Reef fishes are an exceptionally speciose vertebrate assemblage, yet the main drivers of their diversification remain unclear. It has been suggested that Miocene reef rearrangements promoted opportunities for lineage diversification, however, the specific mechanisms are not well understood. Here, we assemble near-complete reef fish phylogenies to assess the importance of ecological and geographical factors in explaining lineage origination patterns. We reveal that reef fish diversification is strongly associated with species’ trophic identity and body size. Large-bodied herbivorous fishes outpace all other trophic groups in recent diversification rates, a pattern that is consistent through time. Additionally, we show that omnivory acts as an intermediate evolutionary step between higher and lower trophic levels, while planktivory represents a common transition destination. Overall, these results suggest that Miocene changes in reef configurations were likely driven by, and subsequently promoted, trophic innovations. This highlights trophic evolution as a key element in enhancing reef fish diversification. Both geography and ecology can drive the origins of new species. Siqueira et al. show how geological changes in the structure of Miocene reefs and the concurrent evolution of new feeding strategies combine to explain why coral reefs contain such a diversity of fish species. As a product of speciation and extinction rates, diversification varies through time4 and among lineages1, being ultimately influenced by both biotic and abiotic factors5. Consequently, understanding how biotic and abiotic factors interact in space and time is paramount in explaining underlying patterns of species diversification. For instance, recent studies have shown that distinct diversification trajectories within vertebrate groups can be explained by geographical7 and species-specific biological traits9. Although these studies provided important insights into individual drivers of vertebrate radiations, disentangling the simultaneous influence of multiple factors on rate heterogeneities is still challenging. This will require a comprehensive approach with methods that can account for multi-order interactions among the underlying drivers of species diversification.The heterogeneity in rates of species formation across the tree of life is a widely recognized macroevolutionary pattern10. Particularly important within these high-diversity systems, fishes represent key energetic conduits, taking part in a large proportion of the recognized biotic interactions. This ecological diversity is also reflected in taxonomic terms, with reef-associated fishes being one of the most speciose vertebrate assemblages in the world11. Although it has been shown that the association with reefs was an important promoter of fish cladogenesis12, the specific mechanisms driving this diversification are not yet fully understood. Historical and geological processes have clearly influenced global distribution patterns of reef fishes at large temporal scales18, with Miocene (23–5.3 million years ago [Ma]) changes in reef configuration being posited as one of the most important drivers of lineage expansion19. However, it is still unknown whether this change in the pace of lineage formation in the Miocene occurred under the influence of biotic or predominantly abiotic factors.Coral reefs constitute an excellent system for applying such broad macroevolutionary approaches, given their status as cradles for biodiversity19, the drivers of reef fish diversification in the Miocene seem to have involved a complex mix of history and ecology. On the historical side, this epoch was marked by major geomorphological changes that reshaped marine biogeography with the formation of the Indo-Australian-Archipelago (IAA) marine biodiversity hotspot14. This process was likely associated with rapid diversification of reef fish lineages12, given the extensive opportunities for vicariance and range expansion provided by the geographical complexity of the IAA. On the ecological side, key trophic innovations  in reef-associated fishes have fundamentally altered the nature of Miocene reefs19. While major reef fish trophic groups were already represented in the Eocene (56–33.9 Ma), specialized morphologies associated with the exploitation of detrital and corallivore trophic pathways, for example, only arose in the Miocene24. These morphological and trophic innovations have also been linked to increased lineage origination in selected reef fish groups28, suggesting that trophic evolution might have had a prominent role in driving patterns of reef fish diversification. Although recent studies have independently explored these potential ecological mechanisms or geographical factors underlying reef fish diversification patterns , they are yet to be examined in a comparative analytical framework capable of quantifying relative support.As recently suggestedTo fill this knowledge gap, we apply phylogenetic comparative methods in near-complete phylogenetic trees to examine the relative importance of ecological and geographical factors in explaining recent lineage origination patterns in reef fishes. More specifically, we first estimate the rates of diversification for all lineages of reef-associated fishes. Then, we apply extreme gradient boosting techniques to assess the most important variables in explaining these lineage-specific rates. Finally, we investigate the historical patterns of reef fish trophic evolution in terms of evolutionary rates and guild transitions, after having identified trophic evolution as a major driver of recent reef fish diversification. These approaches provide a complementary picture of both recent and historical rates of evolution in an important vertebrate radiation, reef fishes.−1 [per million years]) in the genus Megalops, to extreme values (1.2 lineages Myr−1) in Hypoplectrus. Most extant lineages  varied by more than two orders of magnitude, ranging from slightly negative , were found to be highest for herbivores/detritivores  Fig. . OmnivorWe also found a clear interaction between body size and trophic group Fig. . Larger Model results and predictions were consistent when we used the estimates derived from the “DR statistic” as an alternative to the BAMM estimates (see Methods), with only geographic range and temperature slightly increasing in importance  have higher diversification rates than higher-quality feeding ones. However, in addition to these lower-quality feeding guilds, we found that planktivorous lineages also diversified disproportionally fast. This suggests that it is recently acquired trophic strategies, rather than low-quality feeding per se, that may have opened up opportunities for shifts in the pace of lineage origination. These trophic innovations in reef fishes predominantly occurred in the past 20 Myr33, a time that closely matches the highest diversification rates of key lineages in our study 18, it was not until the Miocene that they acquired necessary body and fin morphologies to move into this challenging reef zone24. This apparently simple move may have driven profound trophodynamic changes in shallow reefs36, which might help explain our results.This geological period was marked by the rise of high-turnover reef ecosystems in which both fast-growing corals and large bioeroding fishes first appeared36. Second, the intense grazing pressure promoted by large herbivorous populations may have facilitated the expansion of corals in shallow waters, by altering the coral–algal competitive balance19. This is supported by the paleontological evidence, which suggests that, despite some peripheral scleractinian reef formation in the Eocene39, the rise of modern scleractinian-dominated reefs only took place in the late Miocene41. Finally, once corals dominated shallow waters, they had the capacity to promote the expansion of sessile invertivorous and planktivorous lineages. In the case of SIs, this expansion was likely related to more opportunities for resource exploitation, given the general increase in the availability of both shelter and the abundance of organisms exploited as food sources19. In PKs, the expansion was potentially linked to the shelter provided by topographical complexity against predators and water flow in highly productive and highly hydrodynamic shallow reef environments42. Although intense hydrodynamics might offer a constant flow of planktonic resources, without the refuge provided by corals these shallow reef habitats would probably be uninhabitable for many planktivorous species43.There are three key components to this explanation. First, the colonization of the productive reef flats probably allowed herbivorous fishes to expand their population sizes44. However, by doing this, these fishes become more exposed to predation45. As body size is a major determinant of predation risk in reef fishes46, being large may provide herbivorous fishes with a size refuge from predation. Consequently, the colonization of the reef flats in the Miocene might have been particularly beneficial for large-bodied herbivorous fishes. This is because they were free to maintain high grazing rates on highly productive reef flats, while avoiding the typically high predation pressure in these habitats36. Thus, predation could have been a key component in driving differences in the diversification rate of small–medium and large herbivorous fishes. Although speculative, these ideas provide a fertile ground for future studies willing to compare different models of size evolution in herbivorous reef fishes.This hypothesized scenario provides not only a logical explanation for the observed diversification rates among trophic groups, but it also helps elucidating the patterns found for large body-sized herbivores. To meet metabolic demands, herbivorous fishes have to maintain higher feeding rates when compared to other trophic groups47, which highlights the importance of this trait for species’ long-distance colonization capabilities. Although this might promote genetic connectivity in ecological time scales, in evolutionary time scales it might also increase the chances of vicariance, given the variability in effectiveness of marine biogeographical barriers through time49 and the likelihood of fragmentation of previously contiguous populations. While body size can be considered a “universal trait” related to multiple biological processes50, the key element here might be related to use of shallow reef flat habitats. It appears that the remarkably higher diversification rates found for large-bodied HDs , suggesting that animal trophic evolution might follow common rules. However, to our knowledge, this is the