The accurate, timely, and non-destructive estimation of maize total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) under different phenological stages is a substantial part of agricultural remote sensing. The assimilation of UAV and machine learning (ML) data may be successfully applied in predicting maize TAB and TBMP; however, in the Nordic-Baltic region, these technologies are not fully exploited. Therefore, in this study, during the maize growing period, we tracked unmanned aerial vehicle (UAV) based multispectral bands  at the main phenological stages. In the next step, we calculated UAV-based vegetation indices, which were combined with field measurements and different ML models, including generalized linear, random forest, as well as support vector machines. The results showed that the best ML predictions were obtained during the maize blister (R2)–Dough (R4) growth period when the prediction models managed to explain 88–95% of TAB and 88–97% TBMP variation. However, for the practical usage of farmers, the earliest suitable timing for adequate TAB and TBMP prediction in the Nordic-Baltic area is stage V7–V10. We conclude that UAV techniques in combination with ML models were successfully applied for maize TAB and TBMP estimation, but similar research should be continued for further improvements. Anaerobic digestion of organic materials has been developed for more than 100 years and currently has great potential in contributing to sustainable energy production , into a il fuels ,4.3 ha−1, i.e., an amount that no other crop has achieved so far [A study of digestion  highlighd so far .POT) assay has been recognized as a simple and reliable method to determine the biogas yield of organic substrates [Biomethane potential  at harvest at the R3-Milk stage. A study in Denmark [A study conducted by Amon et al.  suggeste Denmark  highligh Denmark  found thTherefore, the objectives of our study were: (i) to track UAV-based multispectral bands at the main phenological stages of maize, and based on these remote data, establish the correlation between VIs, TAB, and TBMP; (ii) to estimate maize total above-ground biomass and theoretical biochemical methane potential yield, combining UAV techniques with ML methods; and (iii) identify the maize phenological stage during which total-above ground biomass (TAB) and theoretical biochemical methane potential (TBMP) predictions are the most specific.The weather conditions during the maize growing season in 2021 were warm with optimal rainfall conditions, but there was a distinct contrast between different months, both in temperature and rainfall regime .During the 2021 maize sowing harvest, the mean air temperature was 17.3 °C, which was 1.5 °C above the 1991–2020 historical average. During the maize vegetative period (VE–VT), the average air temperature was 20.2 °C, which was 3.0 °C higher than the 1991–2020 historical average; however, during the reproductive period (R1–R6), the average temperature was lower compared to the vegetative period and reached 14.5 °C, which was equal to the historical average during the 1991–2020 range. The year 2021, in terms of rainfall regime, was near optimal during the entire maize growing season, and the sum of precipitation was 234.5 mm . During the entire maize growing season, the number of days with heavy precipitation (above 10 mm) was 6 days. Despite the fact that the cumulative rainfall (234.5 mm) during the entire maize growing season was near optimal compared to the long-term average, contrasting differences were observed between the vegetative and reproductive development stages. For instance, during the maize vegetative development stage, the rainfall was 45.6 mm, which was only 41.1% of the precipitation norm between 1990 and 2020. According to the calculated SPI index, the maize vegetative development stage may be attributed to a moderately dry period. In contrast, a significantly higher amount of precipitation occured during the maize reproductive development stage—188.9 mm, which was 117.3% of the precipitation norm between 1990 and 2020. According to the calculated standardized precipitation index (SPI), the maize reproductive development stage may be attributed to relatively normal conditions.−1 to 15.00 t/ha−1 (dry weight). ANOVA analysis indicated the significant (p < 0.05) effect of maize cultivars on TAB variation in 2021. According to the received TAB yield, maize hybrids may be grouped as follow: DKC3201 ≈ DKC2684 ≈ DKC2891 (TAB: 13.3–15.0 t ha−1) > DKC2978 ≈ DKC3079 ≈ DKC3204 (TAB: 12.4–12.6 t ha−1) > DKC3218 (TAB: 9.7 t ha−1).Due to different maize hybrids, total above-ground biomass (TAB) varied from 9.70 t/haAt maize physiological maturity, protein concentration in the TAB yield ranged from 6.6% to 8.0% .p < 0.05) differences between the cultivar treatments. It has been observed that a statistically significant protein concentration was higher in the earlier maturity cultivars, i.e., DKC3218 (FAO190)—8.0% and DKC2978 (FAO190)—7.64%. Slightly lower protein concentration values were determined in the intermediate-maturity maize cultivars: DKC2684—7.56%, DKC3079—7.05%. In the remaining treatments of longer vegetation cultivars, protein concentration was below 7.0%. In all experimental treatments, lipid concentration varied within the range of 2.53–3.04%. Lipid variation was similar to protein concentration, i.e., higher lipids values were determined in earlier maturity cultivars (FAO190); however, these differences were not significant. Structural carbohydrates in the maize TAB yield varied within a rather narrow range, for instance, cellulose varied from 10.82 to 13.30%, hemicellulose from 15.78 to 16.78%, and lignin from 6.41 to 7.88%; these differences between treatments were not significant. Non-structural carbohydrates at the harvest ranged from 35.12 to 43.58%, and these differences were not significant either. However, when analyzing the total amount of carbohydrates in maize TAB, there were significant differences (p < 0.05) between different cultivars, and the order of treatments was as follows: DKC3079 ≈ DKC2891 ≈ DKC3204 > DKC2684 ≈ DKC3201 ≈ DKC2978 > DKC3218. It was also determined that cultivars of different maturities did not have a significant effect on the ash concentration in maize TAB.Performed statistical analysis indicated significant , a support vector machine (SVM), and random forest (RF) models were tested regarding maize TAB estimation. 2, which varied in the range of 0.60–0.65 (p < 0.01); RMSE, which varied in the range of 0.86–0.92 t ha−1; and BIAS, which varied from −0.07 to −0.25. During later vegetative periods, i.e., at the end of the leaf development (V10) and tasseling (VT), TAB prediction results have shown a better level of agreement with R2 0.72–0.77 (p < 0.01), RMSE 0.86–1.03 t ha−1, and BIAS −0.16 to −0.18. During the first maize growing season, the best TAB prediction results were demonstrated by GLM; however, the results of SVM and RF models were very similar while the differences in TAB prediction results between the different models did not exceed 9.1%, which indicates that the maize growth and development stage have a significantly greater impact on the prediction results than the selected model. During the maize reproductive stages, the model’s prediction capability consistently increased from stage R1 to R3, when the best TAB prediction results were achieved over the entire maize growing season. Then, at the R3 stage, high R2—0.95 (p < 0.01), small RMSE—0.35 t ha−1, and BIAS close to zero, i.e., −0.06, indicated perfect prediction results. During maize growth stage R4, TAB prediction results were slightly worse compared to R3; meanwhile, at stage R5, TAB prediction results were significantly worse than R3 or R4. During the second maize growing season, the best TAB prediction results had been determined using GLM and SVM models; however, the results of the RF model were also rather similar, and the difference between the best and the worst model in a specific maize growing period was no higher than 5.4%.When comparing the main maize growth and development periods, i.e., vegetative (from VE-Emergence to VT-tasseling) and reproductive , it can be stated that slightly better TAB prediction results were obtained during the reproductive period. At the beginning of the maize vegetative period, i.e., during the leaf formation from stage V5 to V7, statistical parameters indicated a generally good level of agreement between the observed and predicted TAB with RThe performance results of the highest performing model in predicting maize TBMP values among GLM, SVM, and RF are shown in 2, varied in the range of 0.73–0.76 (p < 0.01); RMSE, varied in the range of 372.7–459.4 m3 t ha−1; and BIAS values, varied in the range of −72.3 to −83.6, have shown a small TBMP underestimation. Meanwhile, at maize stages V5 and V7, statistical results were only reasonably good, with R2 0.47–0.61 (p < 0.01), RMSE 371.3–644.7 m3 t ha−1, and BIAS −24.9 to −157. During the maize reproductive period, the statistical accuracy between predicted and observed TBMP values was the highest during growth stage R3; while R2 was high—0.97, RMSE was rather low—104.3 m3 t ha−1, and BIAS was close to zero, i.e., 5.3. Slightly worse results when predicting TBMP values were received at maize stages R2 and R4, when R2 slightly varied from 0.88 to 0.90, while RMSE varied from 185.6 to 192.7 m3 t ha−1, and BIAS varied from −11.3 to −19.6. Having compared the models used in this study, it can be stated that GLM was the most accurate model for maize TBMP prediction; however, SVM and RF models have shown slightly poorer results, meaning they can also be used for maize TBMP prediction.The same trend discovered regarding TAB was found regarding the TBMP prediction, i.e., better TBMP prediction results were obtained during the maize reproductive period, compared to the vegetative period. Statistical analysis obviously demonstrated that it is extremely important to choose a suitable prediction period for both maize TAB and TBMP predictions. Among four maize growth and development stages in the vegetative period, the best TBMP prediction performance was determined to be during stages V10 and VT when RThe average weights of an attribute of each calculated VI for the maize TAB, at different growth and development stages, are shown in The analysis has demonstrated the importance of maize growth and development stages as covariates in the use of models for TAB prediction. No clear trends have been found when analyzing the distribution of different VI weights among different maize growth and development stages. For instance, GDVI VI showed very high weight values at stages V5 and VT, but at stages V10, R1, and R3, the weight values were 0. Similar results were obtained using other