Along the west coasts of North, Central, and South America, sea surface temperature (SST) fronts are important for circulation dynamics and promoting biological activity. Prevailing equatorward winds during summer results in offshore Ekman transport and upwelling along the coast, where fronts often form between cold, upwelled water and warmer offshore waters. The interannual variability in winds, coastal upwelling, sea level anomalies, and SST in these regions have been linked to the El Niño‐Southern Oscillation (ENSO), however SST fronts have received less attention. Here, we investigate the interannual variability of SST fronts off North, Central, and South America using satellite SST data spanning 1982–2018. Anomalies of fronts within 0–300 km offshore indicate interannual variability that coincides with ENSO events in most regions. Frontal activity generally decreases during El Niño events and increases during La Niña events. The decrease in fronts off Peru and Chile during El Niño coincides with the seasonal peak in frontal activity, while off the United States the decrease occurs when frontal activity is at a seasonal minimum. We also utilized satellite measurements of wind stress and sea level anomaly to investigate how ENSO oceanic and atmospheric forcing mechanisms affect frontal activity. Decreases in frontal activity during El Niño events are largely due to oceanic forcing  off Central and South America and to both oceanic forcing and atmospheric teleconnections off the United States. This study furthers our understanding of the influence of ENSO on coastal upwelling regions in the eastern Pacific Ocean. Sea surface temperature (SST) frontal activity decreases during El Niño events along most of the west coasts of North and South AmericaEl Niño‐related oceanic forcing impacts frontal activity off North and South AmericaAtmospheric teleconnections during El Niño also affect fronts in the California Current System EBCS are associated with large‐scale anticyclonic flow, and with coastlines generally aligned in the meridional direction, these regions are favorable for coastal upwelling , and SST anomalies have been linked to climatic indices, such as the El Niño‐Southern Oscillation (ENSO) and the Pacific Decadal Oscillation , flagging individual pixels as frontal pixels until the gradient magnitude falls below a smaller threshold T2. This helps to ensure that noisy edges are not broken up into multiple edge fragments. The thresholds employed for T1 and T2 in this study are 0.017 and 0.0085°C km−1, respectively. Comparisons of fronts detected with gradient magnitude maps show that the chosen threshold values allow for capturing most of the main fronts in the domain . The global level 4 SST product provided by GHRSST is produced on a 0.25° grid by optimally interpolating SST observations from satellite (AVHRR only) and in situ platforms . SST fronts are identified off the west coasts of North, Central, and South America using an edge‐detection algorithm Canny,  followinn Figure  also yien Figure . Monthlyn Figure . GHRSST One potential concern with using the GHRSST AVHRR‐only SST product is that it is characterized by a lower feature resolution compared to other SST products, such as MODIS. We repeated the analyses using daily MODIS SST measurements that are available at 4 km resolution from July 2002 to December 2018. Despite the higher spatial resolution of MODIS SST, its shorter temporal coverage is a disadvantage for studying the interannual variability of SST fronts in relation to ENSO. Analyses of fronts using GHRSST AVHRR‐only SST were consistent with the spatial and temporal patterns of fronts detected using MODIS. A regression analysis was used to compare SST gradient magnitudes (which are used to detect fronts) between the two products at monthly time scales. Along the coast of North America, the magnitude of gradients computed using GHRSST were on average 27% less than the magnitude of MODIS gradients, with the intercept centered around zero, and the correlation between the two products was, on average, 0.80. Off South America, the GHRSST gradients are on average 35% less than MODIS gradients, with the intercept centered around zero, and the average correlation was 0.69. Thus, despite the reduction in the magnitude of gradients using GHRSST AVHRR‐only SST compared to MODIS, GHRSST is still suitable for capturing SST fronts.2.2The Cross‐Calibrated Multi‐Platform (CCMP) version 2.0 gridded surface vector winds from July 1987 to December 2018 are used to compute wind stress. Maps of CCMP vector winds are produced daily at 00, 06, 12, and 18Z on a 0.25° grid using a combination of Version‐7 RRS radiometer wind speeds, QuikSCAT and ASCAT scatterometer wind vectors, moored buoy wind data, and ERA‐Interim model wind fields using a Variational Analysis Method  data are obtained from the Copernicus Climate Service. SLA is available daily from January 1993 to December 2018 on a 0.25° grid.2.4We use the Oceanic Niño Index (ONI) produced by NOAA to define El Niño and La Niña events Figure . The ONI2.5n (the number of months in the time series) by the autocorrelation time scale  decompositions are utilized to isolate the dominant modes of variability  for SST frontal probability, alongshore wind stress, and SLA. EOFs are computed using the 3‐month running mean anomaly time series in the 1° latitude boxes. To determine the statistically significant EOF modes for each data set, we used the N‐rule approach outlined by Overland and Preisendorfer  to estimt‐test was used to determine the statistically significant composite anomalies at the 90% and 95% levels.To capture the progression of SST frontal probability, alongshore wind stress, and SLA during