation is repressed by the GAIT complex via the binding of GAIT to its GAIT element  boost immune response by restricting viral replication through the regulation of mRNA translation (either self or foreign mRNA), some acting on bulk translation while other targeting specific transcripts . However, certain cellular mRNAs are selectively translated in the presence of high levels of phosphorylated eIF2α and many viruses are able to overcome this arrest  , induced by IFN-PPP-RNA)  in a seqPPP-RNA) , 246. IFPPP-RNA) . In addiPPP-RNA) . HoweverPPP-RNA) . By doinAltogether, a broad panel of RBPs exists, that regulate RNA dynamics through the modulation of translation. Their molecular and functional structure are divergent but they all converge into shaping the intensity and the efficiency of the innate immune response in time and space.The number of proteins synthesized from any given mRNA molecule is defined by its translation and degradation rates , 250. Ine.g. LPS, TNF-α in myeloid cells or fibroblast)  –267. Everoblast) , 269. Thion rate , 266. HoRapid mRNA degradation mechanisms are essential for shaping the innate immune responses and the binding by RBPs to transcripts happens in a sequence or structure dependent manner. The most widely targeted cis-elements are AU-rich elements (AREs), with RBPs stimulating the deadenylation of mRNA .α, IL−1β or Granulocyte Macrophages Colony Stimulating Factor (GM−CSF) (U/A) nonamer sequence located in the 3’UTR of protein-coding transcripts . Their s(GM−CSF) . Their A(GM−CSF) , 279.via its zinc finger domain. Well known targets are mRNAs displaying high turnover rates such as cytokines and growth factors  zinc finger protein). It has been identified in various organisms from human to yeast –282 and  factors –285. Mec factors , leading factors . TTP is  factors , 294, th factors . Followi factors , 297] lectors  by RBPs, through the recognition of specific sequences and RNA structures in the 3’UTR of targeted mRNAs, that are not ARE. For example, Constitutive Decay Element (CDE) relates to a decay through deadenylation, that is mediated by a 3’UTR conserved sequence with a stem-loop structure specifically recognized by Roquin and Roquin2 –302.α encoding mRNA contains a non-ARE CDE that tightly regulate its expression, and overall prevent excessive TNF-α production in inflamed macrophages . In this case, CUG-BP1 is able to directly recruit the PARN deadenylase to induce target mRNA decay , no-go decay (NGD) and non-stop decay (NSD) also rely on translation to induce degradation of aberrant mRNAs [for a review see ]. Howeveα remains unaffected , a RBP recognizing specific stem loop in 3’UTR and harboring an endoribonucleolytic activity thought to act as a negative regulator of pro-inflammatory processes  . Tegnase-1 .via the release of extracellular vesicles containing high levels of miR-27a. These miR-27a cargos lead naive monocytes to differentiate into M2 macrophages  (MicroRNAs (miRNAs or miR) are other important modulators of mRNA decay and translation by interacting with the 3’UTR of the target transcripts. miRNAs are short (22 to 25 nt long) noncoding RNAs that regulate gene expression in numerous cellular processes. By targeting the 3’UTR of protein coding transcripts, they might directly regulate expression of about 60% of all mammalian genes , 313. miophages) . Furtherophages)  or betweophages) . miRNAs,e.g. IL-6 mRNA) can also be stabilized through the inactivation of miRNAs. Indeed, IL-6 mRNA can be targeted by miR-26 family members for degradation. However, miR-26 family members can be inactivated by TUT4 , a ribonucleotidyltransferase . Initial studies performed in HEK293 cells, by combining data from different experimental systems such as PAR-CLIP, and whole-transcript expression profiling, have shown that HuR is involved in coupling pre-mRNA processing and mRNA stability, highlighting an important role of HuR within the nucleus through its binding at intronic sequences , which is known as one of the most important AU-rich element (ARE)-containing mRNA stabilizing proteins . This RBβ , ISGs anβ , 333. Foβ , increasβ ]. Intereequences . Howeveri.e. IFN response)  cancer therapy, myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) patients are treated with decitabine or azacitidine. This induces the expression of dsRNAs from endogenous retroviruses (ERVs), that are normally silenced by epigenetic regulation. ERVs are in turn recognized by a specific PRR, MDA5, that stimulates an immune response, leading to the death of the cancer cell. Concomitantly, STAU1, which contains multiple dsRBDs, has been shown to cooperate with a long non coding RNA, TINCR, to binds ERV transcripts. The STAU1-TINCR-ERV complex stabilizes ERV transcripts and is required to promote the expected immune response and cell death . Therefoesponse) .In combination, all these RBPs participate in a regulatory network that finely tune inflammation by controlling the fate of diverse mRNAs. RBPs deploy mRNA decay and/or stability mechanisms to maintain immune homeostasis. This is allowed through a timely regulation of RBP activity during immune responses. Indeed, RBPs participate in the positive regulation of rapid pro-inflammatory processes at the onset of immune response and, conversely, appear to play an even more important role in shutting down or reducing inflammation at later time points, thus preventing detrimental tissue damage. Interestingly, this tight regulation appears often regulated by the subcellular localization of RBPs, with RBP harboring different function depending on their local interacting environment. As the mechanisms behind these interactions are still not fully characterized, it will be important to further investigate the different roles and molecular mechanisms by which RBPs regulate mRNA stability during the course of innate immune responses.6A is the most common mRNA reversible post-transcriptional modification (5C) or on the nitrogen of the first position on adenine (N1-methyladenosine or m1A). Other types of RNA modifications such as Cytidine-to-Uracil or Adenine-to-Inosine RNA editing  can lead to changes in the secondary structure of the edited RNA , while the eraser proteins can reverse the modifications by specifically removing the chemical groups from the RNA targets. Finally, the readers are the RNA-binding proteins able to specifically bind the RNA bases bearing the chemical modification , since they need to discriminate between self and non-self dsRNA molecules. While evolutionary elimination of dsRNA sequences within cellular mRNAs has been observed, which should limit innate immune activation against host RNAs, dsRNAs remain frequent in pre-mRNAs  production.It is now well recognized that epitranscriptomics affects several molecular processes. In the immune system, while epitranscriptomics appears important during hematopoietic stem cell differentiation , it is cre-mRNAs . One wayre-mRNAs , 364. InE861A/E861A) are embryonically lethal, and present over-activation of IFNs and dsRNA-sensing pathways. This deregulated innate responses are due to a lack of A-I editing in ADAR1 ADAR1E861A/E861A embryos, as further shown by a decrease in A-I editing in a vast majority of RNA targets. Moreover, the phenotype of ADAR1 ADAR1E861A/E861A can be rescued by a concurrent deletion of MDA5 (RNA Sensing and Regulation of PRR Activity by Non-PRR RBPs). ADAR1 appears as an essential protein in protecting host dsRNA from innate recognition, thus preventing aberrant innate responses against self  while viral RNAs, which are not edited, would trigger robust innate responses , PKR is an IFN-inducible protein that, following dsRNA recognition, phosphorylates eIF2α, favors the formation of stress granules, and acts as a major inhibitor of global translation . ADAR1 mediated inhibition of PKR occurs in a editing-dependent or -independent way, and mostly requires direct interactions between ADAR1 and PKR  induce little or no stimulation , a well know writer of m6A modification  RNA, a negative regulator of ISGs expression . Thus, YTHDF3 participates indirectly to the negative control of antiviral responses by promoting translation of ISG inhibitors to limit the risk of unnecessary inflammation   , 357. Invice versa), suggesting that each of these factors is involved in several cellular mechanisms.Taken together, these results suggest that epitranscriptomics is a strategy used to prevent autoimmunity and balance the risk between an aberrant induction of innate response and self-tolerance , 362. HoThrough their capacity to interact with specific RNA sequences, structural features or chemical modifications, RBPs orchestrate all steps of RNA metabolism from its synthesis, maturation and functional role, to its eventual decay. In the context of innate immunity, RBPs play multiple roles, acting at the first line of defense through their capacity to sense non-self RNAs and induce an immune response, being involved in the activation and effector functions of innate immune cells by finely tuning the amplitude and temporal control of gene expression, and finally acting as key effectors of the antiviral response through their capacity to destroy foreign RNAs. The activity of RBPs is tightly regulated by numerous mechanisms that include control of their subcellular localization, competition with other RBPs for their RNA substrate, or post-translational modifications that modulate their activity. Working in a collaborative network with signal transducers, epigenetic modifiers, and canonical trans-acting factors, RBPs play important roles in developing a global and complex gene regulatory network. Illustrating their importance in finely tuning the innate immune response, numerous human pathologies, such as autoimmune diseases, are associated to mutations in genes coding for RBPs [for a review see ]. TechniAG and ER determined the scope and focus of the review with feedback from AK and MW. AG drafted the manuscript with contributions from ER and MW. AK generated most figures with help from AG and ER. All authors contributed to the article and approved the submitted version.Work in our laboratory is supported by the European Research Council (ERC-StG-LS6-805500) under the European Union’s Horizon 2020 research and innovation programs; ATIP-Avenir program; Fondation FINOVI; Agence Nationale des Recherches sur le SIDA et les Hépatites Virales (ANRS-ECTZ3306) and Agence Nationale de la Recherche (ANR-20-CE15-0025).