VIs, for example, NDVI weight values were very high during the reproductive period, i.e., stages R2, R4, and R5; however, during the vegetative period, the results were the opposite. In general, all of the weight values exhibited high variability across the nine growth stages. For maize TAB prediction, VIs may be grouped according to their importance as follow: NDVI ≈ TGI ≈ GDVI ≈ RGBVI > NDI ≈ VARI ≈ GLI ≈ RGRI > EVI ≈ RDVI ≈ SAVI ≈ GNDVI ≈ NDRE ≈ VEG. It should be noted that the VI of the last group (VEG) had almost no effect on the maize TAB prediction. To summarise the results, the estimated and used UAV-based VIs as input data for ML models were different for each maize growth stage, indicating that the suitable UAV covariates for the TAB prediction were greatly affected by the maize growth stage.The weights of the attributes which reflect the relative importance of the 14 VI used in this study for maize TBMP prediction are shown in For maize TBMP estimation models, the greatest covariates were NDVI  and TGI ; however, it should be noted that in some maize growth stages, the aforementioned VIs did not have a significant impact on maize TBMP prediction. On the contrary, the lowest weight values were obtained using NDRE and VEG indices. To summarise the results, the VIs can be arranged in the following order of importance: NDVI ≈ TGI ≈ VARI > RGBVI ≈ GDVI ≈ RGRI ≈ GLI ≈ NDI > EVI ≈ GNDVI ≈ RDVI ≈ SAVI ≈ VEG ≈ NDRE. When predicting maize TBMP as well as TAB values, a similar trend remained, i.e., the last group VI (VEG) had almost no effect. To summarise the results, it can be stated that the tendencies remained the same as they were during TAB prediction, i.e., UAV covariates for TBMP prediction were considerably affected by maize growth stages.As shown in −1 to 15.00 t/ha−1 (in dry weight), in a region in the Baltic area. Due to the simplicity, easy interpretability, and high acceptance of the RS approach, we obtain high-resolution UAV-based multispectral band data, which have been used for maize TAB and TBMP prediction. Although the VI-based TAB estimation method is considered to be very efficient, the remotely obtained maize TAB and TBMP data cannot effectively describe the mechanistic response of changes in maize growth and development in different environments or under different agronomic management practices. For this reason, 14 VIs derived from UAV-based multispectral images were employed. In addition, some studies demonstrated that the growth stage plays a key role in the sensitivity and performance of VIs when predicting crop biophysical parameters [2 values of 0.90 and 0.93, respectively, while in our study, prediction accuracy during the same period was slightly lower, with the R2 values being 0.73 at V10 and 0.76 at VT. However, the results of both studies confirmed that during the initial maize growth stages (V3–V7), TAB prediction accuracy was lower than it was during later growth periods (V10–VT). In contrast to that, a study by Zhang et al. [2 varied from 0.88 to 0.95. The silking-dough (R1–R4) stages represent the nutrient  concentration growth peak of the maize parts of the leaf, stem, and storage organs , while nutrient concentration in the grain only begins to increase during this period [2 = 0.65) among all of the growth stages analyzed. The results were largely to be expected, as during the initial maize growth stages, the demand for water and nutrients was rather low; thus, the difference in maize hybrids was not so notable. As a result, ML models during those growth stages could not represent well enough the TAB differences caused by the various maize hybrids. On the contrary, maize nutrient demand starts to increase rapidly from around stage V7, and at the end of the vegetative period (V10–V14), the differences are clearly notable [Our study provides experimental evidence regarding different maize hybrids and their total above-ground biomass levels, which varied significantly from 9.70 t/harameters ; there irameters ; they dig et al.  has showg et al.  presentes period . During s period  while ths period , a rapid notable . During  notable . In addi notable . It is i3 ha−1 under suitable climate conditions [3 ha−1 and the correlation between TAB and TBMP was R2—0.98, indicating that TAB is the main factor that mainly determines TBMP levels. This finding, which revealed a strong relationship between TAB and TBMP, was also responsible for the fact that the accuracy of TBMP prediction at different maize growth stages was very similar to that of TAB values. The most accurate TBMP values could be predicted at the R3 and R4 growth stages where R2 was 0.90 and 0.97, respectively. These results nicely coincided with the findings of Amon et al. [Maize is a well-known crop used in methane production and can produce between 7500 and 10,200 mnditions . As has nditions . As a reTo acquire the most suitable model for predicting maize TAB and TBMP, we used three ML methods, i.e., GLM, RF, and SVM models. When comparing the performances of these models during each maize growth stage, the accuracy of all of the used models’ predictions was very similar; although, slightly better results regarding TAB and TBMP prediction were obtained with the GLM and SVM models. However, the differences in the different models’ predictions were not so evident, and thus, all three models used in our study may be successfully employed when predicting maize TAB and TBMP values under Nordic-Baltic climate conditions.When predicting the TAB and TBMP values, it has been observed that when using only spectral bands , the accuracy of the predictions was not sufficient. Thus, in the further steps of this study, only calculated VIs were used and their possible link with dependent TAB and TBMP variables was sought. However, even when using different VIs for maize prediction, no clear insights have been found. For example, during the maize vegetative period for TAB prediction, GDVI performed relatively better than the other VIs at the V5 and VT stages, but later, at the V10, R1, and R3 stages, there was almost no weight effect on the prediction. Similar trends remained when using the other VIs, for example, NDVI was very important at R2, R4, and R5 stages, but during the vegetative period, the results were the opposite. It is only important to note that at that time, i.e., from the R2 to R5 stages, the NDVI values were the highest during the whole maize growing season and varied within the range of 0.68 to 0.73, while the canopy was closed and the NDVI saturated. Another interesting observation is that at the R3 stage, i.e., the time when the accuracy of the TAB predictions was the best, the GLI, NDI, and VARI indices had the greatest impact on these predictions and all of them have a green band in their formulas, as well as not having an NIR band. It has also been noted that most of the VIs used were saturated at the R3 stage of maize growth. When analyzing the TBMP predictions, the trends were very similar to the TAB, i.e., different VIs at different maize growth stages had different effects on the accuracy of the predictions. Certainly, it was greatly affected by the fact that we used different ML models for the prediction; therefore, VI weights at different maize growth stages should be compared carefully.RS technologies, more precisely, UAVs at low altitudes, have important application options for acquiring crop spectral information at the plot scale. One of the greatest advantages of using UAVs compared to satellites is that it is possible to compensate for the lack of spatial and temporal resolution of satellite data in precision agriculture. In this study, we tried an exploration in predicting maize TAB and TBMP, and this is an initial attempt, at least in regards to the Nordic-Baltic area. Our prediction results were rather good, but they could be further improved and more studies that cover different soil types and environmental conditions are required. Our research results are based on a dataset of one-year field experiments under typical weather conditions that prevailed in the area. Thus, this deficiency should be corrected by future multi-year field experiments, testing how TAB and TBMP change under contrasting weather conditions. What is more, the use of more data from field seasonal measurements , as well as climatic and management data, could provide a more detailed description of crop growth and allow for more favorable predictions of TAB and TBMP.According to the Köppen climate classification , the cliZea mays L.) field experiment under rainfed conditions was conducted during the 2021 season at the Lithuanian Research Centre for Agriculture and Forestry (55°23′50′′ N and 23°51′40′′ E) in the Central part of Lithuania. The main soil was Endocalcari–Epihypogleyic Cambisol  with loam texture class ; it had a neutral pH of 6.8 and soil organic matter of 3.2%. In this study, seven maize hybrids with different maturity classes and different suitability levels  were investigated (The maize (stigated . These m−1 (0.75 m row and 0.15 m plant spacing). Weeds were controlled by the herbicide Arrat (tritosulphurone 250 g/kg−1 + dicamba 500 g/kg−1) and commercial formulation wettable granule (WG) at a rate of 0.2 kg/ha−1 was used at the maize V3 growth stage. During the maize growing season, no diseases were observed; thus, other pesticides were not used. All experimental treatments were fertilized according to local practices and followed the same rates as those of mineral fertilizers. Before maize sowing; nitrogen (N), at a rate of 120 kg N ha−1; superphosphate (P), at a rate of 90 kg P ha−1; and potassium chloride (K), at a rate of 170 kg K ha−1 were applied manually and incorporated into the soil. Additionally, at the growth stage of V5, maize was fertilized with N 80 kg N ha−1 to avoid N deficiencies. The mineral fertilizers were in the form of ammonium nitrate (AN) (34.4–0–0), while complex NPK fertilizers were in the form (6–18–34). Maize harvest was performed manually on 6 October 2021. The field experiments included seven treatments that were performed in randomized block design with four replicates. The area of each experimental plot was 27 m2.The maize was grown after conventional tillage and was sown on 30 May 2021, when soil temperature had reached 10–12 °C, with a density of 90,000 plants ha2 were cut to identify the final total aboveground biomass and grain yield. The individual maize components were weighed (fresh mass) and dried at 65 ± 5 °C to constant weight (dry weight).During the maize vegetation period, plant development stages were recorded frequently. The maize vegetative and reproductive development stages were identified on the basis of the entire treatment when 50% or more of the plants were at a particular development stage. The leaf-collar method  