first time that the synergistic effects of species body size and trophic identity have been considered simultaneously when exploring diversification patterns in vertebrates. Body size is regarded as an important component of organismal evolution, given its influence on metabolic rates52 and generation times53. Thus, our results showing lower diversification rates for smaller-bodied species seem counterintuitive considering evolutionary theories. For example, small cryptobenthic fishes contribute to a large proportion of the species richness found on coral reefs54, and their high population turnover and low connectivity should promote faster rates of diversification55. Yet, our results show that gobies, for instance, might be among the slowest evolving families on coral reefs. We propose two possible explanations for these seemingly counterintuitive results. First, although the fast life history of cryptobenthic fishes should be reflected in rapid diversification, some groups might be experiencing high rates of extinction. Unfortunately, estimating extinction rates from phylogenetic trees can be problematic56, making the test of this hypothesis difficult without a good fossil record. Second, our rates of diversification for cryptobenthic fishes might be underestimated due to taxonomic sampling (judging by the rate of species descriptions for these groups and the expected number of undescribed species54). While plausible, when we controlled for this effect by removing key cryptobenthic families54, our trophic results remained practically unchanged and we still found slightly lower diversification rates for smaller-bodied species . Despite being unusual in other trophic identities, these morphological modifications  associated with planktivory seem to arise frequently, no matter the trophic group of the originating lineage  that are unlikely to be simply acquired in evolutionary terms. Not coincidently, these trophic identities represent the most taxonomically restricted groups of reef fishes. Thus, as suggested for selected reef fish families31, the pathway to transition within these trophic groups appears to involve an intermediate generalist stage in which lineages have not yet fully developed the biological traits related to the exploration of specific resources. Interestingly, omnivorous reef fishes have been shown to have very slow rates of morphological evolution29. Alongside our results, this suggests that omnivory might not be an evolutionarily stable trophic strategy; rather, it may represent a transitional stage between reef fish trophic groups.In other recently derived trophic groups, however, transitions occur almost exclusively from omnivorous lineages Fig. , a findi63, our study is unlikely to suffer from this issue. This is because our model relies on estimates of very recent diversification rates (tip rates), which have been shown to be relatively robust to the issues of parameter non-identifiability63. Furthermore, considering the extreme heterogeneity in diversification rates found in reef fishes  process to find distinct diversification regimes within a phylogeny and estimate lineage-specific speciation and extinction rates. For each of our trees, we ran time-variable models for 30 million generations using default operators and priors generated through the “BAMMtools”70 R package. To facilitate convergence, we set a prior expectation of 100 diversification regime shifts. Since we were using near-complete trees, we set the globalSamplingFraction parameter to one. At the end of each run, we removed the initial 10% of the samples as burn-in and assessed convergence through the effective sample sizes using the “coda”71 R package. After running BAMM independently in each of our 100 trees, we combined their results by assessing the median estimated tip diversification rates.To estimate diversification rates within our phylogenies, we used the program BAMM 2.5.073), they have been largely addressed in subsequent studies and program refinements . The current program, therefore, remains a robust framework for estimating diversification rates in large phylogenetic trees. Recently, another framework has been proposed (ClaDS3), providing model improvements in terms of lineage-specific rate estimates. Although this model represents a very strong alternative to BAMM, its implementation is still computationally very intensive, making analyses in large phylogenies such as ours impractical. Therefore, to be able to use another method to cross-validate our main BAMM analysis, we applied the “DR statistic”6 in our near-complete trees. Although this method is mainly focused on speciation, rather than diversification rates76, it is a very useful metric to study speciation rate dynamics alongside BAMM76. We applied this method in our 100 trees and assessed the median lineage-specific speciation rates. The median BAMM and the DR tip estimates were then used independently as the response variables in our predictive model (see Predicting diversification rates in Methods section). Finally, since we focused on patterns of recent (tip) diversification rates, our estimates are unlikely to be influenced by the recently described issues of parameter non-identifiability63 in extant species phylogenies.Although concerns related to BAMM have been raised , and three categorical traits related to species’ ecologies . All the body length data available for our studied species was downloaded from FishBase through “rfishbase”65. For the trophic identity, we grouped species into six major categories related to their diets in the adult life stages: GCs, MIs, OMs, PKs, SIs, and HDs. These categories are related to previously defined dietary groups for reef fishes77; however, we merged the herbivores/macroalgivores category with the general herbivores/detritivores group. This was done to avoid biases in the predictive and the trait-dependent diversification models, given the very small sample size of macroalgivores in our dataset. Our classification considered the most common diets described for each species regardless of potential geographical variation. We also split species between diurnal, nocturnal, or both77, according to their circadian pattern of activity. Lastly, we used the vertical position where fishes are commonly found in the water column as a proxy for their degree of association with the reef matrix, so we classified species as benthic, benthopelagic, and pelagic77.To assess the main drivers of diversification in reef fishes, we generated a dataset with potential explanatory variables. These variables consisted of a set of species’ ecological traits and geographical factors hypothesized to influence the pace of reef fish lineage formation. We used information from the literature, online datasets, and expert assessments79. This dataset consists of global presence–absence records of fishes in 300 × 300 km2 resolution grids, originally sourced from four online biodiversity information systems 7. From this presence–absence data, we filtered those species that were present in our trees and we calculated their geographical range by summing the number of occupied cells. Additionally, we classified species according to their presence in each major oceanic basin , and we calculated the absolute latitude of the centroid of their geographical distribution. By combining the absolute latitude value with the longitudinal centroid of each species, we calculated the distance between that centroid and a central point in the IAA . These variables were added to the model (see Predicting diversification rates in Methods section) to assess predictions related to the influence of biogeography into reef fish diversification rates19.To classify species according to geographical variables, we downloaded the occurrence-based dataset from Rabosky et al.79 that contained environmental variables per grid cell. With these data, we accessed the mean sea surface temperature (SST) and the mean primary productivity (Pr prod) at the centroid grid of each species. Since tropical reef fish lineages have been found to sustain higher net diversification rates30, we used these variables to assess if this might be associated with higher temperatures or energy availability. Our complete dataset containing species’ ecological traits  and geographical variables  had a total of 4875 species.In addition to the presence–absence dataset, we also downloaded the supplementary data from Rabosky et al.80. This machine learning technique represents a state-of-the-art method for modelling complex nonlinear relationships81. It has advantages over other modelling techniques because it automatically handles multi-order interactions among predictors, it does not require prior data transformation or outlier exclusion81, and it provides fast and accurate predictions80. We used the “xgboost”82 R package to build our predictive model. Before running the predictive model, we performed two tuning steps to obtain the combination of parameters  that would result in the minimum root mean square error (rmse). In the first tuning step, we fit models with a range of predefined parameter combinations that were varied systematically to assess which would provide the minimum rmse. In the second tuning step, we refit 1000 models by randomly sampling parameters from a uniform distribution with upper and lower bounds defined as values from the best parameter combination of the first step plus or minus 10%. The parameter combination with the minimum rmse from the second tuning step was then used in the final predictive model. Both tuning steps and the final predictive model were fitted using a gamma distribution for the median tip diversification rates resulting from our BAMM analysis as the response variable.To evaluate the importance of each ecological and geographical variables in predicting reef fish diversification rates, we used the Gradient Boosted Regression Tree method XGBoostR2 of a linear model fitted between the measured and predicted diversification rates. These cross-validation tests were performed 1000 times to assess the mean accuracy and precision values.We used a cross-validation procedure to assess the model’s accuracy and precision in predicting diversification rates. To do that, we divided our dataset into training and testing parts by randomly subsetting 80% and 20% of the datapoints, respectively. We used the training dataset to refit the final model and assess the coefficients of prediction. These coefficients were then used to predict the tip diversification rates in the testing dataset. Accuracy was calculated as the average bias by subtracting each predicted tip diversification from its actual value in the training