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The modification process of RNA methylation is a dynamic reversible regulatory process that is mainly catalyzed by “Writer” m6A methyltransferase, removed by “Eraser” m6A demethylase, and recognized by the m6A binding protein, thereby, linking m6A modification with other mRNA pathways. At various stages of the life cycle, m6A modification plays an extremely important role in regulating mRNA splicing, processing, translation, as well as degradation, and is associated with gametogenesis and fertility for both sexes. Normal gametogenesis is a basic guarantee of fertility. Infertility leads to trauma, affects harmony in the family and seriously affects the quality of life. We review the roles and mechanisms of RNA m6A methylation modification in infertility and provide a potential target for infertility treatment, which can be used for drug development.Eukaryotic messenger mRNAs contain many RNA methyl chemical modifications, in which N Infertility is a reproductive dysfunction that is characterized by a succession of sexual events and interruption of new life. Accordingly, an inability to get pregnant after 12 months of unprotected intercourse is broadly defined as infertility . InfertiThe process through which mature germ cells are formed is called gametogenesis. Gametogenesis is indispensable to the general health and well-being of various species. Overtime, gametes link the various stages of species’ development. Compared to somatic cells, after a series of mitosis and meiosis during the differentiation process, germ cells produce sperms and oocytes. Sexually mature gametes combine during fertilization to produce the all-powerful oosperm . An impoThe transforming growth factor beta-1 proprotein-like (TGF) -β family member proteins  are essential for primordial germ cells (PGC). They act as precursors of male and female gametes to produce oocytes and sperms. Directional interruption of Bmp2, Bmp4, Bmp8b, or BMP signal transducers, SMAD family member s1, 4 and 5, or protein kinase (ALK2) indicates a loss or decrease of PGCs . In mammSperm production is a complex asynchronous differentiation process that is divided into three stages based on the cell types found: Spermatogonia are formed through mitosis, spermatocytes are formed through meiosis, and the haploid stage of sperm cells or sperm formation . ContinuOogenesis is a long-term process in mammals that begins in the embryonic period and ends in menopause. In oogenesis, two basic processes are necessary; the formation and maturation of oocytes and folliculogenesis. Follicles are reproductive units composed of oocytes, somatic cells and follicles. Oteocytes are made up of cumulus granulosa, mural granulosa and theca cells. In the embryonic stage, oogonia are the precursors of female gametes, expanding in number through mitosis. They differentiate into primary oocytes, grow in the follicle and undergo meiosis. Meiosis has a complex early stage that can be divided into five phases: leptotene, zygotene, pachytene, diplotene and diakinesis. Various key events, including homologous chromosome pairing, synapsis and recombination or crossover initially occur in the first period of the prophase . In fema6A modification and its functions as a whole, briefly introduced m6A methyl transferase (“author”), dimethyl enzyme (“eraser”) and binding protein (“reader”)  . Then, wreader”) . FinallyBoth DNA and histone proteins can control gene expression through dynamic reversible chemical modification. Similar to DNA and proteins, RNA, which form the center of the central dogma transmit genetic information. Chemical modification of RNA molecules is more diverse and abundant when compared to DNA. Apart from the four basic bases, there are more than 170 chemically modified nucleotides, which play various roles in many different types of cellular RNAs, including rRNA, tRNA and mRNA, and snRNAamong others . Approxi6A) exerts the most significant effect on gene expression, and is the first modification that has been shown to regulate mRNA abundance. N6-methyladenosine (M6A) modification was first described 40 years ago. M6A was first discovered in mouse L cell polyA-RNA, and was subsequently confirmed to be the most widely distributed and highest internal modification in mRNA  of 3′-UTR, especially in the area near the stop codon , demethylase (eraser) and m6A binding protein (reader). Demethylase is a proof that m6A modification is dynamically reversible.Among these modifications, N6-methyladenosine  catalyzes m 200 kDa . Both MEnslation . METTL3  oocytes . HAKAI,  are modulators that regulate germ cells. After knocking out METTL3 or METTL14, it was found that mppressed . Moreovee of SSC . The knoe of SSC . These rogenesis .6A  is a non-steroidal anti-inflammatory drug that is considered to be a highly selective FTO inhibitor. As an ethyl ester MA derivative, MA-2 can effectively inhibit the growth of glioblastoma stem cells (GSC) and tumor formation induced by GSC. It also has a significant inhibitory effect on the expression of FTO  are closely associated with YTHDC1, a key nuclear factor in the processing of precursor mRNA transcripts. Knockout of YTHDC1 changes the length of the 3′UTR. This is because, extensive selective polyadenylation occurs in oocytes, leading to alternative splicing defects in oocytes.In vitro pulldown assays proved that YTHDC2 preferentially binds m6A  group was significantly higher than that in the YTHDF2cKO group, implying that proliferation of spermatogonia is inhibited after YTHDF2 knockout. Morphologically, wild-type spermatogonia are polygons while YTHDF2cKO spermatogonia are fusiform or round. Cell-adhesion assays revealed that YTHDF2 has a significant effect on the adhesion and spread of spermatogonia .YTHDF2 has the ability to regulate oocytes to reach metaphase stage of meiosis II. During early zygotic development, these oocytes can remain in the metaphase stage of meiosis II. Large amounts of YTHDF2 in GV and MII oocytes imply that it is also expressed during oocyte maturation, spermatogenesis or follicular development. YTHDF2 has been detected in the cytoplasms of germ cells and somatic cells . When th6A modification and its methyltransferase and demethylase, binding protein have an extremely important impact on gametogenesis.With developments in methylation modifications of RNA, studies on RNA methylation modification have gradually developed from relatively concentrated functional research to studies on disease. To maintain generations, the harm caused by reproductive health and its influencing factors to humans should be urgently resolved. RNA methylation modification is an important epigenetic phenomenon. MWe have discussed the role and mechanism of RNA methylation modification in infertility. The mechanism involves the cooperation of METTL3 and METTL14 to control timely translation of methylation transcription, thereby, appropriately regulating the production of proteins that are necessary for spermatogenesis. METTL3 regulates the differentiation of spermatogonia and plays an important regulatory role in oocyte maturation. KIAA1429 plays a vital role in follicle formation and has a regulatory on normal spermatogenesis. The correct regulatory mechanism of YTHDC2 on meiosis in gamete development has not been clearly elucidated. YTHDF2 regulates spermatogonia proliferation and has a role in the maturation of oocytes.6A regulators, potential preventive and treatment methods for infertility can explored. Proteins for m6A regulation can be used as biomarkers to reflect gametogenesis disorders in both sexes and to achieve preventive treatment of infertility. Precise treatment of gene targets such as METL3, WTAP, KIAA1429, FTO, ALKBH5, YTHDC1, YTHDC2, and YTHDF2, by regulating m6A modification ensures normal gametogenesis and exerts a better therapeutic effect on infertility. Entacapone, MA drugs, STM2457, butyrate, miR-186, miR-4429, miR-600 and let-7G, Rhein, MA-2 inhibitors, as well as folate, betaine and methionine methylation donors can be used in the treatment plans for gametogenesis disorders.Secondly, by identifying specific mRNA methylation can prevent and treat infertility by regulating gametogenesis. This discovery has important research significance in theory and practice. However, the mechanisms involved have not been clearly established. For example, it has not been determined whether the specific molecular mechanism that causes infertility is associated with other diseases. Moreover, it has not been determined whether other RNA methylation modification proteins affect gametogenesis. To comprehensively understand the regulatory mechanisms of RNA methylation on infertility, studies should determine how RNA methylation affects gametogenesis under the stimulation of the external environment, drugs and poisons.XL, HW, BL, ZQ, JL, BX, WL, and ZX: revising the manuscript. YD: conception and design, revising it critically for important intellectual content, and final approval of the version to be published. All authors substantially contributed to the review and read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


S. cerevisiae. We found that both tRNA and rRNA transcription is stalled in yeast exposed to stressors such as H2O2, NaAsO2 or methyl methanesulfonate (MMS). From the absence of new transcripts, we concluded that most RNA modification profile changes observed to date are linked to changes happening on the pre-existing RNAs. We confirmed these changes, and we followed the fate of the pre-existing tRNAs and rRNAs during stress recovery. For MMS, we found previously described damage products in tRNA, and in addition, we found evidence for direct base methylation damage of 2′O-ribose methylated nucleosides in rRNA. While we found no evidence for increased RNA degradation after MMS exposure, we observed rapid loss of all methylation damages in all studied RNAs. With NAIL-MS we further established the modification speed in new tRNA and 18S and 25S rRNA from unstressed S. cerevisiae. During stress exposure, the placement of modifications was delayed overall. Only the tRNA modifications 1-methyladenosine and pseudouridine were incorporated as fast in stressed cells as in control cells. Similarly, 2′-O-methyladenosine in both 18S and 25S rRNA was unaffected by the stressor, but all other rRNA modifications were incorporated after a delay. In summary, we present mechanistic insights into stress-dependent RNA modification profiling in S. cerevisiae tRNA and rRNA.RNAs are key players in the cell, and to fulfil their functions, they are enzymatically modified. These modifications have been found to be dynamic and dependent on internal and external factors, such as stress. In this study we used nucleic acid isotope labeling coupled mass spectrometry (NAIL-MS) to address the question of which mechanisms allow the dynamic adaptation of RNA modifications during stress in the model organism  The central dogma of molecular biology states that DNA is the storage of the genetic code, which is transcribed into messenger RNA (mRNA) and translated into proteins with the help of transfer RNA (tRNA) and ribosomal RNA (rRNA). This fundamental life process is dominated by nucleic acids, which are composed of the canonical nucleosides adenosine, guanosine, cytosine and uridine (and thymidine in DNA). The sequence of these building blocks defines the genetic code of an organism. Additional chemical groups on these building blocks, commonly methylations, form a second layer of information on top of the code. In DNA, methylation was found to be dynamic. The addition or removal of a methylation on carbon C5 of cytosine can switch genes off or on ,2. Since6A for human mRNA -methyl . All solutions and buffers were made with ultrapure water . All salts were obtained from Sigma Aldrich  at molecular biology grade, unless stated otherwise. Isotopically labeled compounds: 15N2-uracil, 0.01 g/L 13C6-glucose, 0.72 g/L-methionine-[2H3]-methyl or their unlabeled isotopomers were used. Yeast-nitrogen-base (YNB)  minimal medium was prepared by mixing a 10× YNB stock solution supplemented with a mix of unlabeled aminoacids . Depending on the desired stable isotope labeling, 0.02 g/L S. cerevisiae BY4741 was picked from a YPD-agar plate and used for inoculation of 5 mL YNB. The cells were grown at 30 °C at 250 rpm. The next day, the cell density was assessed by OD600 measurement  and the cell suspension diluted to OD 1. The cells were allowed to grow for 3 h to reach mid-log phase and the