Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides

Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides Natalia Susana Pires, Cecilia Inés Lascano, Julia Ousset, Danilo G. Ceschin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1677791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Transcriptional analysis of the network of transcription regulators and target pathways in exposed organisms may be a hard task when their genome remains unknown. The development of hundreds of qPCR assays, including primers design and results normalization with the appropriate housekeeping genes, seems an unreachable task. Alternatively, we took advantage of a whole transcriptome study on Rhinella arenarum larvae exposed to the organophosphorus pesticides azinphos-methyl and chlorpyrifos to evaluate transcriptional effects on a priori selected groups of genes. This approach allowed us to evaluate the effects on hypothesis-selected pathways such as target esterases, detoxifying enzymes, polyamine metabolism and signaling and regulatory pathways modulating them. We could then compare the responses at the transcriptional level with previously described effects at the enzymatic or metabolic levels to obtain global insight into toxicity-response mechanisms. The effects of both pesticides on the transcript levels of these pathways could be considered moderate, while the responses elicited by chlorpyrifos were more potent and earlier than those elicited by azinphos-methyl. Finally, we inferred a prevailing downregulation effect of pesticides on signaling pathways and transcription factor transcripts encoding products that modulate/control the polyamine and antioxidant response pathways. We additionally tested and selected potential housekeeping genes based on those reported for other species. These results allow us to go through future confirmatory studies on pesticide gene expression modulation in toad larvae. Transcriptomics amphibian organophosphorus pesticides molecular targets signaling pathways housekeeping genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The amphibian Rhinella arenarum (Hensel 1867) is widely distributed throughout Argentina and partially in South America 1 . Its life cycle includes two fundamental stages before reaching the adult stage: the embryonic and larval stages. The physiological reproduction of the species in the Upper Valley of Rio Negro and Neuquén (North Patagonia) occurs once a year during the spring months in backwaters and irrigation channels. The reproductive season coincides with the period of greatest pesticide application to protect fruit production (INTA, 1993). Various kinds of pesticides have been detected in superficial waters where amphibian reproduction occurs 2 . This situation implies that both R. arenarum embryos and larvae are potentially exposed, at least temporarily, to high concentrations of these toxicants, posing a hazard to this and other species that inhabit the area 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . When evaluating the risks of exposure to a contaminant for any species, it is desirable to find early-response biomarkers capable of anticipating irreversible damage that may occur at later stages of development 11 . Molecular targets, effectors or modulators of toxicant effects are among early-response biomarkers. However, their development in autochthonous, nonmodel species such as R. arenarum is difficult due to the lack of both sequenced genomes that allow primer design and transcript analysis, as well as specific antibodies necessary to support protein expression analysis 3 . In these cases, toxicogenomic and particularly transcriptomic analyses through bulk RNA sequencing (RNA-Seq) are powerful tools that enable screening for effects and responses to a toxicant and therefore allow further molecular studies 12 , 13 . RNA-Seq is a highly sensitive and accurate tool for measuring expression across the transcriptome, enabling researchers to detect changes that otherwise would go unnoticed. It is possible to quantify the levels of abundance or relative changes for each transcript during a specific developmental stage or under a specific treatment 14 . In the case of nonmodel organisms such as R. arenarum , RNA-Seq technology allows the obtention of genomic information through a relatively accessible process from an economic point of view and considering the cost-benefit relationship 13 . We have been studying the effects of organophosphorus pesticides in the development of the common toad R. arenarum , focusing on target and detoxifying enzymes, polyamine pathway and signaling, at protein, activity and metabolite levels. However, our advances at the transcript expression level were slow and challenging until we were able to develop a transcriptome study. In the context of this RNA-Seq and transcriptomic analysis performed on R. arenarum larvae exposed to the organophosphorus (OP) pesticides azinphos-methyl (AZM) and chlorpyrifos (CPF) 13 , we were able to perform hypothesis-driven data analysis processing on a priori selected mRNA transcripts. This work was done in parallel with the whole transcriptome bioinformatic (big data) analysis, whose results are to be published elsewhere. The aims of the study were a) to compare transcript expression levels in genes from pathways that have been previously recognized as impacted by OP pesticides and that have also been studied at the biochemical, metabolite or physiological level in R. arenarum to obtain a more comprehensive mechanism of toxicity and response and b) to test a set of potentially adequate housekeeping genes in R. arenarum larval stages for future quantitative PCR studies. Results Gene selection from annotated transcripts for OP effect analysis. We used a list of the available gene transcripts in R. arenarum published by us in the database from massive RNA transcript sequencing and gene annotation (http://rhinella.uncoma.edu.ar/; 13 ). We selected a group of 14 potential housekeeping genes, considering those currently proposed for mRNA expression normalization in Xenopus laevis 15,16,17 (Table S1, GROUP A). We found a considerable number of annotated genes that could be included in the following pathways, which were selected on the basis of available information about OP effects at the biochemical level in amphibians 6,8,18 and other vertebrates: polyamine metabolism genes, (Group B, 14 genes); antioxidant response genes (Group C, 15 genes); OP- metabolizing, primary- and secondary-target enzyme genes (Group D, 16 genes); and gene expression regulators, transcription factors and phosphorylation cascade effectors corresponding to the Mitogen-Activated protein kinase pathway, Transcription factor AP-1, Aryl hydrocarbon receptor (AHR) pathway and Nuclear factor erythroid 2-related factor 2 antioxidant response pathway (Group E, 17 genes) (Figure 1). The complete list of genes is detailed in Supplementary Table S1. Corroboration of gene annotation for selected transcripts Alignments were carried out using the tools available in BLAST (BLASTN, BLASTX and BLASTP) to verify that the sequences of the selected transcripts effectively corresponded to the annotated genes. Those sequences that yielded a match greater than 50% in the vertebrate databases were selected for further analysis. A summary of this sequence validation is shown in Figure 1, and the complete analysis is available in Supplementary Data File I. From a total of 225 transcript sequences that were compared to the corresponding annotated genes, 93.8% could be effectively confirmed for further analysis; the best correspondence was obtained in group E covering transcription factors and signaling pathways with a 100%, and the lowest percentage was approximately 90% for the group of antioxidant stress transcripts (Group C). Validation of adequate transcription levels for filtered transcripts Previously validated transcripts were analyzed to determine if the transcription levels of treatment replicates were appropriate for statistical analysis. These levels resulted from the massive RNAseq amplification and quantitation of each transcript fragment in the different treatments. As ‘not appropriate’ levels, we considered fragments with zero expression levels in any sample and/or replicates with very low and erratic values. The summary of transcripts accepted in each group of selected genes is shown in Figure 1. The raw data corresponding to all transcript fragments are available in Supplementary Data File II. Housekeeping genes stability We carried out a transcription stability analysis of the selected potential housekeeping genes, comparing their variability within and between the treatments. The respective TMM means, minimum and maximum expression values, standard deviations, median expressions and percentual coefficients of variation (CV%) are presented in Table 1. We assumed a maximum CV% of 20% in the TMM as an acceptable limit for a transcript to be considered housekeeping. In this way, we were able to select 9 out of 12 transcripts as appropriate reference genes in the first larval stage of R. arenarum . The genes with the least and acceptable variations in group A were EF1A0, EF1GA, EF1D, EF1B, TBA, TBB, TBB4B, ACTB, and RL8. The transcripts belonging to tubulin TBA1 and glyceraldehyde 3P dehydrogenase (G3P) showed a variability of approximately 22% in their expression levels, roughly in the exclusion limits we considered, and might be considered housekeeping genes if a refined and specific analysis proves better results. In turn, ACT3, belonging to sarcomeric actin, showed the greatest variation in expression at 83%, clearly indicating that it cannot be considered a housekeeping gene in R. arenarum larvae, at least for OP pesticide studies. The TMM values of the selected HKs, rated with respect to their control values, were averaged for each treatment and used to standardize the TMM values of the transcripts corresponding to the genes of groups B, C, D and E (raw and standardized data shown in Supplementary Data File II). Effects of OP on the transcription of polyamine metabolism genes OP mainly decreased the expression of genes related to polyamine synthesis (Figure 2A). Although some differences could be noted between CPF and AZM and the exposure times, decreases reaching 30-40% in the expression levels were found in ornithine decarboxylase (DCOR1), S-adenosylmethionine decarboxylase proenzyme (AMD, transcript 1-b), spermidine synthase (SPEE), and ornithine decarboxylase antizyme (OAZ1). Only AMD transcript 1-a showed relevant increases of nearly 3 times the control values when exposed to both OP pesticides. In turn, the ornithine decarboxylase regulator OAZ2 and the antizyme inhibitors AZIN1 and 2 showed no significant or no relevant changes (Supplementary Table S2). In turn, polyamine-degrading enzymes showed more variable responses to OP pesticides (Figure 2B). AOC1 transcript expression, corresponding to diamine oxidase, showed a significant inhibition of 25-40% by AZM treatment and 35-50% by CPF treatment. The AOC2 transcript showed a similar tendency with even deeper inhibition responses, but the difference was not significant (Table S2). AOC3 and 4 transcript isoforms showed variable and nonsignificant responses to OP pesticide exposure. Acetyl spermidine/spermine oxidase (PAOX) expression showed an induction tendency of 40% with both OPs at 6 h of exposure, while spermine acetylase transcript expression (SAT1, 2-a, 2-b) showed an inhibitory trend with CPF up to 40%. Spermine oxidase (SMOX) expression was scarcely inhibited (20%) by AZM and induced by CPF (20%, 6 h) (Figure 2B). Effects of OP on oxidative stress response - antioxidant enzyme genes Superoxide dismutase transcripts displayed differential responses to OP treatments (Figure 3A). SODC expression showed a decrease in response to AZM exposure at 24 h and to CPF at 6 and 24 h of exposure (up to 30% inhibition) with respect to the control. On the other hand, SODE expression was significantly induced in 60% by CPF at 24 h. Catalase transcription (CATA) was in turn slightly inhibited by AZM exposure at 24 h and by CPF up to 25%. Glutathione-dependent peroxidase transcripts were mainly induced by OP pesticides. GPX1 transcription was considerably induced by AZM at 24 h (3X) and by CPF at both times (up to 2.7X). GPX3 transcription was induced to a lesser extent but in a significant way by CPF (1.5X), while GPX4 also showed an induction of approximately 30% by CPF exposure. In turn, the transcript corresponding to GPX8B presented a tendency to decrease in samples exposed to AZM at 24 and to CPF at 6 and 24 h, up to 60% of control expression values. Two other GPX transcripts, GPX2 and 7, as well as one GSH-reductase transcript (GSHR), showed decreasing but not significant trends due to OP pesticide exposure (Supplementary Table S2). Finally, one transcript for the enzyme glutathione synthetase (GSHB) was scarcely downregulated by OP exposure (AZM at 24 h; CPF at both times, approximately 25%) (Figure 3B). OP pesticide targets and detoxifying enzymes Although some fragments corresponding to cholinesterase transcripts were detected (BCHE), their levels were very low and showed erratic responses (raw data in Supplementary Data File II). Among the esterase group, carboxylesterase (EST5A) transcript showed a relevant decrease in larvae exposed to AZM at both 6 and 24 h, while in larvae exposed to CPF, a decrease at 6 h (50% inhibition) was observed, returning to control values at 24 h (Figure 4A). A similar pattern was observed for another carboxylesterase transcript (EST3B), with no significant effects (Supplementary Table S2). The serum paraoxonase/arylesterase-2 transcript (PON2) showed a small but significant decrease of approximately 20% only in larvae exposed to CPF at 6 h with respect to the controls. For cytochrome P450 enzymes, only the transcripts putatively related to OP metabolization were analyzed; CYP1A1 transcript expression was significantly reduced by AZM at 24 h and by CPF exposure, although the effects were minor (20% inhibition). Cytochrome CYP2C19 expression was more markedly inhibited by both OP pesticides after 6 h of treatment, reaching 50-60% inhibition with respect to controls. Glutathione-S-transferase (GST) transcripts were among the most abundant groups, as 15 different isoforms could be identified. The GST isoforms alpha (GSTA3), pi (GSTP1 and 2), and theta (GSTT3) were downregulated by both OP pesticides, with transcription inhibitions ranging from 20 to 50% with respect to controls (Figure 4B). Isoform GSTP1 showed the highest inhibition and in a significant way. Although GST Mu (GSTM1) showed a decreasing trend, the effects were not significant (Table S2). On the other hand, GST Kappa 1 transcript (GSTK1) was significantly induced by CPF (45%), while GSTM3 showed the highest transcription induction values by CPF and by AZM at 24 h, reaching 2 X - 3.3 X expression values with respect to