was usedA modern, environmentally friendly analysis method, near-infrared (NIR) spectroscopy, which does not require chemical reagents, was used to examine the quality of the maize samples. Maize TAB samples were scanned with a NIRS-6500 device, with a spinning module using wavelengths between 400 and 2500 nm in reflectance, and the obtained spectra were processed with equations installed into the device for maize analysis . For analysis, the samples were dried and ground with an ultra-centrifugal mill ZM 200  to pass a 1 mm screen. The precision of the used equations was sufficient and ranged within the following limits: standard error calibration (SEC)—0.16–0.64, coefficient of determination (RSQ)—0.77–0.94, and standard error cross-validation (SECV)—0.18–0.68. When using this method, maize quality parameters, including protein, lipid, and ash contents, have been determined. The number of water-soluble carbohydrates was determined by Anthron’s method.Soil samples were collected before the implementation of the experiment and they were later air-dried and passed through 2 mm and 0.25 mm size sieves in the laboratory. SOC content was determined by a photometric procedure at a wavelength of 590 nm using a UV–VIS spectrophotometer Cary (Varian) and using glucose as a standard . The soiorgTBMP). orgTBMP was calculated by Equation (1) [VFA), lipids, protein carbohydrates, and lignin were expressed in terms of %. In the next step, TBMP values were recalculated into m3/ha−1 units.In this study, we calculated theoretical biochemical methane potential (TBMP) based on the maize sample’s organic composition (expressed as tion (1) ,(1)TBMP®, red edge, and near-infrared (NIR), with center wavelengths of 450, 560, 650, 730, and 840 nm, respectively. The UAV campaign was carried out 9 times during the main maize growth stages  with an installed real-time kinematic (RTK) module that enhances the precision of position data, derived from satellite-based positioning. Additionally, this used UAV had a sunlight sensor that automatically adjusts radiation reflectance and obtains reflectance data directly. This UAV had a combined multispectral imaging system, including a visible light (RGB) sensor responsible for visible light imaging. There are five additional multispectral bands with 5.74 focal lenses, including blue light (B), green light (G), red light h stages  at a heiThe UAV flights were conducted in clear sky and low wind speed conditions between 11:00 am and 13:00 pm local time. The UAV flights were controlled using the flight planner app , adapted for android OS and using the Huawei P20 smartphone. The average speed of each UAV flight was ≈2.2 m/s, with a camera looking downwards; the flight duration for the full experimental plot cover lasted approximately 20 min; and about 2100 images, with 85% vertical and 85% horizontal overlapping to obtain images with a 1.1 pixel size, were taken during each flight.After each flight UAV, multispectral image data was preprocessed with Pix4D software, which uses the structure-from-motion technique. This technique was used to relate features between overlapping images and calculate the 3D position of the matched points, which are densified and textured with the corresponding images. The ortho-mosaic image was generated by projecting each texture point onto the 2D plane and was exported in a TIFF image format for further analysis. After the image stitching process, the generated ortho-mosaic ground sampling distance was 1.09 cm/pixel. Then, the densified point clouds were generated with 5,049,015 points. The RMSE values in the X, Y, and Z coordinates were 0.020 m., 0.017 m., and 0.052 m., respectively. In order to calculate the vegetation indices (VI) consisting of different combinations of wavelength-specific spectral reflectance, the prepared ortho-mosaic imagery was imported to freely available QGIS software (version 3.2.2.). In the next step, experimental plots (replicates) were marked out with polygons on the ortho-mosaic image, and the vegetation indices were calculated using the raster calculator tool. In this study, we computed 14 widely used VIs for predicting maize TAB and TBMP. The majority of the selected VIs had been used in previous studies for monitoring the growth and predicting the yields of agricultural crops. Based on this, we selected the most commonly used VIs and they were tested in our study .In this study, we used three widely used machine-learning (ML) methods, i.e., generalized linear model (GLM), random forest (RF), and Support Vector Machine (SVM). In order to predict the maize total above-ground biomass (TAB) and theoretical biochemical methane potential (TBMP), a dataset regarding each of the vegetative  and reproductive  period stages, where TAB and TBMP were assigned as dependent variables and VIs as independent variables , was divGeneralized linear models (GLM) are an extension of traditional linear models . This alA random forest (RF) is an ensemble of a certain number of random trees and is more robust with respect to noise . These tThe Support Vector Machine (SVM) is a form of supervised nonparametric modeling, which is defined by using kernels and operating on the margins . The alg2), BIAS, and RMSE, computed as follows [n is the number of observed values, iy and ix are the simulated and observed values, and ith data pair. R2 describes the proportion of the variance in the observed data as explained by the prediction model, and it ranges from 0 to 1, with higher values indicating less error variance. The BIAS measures the average difference between the observed/estimated and predicted values. A positive BIAS value indicates an under-prediction and a negative BIAS indicates an over-prediction. The RMSE is the square root of the mean square error. The smaller the BIAS and RMSE values are, the better the performance of the prediction.The agreement between the observed maize TAB, estimated theoretical methane yield, and predicted parameters was tested by the coefficient of determination (R follows :(2)R2=∑iSignificant differences between the treatments were determined using Tukey’s test at a 0.05 probability level. Statistical analyses were performed using proc GLM, SAS v9.4 .The daily meteorological data, including mean temperature °C, minimum and maximum temperatures °C, mean humidity (%), and sunshine hours per day, were obtained from a meteorological station of the Lithuanian Hydrometeorological Service (Ministry of Environment), located near the maize experimental field.In this study, we investigated the feasibility of using UAV-multispectral images in combination with field measurements and ML algorithms to estimate maize TAB and TBMP values at main phenological stages. Our study suggests that the accuracy of both TAB and TBMP prediction was greatly affected by different maize growth stages; however, different maize hybrids did not have a significant effect on prediction accuracy. The best ML prediction results were obtained during the R2–R4 maize growth period; the prediction models managed to explain 88–95% of TAB and 88–97% TBMP variation. We also found that, for the practical usage of farmers, the earliest suitable timing for TAB and TBMP prediction in the Nordic-Baltic area is within stages V7–V10. In this study, three ML algorithms were used when predicting TAB and TBMP values. The best maize TAB and TBMP prediction accuracy results were achieved using the GLM model.


The nucleocapsid (N) protein of SARS-CoV-2 binds viral RNA, condensing it inside the virion, and phase separating with RNA to form liquid–liquid condensates. There is little consensus on what differentiates sequence-independent N–RNA interactions in the virion or in liquid droplets from those with specific genomic RNA (gRNA) motifs necessary for viral function inside infected cells. To identify the RNA structures and the N domains responsible for specific interactions and phase separation, we use the first 1,000 nt of viral RNA and short RNA segments designed as models for single-stranded and paired RNA. Binding affinities estimated from fluorescence anisotropy of these RNAs to the two-folded domains of N (the NTD and CTD) and comparison to full-length N demonstrate that the NTD binds preferentially to single-stranded RNA, and while it is the primary RNA-binding site, it is not essential to phase separation. Nuclear magnetic resonance spectroscopy identifies two RNA-binding sites on the NTD: a previously characterized site and an additional although weaker RNA-binding face that becomes prominent when binding to the primary site is weak, such as with dsRNA or a binding-impaired mutant. Phase separation assays of nucleocapsid domains with double-stranded and single-stranded RNA structures support a model where multiple weak interactions, such as with the CTD or the NTD's secondary face promote phase separation, while strong, specific interactions do not. These studies indicate that both strong and multivalent weak N–RNA interactions underlie the multifunctional abilities of N. The nucleocapsid protein of the SARS-CoV-2 coronavirus binds to viral RNA, both to protect and condense it inside the viral particle and to facilitate viral transcription inside infected cells. Evidence suggests that variations in RNA structure impact how and where it binds to the nucleocapsid, but these differences are not well understood at a structural level. Using nuclear magnetic resonance spectroscopy, we examine the interactions between each folded domain of the nucleocapsid and both structured and unstructured RNA. Binding affinities and NMR chemical shift profiles demonstrate that binding between the N-terminal domain (NTD) and single-stranded RNA is driven by strong interactions at a specific site, while multiple weak interactions at newly discovered sites lead to phase separation and RNA condensation.The SARS-CoV-2 coronavirus is a positive sense RNA virus with a genome of approximately 30,000 bases in length that encodes 29 proteins, including four primary structural proteins: the envelope, membrane, spike, and nucleocapsid , 2. AmonStructurally, the nucleocapsid consists of two-folded domains linked and flanked by three intrinsically disordered regions (IDRs) Fig. a, b 13,,13, 14. N binds RNA both promiscuously and specifically—the protein binds tightly to any RNA regardless of sequence, but it is also recruited to specific motifs within the viral genome. In SARS-CoV—the virus which caused the 2002 SARS outbreak—N recognizes specific sequences within gRNA, such as the highly conserved transcription regulation sequence  and packWhile structural details on what differentiates specific binding to sites like those in the 5′ UTR from promiscuous interactions that promote phase separation or encapsidation are of particular interest , 21, 31,As the