ENSO events, monthly anomalies for the 24 months covering each event  were extracted from the datasets. The extracted anomalies were averaged to compute composite anomalies for all El Niño and La Niña events  occurring at 0–2 months, which is consistent with the results in Figure EOF decompositions of monthly SST frontal probability anomalies  along the west coasts of both North and South America indicate that the dominant mode is related to ENSO events Figure . We noter = −0.51 to −0.53) occurring at −1 to +1 months. EOF 1 explains 27.2% of the total variance in frontal probability, with the largest response occurring between 2 and 17°S, where the local variance explained by EOF 1 is ∼45%  and atmospheric  forcing mechanisms associated with ENSO have been shown to affect winds, upwelling, thermocline depth, and SST along the west coasts of North, Central, and South America  within 0–300 km offshore is dominant to the north of 20°N Figure . AlongshMonthly anomalies of the alongshore wind stress within 0–300 km of the coast off North America are most pronounced to the north of 34°N. The anomalies are generally positive in the CCS (34–48°N) in boreal winter during El Niño events  persists year‐round between 0 and 35°S Figure . UpwelliThe monthly anomalies of alongshore wind stress are weakly positive during October–May between 5 and 20°S during El Niño events, with the anomalies extending farther south during some events . To the south of 34°N, EOF 1 captures only ∼1.5% of the local variance of alongshore wind stress  captures the dominant mode of variability along the west coast of North America, which is likely associated with ENSO, as indicated by signals in the amplitude time series that coincide with El Niño events Figure . EOF 1 as Figure . EOF modr = 0.22). The spatial pattern and amplitude time series for EOF 2 indicate small positive alongshore wind stress anomalies (more upwelling favorable in the Southern Hemisphere) between 2 and 16°S and larger positive anomalies between 23 and 34°S during El Niño events, spanning austral spring and summer  likely has a larger influence on frontal activity in these regions compared to higher latitudes. In the gap wind regions (11–15°N), where winds blowing offshore dominate in boreal winter, Romero‐Centeno et al.  and KarnIt is important to note the timing of the reduction in frontal activity during El Niño events in relation to the timing of peak frontal activity. SST frontal probability peaks during boreal summer along the west coast of the United States (33–50°N) and during boreal winter  off southern Mexico (7–20°N), Central America, and South America to the south of 5°S. Thus, along the west coasts of southern Mexico, Central America, and Peru and Chile (5–43°S) the decrease in frontal probability in austral summer  during El Niño events occurs when frontal activity is highest in these regions. This also aligns with the timing of peak El Niño conditions in the equatorial Pacific. Along the west coast of the United States, on the other hand, frontal probability also decreases in boreal winter during El Niño events, but frontal activity is lowest during this time of year. This out of phase response at higher latitudes in the Northern Hemisphere between the reduction in frontal probability during El Niño events and the timing of peak frontal activity, compared to the in‐phase response along South America, may contribute to EOF 1 of frontal probability anomalies explaining more of the total variance in the Southern Hemisphere than in the Northern Hemisphere.The west coasts of North and South America feature very productive ecosystems during local summer when upwelling intensifies and frontal activity peaks Carr, . Fronts The anomalies for frontal probability, alongshore wind stress and SLA during La Niña events are generally of opposite sign compared to El Niño events along both coastlines, but the magnitude of the anomalies is less Figures  and 5. TAlthough this study advances our understanding of low‐frequency variability in SST frontal activity, the use of a 37 years‐long time series of SST only allows for capturing a few El Niño events, and even fewer strong events. The relatively short time series also makes it difficult to identify the influence of other modes of variability, such as the Pacific Decadal Oscillation. As more data are collected and satellite time series are expanded, so will our ability to understand the relationships between SST frontal activity and climate variability.Supporting Information S1Click here for additional data file.


Seasonal ice in lakes plays an important role for local communities and lake ecosystems. Here we use Large Ensemble simulations conducted with the Community Earth System Model version 2, which includes a lake simulator, to quantify the response of lake ice to greenhouse warming and to determine emergence patterns of anthropogenic lake ice loss. Our model simulations show that the average duration of ice coverage and maximum ice thickness are projected to decrease over the next 80 years by 38 days and 0.23 m, respectively. In the Canadian Arctic, lake ice loss is accelerated by the cold-season polar amplification. Lake ice on the Tibetan Plateau decreases rapidly due to a combination of strong insolation forcing and ice-albedo feedbacks. Comparing the anthropogenic signal with natural variability represented by the Large Ensemble, we find that lake ecosystems in these regions may be exposed to no-analogue ice coverage within the next 4-5 decades. Many lakes on Earth are covered by seasonal ice, and as lake ice loss has been increasing, it is ever more important to quantify this. Here the authors present a fully coupled lake ice model projection as a part