dataset. Precision was assessed using the We ran the predictions for all levels of the categorical variables, and for a range of values spanning the minimum and maximum measured continuous variables. These predictions were bootstrapped for 1000 iterations to assess the relative importance of each explanatory variable. Finally, we did another 1000 bootstrap iterations of the final predictive model varying only trophic group and maximum body length , while keeping all the other continuous variables in their mean values and the categorical variables in their most common category.xgboost analysis selecting only the reef fish families considered “consensus” families13 . Second, we ran the predictive model excluding the families defined by Brandl et al.54 as cryptobenthic reef fishes. With the first analysis, we intended to eliminate potential issues of defining what constitutes reef fishes. In the second analysis, we wanted to exclude the potential taxonomic bias associated with smaller body-sized species, that is, we expect more undescribed cryptobenthic species than larger-bodied ones.All of these steps were replicated using the “DR statistic” results as the response variable. Moreover, two model sensitivity analyses were performed. First, we ran the 83) models for the classified trophic groups. Our two sets of diversification analyses differ in the sense that the first (BAMM) was used to estimate rates independently of trait evolution, whereas the second (MuSSE) was specifically used to investigate trait-dependent patterns of diversification. These trait-dependent diversification models allow the analysis of character state evolution coupled with changes in speciation and extinction rates.After detecting trophic identity as the main explanatory variable for recent (tip) patterns of lineage diversification in reef fishes (see Results), we explored the historical patterns of trophic evolution using the whole structure of the phylogenetic trees. This was achieved by building multistate speciation and extinction  associated with each trophic group using an unconstrained MuSSE model with the maximum-likelihood function of the “diversitree”84; however, they are unlikely to affect our analysis. This is because we did not use MuSSE to perform model selection and thus imply that trophic group is the only trait affecting reef fish diversification. Based on our BAMM results  and more recently derived  groups. Using the HiSSE framework, we built an unconstrained model that considered rates to be different between analyzed character states (trophic group) with one hidden diversification regime per state, and compared it to a model in which rates were constrained between states but different from the hidden diversification regime. Results from this HiSSE analysis supported our MuSSE results (see “Results”) and estimated higher diversification rates for the recently derived trophic groups compared to the ancestral ones  to perform this analysis. For each of our near-complete trees, we simulated 10 stochastic maps using a modified version of the make.simmap function from the “phytools”88 R package. We customized the aforementioned function to use the transition rates and the ancestral state reconstruction results derived from the original MuSSE model  as inputs for the stochastic mappings. The combined results of all stochastic maps were summarized to assess the mean number of transitions per trophic group. These estimates were then used to plot chord diagrams representing the directionality of transitions using the “circlize”89 R package.We quantified the transitions between classified trophic groups by using stochastic character mappingsFurther information on experimental design is available in the Nature Research Supplementary InformationPeer ReviewReporting Summary


Radiotherapy can result in temporary or permanent gonadal toxicity in male cancer patients despite the high precision and accuracy of modern radiation treatment techniques. Previous radiobiological studies have shown an effective tissue-sparing response in various tissue types and species following exposure to spatially fractionated radiation. In the present study, we used an ex vivo mouse testicular tissue culture model and a conventional X-ray irradiation device to evaluate the tissue-sparing effect (TSE) of spatially fractionated X-rays for the protection of male fertility from radiotherapy-related adverse effects. We revealed a significant TSE for maintaining spermatogenesis in the ex vivo testes model following spatially fractionated X-ray irradiation. Moreover, we experimentally propose a possible mechanism by which the migration of spermatogonial cells, from the non-irradiated areas to the irradiated ones, in irradiated testicular tissue, is essential for the TSE and maintaining spermatogenesis. Therefore, our findings demonstrate that the control of TSE following spatially fractionated X-rays in the testes has a considerable potential for clinical application. Interdisciplinary research will be essential for further expanding the applicability of this method as an approach for the preservation of male fertility during or after radiotherapy. The protection of normal tissue, as well as the eradication of tumor, is an ultimate goal of radiation treatment. However, although radiotherapy has significantly evolved to have a high level of precision and accuracy, it can still result in temporary, long-term, or permanent gonadal toxicity in male patients ,2. In faTissue-level responses remarkably differ depending on whether radiation is delivered uniformly or non-uniformly . In 1909To verify our hypothesis, in the present study, we used a conventional X-ray source and a unique ex vivo testicular tissue culture, which enabled us to monitor the progress of spermatogenesis and easily assess the radiation-induced biological effects. In addition, we used a transgenic mouse model expressing acrosome-green fluorescent protein (Acr-GFP), which is a meiosis-specific biomarker ,18. As pThe Acr-GFP transgenic mice were obtained from RIKEN BRC, Tsukuba, Japan, through the National BioResource Project of MEXT, Japan. These mice expressed the GFP marker specific for meiosis, which is useful for monitoring the progress of spermatogenesis ,18. All 3 in size [3 in a 12-well culture dish.As previously reported , the ex  in size . Each ti2. The Acr-GFP expressions in the culture tissue showed the progression of spermatogenesis, and in the non-irradiated cultures, the peak expression occurred at around 18–22 dpp [The samples were cultured in a humidified incubator at 34 °C under an atmosphere of 95% air and 5% CO8–22 dpp . The exp8–22 dpp , we confAs stated in a past work ,19, the In accordance with a previous research , the AcrAs previously reported , irradian = 4), 1 Gy (n = 4), 2 Gy (n = 4) and 5 Gy (n = 4) at a dose rate of 1 Gy/min or by leaving them non-irradiated (n = 4). To investigate the fractionated X-ray-induced effects on spermatogenesis, we also used 1 Gy × 2  and 0.67 Gy × 3 . A single tissue sample from each mouse testes was randomly assigned to an experimental group. At least four tissue samples each from different donor mice were used for each experiment; contaminated samples were excluded.The radiation-induced effects on spermatogenesis were observed by irradiating the cultured testicular tissues at 7 dpp with doses of 0.5 Gy (n = 4), equivalent to 5 Gy at the whole tissue level.Then, 3 × 15 × 0.3 mm Pb sheets  and polystyrene plates through which X-rays are transmitted were alternately layered and set downstream of the X-ray source to make the 300 μm-slit-modulated X-ray beams . The micp < 0.05 indicated a significant difference.One-way analysis of variance (ANOVA) was used to assess the differences in the GFP expression grades. Values with First, using the ex vivo mouse tissue spermatogenesis model and a conventional X-ray source, we tested radiation-induced effects on spermatogenesis. Next, we elucidated the fractionated radiation-induced effects on spermatogenesis. Previous clinical studies on radiotherapy-related male infertility showed that total doses to the testes exceeding 2.5 Gy delivered as multiple fractions generally produce permanent azoospermia, whereas for single-fraction exposures, the same effect would require doses over 6 Gy, indicating the temporally fractionated X-ray-induced effects on spermatogenesis ,5. The cTo test our hypothesis, we investigated the TSE of spatially fractionated X-rays for maintaining spermatogenesis. Using Pb and acrylic plates, we constructed a 50% 300 μm-slit-modulated X-ray beams. As shown in To our knowledge, this study is the first to visualize the TSE for maintaining spermatogenesis following exposure to spatially fractionated conventional X-rays. As previously reported, high-precision MRT showed the TSE for spermatogenesis . Taken tTo investigate the TSE expansion process in the progress of spermatogenesis, we used the TSE in co-cultured and physically adjacent tissues . We direThe International Commission on Radiological Protection (ICRP) considers the characteristics of stem cells and the niche as one of the most important factors for radiation-induced biological effects . In factIn the present study, our findings revealed that the TSE of spatially fractionated radiation has considerable promise, as it responds to a wide range of energy of X-rays. In addition, we experimentally proposed a possible mechanism of TSE for spermatogenesis by which the migration of spermatogonial cells occurs from the non-irradiated areas to the irradiated ones via the seminiferous tubules. As a radiobiological approach for the preservation of male fertility, these contributions are crucial for expanding the applicability of this method. In future research, interdisciplinary investigations will be needed for the clinical application of our approach.