controls, but with a p-value of 0.09. The GST omega 1 transcript (GSTO1) also showed a significant increase of 45% in its expression for AZM at 6 h and CPF at 24 h. Microsomal GST transcripts were also analyzed, finding increases in the expression levels of isoforms 1, 2 and 3 (M-GST1, M-GST2, M-GST3, respectively, Figure 4C). The M-GST1 transcript was one of the most affected transcripts, with increases of 40% (AZM-24 h; CPF-6 h) to 70% (CPF-24 h). One M-GST3 transcript showed roughly similar effects (30-60% increase), while a second transcript showed a moderate increase (20%) only for CPF at 24 h of exposure. For the M-GST2 isoform, we also identified two transcripts, one of which showed a moderate increase (up to 35%) after CPF exposure, and the other showed slight decreases of approximately 20% in its expression with respect to the controls. OP effects on transcription factor- and signaling pathway-related genes The MAP kinase phosphorylation pathway seemed to be affected solely at the first level of regulation, as mitogen-activated protein kinase kinases (MP2K) were downregulated early by CPF at 6 h and late by AZM at 24 h (Figure 5); the MP2K2 transcript was diminished to 70% of control values by both OP pesticides, while the MP2K1 transcript was affected only by CPF but not at a relevant level (Supplementary Table S2). Similarly, the mitogen-activated protein kinase p38 transcript was scarcely affected by both OP pesticides (approximately 12%), while the JUNK transcript remained unaffected. None of the transcription factor JUN transcripts detected (JUN-1, JUN-B, JUN-D1) were significantly affected by OP exposure (Table S2). In turn, the transcription factor FOS, which associates with JUN as a heterodimer partner in activator protein-1 (AP1), showed a severe reduction in its transcription levels by AZM at 6 h of exposure (to 35% of control levels) but recovered at 24 h and surpassed controls by 25%; CPF caused an inverse pattern, inducing transcription of FOS at 6 h (40%) but inhibiting it at 24 h (30%) (Figure 5). On the other hand, nuclear factor erythroid 2-related factor 2 (NRF2), linked to the antioxidant response through the antioxidant response element (ARE) pathway, showed a decrease in transcription of approximately 30% in the larvae exposed to AZM for 24 h and in those exposed to CPF for 6 and 24 h compared to controls (Figure 5). Within the aryl hydrocarbon receptor (AhR) pathway, some differences were found for the nuclear translocator (ARNT) and the aryl receptor repressor (AHRR). Two transcripts for ARNT were analyzed. The first ARNT transcript showed a decrease of nearly 30% in larvae exposed to AZM at 24 h and to CPF at 6 and 24 h compared to the controls. The second transcript, ARNT2, showed a relevant induction in transcription of nearly 2X after 6 h of exposure to CPF. The AHRR transcript showed an increase in larvae after 24 h of exposure to AZM (80%) and CPF (40%) at 24 h with respect to the controls. The AhR transcript itself showed a decreasing but statistically nonsignificant pattern after exposure to CPF (Table S2). The transcript corresponding to HSP90AB1, the chaperone that binds AhR in the cytosol, showed a decrease of 20% after 24 h of exposure to CPF, while the transcript corresponding to the cochaperone aryl-hydrocarbon-interacting protein-like-1, AIPL1, was downregulated by AZM (approximately 50%) but increased by CPF (up to 75%). Another isoform, AIP, showed no effects, as well as the third cochaperone prostaglandin E synthase-3, PTGES3. Overview of OP effects in hypothesis-selected pathway genes A visual comparison of the number of genes showing significant differential expression, their level of fold-changes and their sense towards up or downregulation, was performed for the different OP treatments using a heatmap representation (Figure 6). The exposure of R. arenarum larvae to AZM or CPF at sublethal concentrations and up to 24 h did not cause remarkable fold-changes in gene expression in the selected pathways. Most of the selected genes did not show significant variations, or the changes were less than 2-fold; only eight transcripts showed changes between 2- and 4-fold. The exposure to AZM provoked a relatively poorer effect at 6 h compared to 24 h, as evidenced in the number of differentially affected transcript expression levels. In turn, the CPF effects on R. arenarum were more potent, affecting most of the gene expression early at 6 h and sustaining the effects after 24 h of exposure. Another interesting feature was that downregulation cases notably exceeded upregulation cases, in a proportion of 3 to 1. Comparison /validation of statistical approaches and hypothesis-selected genes ranking in differential expression. We performed both edgeR pipeline statistical analysis on the whole transcriptome data, and non-parametric Kruskal-Wallis ANOVA and Median tests to the hypothesis-selected genes using the filtered database. The main reasons for using both approaches are that edgeR a priori considers all the possible fragments for one annotated gene as different and that it is not feasible to filter erratic, usually low level-expressed transcripts; in turn, the specific filtering and checking of fragments enables their use as repeats in the non-parametric tests. Keeping in mind that edgeR statistical analysis was performed on the raw, not-filtered database after collapsing the different fragments for each annotated gene into one unique set (5 treatments by duplicate), we could compare the performances with non-parametric analysis in 42 of the 62 a priori selected genes. The coincidences between both statistical approaches extended to 66.7% of the analyzed transcripts, and when the treated- vs .- control pairs were considered, the result was similar, coinciding in 65.5% of all the cases. A transcript-detailed comparison may be found in Supplementary Table S3. From 42 transcripts analyzed, 15 showed coincident statistical outputs in the four treatments, 7 transcripts did in 3 out of 4 cases and 10 did in 2 of 4 cases; only one gene showed no coincidences at all. This fact highlights the need of carefully filtering and validating the transcripts sequences and expression levels when any selection is performed for specific analysis. This may be particularly important in some instances, i.e. , when “top-ten” differentially expressed genes (DEG) are considered. We next extracted the lists of significant DEG from edgeR- paired comparisons of pesticide-treated vs. control transcript expressions and ranked them using the logarithm of their fold-changes. We determined the ranking positions for each hypothesis-selected gene showing significant changes (data in Table S3). Ranking of the selected genes for each group let us verify that the effects of both OP were predominantly the downregulation of transcripts expression, that CPF caused noticeable higher effects on gene transcription compared to AZM and that CPF effects became evident earlier, from 6 h on (Figure 7). The OP AZM was the least effective in changing gene expression at 6 h, about ten times lower for downregulation and one-third for upregulation compared with the effects at 24h or respect to CPF. We could also verify in the edgeR analysis that the effects of both OP on the expression of the hypothesis-selected genes positioned them far from the top ranking. None of the pathway-selected genes was within the top-ten, and only three genes were in the 10 th -50 th or -100 th ranks for most of the treatments (GPX1; CYP2C19; and GSTM3; Table S3), while most of the selected transcripts were within the 500-1500 ranks or in the NS group (Figure 7). These results were in fact similar to those obtained from the non-parametric analysis on the filtered and verified list of transcripts resumed in the figure 6. Please note that the analysis of top ranking differentially expressed genes is not the objective of the present work and is in preparation to be published elsewhere. Application of transcriptome data on primer design and PCR analyses We finally developed PCR assays, with the double purpose of checking the fitness of the fragment sequences determined for the hypothesis-selected genes to design adequate primers and verifying their responses to organophosphates in ad hoc exposures. We selected 6 genes from the polyamine pathway, 4 genes from antioxidant and detoxifying pathways, 4 genes from signaling and transcription factors and 2 HK genes and proceeded to design pairs of primers to develop RT-PCR and qPCR analyses. We succeeded in amplifying 13 of those genes by RT-PCR and could further sequence their products and verify the identities of 8 them by alignment with the original sequences (Supplementary Table S4). We next designed primers suitable for qPCR studies for 10 genes, being able to amplify all of them. Thus, we conclude that the transcriptome de novo assembly in R. arenarum was of very good quality and that annotated gene sequences were adequate for primer design. The general workflow for RT-PCR and qPCR analyses, a detailed description of the methodology and the corresponding results are presented in the accompanying Supplementary file PCR Methods in Brief. Regarding qPCR results, we found very low levels (high Ct values) for most of the polyamine pathway genes and the FOS gene and could not obtain good calibration parameters. We then carried out a differential expression analysis of two genes, AMD1 and SODC, and ACTB and RL8 as HK genes, in R. arenarum larvae exposed to 0.5 and 1.0 mg/L chlorpyrifos during 6-12 h. We applied a geometric means approach to normalize the data with respect to HK levels and to calculate the relative expression levels. The increase of about 3X of AMD1 expression was in very good accordance with a similar increase with both organophosphates in the transcriptome study (Figure 8). The decrease in SODC expression in qPCR assay was also coincident with the results in the chlorpyrifos-exposed larvae analyzed by RNAseq and transcriptome assembly. Thus, we also conclude that the results of differential gene expression analysis performed from curated and filtered transcriptome data in the hypothesis-driven approach is adequate and that results are reliable. Discussion We succeeded in applying a hypothesis-driven transcript analysis from a transcriptome database on R. arenarum larvae exposed to two OP pesticides, with two purposes into mind: 1) to test HK genes currently used in model species and 2) to compare the effects of AZM and CPF on transcript levels for pathways previously described as impacted at biochemical or protein expression levels. These goals in a native, nonmodel species such as the amphibian R. arenarum are a very good example of what transcriptomics analysis can do, solve or imply for the advance in molecular toxicology when other tools are not readily available 12 , 19 , 20 . Considering the results obtained, it is evident that the transcriptomic expression data allow a screening analysis into selected pathways to decide for further qPCR studies if necessary. Furthermore, we can confirm that transcriptomic information is an essential tool when developing molecular biology assays in nonmodel organisms. Data normalization in gene transcription analysis is commonly achieved using HK genes. In a comprehensive study conducted with Xenopus laevis , RL8 and GAPDH were stable as reference genes during the first embryonic stages, ODC1 was stable in the initial and final stages, and H4 was adequate throughout embryonic development 15 . In another study in X. laevis , eEF1A1 and SUB1 L were identified as the genes whose expression remained more stable, which would allow their use as reference genes 16 . In this work, we were able to analyze the expression levels of potential reference genes from the R. arenarum transcriptome data considering those proposed for X. laevis , selecting a list of 9 genes that would fit to the different requirements in future qPCR studies. In fact, we performed several qPCR analyses in R. arenarum larvae exposed to CPF, and successfully used ACTB and RL8 as HK to achieve results normalization. We thus confirm that RNA-Seq data have the potential to identify genes with less variation in their expression, as suggested by other authors 21 , 22 , 23 . By analyzing the transcriptomic data of selected pathways of R. arenarum larvae exposed to the OP pesticides AZM and CPF, we obtained an overview of their regulatory effects on gene transcription at previously known targets. The qualitative heatmap representation shown in Fig. 6 allows a rapid comparison among the different treatments or states 12 . The first conclusion to be highlighted is that, in general, the exposure of R. arenarum larvae to AZM or CPF at sublethal concentrations and up to 24 h did not cause remarkable fold-changes in gene expression in the selected pathways, regardless of these changes being statistically significant. This is very interesting, considering that target esterase genes, detoxifying genes and oxidative stress response genes might be a priori expected to be upregulated, as their protein products are either inactivated by OP pesticides or their activities are engaged in xenobiotic transformation and detoxification 24 , 25 , 26 , 27 . Most of the transcripts showing changes in their levels after OP exposure did so in a moderate way, and only a few showed fold-changes higher than 2X. Thus, moderate changes in mRNA expression in these genes would be enough for ample response changes in their product activities. Another observation is that exposure to AZM clearly provokes a relatively poor effect on the genes analyzed at the short exposure time of 6 h; AZM effects are mainly evident after 24 h of exposure. This may be seen in all the selected groups, except for polyamine metabolism genes, where most of their expression levels were altered. In turn, the CPF effects on R. arenarum were more potent, affecting most of the gene expression early at 6 h and sustaining the effects after 24 h of exposure. Notably, we may also infer that OP effects on gene expression, in addition to being moderate in their amplitude, were also predominantly towards a downregulation. We could also verify the moderate effects of both OP on the hypothesis-selected gene expressions and the differences in favor of AZM as milder than CPF in triggering responses, using edgeR pipeline analysis. To perform this analysis, we had to collapse fragments from a same identified and annotated gene into a unique set of expression data, adding the TMM values for each treatment replicate. However, it was not feasible to cure and/or verify each transcript in the whole big database, so the information extracted for each hypothesis-selected transcript might contain biased errors. In this way, we could find coincidences in the statistical significances of the differential gene expressions on two-thirds of the cases identified in the robust non-parametric analysis. Nevertheless, edgeR pipeline let us additionally