of the new CESM2 large ensemble modelling project and show that unprecedented lake ice loss is emerging globally as a result of anthropogenic-induced warming. Over recent decades a widespread loss of lake ice has been reported6, with many lakes experiencing delayed winter freezing or an earlier spring melt. Offline-lake model simulations forced with the output obtained from multi-model climate simulations7 were able to link the observed lake ice loss to greenhouse warming. Moreover, it was found that the global reduction of lake ice will further intensify in response to projected future warming8.More than 58.5 million lakes on Earth are covered by seasonal and perennial lake ice9. Their statistical projections document that a large number of lakes are likely to experience intermittent or permanent winter ice cover loss in the Northern Hemisphere until the end of this century, but they do not provide projection of changes in thickness and duration of ice cover. Physical process-based lake-ice model simulations, on the other hand, have been used in recent years10 to assess future climate change in lake systems. They run as offline models which are typically forced by daily meteorological data derived from climate model simulations. While thermodynamic processes of water and ice are resolved explicitly in these simulations, the three-way interaction between lake, lake ice and atmosphere, is not represented. Such coupling will play an important role in particular for large lake systems which are known to have considerable impacts on local climate13. Another caveat in such offline model simulations is the fact that diurnal cycle thermodynamics are not resolved. We address these limitations here by including lake simulators into Earth system models as an interactive component15.Other studies that address lake ice responses to greenhouse warming were conducted with statistical models, which are built on the empirical relationship between the observational ice phenology and meteorological variables16, and lake organisms are suffering severe changes in their habitat with the shortening of ice duration which had led to reorganization of aquatic ecosystems, for example, in circumpolar lakes17. Exceeding climate-related thresholds of lake ice phenology can even accelerate such biological processes18. Lake organisms may be exposed to no-analog ice conditions in the future, which are unlikely under natural climates. What’s more, disappearance of extant ice conditions will increase the risk of local extirpation of populations for cold species19. It is thus fundamental to project time and the corresponding global warming levels for the emergence of no-analog conditions for risk assessment and adaptation in the future. Here, we set out to provide a mechanistic account for the sensitivity of lake systems to greenhouse warming, as well as to establish emergence timescales for anthropogenic signals above the natural noise background with a fully coupled Earth system model that includes an interactive lake simulator. The lake model used here represents thermodynamic processes including ice growth and melt, and vertical mixing20 and is coupled to the atmosphere through the exchange of heat and momentum.Lake ice has experienced unprecedented loss rates in the last decades22. Therefore, when no-analog conditions of lake ice will emerge still remains to be answered. Here we pursue a different modeling approach: we focus on only one Earth system model, which has been run for the historical period and a future anthropogenic emission forcing scenario 100 times. These so-called large ensemble simulations are obtained by perturbing the initial conditions. At every point in time, we therefore have a physically consistent single-model-based estimate of the anthropogenic signal (ensemble mean) and of natural variability (ensemble spread). This is advantageous when calculating the time of emergence of anthropogenic signals and the emergence of no-analog conditions for aquatic ecosystems, relative to the natural variability baseline. Moreover, this approach aggregates the statistics of one physical model, rather than the statistics and physics of different models. Therefore, it maintains the physical consistency of the projections, which is particularly relevant for nonlinear processes such as ice formation/melting and lake mixing, which also depend on high-frequency  processes.Detecting the human influences on lake ice phenology and estimating when the anthropogenic signal will emerge from the natural background noise is contingent on knowing the degree of natural variability of the regional lake ice fluctuations. Previous studies have focused on multi-model ensemble forcing approaches, which typically result in a suppression of natural variability and mix the sensitivity from different climate models21, we choose to analyze output from a Large Ensemble of greenhouse warming simulations23 conducted with the Community Earth System Model version 224 (CESM2-LE), for which 100 members have been run over 1850–2100 under a historical/SSP3-7.0 pathway (see “Methods”). We use here a subset of 90 of the 100 ensemble members for which daily lake ice thickness was saved as an output variable. This facilitates a definition of the forced signal as the ensemble mean of daily mean output across the 90 members, facilitating identification of forced trends in the phasing of seasonality in addition to mean state changes. The forced response is determined as the 90-member ensemble mean, whereas the natural variability is characterized by the amplitude of the spread across the ensemble members. For our analysis, we exclude lakes from Antarctica and Greenland (see “Methods”).Given the challenges in