The isolation of high-quality RNA from strawberry leaves and fruits is notoriously cumbersome. Both tissues are extremely rich in active secondary metabolites, as phenolics and pigments that inevitably perturb the isolation of RNA. Many protocols have been developed to address this problem. However, they are either costly, or time-consuming, in particular for high number of many plant samples. We describe here a new method with an easy-to-handle approach to obtain high-quality RNA from strawberry leaves and fruits. The method, referred to as double lysis, uses a novel combination of CTAB and Trizol protocols.Compared to conventional Trizol-dependent protocols, either used individually, or in a commercial spin-column kit, the new method yields RNA at lower costs, but of significantly improved quality. The RNA obtained by double lysis was very pure as indicated by 260/280 ratios of 2.06 (leaves) and 2.07 (fruits), while 260/230 ratio was 2.07 and 1.75, respectively. Also visually, RNA sediments from double lysis showed a white color, indicative of efficient elimination of polyphenolics and pigments. In contrast, traditional Trizol method produced reddish precipitates. The purity of RNA isolated by double lysis enabled successful removal of genomic DNA and, thus, allowed for more efficient cDNA synthesis and RT-PCR. In addition, the suggested method is able to yield RNA with fully preserved integrity from strawberry leaves, fruits in addition to petals and roots.Double lysis is a new RNA isolation protocol developed through the integrative coupling of CTAB and Trizol reagents. The method is cost-effective, robust, time-saving, and can cope even with recalcitrant plant tissues with high contents of phenolics and pigments, such as strawberry leaves and fruits. Fragaria × ananassa Duch.) is one of the most valuable fruit crops, but highly susceptible to both abiotic  and biotic  stress factors. To study stress-related gene expression, it requires to obtain high-quality RNA. In this context purity, integrity and quantity of the RNA are keys for successful molecular analysis by quantitative, semi-quantitative RT-PCR, or RNAseq [Cultivated strawberry  after transplanting. Both tissues were immediately shock-frozen in liquid nitrogen and then stored in -80°C for subsequent extraction of RNA and semi-quantitative RT-PCR.Fully extended leaves and ripe fruits were harvested from 3-month-old strawberry plants , chloroform:isoamylalcohol (24:1), isopropanol, 75% ethanol, absolute ethanol, and DNase-RNase free water, or double-distilled water.Direct-zol RNA miniprep .All centrifugation steps must be performed under cooling (+ 4 °C)Note: while the dissolution of the sediments requires several steps of short vortexing during preparation, never vortex the purified RNA, especially while dissolving it in water.RNA isolation via the double-lysis protocol:1. Weigh in maximally 0.03 g of leaf tissue or nearly 0.5 g of fruit tissue.g on 4 °C. Completely discard the supernatant including any floating tissues. Repeat this step once more and then go to step 3.For fruits only, add 1 ml of prewashing sorbitol buffer (ice-cold) supplied with 30 μl β-ME for each sample, vortex thoroughly for 30 s, then spin down for 5 min at 3700×For leaves, go directly to step 3.2. Grind the tissue in liquid nitrogen to fine powder using mortar and pestle.3. Add 1 ml of the lysis buffer (buffer 1) containing freshly added 50 μl of β-ME and 20 mg PVP. Resuspend the sediment by pipetting several times with cut tips till the tissue is resuspended completely, then vortex 30 s.g on 4 °C.4. Incubate 10 min at 65 °C, invert the tubes gently a couple of times every 2 min (do not vortex during heat incubation) and centrifuge 5 min at 17,600×g on 4 °C.5. Transfer 700 μl of the supernatant into a separate tube and add an equal volume of chloroform:isoamylalcohol (24:1). Vortex 10 s, then let settle at room temperature for 5 min. Spin down 15 min at 21,000×6. Take the 400 μl supernatant and add 800 μl Trizol (ice cold), vortex 30 s, then let it at room temperature for 5 min.7. Add 200 μl of chloroform, vortex 10 s, and incubate at room temperature for 5 min.g on 4 °C, and then transfer the upper 750 μl from the upper phase into a separate tube, where 500 μl isopropanol are added, and the mixture is then mixed by pipetting several times. Incubate the mixture at room temperature for 10–15 min, then transfer to – 20 °C for 10–15min.8. Spin down for 15 min at 21,000×g on 4 °C, to obtain a visible precipitate and discard the supernatant.9. Spin down for 15 min at 17,600×g and 4 °C. Discard the supernatant.10. Add 1 ml of 75% cold ethanol, then vortex 10 s. Re-spin for 3 min at 17,600×11. Repeat step 10.g on 4 °C.12. Add 1 ml of absolute ethanol, vortex 10 s, then spin down for 5 min at 17,600×13. Carefully discard the supernatant completely and let the precipitates to dry for 5 min only at room temperature. Do not lose the precipitate as at that stage it tends to be loose.14. Add 30–50 μl of DNase-RNase free water (do not pipette or vortex) and keep in room temperature for 15 min then dissolve the white sediments carefully by gentle pipetting for few times. Note: never vortex at that stage, since it will disrupt RNA integrity.The protocol applied with leaves was also used to isolate RNA from petals (0.3 g) and roots (0.1 g) of strawberry plants, just omitting the prewashing step.Trizol  according to the manufacturer instructions.TM RNA miniprep according to the manufacturer instructions.Direct-zolOther used protocols:A260/A280 for protein contamination, and A260/A230 for determining the contamination with polysaccharides and polyphenolics.The RNA was quantified spectroscopically  at 260 nm. The level of purity was inferred from the ratios of The isolated RNA of all samples was further digested with DNase using the product DNase I, RNase-free  according to the manufacturer instructions. The obtained RNA was directly used in subsequent cDNA synthesis reactions. It is recommended to not add more than 1 μl of DNase for more than 1 μg of isolated RNA, since this might affect RNA integrity.18 primers, mixed well, and heated at 65 °C for 5 min, before keeping the sample on ice for at least 2 min. Subsequently, the following mixture is added: 5× reaction buffer (1 μl), RiboLock RNase Inhibitor, 20 U/μL (1 μl), 10 mM dNTP Mix (2 μl), and RevertAid M-MuLV Reverse Transcriptase, 200 U/μl (1 μl). The whole mixture was incubated at 42 °C for 60 min and at 70 °C for 5 min to deactivate the reverse transcriptase and terminate the reaction. The mixture was then cooled on ice for at least 5 min and then diluted tenfold with 10 DEPC-treated water. Aliquots of 3 μl were used to amplify FaGAPDH2 (AF421493.1) encoding glyceraldhyde-3-phophate dehydrogenase using the forward primer 5′-CTTGAGAAGAAGGCCACCTATG-3′, and reverse primer 5′-CTTCGGTGTAACCCAAGATACC-3′. The product size should be 91 bp in case of purified RNA with no DNA contamination, but 200 bp with DNA template [The cDNA was synthesized using the RevertAid First Strand cDNA synthesis kit . As template, 280 ng of RNA for at least three samples of leaves and fruits were added to 1 μl of oligo (dT)template . The PCRThe isolated RNA and the PCR amplicons were separated on an 1.5% agarose gel at 5 V/cm for 30 min and visualized with ethidium bromide along with a 100-bp size marker .The total RNA was planned to be isolated from strawberry leaves and fruits using three different isolation protocols: Direct-zol , conventional Trizol method, and the double-lysis method that has been proposed in this work. During the process of isolation, the RNA sediments were observed to be larger in size but apparently reddish in color Fig. a while dA260/280  comparing to double lysis and Direct-zol kit, 96.7 and 25 μg/g FW, respectively. Nevertheless, the ratio of In order to investigate the suitability of the isolated RNA with double-lysis method for sensitive molecular studies, RT-PCR test was accomplished using a specific pair of primers for FaGAPDH2 . As showA260/A280  and integrity.In this study, we have used three different RNA isolation protocols: either Trizol or Direct-zol, which is a Trizol-dependent kit , or a new integrative method, referred to as double lysis, using strawberry leaves and fully matured, red, and soft fruits. The RNA precipitates obtained by the Trizol and the double-lysis method differed with respect to amount and color Fig. a, b. Unl280 Fig. a, and thios Fig. b, the doios Fig. . The A26NA Table , produceNA Table , but witDimorphandra mollis [Lafoensia spp. [A260/A280 and A260/A230 ratios of 2.1 and 1.75, respectively (Fig. Compared to leaves, fruit tissue of strawberries is even more challenging, because it is soft with high water content, pectin-rich cell walls, and fully expanded vacuoles, such that the genetic material including RNA is diluted by a chemically adverse environment . To compa mollis . This prsia spp. . After tely Fig. a, b. AddTo further assess the quality of the purified RNA, we amplified a diagnostic fragment from FaGAPDH2  by RT-PCThe critical issue for RNA isolation from challenging tissues, such as strawberry leaves and/or fruits, is to minimize contamination by polysaccharides and polyphenols rather than the quantity. Our method addresses this issue in a simple, robust, cost-effective, and time-saving way and even can cope with strawberry leaves and fruits as particularly challenging material to yield RNA in satisfying quantity, yet in high quality (see Fig.


Eriocheir sinensis is a freshwater crab and is considered as one of the most important cost-effective species for freshwater aquaculture. E. sinensis can grow in both freshwater and brackish waters. In order to adapt to this changing salinity, E. sinensis can regulate the osmotic concentration of its hemolymph. Additionally, studies have shown that gills are one of the most important tissues in osmoregulation. In this work, we performed the first meta-analysis of publicly available RNA-Seq datasets to identify differentially expressed genes in the gills under different salinity conditions. The results highlighted that many different types of transporters show altered expression because of salinity change. Some of these transporters may serve as novel or new biomarkers for osmoregulation. The findings of this work also suggest that cellular processes related to many morphological changes are also affected.Eriocheir sinensis is a strong osmoregulator that can keep rigorous cellular homeostasis. CMC can flourish in freshwater, as well as seawater, habitats and represents the most important species for freshwater aquaculture. Salt stress can have direct effects on several stages  of the CMC life cycle. To get a better overview of the genes involved in the gills of CMC under different salinity conditions, we conducted an RNA-Seq meta-analysis on the transcriptomes of four publicly available datasets. The meta-analysis identified 405 differentially expressed transcripts (DETs), of which 40% were classified into various transporter classes, including accessory factors and primary active transporters as the major transport classes. A network analysis of the DETs revealed that adaptation to salinity is a highly regulated mechanism in which different functional modules play essential roles. To the best of our knowledge, this study is the first to conduct a transcriptome meta-analysis of gills from crab RNA-Seq datasets under salinity. Additionally, this study is also the first to focus on the differential expression of diverse transporters and channels (transportome) in CMC. Our meta-analysis opens new avenues for a better understanding of the osmoregulation mechanism and the selection of potential transporters associated with salinity change.Chinese mitten crab (CMC) or  Eriocheir sinensis, is a freshwater crab native to China and found as an invasive species in Europe and the United States of America (USA) [+/K+-ATPase) of the gill epithelium is one of the essential ion transporters for hyperosmotic crustaceans and has been implicated in osmoregulation, as well as ion regulation [+-K+-2Cl cotransporter-1 (NKCC1) that belongs to the chloride–cation cotransporters gene family [− from the body of crustaceans to accomplish salt and water retention [Chinese mitten crab (CMC), also known as ca (USA) . This crca (USA) . CMC is ca (USA) . Thus, Cca (USA) . Therefoca (USA) . Howeverca (USA) ,7,8. Howca (USA) . The sigca (USA) ,10. One ca (USA) . All theca (USA) . Sodium/gulation . Transpoe family  are invoetention . Similaretention  and, theetention .E. sinensis have mainly focused on the key transporter genes, such as Na+/K+-ATPase, NKCC1, and V-type ATPase [E. sinensis), we carried out a meta-analysis of four independent RNA-Seq studies. RNA-Seq has become the method of choice for differential expression analyses; however, due to their high costs, limited number of biological replicates are common in studies, which, in turn, leads to a low detection of differentially expressed genes (DEGs) [E. sinensis.Some of these transporters have been highlighted in transcriptome-based studies of CMC during salinity stress. Specifically, such RNA-Seq-based studies in the gills of e ATPase ,9,16. Hoe ATPase . Moreovee ATPase . Currente ATPase . Broadlye ATPase , of whics (DEGs) . Therefos (DEGs) . In a mes (DEGs) ,24. To thttps://www.ncbi.nlm.nih.gov/bioproject) database was queried to search for RNA-Seq studies in response to salinity changes in E. sinensis. Studies that focused on a change in salinity from freshwater to higher salinity using tissue samples from gills were selected only. RNA-Seq reads (fastq files) for the selected samples from each study (https://trace.ncbi.nlm.nih.gov/Traces/sra/) database by using the fastq-dump program of the SRA Toolkit (http://ncbi.github.io/sra-tools/).The National Center for Biotechnology Information (NCBI) BioProject  were predicted with the TransDecoder  pipelinep-values for each gene from individual studies using the following formula [Differential expression (DE) analysis for individual studies was performed at the isoform level using the edgeR  package  formula ,40:Fg=−2gsp corresponds to the raw p-value calculated for a gene (g) in a differential analysis for the study (s). With independent p-values, gF has a χ2 distribution with 2S degrees of freedom. Smaller p-values result in larger gF, and therefore, the null hypothesis is rejected [p-values were adjusted for the Benjamini–Hochberg false discovery rate (FDR), and a corrected p-value < 0.05 was considered as statistically significant [2FC ≤ −1 or log2FC ≥ 1) were considered as DETs in the meta-analysis. Heatmap and correlation analyses were conducted by using the pheatmap (https://cran.r-project.org/package=pheatmap) and Hmisc (https://cran.r-project.org/package=Hmisc) packages, respectively, within the R (https://www.r-project.org/) programming environment.Here, rejected . p-valuenificant . TranscrDrosophila melanogaster (fruit fly) as the reference organism. The interaction network of the DETs was visualized and further analyzed with Cytoscape v.3.8.0 [The amino acid sequences of the DETs were mapped to the STRING v.11.0  database v.3.8.0 . Node de v.3.8.0  and GLay v.3.8.0  plugins E. sinensis, BUSCO scores within the range of 50–95% are expected [Based on the search criteria (see Methods), four RNA-Seq datasets were retrieved for this study. Although all these studies used freshwater and high salinity samples, the concentration of salt in higher salinity groups varied. For example, the salinity in two of these studies was 30% , whereasexpected .TransDecoder identified 179,118, 279,120, 52,132, and 147,537 transcripts with the longest open reading frames (ORFs) for four CMC datasets , respectively. Each of these ORFs were at least 100 amino acids long, and sequences shorter than 100 residues were excluded. These transcripts were then scanned against the UniProt, Crabdb, and Pfam databases to identify ORFs with homology to known proteins. Based on the BLASTp search, the percentage of sequences that mapped to the UniProt database ranged between 15.71% (DS2) and 27.89% (DS1). These numbers are consistent and slightly better in some cases as compared to the previous studies on this non-model organism . Interes2FC ≤ −1 or log2FC ≥ 1) and an adjusted p-value of <0.05. This resulted in 1748 DETs, of which a large number of transcripts (~1300) were further excluded from the analysis because of their inconsistent or conflicting expression patterns. Such inconsistent transcripts were upregulated in some studies and exhibited downregulation in others. Therefore, only those transcripts that showed consistent expression in the four datasets were considered as DE , whereas mitochondrial succinate–CoA ligase (GDP-forming) subunit beta (SUCLG2), also known as GTP-specific succinyl-CoA synthetase (G-SCS), was encoded by the topmost upregulated transcript. Moreover, several of the 405 DETs exhibited very high correlations . From thSUCLG2) were identified as DE across all four datasets, as well as by the Fisher method . This enzyme was found to be part of various enriched biological processes, such as the response to stimulus and nervous system development, in the meta-analysis.Using the ShinyGO tool, enriched GO categories associated with DETs were identified in terms of the biological process (BP), molecular function (MF), and cellular component (CC). Based on this analysis, the GO term “response to stimulus” was the most enriched biological process, followed by several others related to development, such as anatomical structure development, multicellular organism development, and system development . BesidesATP6V0A1 gene), which was downregulated. Among these, some of them, such as subunit H, were annotated to be involved in developmental processes (BP), whereas others  were involved in various molecular functions (MF), including nucleoside-triphosphatase activity, ATPase activity, etc. Additionally, the meta-analysis also identified subunits B, E, and G of the V1 cytoplasmic domain and d1 and e2 of the V0 membrane-bound domain of the V-ATPase complex to be differentially expressed. However, their expression patterns across all studies were conflicting.We also identified DETs that encode five V-ATPase subunits . The transcripts for all these subunits were upregulated by more than two FC, except in the case of subunit a1 (V-type proton ATPase 116-kDa subunit a1 encoded by the Drosophila encoded by an inflated (if) gene. This transcript was identified in 27 out of 30 enriched GO biological processes, e.g., development, differentiation, morphogenesis, locomotion, and chemotaxis. The remaining three enriched processes in which integrin alpha-PS2 was not found were related to mRNA splicing. In Drosophila, such integrins are known to mediate the specific migration of tracheal cells in a given direction [Myo1C), which has been implicated in many roles, including tension sensing and calcium-dependent cargo binding [Myo1C, which was identified to be downregulated in the current study, belongs to a superfamily of actin-based motors that have roles in various cellular processes. Another unconventional myosin that was downregulated by more than four-fold was Myo18a, which plays an essential role in the organization and structure of the Golgi complex.One of the downregulated transcripts in the meta-analysis was identified to share similarity with integrin alpha-PS2 of irection . In cont binding . Myo1C, Among the GO cellular components, the most enriched were related to membranes ; the cytoplasm; cell junction; and nucleus C. The prCHMP2a), which belongs to the Snf family of small coil-coiled proteins. Such proteins are key components of the endosomal sorting required for transport complex III (ESCRT-III), which are essential for the formation of multivesicular bodies (MVBs) and the sorting of cargo proteins to MVBs [SUCLG2, was also implicated in KEGG metabolic pathways. The regulation of osmotic pressure via different transporters and channels is an energy-dependent process [The KEGG enrichment analysis identified 12 statistically significant pathways to be most important in the DETs. Most of these enriched pathways were related to metabolism, ECM-receptor interaction, and oxidative phosphorylation . A total to MVBs . V-ATPas process . In a gi process . The act process ,9,10.Drosophila melanogaster) as the source organism. Of these DETs, the amino acid sequences of only 57 (13 up- and 44 downregulated) transcripts were mapped to the fruit fly proteome, whereas an additional 231 proteins were predicted as their interacting partners. Therefore, the interaction network consisted of 288 nodes, each representing a gene, and 905 edges (interactions between nodes) (GluProRS: node degree = 31) and a DNA-directed RNA polymerase II subunit RPB2 (RpII140: node degree = 30). Of these two, GluProRS was predicted by STRING-DB, whereas RpII140 was one of the downregulated transcripts identified in the meta-analysis.STRING-DB was used to predict the interactions between these DETs using the common fruit fly (n nodes) . The nodThere is a growing interest in the application of biological networks to address important biological phenomena . Networkp < 0.05) with different transporter classes  represented class 8 transporters A. AlmostThis subclass represents proteins that aid in transporting across one or more biological membranes but do not themselves take part in direct transport . Such tr+/H+ exchange regulatory cofactor (NHE-RF), is an adaptor protein that is known to manage a number of cell receptors and channels [SAV1) was upregulated. SAV1 consists of two WW domains, each consisting of about 33 amino acids. The name (WW) refers to two trademark tryptophan (W) residues placed 20–22 amino acids apart. Proteins with WW domains are involved in functions related to signaling or may play structural roles [Family TC 8.A.24: This was the second-most abundant transport family found in the meta-analysis C. Proteichannels . Severalchannels . Six DETal roles . In addiFamily TC 8.A.23: Transcripts belonging to this group represent the basigin family of proteins that regulate transporters . All theAMPK), energy sensor protein kinases that are involved in regulating cellular energy