protein's primary RNA-binding domain, the NTD is a likely candidate for driving specific N–RNA binding. There are multiple structural investigations of RNA binding to the NTD , 31, 40.The CTD is also known to bind RNA , howeverWith a focus on the NTD and CTD, we examine binding to the first 1,000 nucleotides of the CoV-2 genomic RNA  and to short 14-nucleotide RNAs designed to form pure single- and double-stranded RNA species. We find that the NTD binds tightly to ssRNA and weakly to dsRNA, especially in the context of the full-length nucleocapsid protein. We demonstrate that an additional face of the NTD interacts weakly with RNA and promotes liquid–liquid phase separation. Further, a mutation designed to abrogate interaction between the NTD and RNA promotes binding at this second face, suggesting that the RNA binding to the NTD is in equilibrium between these two distinct binding modes. With the CTD, we confirm that the protein interacts only weakly with both ss- and dsRNA, and that dsRNA binds to the positively charged face of the CTD. Examining the propensity of each domain to form phase-separated droplets, we find that weak, nonspecific interactions are the primary drivers of droplet formation, and that while the CTD drives phase separation, binding at the weaker face of the NTD can also promote droplet formation. From these results, we propose that both strong and weak interactions with RNA are essential for nucleocapsid function.To map RNA-interacting sites on the nucleocapsid, we previously measured changes in NMR spectra of the nucleocapsid following addition of the first 1,000 bases of the coronavirus genomic RNA  . Since sThe NTD binds RNA at the positively charged groove Fig. f around The CTD binds g1–1,000 RNA along its positively charged α-face , containing the IDRs to the N- and C-term of the CTD. Removal of the N-terminal tail did not have an impact on phase separation propensity, but addition of the IDRs surprisingly inhibited phase separation, even at high (7.2 μM) protein concentrations . To examine this phenomenon more comprehensively, we collected images of samples of the WT and mutant NTD with the ss- and ds-14mer at NMR conditions. At 37°C, a temperature that promotes nucleocapsid phase separation relative to the 25°C used in the NMR experiments, the NTD forms droplets with the ds-14mer Fig. . InteresWe last turned our attention to interactions between the CTD and the 14mer RNAs. Attempts to assay this interaction by NMR were hampered overall by peak broadening, minimal perturbations in chemical shifts, and the propensity of the CTD to phase separate with RNA, which necessitated collecting spectra at high salt conditions. The addition of g1–1,000 to the CTD produced no chemical shift perturbations, but induced dips in peak intensity at parts of the CTD corresponding to the domain's binding face Fig. a. While The SARS-CoV-2 nucleocapsid protein interacts multivalently with RNA to facilitate a range of functions within an infected cell. Due to the complexity and multiplicity of domains of both the nucleocapsid and the viral RNA, there is a large variety in the potential structures of nucleocapsid–RNA complexes. To identify structural preferences within the viral gRNA, we designed 14mer RNAs consisting entirely of ssRNA (ss-14mer) as a model of regions of unpaired RNA and dsRNA (ds-14mer) as a model for paired stem regions and examined interactions between these RNAs and both nucleocapsid domains, as well as the full-length protein, and their propensity to form liquid condensates. We find that both domains bind to both RNAs, each with different affinities and different propensities for phase separation. In particular, interactions between N and ssRNA are strong, and a mutation that abrogates binding at the NTD dramatically weakens binding between N and RNA, indicating that high-affinity interactions are mediated through single-stranded RNAs and the primary site of the NTD. We additionally describe a second RNA-binding face of the NTD that interacts only weakly with RNA and confirm the α-face of the CTD as the region that binds RNA, with preference for dsRNA. Lastly, we examine how these interactions correlate with the ability of the nucleocapsid to phase separate with RNA, demonstrating that multiple weak, nonspecific interactions at the CTD and at the NTD's secondary face drive liquid droplet formation.In addition to confirming the primary binding face, NMR titration experiments identified a second site containing several residues  at 18°C for 20 h. CTD, CTD248–361, and linker-CTD-CIDR were grown to an OD600 of 2 in terrific broth, then induced with 0.5 mM IPTG at 18°C overnight for unlabeled expression. For 15N labeling, cells of FL-N or CTD were grown in 2× YT to an OD of 0.7, pelleted at low speed, then washed and resuspended in 15N-enriched MJ9 media, grown for an hour then induced with 0.5 mM IPTG at 30°C for 4 h (CTD) or 18°C overnight (FL-N). The NTD and Y109A NTD were expressed in either ZYM-5052 autoinduction media (for unlabeled protein) or MD-5052 autoinduction media with 15N ammonium chloride (for stable isotope labeling)  .3, 5 mM Imidazole, pH 8.0, 0.6 mg/mL lysozyme with protease inhibitor) for 1 h at 4°C. Cells were further sonicated and centrifuged at 20,000 RPM for 45 min to separate cell debris. The supernatant was incubated with Cobalt resin in a gravity column. All proteins were washed on-column with 3 M NaCl (20 column volumes) to remove bound bacterial RNA. FL-N, Y109A FL-N, and CTD constructs were eluted with four column volumes of elution buffer . Purified NTD and Y109A NTD were eluted by on-column proteolysis with 30 nM untagged fast-acting protease bdSENP1 for 1 h at 4°C. Proteins were further purified on a Superdex 75 gel filtration column  in 50 mM sodium phosphate, 150 mM NaCl, pH 6.5. The purity of the recombinant proteins, assessed by SDS-PAGE, was >95%. Protein concentrations were determined using absorbance at 280 nm, calculated from standard extinction coefficients of 1,490 and 5,500 M−1 cm−1 for tyrosine and tryptophan, respectively. Purified proteins were either stored at 4°C and used within one week or flash frozen to −80°C for long-term storage.Proteins were purified using TALON His-tag purification protocol (Clonetech). Cells were lysed in lysis buffer , either labeled with 5′,6-carboxyfluorescein or unlabeled and reverse complement 14mer (5′-GACGUCCAUGUGCC-3′) were purchased from GenScript. The dsRNA was prepared by annealing the ss-14mer (5′-GGCACAUGGACGUC-3′) with the reverse complement (5′-GACGUCCAUGUGCC-3′) at an equal concentration of each oligonucleotide and 50 mM NaCl. The mixture was incubated at 60°C for 15 min and cooled slowly to 26°C . The purFor analysis of the published RNP-MaP data in Fig. 2O for signal locking, 1 mM sodium trimethylsilylpropanesulfonate (DSS) as a reference, and a commercial protease inhibitor cocktail (Roche Applied Science). Samples used in RNA titrations additionally contained a Murine RNAse inhibitor (NE Biolabs) at a concentration of 1 unit/μL. Assignments for the NTD  and CTDRB 34511 ) were trRB 34511 , assignmRB 34511 , and titRB 34511 . One-dim NMRGlue , and bas NMRGlue . Chemicar of 1/5 .Spectra of the NTD and Y109A NTD were collected at 25°C in a buffer of 20 mM sodium phosphate and 20 mM NaCl, at pH 6.5. For titrations of the ss- and ds-14mer, we collected spectra at 100 μM NTD titrated with RNA (concentrated to 4 mM to minimize dilution) up to 2:1 RNA:NTD, at intervals of 0.25, 0.5, 0.75, 1, 1.5, and 2:1 RNA:NTD. Figures of the spectra Figs. . At 25°CFor titrations of NTD into RNA, we performed experiments in a 3 mm NMR tube at 100 μM RNA in NTD NMR buffer and titrated NTD at 400 μM into the RNA samples up to a 1.5:1 NTD:RNA ratio, at intervals of 0.05, 0.1, 0.2, 0.3, 0.5, 0.7, 1, and 1.5:1 NTD:RNA. Plots Fig.  show theExperiments on the CTD were carried out at 25°C in a buffer of 20 mM sodium phosphate and 200 mM NaCl at pH 6 in a shaped NMR tube. At 200 mM NaCl, we observed no evidence of phase separation of the CTD with RNA. All spectra were collected at 100 μM CTD, and at 1 μM g1–1,000  was calculated from intensities measured in NMRViewJ, and uncertainty propagated through the standard deviation of the noise floor of the spectra.Measurements of equences  were useuence in , with a 3 was used. For titrations with the N domains . The excitation wavelength was set to 492 nm and the emission wavelength to 516 nm. For all experiments, a buffer of 50 mM sodium phosphate at pH 6.5, 150 mM NaCl, and 1 mM NaNins Fig. , using ains Fig.  with a fFluorescence and bright-field microscopy images were taken on a Keyence BZ-X700/BZ-X710 microscope and a 384-well plate (Cellvis P384-1.5H-N); images were processed using BZ-x viewer and BZ-x analyzer software. For all samples except the NTD with 14mer RNAs, protein and RNA were mixed at different ratios  binding to the ss-14mer and ds-14mer was visualized by electrophoretic mobility shift assay in 2.5% agarose gels. Increasing concentrations of protein in the range of 0–125 μM were added to 25 μM RNA and incubated for 20 min at room temperature in a total reaction volume of 10 μL. About 2 microlitre loading dye was added to the reaction before loading. RNA bands were stained with Midori Green Nucleic Acid staining solution  and visualized using a Bio-Rad Gel Doc Image system.As binding between N and RNA is charge-driven, and therefore highly dependent on salt concentrations, we have summarized the buffer conditions for all experiments performed in Table Experimental conditions were selected to find a compromise between the need to draw comparisons between experiments, instrumental and sample limitations (e.g. phase separation), and relevance to biological conditions. NMR experiments on the CTD necessitated high salt conditions to prevent condensation. As a result, these cannot be easily compared to experiments on the NTD, performed at low salt concentrations preferred for NMR sensitivity. Likewise, when examining phase separation properties of the NTD and FL-N with 14-mer RNAs by microscopy, we elected to use the NTD NMR conditions, to allow for comparison between our NMR results and phase separation properties with 14-mer RNAs. Phase separation of FL-N was performed at reduced protein (3.6 μM) and RNA (18 μM) concentrations, to make the experiments more comparable to prior phase separation experiments involving g1–1,000. All fluorescence anisotropy experiments were performed at pH 6.5 and 150 mM NaCl conditions, to allow comparison across anisotropy experiments among all protein domains and all RNAs used. Lastly, EMSAs and microscopy assays of phase separation with g1–1,000 were collected at pH 7.5 and 150 mM NaCl to mimic physiological pH and salt concentrations.pgad333_Supplementary_DataClick here for additional data file.