deconvolving forced anthropogenic trends and natural variability25 (see “Methods“). The spatial variance in terms of climatological mean ice duration, ice freeze date, and ice breakup date are well reproduced by the fully coupled lake model, and the correlation coefficients  are 0.94, 0.78, and 0.94, respectively . The observed annual maximum ice thickness is reproduced well by the simulation with a high spatial correlation coefficient , even though the model tends to underestimate the amplitude of seasonal to interannual ice thickness variations    Fig. . This co47) Fig. . On aver23  in comparison with northern Canada  range around the temporal and ensemble mean value (μ) of ice duration variability during the 1850–1950 period . Over the Tibetan Plateau the most rapid ice loss occurs in spring and autumn when strong lake ice-albedo feedback associated with high climatological incoming solar radiation delays the formation of ice. This leads to more absorption of heat by the lakes, which further inhibits seasonal lake ice formation.45. The corresponding species-dependent phenology is oftentimes tied to the presence or absence of ice, which in turn impacts the seasonal cycle of prey and predators43. Ice-related phenology also influences nutrient cycling47, dissolved oxygen48, and the timing of algal blooms49. In many cases, the cascading effects of lake ice can impact ecological processes even in summertime44. In accordance with our projection of 38 ± 11 days reduction in ice duration by the end of this century, a recent study50 projected a prolongation by 33 ± 12 days of lake stratification of the Northern Hemisphere under a similarly high emissions scenario (RCP 8.5).The seasonal cycle of temperature, mixing, light availability, and ice coverage can orchestrate important biological processes in lakes. Observations substantiate that below-ice ecosystems play a fundamental role in aquatic biodiversity16. In comparison, ice loss rates were insignificant in the first half of 20th century when the impact of anthropogenic forcings was minimal  in Finland  is based on the Community Earth System Model24 , with a nominally 1° × 1° degree horizontal resolution. The model is forced by the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical forcing from 1850 to 2014 and the Shared Socioeconomic Pathways forcing scenario61 (SSP3-7.0) from 2015 to 2100. The 90 members with daily lake ice thickness output fields (out of 100 ensemble members simulated) are initialized from a set of different combinations of initial states of ocean and atmosphere23. Among them, 80 members are initialized from 4 different years of a pre-industrial control simulation conducted with CESM224: 1231, 1251, 1281, and 1301  with micro-perturbations to the temperature field . In addition, 10 macro-perturbation members are initialized from 10 different years: 1011, 1031, 1051, 1071, 1091, 1111, 1131, 1151, 1171, and 1191 . Attribution analysis shown in Fig. 15  used in this study is the land module of the fully coupled CESM2. CLM5 uses a nested subgrid hierarchy  to represent the heterogeneity of the land surface. Each grid cell can have up to five one-dimensional land units  which are modeled separately, and there is no horizontal flux exchange among the land units.The ICCP/CESM Large Ensemble simulation20 (LISSS) is a one-dimensional thermodynamic model, which solves a vertical thermal diffusion equation. A lake consists of snow (up to five layers), the lake water body , soil (ten layers), and bedrock (five layers) in the vertical profile. Water volume of the lake water layer is fixed, and the thickness of the lake water layer is set according to the lake depth by the nominal method. Each grid cell has one lake modeled. The mean lake depth and lake-area fraction of the grid cell are aggregated from the high-resolution (0.05° × 0.05°) Global Lake and Wetland Database62 and a global gridded lake depth dataset63 to the CLM5 grid (0.9° × 1.25°). LISSS uses a bulk transfer approach to calculate the surface fluxes (momentum fluxes and heat fluxes) between the lake surface and the atmospheric boundary layer. The mixing processes within the lake water column include wind stirring, convection, molecular diffusion, and unresolved mixing processes. LISSS calculates the ice fraction in each layer of the lake body, instead of simulating a separate ice layer above water layers. To account for the effects of puddling and disintegration on the ice albedo, LISSS decreases the albedo of lake ice during the melting process. The albedo of snow is calculated by the method applied for non-vegetated land units. As a fully coupled lake model, the default timestep is 1800 s, so the diurnal cycle is resolved in our lake model which, however, is less represented in the offline-lake model simulations due to the temporal resolution of forcing fields. In our calculation, an ice-covered year is determined as a year in which ice cover lasts for more than 5 days. Lakes from Antarctica and Greenland are excluded in our analysis, as lake simulations there are subject to large uncertainties due to ice sheet processes in the two regions.CLM5’s Lake, Ice, Snow, and Sediment Simulator25 contains 471 in situ observational ice phenological records for lakes in the Northern Hemisphere, of which 331 records have been observed for more than 20 years since 1850  reproduced by the simulation. By comparison, the melting process is rapid, so the breakup date is more consistent  between observations and the CESM2-LE simulation.In the simulations, ice cover is assumed to be homogeneous horizontally. The ice freeze date is determined as the first day of ice cover appearing (ice thickness > 0 m), the ice breakup date is determined as the last