rank the significant DEG and determine the positioning of most of the hypothesis-selected transcripts. As seen in Fig. 7 , the results confirm that these a priori pathway-selected genes are not among the most affected ones in terms of fold-changes by OP exposure in R. arenarum larvae. The polyamine metabolism pathway was probably the most affected group of those analyzed in this work, as shown in Fig. 6 . The general pattern of the effects of both OP pesticides on polyamine synthesis suggests downregulation through successive involved genes. Ornithine decarboxylase, S-adenosyl methionine decarboxylase precursor and spermidine-spermine synthase expression were mainly inhibited, except for one AMD-1 transcript that showed an induction reaching 2-fold or more. Antizyme expression is also inhibited, but this may be a feedback effect due to ODC inhibition itself. Complementary to these effects on polyamine synthesis, degradation genes tend to be downregulated by OP pesticides, probably as a cellular response intended to avoid a lethal drop in polyamine levels 28 , 29 , 30 , 31 . Nevertheless, some enzymes related to spermidine and spermine degradation show increasing expression in larvae exposed to CPF, which seems to elicit stronger effects than AZM. A decrease in putrescine and spermine was observed in R. arenarum embryos of the complete operculum stage exposed to AZM 32 . Similarly, CPF downregulated ODC activity and decreased putrescine and spermidine levels in early R. arenarum embryos, correlated with the percentage of embryonic developmental arrest 18 . The opposite effect was reported in R. arenarum late embryos exposed to AZM, where ODC activity and putrescine content were increased 31 . We also found concordance between the transcript expression levels and some of the polyamine-degrading enzymes and previously reported activity values in R. arenarum embryos exposed to AZM or CPF, i.e. the inhibition of DAO and SMOX activities and the increase in PAOX activity 32 . Therefore, there is a good correspondence between the expression levels found at the transcriptomic level for the regulatory enzymes, some of the polyamine-metabolizing enzyme activities and the polyamine contents in R. arenarum . These findings are related to the fine regulation of polyamine metabolism, both at the transcriptional level and at the enzymatic level. Oxidative stress has been reported in different organisms exposed to OP pesticides. In particular, the effects of AZM and CPF on oxidative stress and antioxidant responses at the metabolite and enzymatic levels have been studied in R. arenarum development. Indeed, we have proposed that there is a link between OP effects on polyamine metabolism and levels, oxidative stress and teratogenic effects in R. arenarum embryos and larvae 5 , 6 , 7 , 8 , 18 , 24 , 31 , 32 , 33 , 34 . Smirnova et al. (2012) 35 analyzed oxidative stress as the cause of alterations in polyamine metabolism due to the dysregulation of ODC and SSAT; human hepatoma cells chemically induced to increase ROS production showed overexpression of ODC and SSAT, which are transcriptionally regulated by NRF2 through a specific recognition site. The downregulation of NRF2 mRNA expression with elevated Nrf2 protein levels has been reported in liver pathologies 36 . We found that OP exposure in R. arenarum larvae caused a downregulation of NRF2 mRNA expression, and the accompanying downregulation at the transcriptional level of ODC, SSAT and several antioxidant-detoxifying enzyme genes containing ARE sequences (such as GSTA and GSTP) suggests that Nrf2 protein or activity might also be downregulated. Ma et al. (2018) 37 refer to the alterations in transcription levels of detoxifying and oxidative stress-related enzymes in the GST and CYP groups in a transcriptomic analysis performed on trichlorfon-exposed Rana chensinensis . This OP pesticide upregulated CYP2C transcripts but downregulated CYP3A and GSTK1. We report an increase in GSTK1 expression because of CPF exposure in R. arenarum , as well as for other GST isoforms, such as mu and the microsomal isoforms. This upregulation in several GST transcripts is in concordance with the reported increase in GST activity using CDNB as a substrate in R. arenarum embryos and larvae after exposure to both AZM and CPF, among other OP pesticides 7 , 33 , 38 . The effect on GST activity was associated with an increase in GST-Pi1 protein in R. arenarum larvae exposed to arsenic 11 . Other genes under Nrf2 regulation are those belonging to antioxidant defenses, such as SOD, glutathione peroxidases (GPx2, 3, 6 and 8), and glutathione reductases (GSHR1) 37 , 39 . Accordingly, we observed downregulated levels of SODC and GPX8 accompanied by NRF2 downregulation. Glutathione synthase GSHB, and CATA, also seemed downregulated, while SODE, GPX1, 3 and 4 were induced, mainly by CPF. In our experience, the antioxidant enzymatic activity response varies greatly in R. arenarum embryos and larvae exposed to OP pesticides, showing cycles of induction followed by inhibition attributed to catalytic site inactivation due to ROS attack 8 , 24 , 33 , 38 , 40 . We also report here the downregulation of CYP1A1 and CYP2A19 for both AZM and CPF exposures, probably as a negative regulation after a previous detoxification response. CYP1A1/2 are among the genes regulated by the AhR pathway by the binding of activated and nuclear-translocated AhR-Arnt heterodimeric transcription factor to DRE/XREs in their regulatory sequences. Studies carried out with Sparus aurata exposed to PCBs have shown a different expression pattern between AHRR and AhR-ARNT, in accordance with multiple mechanisms contributing to the downregulation of AhR 41 . Our findings coincide with these studies, as we observed induced levels of AHRR, while the levels of ARNT were decreased in R. arenarum larvae exposed to both CPF and AZM. Furthermore, the decreased levels of AhR-ARNT are consistent with the decreased expression found in CYP1A, since the entire mechanism is downregulated. A decrease in HSP90 and AIP transcripts, supposing reduced chaperone levels, would also contribute to lower AhR protein levels 42 . The AhR pathway is also involved in paraoxonase-1 expression regulation, according to results on exposure to polyphenols and specific inducing ligands 43 . In turn, the PON-2 gene has at least a CRE regulatory sequence for AP-1 regulation linked to the oxidative response and JNK activation and a PAPR-regulated site related to polyphenol activation and MEK pathway downregulation by phosphorylation 44 , 45 . Our results agree with downregulated MEK (MP2Ks), AP-1 (cFOS) and AHR pathways causing PON2 transcript repression in R. arenarum larvae exposed mainly to CPF. Similarly, the xenobiotic/endobiotic detoxifying carboxylesterase family (CES) is transcriptionally regulated by a series of nuclear factors and pathways: AhR, constitutive androstane receptor (CAR), pregnane X receptor (PXR) and Nrf2 are involved mostly in the upregulation of some of these families 46 . Although we were not able to annotate putative transcripts corresponding to CAR and PXR pathways in our R. arenarum transcriptome, the downregulation trends followed by AhR and Nrf2 pathways in larvae exposed to AZM and CPF are in line with CES5A transcript downregulation. This may be surprising, in the sense that carboxylesterases are recognized suicide “buffer” enzymes that irreversibly react with OP pesticides to protect the primary target acetylcholinesterase in the nervous system, decreasing their activities. This is in fact corroborated in toad embryos and larvae exposed to different OP pesticides, including AZM and CPF, in most of our reports in R. arenarum 3 , 7 , 8 , 33 , 47 . Thus, an induction of carboxylesterase and cholinesterase mRNA and/or protein synthesis would be expected to reestablish normal activity levels. Finally, remarking on the complexity of the responses and crosstalk between pathways, we have reported for different developmental states in R. arenarum and cell cultures exposed to CPF, other OP pesticides or arsenic, an increase in MEK1/2 and ERK1/2 proteins, their translocation to the nucleus, and ERK phosphorylation, considering that the MAP kinase pathway regulates the Nrf2-mediated response. Additionally, cFOS and cJUN proteins are increased after oxidative stress in R. arenarum embryos and larvae 3 , 43 , 48 . Concluding remarks In conclusion, we acknowledge the power of a hypothesis-driven transcriptomic data analysis on selected pathways. First, we identified an appropriate battery of potential housekeeping genes in R. arenarum larvae to further analyze gene expression by conventional or quantitative PCR after pesticide exposure. Second, we were able to visualize the effects of two OP pesticides in a priori selected metabolic and signaling pathways and compare them to previous metabolite and enzyme activity analyses. In our analysis, we infer a gradient of effects since CPF is more potent than AZM and acts earlier on gene transcription. Finally, we found a prevailing downregulation in signaling cascades and transcription factors that act upstream of the polyamine metabolism pathway and antioxidant responses, which partially coincides with previously well-characterized responses at the protein and/or activity and metabolite levels. Materials And Methods Chemicals High purity-certified standards of azinphos-methyl (98.3% AZM) and chlorpyrifos (CPF; 99.5% purity) were purchased from Chem Service Inc. (West Chester, PA, USA). Standard solutions of 18 g L -1 AZM and 1 g L -1 CPF were prepared by dissolving the pesticide standards in acetone. The exact concentrations of AZM and CPF in the standard solution were checked by capillary gas chromatography coupled to a nitrogen-phosphorus detector (GC-NPD). Biological material Adult females and males of the South American common toad ( Rhinella arenarum ) were collected in reference areas, free of pesticide application, in agreement with the corresponding collection permission 040/2020 from the Environment Secretary of Río Negro Province, Argentina. Animals used in this study were maintained and treated with regard to the alleviation of suffering according to recommendations of the Guide for the Care and Use of Laboratory Animals (National Research Council 2011) 49 . The animals were kept in captivity outdoors for 24-48 h until their use. Female ovulation was induced by intraperitoneal injection of 2500 international units (IU) of human chorionic gonadotropin (ELEA Laboratory, Buenos Aires, Argentina.) and embryos were obtained by in vitro fertilization 5 . Embryos were maintained until they reached the complete operculum (CO) stage (stage 25, according to 50 ). Ten days after reaching the CO stage, the larvae were used for acute toxicity assays. Acute toxicity assays The whole protocol was approved by the Faculty Committee for Care and Use of Experimental Animals (CICUAL- Facultad de Ciencias Agrarias Universidad Nacional del Comahue 01/13- 7 -2020). Larvae were randomly collected to perform the assays. Sublethal concentrations of AZM (0.5 mg L -1 , 1/20 96 h-LC50; 4 ) and CPF (0.1 mg L -1 ), 1/15 96 h-LC50; 8 ) were selected to carry out exposures for up to 24 h in glass dishes, maintaining a ratio of 1 larva/10 mL in amphibian Ringer's solution with 0.3% acetone (final v/v). These concentrations and higher ones in the order of 1 mg L -1 might be transiently found at the irrigation channels in fruit-producing orchards where this species reproduces 2,7 . The exact pesticide concentrations were checked by gas chromatography and nitrogen-phosphorus detection. Control acetone treatment was included to discard possible solvent effects. The treatments were carried out in duplicate, and R. arenarum larvae were grown in 10 different glass receptacles to perform AZM/CPF 6 h exposures, AZM/CPF 24 h exposures, and control treatments. From each receptacle, fifteen random larvae were collected and pooled at the corresponding times. Larvae were washed three times with cold Ringer's solution, placed in 1.5 mL tubes with RNALater® (Thermo Fisher Scientific Inc.) and stored at -20 ° C until processed. RNA extraction, cDNA library generation and massive parallel sequencing RNA extraction, cDNA library generation and massive parallel sequencing were carried out as described by Ceschin et al. (2020) 13 . Briefly, total RNA of each sample was extracted, and the cDNA library for transcriptome analysis was prepared. The ten library samples were normalized to 10 nM cDNA to be sequenced on a HiSeq 1500 Illumina platform, generating nonstrand specific “paired-ends” (PE) 2 × 100 bp readings. Bioinformatic construction of R. arenarum transcriptome and statistical analysis of expression levels A detailed description of the de novo transcriptome assembly, annotation and gene prediction was previously provided by Ceschin et al. (2020) 13 . Briefly, the readings obtained by massive sequencing of the R. arenarum transcriptome were aligned with Bowtie2 v2.3.5 51 against TSA: GHCG00000000.1 (BioProject PRJNA485066), and transcript expression quantification was performed using RSEM v1.3.0 52,53 . Expression values were normalized by the TMM method using the R and EdgeR packages 54,55 . Once the adequate HK transcripts were identified, the TMM values were standardized both by the average of the selected reference genes and by the respective control values. Finally, a nonparametric analysis was performed by the median and Kruskal-Wallis tests to assess significant differences or tendencies using the exact p-values. Raw data, standardization steps and statistical analyses are available in Supplementary Data File II. Validation of transcript expression results was performed by qPCR analysis on CPF-exposed R. arenarum larvae, following the same procedures described above for the treatments and RNA extraction. The different steps for PCR development are detailed in the “Supplementary file PCR data methods in brief”. Declarations Acknowledgments This work was supported by Grant 04A134 from the Universidad Nacional del Comahue and PICT 2017-1529 from Agencia Nacional de Promoción Científica y Tecnológica. Competing interests statement The authors have no competing interests to declare. Data availability and ARRIVE statement All data generated and analyzed during this study are included in the Supplementary Data Files I and II. This study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org). Author contributions NSP performed annotated gene data mining for selected pathways, statistical analyses and initial writing; CIL collaborated with experimental procedures, data calculations, manuscript revision and language editing; JO performed primer designs, sample RNA extraction, RT-PCR and qPCR assays together with NSP; DGC performed the experiments, sample processing, RNAseq and bioinformatic data assembly, including gene annotation; AV contributed to project design, gene selection, data calculation, statistical analysis, manuscript writing and processing. CIL, DGC and AV are staff researchers of CONICET-Argentina. References Kwet, S. R., Silvano, D., Úbeda, C., Baldo, D. & Tada, I. Di. Rhinella arenarum. IUCN 2011. IUCN Red List Threat. Species. http //www.iucnredlist.org/apps/redlist/details/54576/0 8235 , (2004). Loewy, R. M., Monza, L. B., Kirs, V. E. & Savini, M. C. Pesticide distribution in an agricultural environment in Argentina. J. Environ. Sci. Heal. - Part B Pestic. 