Coral reefs in the Central Indo-Pacific region comprise some of the most diverse and yet threatened marine habitats. While reef monitoring has grown throughout the region in recent years, studies of coral reef benthic cover remain limited in spatial and temporal scales. Here, we analysed 24,365 reef surveys performed over 37 years at 1972 sites throughout East Asia by the Global Coral Reef Monitoring Network using Bayesian approaches. Our results show that overall coral cover at surveyed reefs has not declined as suggested in previous studies and compared to reef regions like the Caribbean. Concurrently, macroalgal cover has not increased, with no indications of phase shifts from coral to macroalgal dominance on reefs. Yet, models incorporating socio-economic and environmental variables reveal negative associations of coral cover with coastal urbanisation and sea surface temperature. The diversity of reef assemblages may have mitigated cover declines thus far, but climate change could threaten reef resilience. We recommend prioritisation of regionally coordinated, locally collaborative long-term studies for better contextualisation of monitoring data and analyses, which are essential for achieving reef conservation goals. An overview of coral reefs across East and Southeast Asia spanning >24,000 surveys in almost 2,000 sites finds that coral and macroalgal cover are surprisingly stable through time, though correlated environmental variables reveal hidden impact. Against the backdrop of global sea-level rise, ocean warming and acidification, coral reefs across the world are facing unprecedented degradation6. Local anthropogenic stressors, including coastal urbanisation, overfishing and pollution vary among regions but can interact with global stressors to exacerbate reef degradation11. Identifying the changes that have affected reefs over longer time frames necessitates the analysis of long-term datasets. While long-term monitoring data enable changes that affect the health of the ecosystems to be detected, allowing for early intervention by influencing management and policy decisions14, the availability of data remains limited for many reefs16. Recent monitoring efforts have improved the spatial scale and precision of their products with technological advancements and dedicated resources18, but past monitoring data are still critical for establishing appropriate baselines to compare ecosystems across larger temporal windows20. Indeed, a crucial objective of long-term ecological research is to extend the analysis of reef trends back to the earliest baseline against which more contemporary changes can be compared and interpreted22.Coral reefs are some of the most diverse ecosystems on Earth, supporting numerous species, ecosystem services and livelihoods17. In the Great Barrier Reef, reef monitoring is conducted as part of the Great Barrier Reef Marine Monitoring Program23, while various programs exist for the Caribbean such as the Caribbean Coastal Marine Productivity24. Within the Great Barrier Reef, long-term monitoring revealed high recent declines and differing trajectories of coral and macroalgal cover on reefs across different continental shelf zones26, with a recent report reporting dramatic coral recovery27. The Caribbean monitoring programs produced clear evidence for the region-wide phase shift from coral to macroalgal cover, later revealed to have resulted from a myriad of interplaying factors including parrotfish and sea urchin declines30. In comparison, long-term monitoring studies are more limited and have not been as coordinated among the reefs in the Northeast and Southeast Asia regions, typically with Reef Check initiatives, national or non-governmental organisations and academic institutions running distinct programs16. This is in spite of the area containing some of the world’s largest and most diverse reef areas nestled among archipelagic states with long coastlines32, encompassing most of the Coral Triangle33, as well as representing substantial coral cover34 and high diversity of corals and other reef organisms38. Incidentally, this high diversity also makes it challenging to assess resilience as different species can fill the same niche at different reefs, so comparisons across the region would be difficult41. As Indo-Pacific reefs continue to face serious threats from both global and local stressors43, establishing a historical ecological baseline across the East Asian Seas is more critical than ever to understand the spatial distribution and environmental correlates of the wide-ranging changes on these reefs over the decades they have been monitored45.Various long-term coral reef monitoring programs have been established for many reef regions46 as well as recent quantitative reports for other regions including the Western Indian Ocean and the Pacific islands48.In the absence of coordinated long-term monitoring programs in the region, we leveraged data gathered by the Global Coral Reef Monitoring Network East Asia (GCRMN EA) node. The network was established through the regional partnership among national coordinators representing the Northeast Asia  and Southeast Asia nodes  of the GCRMN Fig. . The preSpecifically, we collate, synthesise, and analyse benthic datasets from within the GCRMN EA nodes to present a rigorous, data-driven trend of the region’s coral reefs from the 1980s through 2019. We hypothesise that coral cover has generally declined over time, with reefs experiencing recovery following disturbance events, while macroalgae have increased in abundance on the reefs in the region. We also hypothesise that changes in benthic cover are associated with both anthropogenic pressures and sea surface temperature rise, with both factors negatively correlated with coral cover and positively correlated with macroalgal cover. To test these hypotheses, this study identifies variations in hard coral and macroalgal cover among localities over nearly four decades, tests for associations between various environmental and socio-economic factors with reef benthic cover, and contextualises the implications of such changes for regional reef management and monitoring into the future.We compiled data from a total of 24,365 surveys at 1,972 reef sites throughout the East Asia region surveyed from 1983 to 2019  being much higher than the unadjusted marginal Bayesian R2 value (0.155 ± 0.020), highlighting that most of the variation was explained by the random effect variables site and location instead.There were considerable variations in coral cover and how these changed temporally among the localities examined according to the data visualisations and Bayesian-modelled coefficients Fig. . Across R2 of the macroalgal model was much lower with an unadjusted Bayesian conditional R2 (0.295 ± 0.010) and an unadjusted Bayesian marginal R2 (0.071 ± 0.018), with most of the variation explained by the random effect variables site and location.Temporal trends in macroalgal cover also differed between localities, though variations were less clear than for coral cover Fig. . Across To validate the inferred temporal trends and determine the degree to which the global-scale coral bleaching events affect coral cover, we organised the dataset into three roughly decadal time periods separated by the global-scale coral bleaching events in 1998 and 2010. When these data subsets were modelled using the same Bayesian GAMM model structure as above, the analysis indicated similar trends among the three time periods Fig. , with onThree global-scale coral bleaching events occurred within the timeframe of the surveys analysed here—in 1998, 2010 and 2016. To determine if there were region-wide bleaching effects on coral cover, we further subset the data to within 2 years of the bleaching events and assigned the surveys to either pre-bleaching, during bleaching, and post-bleaching with reference to the coral bleaching events. We then tested if these individual bleaching events and the timing of events relative to the surveys affected coral cover using a Bayesian hierarchical model with an interaction between the locality and bleaching event. The simplified model showed that coral cover was the lowest pre-bleaching, with similar cover during bleaching and post-bleaching Fig. . The intTo test the effects of environmental and socio-economic variables on benthic cover, we analysed these variables and benthic cover proportions via multi-modal inference with model selection and aggregation performed through an information criterion approach. In most cases, both coral and macroalgal cover showed inverse correlations with temperature (Sea Surface Temperature (SST), SST Anomalies (SSTA), Degree Heating Weeks (DHW) and Bleaching Alert Levels (BAA)), nutrients (Net Primary Productivity (NPP)) and anthropogenic (population and distance) variables. Variables associated with coral cover were aggregated using only two models with similar variables Table , while mCoral cover was negatively affected by some environmental variables like max BAA and NPP SDs, while max NPP showed a positive correlation. Anthropogenic impacts were apparent based on the negative correlation between human population and coral cover. Additionally, while the negative correlation between depth and coral cover was expected due to attenuating light with depth, the negative correlation between reef area and cover was surprising. While locality showed up as a variable within the aggregated model, no specific localities were significant except for Hong Kong, indicating that locality was not a strong predictor of coral cover R2 values highlight the importance of separating site-specific and regional trends since they can differ considerably. Indeed, some of the depth variations in the trends uncovered may also be due to differences in the depths surveyed, even though the main trends across depths hold true at the regional level. The rise of other complementary monitoring methods and availability of historical imagery can potentially be utilised in some cases to infill past data by juxtaposing surveyed sites with past images to estimate coral cover for comparisons through time. However, such an approach remains feasible only at much smaller scales and at the most well-documented sites19.The high variabilities in coral cover at most sites as shown in the modelled trends and marginal increases in Bayesian 59. Thus, the lack of perceived coral cover decline might imply that diversity has been mitigating some of the potential losses. As a consequence of the region’s high diversity of corals and other reef-associated species38, habitats here may exhibit reduced impacts of stressors and increased resilience59. Ecological redundancy provided by multiple species performing similar functions on the reef can also confer greater reef resilience39. Furthermore, shifts in community composition in response to stressors are possible at the levels of the coral host, algal symbionts and microbiomes63 for maintaining a high baseline of ecosystem functioning. However, as stressors increase and recovery periods shorten, competitive species with high growth rates may be replaced by more stress-tolerant and weedy species with greater tolerance to environmental variations65. While competitive species may be able to recolonise reefs in recovery, relentless impacts from current anthropogenic stressors can drive the homogenisation of communities across the components of the holobiont at various geographic scales67. Aside from coral colony-level changes, reef-scale shifts in community assemblages are not uncommon and have already been reported in multiple studies across the