The D2 dopamine receptor (D2R) signals through both G proteins and β-arrestins to regulate important physiological processes, such as movement, reward circuitry, emotion, and cognition. β-arrestins are believed to interact with G protein-coupled receptors (GPCRs) at the phosphorylated C-terminal tail or intracellular loops. GPCR kinases (GRKs) are the primary drivers of GPCR phosphorylation, and for many receptors, receptor phosphorylation is indispensable for β-arrestin recruitment. However, GRK-mediated receptor phosphorylation is not required for β-arrestin recruitment to the D2R, and the role of GRKs in D2R–β-arrestin interactions remains largely unexplored. In this study, we used GRK knockout cells engineered using CRISPR-Cas9 technology to determine the extent to which β-arrestin recruitment to the D2R is GRK-dependent. Genetic elimination of all GRK expression decreased, but did not eliminate, agonist-stimulated β-arrestin recruitment to the D2R or its subsequent internalization. However, these processes were rescued upon the re-introduction of various GRK isoforms in the cells with GRK2/3 also enhancing dopamine potency. Further, treatment with compound 101, a pharmacological inhibitor of GRK2/3 isoforms, decreased β-arrestin recruitment and receptor internalization, highlighting the importance of this GRK subfamily for D2R–β-arrestin interactions. These results were recapitulated using a phosphorylation-deficient D2R mutant, emphasizing that GRKs can enhance β-arrestin recruitment and activation independently of receptor phosphorylation. The D2-like subfamily includes the D2, D3, and D4 receptors, which couple to inhibitory Gi/o/z proteins to reduce adenylyl cyclase activity as well as modulate voltage-gated K+ or Ca2+ channels. Within the central nervous system, these receptors modulate movement, learning and memory, reward and addiction, cognition, and certain neuroendocrine functions. DARs are also critically important drug targets. D1-like agonists are used to regulate blood pressure, while D2R/D3R agonists are used to treat the symptoms of Parkinson’s disease. Further, all FDA-approved antipsychotics function as D2R antagonists. As with other GPCRs, DARs are subject to a wide variety of regulatory mechanisms, which can either positively or negatively modulate their expression or functional activity.Dopamine receptors (DARs) are members of the GPCR super-family and consist of five structurally distinct subtypes ,2,3. TheOne common form of GPCR regulation is that of agonist-induced desensitization, which is typically initiated via phosphorylation of the activated GPCR by one or more members of the G protein-coupled receptor kinase family (GRKs). GRKs are serine/threonine protein kinases and consist of seven isoforms that can be divided into three subfamilies based on their structure ,5,6. TheWhile some DAR subtypes, such as the D1R ,15, follSimilarly, the GRK specificity for regulating D2R signaling is not entirely clear. Most studies have implicated the GRK2/3 subfamily in modulating the D2R ,23,24,253H]-Sulpiride was purchased from Perkin Elmer Life Sciences  or Novandi . Compound 101 was purchased from Tocris Bioscience . HEK293 cells originated from the American Type Culture Collection . HEK293 cell lines in which the expression of individual or multiple GRK isoforms was eliminated using CRISPR/Cas9 technology were generated as described in Drube et al., 2022 sulpiride. Cells were seeded into poly-D-lysine-coated 6-well plates 1 day before the assay at a density of 0.6 × 106 cells/well. A total of 24 h after plating, cells were incubated in the presence of either 200 µM sodium metabisulfite (control) or 200 µM sodium metabisulfite plus 30 μM dopamine in DMEM for 1.5 h at 37 °C. Stimulation was terminated by rapidly cooling the plates on ice and washing the cells three times with ice-cold Earle’s Balanced Salt Solution (EBSS). Cells were then incubated with 0.5 mL of [3H]sulpiride in EBSS  at 4 °C for 3.5 h. Nonspecific binding was determined in the presence of 7.5 μM (+)-butaclamol. Cells were washed three times with ice-cold EBSS and removed from plates with 0.5 mL of 1% Triton X-100 and 5 mM EDTA in EBSS. Samples were mixed with 2 mL of liquid scintillation mixture and counted with a Beckman LS6500 scintillation counter. Cells used to measure protein concentration were incubated with EBSS without [3H]sulpiride, and a BCA assay was performed to determine total cellular protein concentration per well.Cell surface D2R expression was determined as described previously  using ing at 4 °C for 20 min. Cell lysates were resolved with 4–12% SDS-PAGE and transferred to PVDF membrane. Blots were blocked with 5% milk in TBST  for 1 h at room temperature. For detection of GRK2 protein, the blots were incubated with anti-GRK2 antibody or anti-GAPDH antibody overnight at 4 °C at 1:500 or 1:10,000 dilution, respectively. Membranes were washed with TBST and then incubated with secondary antibodies coupled to horseradish peroxidase at 1:10,000 dilution in TBST with 5% milk for 1 h at room temperature. Membranes were washed, and immunoreactive proteins were visualized using chemiluminescence  using a ChemiDoc MP Imaging System .HEK293 cells seeded in 35 mm dishes were transiently transfected with WT D2R-Rluc8 (0.5 µg), β-arrestin2-mvenus (5 µg), and the indicated amount of GRK2 construct or pcDNA control. A total of 48 h after transfection, the cells were washed in ice-cold EBSS supplemented with calcium and magnesium, then lysed in ice-cold RIPA buffer supplemented with protease inhibitors . The samples were rotated at 4 °C for 1 h and the supernatant was isolated following centrifugation at 20,000× 50 and Emax values were calculated from individual concentration-response curves and then averaged to generate mean ± SEM values. Statistical tests were performed using GraphPad Prism as described in the figure legends.Data were analyzed using GraphPad Prism 9 . Concentration-response curves were fit to a log(agonist) vs. response with three parameters and transformed by subtracting baseline response. Data are presented graphically as the mean ± SEM values derived from at least three individual experiments each performed in either triplicate or duplicate, as indicated. EC50) of dopamine for stimulating β-arrestin2 recruitment  for promoting both β-arrestin2 recruitment (~10-fold) and D2R internalization (~40-fold), whereas the expression of GRK5 or GRK6 had no effect on the EC50 for dopamine (50)  . These ro activation BRET assay, we initially determined that Cmpd101 treatment of HEK293 cells does not alter D2R signaling through G proteins , a small heterocyclic molecule that binds within the catalytic sites of GRK2 and GRK3 to inhibit their enzymatic activities . Cmpd101proteins . We nextproteins . Cmpd101proteins  is similproteins . In Figuproteins . Again, proteins .We have previously shown that dopamine can stimulate the physical association of GRK2 with the D2R using a BRET assay  and wereTypically, GRK phosphorylation of GPCRs is thought to mediate β-arrestin recruitment, receptor internalization, and downstream signaling pathways ,6,14,37.50) that is unique to the GRK2/3 subfamily was also observed using the phospho-null D2R, additionally suggesting that this phenomenon is not mediated by receptor phosphorylation , whereas GRK5 and GRK6 do not  in addition to an increase in the maximum response (Emax) . This has the effect of rendering GRK2 catalytically inactive, although it can still form complexes with activated GPCRs, which can block the association of endogenous GRKs with receptors, thus functioning as a dominant-negative mutant ,42. Becae (Emax) . These r50) (50) of dopamine for stimulating β-arrestin2 recruitment to the D2R, but not for enhancing the efficacy (Emax) of this response.Interestingly, the expression of K220R GRK2 in the ΔQ-GRK KO cells also rescued the dopamine-stimulated β-arrestin2 recruitment response for both the WT and phospho-null D2R A,B. Howe50) . Thus, t50) C, which 50 values observed using the parental HEK293 cells  when overexpressed in HEK293 cells, all of the tested GRKs were capable of enhancing β-arrestin recruitment as well as D2R internalization, and (2) only GRK2/3 enhanced the potency of dopamine for stimulating these responses. This suggests that: (1) GRK isoform specificity for regulating D2R signaling exists but is “nuanced” in nature and that (2) more than one mechanism exists for enhancing β-arrestin recruitment—one shared by all of the GRK isoforms and another restricted to the GRK2/3 subfamily.In the current study, we investigated the mechanisms and specificity of GRK-mediated enhancement of β-arrestin recruitment to the D2R and an associated functional response–receptor internalization. When examined in HEK293 cells, we found that overexpression of GRK2, GRK3, GRK5, and GRK6 all enhanced the efficacy of dopamine for stimulating β-arrestin recruitment to the D2R. In addition, GRK2 and GRK3 promoted a leftward shift in the dopamine concentration-response curve such that the ECTo address GRK specificity in greater detail, we used HEK293 cells in which the endogenous expression of either single GRK isoforms , both isoforms of a GRK subfamily , or all GRK isoforms were eliminated using CRISPR-Cas9 technology . It is iactivation of β-arrestin. Alternatively, GRK2 may exert a facilitatory effect on internalization that does not involve β-arrestin.It was interesting to note that in the complete absence of GRK expression, the D2R was still able to recruit β-arrestin when stimulated with dopamine, albeit to only 30–40% of the extent seen in the presence of GRKs. This result suggests that, in the absence of GRKs, the interaction of β-arrestin with the active conformation of the D2R is suboptimal and that GRK expression (GRK2 in particular) is required for effective interactions. Since the D2R–G protein interactions were found to be normal in these cells, these results also indicate that the active state for recruiting β-arrestin differs from that for recruiting G proteins. Evidence for this has also been provided by Pack et al., 2018, using mutant D2Rs that are either G protein or β-arrestin signaling biased . We haveWe also investigated the isoform specificity of GRK-mediated enhancement of β-arrestin recruitment to the D2R using compound 101, a small molecule that selectively inhibits GRK2 and GRK3. Titration of parental HEK293 cells with compound 101 dose-dependently reduced dopamine-stimulated β-arrestin recruitment to the D2R and receptor internalization, in agreement with the notion that GRK2 enhances these processes. However, similar to the GRK2 KO experiments, maximally effective concentrations of compound 101 were unable to completely inhibit the β-arrestin response, although it completely inhibited receptor internalization. Thus, pharmacological inhibition of endogenous GRK2 mimicked the effects of knocking out GRK2 expression. Even with complete inhibition of GRK2, the agonist-activated D2R is still able to interact with β-arrestin, albeit to a reduced extent. This observation further suggests that either there is a GRK-independent mechanism for D2R-mediated β-arrestin recruitment, or that the functions of GRKs in this response are simply facilitatory as opposed to enabling in nature.To further investigate the interactions of GRK2 and the D2R, we used a D2R-GRK2 BRET assay that measures the physical