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Nrf2, the master regulator of anti-oxidative responses. Int. J. Mol. Sci. 18 , 1–19 (2017). Sotomayor, V., Chiriotto, T. S., Pechen D'Angelo, A. M. & Venturino, A. Biochemical biomarkers of sublethal effects in Rhinella arenarum late gastrula exposed to the organophosphate chlorpyrifos. Pestic. Biochem. Physiol. 119 , 48–53 (2015). Calò, M. et al. Role of AHR, AHRR and ARNT in response to dioxin-like PCBs in Spaurus aurata. Environ. Sci. Pollut. Res. 21 , 14226–14231 (2014). Pappas, B. et al. P23 Protects the Human Aryl Hydrocarbon Receptor From Degradation Via a Heat Shock Protein 90-Independent Mechanism. Biochem. Pharmacol. 152 , 34–44 (2018). Gouédard, C., Barouki, R. & Morel, Y. Dietary Polyphenols Increase Paraoxonase 1 Gene Expression by an Aryl Hydrocarbon Receptor-Dependent Mechanism. Mol. Cell. Biol. 24 , 5209–5222 (2004). Shiner, M., Fuhrman, B. & Aviram, M. Macrophage paraoxonase 2 (PON2) expression is up-regulated by pomegranate juice phenolic anti-oxidants via PPARγ and AP-1 pathway activation. Atherosclerosis 195 , 313–321 (2007). Shiner, M., Fuhrman, B. & Aviram, M. Paraoxonase 2 (PON2) expression is upregulated via a reduced-nicotinamide- adenine-dinucleotide-phosphate (NADPH)-oxidase-dependent mechanism during monocytes differentiation into macrophages. Free Radic. Biol. Med. 37 , 2052–2063 (2004). Zhang, Y., Cheng, X., Aleksunes, L. & Klaassen, C. D. Transcription factor-mediated regulation of carboxylesterase enzymes in livers of mice. Drug Metab. Dispos. 40 , 1191–1197 (2012). Caballero de Castro, A. C., Rosenbaum, E. A. & Pechen D’Angelo, A. M. Effect of malathion on Bufo arenarum hensel development-I. Esterase inhibition and recovery. Biochem. Pharmacol. 41 , 491–495 (1991). Mardirosian, M. N., Lascano, C. I., Bongiovanni, G. A. & Venturino, A. Chronic toxicity of arsenic during Rhinella arenarum embryonic and larval development: Potential biomarkers of oxidative stress and antioxidant response. Environ. Toxicol. Chem. 36 , 1614–1621 (2017). Guide for the Care and Use of Laboratory Animals . Guide for the Care and Use of Laboratory Animals (National Academies Press, 2011). doi: 10.17226/12910 . Del Conte, E.. & Sirlin, J. L. Pattern series of the first embryonary stages in Bufo arenarum. Anat. Rec. (1952) doi: 10.1002/ar.1091120109 . Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9 , 357–359 (2012). Han, S., Liang, Y., Li, Y. & Du, W. Lncident: A Tool for Rapid Identification of Long Noncoding RNAs Utilizing Sequence Intrinsic Composition and Open Reading Frame Information. Int. J. Genomics 2016, (2016). https://doi.org/10.1155/2016/9185496 Li, B. et al. Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol. 15 , 1–21 (2014). Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , 139–140 (2009). Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11 , R25. doi: 10.1186/gb-2010-11-3-r25 . (2010). Tables Table 1: Comparison of distribution statistics and expression variation of transcripts proposed as HK in R. arenarum stage 25 larvae. Gene ID Gene name Mean Minimum Maximum SD Median CV % EF1A0 Elongation factor 1-alpha, somatic form 7329.9 6610.7 8457.7 586.4 7265.4 8.00 EF1B Elongation factor 1-beta 583.4 498.6 676.9 64.3 589.4 11.03 EF1D Elongation factor 1-delta 922.1 800.7 1074.7 98.3 910.4 10.66 EF1GA Elongation factor 1-gamma-A 2993.7 2611.2 3344.1 252.9 2968.2 8.45 G3P Glyceraldehyde-3-phosphate dehydrogenase 1822.0 1289.5 2425.1 411.3 1768.3 22.58 RL8 60S ribosomal protein L8 1108.0 883.6 1309.3 134.8 1131.6 12.16 TBA Tubulin alpha chain 1829.9 1589.5 2014.7 142.9 1826.3 7.81 TBA1 Tubulin alpha-1 chain 146.1 98.1 195.5 31.8 142.1 21.74 TBB Tubulin beta chain 518.4 350.2 586.8 70.4 526.8 13.58 TBB4B Tubulin beta-4B chain 219.0 137.1 253.7 33.3 224.5 15.19 ACTB Actin, cytoplasmic 1 363.6 301.7 394.4 37.2 380.3 10.22 ACT3 Actin, alpha sarcomeric/skeletal 90.1 15.1 259.4 73.3 61.9 81.27 Data calculated from massive RNA-seq and transcriptomic analysis are expressed as TMM values. A limit of 20% for the CV is proposed to consider a transcript as an HK gene in expression analysis. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYDATAFILEItranscriptchecking.xlsx SupplementaryDATAfileIIExpressionlevelTMMchecking.xlsx SupplementaryTablesPiresetal2022.pdf SupplementayfilePCRdatamethodsinbrief.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 22 Aug, 2022 Reviews received at journal 18 Aug, 2022 Reviews received at journal 16 Jun, 2022 Reviewers agreed at journal 04 Jun, 2022 Reviewers invited by journal 29 May, 2022 Editor assigned by journal 29 May, 2022 Editor invited by journal 26 May, 2022 Submission checks completed at journal 26 May, 2022 First submitted to journal 20 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1677791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":109056687,"identity":"5599cb7b-e24e-485d-be33-973d3a4850e9","order_by":0,"name":"Natalia Susana Pires","email":"","orcid":"","institution":"Universidad Nacional del Comahue-CONICET","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"Susana","lastName":"Pires","suffix":""},{"id":109056689,"identity":"5a90d392-1910-4590-adf1-c49fedae9b06","order_by":1,"name":"Cecilia Inés Lascano","email":"","orcid":"","institution":"Universidad Nacional del Comahue-CONICET","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cecilia","middleName":"Inés","lastName":"Lascano","suffix":""},{"id":109056690,"identity":"f5aed547-32ac-4c3f-98d7-59a8c982af18","order_by":2,"name":"Julia Ousset","email":"","orcid":"","institution":"Universidad Nacional del Comahue-CONICET","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Ousset","suffix":""},{"id":109056692,"identity":"47deee8b-7fac-42a6-bcfc-116e05f3dd6a","order_by":3,"name":"Danilo G. Ceschin","email":"","orcid":"","institution":"Instituto Universitario de Ciencias Biomédicas de Córdoba, CITAAC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Danilo","middleName":"G.","lastName":"Ceschin","suffix":""},{"id":109056694,"identity":"5e81de3d-b9da-45be-8118-54d3fa3d0925","order_by":4,"name":"Andrés Venturino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3SMUvDQBTA8XccZHqY9UpC8hUuFAQx4FcJBK6LIOJyo0VIl4JrwS9z5UFcju5FB0vBWdHhwMXY2C2h6SZy/yEh8H48jguAz/eXCwDYS/PGgfPFjnDZEj6Q/CwS7dcBEt5itr12dHUS3dXa6ec4nN0b9qkJwgfTSYTB8XhR0FkV12o9t68oLAGPm4dYFd1rTKAiLEgG4vL0iVWEUpTAR5UCsN0iNcHka09udiTdtiTtIdLwmu8Jb7dwYO9VDrKHZMQpQjVpiCpHc0vNWUpJYHPMekjyOJ1+YH4u00W5fHOaLsLZcrNxWiRJD+m+BMLhv8FvzB037/P5fP+7b7PkUTUf1QVpAAAAAElFTkSuQmCC","orcid":"","institution":"Universidad Nacional del Comahue-CONICET","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Andrés","middleName":"","lastName":"Venturino","suffix":""}],"badges":[],"createdAt":"2022-05-20 18:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1677791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1677791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22099301,"identity":"ac1a5785-fad1-4e0a-83ad-d83667450c55","added_by":"auto","created_at":"2022-05-31 21:26:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91691,"visible":true,"origin":"","legend":"\u003cp\u003eGene selection process by metabolic pathway.\u003c/p\u003e\u003cp\u003eOur starting point was the database with 55,000 annotated genes from the R. arenarum transcriptome previously assembled by us\u003csup\u003e13\u003c/sup\u003e. The first step was a hypothesis-driven selection of annotated genes from five pathways recognized as affected by organophosphorus pesticides at enzyme activity or metabolic product levels: PM: polyamine metabolism; AS: antioxidant system; DS: detoxifying systems; TF: transcription factors, signaling pathways; and Housekeeping (HK) genes tested in amphibians. The next step was checking the selected transcript sequences and their predicted amino acid sequence identities with the annotated genes by BLASTN and BLASTP, from which about 94% of isoforms were confirmed (light blue vs. brown slices). The last step was checking the transcription levels for each isoform to discard erratic low values, using the differential expression data corrected by the trimmed mean of M-values (TMM) normalization method, accepting 76% of them (light green vs. yellow slices). Note that, on average, there were 2 accepted transcripts per annotated gene.\u003c/p\u003e","description":"","filename":"figure1Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/50583435fa5058e4a6bfe5ac.jpg"},{"id":22099302,"identity":"b2bc5b63-37ee-4e38-b005-765a32bd0a03","added_by":"auto","created_at":"2022-05-31 21:26:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93891,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of transcripts corresponding to polyamine metabolism genes in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e\u003cp\u003eA, Genes related to polyamine synthesis and its regulation. B, genes related to polyamine degradation. Significance levels for Kruskal-Wallis and median tests, * p=0.09; ¤ p=0.08; † p=0.04; III p=0.0001. AZM: azinphosmethyl, CPF: chlorpyrifos, at 6 h and 24 h exposures; gene codes are detailed in the text.\u003c/p\u003e","description":"","filename":"figure2Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/7fe98b9bd6a2ec9ee0534374.jpg"},{"id":22099842,"identity":"4e02c727-2ec3-45fc-b841-94505214e0f2","added_by":"auto","created_at":"2022-05-31 21:31:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83136,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of transcripts corresponding to antioxidant response genes in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e\u003cp\u003eA, Superoxide dismutase (SOD) and catalase (CATA) genes. B, GSH-dependent antioxidant response and GSH synthesis genes. Significance levels for Kruskal-Wallis and median tests, * p=0.09; ¤ p=0.08; I p=0.07; ** p=0.01; *** p=0.003; ††† p=0.0007. AZM: azinfosmethyl, CPF: chlorpyrifos, at 6 h and 24 h exposures; gene codes are detailed in the text.\u003c/p\u003e","description":"","filename":"figure3Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/d9400b492d6b656aaf4f3c8d.jpg"},{"id":22100216,"identity":"38f6929e-16db-4a73-b755-09d3752bb0c5","added_by":"auto","created_at":"2022-05-31 21:36:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150808,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of transcripts corresponding to OP-detoxifying enzyme genes in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e\u003cp\u003eA, Esterases and Cytochrome P-450 isoforms. B, GSH-S Transferases (GST), cytosolic isoforms. C, GST, microsomal isoforms. Significance levels for Kruskal-Wallis and median tests, * p=0.09; ¤ p=0.08; + p=0.05; † p=0.04; †† p=0.007; III p=0.0001. AZM: azinphosmethyl, CPF: chlorpyrifos, at 6 h and 24 h exposures; gene codes are detailed in the text.\u003c/p\u003e","description":"","filename":"figure4Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/2cfce92d462cfb632c79edad.jpg"},{"id":22099304,"identity":"c42ec56b-8e7d-40c7-a7f4-526ed926c3dd","added_by":"auto","created_at":"2022-05-31 21:26:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50559,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of transcripts corresponding to transcription factors and signaling pathway genes in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e\u003cp\u003eSignificance levels for Kruskal-Wallis and median tests, * p=0.09; ¤ p=0.08. AZM: azinphosmethyl, CPF: chlorpyrifos, at 6 h and 24 h exposures; gene codes are detailed in the text.\u003c/p\u003e","description":"","filename":"figure5Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/53b21d2adb8c9c924a21e698.jpg"},{"id":22099306,"identity":"0357d31c-f144-41ca-b835-0221da10a01e","added_by":"auto","created_at":"2022-05-31 21:26:59","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130132,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representation of OP effects on selected gene expression in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e\u003cp\u003eData are presented for four groups of selected genes whose products or activities are known or suspected targets of OP pesticides. Larvae were exposed to azinphos-methyl (AZM) and chlorpyrifos (CPF) for 6 and 24 h. Gene codes are detailed in the text.\u003c/p\u003e","description":"","filename":"figure6Piresetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/8d63263b83630f0fcd6a4bd5.jpg"},{"id":22099843,"identity":"b1e66ea7-61dc-45f6-b82a-313cc6300a2e","added_by":"auto","created_at":"2022-05-31 21:31:59","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47009,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of differentially expressed genes (DEG) in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to OP pesticides.\u003c/p\u003e\u003cp\u003eThe expression levels of annotated transcripts were normalized by TMM and analyzed by edgeR pipeline in treated-vs.-control pairs for azinphosmethyl (AZM) and chlorpyrifos (CPF) at 6 and 24 h-exposures. The significant DEG were further selected and ranked according to the log-fold changes. From the generated results, hypothesis-selected transcripts were identified, classified by their ranking into the different groups indicated in the figure bar codes either as downregulated (#-, in blue scale) or upregulated (#+, in red scale) DEG, or as NS according to the p-values, being N the counts in each category. Comparatively, the total downregulated, upregulated or NS/unaffected transcripts are shown by the dotted rectangles with the corresponding number of transcripts in italics. The data for hypothesis-selected transcripts is detailed in Table S3.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/5851585950f5720261872899.jpg"},{"id":22099308,"identity":"45746e52-f459-4aa8-a0ca-3067836ed984","added_by":"auto","created_at":"2022-05-31 21:26:59","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":32149,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of gene expression by qPCR in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to chlorpyrifos.\u003c/p\u003e\u003cp\u003eLarvae were exposed to the OP at 0.5 or 1.0 mg/L during 6-12 h. The expressions of two of the selected transcripts were normalized using ACTB and RL8 as HK genes, applying a geometric mean-methodology, and the relative expression levels were calculated.