metabolism [AMPK triggers energy-producing pathways and suppress the processes that consume energy [Family TC 8.A.104: This family comprises the catalytic subunits of AMP-activated protein kinases  family of transporters (TC 8.B.14). Such toxins are known to block potassium channels .TC 3 was the second-most abundant transport class after TC 8 and constituted about 23% (37 DETs) of the total transporters identified in the DETs A. These +- or Na+-translocating F-type, V-type, and A-type ATPase (F-ATPase) superfamily (TC 3.A.2), the Endoplasmic Reticular Retrotranslocon (ER-RT or ERAD) family (TC 3.A.16), and the general secretory pathway (Sec) family (TC 3.A.5).Of the 33 DETs identified in this group, TC 3.A was the second-most abundant subclass B, represRNASEK).Family TC 3.A.2: Of the six DETs representing the TC 3.A.2 family, five depicted various V-ATPase subunits , as described previously (see above), whereas the sixth DET encodes ribonuclease kappa (Family TC 3.A.16: In the case of the six transporters belonging to the ERAD family (TC 3.A.16.), DETs representing 26S proteasome regulatory subunit 6A, cell division cycle protein 48 homolog MJ1156, and putative deoxyribonuclease TATDN1 were upregulated, whereas E3 ubiquitin-protein ligase AMFR, ubiquitin-conjugating enzyme E2 C, and ER degradation-enhancing alpha-mannosidase-like protein 2 were downregulated. Initially, it was thought that the ERAD system was entirely involved in the degradation of misfolded and orphan secretory proteins; however, the system has broad implications in various cellular processes, such as protein folding and transport, the regulation of metabolism, immune response, and ubiquitin-proteasome-dependent degradation .Family TC 3.A.5: In addition to the TC families 3.A.2 and 3.A.16, the third topmost family in which all the four mapped DETs were downregulated by more than two-fold was TC 3.A.5  family). These 3.A.5 members were identified as calpain-3, signal recognition particle 54-kDa protein, transport protein Sec31a, and putative u5 small nuclear ribonucleoprotein 200-kDa helicase. Protein complexes belonging to the Sec family are found in both the prokaryotes, as well as eukaryotes. The Sec system essentially consists of two major components: (a) three integral inner membrane proteins, SecYEG, and (b) the ATPase motor protein SecA and represents one of the major ways by which protein export or membrane integration occurs in bacteria .+ or Na+-translocating NADH dehydrogenase (NDH) family (TC 3.D.1) and the proton-translocating cytochrome oxidase (COX) superfamily (3.D.4)—were included in this subclass. All the four transcripts assigned to this subclass were upregulated by ≥two-fold.Only four DETs distributed between two families—namely, the H+ [NDUFS8), NADH dehydrogenase (ubiquinone) 1 alpha subcomplex subunit 13 (NDUFA13), and probable NADH dehydrogenase (ubiquinone) 1 alpha subcomplex subunit 12 (Y94H6A.8), respectively. The vertebrate H+-translocating NADH dehydrogenase (NDH) complex consists of 45 subunits [NDUFA13 is crucial for membrane potential formation and NADH assembly [Family TC 3.D.1: As the name suggests, NADH:ubiquinone oxidoreductases type I (NDH-Is) of bacterial, as well as of eukaryotic, mitochondria and chloroplast origin, coupled with electron transfer to the electrogenic transport of protons or Na+ ,67. Of tsubunits , and amoassembly .COX7A1). These multi-subunit enzyme complexes reduce O2 to water and, consequently, pump four protons across the membrane [Family TC 3.D.4: Only one DET was found in this family and represents mitochondrial cytochrome c oxidase subunit 7A1 (membrane .The third-most abundant class of transporters was channels/pores with about 34/162 (~21%) DE transporters found in this class . TranspoIn general, these transporters consist mostly of α-helical spanners, although they may also contain some β-strands . Of all AYR1) family. Transporters like AYR1 form an NADPH-regulated channel in lipid bilayers, as well as in the outer mitochondrial membranes [Family TC 1.A.115: This was the most abundant family among all α-type channel transporters, with six DETs found in this group, and is represented by the pore-forming NADPH-dependent 1-acyldihydroxyacetone phosphate reductase  family. Only two  DETs were mapped to the Ca-ClC family. Both these transporters were downregulated.In addition to the above-mentioned families, there were nine additional TC 1.A families, each represented by only one representative on the list of transporters found in this work . Among tIn contrast, the transporters that represented families 1.A.77 (Protein MAK16 homolog B), 1.A.75 (Piezo-type mechanosensitive ion channel component), 1.A.46 (Bestrophin-2), and 1.A.28 (Urea transporter 1) were all upregulated . Of thesSLC12A6 isoform 2, SLC12A1, and SLC12A2 (NKCC1)), thereby initiating the cellular response to environmental stress [EED), two unconventional myosins (Myo1C and Myo18a), serine/threonine-protein kinase 16 (STK16), activating molecule in BECN1-regulated autophagy protein 1 (AMBRA1), mitogen-activated protein kinase kinase kinase kinase 3 (MAP4K3), ATP-dependent RNA helicase (DDX3X), cyclin-dependent kinase 9 (CDK9), and MAPk-Ak2). Among these, MAPk-Ak2 was the most downregulated. MAPk-Ak2 is a substrate of the p38 MAPK that is responsible for the signaling events affecting several cellular processes such as inflammation, division and differentiation, apoptosis, and motility in response to a variety of extracellular stimuli [These transporters allow the transport of molecules across cells or organelle membranes . Ten tral stress . In cont stimuli . p38 MAP stimuli  mediated stimuli ,78.ATJ1) was downregulated. Currently, there is only one family, the polyoma virus SV40 ER penetration channels (VPEC) (TC 1.P.1), known in this subclass. It is reported that a non-enveloped virus stimulates its own membrane translocation by initiating the release and recruitment of essential transport factors to the endoplasmic reticulum (ER) membrane [In addition to TC 1.A and 1.I, two proteins each from subclass vesicle fusion pores (TC 1.F) and the non-envelope virus penetration complex (1.P) were seen among the list of DETs . Both thmembrane . The othThis class consists of transporters that are unclassified and comprises three main subclasses  in the TCDB classification system. A total of 27/162 DE transcripts (that encode transporters) shared between subclasses 9.A (12) and 9.B (15) were identified from this class of transporters . No tranThis subclass represents proteins that are recognized as transporters, but their mechanism of action is unknown . TC 9.A SNX27)-retromer assembly apparatus (RetromerAA) of transporters. Four transporters were identified in this family, and all of them were downregulated. The PDZ domain of SNX27 helps in the recycling of internalized transmembrane proteins from endosomes to the plasma membrane by linking PDZ-dependent cargo identification to retromer-mediated transport [Family TC 9.A.3: The members of this family represent the sorting nexin 27 (ransport .Family TC 9.A.63: The second-most abundant category with three members identified in this group represented the retromer-dependent vacuolar protein sorting (R-VPS) family. Both of these components are necessary for maintaining the vacuole membrane organization . All thrTLK2) by more than four-fold, while the other one—namely, lactoylglutathione lyase (GLO1)—was downregulated. Autophagy-related genes are known to play essential roles in the development and stress responses in plants [Two DETs were also found in the autophagy-related phagophore-formation transporter (APT) family (TC 9.A.15) . One of n plants . In addiThis category consists of proteins for which a transport function has been proposed; however, there is no information to back this suggestion . The proFamily TC 9.B.87: This group represents the selenoprotein P receptor (SelP-Receptor) family of putative transporters. SelP comprises most of the selenium in blood plasma, and it is used by various organs  as a selenium source for the biosynthesis of selenoprotein . SelP hoRXR) found in the TC 9.B.208 (vitamin D3 receptor (VDR) family), which was upregulated. RXR is a ligand-dependent transcription factor that may play a role in the retinoic acid response pathway [Additional families in which at least one DET was found are TCs 9.B.105, 9.B.135, 9.B.142, 9.B.198, 9.B.208, 9.B.229, 9.B.265, 9.B.278, 9.B.311, and 9.B.371 . All the pathway .Additionally known as secondary carrier-type facilitators, TC 2 transporters use a carrier-mediated process to catalyze uniport, antiport, or symport systems . Among aSLC37A2) and facilitated trehalose transporter Tret1-2 homolog (TRET1-2), putative inorganic phosphate cotransporter (Picot), and solute carrier family 49-member 4 homolog (SLC49A4) were identified in this family. Among these, only SLC37A2 was upregulated, whereas the other three were downregulated.Family TC 2.A.1: Proteins of this ancient, huge, and diverse family represent the major facilitator superfamily (MFS) and comprise millions of sequenced proteins . MembersSLC25A42), mitochondrial ornithine transporter 1 (SLC25A15), Graves’ disease carrier protein homolog (SLC25A16), and mitochondrial glycine transporter (SLC25A38) were found in this family. Among these, only SLC25A38 was downregulated.Family TC 2.A.29: Similar to TC 2.A.1, only four DETs that encode for mitochondrial coenzyme A transporter (SLC35B3), acid sphingomyelinase-like phosphodiesterase 3b (SMPDL3B), and solute carrier family 35 member F5 (SLC35F5)—were assigned to this drug/metabolite transporter (DMT) superfamily. All the three transcripts representing this superfamily were downregulated by more than four-fold. In general, most of the DMT superfamily members are nucleotide–sugar transporters that are involved in the endoplasmic reticulum and Golgi of eukaryotic cells [Family TC 2.A.7: Only three DETs—namely, adenosine 3’-phospho 5’-phosphosulfate transporter 2 (ic cells .2+)-iron (Fe2+) permease (ZIP) (2.A.5); neurotransmitter:sodium symporter (NSS) (2.A.22); bile acid:Na+ symporter (BASS) (2.A.28); glycerol uptake (GUP) or membrane-bound acyl transferase (MBOAT) (2.A.50); and the sweet, PQ-loop, saliva;, and MtN3 (Sweet) (2.A.123) families. Only a single representative from each of these families was found in the DETs, and each of these transcripts were downregulated (Other categories of subclass TC 2.A included the zinc (Znegulated .CG8399) was the most downregulated and belonged to the eukaryotic cytochrome b561 (Cytb561) family (TC 5.B.2) of the class transmembrane electron carriers (TC 5)  . Trans-ps (TC 5) . MembersOverall, of the 162 DETs that encode various