stressor was added. After 1 h of stress exposure the medium was exchanged by centrifugation . The resulting pellet was resuspended in fresh medium to initiate the recovery phase. Afterwards, 2 mL of cell suspension was harvested at set time points by centrifugation . The RNA was isolated as described in A single colony of 50 values, yeast was cultivated as described in 2.3 and exposed to various concentrations of H2O2, MMS, NaAsO2, TBH or HOCl. After 1 h, 100 µL of each culture was diluted to 1/105 with sterile water. From this dilution 70 µL was plated on a pre-warmed YPD agar plate. The YPD plates were incubated at 30 °C for 48 h and the colonies were counted.For determination of LD50 concentrations of H2O2 (2 mM), NaAsO2 (40 mM), HOCl (3 mM), MMS (12 mM) and TBH (10 mM). Then, 2 mL samples were drawn as indicated in the For assessment of tRNA and rRNA modification profiles under stress, yeast was cultivated as described in S. cerevisiae BY4741 was grown overnight in unlabeled YNB medium or in 13C6-glucose/15N2-uracil labeled YNB medium, as described in For validation of NAIL-MS conditions, S. cerevisiae BY4741 was grown in 13C6-glucose and 15N2-uracil labeled YNB medium. The next day, the culture was diluted with 13C6-glucose and 15N2-uracil labeled YNB medium to OD 1 with a total volume of 32 mL. After 3 h of growth, the first sample was harvested. Afterwards the culture was split, and one half was exposed to the LD50 concentration of the respective stressor. After one hour of stress exposure, the next sample was harvested and afterwards the medium was exchanged by centrifugation . The resulting pellet was resuspended L-methionine-[2H3]-methyl labeled YNB medium. More samples were harvested (2 mL) at set timepoints after the initiation of the recovery phase. The RNA was extracted and purified as described in A 5 mL overnight culture of 2H3]-methyl labeled YNB medium was used, following the culturing method described in 2H3]-methyl labeled YNB medium was used. L-Methionine-[2H3]-methyl labeled YNB medium in the overnight culture. The next day the culture was diluted with L-methionine-[2H3]-methyl labeled medium to OD 1, grown for 3 h and exposed to 12 mM MMS. After 1 h of exposure, the medium was exchanged by centrifugation , and the resulting pellet was resuspended in 13C6-glucose, 15N2-uracil and L-methionine-[2H3]-methyl labeled YNB medium. To monitor MMS induced RNA damage repair, cells were grown as described in An overnight culture of each knock-out strain was grown in unlabeled YNB medium as described in 4OAc and 2.5xV 100% ice cold ethanol were added to the aqueous phase; the mixture was stored at −20 °C overnight. The next day, samples were centrifuged , the supernatant was discarded and the reaction tube was rinsed with 200 µL of 70% ice-cold ethanol. After another step of centrifugation , the supernatant was discarded, and the ethanol was air dried for 10 min. Afterwards, the total RNA was suspended in 50 µL H2O.Total RNA was isolated according to the hot-phenol extraction protocol of Collart et al. . The ext2O.18S and 25S rRNA and tRNA were purified by size exclusion chromatography (SEC)  according to our published protocol . After p2 (1 mM) containing buffer. Furthermore, 5 µg tetrahydrouridine , 10 µM butylated hydroxytoluene and 1 µg pentostatin were added to avoid deamination and oxidation of the nucleosides. The mixture was incubated 2 h at 37 °C and then filtered through 96-well 10 kDa molecular-weight cut-off plates  at 3000× g and 4 °C for 30 min. Then, 1/10 Vol. of SILIS  was added to each filtrate before analysis by QQQ mass spectrometry.RNA (300–500 ng) in aqueous digestion mix (30 μL) was digested to single nucleosides by using 2 U alkaline phosphatase, 0.2 U phosphodiesterase I  and 2 U benzonase in Tris  and MgClS. cerevisiae BY4741 was grown in 5 mL of 13C, 15N Silantes rich growth medium  supplemented with 1% (w/w) 13C6-glucose. The culture was incubated overnight, and the next day it was diluted to OD 0.1 with fresh 13C, 15N Silantes rich growth medium supplemented with 1% (w/w) 13C6-glucose. The culture was incubated for another 2 days at 30 °C. The cells were harvested and RNA was extracted according to Collart et al. -methyl enzymatic methylations, they receive a +3 mass increase. As shown in 7G, m1A and m3C was achieved with 18 mM L-methionine-[2H3]-methyl. Cells were exposed to 12 mM MMS in the continuous presence of 18 mM L-methionine-[2H3]-methyl. After one hour of MMS exposure, tRNA, 18S and 25S rRNA were extracted, and mass spectrometry analysis revealed the ratio of enzymatically placed methylations (m/z +3) to damage-derived methylations (m/z ± 0). As shown in 7G marks were caused by direct methylation with MMS. To a lower extent, m3C, m1A and m6A were caused by direct methylation of canonical nucleosides in tRNA. For rRNA, we found the same damage products U, t6A and i6A also appeared early on in total tRNA  was scrambled during and after the stress exposure, which must be taken into account if RNA modification densities are quantified. Exposure to Hhemistry ,21,22, ahemistry  and the hemistry . Our anaE. coli and human cells [7Gm is a new type of RNA damage; however, m7Gm requires further structural validation by comparison with synthetic standards, which are currently not available. In addition, we observed both m1Am and m6Am. However, similarly to m6A, we are unsure about the mechanistic origin of m6Am. Recently, the Dedon laboratory presented convincing evidence that m6A might emerge from m1A through dimroth rearrangement due to the RNA hydrolysis protocol required for LC-MS analysis [6Am is in fact a secondary damage product of m1Am which undergoes dimroth rearrangement. In the context of methylation stress, we have previously reported on RNA damage products in an cells ,11. Untianalysis . Thus, iE. coli, the repair of m3C takes two hours through AlkB. m1A takes with more than four hours, and m7G is not removed at all. The slow speed of repair in combination with a clear substrate specificity observed in E. coli makes us wonder how all methylation damage types are repaired within one hour in S. cerevisiae. Maybe, there are no demethylases involved, but a more global mechanism. Interestingly, we studied methylation damage in human RNA after MMS exposure, and to our surprise, we observed only minor quantities of RNA damage products which might argue towards instantaneous repair of the damage or a more sophisticated detoxification processes [Regarding the increases and sudden disappearance of all tRNA and rRNA methylation damage products, we can only speculate. In rocesses . Thus, e


Quantification of isoform abundance has been extensively studied at the mature RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing.We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA-sequencing data (DENR), which models nascent RNA-sequencing read-counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical ‘shape profile’ of read-counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes.https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0).DENR and nascentRNASim are freely available at Bioinformatics online. For about the last 15 years, most large-scale transcriptomic studies have relied on high-throughput short-read sequencing technologies as the readout for the relative abundances of RNA transcripts , alternative polyadenylation and cleavage sites (PASs) and alternative splicing  isoforms, having different TSSs and/or PASs. Moreover, the problem of decomposing this mixture can be more challenging than for RNA-seq in some respects, both because pre-RNA isoforms have fewer differentiating features than mature RNA isoforms, and because nascent RNA read depths tend to be substantially reduced, since introns as well as exons are sequenced. Distinguishing among pre-RNA isoforms in nascent RNA sequence data can be critical for a wide variety of downstream analyses . Neverth+ T-cells and CD14+ monocytes.In this article, we introduce a new computational method and implementation in R, called Deconvolution of Expression for Nascent RNA-sequencing data (DENR), that addresses the problem of isoform-abundance quantification at the pre-RNA level. DENR also solves the closely related problems of estimating abundance at the gene level, summing over all isoforms, and identifying the ‘dominant isoform’, i.e. the one exhibiting the greatest abundance. DENR makes use of a straightforward non-negative least-squares strategy for decomposing the mixture of isoforms present in the data, but then improves on this baseline approach by taking advantage of machine-learning predictions of TSSs and an adjustment for the typical shape-profile in the read-counts along a transcription unit. We show that the method performs well on simulated data, and then use it to reveal a high level of diversity in the pre-RNA isoforms inferred from PRO-seq data for several human cell types, including K562, CD4n isoforms spanning m genomic bins, let  m × n design matrix such that j spans bin i and iβ values. Notice that, when the shape-profile correction is applied, the non-zero values in the design matrix DENR estimates the abundance of each isoform by non-negative least-squares optimization, separately at each cluster. For a given cluster of therwise . DENR esiβ, we normalize them by the total library depth to facilitate comparisons between samples. Isoform-level abundances are then converted to gene-level abundances by summing over all isoforms associated with each gene.After obtaining estimates for all isoform abundances z-scores for scale-independence. The CNN was trained using the Adam optimizer  classifier using the Keras interface to TensorFlow . While o(≤25 bp) . The CNN(≤25 bp) . The modptimizer  with earWhen the optional TSS-calling feature is in use, only isoforms corresponding to predict active TSSs are allowed to have non-zero weights. However, because the TSS predictor inevitably misses some active TSSs, DENR makes use of a heuristic method to identify and reconsider regions of ‘unexplained’ high-density polymerase. Specifically, an upstream polymerase ratio (UPR) statistic is calculated by taking the ratio of the read-count density inside the isoform (+0.5 to +2 kb relative to the TSS) to the density upstream of the isoform  is ignored. As an example, the shape-profile for a set of isoforms based on PRO-seq data from K562 cells , and maps those bins to a canonical  interval. This mapping is intended to fix the scales of the promoter-proximal and termination regions, and allow the remaining gene-body to be compressed or expanded as needed. Specifically, the first 3 kb of each isoform is mapped  to the interval , the last 3 kb is mapped to  and the remaining portion is mapped to the  interval. Finally, the canonical shape-profile is obtained by averaging the relative read-count densities of the entire -rescaled reference set of isoforms, using a loess fit for smoothing, and scaling the density such that the median value across the entire interval is one. This shape-profile is then used to adjust the design matrix Our non-parametric simulator for nascent RNA-sequencing data, called nascentRNASim, makes use of a template set of isoform annotations and a designated collection of well-defined isoform ‘archetypes’ and corresponding read-counts. The archetypes are selected as cases where the observed read-counts can be attributed to a single isoform (see below). Given these inputs, we simulate a synthetic dataset in five steps. First, we group the