\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/4b007149d0f9e92277c21e68.jpg"},{"id":22100254,"identity":"e0876316-7238-4ab1-9133-7c5f8278b56f","added_by":"auto","created_at":"2022-05-31 21:37:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":941764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/18d623fc-0db0-4841-8226-ab3015a9e045.pdf"},{"id":22099310,"identity":"2356e56f-7443-49da-877f-f6a423e61f9e","added_by":"auto","created_at":"2022-05-31 21:27:00","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":137780,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYDATAFILEItranscriptchecking.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/52b9e16e80a03c9daf3affde.xlsx"},{"id":22099311,"identity":"59efe67a-7db4-4fc3-85ac-1857d0393b44","added_by":"auto","created_at":"2022-05-31 21:27:00","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":175694,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDATAfileIIExpressionlevelTMMchecking.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/cd2f4e5fef485dd11b086735.xlsx"},{"id":22099845,"identity":"d7db00fa-8d81-4a7d-bcfe-1583aa56e94a","added_by":"auto","created_at":"2022-05-31 21:32:00","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":199742,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesPiresetal2022.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/ffdfb90d3f4730c1c9689802.pdf"},{"id":22099309,"identity":"bfc950c3-1249-4ffa-8695-48028b2dd1ff","added_by":"auto","created_at":"2022-05-31 21:27:00","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":294318,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementayfilePCRdatamethodsinbrief.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1677791/v1/33edf18c620e3be3164f3962.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe amphibian \u003cem\u003eRhinella arenarum\u003c/em\u003e (Hensel 1867) is widely distributed throughout Argentina and partially in South America \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Its life cycle includes two fundamental stages before reaching the adult stage: the embryonic and larval stages. The physiological reproduction of the species in the Upper Valley of Rio Negro and Neuqu\u0026eacute;n (North Patagonia) occurs once a year during the spring months in backwaters and irrigation channels. The reproductive season coincides with the period of greatest pesticide application to protect fruit production (INTA, 1993). Various kinds of pesticides have been detected in superficial waters where amphibian reproduction occurs \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This situation implies that both \u003cem\u003eR. arenarum\u003c/em\u003e embryos and larvae are potentially exposed, at least temporarily, to high concentrations of these toxicants, posing a hazard to this and other species that inhabit the area \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhen evaluating the risks of exposure to a contaminant for any species, it is desirable to find early-response biomarkers capable of anticipating irreversible damage that may occur at later stages of development \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Molecular targets, effectors or modulators of toxicant effects are among early-response biomarkers. However, their development in autochthonous, nonmodel species such as \u003cem\u003eR. arenarum\u003c/em\u003e is difficult due to the lack of both sequenced genomes that allow primer design and transcript analysis, as well as specific antibodies necessary to support protein expression analysis \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In these cases, toxicogenomic and particularly transcriptomic analyses through bulk RNA sequencing (RNA-Seq) are powerful tools that enable screening for effects and responses to a toxicant and therefore allow further molecular studies \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. RNA-Seq is a highly sensitive and accurate tool for measuring expression across the transcriptome, enabling researchers to detect changes that otherwise would go unnoticed. It is possible to quantify the levels of abundance or relative changes for each transcript during a specific developmental stage or under a specific treatment \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the case of nonmodel organisms such as \u003cem\u003eR. arenarum\u003c/em\u003e, RNA-Seq technology allows the obtention of genomic information through a relatively accessible process from an economic point of view and considering the cost-benefit relationship \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe have been studying the effects of organophosphorus pesticides in the development of the common toad \u003cem\u003eR. arenarum\u003c/em\u003e, focusing on target and detoxifying enzymes, polyamine pathway and signaling, at protein, activity and metabolite levels. However, our advances at the transcript expression level were slow and challenging until we were able to develop a transcriptome study. In the context of this RNA-Seq and transcriptomic analysis performed on \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to the organophosphorus (OP) pesticides azinphos-methyl (AZM) and chlorpyrifos (CPF) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, we were able to perform hypothesis-driven data analysis processing on \u003cem\u003ea priori\u003c/em\u003e selected mRNA transcripts. This work was done in parallel with the whole transcriptome bioinformatic (big data) analysis, whose results are to be published elsewhere. The aims of the study were a) to compare transcript expression levels in genes from pathways that have been previously recognized as impacted by OP pesticides and that have also been studied at the biochemical, metabolite or physiological level in \u003cem\u003eR. arenarum\u003c/em\u003e to obtain a more comprehensive mechanism of toxicity and response and b) to test a set of potentially adequate housekeeping genes in \u003cem\u003eR. arenarum\u003c/em\u003e larval stages for future quantitative PCR studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGene selection from annotated transcripts for OP effect analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a list of the available gene transcripts in \u003cem\u003eR. arenarum\u003c/em\u003e published by us in the database from massive RNA transcript sequencing and gene annotation (http://rhinella.uncoma.edu.ar/; \u003csup\u003e13\u003c/sup\u003e). We selected a group of 14 potential housekeeping genes, considering those currently proposed for mRNA expression normalization in \u003cem\u003eXenopus laevis\u003c/em\u003e \u003csup\u003e15,16,17\u003c/sup\u003e (Table S1, GROUP A). We found a considerable number of annotated genes that could be included in the following pathways, which were selected on the basis of available information about OP effects at the biochemical level in amphibians \u003csup\u003e6,8,18\u003c/sup\u003e and other vertebrates: polyamine metabolism genes, (Group B, 14 genes); antioxidant response genes (Group C, 15 genes); OP- metabolizing, primary- and secondary-target enzyme genes (Group D, 16 genes); and gene expression regulators, transcription factors and phosphorylation cascade effectors corresponding to the Mitogen-Activated protein kinase pathway, Transcription factor AP-1, Aryl hydrocarbon receptor (AHR) pathway and Nuclear factor erythroid 2-related factor 2 antioxidant response pathway (Group E, 17 genes) (Figure 1). The complete list of genes is detailed in Supplementary Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorroboration of gene annotation for selected transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlignments were carried out using the tools available in BLAST (BLASTN, BLASTX and BLASTP) to verify that the sequences of the selected transcripts effectively corresponded to the annotated genes. Those sequences that yielded a match greater than 50% in the vertebrate databases were selected for further analysis. A summary of this sequence validation is shown in Figure 1, and the complete analysis is available in Supplementary Data File I. From a total of 225 transcript sequences that were compared to the corresponding annotated genes, 93.8% could be effectively confirmed for further analysis; the best correspondence was obtained in group E covering transcription factors and signaling pathways with a 100%, and the lowest percentage was approximately 90% for the group of antioxidant stress transcripts (Group C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of adequate transcription levels for filtered transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreviously validated transcripts were analyzed to determine if the transcription levels of treatment replicates were appropriate for statistical analysis. These levels resulted from the massive RNAseq amplification and quantitation of each transcript fragment in the different treatments. As \u0026lsquo;not appropriate\u0026rsquo; levels, we considered fragments with zero expression levels in any sample and/or replicates with very low and erratic values. The summary of transcripts accepted in each group of selected genes is shown in Figure 1. The raw data corresponding to all transcript fragments are available in Supplementary Data File II. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousekeeping genes stability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe carried out a transcription stability analysis of the selected potential housekeeping genes, comparing their variability within and between the treatments. The respective TMM means, minimum and maximum expression values, standard deviations, median expressions and percentual coefficients of variation (CV%) are presented in Table 1. We assumed a maximum CV% of 20% in the TMM as an acceptable limit for a transcript to be considered housekeeping. In this way, we were able to select 9 out of 12 transcripts as appropriate reference genes in the first larval stage of \u003cem\u003eR. arenarum\u003c/em\u003e. The genes with the least and acceptable variations in group A were EF1A0, EF1GA, EF1D, EF1B, TBA, TBB, TBB4B, ACTB, and RL8.\u003c/p\u003e\n\u003cp\u003eThe transcripts belonging to tubulin TBA1 and glyceraldehyde 3P dehydrogenase (G3P) showed a variability of approximately 22% in their expression levels, roughly in the exclusion limits we considered, and might be considered housekeeping genes if a refined and specific analysis proves better results. In turn, ACT3, belonging to sarcomeric actin, showed the greatest variation in expression at 83%, clearly indicating that it cannot be considered a housekeeping gene in \u003cem\u003eR. arenarum\u003c/em\u003e larvae, at least for OP pesticide studies. The TMM values of the selected HKs, rated with respect to their control values, were averaged for each treatment and used to standardize the TMM values of the transcripts corresponding to the genes of groups B, C, D and E (raw and standardized data shown in Supplementary Data File II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of OP on the transcription of polyamine metabolism genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOP mainly decreased the expression of genes related to polyamine synthesis (Figure 2A). Although some differences could be noted between CPF and AZM and the exposure times, decreases reaching 30-40% in the expression levels were found in ornithine decarboxylase (DCOR1), S-adenosylmethionine decarboxylase proenzyme (AMD, transcript 1-b), spermidine synthase (SPEE), and ornithine decarboxylase antizyme (OAZ1). Only AMD transcript 1-a showed relevant increases of nearly 3 times the control values when exposed to both OP pesticides. In turn, the ornithine decarboxylase regulator OAZ2 and the antizyme inhibitors AZIN1 and 2 showed no significant or no relevant changes (Supplementary Table S2).\u003c/p\u003e\n\u003cp\u003eIn turn, polyamine-degrading enzymes showed more variable responses to OP pesticides (Figure 2B). AOC1 transcript expression, corresponding to diamine oxidase, showed a significant inhibition of 25-40% by AZM treatment and 35-50% by CPF treatment. The AOC2 transcript showed a similar tendency with even deeper inhibition responses, but the difference was not significant (Table S2). AOC3 and 4 transcript isoforms showed variable and nonsignificant responses to OP pesticide exposure. Acetyl spermidine/spermine oxidase (PAOX) expression showed an induction tendency of 40% with both OPs at 6 h of exposure, while spermine acetylase transcript expression (SAT1, 2-a, 2-b) showed an inhibitory trend with CPF up to 40%. Spermine oxidase (SMOX) expression was scarcely inhibited (20%) by AZM and induced by CPF (20%, 6 h) (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of OP on oxidative stress response - antioxidant enzyme genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuperoxide dismutase transcripts displayed differential responses to OP treatments (Figure 3A). SODC expression showed a decrease in response to AZM exposure at 24 h and to CPF at 6 and 24 h of exposure (up to 30% inhibition) with respect to the control. On the other hand, SODE expression was significantly induced in 60% by CPF at 24 h. Catalase transcription (CATA) was in turn slightly inhibited by AZM exposure at 24 h and by CPF up to 25%.\u003c/p\u003e\n\u003cp\u003eGlutathione-dependent peroxidase transcripts were mainly induced by OP pesticides. GPX1 transcription was considerably induced by AZM at 24 h (3X) and by CPF at both times (up to 2.7X). GPX3 transcription was induced to a lesser extent but in a significant way by CPF (1.5X), while GPX4 also showed an induction of approximately 30% by CPF exposure. In turn, the transcript corresponding to GPX8B presented a tendency to decrease in samples exposed to AZM at 24 and to CPF at 6 and 24 h, up to 60% of control expression values. Two other GPX transcripts, GPX2 and 7, as well as one GSH-reductase transcript (GSHR), showed decreasing but not significant trends due to OP pesticide exposure (Supplementary Table S2). Finally, one transcript for the enzyme glutathione synthetase (GSHB) was scarcely downregulated by OP exposure (AZM at 24 h; CPF at both times, approximately 25%) (Figure 3B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOP pesticide targets and detoxifying enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough some fragments corresponding to cholinesterase transcripts were detected (BCHE), their levels were very low and showed erratic responses (raw data in Supplementary Data File II). Among the esterase group, carboxylesterase (EST5A) transcript showed a relevant decrease in larvae exposed to AZM at both 6 and 24 h, while in larvae exposed to CPF, a decrease at 6 h (50% inhibition) was observed, returning to control values at 24 h (Figure 4A). A similar pattern was observed for another carboxylesterase transcript (EST3B), with no significant effects (Supplementary Table S2). The serum paraoxonase/arylesterase-2 transcript (PON2) showed a small but significant decrease of approximately 20% only in larvae exposed to CPF at 6 h with respect to the controls. For cytochrome P450 enzymes, only the transcripts putatively related to OP metabolization were analyzed; CYP1A1 transcript expression was significantly reduced by AZM at 24 h and by CPF exposure, although the effects were minor (20% inhibition). Cytochrome CYP2C19 expression was more markedly inhibited by both OP pesticides after 6 h of treatment, reaching 50-60% inhibition with respect to controls.