Early detection of infection is a central and critical component of our innate immune system. Mammalian cells have developed specialized receptors that detect RNA with unusual structures or of foreign origin – a hallmark of many virus infections. Activation of these receptors induces inflammatory responses and an antiviral state. However, it is increasingly appreciated that these RNA sensors can also be activated in the absence of infection, and that this ‘self-activation’ can be pathogenic and promote disease. Here, we review recent discoveries in sterile activation of the cytosolic innate immune receptors that bind RNA. We focus on new aspects of endogenous ligand recognition uncovered in these studies, and their roles in disease pathogenesis. The ability of the immune system to discern ‘self’ from ‘non-self’ is critical for its effective function and protection from invading pathogens. The innate immune system utilizes specialized receptors, termed Pattern Recognition Receptors (PRRs), that detect ligands associated with pathogens and usually absent in the host, termed Pathogen Associated Molecular Patterns (PAMPs) .Aberrant RNA species generated during viral infections constitute a group of PAMPs that is recognized by specific PRRs in the cytosol, which is the location of many replicating viruses . As viruThe cytosolic RNA receptors of the innate immune system can sense different types of RNA and induce different types of responses Figure 1Although RNA sensing is important in defence against pathogens, dysregulation or inappropriate activation of RNA sensing can also lead to disease. A well-known example is the group of diseases termed “type I interferonopathies”, which develop as a result of excessive and chronic production of type I interferons (T1-IFNs) . T1-IFNsDDX58), melanoma differentiation-associated gene 5  and laboratory of genetics and physiology 2 . RLRs play an important role in protection from viral infection . These IR-Alus can form dsRNA structures due to sequence complementarity, and stimulate MDA5. Indeed, repetitive RNA structures have been implicated in MDA5 sensing in other scenarios: treatment of cancer cells with DNA demethylating drugs induced the expression of endogenous retroviral elements (EREs), which also have the propensity to form dsRNA structures and thus activate MDA5  was found in the absence of mitochondrial RNA helicase SUV3 and polynucleotide phosphorylase PNPase, which mediate mt-dsRNA degradation . The mt-patients . NeverthSAM domain and HD domain-containing protein 1 (SAMHD1) is a phosphohydrolase degrading deoxyribonucleoside triphosphate (dNTP), and loss of functional SAMHD1 can also cause AGS . SurprisThe propensity of MDA5 activation by endogenous RNAs is best highlighted through the role of adenosine deaminase acting on RNA 1 (ADAR1). ADAR1 is an enzyme that binds dsRNA and catalyses the conversion of adenosine base to inosine (A-to-I editing). Inosine and uridine do not form hydrogen bonds, thus A-to-I editing reduces base pairing of dsRNA. ADAR1 knockout mice are embryonically lethal due to chronic production of T1-IFN, but can be rescued with concomitant knockout of MDA5 . ADAR1 hin vitro, and most were found to be hyper-edited in sequencing data, indicating their immunogenic potential and targeting by ADAR1. The authors also suggest that cis-NATs might be an important and preferred endogenous ligand for MDA5, in addition to IR-Alus previously linked to MDA5 mutants in AGS  mapping to define RNA-editing QTLs (edQTLs), thus linking RNA editing levels to disease traits . They des in AGS .Alus, but in contrast to previous work , and human vs mouse ADAR1p150 overexpression systems, to investigate inter-cell and inter-species differences in dsRNA expression and editing. They demonstrated that differences in expression patterns of immunogenic dsRNAs across cell types and species drove the effects seen in stimulation of the ADAR1-dsRNA-MDA5 axis. Simply put, an immunogenic dsRNA will drive MDA5 activation when it is expressed in a particular cell, and this expression timing and level, in conjunction with ADAR1 activity and MDA5 presence, will drive a T1-IFN response. These recent advancements further define endogenous RNAs that activate MDA5 in the absence of virus infection, as well as link RNA sensing to specific disease states.Additionally, a pre-print by Sun et al. experimentally investigated ADAR1-editing of endogenous dsRNA on a panel of genetically manipulated cells that lacked different ADAR1 isoforms and/or MDA5 . Using Rous work , were moIn addition to the dsRNA binding region of ADAR1, present in both p110 and p150 isoforms, the cytoplasmic p150 isoform also contains a Z-nucleic acid binding domain. The Z-form is a left-handed helix conformation of double stranded nucleic acids that is thermodynamically less stable compared to the more common right-handed helices in B-DNA and A-RNA structures in most conditions . Backbonin vivo models used showed discrepancies in the phenotypes detected. de Reuver et al.  or Epstein-Barr virus (EBV), both DNA viruses of the nfection . Host 5Sult RNAs . Vault Rult RNAs . In the ult RNAs . Y RNAs ult RNAs . This wault RNAs . Howeverult RNAs . CombineAlthough the interferonopathy SMS is more commonly associated with gain-of-function mutations in MDA5, activating mutations in RIG-I have also been found to cause SMS –57. ThesThe least understood and studied receptor of the RLR family is LGP2. It can bind with different affinities to dsRNA of various lengths and structural features , but sinin vitro cell models, Stok et al.  is an IFN-inducible, antiviral dsRNA-sensing kinase that is constitutively expressed at a low level  through the activation of transcription factors, like activating transcription factor 4 (ATF4), which enable return to cellular homeostasis or, under sustained stress, induce cell death , 83. PKRP195A/p150- mice, which lack the catalytically active, IFN-inducible Adar1 isoform p150, has previously been described as MDA5-dependent , which induce PKR-dependent eIF2α phosphorylation –86. The ependent . A recenresponse . A CRISPresponse . These fPKR can also be activated by mt-dsRNA, as previously described for MDA5. Cellular stress or mitochondrial damage can lead to the release of mt-dsRNA into the cytoplasm, which has been shown to activate MDA5 in some cases . In contIn a subsequent study, it was reported that disruption of mitochondrial membrane potential and structure by small molecule inhibitors led to PKR-dependent ISG induction, eIF2α phosphorylation and cell death . FurtherIn a recent study, another class of duplex structure-forming RNAs, the circular RNA (circRNA) was described in context of PKR . circRNADifferent dsRNA structures are not the only endogenous, activating ligands for PKR. PKR can be activated by direct interaction with other ligands in the absence of dsRNA, such as the protein activator of PKR (PACT). Cellular stress can induce phosphorylation of PACT which leads to increased interaction with and activation of PKR. This can result in osmotic stress induced expression of pro-inflammatory genes –92. A moThe recent findings on sterile PKR activation described here clearly show the large number and variety of endogenous ligands recognized by PKR. Many of these interactions are cell-type and context dependent and require further investigation and characterization to understand them in detail. However, the studies have already shown their future potential in treating PKR-mediated autoimmune and autoinflammatory diseases.The OASs are a family of IFN-inducible cytosolic dsRNA sensors consisting of the catalytically inactive OASL and the catalytically active OAS1, OAS2 and OAS3 proteins. OASL has antiviral functions, despite being catalytically inactive, which are mediated by interaction with and activation of RIG-I through mimicking polyubiquitin . OAS1, Oin vitro transcription that resembles viral dsRNA , while OAS2 appears to require a minimum of 35 bp duplex structures and its target affinity increases with increasing dsRNA length . OAS3 onal dsRNA , 104. ThSimilar to the previously discussed dsRNA sensors, OAS overactivation or dysfunction is associated with autoinflammatory diseases, such as Alzheimer’s disease and AGS, as well as a higher risk of development of severe disease outcomes like COVID-19 , 106. ReA separate study described a potential role for OAS3 and RNase L in RNA homeostasis . The autAlus, and mt-dsRNA have been reported as ligands for several receptors. Similarly, cancer treatments that generate endogenous RNA ligands have also revealed roles for different receptors. Since most of these phenomena have been investigated in isolation, it remains unclear whether these same ligands activate multiple receptors simultaneously, and how potential cooperative signaling from these receptors might affect disease outcome.The recent wave of new discoveries on the activation of cytosolic RNA receptors in non-infectious disease has started to uncover the identity and structure of endogenous ligands of these receptors Figure 2in vitro studies have been imperative to our understanding of how the cytosolic RNA receptors work. However, it is evident that receptor activation in sterile disease is context-dependent and requires in situ studies. Importantly, experiments using endogenous proteins and in disease-relevant tissues will be necessary to fully delineate the endogenous ligands of these receptors. The many technical and methodological advancements of the last few years should facilitate such investigations, such as SHAPE-MaP, a tool developed to determine sequence and structure of RNA ligands, or iCLIP, a method to investigate the precise interaction of RNA ligands with their receptors. Combining these different methods and applying them in disease-specific human tissues, for example iPSC-derived models or organoids, could reveal the exact ligands of each of these receptors in appropriate physiological contexts, which remains unknown in many cases.Structural and Furthermore, several receptors are expressed in the same cell type and tissue and appear to share activating ligands, suggesting a potential for combined activation. Evaluating the contribution of the different receptors in these settings will be an important aspect of future studies. Such investigations will broaden our understanding of innate immunity in non-infectious contexts, and open opportunities for novel treatment strategies.NS and SS wrote and revised the manuscript. All authors contributed to the article and approved the submitted version.


Hevea brasiliensis), previously known as protein LOC110648447, which accumulates in leaves in response to root infection by white root rot fungi, Rigidoporus microporus (HbPR10 is a member of the Bet v 1/PR-10 protein family.Class 10 of pathogenesis-related proteins (PR-10) is a class of PR proteins localized in the cytoplasm . Memberscroporus . SequencHbPR10 gene consists of four exons that can be combined in three ways  with 1 μg of purified HbPR10.1-His protein for 180 min at 37oC, separated the RNA on 1% agarose gel and stained the gel with ethidium bromide. In the presence of HbPR10.1-His, the ribosomal RNA (rRNA) was degraded and low-molecular-weight signals appeared , followed by incubation with RNA, resulted in a reduced RNA degradation compared to unheated HbPR10.1-His  was included as a negative control for the in-solution ribonuclease assay of rice OsPR10  and SPE1OsPR10 . Likewis subgroup of the PR-10 family. A review article by The absence of detectable ribonuclease activity for VpPR10.9 . The conpcad062_SuppClick here for additional data file.