isoform annotations into non-overlapping strand-specific clusters, as in a DENR analysis. Second, we sample randomly (with resampling) from this set of clusters, and similarly, from the set of inter-cluster distances. Third, within each sampled cluster, we substitute for each isoform the archetype that is closest to it in genomic length, keeping the TSS at its original position relative to the beginning of the cluster. Fourth, we sample a new overall isoform abundance for each synthetic isoform from a distribution fitted by kernel density estimation to isoform-abundance estimates from GTEx for skeletal muscle  of masked bins at both the 5most TSS . At the i is estimated in transcripts per million as follows:ir is number of reads mapped to the genomic region in question (corresponding either to an isoform or the union of isoforms associated with a gene), if is the length of that region and, G is the set of all genes in the simulation  in single-end mode (n = 7732) and CD4+ T-cells (n = 7632) were retained for analysis. To survey predominant usage of internal TSSs for transcription, genes with dominant pre-RNA isoforms transcribed from internal TSSs 1 kb downstream from the 5To prepare bigWig files as input for DENR, we first processed published K562  were analyzed. We focused on cases in which the dominant isoforms differed in the two cell types. The Gene Ontology (GO) analysis was performed using the online tool DAVID  (primary transcription) and X and Y, H, and make use of the chain rule, H is the same as H(Y). Specifically, for each i,k is compatible (in TSS and PAS) with pre-RNA isoform j. Thus, we estimate the post-transcriptional contribution as To decompose entropy into components from DENR is implemented as a package in the R programming environment. It requires two main inputs: a set of isoform annotations and a set of corresponding strand-specific nascent RNA-sequencing read-counts. Mature RNA isoform annotations can be easily downloaded by making use of biomaRt  the coordinates of predicted TSSs, (ii) a list of inactive isoforms and (iii) a shape-profile correction. Separate predictions of TSSs are useful because they help to distinguish the start of one isoform (particularly one downstream from the start of a cluster) from the continuation of another isoform. The DENR package includes a lightweight, pre-trained machine-learning classifier, implemented using TensorFlow, that can predict the locations of likely TSSs based on their characteristic patterns of bidirectional transcription and symmetric pause peaks . An option is also provided to perform this optimization in logarithmic space, i.e. by comparing the logarithm of the expected density and the logarithm of the read-counts, corresponding to an assumption of a log-normal distribution for read-counts (see Section 4).DENR is designed to be fast and efficient, and in our experiments on an Intel i7-10700 CPU (using a single thread) it was able to process a typical human dataset  in about 10 min, with <8 GB of RAM. Notably, we used a bin size of 250 bp for all results reported in this article, finding that this size appropriately smoothed the raw PRO-seq signal and struck a good balance between genomic resolution and computational cost. However, we experimented with a smaller bin size of 125 bp and observed similar results (see below). For different datasets, users may wish to experiment with other bin sizes, ranging from, say, 50 to 500 bp.We evaluated DENR’s accuracy in quantifying RNA abundance at both the gene and isoform levels. Lacking an appropriate ‘gold-standard’ in the form of real biological data, we chose to benchmark the software using simulated data. Because, to our knowledge, there is no available simulator for nascent RNA-sequencing data that accommodates multiple isoforms per gene, we developed a new R package, called nascentRNASim, to provide a ground truth against which to compare DENR’s estimates . To maker) of the estimated and ‘true’ abundances at both the gene . The shape-profile correction also appeared to improve performance consistently at isoform level, although to a lesser extent . The effect of the masking strategy was more variable, but we found that masks of one bin at the We first evaluated the impact of the various optional features by running the program with and without TSS prediction, shape-profile correction, log-transformation of read-counts and with various numbers  of masked bins at the 5the gene  and isofthe gene  levels. r = 0.97) (r = 0.85)  method commonly used in the field. For the RCB method, we simply estimated the abundance of a gene by the number of sequence reads that overlap any annotated isoform for that gene divided by the gene’s total length (see Section 2). At the gene level, DENR’s estimates were highly concordant with true abundances ( = 0.97) , substan = 0.85) . Accordi = 0.85) . DENR of = 0.85) , where t = 0.85) , apparen = 0.85) . These cr = 0.89) (r = 0.96) (r = 0.89 versus 0.59) and RMSE (297.5 versus 1117.3) . At the isoform level, DENR’s estimates of abundance were still well correlated with the true values ( = 0.89) , althoug = 0.89) . The est = 0.96) , but the 1117.3) . These b 1117.3)  and on a 1117.3) , and fou+ T-cells  displayed evidence of expression at two or more isoforms (see Section 2), indicating frequent use of alternative TSSs or PASs . We observed a similar pattern in CD4+ cells, with 48.9% (3734 of 7632) of genes producing two or more pre-RNA isoforms. Moreover, we found that the dominant isoforms for 1178 (15.2%) and 1262 (16.5%) of genes, respectively, made use of an internal TSS, at least 1 kb downstream from the 5Having demonstrated that DENR has good power to recover true gene and isoform abundances in simulated data, we next applied it to real data from K562  all overlap  for two similar but distinct human cell types, CD4X (estimated from PRO-seq data using DENR) and mature RNA isoform-abundance relative frequencies Y (estimated from RNA-seq data using StringTie), the joint entropy H can be decomposed into a component arising from primary transcription, H(X), and a conditional-entropy component arising from post-transcriptional processes, H(X) across any set of expressed genes using DENR, estimate H for the same set of genes using StringTie, and then estimate the post-transcriptional entropy, To quantify isoform diversity at the pre-RNA and mature RNA levels, we made use of the information-theoretic measure of Shannon entropy. We observed that, given pre-RNA isoform-abundance relative frequencies + T-cell and CD14+ monocyte datasets individually, we observed reasonably good concordance, with estimates of H = 0.94–1.01 bits/gene in total entropy, of which a clear majority, 63–64%, comes from transcriptional entropy [H(X)] and the remaining 36–37% derives from post-transcriptional processes  increased by about 10%, indicating higher levels of isoform diversity across cell types than within them. Interestingly, however, the fractional contribution from primary transcription, When applying these methods to the CD4rocesses . When we+ T-cells play an important role in the adaptive immune system whereas CD14+ monocytes are part of the innate immune system. Therefore, we extracted 116 and 287 genes associated with the GO terms ‘adaptive immune response’ and ‘innate immune response’, respectively, and calculated + T-cells , and slightly elevated in innate-immunity-related genes in CD14+ monocytes (P = 8.59e-14), suggesting that primary transcription may disproportionally contribute to isoform diversity in the genes most relevant to the specific immune-related functions of each cell type. Examining several other classes of genes  display evidence of expression at multiple distinct pre-RNA isoforms. Moreover, we found that the dominant isoform fairly commonly (in ∼15% of cases) makes use of a TSS that is >1 kb downstream of the 5+ T-cells, we identified more than 200 genes that use different dominant isoforms in these two cell types, including prominent examples, such as RUNX1.In analyses of real data, we found that many genes  into a component estimated at the pre-RNA level (by applying DENR to PRO-seq data) and the remainder, which we argue can be interpreted as the conditional entropy introduced at the post-transcriptional level. Our observations are qualitatively similar to those from a number of previous studies reporting widespread, regulated alternative TSS usage, often in a tissue-specific manner . HoweverThere are a number of potential avenues for improvement of our current implementation of DENR. First, the method assumes a sum-of-squares loss function, which is equivalent to maximum-likelihood estimation under a Gaussian  generating distribution for read-counts, with the counts for each bin assumed to be independent and identically distributed. Real nascent RNA-sequencing read-counts, however, tends to be not only overdispersed but non-uniform along the genome, with fairly pronounced spikes separated by intervals of reduced signal. The method could be extended to allow for maximum-likelihood estimation under an arbitrary generating distribution for read-counts, by making use of a general probabilistic model for nascent RNA-sequencing data that we have recently proposed . This moa priori or completely excluding them (i.e. assigning a prior probability of zero) based on the absence of a TSS prediction or other evidence of inactivity. A natural generalization would be to accept an arbitrary prior probability for each candidate isoform. These weights could potentially be determined based on a variety of relevant covariates, including not only TSS predictions but also, say, chromatin accessibility, chromatin contact, histone modification or RNA-seq data from a relevant cell type. The model would then combine the prior probabilities with the data likelihood to enable full Bayesian estimation of isoform abundances. A related extension would be to consider not only annotated isoforms but also ones suggested by the nascent RNA-sequencing data but not annotated. Such candidates could potentially be identified using a separate method [e.g. A second limitation is that DENR effectively uses a ‘hard prior’ for candidate isoforms, either treating them as equally likely Finally, the current inference method does not make use of a sparsity penalty to encourage the observed data to be explained using as few isoforms as possible. In initial experiments, we did not find that such penalties made a noticeable difference in our prediction performance, and in general, we do not observe a proliferation of isoforms with small weights. However, we do occasionally find that DENR gives high weights to short transcripts that happen to coincide with spikes in the data or pause peaks, apparently owing to a failure to account for spikes in the read-count data, as well as inadequacies in the shape-profile correction when applied to short isoforms. It is possible that a sparsity penalty—perhaps combined with the use of a richer model for read-counts—would help to eliminate some of these apparently spurious predictions.Despite these limitations, we have shown that DENR is generally an effective tool for quantifying pre-RNA abundance at both the gene and isoform levels, with many possible downstream applications. We expect this method to be increasingly useful to the community as nascent RNA-sequencing data grow more abundant and are used for a wider variety of downstream applications.btab582_Supplementary_DataClick here for additional data file.