region70. While some studies have linked these changes to specific causes, such as compositional shifts following bleaching events or cyclone impacts73, more work needs to be done to identify direct drivers of losses of reef ecosystem resilience over longer timescales.Biodiversity is known to be able to buffer against changes in ecosystem state, functioning and resilience43. Additionally, while our models do not show any clear effects of bleaching on coral cover, variations observed through the bleaching periods could be indicative of some bleaching impact occurring in tandem with other stressors at various time periods. Higher temperature maxima and variations are associated with bleaching stress, with prolonged bleaching effects potentially leading to coral tissue death and benthic replacement by macroalgae74. Notably, our analyses have not revealed significant lag effects associated with coral bleaching, suggesting that while bleaching did occur globally, reefs across the large latitudinal range examined here had variable trajectories during and following bleaching events.Despite the lack of benthic cover changes seen in the present study, analyses of the environmental and socio-economic variables suggest the impacts of anthropogenic climate change and coastal development adjacent to reefs in the region are conspicuous, corroborating results of past studies75. The proximity of human populations to reefs has also been hypothesised to adversely affect coral cover, with both larger populations and smaller distances having negative impacts on reef conditions45 but see also Bruno and Valdivia (2016)76. Our results corroborate these general patterns, showing that anthropogenic impacts such as human populations had negative relationships with coral cover alongside positive correlations with macroalgal cover.While higher net primary productivity can be linked to greater coral and macroalgal photosynthesis and growth, excessive levels and large variations in nutrients can negatively impact coral growth while encouraging macroalgal proliferation47 emphasise that regional variations in reef trends must be considered when interpreting findings across spatial scales. Despite encompassing a large reef area, increasing coastal populations and the growing body of research here, the East Asian Seas are relatively understudied and poorly surveyed compared to other regions34. While the results of most reef monitoring studies may be generalisable to global scales and have advanced our understanding of reef dynamics, variations between regions can provide further insights on the interpretation of monitoring data, especially for local reef management. Contextualizing these differences is especially important for making conservation and policy decisions. While coral cover declines in East Asia are not as apparent when compared to other regions, it is important to note that degradation of reef health can manifest as changes in reef community structure and coral species composition instead68. Shifts in community composition can alter the complexity, life history strategies, and functioning of coral reefs65. For example, phase shifts from coral to algae or other benthic zoantharian or soft coral dominance can have broader implications on reef organisms and the ecosystem services provided by reefs55. At the local scale, even with few changes in coral assemblage, community functioning can be severely impacted71. For instance, declines in fast-growing, competitive Acropora species with replacement by slower-growing, more stress-tolerant Porites species can reduce reef complexity, leading to fewer habitats for reef-associated fish and crabs, and depress carbonate production71. Such changes to reef functioning and the dependent reef fauna can drive further ecosystem declines39 and therefore need to be considered in future studies.The distinct trajectories of East Asian reefs uncovered by this study compared to other monitored regions77. Local research networks are needed to access the data, with inputs to translate and contextualise the associated information, and to provide justifications for use of the data. The collaborative approach of the GCRMN and its members’ networks have been critical for assembling the data presented here, especially from localities that are traditionally underrepresented in coral reef science79. Expanding these networks would enable greater research participation and make available more relevant data for analysis.This study uncovers the decadal stability of hard coral and macroalgal cover of reefs in the East Asia region, and also underscores the continued need for data and more effective long-term monitoring. The present dataset, while extensive, does not adequately represent the state of most reefs in the region prior to the 1990s. Potentially, some of these data exist but are recorded in the multiple native languages used in East Asia or are stored within governmental organisations and not available for public use80. Coral and macroalgal cover data on their own are inadequate for precise examination of reef health; more variables are needed to understand how reefs are fundamentally changing17. Species-poor stands are not likely to maintain the same level of reef functioning needed for sustained ecosystem health as more diverse reefs82. The high diversity on many East Asian reefs, located within the Coral Triangle and its periphery, stands in contrast against other regions with less speciose reefs37. Recent global biogeographical comparisons have also highlighted the functional redundancies built into the more diverse reefs40. While it is difficult to obtain precise species richness estimates throughout all the localities analysed here, many studies of community composition on these reefs do exhibit high diversity45, which can both confer greater ecosystem redundancy while also increasing the difficulty of taxonomic identification during surveys. The level of reef functioning needs to be quantified and various studies have proposed different suites of variables to be measured and analysed39.Detailed local knowledge is also necessary for understanding monitoring needs and overcoming data limitations. Long-term monitoring must balance between ease of implementation, availability of resources and trained manpower, as well as data comprehensiveness to identify and substantiate changes through time80. Such datasets generate new opportunities to identify major reef changes and evaluate ecosystem functioning, while the methodology remains easy to implement throughout the region. Environmental variables should also be measured in situ alongside these surveys to validate the burgeoning amount of remotely sensed data and enhance the precision of statistical models. Together, these recommendations provide the basis for improved region-wide analyses of reef changes and their timely detection to inform management decisions.Here, we suggest a standardised survey methodology, ideally along permanent transects, that at minimum records the genus identities, abundances and sizes of the benthic organisms present, including hard corals (and their growth forms), reef fish and invertebrates83. Surveys differed in methodology, length and replication, but generally were comparable through transect methodologies for the measurement of benthic cover84. Here, to standardise the taxonomic conventions across regions, we define coral or hard coral as Scleractinia and Heliopora corals only, while macroalgae is defined as all fleshy seaweed, excluding turf algae. Benthic proportional data were transformed according to Smithson and Verkuilen85 to constrain values at the end of the beta scales (0 and 1) and to scale other values in proportion. Missing geographic data were infilled based on other corresponding available information, using either combinations of site names or GPS coordinates or location, and sites were then merged according to relative GPS coordinates. Surveys with missing or unverifiable coral data were removed from the analyses. All analyses were conducted in R86 in RStudio87. Data processing was performed with ‘dplyr’ v0.8.3, ‘tidyr’ v1.0.2, ‘ggplot2’ v3.2.1 within ‘tidyverse’ v1.3.0.Reef surveys were collated from various coordinators of the Global Coral Reef Monitoring Network East Asia (GCRMN EA), including sources from government, non-governmental organisations, research institutes and universities. The ASEAN-Australia Living Coastal Resources project also contributed data for five countries  between 1986 to 199288 to examine patterns in the benthic proportional data and to model coral and macroalgal cover trends through time, with 1 representing 100% benthic cover and 0 representing 0% benthic cover. We used a weakly informative prior based on reef survey values to constrain the models. These priors generally helped to constrain model values to achieve better convergence, but did not strongly change model predictors. Models were first checked for convergence graphically and through model diagnostics using both ‘brms’ diagnostics and ‘performance’ v0.4.4 , and environmental variables such as primary productivity, reef area and wave energy. Human population numbers were converted to annual data approximated from the 5-yearly intervals using both linear (LM) and generalised additive models (GAM) in ‘mgcv’ v1.8–28. GAM approximations were preferred over LM values as they accounted for variable changes in population through time, but LM approximations were used when population values declined substantially by >50%, most probably due to changes in methodology as detailed in Yeager et al.90. Temperature data were extracted from the National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch91 at 5 km intervals, from monthly composites of degree heating weeks, sea surface temperatures and anomalies. Means and standard deviations were calculated for these extracted variables. Reef areas within 5 km and 100 km radii were also obtained from Yeager et al.90. All sites were then associated with the nearest measurement for that year. The full list of variables can be found in Supplementary Table Environmental and socio-economic variables were obtained from Yeager et al.92 using an information theoretic approach. As no distinct top model was identified, conditional model averaging was carried out for all models with ΔAICc values within 6 of the best model93.We analysed the effects of the above variables on coral and macroalgal cover through time using multi-model averaging. Variables were scaled and centred before selection with the ‘dredge’ function in ‘MuMIn’ v1.43.17, limited to a maximum of 6 variables, following Grueber et al.Further information on research design is available in the Supplementary InformationReporting Summary