SoLute Carrier (SLC25) family of mitochondrial metabolite transporters to survey this coupling in a family-wide manner. The SLC25 family is the largest family of mitochondrial metabolite transporters in eukaryotes that translocate ligands of different chemical properties, ranging from nucleotides, amino acids, carboxylic acids and cofactors, presenting adequate experimentally validated functional diversification in ligand transport. Here, we combine phylogenetic analysis to profile SLC25 transporters across common eukaryotic model organisms, from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, to Homo sapiens, and assess their sequence adaptations to the transported ligands within individual subfamilies. Using several recently studied and poorly characterized SLC25 transporters, we discuss the potentials and limitations of phylogenetic analysis in guiding functional characterization.Homology search and phylogenetic analysis have commonly been used to annotate gene function, although they are prone to error. We hypothesize that the power of homology search in functional annotation depends on the coupling of sequence variation to functional diversification, and we herein focus on the  It is estimated that at least 20–30% of the coding proteins derived from any model organism’s genome have poorly characterized functions . To predIn the context of cellular metabolism, specifically for metabolic enzymes and metabolite transporters of poorly characterized function, we consider functional prediction, the “deorphanization” effort, as predicting the ligands specifically binding at the evolutionarily conserved, putative catalytic pocket. This binding ligand can be a translocating ligand for a transporter, a substrate or product of a metabolic enzyme, or a regulatory molecule bound at the same pocket of these pseudogenized enzymes or transporters that gain additional moonlighting functions . To test3− for ATP4− to support cellular bioenergetics and UCP1, which is exclusively expressed in brown adipose tissue and dissipates the proton motive force to generate heat. Despite their critical roles, at least one-third of human SLC25 transporters remain poorly characterized, leaving ample space for deorphanization efforts. Meanwhile, decades of elegant experimental studies have provided functional and mechanistic insights, presenting an ideal case for evaluating the coupling of sequence variation and functional diversification for proteins within the SLC25 family. The SLC25 family is the largest protein family responsible for translocating metabolites across the mitochondrial inner membrane, a process that critically controls all aspects of mitochondrial physiology ,5,6,7,8.The SLC25 family is a prime candidate for our investigation into phylogenetic-analysis-guided studies for many reasons. First, the family is extraordinarily functionally diverse, being the largest protein family (53 in humans) that translocate a variety of chemically distinct metabolite ligands across the inner mitochondrial membrane, ranging from nucleotides to amino acids, carboxylic acids, inorganic ions, and different cofactors to protons with high specificitySecond, structure–function analysis, leveraging elegant primary sequence interrogation ,10, atomGiardia lamblia and Encephalitozoon cuniculi [Monocercomonoides sp. [Third, and most remarkably, the SLC25 family is highly evolutionarily conserved yet functionally diverse in eukaryotes. This family is present in all eukaryotic species , with only a few exceptions in protist species that have significantly reduced mitochondrial function, cuniculi , or thatides sp. . We postides sp. ,18,19, tides sp. ,21 alreaHere, to evaluate the sequence adaption for transporting ligands within the SLC25 family, we performed a phylogenetic analysis of SLC25 transporters from humans and commonly studied model organisms and annotated the tree based on the chemistry of the experimentally validated transporting ligands. We observed that for majority of the transporters, there exists a clustering of orthologous transporters across different species that transport the same ligand, suggesting an early divergence of these metabolite transporters as well as the coupling of sequence variation to ligand adaptation. They include ADP/ATP exchangers and transporters for CoA, carboxylates, glutathione, SAM and several amino acids, among others. In other cases, the sequence variation might be coupled to a conserved metabolite regulation mechanism or to other functional innovations, for instance, targeting different subcellular membranes, including outer mitochondrial membranes or peroxisomes. A lack of homology for several species-specific transporters is also noted. In summary, we explore the power of phylogenetic analysis in ligand prediction for the SLC25 transporter family, which might guide the “deorphanization” effort for some of the poorly characterized SLC25 transporters as well as provide an example for the exploration of other transporter families.Saccharomyces cerevisiae , Caenorhabditis elegans , Drosophila melanogaster , Danio rerio  and Homo sapiens . We reasoned that these organisms are diverse enough to represent different eukaryotic branches, and as model organisms, the sequences are more likely to be experimentally studied and thus suitable for our analysis.We chose the following five genomes, including commonly studied model organisms, to evaluate the SLC25 protein family: SLC25 sequences from each genome were identified via a text-mining search in the UNIPROT database , using aWhile validating the yeast sequences, we noticed that the Cmc1 protein from the S288c yeast strain (D6W196) was curated as a truncated form of the protein due to a frameshift in position 403 in Strain S288c. We therefore replaced this sequence with the full-length sequence of the same protein from the CG379 yeast strain (P0CI40).Upon validating the yeast (35) and human (53) sequences, we then manually curated the SLC25 sequences in worm (49), fly (120) and zebrafish (111) using several rounds of multiple-sequence alignment (MUSCLE) ,24 withiIn summary, we included in our analysis 53 human, 35 yeast, 35 worm, 48 fly and 57 zebrafish SLC25 protein sequences. We acknowledge that our curation might contain errors for the worm, fly and zebrafish sequences, especially for paralogs within the same subfamily. First, for several subfamilies that are known to contain multiple paralogous genes, for instance, ADP/ATP exchangers and CMCs, multiple sequences might have identity values <0.01 but would either map to different chromosomal regions in tBLASTn or the mapping was of low confidence; therefore, the number of paralogs might be incorrect. Second, the zebrafish genome is known to have local genome duplication, so paralogs might be missing in our analysis. Regardless of the exact number of sequences, our analysis should sufficiently represent the complete functional diversity of the family.We then manually trimmed all protein sequences to the regions that are predicted to encompass the three “Solcar” domains: the core transporter regions. For instance, the predicted EF-hand domains in CMCs and Aralars were removed. We then included the human MCU, another inner membrane channel, as the outgroup.We aligned the protein sequences using MAFFT v7.505  and deteHomo sapiens (human), Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly) and Danio rerio (zebrafish). We chose these model organisms because they are evolutionarily diverse and are likely to be experimentally studied and annotated. Upon manual curation (see method), we finalized the sequence entry, which included 53 human sequences, 57 zebrafish sequences, 48 fly sequences, 35 worm sequences and 35 sequences. The large numbers of SLC25 transporters in all model organisms are consistent with a putative massive gene family expansion prior to the time when these eukaryotic lineages diverged. We then performed a multiple sequence analysis, using a nonhomologous mitochondrial calcium uniporter sequence in human (MCU) as the outgroup, and constructed the phylogenetic tree with bootstrap values to highlight clade confidence .To perform the phylogenetic analysis, we collected amino acid sequences via a text-mining search (using “mito_carr” and “Solcar” in the UNIPROT database) against each model organism using their taxonomy codes. These organisms included We then sought to functionally annotate the tree by color-coding major clades based on the chemistry of the transporting ligands identified for the known transporters in the clade. Specifically, the subfamily is manually annotated by ligand based on the biochemical activity of the well-characterized representative yeast or human SLC25 proteins in each clade and then expanded to the other SLC25 proteins in the clade with bootstrapping values > 90 . Indeed,Glutathione for SLC25A39 and SLC25A40SLC25A39 was recently identified as the evolutionarily conserved, putative transporter for the major cellular antioxidant glutathione, the tripeptide in which the γ-peptide bond preserves the amino acid chemical group of the glutamate ,33. The BCAAs for SLC25A44The recently identified branched-chain amino acid (BCAA) transporter SLC25A44  containsBoth the SLC25A39 and SLC25A44 clades are nested within a cluster of transporters (despite having low homology with the other groups) in which the majority translocate amino acids, supporting their putative identification.NAD+ for SLC25A51 and SLC25A52Recently identified putative human NAD+ transporters, SLC25A51 ,36,37 anSLC25A32The evolutionarily conserved SLC25A32 subfamily has been proposed to transport both dinucleotide cofactor FAD and tetrahydrofolate (THF) cofactors supporting a mitochondrial one-carbon metabolism. While in vitro , mouse gSLC25A38The previously proposed putative glycine transporter SLC25A38 ,48 was fPeroxisomal SLC25A17The only non-mitochondrial SLC25 protein, peroxisomal SLC25A17 , which iSLC25A34 and SLC25A35UnCoupling Protein subfamily UCP1-5  (bootstrap = 95), in which dicarboxylate transport activities have been proposed for several UCPs [The distinct SLC25A34 and SLC25A35 subfamily of completely unknown function contains human, fly, zebrafish and yeast orthologs, such as yeast Oac1 , but doeral UCPs ,54,55. SSLC25A43The vertebrate-specific SLC25A43 (only in human and zebrafish) of completely unknown function sparked interest because the clade is nested within a majority nucleotide group with high confidence (bootstrap = 86). The clade’s high homology to ADP/ATP carriers , CoA transporters (A16 and A42) and the nucleotide-importer SCAMC subfamily, followed by thiamine pyrophosphate transporters, might guide the deorphanization of SLC25A43.Despite a close clustering of high confidence, we cannot validate the homology via BBH search between SLC25A43 and the yeast Ugo1, an outer mitochondrial membrane SLC25 protein that is important for mitochondrial fusion with unknown ligand significance . We therSLC25A45, SLC25A47 and SLC25A48Slc25a47 leads to a reduction in respiration [The homologous SLC25A45, SLC25A47 and SLC25A48 are three poorly characterized transporters that are clustered within a distinct clade within the amino acid transporters, separated (bootstrap = 83) but homologous (bootstrap = 78) with the putative basic amino acid transporter SLC25A29, followed by a homology with the carnitine transporter SLC25A20 and the ornithine transporters SLC25A15 and SLC25A2 (bootstrap = 100). Recent loss-of-function studies in mouse livers suggest that deleting the liver-specific ,59,60 Slpiration , an incrpiration ,60 and aOuter mitochondrial membrane SLC25A46, MTCH1 and MTCH2The three human outer mitochondrial membrane SLC25 proteins, SLC25A46, MTCH1 and MTCH2, are homologous with high levels of confidence with other animal sequences in worm, fly and zebrafish. A lack of homology with the yeast outer mitochondrial membrane protein Ugo1 might suggest two independent evolutionary innovations to utilize the inner membrane SLC25 protein scaffold for new outer membrane function. The recently proposed novel role of MTCH2 as an outer mitochondrial membrane insertase  is intriHere, we performed a phylogenetic analysis of SLC25 family transporters from common model organisms to evaluate the power of homology search in guiding ligand characterization. Our analysis exhibited an extremely high degree of homology between SLC25 transporters across evolution that transport the same ligand , consistent with an early functional divergence of SLC25 proteins. Consistent with previous phylogenetic analyses ,21, we aWhile several phylogenetic analyses have been performed on automatically collected SLC25 sequences across a broad range of eukaryotic taxa ,66,67, oArabidopsis thaliana, house mouse, Mus musculus, and western clawed frog, Xenopus tropicalis (often used for the study of ion channels). For recently innovated SLC25 proteins, a phylogenetic analysis focusing on sequenced vertebrate [Our analysis should not be affected by the potentially incorrect number of SLC25 family proteins in each species because the error should only occur for species-specific paralogs for which we cannot distinguish whether certain transporter sequences have inaccurate annotations or are taxa-specific gene duplications. A more complete analysis might benefit from the inclusion of other model organisms, including the plant rtebrate  or mammartebrate  might inUnCoupling Proteins (UCPs) might potentially suggest an adapted metabolite regulation mechanism instead of an adaptation to transported ligands—for example, the regulation of UCP1 by purine nucleotides—demonstrated via structure [Several limitations to utilizing homology search for ligand characterization are noted. First, singleton transporters with little homology with other transporters provide little guidance toward their characterization. Examples of this include the peroxisome SLC25A17 clade (discussed above) and many species-specific transporters, for instance, the fruit fly MME1   and the tructure ,73 and mtructure .Cluster of Orthologous Group (COG) in bacteria and in eukaryotes [A phylogenetic analysis could potentially guide experimental ligand characterization efforts for certain SLC25 transporters as an orthologous strategy to in vitro proteoliposome-based transport assays, cell-based assays and in vivo studies. Other molecular evolution approaches could also provide exciting insights . A comprkaryotes .