\u003c/p\u003e\n\u003cp\u003eGlutathione-S-transferase (GST) transcripts were among the most abundant groups, as 15 different isoforms could be identified. The GST isoforms alpha (GSTA3), pi (GSTP1 and 2), and theta (GSTT3) were downregulated by both OP pesticides, with transcription inhibitions ranging from 20 to 50% with respect to controls (Figure 4B). Isoform GSTP1 showed the highest inhibition and in a significant way. Although GST Mu (GSTM1) showed a decreasing trend, the effects were not significant (Table S2). On the other hand, GST Kappa 1 transcript (GSTK1) was significantly induced by CPF (45%), while GSTM3 showed the highest transcription induction values by CPF and by AZM at 24 h, reaching 2 X - 3.3 X expression values with respect to controls, but with a p-value of 0.09. The GST omega 1 transcript (GSTO1) also showed a significant increase of 45% in its expression for AZM at 6 h and CPF at 24 h.\u003c/p\u003e\n\u003cp\u003eMicrosomal GST transcripts were also analyzed, finding increases in the expression levels of isoforms 1, 2 and 3 (M-GST1, M-GST2, M-GST3, respectively, Figure 4C). The M-GST1 transcript was one of the most affected transcripts, with increases of 40% (AZM-24 h; CPF-6 h) to 70% (CPF-24 h). One M-GST3 transcript showed roughly similar effects (30-60% increase), while a second transcript showed a moderate increase (20%) only for CPF at 24 h of exposure. For the M-GST2 isoform, we also identified two transcripts, one of which showed a moderate increase (up to 35%) after CPF exposure, and the other showed slight decreases of approximately 20% in its expression with respect to the controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOP effects on transcription factor- and signaling pathway-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MAP kinase phosphorylation pathway seemed to be affected solely at the first level of regulation, as mitogen-activated protein kinase kinases (MP2K) were downregulated early by CPF at 6 h and late by AZM at 24 h (Figure 5); the MP2K2 transcript was diminished to 70% of control values by both OP pesticides, while the MP2K1 transcript was affected only by CPF but not at a relevant level (Supplementary Table S2). Similarly, the mitogen-activated protein kinase p38 transcript was scarcely affected by both OP pesticides (approximately 12%), while the JUNK transcript remained unaffected. None of the transcription factor JUN transcripts detected (JUN-1, JUN-B, JUN-D1) were significantly affected by OP exposure (Table S2). In turn, the transcription factor FOS, which associates with JUN as a heterodimer partner in activator protein-1 (AP1), showed a severe reduction in its transcription levels by AZM at 6 h of exposure (to 35% of control levels) but recovered at 24 h and surpassed controls by 25%; CPF caused an inverse pattern, inducing transcription of FOS at 6 h (40%) but inhibiting it at 24 h (30%) (Figure 5).\u003c/p\u003e\n\u003cp\u003eOn the other hand, nuclear factor erythroid 2-related factor 2 (NRF2), linked to the antioxidant response through the antioxidant response element (ARE) pathway, showed a decrease in transcription of approximately 30% in the larvae exposed to AZM for 24 h and in those exposed to CPF for 6 and 24 h compared to controls (Figure 5).\u003c/p\u003e\n\u003cp\u003eWithin the aryl hydrocarbon receptor (AhR) pathway, some differences were found for the nuclear translocator (ARNT) and the aryl receptor repressor (AHRR). Two transcripts for ARNT were analyzed. The first ARNT transcript showed a decrease of nearly 30% in larvae exposed to AZM at 24 h and to CPF at 6 and 24 h compared to the controls. The second transcript, ARNT2, showed a relevant induction in transcription of nearly 2X after 6 h of exposure to CPF. The AHRR transcript showed an increase in larvae after 24 h of exposure to AZM (80%) and CPF (40%) at 24 h with respect to the controls. The AhR transcript itself showed a decreasing but statistically nonsignificant pattern after exposure to CPF (Table S2). The transcript corresponding to HSP90AB1, the chaperone that binds AhR in the cytosol, showed a decrease of 20% after 24 h of exposure to CPF, while the transcript corresponding to the cochaperone aryl-hydrocarbon-interacting protein-like-1, AIPL1, was downregulated by AZM (approximately 50%) but increased by CPF (up to 75%). Another isoform, AIP, showed no effects, as well as the third cochaperone prostaglandin E synthase-3, PTGES3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverview of OP effects in hypothesis-selected pathway genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA visual comparison of the number of genes showing significant differential expression, their level of fold-changes and their sense towards up or downregulation, was performed for the different OP treatments using a heatmap representation (Figure 6). The exposure of \u003cem\u003eR. arenarum\u003c/em\u003e larvae to AZM or CPF at sublethal concentrations and up to 24 h did not cause remarkable fold-changes in gene expression in the selected pathways. Most of the selected genes did not show significant variations, or the changes were less than 2-fold; only eight transcripts showed changes between 2- and 4-fold. The exposure to AZM provoked a relatively poorer effect at 6 h compared to 24 h, as evidenced in the number of differentially affected transcript expression levels. In turn, the CPF effects on \u003cem\u003eR. arenarum\u003c/em\u003e were more potent, affecting most of the gene expression early at 6 h and sustaining the effects after 24 h of exposure. Another interesting feature was that downregulation cases notably exceeded upregulation cases, in a proportion of 3 to 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison /validation of statistical approaches and hypothesis-selected genes ranking in differential expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed both edgeR pipeline statistical analysis on the whole transcriptome data, and non-parametric Kruskal-Wallis ANOVA and Median tests to the hypothesis-selected genes using the filtered database. The main reasons for using both approaches are that edgeR \u003cem\u003ea priori\u003c/em\u003e considers all the possible fragments for one annotated gene as different and that it is not feasible to filter erratic, usually low level-expressed transcripts; in turn, the specific filtering and checking of fragments enables their use as repeats in the non-parametric tests. Keeping in mind that edgeR statistical analysis was performed on the raw, not-filtered database after collapsing the different fragments for each annotated gene into one unique set (5 treatments by duplicate), we could compare the performances with non-parametric analysis in 42 of the 62 \u003cem\u003ea priori\u003c/em\u003e selected genes. The coincidences between both statistical approaches extended to 66.7% of the analyzed transcripts, and when the treated- \u003cem\u003evs\u003c/em\u003e.- control pairs were considered, the result was similar, coinciding in 65.5% of all the cases. A transcript-detailed comparison may be found in Supplementary Table S3. From 42 transcripts analyzed, 15 showed coincident statistical outputs in the four treatments, 7 transcripts did in 3 out of 4 cases and 10 did in 2 of 4 cases; only one gene showed no coincidences at all. This fact highlights the need of carefully filtering and validating the transcripts sequences and expression levels when any selection is performed for specific analysis. This may be particularly important in some instances, \u003cem\u003ei.e.\u003c/em\u003e, when \u0026ldquo;top-ten\u0026rdquo; differentially expressed genes (DEG) are considered.\u003c/p\u003e\n\u003cp\u003eWe next extracted the lists of significant DEG from edgeR- paired comparisons of pesticide-treated vs. control transcript expressions and ranked them using the logarithm of their fold-changes. We determined the ranking positions for each hypothesis-selected gene showing significant changes (data in Table S3). Ranking of the selected genes for each group let us verify that the effects of both OP were predominantly the downregulation of transcripts expression, that CPF caused noticeable higher effects on gene transcription compared to AZM and that CPF effects became evident earlier, from 6 h on (Figure 7). The OP AZM was the least effective in changing gene expression at 6 h, about ten times lower for downregulation and one-third for upregulation compared with the effects at 24h or respect to CPF. We could also verify in the edgeR analysis that the effects of both OP on the expression of the hypothesis-selected genes positioned them far from the top ranking. None of the pathway-selected genes was within the top-ten, and only three genes were in the 10\u003csup\u003eth\u003c/sup\u003e-50\u003csup\u003eth\u003c/sup\u003e or -100\u003csup\u003eth\u003c/sup\u003e ranks for most of the treatments (GPX1; CYP2C19; and GSTM3; Table S3), while most of the selected transcripts were within the 500-1500 ranks or in the NS group (Figure 7). These results were in fact similar to those obtained from the non-parametric analysis on the filtered and verified list of transcripts resumed in the figure 6. Please note that the analysis of top ranking differentially expressed genes is not the objective of the present work and is in preparation to be published elsewhere. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApplication of transcriptome data on primer design and PCR analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe finally developed PCR assays, with the double purpose of checking the fitness of the fragment sequences determined for the hypothesis-selected genes to design adequate primers and verifying their responses to organophosphates in \u003cem\u003ead hoc\u003c/em\u003e exposures. We selected 6 genes from the polyamine pathway, 4 genes from antioxidant and detoxifying pathways, 4 genes from signaling and transcription factors and 2 HK genes and proceeded to design pairs of primers to develop RT-PCR and qPCR analyses. We succeeded in amplifying 13 of those genes by RT-PCR and could further sequence their products and verify the identities of 8 them by alignment with the original sequences (Supplementary Table S4). We next designed primers suitable for qPCR studies for 10 genes, being able to amplify all of them. Thus, we conclude that the transcriptome \u003cem\u003ede novo\u003c/em\u003e assembly in R. arenarum was of very good quality and that annotated gene sequences were adequate for primer design. The general workflow for RT-PCR and qPCR analyses, a detailed description of the methodology and the corresponding results are presented in the accompanying Supplementary file PCR Methods in Brief.\u003c/p\u003e\n\u003cp\u003eRegarding qPCR results, we found very low levels (high Ct values) for most of the polyamine pathway genes and the FOS gene and could not obtain good calibration parameters. We then carried out a differential expression analysis of two genes, AMD1 and SODC, and ACTB and RL8 as HK genes, in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to 0.5 and 1.0 mg/L chlorpyrifos during 6-12 h. We applied a geometric means approach to normalize the data with respect to HK levels and to calculate the relative expression levels. The increase of about 3X of AMD1 expression was in very good accordance with a similar increase with both organophosphates in the transcriptome study (Figure 8). The decrease in SODC expression in qPCR assay was also coincident with the results in the chlorpyrifos-exposed larvae analyzed by RNAseq and transcriptome assembly. Thus, we also conclude that the results of differential gene expression analysis performed from curated and filtered transcriptome data in the hypothesis-driven approach is adequate and that results are reliable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe succeeded in applying a hypothesis-driven transcript analysis from a transcriptome database on \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to two OP pesticides, with two purposes into mind: 1) to test HK genes currently used in model species and 2) to compare the effects of AZM and CPF on transcript levels for pathways previously described as impacted at biochemical or protein expression levels. These goals in a native, nonmodel species such as the amphibian \u003cem\u003eR. arenarum\u003c/em\u003e are a very good example of what transcriptomics analysis can do, solve or imply for the advance in molecular toxicology when other tools are not readily available \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Considering the results obtained, it is evident that the transcriptomic expression data allow a screening analysis into selected pathways to decide for further qPCR studies if necessary. Furthermore, we can confirm that transcriptomic information is an essential tool when developing molecular biology assays in nonmodel organisms.\u003c/p\u003e \u003cp\u003eData normalization in gene transcription analysis is commonly achieved using HK genes. In a comprehensive study conducted with \u003cem\u003eXenopus laevis\u003c/em\u003e, RL8 and GAPDH were stable as reference genes during the first embryonic stages, ODC1 was stable in the initial and final stages, and H4 was adequate throughout embryonic development \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In another study in \u003cem\u003eX. laevis\u003c/em\u003e, eEF1A1 and SUB1 L were identified as the genes whose expression remained more stable, which would allow their use as reference genes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In this work, we were able to analyze the expression levels of potential reference genes from the \u003cem\u003eR. arenarum\u003c/em\u003e transcriptome data considering those proposed for \u003cem\u003eX. laevis\u003c/em\u003e, selecting a list of 9 genes that would fit to the different requirements in future qPCR studies. In fact, we performed several qPCR analyses in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to CPF, and successfully used ACTB and RL8 as HK to achieve results normalization. We thus confirm that RNA-Seq data have the potential to identify genes with less variation in their expression, as suggested by other authors \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBy analyzing the transcriptomic data of selected pathways of \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to the OP pesticides AZM and CPF, we obtained an overview of their regulatory effects on gene transcription at previously known targets. The qualitative heatmap representation shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e allows a rapid comparison among the different treatments or states \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The first conclusion to be highlighted is that, in general, the exposure of \u003cem\u003eR. arenarum\u003c/em\u003e larvae to AZM or CPF at sublethal concentrations and up to 24 h did not cause remarkable fold-changes in gene expression in the selected pathways, regardless of these changes being statistically significant. This is very interesting, considering that target esterase genes, detoxifying genes and oxidative stress response genes might be a priori expected to be upregulated, as their protein products are either inactivated by OP pesticides or their activities are engaged in xenobiotic transformation and detoxification \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Most of the transcripts showing changes in their levels after OP exposure did so in a moderate way, and only a few showed fold-changes higher than 2X. Thus, moderate changes in mRNA expression in these genes would be enough for ample response changes in their product activities. Another observation is that exposure to AZM clearly provokes a relatively poor effect on the genes analyzed at the short exposure time of 6 h; AZM effects are mainly evident after 24 h of exposure. This may be seen in all the selected groups, except for polyamine metabolism genes, where most of their expression levels were altered. In turn, the CPF effects on \u003cem\u003eR. arenarum\u003c/em\u003e were more potent, affecting most of the gene expression early at 6 h and sustaining the effects after 24 h of exposure. Notably, we may also infer that OP effects on gene expression, in addition to being moderate in their amplitude, were also predominantly towards a downregulation.