Cell type-specific differential gene expression analyses based on single-cell transcriptome datasets are sensitive to the presence of cell-free mRNA in the droplets containing single cells. This so-called ambient RNA contamination may differ between samples obtained from patients and healthy controls. Current ambient RNA correction methods were not developed specifically for single-cell differential gene expression (sc-DGE) analyses and might therefore not sufficiently correct for ambient RNA-derived signals. Here, we show that ambient RNA levels are highly sample-specific. We found that without ambient RNA correction, sc-DGE analyses erroneously identify transcripts originating from ambient RNA as cell type-specific disease-associated genes. We therefore developed a computationally lean and intuitive correction method, Fast Correction for Ambient RNA (FastCAR), optimized for sc-DGE analysis of scRNA-Seq datasets generated by droplet-based methods including the 10XGenomics Chromium platform. FastCAR uses the profile of transcripts observed in libraries that likely represent empty droplets to determine the level of ambient RNA in each individual sample, and then corrects for these ambient RNA gene expression values. FastCAR can be applied as part of the data pre-processing and QC in sc-DGE workflows comparing scRNA-Seq data in a health versus disease experimental design. We compared FastCAR with two methods previously developed to remove ambient RNA, SoupX and CellBender. All three methods identified additional genes in sc-DGE analyses that were not identified in the absence of ambient RNA correction. However, we show that FastCAR performs better at correcting gene expression values attributed to ambient RNA, resulting in a lower frequency of false-positive observations. Moreover, the use of FastCAR in a sc-DGE workflow increases the cell-type specificity of sc-DGE analyses across disease conditions.The online version contains supplementary material available at 10.1186/s12864-023-09822-3. Single cell RNA sequencing (scRNA-Seq) is revolutionizing basic and translational biomedical research. The ability to quantify RNA expression in individual cells with high throughput enables quantification of cell type composition of complex tissue samples and characterization of their transcriptional phenotypes, or cell states, in great detail . The useWe previously published a comparison of the cellular landscape in airway wall samples between healthy controls and patients with asthma . While cAmbient RNA is cell-free mRNA that is released during preparation of single-cell suspensions for scRNA-Seq analysis and is one of the features that limits sc-DGE analyses, next to sparsity of data and the presence of doublets , 6. Sc-DThe composition of ambient RNA depends on the cell type composition and processing of the tissue, and is therefore highly sample-specific. When comparing gene expression profiles across cell types within a single sample, transcripts of ambient RNA will be shared and will not be identified as differentially expressed genes. In contrast, when comparing gene expression profiles in a cell-type specific fashion between different samples belonging to an experimental or disease condition, the ambient RNA composition might be different across the contrast used in the DGE analysis. In such a case, transcripts identified as differentially expressed genes may be derived from either cellular RNA or from contaminating ambient RNA, leading to false-positive results in the latter case. Therefore, a new, stringent method for ambient RNA correction that allows sc-DGE studies comparing healthy to diseased tissue samples is urgently needed.Here, we characterized the contamination by ambient RNA in sc-DGE analyses and present a novel method ‘FastCAR’ (Fast Correction for Ambient RNA) to quickly identify and correct for ambient RNA in droplet-based scRNA-Seq data. We provide a rationale for selection of the genes that should be corrected on the basis of the data retained within the gene expression matrix, without the need for prior knowledge on the expected cell-type specific gene expression patterns. Furthermore we compare its performance to the other ambient RNA correction methods SoupX and CellBender-remove-background which were either not thorough enough or computationally prohibitive . The useTo test FastCAR, we used our previously published  scRNA-SeAs another disease/control dataset we used PBMCs from seven healthy donors and seven hospital admitted COVID-19 patients  platformTo perform the differential expression analyses we used R package EdgeR  using thautoest function of the original tutorial starting from the count matrices. The cell selection step was not applied as we used only the libraries that were selected as live, high-quality cells in our previous publication [We applied SoupX using the suggested settings by the lication . For theTo perform the ambient RNA correction using CellBender remove background we used the default recommended settings . The cell selection was not applied as we used only the libraries that were selected as live, high-quality cells in our previous publication .SCGB3A1, expressed in secretory cells [IGKC from B cells [HBB, originating from erythrocytes [In order to develop a method that can be used to correct for ambient RNA in a sc-DGE analysis, we took advantage of a previously reported data set in which we reported the changes in the cellular landscape of the airway wall in patients with asthma compared to healthy controls . After ury cells , 14, IGK B cells , 16 and hrocytes  which whThis ambient RNA is sample-specific which leads to samples where the total ambient RNA (summed UMI in libraries with = < 100 UMI) of a gene is higher to also have relatively higher presence of that gene in non-expressing cells compared to other samples , every library (j) with that number of UMIs or fewer is used to generate the ambient RNA profile. For every gene (g), the fraction of these selected libraries containing any UMIs of that gene (frC) is determined, as well as the highest number of UMIs of that gene occurring in a single library (gMax). If frC exceeds the user provided allowable fraction of ambient affected cells (frAA), the UMI counts for that gene in each cell is reduced by gMax. If this results in negative counts, the number of counts of that gene in the cell is set to 0.FastCAR determines the ambient RNA profile to correct the cell expression for on a gene by gene basis. The user provides a threshold for the number of Unique Molecular Identifiers (UMI) per sequencing library . Secondly, the maximum UMI-per-library threshold at which libraries are considered to only contain ambient RNA(thE).As stated earlier, there are two variables to consider when running FastCAR. Firstly, the minimum allowable fraction of libraries that contains ambient RNA of each gene.  will not be identified in the sc-DGE method, and therefore do not need to be corrected for. The default setting for this contamination chance parameter is 0.005; any genes present in less than 5 out of each 1000 ambient RNA libraries are ignored. This limits the total profile of ambient RNA that needs to be corrected for quite significantly.8. We have established a workflow  IGKC level in the non-expressing libraries will be mostly corrected for while expression levels of IGKC in the B cells remains high , a computational method that provides an unbiased way to identify and correct for the ambient RNA likely to affect DGE analyses on a per-sample basis that is intuitive and easy-to-use. FastCAR identifies the genes in the ambient RNA and applies a threshold for filtering that can be adapted to the settings of the DGE analyses, effectively removing the ambient RNA from the DE gene results. We show that this method effectively removes ambient RNA, but still retains a large proportion (~ 92%) of the DE genes observed prior to ambient RNA removal.IGKC and SCGB1A1, we could show that the performance for ambient RNA removal of these genes with FastCAR is superior compared to CellBender and SoupX. Other genes that FastCAR identifies and corrects for are also more thoroughly corrected for meaning that applying the other methods would still result on falsely identifying genes as differentially expressed that result from ambient RNA.When comparing the results of DGE analysis before and after ambient RNA correction using FastCAR to the other methods we tested, there is a striking difference in the number of genes affected by correction procedures. Because FastCAR is made to only adjust for the expression of genes likely to affect DGE analyses, it corrects only a small subset of genes which leaves most results are identical to the uncorrected results. Both SoupX and CellBender adjust for the expression of many genes which results in large differences in the number and identity of the differentially expressed genes before and after ambient RNA correction. While both CellBender and FastCAR apply linear transformation of the data during correction, SoupX also applies a normalization that might interfere with certain downstream analyses. In the absence of a gold standard to compare the results of the DGE analyses and the low sample numbers in the dataset we used for testing, it is not possible now to determine with certainty which of these methods is optimal for the identification of DE genes that reflect the biological truth. However, using well-established cell type-specific genes like DGE analyses between healthy and control or other experimental groups in specific sub-populations of cells is a promising use of single cell data that may have large impact on our understanding of how the in-situ behaviour of cells of the same type differs between groups. Correcting for the presence of ambient RNA will be vital to finding meaningful results in these analyses and FastCAR is an effective method to do so. We developed FastCAR as part of a workflow for sc-DGE analysis, and therefore propose to use FastCAR for this specific application. We did not compare performance of FastCAR or any of the other ambient RNA correction methods for any other downstream applications.FastCAR can be run on a per-sample basis as part of the pre-processing pipeline used for scRNA-seq data and does not require a specific computational infrastructure.FastCAR can also be used to merely report the mRNA profile of the ambient RNA fraction on a per-samples basis, which can then be used to interpret results from the sc-DGE analysis performed without ambient RNA correction.Because of the large variability in cells and processing there is no perfect threshold to use to profile the ambient RNA, or a method to determine with certainty what this threshold should be for a specific sample. This necessitates the use of arbitrary thresholds or user-defined thresholds. The FastCAR R package includes functions that help the user set these thresholds and allows for profiling the ambient RNA without performing the correction to facilitate choosing these thresholds.A reasonable concern is whether removing the highest expression found in the ambient RNA is not too strict. We analysed the cell-type specific gene expression compared to the ambient expression levels and found these to be an order of magnitude higher. Consequently, the cell-type specific expression levels are well retained even for the genes that are highly expressed in the ambient RNA. Moreover, the FastCAR correction does not strongly affect clustering of the cells. This is not unexpected as the ambient RNA is a low and ubiquitous signal that has an equal chance of affecting each cell in a sample.The correction method works under the assumption that cell containing libraries are equally likely to contain ambient RNA and that mRNA from lysed cells is the only meaningful source of transcripts that are not expressed in the measured cell. Other possible sources of such transcripts are barcode switching, where spontaneous errors in the cell barcodes cause transcripts to be assigned to the wrong cell. This may also be partially responsible for some of the signal . FastCARWhile there are datasets of mixed human/murine cells that can be used to benchmark ambient RNA correction methods that strive for precise removal for ambient RNA, these datasets don’t have test/control groups with possible effects on ambient RNA and resulting DEGs. In conclusion, to perform cell type-specific DGE analyses between groups there needs to be some correction to account for the sample-specific ambient RNA. FastCAR is a resource efficient method of thoroughly correcting the expression of genes most likely to be affected by ambient RNA. While other tools are available for general ambient RNA correction, FastCAR is more thorough in its correction than the other tested methods for genes it identifies as likely to affect sc-DGE.Below is the link to the electronic supplementary material.Supplementary Material 1: Tables of all differential gene expression results between conditions with no correction, and the tested ambient RNA correction methods appliedSupplementary Material 2: Supplementary Figure 1: FastCAR correction does not strongly affect clustering. A) Effect of FastCAR on the UMAP and clustering in the same cells from bronchial biopsies. B) Jaccard index of the overlap of the cell contained in the clusters between corrected and non-corrected bronchial biopsies. The same cells cluster together even if the clusters get split differently along a gradient. Supplementary Figure 2: Comparison of applying different ambient RNA correction methods between asthma and control in selected cell types in bronchial biopsies. A) IGKC levels in selected expressing and non-expressing cell types without correction and after applying other correction methods. B) SCGB3A1 levels in selected expressing and non-expressing cell types without correction and after applying other correction methods