day of ice cover disappearing (ice thickness = 0 m), and the ice duration is determined as the total number of days with ice cover which is roughly equal to the difference between the breakup date and the freeze date. In reality, freeze dates and breakup dates in different parts of the lake might be different due to the heterogeneity of ice cover, but the in situ observations oftentimes represent ice phenology in the shoreline area covered by the observer’s sight. Therefore, this heterogeneity may lead to the deviation between observations and simulations, especially for large lakes, as the larger lakes tend to have higher heterogeneity in ice cover. Notably, the long freezing process contributes to this bias. Consequently, as shown in Supplementary Fig. Although the method of using one representative lake for each grid point may overlook the influence of lake-specific factors (such as morphology and discharge), the high correlation between observations and simulations implies such influences are small, because to a first order the spatial and temporal variance are controlled by climatic factors. Additionally, timeseries of 16 long records are validated, respectively  model in the Inter-Sectoral Impact Model Intercomparison Project phase 2b66 (ISIMIP 2b) using the Global Lake and River ice dataset. In the ISIMIP 2b, there are five lake models in total, and SimStrat-UoG is the best model regarding its performance on ice phenology7 (Supplementary Fig. 63. However, the spatial resolution there is 0.5° × 0.5° relative to 0.9° × 1.25° used for CLM5. The SimStrat-UoG model is forced by the daily output fields, including surface air temperature, surface wind speed, longwave and shortwave radiation, and specific humidity from GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 from the CMIP5 project. All the fields were interpolated to a 0.5° × 0.5° grid and then were bias-corrected against the ERA-Interim reanalysis data. The MAE for the climatological mean ice duration is 15.7 days (Supplementary Fig. r value = 0.77; Supplementary Fig. r value = 0.93; Supplementary Fig. As a comparison, we also evaluate the lake ice simulation conducted by the SimStrat-UoG67. The calculation of the time of emergence for the anthropogenic signal and no-analog situation in ice phenology is described in the caption of Fig. In the large ensemble simulations analyzed here, natural variability is represented by the standard deviation across the ensemble members, and the ensemble mean represents the forced response to the anthropogenic emissions. The changes in the ice duration, freeze date, breakup date, and annual maximum ice thickness over 2020–2100 are determined as the difference between the values of all ensemble members in 2100 and the values in 2020. The difference in the mean across all ensemble members is the forced response, and the standard deviation represents the natural variability, which itself can be influenced by greenhouse warming. Similarly, changes in intermittent ice coverage between 2090–2100 and 2000–2010 were first calculated for individual ensemble members. Then, the forced response is determined as the average changes over all ensemble members. Changes in lake surface albedo and absorbed shortwave radiation in Fig. Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Mycobacterium tuberculosis complex (MTBC). We compared herein the spoligotyping of MTBC clinical isolates using a membrane-based method  and whole-genome sequencing (WGS)-based method . All MTBC strains isolated at the Lyon University Hospital, France, between November 2016 and December 2020 were included (n = 597). Spoligotyping profiles were also used for species identification among the MTBC. Outputs of both methods were analyzed, and discrepant results were investigated thanks to CRISPRbuilder-TB. The overall agreement was 85.7%. Spacer discrepancies observed between the methods were due to the insertion of IS6110 within the direct repeat (DR) sequence upstream or downstream of spacers, mutated DR sequences, or truncated spacers. Discrepancies did not impact species identification. Although spoligotyping-based species identification was inconclusive for 29 isolates, SNP-based phylogeny conducted after WGS allowed the identification of 23 M. tuberculosis (Mtb), 2 M. canettii, and 4 mixed MTBC infections. WGS yielded very few discrepancies compared to membrane-based spoligotyping. Overall agreement was significantly improved (92.4%) by the CRISPR locus reconstruction using CRISPRbuilder-TB for the MTBC isolates with the shared international type 53 in silico spoligotyping. A smooth transition from the membrane-based to the in silico-based genotyping of M. tuberculosis isolates is, therefore, possible for TB diagnosis and epidemiologic survey.To tackle the spread of tuberculosis (TB), epidemiological studies are undertaken worldwide to investigate TB transmission chains. Clustered regulatory interspaced short palindromic repeats (CRISPR) locus diversity, also called spoligotyping, is a widely used genotyping assay for the characterization of IMPORTANCE Whole-genome sequencing (WGS) has profoundly transformed the perspectives of tuberculosis (TB) diagnosis, providing a better discriminatory power to determine relatedness between Mycobacterium tuberculosis complex (MTBC) isolates. Previous genotyping approaches, such as spoligotyping consisting of an initial PCR step followed by reverse dot hybridization, are currently being replaced by WGS. Several pipelines have been developed to extract a spoligotype from WGS data (in silico spoligotyping) allowing for the continuity of MTBC molecular surveys before and after WGS implementation. The present study found very good overall agreement