\u003c/p\u003e \u003cp\u003eWe could also verify the moderate effects of both OP on the hypothesis-selected gene expressions and the differences in favor of AZM as milder than CPF in triggering responses, using edgeR pipeline analysis. To perform this analysis, we had to collapse fragments from a same identified and annotated gene into a unique set of expression data, adding the TMM values for each treatment replicate. However, it was not feasible to cure and/or verify each transcript in the whole big database, so the information extracted for each hypothesis-selected transcript might contain biased errors. In this way, we could find coincidences in the statistical significances of the differential gene expressions on two-thirds of the cases identified in the robust non-parametric analysis. Nevertheless, edgeR pipeline let us additionally rank the significant DEG and determine the positioning of most of the hypothesis-selected transcripts. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the results confirm that these \u003cem\u003ea priori\u003c/em\u003e pathway-selected genes are not among the most affected ones in terms of fold-changes by OP exposure in \u003cem\u003eR. arenarum\u003c/em\u003e larvae.\u003c/p\u003e \u003cp\u003eThe polyamine metabolism pathway was probably the most affected group of those analyzed in this work, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The general pattern of the effects of both OP pesticides on polyamine synthesis suggests downregulation through successive involved genes. Ornithine decarboxylase, S-adenosyl methionine decarboxylase precursor and spermidine-spermine synthase expression were mainly inhibited, except for one AMD-1 transcript that showed an induction reaching 2-fold or more. Antizyme expression is also inhibited, but this may be a feedback effect due to ODC inhibition itself. Complementary to these effects on polyamine synthesis, degradation genes tend to be downregulated by OP pesticides, probably as a cellular response intended to avoid a lethal drop in polyamine levels \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Nevertheless, some enzymes related to spermidine and spermine degradation show increasing expression in larvae exposed to CPF, which seems to elicit stronger effects than AZM. A decrease in putrescine and spermine was observed in \u003cem\u003eR. arenarum\u003c/em\u003e embryos of the complete operculum stage exposed to AZM \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Similarly, CPF downregulated ODC activity and decreased putrescine and spermidine levels in early \u003cem\u003eR. arenarum\u003c/em\u003e embryos, correlated with the percentage of embryonic developmental arrest \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The opposite effect was reported in \u003cem\u003eR. arenarum\u003c/em\u003e late embryos exposed to AZM, where ODC activity and putrescine content were increased \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. We also found concordance between the transcript expression levels and some of the polyamine-degrading enzymes and previously reported activity values in \u003cem\u003eR. arenarum\u003c/em\u003e embryos exposed to AZM or CPF, i.e. the inhibition of DAO and SMOX activities and the increase in PAOX activity \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Therefore, there is a good correspondence between the expression levels found at the transcriptomic level for the regulatory enzymes, some of the polyamine-metabolizing enzyme activities and the polyamine contents in \u003cem\u003eR. arenarum\u003c/em\u003e. These findings are related to the fine regulation of polyamine metabolism, both at the transcriptional level and at the enzymatic level.\u003c/p\u003e \u003cp\u003eOxidative stress has been reported in different organisms exposed to OP pesticides. In particular, the effects of AZM and CPF on oxidative stress and antioxidant responses at the metabolite and enzymatic levels have been studied in \u003cem\u003eR. arenarum\u003c/em\u003e development. Indeed, we have proposed that there is a link between OP effects on polyamine metabolism and levels, oxidative stress and teratogenic effects in \u003cem\u003eR. arenarum\u003c/em\u003e embryos and larvae \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Smirnova et al. (2012)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e analyzed oxidative stress as the cause of alterations in polyamine metabolism due to the dysregulation of ODC and SSAT; human hepatoma cells chemically induced to increase ROS production showed overexpression of ODC and SSAT, which are transcriptionally regulated by NRF2 through a specific recognition site. The downregulation of NRF2 mRNA expression with elevated Nrf2 protein levels has been reported in liver pathologies \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We found that OP exposure in \u003cem\u003eR. arenarum\u003c/em\u003e larvae caused a downregulation of NRF2 mRNA expression, and the accompanying downregulation at the transcriptional level of ODC, SSAT and several antioxidant-detoxifying enzyme genes containing ARE sequences (such as GSTA and GSTP) suggests that Nrf2 protein or activity might also be downregulated.\u003c/p\u003e \u003cp\u003eMa et al. (2018)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e refer to the alterations in transcription levels of detoxifying and oxidative stress-related enzymes in the GST and CYP groups in a transcriptomic analysis performed on trichlorfon-exposed \u003cem\u003eRana chensinensis\u003c/em\u003e. This OP pesticide upregulated CYP2C transcripts but downregulated CYP3A and GSTK1. We report an increase in GSTK1 expression because of CPF exposure in \u003cem\u003eR. arenarum\u003c/em\u003e, as well as for other GST isoforms, such as mu and the microsomal isoforms. This upregulation in several GST transcripts is in concordance with the reported increase in GST activity using CDNB as a substrate in \u003cem\u003eR. arenarum\u003c/em\u003e embryos and larvae after exposure to both AZM and CPF, among other OP pesticides \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The effect on GST activity was associated with an increase in GST-Pi1 protein in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to arsenic \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Other genes under Nrf2 regulation are those belonging to antioxidant defenses, such as SOD, glutathione peroxidases (GPx2, 3, 6 and 8), and glutathione reductases (GSHR1) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Accordingly, we observed downregulated levels of SODC and GPX8 accompanied by NRF2 downregulation. Glutathione synthase GSHB, and CATA, also seemed downregulated, while SODE, GPX1, 3 and 4 were induced, mainly by CPF. In our experience, the antioxidant enzymatic activity response varies greatly in \u003cem\u003eR. arenarum\u003c/em\u003e embryos and larvae exposed to OP pesticides, showing cycles of induction followed by inhibition attributed to catalytic site inactivation due to ROS attack \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also report here the downregulation of CYP1A1 and CYP2A19 for both AZM and CPF exposures, probably as a negative regulation after a previous detoxification response. CYP1A1/2 are among the genes regulated by the AhR pathway by the binding of activated and nuclear-translocated AhR-Arnt heterodimeric transcription factor to DRE/XREs in their regulatory sequences. Studies carried out with \u003cem\u003eSparus aurata\u003c/em\u003e exposed to PCBs have shown a different expression pattern between AHRR and AhR-ARNT, in accordance with multiple mechanisms contributing to the downregulation of AhR \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our findings coincide with these studies, as we observed induced levels of AHRR, while the levels of ARNT were decreased in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed to both CPF and AZM. Furthermore, the decreased levels of AhR-ARNT are consistent with the decreased expression found in CYP1A, since the entire mechanism is downregulated. A decrease in HSP90 and AIP transcripts, supposing reduced chaperone levels, would also contribute to lower AhR protein levels \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The AhR pathway is also involved in paraoxonase-1 expression regulation, according to results on exposure to polyphenols and specific inducing ligands \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In turn, the PON-2 gene has at least a CRE regulatory sequence for AP-1 regulation linked to the oxidative response and JNK activation and a PAPR-regulated site related to polyphenol activation and MEK pathway downregulation by phosphorylation \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Our results agree with downregulated MEK (MP2Ks), AP-1 (cFOS) and AHR pathways causing PON2 transcript repression in \u003cem\u003eR. arenarum\u003c/em\u003e larvae exposed mainly to CPF. Similarly, the xenobiotic/endobiotic detoxifying carboxylesterase family (CES) is transcriptionally regulated by a series of nuclear factors and pathways: AhR, constitutive androstane receptor (CAR), pregnane X receptor (PXR) and Nrf2 are involved mostly in the upregulation of some of these families \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Although we were not able to annotate putative transcripts corresponding to CAR and PXR pathways in our \u003cem\u003eR. arenarum\u003c/em\u003e transcriptome, the downregulation trends followed by AhR and Nrf2 pathways in larvae exposed to AZM and CPF are in line with CES5A transcript downregulation. This may be surprising, in the sense that carboxylesterases are recognized suicide \u0026ldquo;buffer\u0026rdquo; enzymes that irreversibly react with OP pesticides to protect the primary target acetylcholinesterase in the nervous system, decreasing their activities. This is in fact corroborated in toad embryos and larvae exposed to different OP pesticides, including AZM and CPF, in most of our reports in \u003cem\u003eR. arenarum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Thus, an induction of carboxylesterase and cholinesterase mRNA and/or protein synthesis would be expected to reestablish normal activity levels. Finally, remarking on the complexity of the responses and crosstalk between pathways, we have reported for different developmental states in \u003cem\u003eR. arenarum\u003c/em\u003e and cell cultures exposed to CPF, other OP pesticides or arsenic, an increase in MEK1/2 and ERK1/2 proteins, their translocation to the nucleus, and ERK phosphorylation, considering that the MAP kinase pathway regulates the Nrf2-mediated response. Additionally, cFOS and cJUN proteins are increased after oxidative stress in \u003cem\u003eR. arenarum\u003c/em\u003e embryos and larvae \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcluding remarks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, we acknowledge the power of a hypothesis-driven transcriptomic data analysis on selected pathways. First, we identified an appropriate battery of potential housekeeping genes in \u003cem\u003eR. arenarum\u003c/em\u003e larvae to further analyze gene expression by conventional or quantitative PCR after pesticide exposure. Second, we were able to visualize the effects of two OP pesticides in \u003cem\u003ea priori\u003c/em\u003e selected metabolic and signaling pathways and compare them to previous metabolite and enzyme activity analyses. In our analysis, we infer a gradient of effects since CPF is more potent than AZM and acts earlier on gene transcription. Finally, we found a prevailing downregulation in signaling cascades and transcription factors that act upstream of the polyamine metabolism pathway and antioxidant responses, which partially coincides with previously well-characterized responses at the protein and/or activity and metabolite levels.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eChemicals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh purity-certified standards of azinphos-methyl (98.3% AZM) and chlorpyrifos (CPF; 99.5% purity) were purchased from Chem Service Inc. (West Chester, PA, USA). Standard solutions of 18 g L\u003csup\u003e-1\u003c/sup\u003e AZM and 1 g L\u003csup\u003e-1\u003c/sup\u003e CPF were prepared by dissolving the pesticide standards in acetone. The exact concentrations of AZM and CPF in the standard solution were checked by capillary gas chromatography coupled to a nitrogen-phosphorus detector (GC-NPD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdult females and males of the South American common toad (\u003cem\u003eRhinella arenarum\u003c/em\u003e) were collected in reference areas, free of pesticide application, in agreement with the corresponding collection permission 040/2020 from the Environment Secretary of R\u0026iacute;o Negro Province, Argentina. Animals used in this study were maintained and treated with regard to the alleviation of suffering according to recommendations of the\u0026nbsp;Guide for the Care and Use of Laboratory Animals (National Research Council 2011)\u0026nbsp;\u003csup\u003e49\u003c/sup\u003e. The animals were kept in captivity outdoors for 24-48 h until their use. Female ovulation was induced by intraperitoneal injection of 2500 international units (IU) of human chorionic gonadotropin (ELEA Laboratory, Buenos Aires, Argentina.) and embryos were obtained by \u003cem\u003ein vitro\u003c/em\u003e fertilization\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e. Embryos were maintained until they reached the complete operculum (CO) stage (stage 25, according to\u0026nbsp;\u003csup\u003e50\u003c/sup\u003e). Ten days after reaching the CO stage, the larvae were used for acute toxicity assays.