transporters, the majority of them were downregulated, whereas only 41 were upregulated. The analysis suggested that change in salinity triggers the differential expression of almost all the major transport classes , as well as several unclassified transporters (TC 9). Moreover, transporters belonging to TC 8 were the most diverse and represented 25 different families. In contrast, only three transporters belonging to TC 4 and TC 5 were found. Some of these numbers might change in the future when TC 9 (unclassified) transporters are assigned to well-defined classes or removed from the transporter classification system.Studies have shown that gills are the most relevant tissues during osmoregulation in CMC, and to understand the expression profiles and the associated pathways, many studies have applied RNA-Seq techniques under different salinity conditions ,9,10,16.AGO2) and mitochondrial succinate–CoA ligase (GDP-forming) subunit beta (SUCLG2) were encoded by the topmost down- and upregulated transcripts, respectively. Proteins belonging to the argonaute family associate with small RNAs and help in mRNA degradation, translational repression, or both [SUCLG2 catalyzes the only step in the citric acid cycle that provides substrate-level phosphorylation to synthesize GTP from GDP [SUCLG2 may directly influence the anabolic functions of the citric acid cycle inside the mitochondria by supplying GTP for the GTP-dependent steps of protein synthesis [ttk) (Drosophila melanogaster (fruit fly). ttk is a transcription factor that has been implicated in several roles, including cell fate specification, cell proliferation, and cell cycle regulation. Besides these established roles, ttk also plays a crucial role in tracheal development [Our meta-analysis identified 405 DETs between freshwater and high-salinity conditions. Of these, the protein argonaute-2 ( or both . In contfrom GDP . While Aynthesis . One of is [ttk) , which eelopment . Gills aelopment . Therefoelopment . These r+/K+-ATPase and V-type ATPase are well-known biomarkers of crustacean gills [SDCBP), also known as syndecan-binding protein 1, was the most downregulated transcript from the family TC 8.A.23. Syndecans are receptors for a diverse type of proteins and play essential roles in many cellular processes, such as development, maintenance, repair, cell adhesion, and migration. Reports have shown that syndecans can also regulate stretch-activated ion channels [+/K+-ATPase and connexin 43 [As discussed above, a large number (162/405) of DETs were found to be transporters belonging to various classes. Transporters such as Naan gills  and havean gills . This isan gills . Severalchannels . Additiochannels  and regunexin 43  or are inexin 43 . Therefo+/K+-ATPase (TC 3.A.3) and V-type ATPase (TC 3.A.2) are two well-known examples of this class. Studies have shown that Na+/K+-ATPase is an essential component for low-salinity adaptation in blue crab  [Drosophila, there are 14 subunits of V-ATPase encoded by at least 33 genes [RNASEK, which closely associates with this enzyme [RNASEK changes the localization of various V-ATPase subunits and effects the expression levels of some of its subunits. Therefore, to better understand a complete mechanism such as salinity change, it is of importance to identify the interacting partners (or co-expressed genes) of the well-known biomarkers and explore the consequent effects on the changing condition.The number of TC 3 (primary active) transporters was slightly lower as compared to the accessory factors but higher than other transport classes. TC 3 transporters help in the active transport of solutes against a concentration gradient that is derived by a primary source of energy . For exasapidus) . Similarsapidus) . In Dros33 genes . Some of33 genes  or highl33 genes ,16. As ds enzyme . Decline2+ concentration is one of the initial events observed after osmotic stress [TC 1 proteins aid in transport through an energy-independent (facilitated diffusion) process in which the substrate moves across the channel or pore without the coupling of the translocation step to another chemical or vectorial process . Some ofc stress . Membersc stress . In animc stress . Similarc stress .+) gradient across the membrane as the source of energy, a mechanism well-conserved among living organisms [2+-cell signaling, and cell death [In our meta-analysis, we observed that the number of secondary carriers is much lower as compared to other transporter classes . Additiorganisms . Anotherrganisms . Individll death . AlthougSeveral transporters of unknown classification were also found distributed between two subclasses (TC 9.A and 9.B) of this group. The proteins of TC 9.A include transporters with unknown biochemical mechanisms; however, their role in transport has been established . In contE. sinensis as compared to the genes and pathways reported from individual studies. This study, in addition to being the first meta-analysis of CMC in response to salinity, also represents the first such study that provides a focused overview of various transporters involved in salinity change. The various types of transporters highlighted in this meta-analysis can provide significant clues for experimental scientists to design future experiments to get a better understanding of the adaptation to salinity change and, consequently, the underlying mechanisms of osmotic regulation at varying salinity conditions in Chinese mitten crabs.Overall, in this meta-analysis of RNA-Seq data, we successfully identified a number of transcripts whose expressions were apparently altered in the gills of Chinese mitten crabs in response to ambient changes in the salinity conditions. Of these DETs, a large number were found to be transporters from diverse families, again highlighting the significance and greater contribution of the transportome in osmotic regulation. Coordinated responses induced by salinity changes triggered several processes related to cell development, differentiation, motility, and protein synthesis, including others, as some of the most enriched processes. The processes and the DETs identified from our meta-analysis will serve as more robust candidates for understanding the mechanism of osmotic regulation in


Tomato spotted wilt tospovirus (TSWV), causes systemic infection in susceptible tomato cultivars, whereas its infection is localized in cultivars carrying the Sw-5 resistance gene. The response to TSWV infection in tomato cultivars with or without Sw-5 was determined at the virus small RNA level in the locally infected leaf. Predicted reads were aligned to TSWV reference sequences. The TSWV genome was found to be differentially processed among each of the three-viral genomic RNAs—Large (L), Medium (M) and Small (S)—in the Sw-5(+) compared to Sw-5(−) genotypes. In the Sw-5(+) cultivar, the L RNA had the highest number of viral small-interfering RNAs (vsiRNAs), whereas in the Sw-5(−) cultivar the number was higher in the S RNA. Among the three-viral genomic RNAs, the distribution of hotspots showed a higher number of reads per million reads of vsiRNAs of 21 and 22 nt class at the 5′ and 3′ ends of the L and the S RNAs, with less coverage in the M RNA. In the Sw-5(−) cultivar, the nature of the 5′ nucleotide-end in the siRNAs varied significantly; reads with 5′-adenine-end were most abundant in the mock control, whereas cytosine and uracil were more abundant in the infected plants. No such differences were seen in case of the resistant genotype. Findings provided insights into the response of tomato cultivars to TSWV infection.Tospoviruses cause significant losses to a wide range of agronomic and horticultural crops worldwide. The type member,  Orthotospovirus, family Tospoviridae , the sole genus of one of the three plant-infecting families in the order  and susceptible [Sw-5(−)] in the locally infected leaves.A better understanding of the different molecular mechanisms during the interaction between the virus and the host silencing machinery is useful for the development of novel technologies to control the virus. The vsiRNA outputs of TSWV in susceptible tomato and in systemically infected leaves have been studied previously. However, it is unknown how the RNAi machinery operates against TSWV in resistant genotypes and in a local infection scenario. Moreover, the output of RNAi in resistant cultivars under incompatible infections has not been addressed. the objective of this study was to determine the small RNA profiles of TSWV in infected tomato cultivars resistant  resistant to TSWV, and Marglobe  susceptible to TSWV, were sown in trays in a growth chamber and transplanted to individual pots in a greenhouse. All plants were kept in nylon mesh tents to protect them against insect infestation including thrips. Greenhouse conditions were 26 °C with 16 h day and 8 h night. Twenty-eight days post-emergence, the first three fully expanded leaves of tomato plants were manually inoculated with TSWV inoculum. Frozen TSWV-infected N. benthamiana tissue was homogenized by grinding in 0.01 M sodium phosphate buffer (pH 7.0) containing 0.4% β-Mercaptoethanol and carborundum. Leaves #1 to #3 of control tomato plants of both varieties were mock-inoculated with buffer.TSWV was originally isolated from a naturally infected peanut , immediately frozen in liquid nitrogen and then stored at −80 °C. The tissues were ground in liquid nitrogen and sets of 100 mgs were stored in 2 ml tubes for DAS-ELISA  and RNA isolation. Treatments consisted of resistant (Red Defender) and susceptible (Marglobe) tomato cultivars that were mock- or TSWV-inoculated, with three biological replicates per each treatment.N. benthamiana were used as negative and positive controls, respectively.TSWV levels were tested by DAS-ELISA using a commercial kit  following manufacturer’s instructions. Healthy and TSWV-infected Based on the ELISA test, nine plants of each treatment with absorbance values close to the average were selected and mixed into groups of three to get three biological replicates. Thus, each biological replicate consisted of a mixture of total RNA of three samples. Total RNA was extracted using Trizol  following the manufacturer’s instructions and then treated with Turbo-DNase (Invitrogen). Total RNA was quantified using Nanodrop, replicates were mixed and RNA quality numbers (RQN) were measured in a fragment analyzer at the Washington State University (WSU) Genomic Core Facilities.RNA integrity was determined with a fragment analyzer , and samples with more than 5.0 RQN were sent to the Beijing Genomic Institute (BGI) for library construction and small RNA sequencing using Illumina HiSeq 4000, with a single-end library read length of 50 bp.Solanum lycopersicum reference genome (Solanum lycopersicum assembly SL2.50) using BLAST. Contigs were mapped back to the reads of each treatment to the assembled contigs.Raw data were filtered using an in-house BGI method including removal of the low-quality sequences, 5′ adaptor contaminants from the 50 nucleotide tags. Length of small RNAs between 18 and 30 nucleotides were used for the analysis. Quality Control analysis of clean reads was also checked using the platform CLC Genomics Workbench, version 8.0.  