between hybridization to membrane-based spoligotyping and in silico spoligotyping, indicating the possibility of a smooth transition from the traditional to the in silico-based genotyping of MTBC isolates for TB diagnosis and epidemiological survey. Mycobacterium tuberculosis complex (MTBC) transmission in high-income and low tuberculosis (TB) prevalence countries remains a public health priority given the constant changes in MTBC epidemiology worldwide. Key measures for TB control rely on the linkage of cases and identification of transmission chains, through a population-based systematic molecular TB survey, to uncover outbreaks, even between unrelated cases   and the mycobacterial identification repetitive unit-variable number of tandem repeats 15 (MIRU-VNTR15) typing –5. Histo–anettii) . Becauseanettii) .in silico spoligotyping  may introduce bias in the output sequences mainly due to heterogeneous or variant-sensitive primer affinity (Since the advent of next-generation sequencing (NGS), WGS has been implemented in high-income countries and profoundly transformed the perspectives of TB diagnosis. WGS provides a better discriminatory power than spoligotyping and MIRU-VNTR15-typing to determine relatedness between MTBC isolates . In addigotyping , enablinaffinity . In a reaffinity  comparedA total of 597 MTBC isolates were analyzed, among which there were 4 cases of mixed infections (infection with 2 different strains of MTBC), not allowing species or lineage identification using spoligotyping. For the 593 remaining MTBC isolates, identification of MTBC species using membrane-based spoligotyping was feasible for 568 isolates (95.8%). For 25 MTBC isolates, spoligotyping did not allow species identification, including 21 “unknown” spoligotypes, i.e., spoligotyping was not related to an MTBC species in the SITVIT database, and 4 isolates with the shared international type (SIT) 2669 for which none of the 43 spacers was detected.M. tuberculosis , including 401 Euro-American lineage 4, 74 East-Asian lineage 2, 30 East African-Indian lineage 3, and 19 Indo-oceanic lineage 1, 29 M. bovis, and 15 M. africanum (M. canettii (spoligotyping 2669 for which no spacer was detected) and 23 were M. tuberculosis, including 21 Euro-American lineage 4 (including the other two SIT 2669), 1 East African-Indian lineage 3, and 1 Indo-oceanic lineage 1 . Of the 85 isolates with discordant MTBC spoligotypes, 75 differed by only 1 spacer, and 10 isolates differed by 2 spacers.Excluding mixed infections, the overall agreement between membrane-based spoligotyping and in silico spoligotyping . It was always absent in the membrane-based method but present in gotyping .in silico spoligotyping. Of note, in the study data set the SIT 50 was never obtained with in silico spoligotyping: all SIT 50 on membrane-based spoligotyping were SIT 53 on in silico spoligotyping  were found in MTBC isolates with the SIT 50 on membrane-based spoligotyping and SIT 53 on in silico spoligotyping, the 85 discrepant spoligotypes were analyzed using CRISPRbuilder-TB, allowing a reconstruction of the CRISPR locus in MTBC  and those observed for the spacer 42 (DR6 downstream of the spacer). It also found truncated spacers for the discrepant spacer 38 and some of the discrepancies observed for the spacers 14 and 15. Analysis of the 4 SIT 2669 for which no spacer of the 43 investigated by the conventional spoligotyping methods were detected using CRISPRbuilder-TB found that 2 M. canettii isolates had spacers of the 98-spacer spoligotyping format reported by Guyeux et al.  on a set of 166 MTBC isolates  to 92.4% .The overall agreement between membrane-based and isolates . As obsepacer 31 , 20. DueM. canetti, and the others that had a completely deleted DR/CRISPR region belonged to genetically unrelated L4-EuroAmerican M. tuberculosis lineage as previously described for the SIT 2669 isolates  by supplementary analysis using CRISPRbuilder-TB for certain isolates. In addition to more accurate epidemiological monitoring than that provided by spoligotyping, WGS had an added value in some cases of species identification. These data support a smooth transition from the membrane-based to the Between November 2016 and December 2020, MTBC isolated from specimens taken from patients during routine care in the Lyon University Hospital, France were prospectively included.All data were maintained in an electronic database, in accordance with the ethics committee of the Lyon university hospital, France , and the national data protection commission . In accordance with French legislation, written informed consent from patients was not required.https://github.com/dcouvin/SpolLineages) (Membrane-based spoligotyping was performed as described elsewhere . MTBC spineages) .For MTBC WGS, genomic DNA was purified from cleared lysates using the Maxwell RSC Instrument  automated DNA extraction system and the Maxwell RSC Blood DNA kit (Promega). Libraries were generated using a bead-based tagmentation system . A nanoliter liquid handler  was used to reduce the by 10 times the reaction volumes. Miniaturized libraries were sequenced on the Nextseq or Miseq system (Illumina) to produce 150 or 300 base-pair paired-end reads, respectively. Reference genome coverage was at least 96% and depth of coverage at least 30×.in silico spoligotyping was determined through the open-access tool SpoTyping (https://github.com/xiaeryu/SpoTyping-v2.0.) (in silico spoligotyping were determined using CRISPRbuilder-TB (https://github.com/cguyeux/CRISPRbuilder-TB) (in silico spoligotyping or vice versa.MTBC g-v2.0.) . MTBC splder-TB)  to identhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/), and potential cross-species contaminations were monitored by FastQ Screen (Mycobacterium tuberculosis reference genome (NC_000962.3) using Burrow-Wheeler Aligner (BWA) (https://github.com/lh3/bwa) (https://github.com/broadinstitute/GATK-for-Microbes), samples were aligned both on reference and on shifted reference. Duplicated reads were identified using Picard MarkDuplicates (http://broadinstitute.github.io/picard/). Variant calling with MuTect2 in microbial mode was performed  . Followierformed .https://github.com/samlipworth/snpit)   to identh/snpit) .Cohen’s Kappa values were calculated using XLSTAT 2020.5.1 , and interpreted according to Landis and Koch criteria . Minimum