IGF1, TGFB1, TGFBR1 and EGFR may control the development of the testis from immature to sexually mature, which might be important candidate genes for the development of goat testis. The current study provides novel insights into goat testicular development and spermatogenesis.Reproductive competence in male mammals depends on testicular function. Testicular development and spermatogenesis in goats involve highly complex physiological processes. In this study, six testes were, respectively, obtained from each age group, immature (1 month), sexually mature (6 months) and physically mature (12 months old) Qianbei Ma goats. RNA-Seq was performed to assess testicular mRNA expression in Qianbei Ma goats at different developmental stages. Totally, 18 libraries were constructed to screen genes and pathways involved in testis development and spermatogenesis. Totally, 9724 upregulated and 4153 downregulated DEGs were found between immature (I) and sexually mature (S) samples; 7 upregulated and 3 downregulated DEGs were found between sexually mature (S) and physically mature (P) samples, and about 4% of the DEGs underwent alternative splicing events between I and S. Select genes were assessed by qRT-PCR, corroborating RNA-Seq findings. The detected genes have key roles in multiple developmental stages of goat testicular development and spermatogenesis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to determine differentially expressed genes (DEGs). GO analysis revealed DEGs between S and P contributed to “reproduction process”, “channel activity” and “cell periphery part” between I and S, and in “ion transport process”, “channel activity” and “transporter complex part”. KEGG analysis suggested the involvement of “glycerolipid metabolism”, “steroid hormone biosynthesis” and “MAPK signaling pathway” in testis development and spermatogenesis. Genes including  The Qianbei Ma goat is a unique goat breed in the mountainous area of Guizhou Plateau. It is large in size, strong in constitution and delicious in taste. It is a goat breed whose skin and meat are both valuable, and has many advantages such as strong adaptability, high fertility, stable genetic inheritance and strong disease resistance. However, the research on its reproductive performance mainly focuses on lambing and estrus in she-goat and the molecular biology understanding of testicular development or spermatogenesis in Qianbei Ma goat is very limited, deserving further investigation. The breeding performance of male animals is also important to the total fertility of the population. Additionally, the most important guarantee for the reproductive performance of rams is the normal development of testis and spermatogenesis. Testicular development and spermatogenesis are very complex and precise processes which are regulated by multiple genes.The testis represents a critical organ of the male reproductive system in mammals, producing spermatozoids and androgens. Spermatogenesis constitutes a developmental event, which produces haploid spermatozoa from diploid spermatogonial stem cells during meiosis in the testis . SpermatRNA sequencing (RNA-Seq) allows an expression profiling of genes and might help map and quantify the transcriptome ,5. This The present work aimed to perform transcriptome profiling of immature and mature Qianbei Ma goat testis specimens by RNA-Seq and bioinformatic analysis. The findings provide novel insights into the mechanisms regulating goat testicular development and spermatogenesis.This study had approval from the Animal Ethics Committee of Guizhou University  .Permission was granted to castrate eighteen healthy Qianbei Ma goats in Fuxing Husbandry Co., Ltd., Zunyi, Guizhou, China. Goat ages were obtained from goat farming records. There were six immature goats , six sexually mature goats  and six physically mature goats . We surgically collected the right testes from the eighteen goats by castration after anesthesia, followed by storage in RNA/DNA sample protector . The testis from each goat was cut longitudinally, and a small amount (3–5 g) of the parenchyma, including seminiferous tubules and Leydig cells, underwent snap freezing in liquid nitrogen and was transported to the lab for further studies. All castrated goats remained in Fuxing Husbandry Co., Ltd.  after our study, for fatten feeding.Total RNA extraction was carried out from the testicular tissue in groups I, S and P using TRIzol reagent  and RNeasy RNA purification kit  with DNase as directed by the manufacturer. A NanoDrop™ One spectrophotometer  was utilized to assess RNA purity and amounts. RNA quality assessment utilized 1% agarose gel electrophoresis. High-quality RNA samples  were sequenced on an Illumina NovaSeq 6000 system, generating 150-bp paired end reads.® Ultra™ Directional RNA Library Prep Kit for Illumina® according to a protocol provided by the manufacturer. Upon clustering, the prepared libraries underwent sequencing on an Illumina NovaSeq 6000 . The image data of the sequences yielded by the high-throughput sequencer were converted into sequence data (reads) by CASA V A base recognition to obtain FASTQ files. Raw RNA-Seq FASTQ data next underwent filtration with Fastp v [C. hircus) (ARS1.2) reference genome [Approximately 5 μg RNA/sample constituted the input material for RNA sample preparation. Index-coded samples were clustered with NEBNext Fastp v , a qualie genome  using HIe genome .Reads mapped to a given gene were counted with featurerts for estimating the expression of various gene transcripts. Gene expression was determined from million mapped reads per kilobase (FPKM) values , the curp values (p-adj) to control for false discovery rate. |log2 (FoldChange)| ≥ 1 and padj < 0.05 was set as the significance threshold for differential expression [The DESeq2 software (1.20.0) was used to analyze differential expression between the treatment and control groups. The Benjamini–Hochberg algorithm was utilized to adjust pression .http://www.genome.jp/kegg/, accessed on 21 July 2022) [GO and KEGG analyses of DEGs were implemented with ClusterProfiler, correcting for gene length bias. KEGG is an information database based on molecular findings, particularly via genome sequencing and additional high-throughput techniques to produce large-scale molecular data sets, allowing a deep understanding of biological systems (ly 2022) . GO and ly 2022) .StringTie was utilized to build and identify previously reported and new transcripts from HISAT2 alignment data. StringTie utilizes a network-flow algorithm with optional de novo assembly to splice transcripts. Compared with cufflinks, StringTie has the following classifying AS events, which were assessed in various samples separately.http://rnaseqmats.sourceforge.net/index.html, accessed on 22 July 2022) was utilized to classify events.Advantages: (1) it yields more complete transcripts; (2) assembles more accurate transcripts; (3) better estimates the transcript’s expression level and (4) has greater splicing speed ,17. rMATTGFBR1, TGFB1, EGFR, IGF1, MAPK3 and SMAD4 associated with testicular development or spermatogenesis. These DEGs were significantly up-regulated or down-regulated in our RNA sequencing results. The six selected DEGs were examined by qRT-PCR to validate the correctness of our RNA-Seq findings. Total RNA (1000 ng) was utilized to produce complementary DNA (cDNA) with 2×SYBR Green qPCR Master Mix None ROX  at 25 °C (5 min), 42 °C (30 min) and 85 °C (5 s). The primers used for qRT-PCR are shown in GAPDH was utilized for normalization in data analysis by the 2−∆∆Ct method.By analyzing the results of RNA sequencing and combining with the results of other earlier studies ,21,22,23To determine the genes associated with testicular development and sperm formation, 18 libraries, including 6 each from immature, sexually mature and physically mature testes were sequenced. C. hircus (goats) [Paired-end clean reads underwent alignment to the goat reference genome ARS1.2 that is commonly utilized for  (goats)  with HISAdditionally, an overview of the percentages of clean reads mapped to exon borders (junction reads) is shown in All RNA-Seq data in our study had been submitted to NCBI (accession number BioProject: PRJNA879963).http://rnaseq-mats.sourceforge.net/index.html, accessed on 21 July 2022). By determining the types and amounts of AS events, and analyzing each AS type, a large number of AS events were found in testicular development and sperm formation. Of all the DEGs detected between immature (I) and sexually mature (S) testes in our present work, 544 genes underwent AS events between sexually mature (S) and physically mature (P) testes, with no detected AS events (padj ≤ 0.05). Therefore, approximately 4% of genes showed AS between I and S, and no gene had AS between S and P in this study. The above findings indicated AS was very important in the complexity of gene expression during testis development, especially in the period from immaturity to sexual maturity.In this work, AS events were categorized into five types with rMATS , mutually exclusive exon (MXE), alternative 3′ splice site (A3SS), alternative 5′ splice site (A5SS) and skipped exon (SE). All five types of AS were found in the S vs. I group comparison. RI, MXE, A3SS and SE were found in the P vs. S group comparison. These findings showed SE as the most common AS event amounting to 368 in S vs. I. Other identified AS events were RI (48), MXE (110), A3SS (66) and A5SS (54) .In order to clarify the difference in gene expression in testis of Qianbei Ma goat at different developmental stages, we compared and analyzed the differentially expressed genes in three different developmental stages. DEGs were determined with |log2 (fold−change)| ≥ 1 and padj < 0.05. As a result, a total of 9,724 upregulated and 4,153 downregulated DEGs were detected between the I and S groups; 7 upregulated and 3 downregulated genes were detected between the S and P groups A2. AmongGO analysis was carried out to examine the functions of DEGs in testicular development. Totally, 106 GO terms associated with “biological processes”, “molecular functions” and “cellular components” were markedly enriched between immature (I) and sexually mature (S) testes. We found 43 GO terms belonged to “biological process”, 56 GO terms belonged to “molecular functions”, and 7 GO terms belonged to “cellular components”. Totally, 36 GO terms associated with “biological processes”, “molecular functions” and “cellular components” had significant enrichment in the sexually mature (S) vs. physically mature (P) testes. We found 5 GO terms belonged to “biological processes”, 21 GO terms belonged to “molecular functions”, and 10 GO terms belonged to “cellular components” A,B.KEGG pathway analysis of DEGs (|log2 (fold−change)| ≥ 1 and padj < 0.05) was carried out. For I vs. S, 8 KEGG pathways were upregulated, including “glycerolipid metabolism”, “protein digestion and absorption” and “steroid hormone biosynthesis”. Totally, 90 pathways were downregulated, including +“lysosome”, “MAPK signaling pathway” and “chemokine signaling pathway” A. For S TGFBR1, TGFB1, EGFR, IGF1, MAPK3 and SMAD4, to validate RNA-Seq data by qRT-PCR. qRT-PCR data corroborated RNA-Seq findings, suggesting the reliability of RNA-Seq data (To verify the DEGs in immature (I), sexually mature (S) and physically mature (P) testes, we selected 6 DEGs, including Seq data .With the current development of detection technology, more and more mRNAs on testis related to testis development and spermatogenesis have been reported. RNA-Seq has emerged as a tool for efficiently and inexpensively detecting new transcripts and genes. RNA-Seq methods have been broadly utilized to determine DEGs or gene expression patterns, new transcripts, AS events and SNPS, and have empowered studies examining porcine ,25, cattSECISBP2) was the gene with the highest number of SE events, i.e., a total of ten SE events. SECISBP2 underwent the most AS events during the development, indicating that SECISBP2 protein synthesis is complex and important to Qianbei Ma goats’ testis development. Mutation of the gene altered thyroid hormone metabolism [Alternative splicing (AS) represents a commonly encountered phenomenon in eukaryotes, which could lead to the production of various protein forms at different times under different circumstances, increasing species/body fitness. Although AS research is a known subfield of molecular biology, only in recent years has this subfield attracted sufficient attention. AS is critical for the complex proteomes and functions found in higher organisms. AS is an important mechanism in the regulation of gene expression and promotes proteome diversity . It is etabolism ,43. Thyrtabolism ,45,46. Ttabolism . Based oTGFB1, EGFR and IGF1, which have critical functions in testis growth, hormone secretion, spermatogenesis and Leydig cell differentiation. Combining previous relevant reports, and KEGG and GO data in the current study, the genes involved in the regulation of testis development and sperm formation through protein phosphorylation were mainly TGFB1) plays multiple biological roles, including the control of proliferative and differentiation potentials of cells in rams [TGFB1 regulates tight junctions in Sertoli cells and controls spermatogenesis. It modifies the blood-testicular barrier (BTB) by downregulating tight junction proteins [TGFB1 might play an important role in testicular development because of its high expression in the immature testis and markedly reduced expression in sexual maturity, as spermatogenesis begins. A comparable expression pattern was found for TGFB receptor type 1 (TGFBR1) [TGFB1 resulted in lower testosterone levels in the testis and serum, and decreased the ability to mate with females [TGFB1 may not only directly regulate goat testicular development and sperm formation, but also ensure the normal development of male external genitalia and affect fertility.Transforming growth factor β-1  . In addi females . Based oEGFR) represents a receptor gene for EGF and controls testicular function in the human, mouse, rat and livestock species as well as in alpacas [EGFR were critical paracrine and/or autocrine modulators of testicular development and sperm formation and regulate testosterone production by testicular interstitial cells [EGFR may also be expressed in various goat testicular cells, stimulate testosterone secretion, and regulate testis development and spermatogenesis.Epidermal growth factor receptor  contributes to the regulation of testicular function [IGF1 and its receptor IGF1R were expressed in testicles, and their hormones act directly on male gonads [IGF1 promotes the development of sustentacular cells, Leydig cells and gonocytes. In mature testis, the IGF1 gene induces spermatogenesis and regulates Leydig cell function [IGF1 may act as an autocrine/paracrine or endocrine signal to regulate testicular steroid production as well as germ cell and Sertoli cell functions [IGF1 plays different roles in testicular function at different stages of testicular development [Insulin-like growth factor I  TGFBR1, TGFB1, EGFR, IGF1 and MAPK3 belonged to the “MAPK signaling pathway”, corresponding to the GO term “protein phosphorylation”. However, the specific function and pathway mechanism of the selected genes still need to be further studied at the cellular level. The findings should further our understanding of gene regulation during testicular development and sperm formation. Our study also suggests that RNA-seq has great potential in the study of the transcriptome during the development of goat testis. This study first used RNA-Seq to profile the expression of genes during testicular development in Qianbei Ma goats. We identified 544 genes with AS events between I and S, which suggests that AS of differential genes might be critical in the regulation of testicular development in goats. Totally, eight KEGG pathways were upregulated and 90 were downregulated between I and S. Totally, seven KEGG pathways were upregulated between S and P. Among the six screened DEGs (


Mixed-lineage leukemia 1 (MLL1) is a transcription activator of the HOX family, which binds to specific epigenetic marks on histone H3 through its third plant homeodomain (PHD3) domain. Through an unknown mechanism, MLL1 activity is repressed by cyclophilin 33 (Cyp33), which binds to MLL1 PHD3. We determined solution structures of Cyp33 RNA recognition motif (RRM) free, bound to RNA, to MLL1 PHD3, and to both MLL1 and the histone H3 lysine N6-trimethylated. We found that a conserved α helix, amino-terminal to the RRM domain, adopts three different positions facilitating a cascade of binding events. These conformational changes are triggered by Cyp33 RNA binding and ultimately lead to MLL1 release from the histone mark. Together, our mechanistic findings rationalize how Cyp33 binding to MLL1 can switch chromatin to a transcriptional repressive state triggered by RNA binding as a negative feedback loop. A three-position alpha-helix of Cyp33 acts as an allosteric switch induced by RNA binding to repress MLL1 mediated transcription. Cyclophilin 33 (Cyp33) is a 33-kDa protein with a C-terminal cyclophilin domain  . N and MLL1C, which associate to form a holocomplex  and an adjacent BRD, followed by an extended atypical PHD (PHD4) and the FYRN domain. The methyltransferase active site, the SET domain, is located in the MLL1C subunit, preceded by the FYRC domain and a transactivation domain (TAD) .MLL1 (430 kDa) is a positive regulator of gene transcription in early development and hematopoiesis . The thiin (TAD)  (17).Human MLL1 congenital mutations result in developmental abnormalities known as the Wiedemann-Steiner syndrome (4 methyl marks on the histone H3 protein tail (H3K4me)  and the C-terminal extension experienced some line broadening, indicating conformational exchange. Using 3112 nuclear Overhauser effect (nOe)-based distance restraints , Phe49, and Phe51 (RNP1) together with Pro38 at the end of β2  and moderate dynamics for the C-terminal α helix (residues 95 to 106) together with the β2β3-loop  suggests that, in the free form, α3 is loosely interacting with the RRM β sheet surface and predominantly oriented perpendicular to it.The structure of the RRM is similar to the crystal structure determined previously . Trp101,nd of β2 . In this2β3-loop . In conc3 occludes the canonical binding site of Cyp33 RRM, its interaction with RNA could be compromised. Therefore, we investigated whether the binding of the RRM to RNA was still possible. We first performed NMR titrations using the mRNA poly-A signal sequence AAUAAA that was proposed to be targeted by Cyp33 , which are transcribed from the intergenic sequence of MLL1 target genes HOXC8 and HOXC6. Consequently, we titrated Cyp33 RRM to UAAUGUCG that contains the consensus motif and two additional nucleotides. Similar chemical shift perturbations were observed for AAUAAA and UAAUGUCG, indicating a similar mode of interaction with the RRM for both sequences   very close from the AAUAAUAA motif identified recently using the same method  as those used to interact with the β sheet surface in the free form  values around 7 μM; table S1], indicating that they only affect RNA binding. For the Cyp33-WLF mutant, the RNA binding affinity was measured by both isothermal titration calorimetry (ITC) and NMR, whereas for the Cyp33-WT and the other mutants affinities were measured only by NMR titrations. Cyp33-WT binds to UAAUGU RNA with a Kd of 300 μM. As we predicted, the α3 mutant Cyp33-WLF has a higher RNA binding affinity (Kd of about 70 μM), whereas the Cyp33-K, Cyp33-RK, and Cyp33-KRK mutants bind RNA with a much lower affinity  (table S1 and fig. S2C).Our structure of Cyp33 bound to RNA revealed that the α3 helix had to be displaced from the β sheet surface to allow the binding of the RRM to RNA. Next, we investigated whether this RNA-driven unusual structural rearrangement had any relevance to Cyp33 function as a regulator of MLL1 activity. On the basis of our structural findings, we designed mutants for which the RNA binding affinity of Cyp33 should be decreased  and a W101A, L102A, and F105A triple mutant (Cyp33-WLF) for which the RNA binding affinity of the RRM should be enhanced (fig. S2A). The mutated lysines and arginine are involved in interactions of the RRM with the backbone and bases of the RNA (fig. S1H). Because K83 was also reported to be a ubiquitination site of Cyp33, we did not use this mutation for in vivo investigations and rather based our conclusions on the double-mutant Cyp33-RK86-88A. Conversely, the tryptophan, leucine, and phenylalanine do not contact RNA. They are the core residues in α3 responsible for the hydrophobic packing of the helix on the β sheet (fig. S2A). In good agreement with the expected effect of these mutations, the chemical shift perturbations on the β sheet induced by the WLF-mutated helix in the free protein are smaller than those of the wild type (WT) compared to Cyp33 RRMΔα, confirming that α3 interacts less efficiently with the β sheet of Cyp33 RRM in this mutant (fig. S2B). We then measured the affinities of all the Cyp33 mutants for UAAUGU RNA and for the MLL1 PHD3 domain . The binding affinities for PHD3 were measured by ITC and were unchanged for all the mutants  and H3K4me3 bound to MLL1 PHD3-BRD (PDB 3LQJ) . In the L1 PHD3) .To more comprehensively understand the ensemble of interactions, we integrated our data into a network of biochemical reactions to calculate a dynamic model based on ordinary differential equations (fig. S4). The model was parameterized on the basis of the measured reaction constants from this work and data from previous literature, with additional kinetic parameters approximated based on diffusion limits (table S2). Simulations of the model indicated that the expression of both RNA and Cyp33 is needed to switch the chromatin