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcute toxicity assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole protocol was approved by the Faculty Committee for Care and Use of Experimental Animals (CICUAL- Facultad de Ciencias Agrarias Universidad Nacional del Comahue 01/13- 7 -2020). Larvae were randomly collected to perform the assays. Sublethal concentrations of AZM (0.5 mg L\u003csup\u003e-1\u003c/sup\u003e, 1/20 96 h-LC50;\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e) and CPF (0.1 mg L\u003csup\u003e-1\u003c/sup\u003e), 1/15 96 h-LC50;\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e) were selected to carry out exposures for up to 24 h in glass dishes, maintaining a ratio of 1 larva/10 mL in amphibian Ringer\u0026apos;s solution with 0.3% acetone (final v/v). These concentrations and higher ones in the order of 1 mg L\u003csup\u003e-1\u003c/sup\u003e might be transiently found at the irrigation channels in fruit-producing orchards where this species reproduces \u003csup\u003e2,7\u003c/sup\u003e. The exact pesticide concentrations were checked by gas chromatography and nitrogen-phosphorus detection. Control acetone treatment was included to discard possible solvent effects. The treatments were carried out in duplicate, and \u003cem\u003eR. arenarum\u003c/em\u003e larvae were grown in 10 different glass receptacles to perform AZM/CPF 6 h exposures, AZM/CPF 24 h exposures, and control treatments. From each receptacle, fifteen random larvae were collected and pooled at the corresponding times. Larvae were washed three times with cold Ringer\u0026apos;s solution, placed in 1.5 mL tubes with RNALater\u0026reg; (Thermo Fisher Scientific Inc.) and stored at -20 \u0026deg; C until processed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA extraction, cDNA library generation and massive parallel sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA extraction, cDNA library generation and massive parallel sequencing were carried out as described by\u0026nbsp;Ceschin et al. (2020)\u003csup\u003e13\u003c/sup\u003e. Briefly, total RNA of each sample was extracted, and the cDNA library for transcriptome analysis was prepared. The ten library samples were normalized to 10 nM cDNA to be sequenced on a HiSeq 1500 Illumina platform, generating nonstrand specific \u0026ldquo;paired-ends\u0026rdquo; (PE) 2 \u0026times; 100 bp readings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatic construction of R. arenarum transcriptome and statistical analysis of expression levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA detailed description of the de novo transcriptome assembly, annotation and gene prediction was previously provided by\u0026nbsp;Ceschin et al. (2020)\u003csup\u003e13\u003c/sup\u003e. Briefly, the readings obtained by massive sequencing of the \u003cem\u003eR. arenarum\u003c/em\u003e transcriptome were aligned with Bowtie2 v2.3.5\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e against TSA: GHCG00000000.1 (BioProject PRJNA485066), and transcript expression quantification was performed using RSEM v1.3.0\u0026nbsp;\u003csup\u003e52,53\u003c/sup\u003e. Expression values were normalized by the TMM method using the R and EdgeR packages\u0026nbsp;\u003csup\u003e54,55\u003c/sup\u003e. Once the adequate HK transcripts were identified, the TMM values were standardized both by the average of the selected reference genes and by the respective control values. Finally, a nonparametric analysis was performed by the median and Kruskal-Wallis tests to assess significant differences or tendencies using the exact p-values. Raw data, standardization steps and statistical analyses are available in Supplementary Data File II.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValidation of transcript expression results was performed by qPCR analysis on CPF-exposed \u003cem\u003eR. arenarum\u003c/em\u003e larvae, following the same procedures described above for the treatments and RNA extraction. The different steps for PCR development are detailed in the \u0026ldquo;Supplementary file PCR data methods in brief\u0026rdquo;.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grant 04A134 from the Universidad Nacional del Comahue and PICT 2017-1529 from Agencia Nacional de Promoci\u0026oacute;n Cient\u0026iacute;fica y Tecnol\u0026oacute;gica.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability and ARRIVE statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in the Supplementary Data Files I and II. This study is reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNSP performed annotated gene data mining for selected pathways, statistical analyses and initial writing; CIL collaborated with experimental procedures, data calculations, manuscript revision and language editing; JO performed primer designs, sample RNA extraction, RT-PCR and qPCR assays together with NSP; DGC performed the experiments, sample processing, RNAseq and bioinformatic data assembly, including gene annotation; AV contributed to project design, gene selection, data calculation, statistical analysis, manuscript writing and processing. CIL, DGC and AV are staff researchers of CONICET-Argentina.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKwet, S. R., Silvano, D., \u0026Uacute;beda, C., Baldo, D. \u0026amp; Tada, I. Di. 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(2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Comparison of distribution statistics and expression variation of transcripts proposed as HK in \u003cem\u003eR. arenarum\u003c/em\u003e stage 25 larvae.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.340819022457067%\"\u003e\n \u003cp\u003eCV %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eEF1A0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eElongation factor 1-alpha, somatic form\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e7329.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e6610.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e8457.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e586.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e7265.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eEF1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eElongation factor 1-beta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e583.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e498.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e676.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e64.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e589.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e11.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eEF1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eElongation factor 1-delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e922.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e800.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e1074.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e910.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eEF1GA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eElongation factor 1-gamma-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e2993.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e2611.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e3344.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e252.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e2968.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eG3P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1822.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e1289.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e2425.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e411.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1768.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e22.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eRL8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003e60S ribosomal protein L8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1108.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e883.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e1309.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e134.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1131.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eTBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eTubulin alpha chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1829.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e1589.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e2014.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e142.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e1826.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eTBA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eTubulin alpha-1 chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e146.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e98.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e195.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e142.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e21.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eTBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eTubulin beta chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e518.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e350.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e586.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e526.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e13.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eTBB4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eTubulin beta-4B chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e219.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e253.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e224.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eACTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eActin, cytoplasmic 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e363.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e301.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e394.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e380.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"10.435931307793924%\"\u003e\n \u003cp\u003eACT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"38.177014531043596%\"\u003e\n \u003cp\u003eActin, alpha sarcomeric/skeletal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e90.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.775429326287979%\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.039630118890356%\"\u003e\n \u003cp\u003e259.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"7.265521796565389%\"\u003e\n \u003cp\u003e73.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.982826948480845%\"\u003e\n \u003cp\u003e61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"6.340819022457067%\"\u003e\n \u003cp\u003e81.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData calculated from massive RNA-seq and transcriptomic analysis are expressed as TMM values. A limit of 20% for the CV is proposed to consider a transcript as an HK gene in expression analysis.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Transcriptomics, amphibian, organophosphorus pesticides, molecular targets, signaling pathways, housekeeping genes","lastPublishedDoi":"10.21203/rs.3.rs-1677791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1677791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTranscriptional analysis of the network of transcription regulators and target pathways in exposed organisms may be a hard task when their genome remains unknown. The development of hundreds of qPCR assays, including primers design and results normalization with the appropriate housekeeping genes, seems an unreachable task. Alternatively, we took advantage of a whole transcriptome study on \u003cem\u003eRhinella arenarum\u003c/em\u003e larvae exposed to the organophosphorus pesticides azinphos-methyl and chlorpyrifos to evaluate transcriptional effects on \u003cem\u003ea priori\u003c/em\u003e selected groups of genes. This approach allowed us to evaluate the effects on hypothesis-selected pathways such as target esterases, detoxifying enzymes, polyamine metabolism and signaling and regulatory pathways modulating them. We could then compare the responses at the transcriptional level with previously described effects at the enzymatic or metabolic levels to obtain global insight into toxicity-response mechanisms. The effects of both pesticides on the transcript levels of these pathways could be considered moderate, while the responses elicited by chlorpyrifos were more potent and earlier than those elicited by azinphos-methyl. Finally, we inferred a prevailing downregulation effect of pesticides on signaling pathways and transcription factor transcripts encoding products that modulate/control the polyamine and antioxidant response pathways. We additionally tested and selected potential housekeeping genes based on those reported for other species. These results allow us to go through future confirmatory studies on pesticide gene expression modulation in toad larvae.\u003c/p\u003e","manuscriptTitle":"Hypothesis-driven dragging of transcriptomic data to analyze proven targeted pathways in Rhinella arenarum larvae exposed to organophosphorus pesticides","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-31 21:26:57","doi":"10.21203/rs.3.rs-1677791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-08-22T18:19:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-08-18T16:14:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-06-16T11:55:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ef760824-07c4-414d-a23e-15a221b50a10","date":"2022-06-04T20:48:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-05-29T19:33:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-05-29T19:31:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-05-26T16:28:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-05-26T16:26:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-05-20T18:19:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6609dd3a-e097-4760-b7c9-3466cdb506e8","owner":[],"postedDate":"May 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-09-30T16:59:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-31 21:26:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1677791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1677791","identity":"rs-1677791","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}