proximity of D2R and GRK2. We found that dopamine dose-dependently stimulated a BRET signal between the D2R and GRK2 and that compound 101 dose-dependently inhibited this interaction. Notably, however, maximally effective concentrations of compound 101 were unable to completely inhibit the D2R-GRK2 BRET response, similar to its effects on the β-arrestin recruitment response. Compound 101 is known to bind in the catalytic site of GRK2 to inhibit its enzymatic activity ,46; howe50). These results indicate that GRK modulation of the β-arrestin recruitment and receptor internalization responses occur through mechanisms that do not involve D2R phosphorylation.GRK modulation of β-arrestin recruitment to GPCRs typically occurs through the phosphorylation of serine and/or threonine residues within cytoplasmic regions of the GPCR, resulting in an increase in affinity for GPCR–β-arrestin interactions, as well as facilitating GPCR-mediated activation of β-arrestins ,47. We pAs GPCRs are intimately involved in the activation of β-arrestins ,10,29, w50). These results argue that the kinase activity of GRK2 (or GRK3) is required to enhance the potency of dopamine for stimulating β-arrestin recruitment to the D2R but is not required for enhancing the efficacy of this response. It is not immediately clear what GRK2 substrate may be involved in promoting this effect, although we have previously speculated that another regulatory protein may be involved in modulating D2R–β-arrestin interactions [Our results raised an obvious question: if receptor phosphorylation is dispensable for D2R-mediated β-arrestin recruitment and activation, then what is the role of GRKs in modulating these responses for the D2R, and is kinase activity even required? To address these questions, we used the K220R kinase-defective mutant of GRK2, which still forms active complexes with GPCRs ,42. We tractions ,17.+ currents elicited by the short (D2S) and long (D2L) D2R isoforms in Xenopus oocytes [2+ was also required for desensitization of the D2S receptor. These results suggest the existence of a Ca2+-dependent protein involved in regulating D2R activity, at least for the D2S isoform. However, we have not found any differences between the D2S and D2L isoforms with respect to their phosphorylation by GRK2 or their desensitization using either cAMP or GTPγS binding assays ([Xenopus oocytes vs. HEK293 cells) or the functional readouts (K+ currents vs. cAMP/GTPγS binding) that were assessed, although the future interrogation of Ca2+-dependent proteins known to interact with GRK2 (see below) might prove to be informative. Finally, it should be noted that we have performed phospho-proteomics scans of the ΔQ-GRK KO cells with or without GRK2 expression in search of novel GRK2 substrates; however, these experiments were inconclusive (unpublished observations).Importantly, a clue may come from studies measuring the desensitization of K oocytes ,49. Dese assays (, and unpInterestingly, the effects of the K220R GRK2 mutant in enhancing the efficacy of dopamine-stimulated β-arrestin recruitment to the D2R appeared similar to those of GRK5 and GRK6, neither of which have been shown to phosphorylate the D2R . Taken tIn summary, our results have confirmed and extended our previous findings that receptor phosphorylation is not required for β-arrestin recruitment to, nor activation by, the D2R. Rather, it appears that GRK-mediated phosphorylation of the D2R primarily mediates its post-endocytic trafficking, as previously shown ,18,19. W


Phosphorylation is a post-translational modificationof great interestto researchers due to its relevance in many biological processes.LC-MS/MS techniques have enabled high-throughput data acquisition,with studies claiming identification and localization of thousandsof phosphosites. The identification and localization of phosphositesemerge from different analytical pipelines and scoring algorithms,with uncertainty embedded throughout the pipeline. For many pipelinesand algorithms, arbitrary thresholding is used, but little is knownabout the actual global false localization rate in these studies.Recently, it has been suggested to use decoy amino acids to estimateglobal false localization rates of phosphosites, among the peptide–spectrummatches reported. Here, we describe a simple pipeline aiming to maximizethe information extracted from these studies by objectively collapsingfrom peptide–spectrum match to the peptidoform-site level,as well as combining findings from multiple studies while maintainingtrack of false localization rates. We show that the approach is moreeffective than current processes that use a simpler mechanism forhandling phosphosite identification redundancy within and across studies.In our case study using eight rice phosphoproteomics data sets, 6368unique sites were confidently identified using our decoy approachcompared to 4687 using traditional thresholding in which false localizationrates are unknown. The search algorithm uses the mass/charge measured from precursors,that is, intact peptide sequence (plus PTMs where present) in theMS1 scan, and from their fragment ions (in the MS2 scan), to compare against theoretical spectra generated computationallyfrom a peptide-sequence database. For analysis of phosphoproteomedata , a search would include a parameter for a variablemodification, whereby every STY residue is assessed with and withoutthe addition of the phosphate mass . There are a largenumber of MS search engines, including both open- and closed-sourcedsoftware; for a review of phosphoproteomics pipelines, see Locard-Paulet et al. 2020,4 or for generalphosphoproteomics methods, see Riley et al. 2016.5Phosphorylation is a post-translationalmodification (PTM) involvinga phosphate group being bound to one of multiple possible amino acids,including the “canonical” Ser, Thr, and Tyr, with reportsof more rare sites on Arg, His, Cys, Lys, and potentially severalother residues.2 spectra,and multiple other causes. These challenges will lead to some incorrectmatches, due to the inferential nature of LC-MS/MS proteomics analyses.There will also be different degrees of confidence among identificationsaccording to how closely the observed data match those extracted fromtheoretical databases. Hence, analysis pipelines have developed algorithmsto calculate scores grading the level of confidence in those identifications.7 There are two main aspects to PTM identification: first, a peptidemust be “identified” in relation to a reference database,generating a score as a metric of confidence or similarity betweenthe precursor ion and the candidate peptides in the reference database.Depending on the tool or algorithm, these scores might or might notbe probabilistic.8 Second, once a candidatepeptide with n modifications has been identifiedas the best candidate for a particular spectrum or peptide–spectrummatch (PSM), it is common practice in PTM analysis to run anotheralgorithm (within the search engine or as post-processing), whichthen goes on to identify the n modifications withinthe possible sites m in that peptide, where n < m. In this second step, scores arecalculated for all combinations of n modified sitesin m possible positions.10Protein modifications may be biologicalPTMs, or technical artifactsinduced during sample handling. Completely unambiguous identificationof peptidoforms (a peptide’s amino acid sequence and exactpositions of modifications) from LC-MS/MS is often impossible, dueto imperfect fragmentation of peptides during MS, multiple peptidoformsin a database sharing some identical or similar sets of fragmentsions, near-isobaric peptides eluting from the LC column at the sametime, causing difficult-to-interpret chimeric MS4 which hinders the comparison or combination of PTM datafrom multiple data sets.Some search engines claim these scores to be probabilistic. Trulyprobabilistic scores would allow unbiased interpretation of scoreswithin and between data sets; for example, a score of 0.98 would indicatethat the PTM is correct with a confidence of 98%, or among the populationof matches yielding a score of 0.98, then 98% would be correct and2% incorrect matches, independently of the experimental characteristicsof the study or data set being analyzed. It is difficult to assesswhether these scores are truly probabilistic or not, but it has beenobserved that the same matches obtained from different probabilisticpipelines could have significantly different scores,7 and ptmRS.10 PTMProphetuses peak intensities and the number of peaks as parameters in Bayesianmixture models to estimate the probability for each candidate sitebeing modified, before normalizing these probabilities according tothe number of modified sites in the PSM. While ptmRS utilizes thenumber of characterizing ions, those exclusive to the modification,to calculate the probability of each match being random using thehypergeometric distribution. In both cases, PTMProphet and ptmRS,the specificity of the observed data is assessed by comparing thebest score for each candidate site versus the next best score fora different site. It is typical practice for PSM false discovery rate(FDR) thresholds to be used to filter the most confident peptide identifications(often at 1% FDR) before site localization scores are generated. Forexample, users of the trans-proteomic pipeline (TPP)11 would generally use PeptideProphet12 to select PSMs based on a pre-defined FDR threshold; this wouldbe followed by PTMProphet scoring, which is used to select PTM sitelocalization or, alternatively, the final score for each site couldbe a combination of the PSM identification score probability and thelocalization score.13 Simple cutoff pointsof the final PTM localization scores are used to establish differentlevels of uncertainty among the PTM identified. The cutoffs are usuallydetermined by benchmarking the scores with respect to synthetic datasets for which the key answer is known and hence allow for approximateestimation of false localization rates (FLRs). Although there havebeen attempts to calibrate and demonstrate performance using syntheticdata sets,14 it has also been argued whethersynthetic data sets are representative of biological variability foundin natural data sets,15 and although theyare a useful means to provide guidance on performance, they are notnecessarily an exact indication of how scores could behave on naturaldata sets. Furthermore, a ptmRS score is a local statistic, givingonly information about the confidence in one localization from onePSM. It does not follow that thresholding at a given ptmRS score woulddeliver a particular performance for a global statistic (FLR) acrossthe entire data set.Two well-known PTM localization scoringalgorithms are PTMProphet17 In this approach, incorrect matches to the decoy database in a listof PSMs ranked by score provide FDR estimates that are used to establishmore or less stringent thresholds—commonly, trading betweenthe overall number of identifications and the proportion of falsepositives that the researcher is willing to accept. The proteomicsfield has largely stabilized at 1% FDR, regarded as acceptable .Within this context, scores are used asnominal indicators to setarbitrary thresholds following the software developers’ guidance.Given that the scores are not likely to be exactly probabilistic andconfirmation of identifications via follow-up studies would be