to a “repressive” state. This is indicated by an increase of free and RNA-bound H3K4me3 tail (in red) and a strong decrease (in blue) of MLL1-PHD3 bound to the H3 tail . PresencIn term of structural plasticity, the three positions adopted by the conserved α helix downstream of the RRM in free Cyp33, bound to RNA, and bound to MLL1 PHD3 is unprecedented . Change Although the structure of MLL1 PHD3 and Cyp33 had been solved and their interaction had been studied .HOX genes. MLL1 binds H3K4me3 via its PHD3 domain and maintains a high level of this modification via its catalytic SET domain (Kd of 4 μM) , the affinity is increased by avidity due to the presence of multiple copies of this motif. The lincRNAs NC3 and NC4 could therefore recruit Cyp33 to the site of transcription of the HOX gene and in proximity to MLL1 using a very sophisticated mode of regulation. The interaction of the α3 helix with the RNA binding interface of Cyp33 in its free form prevents a premature recruitment of the protein at the transcription site. A minimal amount of transcribed lincRNA will be needed to compete out the α3 helix from the β sheet surface and initiate the repressive mode of action of Cyp33. The Cyp33-MLL1 interaction happens then in two steps. First, Cyp33 PPIase binds to MLL1 and induces the isomerization of the Pro1629 from cis to trans, which weakens the interaction between the BRD domain and the PHD3 but still maintains the H3K4me3 bound to the PHD domain (Kd of 5 versus 300 μM). So contrary to what was anticipated, RNA binding does not inhibit Cyp33-MLL1 interaction but would rather stimulate it by recruiting Cyp33 to the site of transcription and by repositioning the α3 helix to facilitate its binding to MLL1. Our data suggest that the complex in step 3 is only transient (Kd of 70 μM) than for MLL1/Cyp33 RRMΔα3 (Kd of 24 μM) due to the interaction of Cyp33 α3 with the α helix of MLL1 PHD3 . Our results reveal a very sophisticated mechanism of negative feedback regulation of transcription mediated by Cyp33, RNA, and MLL1 .NC3 and NC4, which are bound by Cyp33 RRM when the expression level of these RNAs increases. This interaction induces a change in position of Cyp33 α3-helix that allows its interaction with MLL1, forces the release of the protein from H3K4me3 and represses transcription by negative feedback  expression system. Expression was performed in either LB for unlabeled protein or M9 minimal medium enriched with 13C-glucose and/or 15NH4Cl for 15N and 13C or only 15N labeling schemes.Vectors encoding the Cyp33 RRM∆α and Cyp33 RRM constructs were transformed into chemical competent BL21-CodonPlus (RIL), and the vector encoding the codon-optimized construct for MLL1 PHD3 was transformed into chemical competent BL21-(DE3) 600) reached 0.8 and were then induced with 0.5 mM isopropyl-β-d-thiogalactopyranoside at 20°C and incubated for another 24 hours. The cells were harvested, centrifuged, and resuspended in 30 ml of lysis buffer [30 mM Hepes and 0.5 M NaCl (pH 8.0)] and 3 μl of 1 M phenylmethylsulfonyl fluoride protease inhibitor. This cell suspensions were lysed using a M110S homogenizer of Microfluidics and purified on chitin beads (New England Biolabs) by washing with lysis buffer, high salt buffer [30 mM Hepes and 2 M NaCl (pH 8.0)], and again lysis buffer. In case of purification of one of the Cyp33 variants, intein autocleavage was induced with lysis buffer containing 50 mM dithiothreitol (DTT) followed by at least 12 hours of incubation at room temperature and subsequent elution using twice 20 ml of NMR buffer [40 mM KCl and 20 mM KH2PO4 (pH 7.0)]. Cleavage of MLL1 PHD3 protein could not be achieved by intein autocleavage, because DTT at high concentrations has the tendency to complex Zn2+ ions. Instead, 1 mg of sequence specific protease from Tobacco Etch Virus (TEV) protease was added onto the chitin column and incubated overnight at room temperature. Except the addition of 10 μM ZnCl to the NMR buffer, the same elution protocol was applied as described for the Cyp33 variants. The NMR buffer was selected using differential scanning fluorimetry. In this method, the melting temperature of a protein is tested in presence of a variety of buffer conditions (96 conditions) and a dye with affinity for the hydrophobic parts of the protein by monitoring fluorescence-based thermal shifts  were supplemented to the sample before applying it to the column.2O and 90% H2O/10% D2O for all other purposes. Homonuclear 2D, 15N- and 13C-edited 3D Nuclear Overhauser and Exchange Spectroscopy (NOESY) experiments for structure calculation and assignments of aromatic residues were all acquired on a Bruker Avance III HD 900 spectrometer equipped with a TCI cryo probe (TM = 120 ms). All spectra were processed with TopSpin 3.0 and analyzed with Sparky 3.1.1.4.NMR spectra were acquired at 303.15 K for the free Cyp33 RRM domain and at 310.15 K for all complexes. Triple-resonance experiments [experiment correlating atom names HN and CA (HNCA) and CBCAcoNH] for backbone assignment and three-dimensional (3D) Total Correlation SpectroscopY (TOCSY) experiments (hCccoNH and HcccoNH) for side-chain assignments (1H-13C]-HSQCs (Heteronuclear Single Quantum Coherence) using the natural abundance of the 13C isotope, homonuclear 2D TOCSY (spin_lock = 50 ms), 2D NOESY (TM = 120 ms), and ω2-filtered 2D NOESY (sample with 13C-labeled protein). Assignments of nOes were based on the manual analysis of ω3-filtered 13C-resolved 3D NOESY  of standard 15N- or 13C-resolved 3D NOESY experiments for the protein-protein complex with one, the other, or both protein components, 13C and 15N labeled.RNA resonance assignment was achieved using [15N-[1H] heteronuclear nOes were measured on a Bruker Avance III 750 spectrometer equipped with a TCI room temperature probe at a transmitter frequency of 750.134 MHz for the proton and 76.019 MHz for 15 N (15N-[1H]-nOe and for the reference experiment, a relaxation delay of 2 s and a water gate solvent suppression was used.Backbone 13C-resolved 3D NOESY where only MLL1 PHD3 was 13C labeled but not Cyp33-RRM, then shift assignments of all given shift lists were consolidated. Thus, the respective shift list contains 13C shifts of MLL1 PHD3 and Cyp33 RRM. This led to the problem that peaks were potentially interpreted wrong, caused by picked artifacts. To overcome this problem, the according shift list in cycle one was manually modified by deleting all resonance assignments, which were not detected by manual inspection of the respective NOESY experiment, and ATNOS/CANDID was restarted from cycle two. Because RNA cannot be interpreted by ATNOSCANDID standard library, only protein shifts were given, and nOe signals of RNA were manually picked, assigned, calibrated, and further used as distance restraints.Regarding the structure calculation, initial peak picking and nOe assignments was performed using the ATNOS/CANDID package. For NOESYs of nonuniformly labeled samples, e.g., only one component was labeled, ATNOS/CANDID had to be aborted after completing the first cycle, and the consolidated shift list concatenated to the according NOESY had to be modified. Hence, if ATNOS/CANDID performed the peak picking of an Peak lists of the final seventh cycle and manual derived restraints involving RNA were used as an input for the program CYANA 3.0 (−1 Å−2 for distance restraints and 300 kcal mol−1 rad−2 for torsion angle constraints were applied. First, a short minimization with long-range electrostatics treatment by the particle mesh Ewald method (The AMBER 9 package (


Here, we present the complete coding sequences of two severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains that were recovered from a nasopharyngeal swab from a female patient and the second viral passage in cell culture. After testing, both strains were identified as BA.5.2.20, a subvariant of Omicron. Betacoronavirus and family Coronaviridae, is the causative agent of coronavirus disease 2019 (COVID-19) (TC) values. Sample 1 was a nasopharyngeal specimen collected from a 25-year-old female patient , and sample 2 was recovered from the second viral passage on 293TACE2.TMPRSS2 cells (ATCC NR-55293_12-2021). These cells were modified to express TMPRSS2 and ACE2, hence increasing their permissivity to Omicron (Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the genus OVID-19) . We repoNC_045512.2) and the reference sequence of Omicron variant BA.1  using Unipro UGENE version 45 and a de novo mapping tool (UGENE version 46.0). All tools were run with default parameters unless otherwise specified.Both samples were sequenced. Viral RNAs were extracted using the NucleoSpin RNA virus isolation kit . Sequencing reagents were purchased from Thermo Fisher Scientific (USA). The RNA quantity and quality were evaluated using the Qubit RNA high-sensitivity (HS) assay kit; cDNA was then synthesized from a sufficient amount of RNA (1 to 10 ng) using the SuperScript VILO reverse transcriptase kit. Libraries were prepared using the Ion AmpliSeq SARS-CoV-2 research panel, according to the manufacturer’s instructions, and then were loaded into the Ion Chef instrument for automated template preparation and chip loading. Sequencing was carried out on the Ion S5 sequencer. Finally, consensus sequences were generated by mapping the reads against the Wuhan-Hu-1 reference sequence (GenBank accession number https://pangolin.cog-uk.io/) identified the strains as belonging to subvariant BA.5.2.20 of Omicron lineage B.1.1.529 . Phylogenetic analysis using the Pangolin web application (.1.1.529 . This lihttps://clades.nextstrain.org/) was used to identify 34 identical amino acid substitutions in both samples against the Wuhan-Hu-1 reference sequence  were also submitted to the SRA under BioProject accession numbers PRJNA945815 and PRJNA945874.The SARS-CoV-2 genome sequences were submitted to the GISAID database under the identifiers EPI_ISL_16683686 and EPI_ISL_16683687 and to NCBI GenBank under the accession numbers