Skeletonema dohrnii is a good model diatom for studying environmental stress and has promising applications and prospects in various fields. Antibiotics and heavy metals are commonly exceeded in the nearshore marine habitats. In this work, we investigated the effects of an antibiotic  and a heavy metal ion  stress on marine diatom S. dohrnii, mainly using excitation-emission matrices (EEMs) fluorescence methods and OJIP test. Results indicated that algal cells grown with the antibiotic showed higher biomass, specific growth rate, doubling time, chlorophyll a, and chlorophyll fluorescence variables. Moreover, excess zinc had negative effects on S. dohrnii. We found that zinc not only inhibited the relative photosynthetic electron transfer efficiency but also reduced the Chl a content, which ultimately affected algal growth and organic matter production. In addition, the combined effect of penicillin and Zn2+ further affected the physiological state of S. dohrnii. The dissolved organic matter (DOM) characteristics of the four cultures were also different, including fluorescence indices  and fluorescence peaks . In brief, characterization of chlorophyll fluorescence characteristics and DOM-related variables are important for understanding the effects of environmental stress on microalgae. Phytoplankton (including eukaryotic and prokaryotic cells) play an important role in the ecosystem as the main primary producers in the ocean . They plSkeletonema spp. are widely distributed throughout the ocean in habitats spanning a full range of observed area. Meanwhile, it is easily cultured in the laboratory, and each species within the same genus has its own characteristic behavior curve [2, nutrients, and pH [Skeletonema dohrnii is highly resistant and has been used extensively in the study of environmental stresses [The diatom or curve , reflector curve . Most in, and pH . Photosystresses . The fatS. dohrnii was cultured in batch mode. Subsequently, we established different treatments to investigate: (1) how the addition of antibiotics and heavy metals affects the photosynthetic activity of diatoms; and (2) also to understand S. dohrnii changes in DOM under stress conditions (2 µg/L penicillin and 10 µmol/L Zn2+). Characterization of chlorophyll fluorescence and DOM associated variables were important for our increased understanding of the effects of penicillin and Zn2+ in marine diatoms.In this study, S. dohrnii was originally isolated from the central Yellow Sea, China [S. dohrnii were cultured in transparent conical flasks with artificial seawater (ASW) medium [−2 s−1 and shaking at 160 rpm on a shaker . Cultures were maintained under these conditions for approximately 30 days before the initiation of the experiment.The marine diatom a, China , and sub) medium  at 25 °CS. dohrnii cells were collected via centrifugation  after cultivation in ASW medium. The cells were washed with Milli-Q water and centrifuged a second time at 5000 rpm for 5 min. Then, algal cells were inoculated in 2 L flasks containing 1.5 L ASW medium. The initial biomass concentration of the S. dohrnii was about (2.03 ± 0.02) × 108 cells/L. We chose penicillin (2 µg/L), Zn2+ (10 µmol/L) and penicillin (2 µg/L) + Zn2+ (10 µmol/L) to represent the single and combination experiments, respectively. For simplicity, we named blank  and different treatments . All experiments were carried out in triplicate.−1. To regularly monitor the algal growth, 100 μL of microalgae solution was collected onto the blood cell counting chamber and microalgal cells were counted immediately using an inverted microscope (Olympus BX51). Specific growth rate and doubling time of S. dohrnii cultured in the laboratory were calculated based on the following equations:a and Nb denote the algal abundance (cells/L) at times ta and tb, respectively.Algal cells were cultivated at a constant temperature of 25 °C in a shaker at a shaking rate of 160 rpm mina was determined by referring to Mera et al. [v/v) as blank control. The following formula was used to calculate the concentration of Chl a:The content of Chl a et al.  with a fv/Fm; potential activity of photosystem II, Fv/Fo; and total light energy flux, Pi_Abs) of the chlorophyll was determined concurring to Markou et al. [The algal solution had 20 min of dark acclimation at 26 °C. The fluorescence parameter  onto pre-combusted (450 °C for 6 h) glass vials. Even though these filters have a larger pore size, as compared to 0.2 µm polycarbonate filters, they have advantages that demonstrated their application in this research . Then, tInstrument corrections were performed according to the procedure recommended by the Hitachi F-7100 instruction manual. Blank subtraction using ultrapure water (Milli-Q) was performed for each DOM spectrum in order to remove most of the Raman scatter. The correction was followed by Raman calibration according to the literature . FluoresS. dohrnii, cells were harvested using pre-combusted (450 °C for 6 h) GF/F glass fiber filters (25 mm diameter). The samples were acidified with 200 μL of 0.2 N HCl and dried overnight in an oven at 60 °C before analysis with a Costech Elemental Analyzer The dissolved organic carbon (DOC) was measured using a total organic carbon analyzer . To determine the particulate organic carbon (POC) and particulate organic nitrogen (PON) in t-test analysis using SPSS software (version 26.0). Triplicate samples of each condition were taken into consideration, and comparison was done between groups of cells grown at penicillin and Zn2+. The correlation analysis and chromophoric dissolved organic matter (CDOM) associated variables were obtained using the “ggplot2”, “corrplot”, “RColorBrewer” and “reshape2” package in RStudio software. The measured data were reported as mean ± standard deviation (SD). The value of p < 0.05 was considered a statistically significant difference.In this study, Statistical analysis of all data was performed with unpaired S. dohrnii was cultivated in artificial seawater without the lag phase  × 108 (Blank) and (3.60 ± 0.05) × 108 cells/L (SA), respectively (S. dohrnii significantly decreased from around (2.03 ± 0.05) × 108 (SZ) to (0.04 ± 0.01) × 108 and (0.02 ± 0.01) × 108 cells/L (SAZ), respectively, after 8 days of cultivation. In other words, the cells concentration in the SZ and SAZ treatments decreased from day 0 and approached zero at day 8  d−1 for SA. The doubling times were (1.779 ± 0.011) d and (1.407 ± 0.007) d for Blank and SA. There was no significant difference between both groups. The specific growth rates and doubling times of SZ and SAZ showed a negative increasing trend, so they are not shown in 2+ on the Chl a content of S. dohrnii are shown in a content of S. dohrnii also indicated that SA grew best in artificial seawater compared with the other cultures   b.S. dohrnii in response to various treatments in artificial seawater.Photosynthesis is one of the major metabolic activities of algae cultivated in the natural environment. Nevertheless, chlorophyll fluorescence of cells was analyzed to assess the growth of 2+) on the photosynthetic property of S. dohrnii were evaluated by monitoring the chlorophyll fluorescence characteristics. For these fluorescence variables , there was a significant increase in Blank and SA during the first two days, followed by a general decrease. SZ and SAZ gradually decreased and tended to zero throughout the culture time. Moreover, we found that Blank was generally higher than the other treatments in variables of QY, Fv/Fm, and Fv/Fo, except for the fix area. This suggests that the addition of penicillin and Zn2+ affected the chlorophyll fluorescence properties of S. dohrnii.The effects of different treatments . Looking at v/Fm, Fv/Fo had similar trends, and the differences among treatments were also more significant  being the most obvious , Table 3S. dohrnii. From The fluorescence indices were further used as variables indicating changes in fluorescence characteristics under the different treatments of Generally, the fluorescence intensity changes of the peaks  were basically similar, which increased with increasing culture time . The fluS. dohrnii-associated bacterial community diversity. It was noteworthy that the antibiotic used in this study was penicillin at a concentration of 2 µg/L. The increase in the number of cells in S. dohrnii implies that the cause may be due to the antibiotics removing bacteria that are harmful to the growth of the algae [a in algal cells is also affected to some extent when the algae are stressed. As the concentration of heavy metal ions increases, Chl a suffers more negative effects. The accumulation of reactive oxygen species damages the algal cell structure and limits the synthesis of Chl a. However, based on our observation, the values of algal abundance, Chl a, and chlorophyll fluorescence characteristics were different from the other treatment groups (2+ (10 µmol/L) indirectly affect the physiological state of S. dohrnii.When antibiotic stress is applied, it is well recognized that antibiotics inhibit the growth of bacteria and usually also influence the growth of algae. In addition, antibiotics might be altered the he algae ,28. One he algae ,30. Addit groups . Previouv/Fm is the main parameter that measures photosynthetic performance and is an important index that directly expresses the photosynthetic efficiency of microalgae. It has been suggested that the stress-induced decrease in Fv/Fm is a short-term response of the photosystem under stress conditions, which may be a reconstructed equilibrium between light energy uptake and utilization by microalgae, minimizing damage to the algae [v/Fm and Fv/Fo ratios of the algae decreased rapidly with increasing incubation time, indicating that the photosynthetic system of the microalgae was inhibited due to the addition of Zn2+. This may be due to the fact that heavy metal ions blocked photosynthetic electron transfer between qa and qb, which in turn formed more qb-non-reducing PS II reaction centers and this led to a reduction in oxygen evolution from PS II reaction centers. In this study, we found that the variation in Fv/Fm and Fv/Fo ratios were different for all