tooonerous or impossible, it is not known what proportion of sites arebeing incorrectly identified within the specified threshold. However,in peptide identification, empirical methods to estimate global statisticsare well established, for example, based on the inclusion of decoydatabases in addition to the target database.15 A decoy could be anyamino acid that cannot naturally be modified by the specified PTMand thus can be included as a variable modification option. As perpeptide identification, matches to decoy amino acids could be usefulfor providing objective estimates of global FLR in PTM site identificationstudies. For example, in phosphorylation studies, among several candidates,Ala was chosen as decoy amino acid to generate false-positive distributionsand estimate FLRs.13 Ramsbottom et al. concluded that including decoy amino acids couldenable estimation of the global FLR, with the advantageous propertythat counts of hits to decoy amino acids represent a useful empiricalmethod to estimate the ways in which the analysis pipeline might havegone wrong, including both incorrect peptide sequence identificationand identification of incorrect sites within those peptides. However,further research is required to investigate how the primary outputfrom PTM site identification studies using decoy amino acids shouldbe post-processed, aiming to optimize the information presented toresearchers and facilitate its interpretation, which is the topicof this work. This research assesses core topics in post-processingof PTM sites identification using decoy amino acids leading to therecommendation of a simple post-processing pipeline when using decoysamino acids for PTM site identification.For PTM analysis, it has been suggested that decoy amino acidsshould be introduced as well.The first topic relatesto the cumulative evidence for sites withina data set. The primary output from statistical processing is localscores or statistics for a PSM, which can then be collated to a dataset level , using matches to decoy peptides and decoy aminoacids to assess global FDR/FLR on the set of PSMs supporting PTM sites.For abundant peptidoforms, which we define here as the peptide sequenceplus the exact set of modifications carried out on exact residue positions,it is common for them to be sampled multiple times by the instrumentin different scans, leading to redundant PSMs reporting on the samepeptidoform. A research team would generally wish to “collapse”the redundancy and re-calculate statistics, so that each peptideoformis reported only once, even if it was supported by more than one PSM.Common practice is simply to take the highest-scoring PSM or the highestlocalization score . However, this has the clear disadvantage of information loss;it seems intuitive that peptidoforms supported by multiple PSMs aremore likely to be true by those supported by fewer. We wish to developmethods to explore, understand, and handle this phenomenon appropriately,such that the PSM count supporting a phosphosite can be taken intoaccount.In the next step, we wish to address the concerns regardingappropriatehandling of how PTM site information should be collapsed from thePSM to the peptidoform level. Traditionally, for each peptidoform,researchers have simply selected the maximum score within all PSM-sitecandidates matching to the peptidoform. However, it has not been assessedwhether scores for different PTM sites within a PSM should be consideredindependent from each other (then taking the maximum would be appropriate)or they are not and, a different statistic across the PSM would bepreferable.Finally, there is much to be gained when identifyingPTMs on alarge scale by combining results from multiple studies in a meta-analysis.Particularly, we assess the compatibility of scores from independentanalyses and how information about a site being identified in severalindependent analyses could be used to increase our confidence on thosesites’ localization being correct.Arabidopsisthaliana, 1 for human (both data sets explored before13), and 8 being rice data sets . It is expected that the variety of datasets would allow methodology performance assessment while using thesynthetic data sets and investigate whether similar patterns wouldbe observed in biological data sets, between and within species. Thefull list of data sets with a brief description of the experimentalobjectives extracted from their publications’ abstracts canbe found in The post-processing analyses are illustratedusing 12 different data sets. Two are “synthetic” datasets, which were manufactured to contain specific peptidoforms andthus can be used to assess algorithm performance with a known answer.The other 10 belong to three species: 1 for et al.,13 using the TPP,11 including the Comet search engine,29 ThermoRawFileParser,30 and post-processingvia PeptideProphet,12 iProphet,31 and PTMProphet.7 Theanalysis parameters for TPP are displayed in Table S1. The only difference between Decoy searches (pASTY) anda typical search (pSTY) was the inclusion of alanine as the potentialphosphorylation site for pASTY searches; the rest of the parametersremained the same.An analysis pipelinewas set up for analysis, as described by Ramsbottom 32 Percolator,33 and the ptmRSsite localization.32 The PSM probability values werecalculated using 1-PEP values (reported natively by the pipeline),with the PTM probability being calculated innately though ptmRS scoring.The analysis parameters for Mascot and ptmRS are shown in Table S2.Proteome Discoverer was also used, including the Mascot search engine,Peptide identifications fromPeptideProphet scoring were initiallyfiltered at 1% FDR (PSM level), based on the target-decoy search resultsprovided by this tool. Probabilities of potential modified sites inidentified peptides were computed using PTMProphet. The final scorefor each site before post-processing was calculated as (peptide identificationscore)*(site identification score).pSTY: typical phosphorylation site searchlooking for a phosphate group at S, T, and Y.pASTY: phosphorylationsearch including S, T, and Y as target amino acids and alanine asdecoy amino acid.PSM: peptide sequence with or without modificationsmatching toa single spectrum.pAla: is a match to the decoy amino acidalanine. pAla hits canbe present at any reporting level.PSM-site reporting: at thislevel of reporting, the results includethe scores relating to each PTM site for each PSM passing the peptideidentification threshold of 1% FLR. For each PSM, there will be asmany PSM sites as phosphosites identified in the peptide. At thislevel, there is duplication of identical matches to sequences andsites, as there could be multiple PSMs supporting the same peptidoform.Peptidoform: this is a unique peptide in the data set formed fromthe combination of peptide sequence and the number and position ofthe modifications identified.Peptidoform-site reporting: thisconcept refers to results reportingeach specific phosphosite within each peptidoform. At this level,duplication of identically-modified sequences has been removed, butall unique sequences and sites to target and decoy matches identifiedat the PSM-site level still remain in the data set.Data analyses were performed in R 4.0.3via RStudio Version 1.1.442 . Thefollowing steps were used to assess different approaches for post-processingphosphorylation site scores:Step 1: using phosphosite frequencyto inform scores.Under the hypothesis that modified sites thathave been observedmore frequently in one data set are more likely to be correct comparedto those that have been observed less often, or in other words, randomPTM matches are expected to be observed less frequently than matchesto correct sites; scores can be modified to include information abouthow often sites are observed in a data set.p is the probabilityof obtaining in a spectrum a match to pAla by chance (unique pAlacount/unique spectrum); this considers only incorrect matches to targetamino acids, consistent with the principles used for FLR calculation, q is equal to 1-p, s isthe number of successes or the frequency in which a specific proteinsite has been observed to be phosphorylated across the data set, and n is the number of times that same protein position is seenacross all PSMs in a data set (phosphorylated or not). This binomialprobability can be interpreted as a measure of confidence that thesite is a true phosphosite given that it has been observed to be s times phosphorylated over n possibleopportunities.We could considereach phosphorylated site as dichotomous eventsin which the site can be either phosphorylated or not phosphorylated.Based on matches to decoy amino acids, the probability of obtaininga random match can be estimated and scores adjusted based on how manytimes a site has been observed to be phosphorylated over the possiblechances of being phosphorylated. Using this information, scores forall sites can be penalized, adjusting our confidence in those identifications.This approach takes into account the differences in PSM counts. Weimplemented this approach using the binomial distribution, which followsthe discrete distributionPs) for each PSM site. This approach will bereferred to as Binomial_Adjustment.The final adjusted probabilistic score incorporatesthis informationby simply multiplying the site score by its binomial score  within all PSMs with identical sequence and positionbeing phosphorylated. This approach considers the maximum to be thebest representation of its class and assumes that scores belongingto the same PSM are independent from each other. Chosen scores fora peptidoform may come from different PSMs. This form of collapsewas produced for unadjusted data and data after Binomial_Adjustment,results of which will be referred to as Unadjusted_PformMax and Binomial_PformMax,respectively.Another potential approach we tested for collapsingthe resultsfrom PSM-site level to peptidoform-site level was taking the meanscore of the PSM sites belonging to a peptidoform site. In this approach,the true probabilistic score would be the center of the distributionformed by all PSM site scores linked to the peptidoform site. As forthe maximum approach, PSM-site scores would be related to other PSM-sitescores for the same peptidoform site but independent from any othersite. These results will be referred to as Unadjusted_PformMean andBinomial_PformMean for unadjusted and binomialadjusted PSM data, respectively.In a third approach, we tested using the mean score of the phosphositesin a PSM and then taking the maximum mean score for all PSMs linkedto the same peptidoform, that is, the PSM delivering the best overallexplanation for the sites scored. This approach considers the scoresfrom a PSM to be dependent on each other, which would make a PSM thebasic experimental unit and not the PSM site. Results from this collapsemethod will be referred to as Unadjusted_PformMM and Binomial_PformMM.Finally, considering PSMs for the same site independent from eachother (and independent from other sites within a PSM), the productof [1—(site probability)] could be understood as cumulativeinformation of a modified site being correctly identified given theobserved data. This approach would assume that each observation ofa site is independent of others (an assumption we know is unlikelyto be true). This will be referenced later in the report as Product_Pform.An example of results collapsing from