Adenosine-to-inosine (A-to-I) editing, a key RNA modification widely found in eukaryotes, is catalyzed by adenosine deaminases acting on RNA (ADARs). Such RNA editing destabilizes endogenous dsRNAs, which are subsequently recognized by the sensors of innate immune and other proteins as autologous dsRNAs. This prevents the activation of innate immunity and type I interferon-mediated responses, thereby reducing the downstream cell death induced by the activation of the innate immune sensing system. ADARs-mediated editing can also occur in mRNAs and non-coding RNAs (ncRNAs) in different species. In mRNAs, A-to-I editing may lead to missense mutations and the selective splicing of coding regions. Meanwhile, in ncRNAs, A-to-I editing may affect targeting and disrupt ncRNAs maturation, leading to anomalous cell proliferation, invasion, and responses to immunotherapy. This review highlights the biological functions of A-to-I editing, its role in regulating innate immunity and cell death, and its potential molecular significance in tumorigenesis and cancer targeted therapy and immunotherapy. DNA and RNA modification are crucial for a number of biological processes, and post-transcriptional modifications to RNA, in particular, are being increasingly understood . In receIn mammals, A-to-I editing is one of the greatest commonly detected co-transcriptional/post-transcriptional RNA modifications. It is catalyzed by adenosine deaminases acting on RNA (ADARs), and unlike RNA methylation modifications m6A, m5C), it is irreversible . MoreoveC, it is ADAR1 knockout (KO) (ADAR1-/-) mice have a phenotype that is embryonically deadly with significantly elevated protein levels of type I interferon IFN-α and IFN-β in embryonic tissue and undetectable type II interferon IFN-γ . This inADARs-mediated editing is critical to mammalian survival, and its misregulation may a contributor to tumorigenesis and progression. The RNA editing occurs at different locations and produces different effects. As inosine is mistaken for guanosine during base pairing, A-to-I editing in exonic regions can be the result non-synonymous mutations . In addiStudies have shown that ADARs-mediated editing has important functional and biological significance in processes such as innate immunity, cell death and tumorigenesis. Such editing affects the expression and activity of many transcripts, and abnormal editing is also closely associated with the development of many cancers. Several studies have shown that the RNA editing mainly inhibits the dsRNA-sensing pathway, leading to cancer cell immune tolerance –31, whicA-to-I editing is a key RNA modification found widely in eukaryotes and is catalyzed by ADARs. The inosine formed via deamination exhibits altered hydrogen bonding patterns and readily pairs with cytosine bases, which are decoded as guanine . An esseInnate immunity is host's first defence against foreign agents such as viruses, and its dynamic balance is key for maintaining homeostasis. When the body is invaded by foreign pathogens or abnormal endogenous nucleic acids (NAs) are present, pattern recognition receptors (PRRs) are enriched on innate immune cells such as dendritic cells (DCs). PRRs sense various damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) and initiate signaling cascades that up-regulate a variety of immune genes, including those that encode for inflammatory cytokines and chemokines. In particular, type I IFN is upregulated . An inflIn humans, ADAR1 p150, ADAR1 p110, and ADAR2 have A-to-I editing activities. Although all three enzymes can prevent the dsRNA-induced activation of innate immunity via A-to-I editing, they regulate innate immunity differently. This is probably due to the differences in their domain and localization. ADAR1-/- mice, ADAR1 p150 specific KO (ADAR1 p150-/-) mice, and ADAR1 carrying the editing point mutation E861A (ADAR1 E861A/E861A) mice all show lethality to embryos. These mutations result in large-scale apoptosis and the increased expression of ISG , 40–42. ADAR1 p150 also has a unique Zα domain that promotes A-to-I editing of endogenous Alu elements and prevents pairing of inverted Alu repeat sequences to form dsRNAs. Thereby, it reduces the accumulation of endogenous Z-NAs, inhibiting the PANoptosis and necroptosis induced by the Z-RNA sensor ZBP1 . PANoptoProtein kinase R (PKR), coded by EIF2AK2, is a type I IFN-induced antiviral protein that also recognizes dsRNAs and is involved in innate immunity. Unlike MDA5, PKR does not cause ISG upregulation, which primarily leads to the inhibition of translation initiation and cell growth , 49. StrAlthough there is no direct evidence that the ADAR2-mediated editing is directly involved in innate immune responses, recent studies have suggested that ADAR2 may like ADAR1, prevent RNA sensors such as MDA5 and PKR from recognition of endogenous dsRNAs as nonself entities. ADAR2-mediated editing allows the viral RNA of the Borna disease virus (BoDV), an RNA virus that replicates in the nucleus, to appear as "self" RNA. Hence, this virus can escape innate immune reaction of the host and establish a long-lasting infection in the nucleus. In human embryonic oligodendrocytes, ADAR2 KO (ADAR2-/-) enhances the BoDV-induced immune response and increases the expression of the pro-inflammatory molecules Interleukin-6 (IL-6) and recombinant human C-X-C motif chemokine 10 (CXCL10) in the body. These effects are ameliorated by the overexpression of wild-type ADAR2 .ADAR1-/- and ADAR1 E861A/E861A mice die in utero 12.5 and 13.5, respectively , 40, 42.Although simultaneous deletion of MDA5 or MAVS can reverse the embryonic lethality seen in ADAR1 p150-/- mice, ADAR1 p150/Ifih1 and ADAR1 p150/Mavs double-KO mice have high postnatal mortality rates , 56, 57.2+ permeability, leading to progressive neuronal death [In addition to ADAR1-mediated editing abnormalities, ADAR2-mediated editing abnormalities also cause cell death. In mice, the deletion of ADAR2 or defective ADAR2-mediated editing lead to postnatal death due to epilepsy . After Aal death . In addial death .A-to-I editing levels are frequently elevated in several types of cancer, including hepatocellular carcinoma, non-small cell lung cancer, thyroid cancer, pancreatic cancer, esophageal cancer, cervical cancer, and multiple myeloma , 62–66. Genes known to undergo A-to-I editing include antizyme inhibitor 1 (AZIN1), bladder cancer-associated protein (BLCAP), integrin alpha 2 (ITGA2), glioma-associated oncogene 1 (GLI1), endonuclease 8-like 1 (NEIL1), cell cycle protein I (CCNI), gamma-aminobutyric acid receptor subunit alpha-3 (GABRA3), and CDK13. Of these, AZIN1 , 73–75, Edited AZIN1 promotes tumor cell proliferation, invasion, and migration in a wide range of cancers, including hepatocellular carcinoma , non-smaIn contrast to the above examples, low levels of A-to-I editing are associated with poor prognoses in some cancers , 67. TheSo far, for the editing of introns in the coding region in tumors, focal adhesion kinase (FAK) is more frequently studied. A-to-I editing of the FAK intron region leads to increased transcript stability. In lung adenocarcinoma, the editing of specific intron sites on chr8:141,702,274 in the FAK transcript increases the stability of FAK mRNA and expression of the FAK protein, thereby promoting tumor mesenchymal properties, migration and invasion .Generally, the 3′ UTRs of RhoA GTPase activating protein 26 (ARHGAP26), DNA fragmentation factor alpha (DFFA), and dihydrofolate reductase (DHFR) are most commonly studied in the context of A-to-I editing. These edited 3′ UTRs are mainly found in breast cancer. The editing of the 3′ UTR affects the regulation of these genes through two primary methods. The most common is the A-to-I editing of Alu dsRNA in the 3′ UTR, which — when unedited — binds to and is regulated by miRNAs. The other approach relies on editing to alter the stability of mature mRNAs, primarily via the recruitment of HuR proteins to ADAR1. Several studies have established that RNA editing in the 3′ UTR can create or disrupt miRNA binding sites, thereby altering the stability of cancer-associated mRNAs. When DHFR mRNA is A-to-I edited in breast cancer, it does not bind to miR-25-3p and miR-125a-3p. Therefore, translation is not disturbed, and the elevated levels of DHFR mRNA and protein enhance cell proliferation and methotrexate resistance . AlteredA-to-I editing primarily modifies the targeting and maturation of ncRNAs and thereby affects their role in cancers. In melanoma, A-to-I editing by ADAR1 reduces the ability of pri-miR-455 to bind to Drosha and get processed into mature miR-455-5p, which prevents miR-455-5p from promoting melanoma growth and metastasis in vivo . When miIn most tumors, elevated levels of A-to-I editing promote tumorigenesis and development. For example, AZIN1 and BLCAP promote cancer development after editing , 73–75. Site-directed RNA editing requires a guide RNA (gRNA) that directs the ADAR to the target site for targeted editing. The most commonly used gRNA is an antisense oligonucleotide (ASO) that uses its single-stranded RNA structural domain to bind to the target mRNA through base pairing, while other structural domains take up the ADAR into the RNA and the ADAR converts adenosine A to inosine I. ADAR is further divided into endogenous and exogenous. Exogenous ADAR proteins or their catalytic structural domains are fused to λ-phage N protein (λN peptide) –100, SNATumors are classified as hot or cold based on the infiltration of immune cells and surrounding cells. As hot tumors show infiltrating immune cells, immunosuppressants are effective against them. Meanwhile, cold tumors are immune cell-suppressive tumors, and immunotherapy is less effective against them. However, immune checkpoint blockade (ICB) therapy offers new avenues for cold tumor immunotherapy. Nevertheless, tumors that do not respond to ICB and are resistant to immunosuppression still exist. Recent studies have demonstrated that ADAR1 loss-of-function makes cancer cells more sensitive to immunotherapy, reducing immune tolerance and sensitivity to ICB agents . NotablyThe dsRBDs of ADAR1 bind to promiscuous dsRNAs and perform A-to-I editing of the dsRNAs. As a result, the dsRNAs cannot bind to dsRNAs sensors (MDA-5 and PKR). This inhibits IFN production and ISG expression , 112, esThe Zα structural domain of ADAR1p150 recognizes and binds Z-RNA or Z-DNA and performs the A-to-I editing of the Z-RNA. The modified Z-RNA is not sensed by ZBP1, thereby inhibiting ZBP1-induced PANoptosis, which promotes tumorigenesis , 115. ThA‑to‑I editing is one of the greast important epigenetic modifications in mammals. Unlike other epigenetic modifications, ADAR-mediated editing is irreversible. It has important roles in innate immunity, cell death, and tumor progression. Its dysregulation can result in neurological and developmental defects, autoimmune diseases, and malignancies, and the mechanisms involved in these pathological processes are multifaceted. A-to-I editing primarily affects the stability of dsRNAs, preventing them from being recognized by dsRNAs sensors. This inhibits IFN production, suppressing innate immunity and also reducing cell death downstream of innate immune system activation. In human malignancies, the editing affects the expression and activity of many transcripts, which in turn affect important oncogenes and related regulators. Whether A-to-I editing is a driver of cancer progression remains an open question. Nevertheless, it is clear that A-to-I editing is an important marker of tumor development and a key target for tumor therapy. The role of A-to-I editing in tumors and other diseases is an exciting area of research. The instantaneous targeted modification of a specific RNA is extremely appealing. Such targeted modification technology holds the promise of becoming a practical therapeutic tool for cancers and other diseases.RNA editing technology is a transient alteration of mRNA, which does not change the genome sequence, and involves fewer safety and ethical issues than DNA editing technology, but RNA editing technology can still produce off-target effects, generating unexpected products or effects that may pose a risk of modifying the biological environment to the point of threatening the health of future generations of human beings and the survival of human beings. Before targeted RNA editing technology can be used in clinical practice, in addition to solving the main off-target problem, the following problems need to be solved: 1) the exogenous protein may cause the body to produce an immune response; 2) the fusion protein may have a neutralization reaction with the body's antibodies resulting in editing failure. The development of new gRNAs and delivery strategies for specific tissues or organs will be the main research and development areas for RNA editing technology routes. Improving the efficiency of A-to-I editing and reducing the minimum dose to achieve an effective therapeutic effect is likely to reduce the occurrence of side effects such as immune reactions. Although RNA editing technology is still in the preclinical stage, it has shown promising applications in several fields such as oncology and is expected to become a hot spot for the next generation of precision medicine.