cultures, suggesting that penicillin (2 µg/L) and Zn2+ (10 µmol/L) impact photosynthetic electron transport in algal cells.Photosynthetic activity parameters are widely used to describe the photosynthetic characteristics of algal cells . Besideshe algae . Severalhe algae ,34. In t2+ (10 µmol/L) addition to the different treatments  and fluorescence peaks  in Blank and SA  and Zneatments . Moreoveeatments . The Chlk and SA a,b. Howek and SA c,d. Thesin algae ,36. As tnhibited . Once conhibited . Howeveroduction .2+, which affect the production and fluorescence intensity of diatom S.dohrnii-derived DOM. In addition, it may also be related to the species of microalgae or culture medium. Taken together, our previous study and results suggest that the fluorescence intensities generated in microalgae culture experiments are relatively low when the variability of the instrument is taken into account. Indeed, background fluorescence is helpful for monitoring the low DOM fluorescence by microalgae.Some laboratory experiments have depended on additions of carbon or high nutrients substrates to investigate DOM formation by microalgae or bacteria . In this2+ on algae or microbially-transformed planktonic material. A number of recent studies, CDOM generated in culture work on diatoms, prasinophytes, and bacteria showed similar fluorescence patterns to those reported in this study [2+ affect the generation of DOM fluorescence peaks. All four cultures produced a pattern of two peaks that were dominated by DOM fluorophores, which suggests different compounds . This two-peak pattern has been previously demonstrated in monocultures and oceanic origin . The peais study . Althoug2+ led to the change to a more fluorescent component.The fluorescence index provided a clear insight into the process of DOM changes. The values of FI were all greater than 1.8, indicating that the fluorescence component of OM was mainly produced by microorganisms . The rat2+, the photosynthetic activity and chromophoric dissolved organic matter of S. dohrnii were investigated to evaluate the physiological properties and dissolved organic matter characteristics. For the addition of penicillin (2 µg/L) alone, higher values were shown for certain variables compared to other treatments, such as algal abundance, Chl a, specific growth rate, DOC, POC, POC/PON, HIX, and peak C. High concentrations of Zn2+ (10 µmol/L) can also severely inhibit the electron transfer rate and photochemical efficiency of algal cells. Moreover, Zn2+ plays a dominant role in the combined effect of penicillin and Zn2+. Since only penicillin (2 µg/L) and Zn2+ (10 µmol/L) were selected for this experiment, there were limitations in the choice of pollutant types and concentrations. However, this still provides data to support the investigation of pollutant stress on marine diatoms. In addition, the proliferation of diatoms can cause the initiation of potentially harmful blooms (red tides), and this study also provides a data base for the management of China’s nearshore aquatic environment.In this work, utilizing combined penicillin and Zn


Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment. Tumor heterogeneity is the most complex contributor to misdiagnosis and is often associated with the failure of identifying its margins in different tissues. There is a dire need for the development of new approaches that can precisely detect molecular mechanisms fueling tumor heterogeneity and better help diagnose cancers in clinical practices. Molecular approaches developed so far provide limited information about tumor cells and their microenvironment as these approaches can perform bulk transcriptome analysis without addressing their spatial context . In the Analysis of RNA expression in their spatial context provides important information about cellular heterogeneity of tissues, tumors, and immune cells as far as the application of spatial transcriptomics is concerned. This also helps to decipher subcellular localization and expression of RNA in various conditions, which leads to a better understanding of the architecture of tissues . SpatialThis review article describes potential applications of the emerging single-cell RNA-seq techniques for the analysis of unbiased RNA sequencing within the heterogeneous tumor and highlights the importance of single-cell RNA-seq techniques in terms of their accuracy, sensitivity, reliability, and resolution for exploring and understanding the microenvironment. The review article also provides an in-depth knowledge of the integration of single-cell RNA-seq with data generated by omics technologies towards a comprehensive analysis of intratumor heterogeneity. It is expected that the literature presented here will help researchers expand their work for exploring splicing and post-transcriptional modifications of RNA methylation, without compromising the spatial context of the cancer cell using single-cell RNA-seq techniques. -cell stage [Mycobacterium tuberculosis [The journey of single-cell RNA-seq started in 2009 when this technique was used for the first time for the analysis of mouse blastomere dividing at the fourll stage  and Iscoll stage  were pioll stage . Up tillll stage . The firll stage . In the ll stage , which sll stage . Furtherll stage . A studyll stage . In 2017rculosis . Anotherrculosis . The latrculosis . Recentlrculosis ,23,24. IThere have been tremendous efforts carried out for the development of single-cell RNA seq techniques in wet-lab, bio-informatic or computational tools in recent years, resulting in the general methodology as presented in the methodological pipeline . Among tSingle-cell RNA-seq introduced by Tang and his colleagues involved the use of polyT sequences for the analysis of mRNA from tissue samples . InitialCEL-seq has higher 3′-bias and low sensitivity for lowly expressed transcripts. A transcript that has 5 RNA copies per cell, CEL-seq has only a 50% chance of identifying these transcripts . While sThe challenges described in the CEL-seq were overcome when Hashimoshony et al. developed an improved version of CEL-seq named CEL-seq2 in 2016. This method has a much higher sensitivity for the detection of RNA transcripts from tissue samples and needs reduced costs and time for the analysis of tissue samples . Figure SCRB-seq was developed as it can perform analysis of RNA analytes from a low input concentration. This method involves the use of barcoding and sequencing of single-cell RNA by incorporating unique molecular identifiers (UMIs) in the reaction, which has considerably reduced the amplification bias of target mRNA . Later, One of the state-of-the-art next-generation sequencing methods is SMART-seq. Although it is more suitable for sequencing small transcripts, it can also be used to read full-length genes to show complex structural variation existing in the DNA samples, i.e., indicating where copy number variation has occurred based on the reference sequence . In thisDrop-seq is a cost-friendly technique compared with other sequencing techniques for the analysis of transcriptome at the single-cell level, and the method relies on the use of encapsulation of single cells with DNA barcoded microbeads . This alDrop-seq is a cost-effective technique as USD 0.7 is needed for one cell analysis and library preparation time is very short . The identification of mRNA strands is quite easy with unique molecular and cell barcodes used in this technique. This method results in the creation of a high amount of cDNA using reverse transcription with template-switching PCR. The merits of Drop-seq include evaluation of single-cell sequences of similar patterns, identification of gene-specific mRNA strands via single molecular and cell barcodes, high amount of reads from single cells, cost-effectiveness, and fast library prep . However, it requires a specialized custom microfluidic device for the separation of droplets, has low gene sensitivity per cell compared with other single-cell RNA-seq techniques, and is restricted to only the mRNA transcripts.MARS-seq  was developed for sampling thousands of in vivo cells that used RNA sequencing multiplex by sustaining the control over biases during amplification and labeling errors. For exploring this new technique, RNA from over 4000 mouse spleen single cells was sequenced by focusing on the heterogeneous cell population having a high level of expression of surface marker CD11c to differentiate between the diversity of spleen cells and cells of ductal carcinoma. A new framework was developed from single-cell transcriptional states of tissues to break down the complex functions. MARS-seq can be applied easily to tissues/organs for revealing detailed genome-wide transcriptional profiling in normal and diseased states, thus proving this technique to play a vital role to study the biological functions of cells in vivo . MARS-seAdvantages of MARS-seq include in vivo sampling, high throughput transcriptional profiling, and three barcode levels , which help in vigorous multiplexing skills and are cost-effective. However, disadvantages are three biases in the purification step and removal of strand-specific information during fragmentation. There have been considerable contributions from 10x Genomics in single-cell RNA seq analysis which has led to the identification of various cell types (subpopulations) found in heterogenous cancer tissues. For instance, the GemCode™ Technology from 10x Genomics, which can analyze the transcriptome of a huge number of cell populations (thousands of cells) by combining a microfluidic platform with molecular barcoding and then the analysis of cells transcriptome at single-cell resolution using custom bioinformatics software . There iSpatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections . This teSingle-cell RNA-seq techniques have been the basis for performing the transcriptome analysis by NGS-based approaches as these methods involve the incorporation of a spatial barcode before library preparation . The FirAnother form of NGS-based platform is Slide-Seq, which performs analysis of transcripts by covering the glass slides with randomly barcoded beads to capture mRNA . In thisThere are two