for each treatment. Bulk of contigs with all the reads (de novo assembly) were predicted and compared to Collapsed to identical sequences with maintained counts using the FASTX-Toolkit package, only reads with lengths between 18 and 30 nt were retained for alignment normalization. Alignment of collapsed reads to reference sequences was performed using the Small Complementary RNA Mapper (SCRAM) software package which allowed for exact matches to the reference sequence only . NormaliTSWV siRNAs were aligned to the reference genome L RNA—NC_002052.1; M RNA—NC_002052.1; S RNA—NC_002052.1 using CLC Genomics Workbench, to assemble the genome of our isolate and use it as a reference to determine the viral siRNA profile and predict the hotspots. To the test the accuracy of the prediction, siRNAs were aligned to L RNA sequence (KP827649.1) of the TSWV isolate used in this study.www.idtdna.com/UNAFold). TSWV-RdRp, GN/GC, NSm, and NSs primers (from this study) amplify the RNA transcript for each gene as well as the corresponding genomic RNAs, for this reason, we refer to the templates as TSWV-RdRp, GN/GC, NSm, and NSs  following the instructions. Briefly, for a 20 µL volume reaction of 4 µL of 5× iScript were mixed with the volume containing 1 µg of RNA, plus nuclease free water to the final volume. Incubation conditions consisted of 5 min at 25 °C for priming, followed by 30 min at 42 °C for reverse transcription, with a final incubation at 5 min at 85 °C for inactivation. cDNA was used immediately for either end-point PCR, qPCR or stored at −20 °C for further assays. qPCR was performed with the SSoAdvanced Universal SYBR Green Supermix (Bio-Rad) in 20 µL reaction containing 10 µL of 2× SSoAdvanced Universal SYBR Green Supermix (Bio-Rad), 2 µL of 50 ng cDNA, 0.7 µL (10 µM) of each primer and 6.6 µL of nuclease free water. The qPCR reactions were carried out in iCycler (Bio-Rad); the program began with DNA denaturation at 95 °C for 30 sec, followed by 40 cycles of denaturation at 95 °C for 15 sec and annealing and extension at 60 °C for 30 sec. Melting curve 60 to 95 °C at 5 sec 0.5 °C per cycle. Elongation factor 1-alpha  and Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were chosen for normalization as reference control . The ∆Ctp-value correction using custom Python scripts.Relative expression values were determined by fitting to a normal distribution and assessing the goodness of fit with a Shapiro–Wilks W test. One-way ANOVA was carried out and significant differences were compared with Least Significant Difference (LSD) test. The software used was JMP . siRNA 5′ nucleotide enrichment was compared with pairwise t-tests with Bonferroni Sw-5(−) genotype, while in the Sw-5(+) is restricted to the inoculated leaf  cultivar gave a slightly higher number of reads than the Sw-5(−) (Sw-5(+) cultivar showed a slightly higher number reads-per-million-reads (RPMR) of 22 nt in the mock-inoculated than in the TSWV-infected plants. In both treatments, the number of reads of 24 nt were higher than the other read lengths, but no difference between them. On the other hand, in the susceptible variety infected with TSWV, the number of reads of 21 and 22 nt were higher than in the mock-inoculated, but the number of reads of 23 and 24 nt was smaller. The 23 nt class showed a subtle difference in the susceptible variety, whereas the TSWV-infected plants had 50,000 RPMR less than the mock-inoculated . In both tomato genotypes, the mock-inoculated leaves yielded 500,000 more reads than the virus-inoculated leaves. Also,  Sw-5(−) . The sizoculated . SimilarSw-5(+) and Sw-5(−) tomatoes (Sw-5(+) tomato, the hotspots in the L RNA had more abundance of vsiRNAs of 21 nt contrasting with the same spots in the Sw-5(−). The hotspot graph showed a higher number vsiRNAs aligned with the L RNA of Sw-5(+) cultivar, in comparison with the other RNAs, while in Sw-5(−) cultivar, the S RNA was targeted more in comparison to the other RNAs. However, the hotspot enrichment pattern was similar for the M RNA in both varieties.  cultivar in comparison with the Sw-5(−) cultivar, but also when compared with the other RNAs. On the contrary, the percentage of the vsiRNAs aligned with the S RNA were higher in Sw-5(−) compared to the resistant variety, and the other RNAs. Interestingly, the M RNA was processed in similar proportions in both varieties. This result suggested a tendency to target the RNAs differentially in a genotype (resistant versus susceptible)-dependent manner. However, it is important to note that as expected, the vsiRNAs processing was higher in the susceptible tomato than in the resistant one ], the reads with a 5′-adenine were more abundant in the mock-inoculated control compared to the TSWV-inoculated, whereas cytosine (C) and uracil (U) were more abundant in the TSWV-inoculated plants. No such difference was seen in case of virus-infected and mock-inoculated plants containing the Sw-5 gene. No difference was seen between TSWV-infected resistant and mock-inoculated control as well with the mock-inoculated control of the susceptible genotype  and BadSw-5(−) and in Sw-5(+) cultivar, with NSm showing most dramatic difference (Sw-5(−) and Sw-5(+). Comparative analysis of all genes showed a highest level of the NSm gene followed by N, and with lowest expression levels of L and NSs in Sw-5(−). A similar pattern was found in Sw-5(+), but with a very low expression of all genes compare to Sw-5(−) genotype , and the primer pair for N gene from Rotenberg et al.  worked ofference . The relgenotype . The norgenotype . One-wayN. benthamiana, TSWV susceptible tomato, and peanut [N. benthamiana, and Fletcher et al. [Polygonum ringspot tospovirus (PolRSV) in tomato showed the highest vsiRNAs correspond to 22 nt followed by 21 nt. While the virus induced similar accumulation of sRNAs in both hosts [N. benthamiana and susceptible tomato (cv. Sunny), at 17 dpi, Mitter et al. [N. benthamiana. Noticeably, TSWV induced a reduction in the peak of 24 nt in peanut, but a slight increase in the peak of 22 nt [Until now, the vsiRNA profiles of tospoviruses were studied in susceptible hosts and in systemically infected, non-inoculated leaves ,32,33,34d peanut ,29,30,32d peanut ,29. MittCassava brown streak virus accumulated predominantly 21 nt class in cassava cultivars [Cucumber mosaic virus satellite RNA was targeted more efficiently by DCL4. In TSWV, the silencing suppressor binds to long and short dsRNA molecules and suppresses local and systemic silencing, but also acts in the biogenesis of siRNAs by sequestration [The size of the siRNAs is related to the type of DCL that is acting, for example DCL4 produces siRNAs of 21 nt, while the 22 nt are produced by DCL2 . In our ultivars . In TSWVultivars , a hairpstration . The spestration .Sw-5(+) tomato, some of the hotspots in L RNA had more abundance of vsiRNA of 21 nt contrasting with the same spots in the Sw-5(−). Besides the low number of RPMR, this could indicate a more modest action of the DCL4. It could be the presence of another unknown mechanism that blocked the virus replication before the necrotic response reached a level that confines the virus to the infected cells thus preventing the virus spread to neighboring cells.On the other hand, DCL3 is involved in the production of 24 nt siRNAs, which indicates more activity in resistant tomato in both scenarios: mock- and TSWV-inoculated, and in the susceptible variety’s mock control. Interestingly, TSWV-infected Marglobe showed a lower peak compared to the 22 nt peak. This could be an indication that in compatible interactions, the activity of DCL3 can be reduced compared to a more prioritized activity of DCL2. Similar results were evident in susceptible host-virus interactions ,32,48. ISw-5(+) peanut was more similar to the one reported by Fletcher et al. [Fletcher et al.  found a r et al. . Moreover et al.  suggesteSw-5 gene is known to reduce/control the movement of the virus. It was surprising that the percentage of vsiRNAs aligned with the NSm was quite similar in both varieties; however, the number of RPMR was higher in the susceptible variety. On the other hand, in the resistant variety, the percentage of vsiRNAs processed, the number of reads was similar between NSs and the N. Interestingly, the polymerase gene had the highest percentage of vsiRNAs in Sw-5(+) in the viral sense. This led us to hypothesize a complementary action in the plant immune machinery to degrade the corresponding RdRp supporting the function of the Sw-5 (=limiting the movement of the virus from cell to cell), or it could be due to a lack of TSWV protective mechanism for the polymerase, reducing its function in producing replicative viral forms. It would be like an early recognition of the NSm protein by the Sw-5 protein, the immune response consisted of the activation of the programmed cell death (PCD) but also a silencing interference machinery that quickly targets the polymerase transcripts, resulting in the high percentage of vsiRNAs aligned with the RdRp in sense polarity.The The higher number to vsiRNAs matching the NSs and the N in the susceptible host can be an indication of either a highly efficient amplification phase focused on those RNAs, or a potential use of the resulting vsiRNAs targeting host genes to allow the viral life infection cycle. This topic is gaining scientific interest, as it has been discovered as a secondary route that the virus used to counterattack the antiviral plant defense by hijacking the host genes.Interestingly, Bai et al. , in TYLCThe fewer number of the vsiRNAs in the resistant variety can also be due to a mechanism that quickly counters the NSs thus limiting the production of RdRp, and resulting in fewer dsRNA replicative forms before the HR is triggered. It would be interesting to determine the siRNA biogenesis close to the time of infection (3 to 6 h post-inoculation), and subsequently a time series such as one, three, five days, etc. Similar results were found in resistant genotypes of cassava infected with Ugandan cassava brown streak virus , and in It is assumed that the main role of vsiRNAs is to target viral mRNA molecules to inhibit viral replication . The RNAFletcher et al.  pointed