Anthropogenic climate change is intensifying natural disturbance regimes, which negatively affects some species, while benefiting others. This could alter the trait composition of ecological communities and influence resilience to disturbance. We investigated how the frequency and intensification of the regional storm regime (and likely other disturbances) is altering coral species composition and in turn resistance and recovery. We developed regional databases of coral cover and composition (3144 reef locations from 1970 to 2017) and of the path and strength of cyclonic storms in the region . We found that total living coral cover declined steadily through 2017 . Our results also indicate that despite the observed increase in the intensity of Atlantic cyclonic storms, their effect on coral cover has decreased markedly. This could be due in part to selection for disturbance-resistant taxa in response to the intensifying disturbance regime. We found that storms accelerated the loss of threatened acroporid corals but had no measurable effect on the cover of more resilient “weedy” corals, thereby increasing their relative cover. Although resistance to disturbance has increased, recovery rates have slowed due to the dominance of small, slow-growing species. This feedback loop is locking coral communities into a low-functioning state dominated by weedy species with limited ecological or societal value. Due to their short-term variability, many of these environmental parameters act as acute, lethal disturbances and powerful selection agents4. The effects of climate change on natural systems are due in part to the growing frequency and intensity of acute disturbances, i.e., anthropogenic acceleration of the natural disturbance regime5. For example, the frequency of anomalously high ocean temperatures (AKA “marine heatwaves”) on Caribbean coral reefs has increased by fivefold and the return time has been cut in half since the mid-1980s8. This intensifying disturbance regime is causing ever more frequent mass coral bleaching events, which is driving reefs globally into a state of continual recovery, with little chance of returning to a pre-disturbance state.The U.S. Fourth National Climate Assessment concluded that the ongoing disruption of ocean ecosystems will intensify as ocean warming, acidification, deoxygenation, and other aspects of climate change increase12. Numerous studies have documented an increase in TC intensity globally  and the IPCC concluded “It is likely that the proportion of intense tropical cyclones has increased over the last four decades and that this cannot be explained entirely by natural variability”13. TCs are an important source of disturbances to coral reefs17. High winds and strong waves can fragment or uproot reef organisms or “sand-blast” the tissue off coral skeletons18. The ecological consequences of storms include the replacement of storm-sensitive species by weedy competitors, changes in species composition and ecosystem functioning, and a general reduction in the surface complexity of reef habitats18.The intensity (but not the frequency) of Tropical Cyclones (TCs) is predicted to increase due to the growing heat content of ocean surface waters19 found that the two category 5 hurricanes that struck the reefs around St. John U.S. Virgin Islands in 2017 had no significant effects on living coral cover. Whereas, studies of the impacts of earlier category 5 hurricanes Hattie in Belize (in 1961)15, Allen in Jamaica (in 1980)17, and Hugo (1989) in the U.S. Virgin Islands18 describe catastrophic damage to coral communities. These findings suggest coral communities are becoming more resistant to storms and possibly other disturbances. However, the generality of and mechanisms underlying reduced hurricane effects on coral communities are unknown, particularly on contemporary, degraded Caribbean reefs.Despite the historical importance of TCs and the past (last ~ four decades) and forecasted near-future increase in TC intensity, recent studies have reported that the effects of TCs on coral reefs have become negligible. For example, Edmunds22. The general pattern is selection for species with physiological, morphological, and demographic characteristics that enable them to tolerate biotic and abiotic disturbances; typically, species with “weedy” life histories21. The subsequent change in community trait composition could modify a wide range of ecological processes, including community responses to the disturbances driving the change. Moreover, disturbance-resistant coral species are typically slow growing and contribute little to reef accretion and habitat provision25. Such feedbacks could lock communities in a state dominated by weedy species  is locking reefs into a resilient but low functioning state.The purpose of this study was to measure the state-dependent effect of storms on Caribbean reef coral communities to better understand the causes and implications of changing coral reef resilience to storms. We quantified both resistance to (the capacity to resist change measured as the impact of a disturbance) and recovery (the rate of return to a pre-disturbance state)29 which suggested that coral loss may have plateaued by the late 1990s, at a regional mean of ~ 16%. However, our results indicate that from 1997 to 2017, the median annual loss rate was ~ 0.25% per year, with a final year regional mean of 9.5% ± 0.59% (SE). The more recent regional effects of stony coral tissue loss disease30 including reports of severe coral loss across the Caribbean32 suggest further decline since the final year of our study. Despite substantial sub-regional variation . Although we have interpreted immediate post-storm changes in coral cover as storm effects, it is important to recognize that, like any natural ecosystem, coral reefs can experience multiple stressors or disturbances simultaneously. Therefore, it is possible that other factors have caused or contributed to observed post-TC changes in coral cover in some specific instances. For example, a storm strike could have coincided with bleaching event or disease outbreak36. Additionally, our method for identifying potential storm impacts could be unreliable for TCs that are either larger or smaller than normal or moving more slowly or more quickly than normal (which introduces uncertainty). Future studies should leverage methods that explicitly model storm wave damage, which would resolve these uncertainties38.Despite the observed increase in Atlantic storm intensity39, and other large-scale disturbances including disease outbreaks and bleaching events42. Although this phenomenon is often attributed to simply having more coral to lose, our results suggest it is being caused in part by the dominance of disturbance-sensitive species on typical high-cover reefs44; particularly coral species in the genus Acropora.One obvious difference between the state of Caribbean reefs in the 1970s and during more recent decades is that pre-storm coral cover has been substantially reduced. Our results indicate that reefs with greater pre-impact live coral cover were less resistant . In U.S. waters, Acropora species are also listed under the Endangered Species Act as threatened. These species dominated Caribbean reefs until the early 1980s, when they were nearly extirpated by white band disease—a regional epizootic linked to ocean warming48. Corals in the genus Acropora are particularly sensitive not only to storms and other forms of physical disturbance (due to their morphology), but also to disease, predator outbreaks, and other environmental perturbations49; i.e., there is positive co-tolerance of sensitivities to these stressors50.Due to their fast growth and branching or plating morphology, these species are considered competitively dominant over other sessile reef organisms, including other coral species52. The more disturbed a community is, the more resistant it becomes. The change in trait composition caused by disturbance is the mechanism underlying what is sometimes called “ecological memory”; the idea that the recent disturbance regime strongly influences current resistance and recovery52. On coral reefs this phenomenon is apparent across both space and time. For example, due to anthropogenic ocean warming, coral communities on Australia’s Great Barrier Reef experienced extreme temperature shocks in both 2016 and 2017, triggering widespread mass-bleaching53. The effect on coral cover in 2017 (even when disturbance intensity and duration were accounted for) was substantially less than in 201653. Moreover, during the 2016 event, there was spatial variance in reef resistance, explained largely by pre-disturbance living coral cover (an indicator of the amount of time since the last disturbance), i.e., higher cover reefs were far less resistant3.There is widespread evidence that disturbance modifies population and community responses to future disturbances through selection for resistant individuals and species46) were negatively affected by storms . Stress tolerant species were negatively affected by storms in terms of absolute cover , but due to their high abundance on many contemporary reefs, the mean change in relative abundance from pre- to post-storm was not significantly different from zero  was not affected by storms , i.e., these species were resistant to this important disturbance. Post-storm recovery rates indicate that the stress tolerant group continued to decline, likely causing the relative abundance of weedy species to increase  on Caribbean reefs appears to have shifted reef composition into a lower cover but more resistant state  than competitive species23 overall post-disturbance recovery likely also declined. Other disturbances are likely reinforcing these changes in coral species composition, particularly mass bleaching and disease, both of which have similar species-specific effects as storms  negative association between system resistance and recovery in response to TCs. A negative covariance between resistance and recovery can be due to evolutionary trade-offs between the characteristics that influence a species’ responses to disturbance62. Our results suggest that there is a trade-off between resistance and recovery for Caribbean coral communities. This characteristic of modern coral reefs (and possibly other marine systems) underlies the protection paradox; when by promoting high coral cover and fast-growing resilient species  managers inadvertently reduce community resistance to the next inevitable disturbance49.Resistance and recovery need not be positively related across sites and often aren’t. For example, Patrick et al.64. The observed compositional shift toward dominance by weedy coral species also degrades other important reef characteristics and functions including vertical reef accretion, surface complexity, and habitat provision65. In the Caribbean competitive coral species and some stress tolerant species in the genus Orbicella disproportionately contribute to increased structural complexity and calcification23. In contrast, weedy species generally have only minimal influence on these functions due to their smaller colony sizes, flatter morphologies, and slower rates of calcification and vertical reef accretion24. The loss of structural complexity is especially important due to the established links between complexity and multiple ecosystem services including the facilitation of community inhabitants such as invertebrates and fishes and subsequently of fisheries productivity65.By increasing resistance and slowing recovery, an intensified storm regime appears to be contributing to a feedback loop, locking reef communities into a low functioning state Fig. . The tra14, we found little evidence of post-storm recovery. Instead, we found a brief period of stasis after the immediate impact of a storm, and then resumption of pre-storm decline (2005). This pattern could be due to delayed disease or coral predator outbreaks triggered by the storm67. For example, physical damage from tropical storms, such as coral fragmentation and increased sedimentation, may provide more opportunity for contact between pathogens and live coral tissue35.Although the immediate effects of storms on coral communities have declined, the longer-term impacts appear to linger for years Fig. . Like Ga69. Thus, the effect of storms on cover, composition, and functioning often continue for a significant period after the event.When comparing the average rate of change in coral