main types of imaging-based approaches that are used for spatial transcriptomics: in situ hybridization and in situ sequencing-based methods. MERFISH (multiplexed error-resistant fluorescence in situ hybridization) can test thousands of RNA molecules in cancer tissue samples. MERFISH uses a two-step approach for the analysis of RNA molecules from cells . The metSingle-molecule fluorescence in situ hybridization (smFISH) offers both quantitative measurements of RNA expression and even RNA spatial localization by directly imaging individual RNA molecules in single cells ,65. The It would be worth mentioning here that in the MERFISH transcriptome analysis method, the analysis of RNA molecules is based on a fluorescent signal generated by fluorescently labeled probes tagged to RNA molecules. The intensity of the signal is usually high enough for the detection of RNA molecules, however, reduced signal intensity renders low detection and may lead to misinterpretation of biological processes ,72. Low Advances in the Fourth-generation RNA-seq platforms, especially in situ sequencing (ISS) and fluorescent ISS (FISSEQ), have greatly helped to achieve the objectives and aims of RNA-seq techniques ,74. ISS Laser capture micro-dissected RNA-seq (LCM-RNAseq) is an advanced approach to overcome the limitations faced by bulk RNA-seq techniques . The LCMSingle-cell RNA-seq techniques can generate the sequence information from slides while preserving the histological context of the area of interest  and idenSingle-cell RNA-seq has been combined with other omics technologies for better analysis and understanding of the RNA analytes in their spatial context. In situ visualization of transcriptome by a single-cell RNA-seq gives multiplexed information for the expression of genes, cell types, and disease progression patterns. On the other hand, the in situ sequencing (ISS) technique, as mentioned earlier, by using padlock probes and amplification by a rolling circle amplification, spatially displays expressions of RNA in a range of tissue sections. The study reported by Gyllborg et al. used hybridization-based ISS (HybISS) for understanding the spatial localization of RNA in human and mouse brain tissue samples . This prSingle-cell RNA-seq studies identified two cancer cell populations for the pancreatic ductal adenocarcinoma (PDAC-A) tumor that seemed to be quite different from each other histologically . There wSingle-cell RNA-seq is a well-known technique for clinicians and it is expected that it will be ready soon for clinical applications. Up till now, the transcriptome of tissue samples of various disease origins obtained from humans has been mapped with the help of single-cell RNA-seq. The target of most single-cell RNA-seq studies has been to map the transcriptome of tissues to explore the molecular mechanism of disease progression. This is deemed very useful for differentiating cellular subpopulations stratifying various types of disease categories as well as assessing different therapeutic responses. In cancers, calculations of transcripts copy number, gene mutations, and modifications at a single-cell resolution are used for determining tumor heterogeneity, clonal lineages, and metastatic variations. For this application, target gene panels for single-cell RNA seq can perform profiling of target RNA molecules with improved efficacy. Tumor heterogeneity is one of the main features of cancers and plays a major role in drug resistance. The infiltration of stroma and the presence of diverse types of cells in tumor tissues largely compromise the results obtained by bulk RNA-seq. However, single-cell RNA-seq techniques, by exploring the intratumoral transcriptomic heterogeneity of cancers , play a Single-cell RNA-seq is applied to identify specific gene signatures for a cell type and the characterization of known and unknown cell types as well as subtypes within and surrounding tumors ,100,101.Single-cell RNA-seq technique and spatial transcriptomics are valuable platforms for the identification of cancer cells in their physical locations, which can be used for exploring intra-tumor heterogeneity and epigenetic modifications for individualized therapeutic approaches. This can have huge effects in clinical applications, but their practical application is restricted by different limitations. Firstly, there is comparatively high experimental time and costs required for their use in common clinical labs and thus it is highly recommended that their costs and processing time should be minimized. There is a dire need for the development of platforms that can perform analysis of formalin-fixed paraffin-embedded tissues, as in routine practice in the clinics most of the samples are preserved in formalin-fixed and are paraffin-embedded ,104. TheOne of the main challenges in the single-cell RNA-seq technique is that it requires the isolation of single cells by complex sorting techniques, which may compromise the structure of cells resulting in unknown transcription changes during the analysis of cells. This challenge is overcome using cell lines and organoids for single-cell RNA-seq analysis, but these cannot be alternative to tissue samples as these lack intricate interactions that exist between cancer cells and their microenvironment. Most RNA-seq methods depend on poly(A) tail capture to enrich mRNA and deplete abundant and uninformative rRNA. Thus, these methods are often restricted to sequencing polyadenylated mRNA molecules. However, recent studies are now starting to appreciate the importance of non-poly(A) RNA, such as long-noncoding RNA and microRNAs in gene expression regulation. This has led to the development of a small-seq technique which is a single-cell method that can capture small RNAs (<300 nucleotides) such as microRNAs, fragments of tRNAs, and small nucleolar RNAs in mammalian cells . This meIn the single-cell RNA-seq sequencing technique, the quality and quantity of RNA isolated and amplified play an important role in the precise analysis of RNA transcripts. The mapping of RNA molecules present in a cell is a random variable that depends upon the number of RNA present in a cell. This is largely affected by reverse transcription reaction, the extent of cDNA amplification, and the efficacy of RNA capture from the cells. This can lead to an obscure relationship between an actual number of reads and counted genes. This challenge is overcome by the use of unique molecular identifiers (UMIs) which help to prevent differences in amplified cDNA molecules and thus provide a useful way for estimating the accuracy of reaction by estimating the number of captured and reverse-transcribed mRNA molecules ,110. 6 cells. The limitation of LCM-RNAseq has been addressed now using alternative techniques or improved technological platforms. For instance, He et al. developed a new algorithm named (ADVOCATE) which is capable of analyzing the data derived by LCM-RNAseq to obtain expression profiles of different genes in the tissue sections of pancreatic ductal adenocarcinoma (PDA) [LCM-RNAseq is a useful technique for the analysis of transcriptome from fresh and FFPE tissue samples, but RNA extracted from FFPE tissue samples results in the extraction of low quantity and quality of mRNA. The analysis of this low-quality RNA with LCM-RNAseq methods affects the integrity of RNA. It is, therefore, imperative to improve LCM-RNAseq analysis techniques to increase the quality and stability of RNA biomolecules. This is achieved by using an LCM instrument with a suitable IR laser and the use of an appropriate RNA-seq library preparation kit which prevents RNA degradation . In thisma (PDA) .Mapping of RNA across tissue sections was originally limited for snap-frozen mammalian tissues and fresh plant tissues . HoweverOne of the main challenges faced in profiling RNA transcripts in their spatial context was that molecular techniques were unable to analyze mRNA expression up to the single-cell level due to limitations of microarray spot size and spacing. The advances made by Stahl’s group in 2019 were able to visualize gene expression with improved spatial resolution up to 1400×. This can also detect spatial gene expression patterns at the single-cell level . This leThe pathological nature of cancer tissues is decided based on the single-cell transcriptomic profile differences of healthy and cancer patients. However, we lack standard data regarding disease subtypes, duration, severities, and drug response outcomes for clinical considerations. One of the major hurdles of single-cell RNA-seq is that we still lack a definition of this platform for clinical application and monitoring responses to drugs at the single-cell level. Recent progress in the integration of single-cell RNA-seq with spatial transcriptomics will immensely help in exploring undiscovered biomarkers and agents involved in tumor development and will pave a path for better therapeutic outcomes.Single-cell RNA-seq techniques have been very useful for performing transcriptome analysis of single cells within and across cancerous tissues. The techniques applied in recent years are mostly focused on the isolation of cancer cells from cancer tissues, extraction of RNA, and their analysis by NGS techniques. Few studies have attempted to visualize RNA expression in their native tissue context in combination with other techniques over the last few years, but the interest in profiling transcriptome at a single-cell level in its spatial context is growing every day as evident from the number of published works. Nevertheless, recent developments in single-cell RNA-seq techniques with other allied approaches have a huge impact on exploring the tumor heterogeneity that will have a profound influence on finding the personalized medicine to prevent cancer relapse, which is a major issue in oncology. Moreover, in the field of translational research, single-cell RNA-seq possesses a huge potential in determining single-nucleotide variation (SNV), methylation patterns, copy number variation (CNV), microsatellite instability, and gene rearrangements/translocations for understanding tumor heterogeneity of cancer tissues for use in clinical settings.