the PSM-site level to thepeptidoform level is displayed in et al. wherematches to decoy elements are effectively removed from the reportedresults. Hence, the new FLR formula isT is the total countof target amino acids (STY), A is the total countof decoy amino acids in the data set, n observations in the data set, and n is the count of observations at a given position in thedecreasing ranked list of scores. We took a conservative approachwhen calculating the pAla FLR by considering decoys, all sites ina PSM with a decoy match. We have adapted the FLR calculation fromRamsbottom et al.. In the bottom panel of i.e., filtered for high-quality observations), it can be observed thatthe PSM count for phosphosites localized to S or T have broadly thesame distribution, even though phosphosites on S are much more common.We can thus conclude that the differences between PSM counts for decoysand targets are not explained by the total number of target sitesin the data set. It is also worth noting that pY sites are infrequentin plants  and difficult to identifyunless specialized enrichment protocols are employed. In most datasets, pY have a similar distribution to pA, indicating that pY sitesare most likely false positives as well. Overall, we can concludethat PSM count supporting a PTM site identification is informative,and that taking a maximum PTM score to collapse redundancy is likelyto lead to loss of valuable information, as a PTM site, for example,with score = 0.97 with 1 observation, or with 100 observations wouldscore equally after collapse, but are not equally likely to be true.Giventhe premise that random incorrect PTMs occur less frequently thancorrect matches, information about the number of matches to each siteobserved in the data set could be used to modify our confidence ofeach site being correct. s times over the n times that site occurred in the data set, independentlyof being phosphorylated or not. Then, the complement of these probabilitiescan be used to penalize the unadjusted scores for all sites, penalizingmore heavily those that have been observed less often. We used twonatural data sets (Ara_PXD008355 and Hum_PXD000612) and two synthetic(Syn_PXD000138 and Syn_PXD007058) to make those comparisons basedon the pAla FLR . The results afterusing the binomial adjustment appear to also improve at 1% FLR withrespect to unadjusted (Table S2).The PD pipeline seems to be more successful than TPP,avoidingearly peaks  across PSM sites correspondingto a same protein site and then removing redundancies in the datasets. This leads to the seven approaches explained in the Methodssection and with the pAla FLR curve generated using these methodsdisplayed in Clearly, the product adjustment does not achievethe desired results4. For thAmong the collapsing methods applied after Binomial_Adjustment,the Pform_Mean method appears to underperform compared to the Pform_Maxand Pform_MM approaches. It could be expected that not all the distributionsof scores for all sites would be centered or well characterized, giventhat for most sites, there are only few observations per site in thedata sets. Thus, we concluded that the site score mean is not a suitablestatistic to represent peptidoform-site scores.A very similarperformance can be observed between Pform_Max andPform_MM approaches for both binomial adjusted and unadjusted dataacross all data sets 3. The PfFigure S3 and Table S4). Binomial adjustmentappears to have little effect on PD output; similar patterns can beobserved between the unadjusted and adjusted data . As observed by comparing the results at the peptidoformlevel, the PD pipeline returns a larger number of sites than the TPPpipeline for the natural data sets at all pAla FLR levels. For thelargest synthetic data set, both pipelines return similar results,while for the small data set, the performance of PD is very poor.For the PD pipeline, the PformMM clearly outperformsany of theother approaches, especially in the natural data sets  incorrect sites. Therefore, in additionto simply taking the matches passing a common threshold, further criteriabased on those thresholds could be introduced to indicate a differentlevel of confidence among those identifications.In our examplethere are 11,828 unique protein sites at 5% FLR,and of those, 6368 were identified at 1% FLR peptide-sequence-sitelevel based on the Combined Algorithm 5. We couResults from thisclassification are displayed on Results applying these simple criteria seem toreturn a satisfactorylevel of decoy matches for our example using rice data sets. However,the relationship between decoy matches and thresholding may vary dependingon the nature of the species and data being investigated, and therefore,the criteria for Gold–Silver–Bronze may need to be learnton a case-by-case basis, for example, optimizing how many data setsare required for promotion of a site to Gold standard.There is a wide range of scoring algorithmsused for PTM identificationand localization. It is common practice for PTM score thresholds tobe applied for real data sets, which have been calibrated based onanalyses of synthetic data sets for which the correct matches areknown. However, it can be questioned whether these scores are trulyprobabilistic, hence limiting the interpretability of scores acrossdata sets. Additionally, in the context of PTM localization, syntheticdata sets may not provide a good representation of natural data sets,and therefore, PTM scoring calibration efforts based on syntheticdata sets are not likely to be entirely reliable. These two reasonsmake it questionable to use a fixed scoring threshold across studies.An alternative to scoring thresholds has been suggested, which involvesthe introduction of decoy amino acids in addition to target modifiableamino acids as variable modifications in search engines to enablethe calculation of data-set-specific FLR statistics. This approachshould provide objective comparable results between independent studiesbased on global FLR thresholds.Results from searches usingdecoy amino acids can yield severalthousands of PSMs with high levels of duplication and ambiguity, whichcould hinder their interpretation. In this paper, we suggest a post-processingworkflow for LC-MS/MS-based PTM localization studies using decoy aminoacids. Our workflow has simplicity at its core in which for each step,simple methods are chosen to enable implementation with any basicstatistical software. The workflow is data and search engine agnosticfavoring the exchange of findings from different sources and methodologiesas long as there is a direct correlation between scores and the confidenceof a PTM site being correctly identified. We suggest three steps,as follows.First, we aim to mitigate the effect of spuriousrandom matches,by using the binomial distribution to adjust PTM scores, with scoresϵ . This adjustment takes into account differences in abundancefor different peptides in a way that all scores are penalized butmore heavily for sites that are observed less frequently. Our resultssuggested that data obtained via the TPP pipeline could benefit morefrom this adjustment than data extracted using the PD pipeline.Second, we investigated simple approaches for collapsing from thePSM level to the peptidoform level. Traditionally, the most commoncollapsing approach has been using the maximum score across all PSMsites belonging to a peptidoform site, even if the scores for a peptidoformwith multiple PTMs come from different PSMs. This would be conceptuallycorrect if scores for PSM sites within a PSM are considered to beindependent from each other. However, at least for the scoring methodsused in this paper, PSM sites scores do not seem to be independentand the PSM average score would be a more suitable statistic thanthe independent scores when comparing scores across PSMs. However,given that in highly correlated scores within a PSM, to have a maximumscore will lead to other PTM sites to have high scores as well. ForTPP, we showed that these two approaches would lead to similar resultsat least for the TPP pipeline, while bigger differences were observedfor the PD pipeline. Therefore, the traditional approach of takingthe maximum score would be preferred in general as it would performwell for PSM level with correlated and independent scores, while thoseusing the pipelines with PD at its core may obtain better performanceby using the mean PSM score.Third, we have also shown thatscores from independent studieswere not comparable as they are likely to be closely linked to theexperimental and data collection procedures of each study. Therefore,we suggested a simple approach to combining evidence from independentstudies. By retaining the matches to decoy PTM sites based on differentFLR thresholds, the overall FLR estimates of combined data can bedetermined.A limitation to the results presented here is thatthese are basedon two analysis pipelines and scoring methods. Further analyses wouldbe required to demonstrate that the simple post-processing approachesexplained above would also perform well for data acquisition via otheranalysis pipelines and PTM scoring algorithms. This is especiallyimportant for those scores ∉  for which an additionalstep might be required.We believe our FLR estimates to be broadFLR indicators due tothe complex nature of PTM scoring. We have shown that synthetic datasets often used to calibrate scoring methods are not likely to berepresentative of natural data sets, and therefore, it is not possibleto fully ascertain performance but enables comparisons with currentpractice.We expect our method to work for cases involving enrichmentformodifications other than phosphorylation. However, the method wouldnot be suitable for open modification searches, without substantialmodification.