Contrary to the previous notion that Bcd only activates P2, we find that Bcd activates both promoters via the same two enhancers. P1 activation is less frequent and requires binding of more Bcd molecules than P2 activation. Using a theoretical model to relate promoter activity to enhancer states, we show that the two promoters follow common transcription kinetics driven by sequential Bcd binding at the two enhancers. Bcd binding at either enhancer primarily activates P2, while P1 activation relies more on Bcd binding at both enhancers. These results provide a quantitative framework for understanding the kinetic mechanisms of complex eukaryotic gene regulation.Many eukaryotic genes contain alternative promoters with distinct expression patterns. How these promoters are differentially regulated remains elusive. Here, we apply single-molecule imaging to quantify the transcriptional regulation of two alternative promoters (P1 and P2) of the Bicoid (Bcd) target gene  Drosophila embryonic gene hunchback are regulated by different action modes of two enhancers.Alternative promoters differ in their expression patterns, whose mechanisms are not well understood. Here the authors show that alternative promoters of a  In eukaryotic genomes, many genes contain alternative promoters5, which can produce functionally distinct transcript isoforms under the regulation of multiple enhancers7. During development, the differential expression of these isoforms is critical to cell fate decisions9. Misregulation of these isoforms can lead to diseases, including cancer12. Thus, eukaryotic gene regulation needs to be understood at the level of individual alternative promoters and enhancers.Promoters initiate gene transcription by interacting with specific 13. Typically, TF binding determines the spatial expression pattern14, whereas other events set the expression amplitude18. When multiple promoters and enhancers are present, interactions between these elements often result in complex and varied promoter behaviors19. For example, multiple enhancers may contact each other and synergistically drive a promoter21. Alternatively, different enhancers may be mutually exclusive and compete for promoter activation23. In these cases, enhancers combine their regulatory effects differently, ranging from superadditive to subadditive or even repressive summation12. Similarly, a single enhancer may activate multiple promoters simultaneously25 or one at a time26. So far, although specific mechanisms were proposed for some of these phenomena, it is unclear whether universal mechanisms exist for the complex interactions between multiple enhancers and promoters.Enhancer activation of a single promoter involves a series of molecular events, including transcription factor (TF) binding, local chromatin opening, and physical proximity between the two elementsDrosophila gap gene hunchback (hb), which contains two alternative promoters  10–13), hb is expressed in a bursty manner32 throughout the anterior half of the embryo in response to the concentration gradient of the maternal TF Bicoid (Bcd)34. This expression is believed to be purely from the P2 promoter mediated by two enhancers: a proximal enhancer located next to P234 and a distal shadow enhancer located 4.5 kb upstream35. Cooperative Bcd binding at either enhancer can activate P235. Competitive action of these partially redundant enhancers helps suppress expression noise and ensure a robust expression pattern35.An ideal model to investigate alternative promoter regulation is the 37, due to its lack of Zelda binding sites and TATA box necessary for local chromatin opening38. Instead, the promoter is activated in late nc14 by a Bcd-independent stripe enhancer36. However, previous measurements of endogenous P1 activity relied on traditional in situ hybridization methods, which have limited sensitivity to detect weak mRNA signals. In fact, hb-reporter experiments showed that P1 could respond to Bcd when placed adjacent to the proximal or distal enhancers30. Without precise quantification of endogenous P1 activity, the understanding of early hb regulation is incomplete. It is unclear how P1 interacts with different hb enhancers and whether the mechanisms of P1 and P2 regulation are intrinsically related.Unlike P2, the P1 promoter is believed to be inactive during early development40 to quantify the expression of individual P1 and P2 promoters for each endogenous hb gene locus in nc11–13 embryos. Contrary to the previous notion, we find that P1 contributes a modest yet non-negligible fraction of early hb transcription and affects the expression patterns of other gap genes. Using different TF dosages and enhancer deletion, we show that Bcd activates both promoters via the proximal and distal enhancers. Compared with P2, P1 activation requires cooperative binding of more Bcd molecules and a synergistic (as opposed to competitive) action of the two enhancers. Analyzing the statistics of nascent mRNA signals from individual promoter loci reveals that both promoters follow a unified scheme of three-state transcription kinetics. Cooperative