We then experimentally determine RNA degradation kinetics due to metal‐catalyzed hydrolysis (cleavage) and evaluate whether early Mars could have been permissive toward the accumulation of long‐lived RNA polymers. Our results indicate that a Mg2+‐rich basalt sourcing metals to a slightly acidic (pH 5.4) environment mediates the slowest rates of RNA cleavage, though geologic evidence and basalt weathering models suggest aquifers on Mars would be near neutral (pH ~ 7). Moreover, the early onset of oxidizing conditions on Mars has major consequences regarding the availability of oxygen‐sensitive metals  due to increased RNA degradation rates and precipitation. Overall, (a) low pH decreases RNA cleavage at high metal concentrations; (b) acidic to neutral pH environments with Fe2+ or Mn2+ cleave more RNA than Mg2+; and (c) alkaline environments with Mg2+ dramatically cleaves more RNA while precipitates were observed for Fe2+ and Mn2+.Advances in origins of life research and prebiotic chemistry suggest that life as we know it may have emerged from an earlier RNA World. However, it has been difficult to reconcile the conditions used in laboratory experiments with real‐world geochemical environments that may have existed on the early Earth and hosted the origin(s) of life. This challenge is due to geologic resurfacing and recycling that have erased the overwhelming majority of the Earth's prebiotic history. We therefore propose that Mars, a planet frozen in time, comprised of many surfaces that have remained relatively unchanged since their formation > 4 Gya, is the best alternative to search for environments consistent with geochemical requirements imposed by the RNA world. In this study, we synthesize in situ and orbital observations of Mars and modeling of its early atmosphere into solutions containing a range of pHs and concentrations of prebiotically relevant metals (Fe However, life as we know it is a highly complex collection of molecular machinery and genetic information. The central dogma of molecular biology stipulates that deoxyribonucleic acid (DNA) makes ribonucleic acid (RNA) via transcription, RNA makes protein via translation, and information cannot be transferred backwards from proteins to nucleic acids Crick, . FundameUnder the RNA world scenario, abiotic synthesis of simple RNA molecules from common molecular feedstocks in geologically relevant environments  which have been demonstrated to catalyze hydrolysis  in between deoxyribonucleotides, allowing us to quantify hydrolysis at a single cleavage site by transesterification on a urea polyacrylamide gel Figure  e.g., A. This ch2.32 and H2  that could possibly undergo Fischer‐Tropsch synthesis and reductive transformation into complex biomolecules and (c) sustain thermophilic methanogens/acetogens via the acetyl‐CoA pathway.Little‐to‐no record exists of Earth's prebiotic history during the Hadean. Analysis of isotopic signatures and inclusions preserved within zircons  suggest that a global ocean , however, have since indicated that LUCA was most likely a mesophilic surface‐dweller capable of UV repair  of Mars record alternating periods of acidic groundwater flow (pH ~ 2–4) and desiccation under highly oxidizing conditions  at Gusev Crater indicate acid weathering of primarily olivine‐rich outcrops in a possible hydrothermal environment  deposits, magnetite‐silica facies (Fe2+), and hematite‐phyllosilicate facies (Fe3+)  reflect a long‐lived lacustrine environment with circumneutral pH waters and variable redox states. This is indicated by the presence of manganese oxide ; however, many have not been able to resolve counteracting cooling effects of atmospheric density and albedo due to aerosol and cloud formation  volRamirez, ; (c) impRamirez, ; (d) sub2.52‐dominated atmosphere at variable pressures  inducing their respective shifts in pH  as calculated by Kua & Bada, 2+, Mg2+, or Mn2+ intended to represent a range of dissolved metal concentrations within the water column, variable weathering, residence times, and wet‐dry cycling. High values additionally allow us to easily measure kinetics and extrapolate rates of catalysis for more geochemically reasonable metal concentrations. Mixtures of Fe2+ and Mg2+ at 50:50, 20:80, and 80:20  were included to represent metal concentrations derived from variable bedrock compositions; in particular, 20:80 Fe2+:Mg2+ is closest to the average crustal composition on Mars . The atmosphere inside the glove box was N2 with 2.5% – 3% H2 and internal circulation through a platinum catalyst maintained residual oxygen levels below 10 ppm. All RNA reactions occurred inside the glove box on a miniPCR mini16 thermocycler  kept at 75°C in order to facilitate rapid RNA degradation.Samples were prepared by mixing stock buffer solutions of 1 M Glycine‐HCL  at pH 3.2, 0.5 M MES  at pH 5.4 and 6.7, 1 M Tris pH 8 , and 1 M Tris pH 9  with stock solutions of 0.5 M ammonium iron(II) sulfate hexahydrate , 0.5 M manganese(II) chloride tetrahydrate , or 0.5 M magnesium chloride . A typical RNA incubation consisted of 250 mM buffer, 1 mM EDTA , 0–50 mM of metals, and 5 µM of the RNA oligomer in a final 20 µl reaction successively added (in that order) into a 0.2‐mL PCR tube . In particular, 1mM EDTA added prior to the addition of Fe2.6t = band 1/ band 1 + band 2) at t = time.All RNA degradation experiments were quantified via urea polyacrylamide gel electrophoresis  followed by imaging on a Typhoon 9,410 . Two fluorescent bands were detected representing either the intact 15‐mer (5’‐Cy3‐TTT‐TTT‐rCTT‐TTT‐TTT‐3’—band 1) or the residual 7‐mer  cleaved at the single ribonucleotide site  inside an anaerobic chamber. 1 µl aliquot time points were taken at 5, 15, 30, 60, and 120 min for pH 6.7, 8, and 9 and at 10, 30, 60, 120, and 240 min for pH 3.2 and 5.4. Aliquots were added to 25 µl of a kill buffer solution of 8 M urea, 1x TBE , and 100 mM EDTA in order to arrest further catalysis and collect representative time point measurements. Specifically, EDTA–metal complexes in aliquot‐kill buffer solutions are not active because EDTA sequesters  metal ions and keeps them away from the substrate . Samples were then removed from the anaerobic chamber, and 5 µl of the kill buffer‐time point mixture (2 picomoles RNA) was taken for gel electrophoresis and quantification. Pseudofirst‐order reaction kinetics kobs(h‐1) of RNA hydrolysis were determined with the following relationship, ln[pt] = ‐kt + ln[po] where pt = percent intact oligo at t = time n and po = percent intact oligo at t = 0   versus metal concentration (mM) to a one‐site binding model by assuming that hydrolysis is preceded by metal binding at the single ribonucleotide . This model is represented by kobs(h‐1) = Bmax(h‐1) * metal /  where Bmax = maximum metal binding  and Kd = the metal concentration required to achieve half the maximum metal‐ribonucleotide binding (and ensuing cleavage)  = 1.28 x 10–2) followed by Fe2+ at 50 mM (kobs(h‐1) = 2.47 x 10–2) then Mn2+ at 50 mM (kobs(h‐1) = 2.57 x 10–2) relative to the negative control (kobs(h‐1) = 8.06 x 10–2)  = 0.95 x 10–2) is notably lower than for Fe2+ (Bmax(h‐1) = 7.1 x 10–2)  = 47.4 x 10–2) is more stable than Mg2+ (Bmax(h‐1) = 96.1 x 10–2) although we observed Fe2+ (and Mn2+) precipitate from solution  over Fe2+‐rich solutions   in nearly all pH conditions Figure , Table 144.12+, Fe2+, and Mn2+ form a hydrated hexa aquo species (Mg2+(H2O)6, pKa = 11.4, Fe2+(H2O)6, pKa = 9.6, and Mn2+(H2O)6, pKa = 10.6) which polarize first shell water molecules in a tightly packed octahedral geometry  in solution. Between pH 5.4 and 8, slower rates of metal‐catalyzed hydrolysis occur in the presence of Mg2+ (pKa = 11.4) followed by Fe2+ (pKa = 9.6) and then Mn2+ (pKa = 10.6)  has shown that because of low lying d orbitals, Fe2+ (0.11 e‐) has greater electron‐withdrawing power than Mg2+ (0.08 e‐)  = 47.4 x 10–2) rather than Mg2+ (Bmax(h‐1) = 96.1 x 10–2) contrary to our interpretation for pH 5.4 – 8 2 around pH ~ 9  is mostly Mg2+‐dominated then transitions to Fe2+‐dominated at 20:80 (5 mM Mg2+: 20 mM Fe2+)  optimum while Mg2+ is most effective at alkaline conditions (pH ~ 9). Conversely, while metal catalysis  is slower at acidic values considered in this study    required to sufficiently acidify surface waters are not supported by atmospheric models  could have conceivably enabled novel catalytic activity and catalyzed polymerization while simultaneously tempering degradation rates due to differences in pH optimums  and Mg2+ (0.64–210 mM) depending on modeled water‐rock interactions 6], MgNa2[Fe(CN)6]) has been proposed as a viable mechanism for concentrating metals over large timescales that are consistent with experimental synthesis of RNA, amino acid, and lipid precursors via cyanosulfidic chemistry . Furthermore, the study conducted here notably excluded the role of sulfates  on RNA stability. These metals have been demonstrated to catalyze hydrolysis (cleavage), folding, polymerization, and impart RNA with novel catalytic properties. In addition, we simultaneously adjusted pH to reflect the composition of hypothetical CO2‐dominated atmospheres in equilibrium with surface waters and waters at an acidic and alkaline vent. We determined that RNA stability depends on metal concentration and pH. Our results reproduce the enhanced RNA cleavage rates associated with each metal's respective acid dissociation constant (pKa) and an increase in metal concentration  waters would therefore be the stability optimum (as determined here) for RNA on Mars. However, it is important to note RNA replication chemistry with Mg2+ as the metal cofactor requires mildly alkaline pH values in order to result in net accumulation . However, global oxidizing conditions (due to the lack of a dynamo) on the surface of Mars may have led to significant RNA instability due to the precipitation of RNA–Fe3+ complexes in Fe2+‐rich environments possibly as early as ~ 4.1 Gya. We therefore presume the non‐redox‐sensitive Mg2+ would have been the principal catalyst on Mars as hypothesized on Earth after the great oxidation event ~ 2.6 Gya (e.g., Athavale et al., Discoveries of structural and regulatory RNA molecules suggest that life as we know it may have emerged from an earlier RNA world (Bernhardt, n Figure , Table 1n Figure  though sn Figure . At pH 9s Figure . We concNo competing financial interests exist.