Contrastingly, extreme precipitation in the winters of 2019 and 2020 decreased mass loss by 60 ± 16 Gt yr−1 during those years . These results emphasise the important impact of extreme snowfall variability on the short-term sea level contribution from West Antarctica.Mass loss from the West Antarctic Ice Sheet is dominated by glaciers draining into the Amundsen Sea Embayment (ASE), yet the impact of anomalous precipitation on the mass balance of the ASE is poorly known. Here we present a 25-year (1996–2021) record of ASE input-output mass balance and evaluate how two periods of anomalous precipitation affected its sea level contribution. Since 1996, the ASE has lost 3331 ± 424 Gt ice, contributing 9.2 ± 1.2 mm to global sea level. Overall, surface mass balance anomalies contributed little (7.7%) to total mass loss; however, two anomalous precipitation events had larger, albeit short-lived, impacts on rates of mass change. During 2009–2013, persistently low snowfall led to an additional 51 ± 4 Gt yr The authors combine measurements of ice loss from West Antarctica with climate modelling to show that periods of drought or extremely heavy precipitation can significantly increase or decrease rates of mass loss for periods lasting several years. Changes in the mass of these glaciers occur largely due to differences between net snow accumulation over the surface of each glacier basin and ice discharge into the ocean. In recent decades, the ASE glaciers have accelerated7, thinned10, and have undergone complex reconfigurations of their calving fronts and grounding lines12  of West Antarctica are the dominant contributors to sea level rise from the Antarctic Ice Sheet19. These periods of more rapid basal melting are driven by decadal-scale variations in the thickness of the warm Circumpolar Deep Water (CDW) layer19, which is the main source of oceanic heat on the continental shelf20. These thickness variations are, in turn, thought to be driven largely by westerly wind anomalies at the shelf break26, which causes an eastward undercurrent of CDW to divert southwards onto the continental shelf where it encounters cross-shelf troughs27. These westerly wind anomalies are correlated with sea surface temperature and air pressure anomalies in the tropical Pacific associated with the El Niño-Southern Oscillation (ENSO), the effects of which are transmitted to the Amundsen Sea via standing atmospheric Rossby waves29 and manifest as a weakening and eastward shift of the Amundsen Sea Low (ASL) during El Niño events31. This ENSO-driven decadal variability in CDW thickness appears to be superimposed on a centennial trend of increasing westerly wind anomalies18 and an associated increase in Amundsen Sea heat content32. While influential on the wider Antarctic climate, the Southern Annular Mode (SAM) does not significantly influence winds directly over the Amundsen Sea, or rates of ocean melting at the grounding lines of the ASE ice streams that are rapidly thinning33. Overall, the observed ice dynamic changes of ASE glaciers in recent decades appear to be driven primarily by large-scale changes in atmospheric conditions, via their influence on surface winds, which drive cross-shelf exchange of warm ocean water masses.The ice dynamic changes across the ASE have been attributed to periods of submarine melt-driven thinning of ice shelves in the region37, which is the dominant contributor to surface mass balance (SMB) variations35. Most of the precipitation over the ASE is associated with synoptic-scale (~1000 km) weather systems that drive orographic precipitation near the coast38. In particular, variations in the strength and longitudinal position of the ASL and of anticyclones in the Bellingshausen Sea exert a strong influence on meridional wind and moisture fluxes39. In general, a deeper and more westward ASL, which is typical during winter, encourages the southward transport of maritime air masses and drives heavier precipitation over the ASE, particularly during the presence of a blocking anticyclone over the Bellingshausen Sea and Antarctic Peninsula, compared to times with a more eastward or weaker  ASL37. There are, therefore, seasonal fluctuations in the strength and position of the ASL, which typically cause heavier precipitation in the ASE during winter than in summer30.Change in atmospheric conditions also drives significant precipitation variability over the ASE42, which encourages southward transport of warm, moist air, potentially forming so-called ‘Atmospheric Rivers’ (ARs)45. When these air masses make landfall, they can lead to short-lived but heavy orographic precipitation events46. Although infrequent and short-lived, extreme precipitation events (of which ARs drive a subset) contribute a large proportion of precipitation to the ASE and drive the majority of interannual precipitation variability34, and can cause notable (~18 cm) increases in ice-sheet surface height when integrated over a full winter season47.In addition to seasonal changes, the position and strength of the ASL are influenced by large-scale atmospheric circulation patterns and modes of climate variability. The ASL is deeper during positive phases of the SAM and during La Niña eventsth Century49, but with substantial variability driven by southern hemisphere circulation patterns50 and short-term variations driven by extreme precipitation events51. Over the 21st Century, increased SMB could even cause net ice-sheet mass gain under certain climate scenarios52. We note that these studies have largely focused on extreme increases in precipitation, and that comparatively little attention has been devoted to studying prolonged periods of low snowfall53. Despite the clear importance of atmospheric conditions in driving changes in CDW thickness and ice shelf basal melt rates, the direct effect on ASE mass balance of anomalous precipitation events, and their potential to mitigate or compound mass changes due to ocean-driven changes in grounding line flux from the ASE, has received comparatively little attention. This hinders our ability to determine the relative effects of changes in atmospheric and oceanic conditions on the mass balance of West Antarctica. Some recent observations provide evidence that such an analysis is pertinent. Firstly, a recent mass balance assessment of the Getz region noted that anomalously low precipitation during 2010/2011 winter exacerbated mass loss from the basin5. Secondly, anomalously high precipitation driven partly by ARs during the 2019/2020 winter caused rapid increases in surface height over the Amundsen Sea Embayment basins, despite ongoing dynamic thinning47. These observations indicate that periods of anomalously low or anomalously high precipitation may exert a non-negligible effect on the mass balance of the ASE.The impact of precipitation variations on mass balance has been studied extensively across the Antarctic Ice Sheet and over different time periods. Snowfall over the Antarctic Ice Sheet has increased throughout the 2054, HIRHAM555. and MAR57). The synoptic-scale atmospheric conditions during periods of anomalous precipitation are evaluated using the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis dataset58 and ERA559. Finally, we examine historical records of climate indices, air pressure60, and snow accumulation from ice cores61 to assess the prevalence of, and atmospheric conditions associated with, comparable periods of anomalous precipitation in the 20th Century.Here, we present a 25-year-long record of ice flux and mass balance from West Antarctica, quantifying the sea-level contribution from this region from 1996 to 2021. Secondly, we quantify the effect of periods of anomalous precipitation on the mass balance of the ASE, and characterize the atmospheric conditions associated with those events in order to re-evaluate the impact of changing atmospheric conditions on the mass balance of ASE glaciers. To do this, we calculate grounding line discharge and mass balance for each glacier basin draining the ASE from 1996 to 2021 using existing and new velocity observations and three regional climate models (RACMO2.3p265 with high-resolution (6–12-day) velocity estimates from intensity tracking of Sentinel-1a and 1b image pairs since 2015  in grounding line discharge due to surface lowering66  velocity estimates from 1996 to 2018r−2 Fig. . Changes14, is likely a delayed response to a reduction in cavity ocean heat content first observed in early 201213. The reduced cavity ocean heat content was in turn driven in large part by lowering of the thermocline due to persistent easterly wind anomalies over the Amundsen Sea beginning in 2011, associated with a major La Niña event13. Building on previous observations14, we observe this slight reduction in discharge across the easterly ASE basins, from Pine Island through to Kohler  since 2016, which has subsequently accelerated by over 40% , though the errors are comparable to the mass balance at that time   Fig. . Most of67) Fig. .We focus on two large and opposing changes to SMB during our study period Fig. . The fir5; we provide further details here. Monthly RACMO2.3p2 precipitation during this time was, on average, just 17% below the monthly climatological mean. However, precipitation remained below the monthly climatological mean for 70% of the 2009–2013 period, creating a prolonged period of subdued precipitation  daily precipitation events  and 8.7% (11.2 Gt) of the total winter precipitation during 2019 and 2020 respectively, was provided by ARs. By way of comparison, we also define ‘extreme precipitation events’ as days in the top 10% of long-term (1979–2021) daily precipitation totals34 in RACMO2.3p2. By this metric, extreme precipitation events  and quantify its impact on the mass balance of the ASE. The cumulative mass loss due to this snowfall drought was ~253 ± 81 Gt or 51 ± 4 Gt yr−1 Fig. , contribetz Fig. . The sup31, which exerts a strong control on ASE precipitation variability. A relatively deep ASL is associated with the La Niña phase of ENSO particularly during winter, with a positive SAM during summer, and with a higher amplitude semi-annual oscillation30. During 2009–2013, there was a switch to strong La Niña conditions19 and the SAM was positive . In other words, in terms of their total impact on sea-level rise, the 5-year SMB anomaly we describe is equivalent to the observed 20-year increase in discharge from the mid-1970s to the mid-1990s.The snowfall drought during 2009–2013 caused 51 ± 4 Gt yriod Fig. . Groundith Century, ice core records  across the majority of the ASE basin, with the greatest relative thinning of ~1.5 m concentrated in coastal parts of the Getz basin  and 136.2 Gt (45.3%) of the precipitation in the winters 2019 and 2020 were, according to our classification, caused by ARs and extreme precipitation events respectively. Extreme precipitation events therefore account for a large proportion of the positive winter precipitation anomaly  delivered less than 10% of precipitation during the extreme winters of 2019 and 2020. However, ARs are more common in East Antarctica45, where they can contribute over 30% to the total precipitation in the region81 and where they are expected to increase in frequency and intensity during the 21st Century83 due to the greater moisture-bearing capacity of air masses as air temperature rises in future. On the other hand, ARs bring warm air to the ice-sheet that can cause intense surface melting84 and could in the future cause liquid rather than solid precipitation and even SMB decreases by means of runoff, as currently occurs in Greenland86. To date, there are no dedicated projections of ARs or extreme precipitation events over Antarctica. However, most models project SMB increases over the grounded Antarctic Ice Sheet during the 21st Century87, which may offset increases in grounding line discharge under certain climate scenarios88, despite projected increases in low-elevation surface melting and associated meltwater runoff89.Given the capacity of ARs to rapidly deliver large amounts of precipitation90, with a focus on processes driving cross-shelf exchange of warm CDW, ice-ocean interactions in ice shelf cavities and the resulting glacier response73. Simultaneously, many other studies have shown that substantial temporal variations in precipitation occur due to extreme precipitation events37, and that variations in precipitation can affect the large-scale mass balance of the Antarctic Ice Sheet50. Here, we update the grounding line discharge and mass balance record from the ASE and use a range of atmospheric reanalysis datasets to combine these strands of research and re-evaluate the effect of anomalous precipitation on the mass balance of the ASE. We find that the ASE has lost 3331 ± 424 Gt ice, contributing 9.2 ± 1.2 mm to sea-level since 1996, driven by progressively increasing grounding line discharge. Overall during 1996–2021, changes in SMB had a modest (7.7%) impact on mass balance but anomalously high or low precipitation can have