Biliverdin Reductase B (BLVRB) is an NADPH-dependent reductase that catalyzes the reduction of multiple substrates and is therefore considered a critical cellular redox regulator. In this study, we sought to address whether both structural and dynamics changes occur between different intermediates of the catalytic cycle and whether these were relegated to just the active site or the entirety of the enzyme. Through X-ray crystallography, we determined the apo BLVRB structure for the first time, revealing subtle global changes compared to the holo structure and identifying the loss of a critical hydrogen bond that “clamps” the R78-loop over the coenzyme. Amide and Cα chemical shift perturbations were used to identify environmental and secondary structural changes between intermediates, with more distant global changes observed upon coenzyme binding compared to substrate interactions. NMR relaxation rate measurements provided insights into the dynamic behavior of BLVRB during the catalytic cycle. Specifically, the inherently dynamic R78-loop that becomes ordered upon coenzyme binding persists through the catalytic cycle while similar regions experience dynamic exchange. However, the dynamic exchange processes were found to differ through the catalytic cycle with several groups of residues exhibiting similar dynamic responses. Finally, both local and distal structural and dynamic changes occur within BLVRB that are dependent solely on the oxidative state of the coenzyme. Thus, through a comprehensive analysis here, this study revealed structural and dynamic alterations in BLVRB through its catalytic cycle that are not simply relegated to the active site, but instead, are allosterically coupled throughout the enzyme. For example, largescale spectral changes induced by a coenzyme’s oxidative state have been identified for Rhodospirillum rubrum transhydrogenase  relaxation experiments within the last 20 years has been that active sites of enzymes have evolved to undergo motions on timescales similar to their catalytic rates and these motions are allosterically coupled to distal sites . Such inrogenase  and the rogenase . Identifrogenase . One exprredoxin  and chanrogenase  and the rogenase . In thisBLVRB is an NADPH-dependent oxidoreductase that has emerged as a critical redox regulator within the last decade, as it catalyzes reduction of multiple substrates . BLVRB w12121 as holo BLVRB (PDB accession 1HDO) at both cryogenic (apo-cryo) and room temperature conditions (apo-RT)  . At the (apo-RT) . In both(apo-RT) , is well(apo-RT) , indicat(apo-RT)  and illu(apo-RT) . Further(apo-RT) . Interes(apo-RT) , which i+ complex previously published  calculated from 15N-HSQC spectra were compared to the respective differences for S111A  while H153A has no effect on maximum velocity but increase affinity . Considering that the global CSPs short-circuited by these three mutants are largely similar, it is interesting to speculate why their imparted catalytic changes differ. Although such differences could be due to an array of perturbations during the catalytic cycle, catalytic differences could also be influenced by the way the oxidized and reduced forms of the coenzyme may lie within the active site of each mutant. In general though, these catalytic differences relative to the WT enzyme indicate that disturbing residues involved in hydride-coupled networks broadly affect catalytic function.Amide CSPs between NADPH and NADPor S111A , H153A  to the experimental CSPs measured between all possible BLVRB intermediates  and appeared to undergo a dynamic “switch”. Specifically, Group I exhibited no R2-CPMG dispersion in apo BLVRB but did exhibit dispersion throughout the rest of the catalytic cycle and vice-versa for Group II that was inherently dynamic within apo BLVRB but largely quenched with coenzyme and substrate. In contrast, the final group (Group III) was both proximal to the active site but extended well within the C-terminal lobe. The identification of coupled movements within this C-terminal lobe are consistent with our previous studies that have identified one particular site, T164, which modulates coenzyme affinity allosterically . Specifi regions . For exa regions .+ did we observed a subset of CSPs that were similar to the calculated chemical shift differences derived from R2-CPMG dispersion. While this is at least consistent with our X-ray crystal structures of apo BLVRB that is indeed similar to bound NADP+, the dynamics within the remaining BLVRB intermediates appear not to sample previous or following conformations. Moreover, unlike DHFR where dynamics have been associated with the rate-limiting step of product release  with isopropyl b-D-1-thiogalactopyranoside (IPTG). BLVRB mutations were produced by a nested PCR protocol whereby the initial PCR produced a partial fragment with the mutation that utilized this fragment as a primer for the full-length PCR fragment with insertion into NdeI-cleaved pET22. Hi-Fi PCR amplification (NEB) was used for all PCR reactions and In-Fusion (Takara) was used for vector insertion. Unlabeled proteins were expressed in luria broth (LB) for 3 h and labeled proteins were expressed in M9 minimal media for 5 h 2H,15N,13C-labeling for backbone assignments was grown in 99.9% D2O M9 minimal media supplemented with 15N ammonium chloride and 13C glucose facilitated further assignments of residues initially too line broadened. 2H,15N-labeling for R2-CPMG dispersion experiments was grown in 99.9% D2O M9 minimal media supplemented with 15N ammonium chloride. BLVRB proteins were all purified denatured via Ni-affinity (Sigma), refolded through dialysis, and further purified via Superose-75 size-exclusion-chromatography (Cytiva) as previously described (Expression and purification of recombinant human BLVRB (UniProt P30043) has previously been described . Brieflyescribed .oC and induced at 0.6 ODs (600 nM) with IPTG for 5 h. Soluble protein was purified via Ni-affinity and fractions concentrated for further purification of the tetramer via Superose-200 size-exclusion-chromatography (Cytiva).Human glucose-6-phosphate dehydrogenase  that was used to recycle NADPH, described further below, was also cloned into pET21 for expression at 25http://molprobity.biochem.duke.edu/index.php).Crystals were grown by vapor diffusion where protein/mother liquor drops were equilibrated over a 0.25–0.5 M NaCl solution at 25 °C. 3 μL of human BLVRB at 18 mg/mL were mixed with 3 µL of crystallization solution consisting of 0.1M MES pH 6.5% and 1%–4% PEG 20000 . Initial2O. Holo BLVRB with NADP+ (oxidized coenzyme) comprised 2 mM coenzyme while samples with the coenzyme product also comprised 700 μM bilirubin. For holo BLVRB with NADPH, the recycling system previously exploited was used to extend the life-time of the sample due to the short half-life of the reduced form of this coenzyme CA, HNCACB, and a HN(co)CACB. As the Cα resonances are readily obtained from both the HNCA and HN(co)CA, both the HNCACB and CBCA(co)NH were optimized for CB coupling, specifically by setting the delay for CC coupling to 6 m. All 3D assignment spectra utilized non-uniform sampling (NUS) reconstructed to 72 and 96 increments in the nitrogen and carbon dimensions, respectively  with accession numbers 51658 and 51657, respectively, and BLVRB alone and in the presence of NADP+ have previously been deposited (All BLVRB samples were prepared in 50 mM bis-tris, pH 6.5, 50 mM NaCl, 1 mM DTT at 500 μM protein with 5% Dcoenzyme . Specifiectively . Backboneposited .oC with R1, R2, and R1ρ relaxation experiments utilizing a standard BioPack sequence and R2-CPMG dispersions were collected at 0oC, 5oC, and 10oC with TROSY selection as previously described (All relaxation experiments were conducted on the Rocky Mountain Varian 900 spectrometer equipped with a cryo-probe at 20escribed . Standarescribed  and R2-Cescribed .+ or NADPH with the above-described recycling system were used and amide CSPs calculated accordingly.Cα CSPs atoms were calculated by taking absolute differences between each intermediate while amide CSPs were calculated by the square root of the sum of squares for both nitrogen and protein shifts between intermediates. For hydride-mediated coupling experiments, BLVRB WT, S111A, H153A, and T164S were compared between reduced and oxidized coenzyme. Specifically, either 2 mM NADPA Varian Cary 50 UV-Vis Spectrophotomer was used with a 1 mm cell and 300 μL total volumes to observe the diminishment in NADPH at 340 nm, as previously described . NADPH c