cover between storm-impacted and unimpacted sites, we found that storm-impacted sites still have a negative recovery rate  in the 2010s while unimpacted sites have a slightly positive rate of change, indicating recovery from other disturbances, likely including coral bleaching and disease Fig. . Coral rThe contribution of storm impacts to short-term coral decline in the Caribbean has decreased over the past few decades; however, localized immediate impacts from storms can still be substantial on reefs with high coral cover or longer time intervals between storm events. Storms, combined with thermal stress and disease, appear to be driving a shift towards coral assemblages dominated by resistant but low-functioning weedy species Fig. C. Increa71. For the 47 years for which we had coral survey data there were 547 storms, 152 of which crossed at least one surveyed reef, resulting in 10,058 unique storm-reef intersections. When coral reef survey data was available for pre- and post-storm years at a site (1 year prior and 1 year post), we quantified the effect of storms on living coral cover and community structure, and their dependency on pre-impact reef state.We compiled a coral reef-storm dataset that catalogues the historical record of storm crossings on coral reefs in the greater Caribbean and Gulf of Mexico Fig. A. This d72 and Schutte et al.27) with more recent coral survey data from longitudinal studies and monitoring datasets. We relied heavily on data from monitoring programs because they provide large amounts of repeated measurements over long time frames and cover broad spatial scales, both of which are essential for making conclusions regarding regional trends and possibly help mitigate the effects of publication bias. Most surveys were not conducted on permanent transects, and GPS coordinates were used to verify the location on the reef for resurveying over time. All monitoring programs collect data on characteristics of the habitat surveyed , which aids in verifying that the same reef area is resurveyed.Coral reef benthic survey data was obtained from primary source databases, peer-reviewed literature, and grey literature. Databases included widely-used and publicly available coral reef monitoring programs, such as Reef Check, Reef Life Survey, and the Atlantic and Gulf Rapid Reef Assessment (AGRRA). Primary databases with download date are listed in Supplementary Table 73. The data collection methods for obtaining coral species data were similar (AGRRA or modified-AGRRA protocols) and conducted by trained scientists.Absolute living scleractinian coral cover was measured using quantitative techniques including line transect intercept  and point count (randomized points taken from video transects or photo quadrats). Despite differences in survey methodology and possibly precision and accuracy, the metric of percent cover of the benthos is recognized as a fairly coarse measurement, resulting in negligible differences that are not statistically affected by the method of collection74 was used to extract raw percent cover data from tables, and ImageJ  and/or Web Digitizer75 was used to extract raw percent cover data from figures. If more than one survey was conducted at the same reef site on any given day, percent coral cover was averaged to produce one value per day/location combination. Coral reef survey locations were considered unique based on the latitude and longitude coordinates provided from the dataset or study. The resulting database includes survey data from 3144 unique reef locations throughout the Caribbean with 11,490 measurements of coral cover between 1971 and 2017. Approximately 23% of the data came from peer-reviewed literature sources and 77% from coral reef monitoring programs.Studies were included if they reported a reef site location, survey date (or year), and a measure of absolute percent cover for scleractinian corals. When latitude and longitude coordinates of survey locations were not provided in the reference, we used site location descriptions and maps from the text to identify approximate coordinates using Google Earth, when possible. In addition to manual data entry from primary literature, three tools were used to extract data from pdf resources: the tabulizer package in R46. These assignments are based on qualities related to species specific growth and reproduction , wind speed (knots), low pressure (millibar), status , date and time, with variables recorded every 6-h. Historical track information from the earliest year (1851) to present was used to analyze overall storm patterns in the Atlantic basin.Historical storm track data was downloaded directly from the National Oceanic and Atmospheric Administration (NOAA) Atlantic Hurricane Database (HURDAT2) using the HURDAT package in R77. For each reef, we searched for all historical storms occurring within a 100km radius of the reef site coordinates. Storms of any strength were retained within a 35km radius of the reef coordinates, storms of category 3–5 on the Saffir-Simpson scale were further retained between 35 and 60km, and only category 4 and 5 storms retained between 65 and 100km. These buffers are based on previously published hurricane path impacts to coral reefs79. It is possible that our buffers could have excluded storms that affected far away reefs  or could have included storms that did not generate damaging waves for the full 100km 81. Methods to more accurately predict storm wave damage can be found in Puotinen et al.38. Each observation in the database is a unique reef-storm intersection. Therefore, reef locations appear multiple times in the database, if more than one storm has hit the reef since 1851, and individual storms appear multiple times if they struck multiple coral reef locations along their path.Functional programming in R was used to catalog which hurricanes cross which reef sites in the coral reef survey dataset. Code for these procedures was adapted from Elsner and Jagger81. Additionally, spatial variability in coral community response to hurricanes exists both between and within storm events. Hurricanes can result in substantial coral mortality, including complete decimation of coral populations in some cases, and changes to the physical reef structure85. However, there are also documented instances of minimal structural damage or reduction in coral cover from hurricanes86. Even within a single storm event at a single location the coral community response may vary across a reef landscape, including at the scale of only a few hundred meters87. Other studies also demonstrate a wide range of differential impacts  between nearby sites hit by the same storm88. This spatial variability in coral response can be a result of the individual storm characteristics , site-specific disturbance history, habitat heterogeneity , environmental gradients along reef sites, or differences in the initial conditions  at a reef site89. All of these factors contribute to patchiness in storm impacts on coral reefs. We attempted to quantify many of these as predictors in our statistical models , approximately 32% of named storms in the southwestern Atlantic Ocean basin have hit a coral reef location . Between 1970 and 2017, the time period of coral survey sampling, there were 547 storms total, 28% of which crossed over at least one coral reef site, for a total of 10,058 unique site-storm intersections. Out of 3144 unique coral reef survey sites, 2754 sites experienced at least one tropical storm since the beginning of storm records in 1851 . Sites that were not impacted were located in the SW Caribbean, along the coast of Panama, Colombia, and Costa Rica.90. Using previously described methods, we tabulated a count vector (Y) of storm events per reef for each year between 1851 and 2017. The dispersion statistics (Ψ) is calculated as:For each unique reef site, we calculated several measurements pertaining to the disturbance regime of tropical storms, including the total number of storms to ever hit that reef, historical return time (average number of years between storm events), storm dispersion patterns, and the average historical maximum intensity of all storms, weighted by their distance to the reef. All of these variables were calculated from coral-storm intersections that occurred between 1851 and 2017. The dispersion statistic is used to assess the temporal clustering of hurricanes and has demonstrated ecological impacts on coral reef ecosystemsA subset of the larger coral cover database was identified to serve as a “control” dataset. This included coral cover data from sites that were either (1) never hit by a storm (as determined with our buffer methods); (2) had a substantial amount of time (> 10 years) between storm events. As previously mentioned, uncertainties exist in the buffer methods, particularly for capturing exposure to large/slow-moving or small/fast-moving storms, and therefore it’s possible some ‘control’ sites were actually impacted.7. The CR value was also calculated for each Caribbean subregion in order to account for anticipated spatial variation in coral cover and potential local conditions contributing to coral decline.For sites that had been hit by a storm, coral cover data was only retained for a period of 10 years after a previous storm until the next storm hit. This is to ensure that we were not including potential storm recovery trajectories as part of a control condition. For each reef site, we calculated the annual rate of change in coral cover (CR) to use as a comparison against the rate of change in coral cover at storm-impacted sitesCoral resistance to tropical storm damage was measured as the change in coral cover from initial conditions (one year prior to a storm) and one-year post-storm and is presented as both the absolute change, and change relative to the initial cover. Relative resistance was calculated as: Paired Wilcoxon tests were used to quantify differences in cover before and after each individual storm event at each reef (i.e. each site-storm combination is one observation for this test). A Kruskal–Wallis test was used to test the hypothesis that coral resistance is greater  in more recent decades.91.Relative recovery = % cover at year relative to storm − % cover before impact.Annual rate of change in coral cover (CR) = (pca-pcb)/d.Quantifying the relative change in coral cover for years both before and after a storm event allows us to compare the impact of storms on pre-disturbance trends. First, we used regression models to evaluate the trend in relative recovery for the time periods pre- and post-storm. Upon visual review of linear regression (using ordinary least squares models), it became apparent that one linear relationship did not persist throughout the time period of recovery, but rather multiple piecewise relationships might exist. We used the segmented package in R to estimate the appropriate breakpoints for the regressions92. We then compared the slopes in the piecewise regressions for several time periods pre- and post-storm to describe patterns of recovery. Next, we quantified the annual rate of change in coral cover (CR) after a storm event. CR is measured over each individual storm event time series, in which pca is the percent cover at the end of the time series, pcb is the coral cover immediately after a storm (post one-year), and d is the duration of the time series, calculated as the number of years between pcb and pca. If two or more storms occurred in the same year/site, the CR time series was kept for the stronger storm and/or later storm. Resistance and recovery were quantified for both absolute coral percent cover  and the relative abundances of coral life history groups. In text, resistance and recovery results are reported as mean ± standard error.Temporal patterns in coral recovery were quantified in two ways: as (1) the relative change in coral cover at any year pre- or post-storm, relative to coral cover in the year preceding a storm, here referred to as the initial conditions; and (2) as the annual rate of change in absolute coral cover (CR), post-storm93 in R. All analyses were conducted in R version 3.6.1.Linear mixed models were used to quantify the effects of a variety of disturbance characteristics on coral resistance and recovery. Predictors included a mix of event specific characteristics and disturbance regime characteristics (Supplementary Table 94.A previous version of this manuscript was published as a preprintSupplementary Information.