a larger (over 50%) impact on mass balance over shorter (2–5 year) timescales. Therefore, whilst discharge has progressively increased during the study period, the mass balance of the ASE has been comparatively steady since 2010. Thus, periods of anomalous precipitation do have a non-negligible impact on the sea-level contribution from the ASE, and estimates of mass balance over short time periods (5 years or less) may not be representative of longer-term mass changes. We suggest that further work is required to understand the effect of historical and future snowfall droughts and ARs on Antarctic Ice-Sheet mass change but that ice-ocean interactions remain a priority for future research as these are the dominant driver of ice dynamic variability and decadal-scale mass change in the ASE.Over the past few decades, many studies have investigated the response of the ASE glaciers to changes in atmospheric and oceanic forcingD, across each flux gate pixel asV is the gate-normal ice velocity, H is the ice-equivalent thickness, w is the pixel width and ρ is ice density (917 kg m−3). We use “flux gate 1” (or FG1) from ref. 65 for our flux gate, which follows areas of direct bed observations and low bed elevation error, rather than the grounding line, and we resample the flux gate pixels to regular 200 m spacing. All plots in the main text are produced using this flux gate. We tested the sensitivity of our results to flux gate location by migrating each gate pixel along a flowline upstream based on the time-average velocity in 0.5-year increments for 10 years, creating 20 individual flux gates. Ice discharge increases by up to 6.3%, and by 1% on average, compared to that using the FG1 gate, as the gate is migrated upstream , which has been filled using an optimised ice-sheet model – BISICLES5. For 2000 and 2005 to 2016, we use 1×1 km MEaSUREs annual velocity mosaics63. We also use a MEaSUREs velocity mosaic incorporating velocity estimates between 1995 and 2001, at 450×450 m resolution91. For 1997–2018, we use 120×120 m ITSLIVE annual mosaics65; for 2009 to 2020, we use quarterly ice velocity mosaics from TerraSAR-X (2009–2016) and Sentinel-1 (2015–2020) just over Pine Island Glacier7. From 2015 onwards, we use high temporal resolution (6- to 12-day) estimates of ice velocity derived from intensity tracking of all 6- and 12-day Sentinel-1 image pairs in GAMMA, using standard methods5. We combine a range of cross-correlation window , which are merged using a signal-to-noise ratio weighted mean, to accommodate the large range in ice velocities in the ASE and to increase coverage. Individual image pair velocities are filtered using a ‘dusting’ approach to remove spatially-isolated pixels and a 2D gaussian filter5, and posted at 100 × 100 m. Image pair velocities are mosaicked to 200 × 200 m monthly-mean velocities after further outlier removal and smoothing using a Discrete Cosine Transform Penalised Least Squares approach, optimised using the generalised cross-validation for fast, unsupervised processing93. Each of these velocity products spans a time period; following ref. 94, we ignore this and treat each product an instantaneous measurement with the timestamp given by the central date in the estimate. We extract easting and northing velocities at each gate pixel using nearest neighbour interpolation. Treating each gate pixel as a timeseries, we remove outliers, which we define as data points more than three standard deviations from the detrended timeseries. We fill temporal gaps using a linear interpolation except at the beginning and end of each timeseries, which are back- and forward-filled with the temporally nearest value for that pixel. Each filled timeseries (with no data gaps) is smoothed using a Savitzky-Golay filter with a 6-month window and a second-degree polynomial. Finally, as in previous studies94, we assume the depth-averaged velocity is the same as the surface velocity.The gate-normal ice velocity is given by96 and the 200 m REMA Digital Elevation Model97 (DEM) to define our baseline thickness estimate. We use observed ice-equivalent thinning rates from 2003–2019 derived from ICESat-1 and ICESat-2 observations66 to adjust the REMA DEM surface, assuming that the REMA DEM is timestamped to 9th May 201597. For velocity measurements prior to 2003, we use the average estimated elevation from 2003 to 2006, rather than extrapolate the thinning rate. Similarly, for velocity measurements after 2019, we use 2017–2019 average ice surface elevation. Our results are insensitive to these thickness adjustments . Basin-scale discharge using alternative basin definitions102 are shown in Supplementary Table Grounding line discharge from each basin is estimated by integrating the pixel-based discharge estimate for all flux gate pixels that fall within the respective basin. In the main text, we use basins from ref. 94, we define our discharge error using the minimum and maximum possible discharge based on the errors in the velocity and thickness data at each gate pixel. The pixel-based maximum discharge is defined asFollowing ref. And the pixel-based minimum discharge is defined as97, and a 0.1 m yr−1 error in the linear surface elevation change66. Following ref. 94, we define invalid thickness estimates as those that are <20 m where the ice is flowing faster than 100 m yr−1, and we estimate the thickness in those pixels using a weak correlation (R2 = 0.55) fit between the time-average velocity and the remaining valid baseline thickness pixels and we assume a 50% thickness error in these locations. Across the ‘FG1’ flux gate, this correction replaces 9.5% of all gate pixels and increases our average FG1 gate thickness by 26 m, resulting in an 1.2% increase in discharge.In all cases throughout the manuscript, the presented discharge errors are defined as54. It has been especially adapted for use in the polar regions by adopting a multilayer snow model that includes routines for melt, percolation, refreezing, runoff, snow albedo, and drifting snow. RACMO2.3p2 has been extensively evaluated and compares favourably with observations of near-surface climate and SMB; in particular, RACMO2.3p2 SMB estimates are similar to and strongly correlated with radar-derived snow accumulation in the Getz and Thwaites Glacier basins54.We use SMB output from three regional climate models. We draw predominantly on the Regional Atmospheric Climate Model version 2.3p2 (RACMO2.3p2), which is forced at its lateral boundaries by ERA5 and run at 27 × 27 km resolution57 and the Danish regional climate model HIRHAM555. MAR is a hydrostatic model specifically designed for polar areas. It is forced at its boundaries by ERA5, runs at 35 × 35 km resolution, and includes routines for meltwater refreezing, snow metamorphism, snow surface albedo, and drifting snow57. MAR monthly-mean SMB output is available for 1980–2021. HIRHAM5 is a hydrostatic model forced at its boundaries by ERA-Interim. It is run at 0.11° resolution and includes a new albedo scheme55, sublimation, surface melt, retention, refreezing, and runoff103 but does not include wind redistribution of snow. HIRHAM5 daily-mean SMB output is available for 1980–2018.These data are supplemented by SMB estimates from the Modèle Atmosphéric Régional (MAR)70, which we take as 14.8% of the annual SMB for all products2.Cumulative SMB anomalies are calculated as follows. In each spatial cell, we cumulatively sum timeseries of anomalies relative to the 1980–2008 climatological mean of that cell. We use 1980–2008 rather than the 1979–2008 mean so that we can use the same climatological period for all SMB datasets. These pixel-based cumulative SMB anomalies are integrated across each glacier drainage basin to obtain regional cumulative SMB anomalies. SMB anomaly errors are taken as the root sum square of the annual errorsWe linearly interpolate both the discharge and SMB timeseries to monthly intervals from January 1996 to December 2021. The mass balance is then the simple difference between these variables. The mass balance error is assumed to be the root sum square of the discharge and SMB errors. Cumulative mass change errors are defined as the root sum square of the annual mass balance errors, excluding errors in initial ice thickness.4 to annual intervals. Ref. 4 did not present grounding line discharge estimates for Getz. For the purpose of this analysis we estimate grounding line discharge from the Getz basin during 1974–1996 by scaling the annually interpolated values from ref. 4 according to the average relative contribution of the Getz basin to the total ASE discharge presented here since 1996. This approach assumes a synchronous response from all basins which is contrary to observations9. These discharge estimates are therefore used only to illustrate the potential impact of historical SMB changes on ASE-wide mass balance and are not a true estimate of historical ASE grounding line discharge.Annual grounding line discharge from the ASE is estimated by linearly interpolating the values presented in ref. 45, which is based on contiguous areas of very high (98th percentile) meridional component of vertically-integrated water vapour transport (vIVT). It is calculated using vertically-integrated specific humidity and meridional wind speeds from MERRA-2. From this catalogue, we include ARs only if they intersect the grounded ASE (basins G-H and F-G) and amalgamate AR detections if they are separated by less than 12 hours (accounting for any changes in AR footprint during that time). The precipitation provided by ARs is calculated by integrating daily RACMO2.3p2 precipitation within the landfalling-portion of the AR footprint from the start of the AR detection period and until 24 hours following the end of the event.We use an existing ice-sheet wide, 3-hourly catalogue of ARs, derived using a Polar-specific AR detection algorithm104 (IPOTPI), of which we use the HADISST1.1 filtered version. The IPOTPI is based on the difference between the sea surface temperature anomaly averaged over the central equatorial Pacific  and the average of the sea surface temperature anomaly in the Northwest  and Southwest Pacific , with anomalies calculated relative to January 1971 to January 2000 period. We also use the Southern Annular Mode (SAM)105, which is a station-based index defined as the zonal pressure difference between 40°S and 65°S. Finally, we also use an ASL index, defined as the annual average of the 500-hPa geopotential height from 75°S to 60°S and 170°E to 70°W, normalised relative to the 1941–1990 period, derived from proxy-based climate reconstructions80.We use four climate indices to provide insight into the mechanistic drivers of potential historical periods of snowfall drought. The first of these indices is the Oceanic Niño Index (ONI) is a key indicator of the El Niño Southern Oscillation. It is a rolling 3-month average sea surface temperature anomaly (relative to the mean of the surrounding 30 years) in the east-central tropical Pacific between 120°–170°W (the Niño-3.4 region). An El Niño is considered to be present when the ONI is +0.5 or greater, whereas a La Niña is considered active when the ONI is −0.5 or lower. In Fig. We define periods of snowfall drought , which we calculate as 550,680 km2. The ice-equivalent height change is then simply given by Hw.e.(ρw/ρi) where ρw and ρi are the densities of water (1000 kg m−3) and ice (917 kg m−3), respectively.The total mass change due to the 2009–2013 snowfall drought was ~260 Gt. We estimate these-equivalent thickness change due to this mass change as follows. When distributed evenly over 1 mSupplementary InformationPeer Review File


Since its discovery, QX-type avian infectious bronchitis virus (IBV) has rapidly spread worldwide and become the most prevalent dominant genotype in Asia and Europe. Currently, although the pathogenicity of QX-type IBV in the reproductive system of hens is widely and deeply understood, its pathogenicity in the reproductive system of roosters remains largely unknown. In this study, 30-week-old specific pathogen-free (SPF) roosters were used to investigate the pathogenicity of QX-type IBV in the reproductive system after infection. The results showed that QX-type IBV infection caused abnormal testicular morphology, moderate atrophy and obvious dilatation of seminiferous tubules, and produced intense inflammation and obvious pathological injuries in the ductus deferens of infected chickens. Immunohistochemistry results showed that QX-type IBV can replicate in spermatogenic cells at various stages and in the mucous layer of the ductus deferens. Further studies showed that QX-type IBV infection affects plasma levels of testosterone, luteinizing hormone, and follicle-stimulating hormone as well as causes changes in transcription levels of their receptors in the testis. Furthermore, the transcription levels of StAR, P450scc, 3βHSD and 17βHSD4 also changed during testosterone synthesis after QX-type IBV infection, indicating that the virus can directly affect steroidogenesis. Finally, we found that QX-type IBV infection leads to extensive germ cell apoptosis in the testis. Collectively, our results suggest that QX-type IBV replicates in the testis and ductus deferens, causing severe tissue damage and disruption of reproductive hormone secretion. These adverse events eventually lead to mass germ cell apoptosis in the testis, affecting the reproductive function of roosters. Coronaviridae family [Mycoplasma synoviae) can aggravate the disease [Avian infectious bronchitis (IB) is an acute, highly infectious disease of chickens caused by infectious bronchitis virus (IBV), which belongs to the e family . IBV maie family . IBV infe family . After i disease ,5.Coronaviridae family, on the reproductive system of roosters.IBV has many serotypes and genotypes, and different types of strains may differ significantly in pathogenicity and tissue tropism. The QX-type  was first discovered in the late 1990s . AccordiIn this study, QX-type IBV, which is highly pathogenic to the reproductive system of hens, was used to infect 30-week-old SPF roosters as experimental subjects, and the pathogenicity and potential impacts of QX-type IBV on the rooster reproductive system were evaluated from the aspects of gross lesions, viral load, plasma reproductive hormone levels and cellular apoptosis.50) of the QXL strain was calculated using the Reed and Muench method as described previously [The QX-type velogenic IBV strain CK/CH/JS/2010/12 (abbreviation: QXL) was originally isolated in 2010 from the trachea and kidney of chickens that exhibited respiratory symptoms and death (GenBank accession NO. JQ900122.1). The 50% chicken embryo infectious dose  and kept in negative-pressure isolators in the secondary biosafety barrier area until 30 weeks of age.All the animal experimental procedures were performed in accordance with the ARRIVE Guidelines 2.0. All experimental protocols applied in this study were approved by the Animal Welfare and Ethical Review Committee of Yangzhou University (202103403).5 EID50 of the QXL strain via the oculonasal route. Birds in the control group were administered 200 μL of phosphate-buffered saline (PBS) via the oculonasal route. At 4 and 8 days post-infection (dpi), five birds from each group were randomly selected for blood and tissue sample collection. Blood samples were collected from the wing vein, gently added to tubes coated with heparin sodium, and immediately centrifuged at 3000 × g for 20 min at 4 °C. Plasma samples were obtained and stored at −70 °C. Then, the birds were euthanized by cervical dislocation, body and testicular weights were recorded, and gross lesions of the testis and ductus deferens were observed. The results are presented as the GSI (expressed as the ratio of testicular weight to body weight). Testis and ductus deferens tissue samples were subsequently collected, some of which were immediately fixed in 10% neutral-buffered formalin. The remaining tissue was placed in a prelabeled cryogenic storage tube, immediately flash-frozen in liquid nitrogen for 1 h, and then frozen at −70 °C.All the birds were randomly divided into two groups with 10 birds in each group and housed in negative-pressure isolators in the secondary biosafety barrier area. All animals were provided ad libitum access to food and water during the experiment. At the age of 30 weeks, animals in the challenge group were inoculated with 200 μL of allantoic fluid containing 10The fixed tissue samples were processed via the standard histological procedure, embedded in paraffin wax, and cut into 5-μm sections. Then, some sections were stained with haematoxylin and eosin (H&E) for histopathological observation. Some sections were prepared for immunohistochemistry (IHC) to detect the distribution and accumulation of the viral antigen in tissues. Briefly, sections were treated with 3% hydrogen peroxide for 6 min to eliminate the activity of endogenous peroxidase. Sections then underwent the antigen retrieval procedures, followed by blocking with 10% (v/v) goat serum for 30 min. The samples were then sequentially incubated with a mouse anti-IBV N protein monoclonal antibody at 37 °C for 2 h and a biotin labelled goat anti-mouse antibody at 37 °C for 30 min, followed by development with a concentrated SABC-POD (mouse IgG) kit . Sections were visualized using a 3,3’-diaminobenzidine (DAB) Chromogenic Substrate Kit (Brown)  and counterstained with Mayer’s haematoxylin before mounting the slides with neutral balsam mounting medium. Sections were observed using a Leica DM2000 LED light microscope  and photographed with the accompanying MShot Image Analysis System V1.1.4 .Concentrations of plasma luteinizing hormone (LH), follicle-stimulating hormone (FSH), testosterone (T) and oestrogen (E2) were determined by enzyme-linked immunosorbent assay (ELISA) kits  according to the manufacturer’s protocols. Absorbance was measured at 450 nm using an ELx800 microplate reader , and the parameters were set using the accompanying Gen5 software. The sensitivity of the assay was 50 ng/L for LH, 0.5 IU/L for FSH, 0.2 nmol/L for T and 2 ng/L for E2. Concentrations of plasma reproductive hormones were calculated using the standard curve based on the standards provided with the respective kits.18 (Takara Bio). The reaction system was prepared with AceQ® qPCR probe Master Mix  for the detection of viral load by qRT‒PCR, and the primers, probe, and methods of obtaining standard curves were referenced in a previous study [−ΔΔCT method [Total RNA of the testis and ductus deferens (100 mg from each sample) was extracted using an Ultrapure RNA Kit  according to the manufacturer’s instructions, and the concentrations of extracted total RNA were determined using a NanoDrop2000 Ultra microspectrophotometer . A total of 5 μg of RNA was then reverse transcribed to cDNA using an EasyScript® Reverse Transcriptase  Kit  and random hexamer primers  or oligo (dT)us study . AceQ® qT method . All theApoptotic cells in the testis were identified by a terminal deoxynucleotidyl transferase dUTP nick-end labelling (TUNEL) assay using a One-step TUNEL Apoptosis In Situ Detection Kit   following the manufacturer’s procedures. Briefly, the prepared dewaxed slides were permeabilized with sodium citrate buffer  in a microwave at medium-high heat for 8 min. Sections were washed in PBS (3 × 5 min), and a positive slide was then prepared by covering the slide with a DNase I (50 U/μL) reaction mixture at 37 °C for 30 min. Sections were sequentially incubated with a TdT enzyme reaction mixture at 37 °C for 60 min and streptavidin-fluorescein labelling solution at 37 °C for 30 min in a humidified box, followed by counterstaining with DAPI before mounting the slides with glycerine mounting medium. Sections were immediately examined with an Olympus IX73 fluorescence microscope (Evident (subsidiary corporation of Olympus), Japan) and photographed with the accompanying cellSens software .t test using GraphPad Prism 8.3 software . Differences between the challenge and control groups were considered statistically significant when P was<0.05.All data were statistically analysed via Student’s To determine the impacts of QX-type IBV on the testis, lesions and histopathological changes were evaluated during necropsy. The normal testis are usually bean-shaped, with slightly different sizes and weights on each side. However, obvious morphological abnormalities were observed in the testis both at 4 and 8 dpi , and the2 and IFN-β in the challenge group were significantly higher than those in the control group, whereas the transcription levels of IFITM3 and OASL did not significantly change  assays were used to observe the distribution of viral antigens in the testis and ductus deferens. IHC results showed that viral antigens were present in Sertoli cells, spermatogenic cells of various stages of the testis, and in the mucosal layer of the ductus deferens . Viral RThe reproductive physiology and behaviour of birds are governed by the endocrine system, and ELISA was therefore used to measure the concentrations of plasma reproductive hormones . A significant increase in plasma T concentration at 4 dpi was accompanied by a significant decrease in the FSH concentration in roosters . At 8 dp2 in the testis were measured by qRT‒PCR. Compared with those in the control group, the transcription levels of 3βHSD and 17βHSD4 were significantly upregulated in the challenge group at 4 dpi  axis , and LH T synthesis proceeds from cholesterol that undergoes a series of enzymatic reactions in the Leydig cells of the testis. The first and rate-limiting step is the translocation of cholesterol from the outer to the inner mitochondrial membrane, which is promoted by StAR, and the conversion of cholesterol to pregnenolone is catalysed by P450scc in the inner mitochondrial membrane . PregnenAs indicated earlier, IBV-caused injuries in the reproductive system and reproductive endocrine disruption may lead to the common endpoint of apoptosis. Previous studies have demonstrated that IBV can induce apoptosis in tracheal, kidney, and HD11 macrophages ,40. HoweIn addition, chickens of all ages are susceptible to IBV, and chicks less than one month old are especially susceptible. Whether infection early in life can also cause lesions in the reproductive system of roosters or even cause permanent injuries still needs to be systematically evaluated through further research.QX-type IBV replicates in the testis and ducts, causing severe damage, and induces endocrine disorders in reproductive organs and apoptosis of germ cells. These results provide a basic understanding of the pathogenicity of QX-type IBV in the male reproductive system and elucidate the possibility of IBV causing infertility in roosters via multiple pathways.


However, this process is challenging due to the more favorable CO adsorption than N2 at the catalytic site, making the formation of the key urea precursor (*NCON) extremely difficult. Herein, we theoretically construct a spatially isolated dual-site (DS) catalyst with the confinement effect to manipulate the competitive CO and N2 adsorption, which successfully guarantees the dominant horizontal N2 adsorption and subsequent efficient *NCON formation via C–N coupling and achieves efficient urea synthesis. Among all the computationally evaluated candidates, the catalyst with dual V sites anchored on 4N-doped graphene (DS-VN4) stands out and shows a moderate energy barrier for C–N coupling and a low theoretical limiting potential of −0.50 V for urea production, which simultaneously suppresses the ammonia production and hydrogen evolution. The confined dual-site introduced in this computational work has the potential to not only properly address part of the challenges toward efficient urea electrosynthesis from CO and N2 but also provide an elegant theoretical strategy for fine-tuning the strength of chemical bonds to achieve a rational catalyst design.Electrocatalytic urea synthesis under mild conditions  2 and CO adsorption to achieve efficient urea electrosynthesis, showing the theoretical feasibility of being a promising alternative to the traditional synthetic protocol.A spatially isolated dual-site catalyst manipulates the competitive N In industry, large-scale urea synthesis was achieved at a high temperature of 150–200 °C and high pressure of 150–250 bar using ammonia (NH3) and carbon dioxide (CO2) as reactants. Another key issue of this route lies in its dependence on NH3 as the nitrogen source, which is industrially produced via the energy- and capital-intensive Haber–Bosch process under harsh conditions.4 It consumes ∼2% of the global fossil energy and emits ∼300 million tons of CO2 annually during the inert N2 fixation to NH3 due to the usage of gray hydrogen, and ultimately impacts the sustainability of the whole urea production industry.4 Therefore, developing a low-carbon and alternative sustainable urea synthesis protocol is significant for achieving and maintaining the sustainable development of human society.As a crucial artificial fertilizer, urea (NH2 and CO2-derived CO) with NH3-free nitrogen sources  has been proposed as a promising and efficient one-step method to produce urea via electrochemical reduction, which has attracted increasing attention from academia due to the sustainability and affordability.5–20 For example, Chen et al. experimentally prepared an electrocatalyst by anchoring PdCu nanoparticles on TiO2 nanosheets for aqueous N2 and CO2 coupling to synthesize urea, which achieved a formation rate of 3.36 mmol g−1 h−1 and a faradaic efficiency of 8.92% at −0.4 V versus RHE.5 From electrochemical CO2 and N2 coupling, Zhang and co-workers reported the urea yield rates/faradaic efficiency of 5.91 mmol g−1 h−1/12.55% and 9.70 mmol g−1 h−1/20.36% on the Mott–Schottky Bi–BiVO4 heterostructures and flower-like nickel borate [Ni3(BO3)2], respectively.6,7 Zhu et al. theoretically predicted Mo2B2, Ti2B2, and Cr2B2 as potential efficient electrocatalysts for urea production via N2 and CO2 coupling under ambient conditions based on the data from mechanistic calculations with the computational hydrogen electrode (CHE) model.8 Moreover, compared with the chemically inert N2 molecule, higher urea yields can be generally obtained by using more activated nitrite and nitrate, but have trouble in large-scale applications due to the limited availabilities of those nitrogen sources.13–20Recently, the direct coupling of carbon-based feedstocks  of C 2s  is inevitable in almost all electrocatalytic processes, true also for urea electrosynthesis, which represents the fourth formidable challenge.Despite the impressive recent progress in electrochemical urea synthesis, the sluggish reaction kinetics and limited yield still seriously impede its industrial integration. In principle, the formation of key *NCON species is a prerequisite for urea synthesis, of C 2s .24 Meanw ensured  to achie2 and CO adsorption with a confined dual active site, which provided an elegant proposal to achieve efficient urea production from N2 and CO. As presented in 2 adsorption by forming one additional stable TM–N bond, whereas the polar CO molecule binding was hardly affected or even destabilized, thereby making horizontal N2 adsorption very competitive and eventually promoting the formation of the *NCON intermediate. A group of metals, mainly first-row transition metals, was chosen as active centers and anchored on nitrogen-doped graphene to construct our confined dual-site catalysts shown in 4 (DS-VN4) catalyst to be a very promising candidate to drive the electrochemical production of urea via the *NCON protonation with a low limiting potential of −0.50 V. Meanwhile, this DS-VN4 catalyst shows tremendous suppression of undesirable ammonia formation and hydrogen evolution, enabling the high reaction selectivity toward urea products. This work not only identified the spatially isolated dual site with the confinement effect for steering the competitive N2 and CO adsorption but also rationalized this theoretical strategy to fine-tune the strengths of chemical bonds, thus paving a promising route to obtain green urea electrosynthesis via rational catalyst design.In this work, we proposed a strategy to properly address the aforementioned challenges by steering the competitive N2.2.1.via N2 and CO coupling, the first priority is to ensure preferential N2 adsorption with horizontal configuration. However, as mentioned in 2 molecule based on the analysis from molecular orbital theory. To this end, we first chose ten transition metals (TM) as active sites  and embedded them in graphene with different coordination environments  shown in Fig. S1.2 and CO. As presented in 2 molecule energetically favors the one-sided vertical binding mode to the single active site, and the binding strength of CO is much stronger than that of the N2 molecule. The two exceptions, NiN4 and NiN3C1, with similar CO and N2 binding strengths are due to their physical adsorptions toward adsorbates. In general, our calculated binding strengths of CO and N2 on the single active site follow the vertical CO > vertical N2 > horizontal N2, which is consistent with the theoretical expectation shown in 2, thereby eliminating the N2 attacking and blocking the C–N coupling for urea synthesis.To achieve an efficient urea electrochemical synthesis 2 and CO adsorption. Noteworthily, our recent work has successfully validated this strategy to enhance N2 adsorption and activation with the dual-site catalyst, making near-ambient conditions of ammonia synthesis possible.26 In our model catalyst shown in Fig. S2,27–30 for O2 electroreduction27 and CO2 electroreduction.28 Thus, the adsorption of N2 and CO molecules was systematically examined on the designed dual-site catalysts. As presented in 2-dominated adsorption such as DS-VN4, DS-VN3C1, DS-VN2C2, DS-CrN2C2, and DS-CrN1C3. The para-configuration of the TMN2C2 moiety was also considered  analysis2 molecule via the additionally constructed stable TM–N bond shown in 2 and CO molecules on the single-site catalysts shown in Fig. S6.2 adsorption and its dominance on the active site by the dual-site strategy are again supported by the electron transfer analysis. Note that the implementation of this strategy is based on the active site with strong binding to the N atom, i.e., guaranteeing a strong TM–N bond. We further examined it at the single TM site by calculating the binding energy of a single N atom using the N2 molecule as the energy reference. As listed in Table S6,2 distribution all exhibit strong N binding, implying the effectiveness of the dual-site strategy for modulating competitive adsorption. As a summary, the aforementioned spin-charge density, COHP, and charge transfer analysis provide the rationale for the fine-tuning of N2 and CO adsorption using our proposed confined dual-site catalysts.Meanwhile, the confined dual-site catalysts will transfer more electrons to the adsorbed N2.3.2 and CO competitive adsorption, we further evaluated the capability of this strategy in tackling the competition between CO–N2 coupling and N2 electrochemical hydrogenation, where the five candidates with dominant N2 adsorption were chosen as model systems. At first, we examined the free energy changes of the coupling process via the Eley–Rideal mechanism (*N2 + CO(g) → *NCON) and the N2 electroreduction process (*N2 + H+ + e− → *N2H) for convenient comparison. As shown in S-VN4, −0.34 eV for DS-VN3C1, −0.43 eV for DS-VN2C2, −0.01 eV for DS-CrN2C2, and −0.15 eV for DS-CrN1C3) on the five catalysts, indicating the thermodynamic feasibility. In contrast, the protonation of the adsorbed N2 molecule to the *N2H intermediate needs to overcome the positive free energy. Thus, at 0 V potential, the C–N coupling is thermodynamically more favorable than N2 electroreduction on the five dual-site catalysts.Since our proposed dual-site strategy was already able to properly address the challenge from the N2H intermediate via N2 reduction would be electrochemically promoted by increasing the applied potential (U), while that of *NCON was less affected due to the noninvolvement of the proton-electron coupling step during the CO–N2 coupling. In other words, the free energy change of N2 protonation will become more negative at the high U, making N2 reduction more favorable than the C–N coupling at a certain U. Thus, the effect of U was further considered to explore this competitive process. As shown in 2H and *NCON intermediates on the DS-VN4 catalyst, which indicates that at least a U of −1.16 V is needed to make the formation of *N2H more competitive than *NCON. In principle, the DS-VN4 catalyst will preferentially form the *NCON intermediate at U < −1.16 V ,21 indicating the kinetic feasibility of generating the *NCON intermediate. Afterward, the *NCON intermediate undergoes consecutive electrochemical protonation to produce the *NHCON, *NHCONH, *NHCONH2, *NH2CONH2 intermediates. The corresponding free energy changes are +0.05, −0.04, +0.47, and +0.50 eV, respectively. In this case, the final protonation process (*NHCONH2 + H+ + e− → *NH2CONH2) shows the largest uphill free energy of +0.50 eV, and is therefore the theoretical potential-determining step. Finally, the desorption of the *NH2CONH2 intermediate is endothermic by 1.25 eV. Theoretically, only a low limiting potential of −0.50 V is required to drive this reduction process toward urea synthesis, indicating the high activity of the designed DS-VN4 catalyst.As presented in s Fig. S9, which i4 catalyst , making the binding of H atoms much less competitive than N2 molecules. Despite the electrochemical binding of H with the catalyst being enhanced with increasing applied potential, N2 adsorption will remain dominant on the DS-VN4 catalyst at UL < −1.135 V and properly suppress the HER due to the lack of active sites to bind H. Based on the above energetic analysis of the whole reaction pathways for urea synthesis, the DS-VN4 catalyst could effectively and selectively produce urea in the limiting potential range of −0.50–−1.135 V, which are able to suppress both the N2 reduction and the HER. We anticipate that our proposed strategy based on confined dual-site catalysts will shed light on the experimental catalyst design with high activity and selectivity toward urea production via CO and N2 coupling.Due to the inevitable occurrence of the hydrogen evolution reaction (HER) in any electrochemical reactions, we further took it into consideration. Our calculated binding free energies of two H atoms on the Ds-VN Fig. S10 are only3.2 molecule with the catalytic dual-site can be enhanced, which guarantees the effective coupling of the pre-adsorbed N2 with CO and facilitates the formation of the *NCON intermediate as the key precursor for urea electrosynthesis. Based on our systematic calculations on a group of transition metal-based dual-site catalysts, DS-VN4, DS-VN3C1, DS-VN2C2, DS-CrN2C2, and DS-CrN1C3 were computationally found to exhibit dominant N2 distribution in a horizontal mode, effectively blocking CO adsorption. Furthermore, the formation of the *NCON intermediate on these screened catalysts is thermodynamically more favorable than the *N2H formation from electrochemical N2 hydrogenation. Among all the theoretically predicted promising candidates, DS-VN4 stands out as an efficient electrocatalyst for urea synthesis with high activity and selectivity. This study demonstrates the feasibility and functionality of the confined dual-site strategy in regulating the competitive adsorption of reactants to achieve efficient C–N coupling and urea production, which can bring important theoretical guidance for the design of experimental catalysts for practical and sustainable urea synthesis.In summary, we proposed a theoretically feasible strategy to steer the competitive adsorption of reactants by constructing a catalyst with a spatially isolated dual-site. With the help of the spatial formation of a more stable TM–N bond than TM–O, the interaction of the N4.Ab Initio Simulation Package (VASP) code.33 The revised Perdew–Burke–Ernzerhof (RPBE) functional was employed to describe the exchange–correlation interactions within the generalized gradient approximation.34,35 The electron–ion interactions were represented by the projector augmented wave (PAW) method.36 The kinetic energy cutoff of the plane wave was set to be 500 eV and the convergence criterion for the residual forces and total energies were set to be 0.03 eV Å−1 and 10−5 eV, respectively. The empirical correction in Grimme's method (DFT + D3) was adopted to describe the van der Waals interaction.37 The transition state with only one imaginary frequency was identified using the climbing image nudged elastic band (CI-NEB) method.38 Bader charge calculation was performed to analyze the charge population and charge transfer.39 Other computational details can be found in the ESI.†All the density functional theory (DFT) calculations with spin-polarization were performed using the Vienna Computational data supporting the findings can be found in the article and ESI,T. W. led and supervised this project; Z. C. performed all the DFT computations and data analysis; Y. H. L. contributed to the plot of The authors declare no competing financial interest.SC-014-D3SC04688E-s001