Bcd binding at either enhancer can drive a promoter to a weak active state, while additional Bcd binding at the second enhancer can turn the promoter to full-power transcription. The two promoters differ in their responses to different Bcd binding configurations. P2 transcription is primarily driven by Bcd binding at a single enhancer, while P1 transcription relies more on Bcd binding at both enhancers. In concert, these results provide a simple and quantitative mechanism for the differential regulation of alternative promoters. Our quantitative approach may be generalized as a framework for deciphering complex eukaryotic gene regulation involving multiple promoters and enhancers.Here, we use single-molecule fluorescence in situ hybridization (smFISH)hb promoter produces a unique transcript isoform  and intron region of hb-RB. A P2-specific probe set targeted the 3′UTR of hb-RA. Finally, a probe set targeted the coding sequence (CDS) region shared by hb-RA and hb-RB  syncytial blastoderm embryos . The boundary position x0 of the P1-specific signals was significantly lower than that of the P2-specific and CDS signals 30, we showed that the average nascent CDS signal from either promoter was proportional to the signal of promoter-specific probes  in the WT embryo is a linear combination of the nascent P1–5′UTR and P2–3′UTR signals (rP1 and rP2):a1 and a2 are ratio parameters that, in theory, depend on the mRNA elongation and termination dynamics  in terminating a nascent mRNA  and knirps (kni), in P1 deletion embryos (ΔP1C)30. Based on the literature, the anterior boundaries of Kr and kni expression domains in nc14 are both set by the repression of Hb protein50. Consistent with this picture, the anterior Kr and kni boundaries shifted anteriorly for ~0.09 EL (from 0.45 ± 0.02 EL in WT to 0.36 ± 0.01 EL in ΔP1C) and ~0.06 EL (from 0.58 ± 0.01 EL in WT to 0.52 ± 0.02 EL in ΔP1C), respectively, in response to P1 deletion  was significantly higher than that of P2-specific (h = 4.6 ± 0.2) and CDS (h = 5.3 ± 0.3) GRFs during nc11–13  containing the hb gene and its regulatory sequence35  signals in the control and enhancer-removed transgenes in nc11–13 embryos , whereas new transcripts are only initiated in active states with rates kiINI,. Following transcription initiation, each nascent mRNA elongates with constant speed VEL, and resides on the gene for an extra termination period TR before being released  and two active states (states 1 and 2) with Poissonian rates sed Fig. . With a sed Fig. . We founsed Fig. . Bcd maik01 and k12 satisfied Hill functions  between the P1- and P2-GRFs. I.e., the differential regulation of P1 and P2 results from their preference for different active states.In addition to the Bcd dependence, the ratio between the activation and inactivation rates determines the probability of each transcription state. In the anterior expression domain, k01 by ~30% in the anterior expression domain , the removal of either enhancer yielded a uniform decrease of ain Fig. . Deletinary Fig. . These rary Fig. . This inary Fig. .hb promoters follow a common kinetic scheme of Bcd-dependent activation  through cooperative binding. This suggests that the two active states of a promoter may correspond to different Bcd binding states at hb. Previous literature has shown evidence of multiple Bcd binding states at hb32. However, only one binding state (with ~4–5 Bcd molecules) was reported to coincide with gene activation32. The biological functions of the other binding states were unclear. Here, we showed that the binding of ~4–5 Bcd molecules happened at a single enhancer . Enhancers at this binding state can competitively drive promoter activation, consistent with a previous study of hb P2 activation23. In contrast, the binding of ~8–10 Bcd molecules involved both enhancers, leading to synergistic activation of a promoter. Such a mechanism was proposed for some eukaryotic genes66 but has never been reported for hb.Our model revealed that Bcd regulates both activation steps  at the proximal enhancer may physically block P1 transcription. It is unclear how P1 coordinates its activation and transcription. One possibility is that TF binding at enhancers is only transiently needed to form the preinitiation complex. Future experiments using high-resolution live imaging techniques would likely solve this puzzle68.P1 and P2 differ in their preference for different Bcd binding states. Specifically, the P1 response to the lower Bcd binding state is much less than that of P2. A possible reason is that P1 lacks specific motif codes for chromatin opening7. Moreover, our combined experimental and theoretical approach directly relates TF binding at individual enhancers with the stochastic activity of each promoter. A generalization of this approach  will help understand the regulatory mechanisms for cell fate decisions and enable the precise design of synthetic gene circuits69.Altogether, these results showed that a single kinetic scheme could create apparently different types of enhancer-promoter interactions. Such a unified scheme may be shared by alternative promoter regulation in many eukaryotic systems and may be crucial for phenotypic complexity in higher eukaryotes30 and were obtained as gifts from Dr. Stephen Small (New York University) and Dr. Pinar Onal (Northwestern University). 1×bcd strain  was developed previously51 and was obtained as a gift from Dr. Jun Ma (Zhejiang University). hb-BAC reporter constructs were previously developed35. The distal-enhancer-removed BAC construct and its control were obtained as gifts from Dr. Michael Perry  and Dr. Alistair Boettiger (Stanford University). The proximal-enhancer-removed construct and its control were rebuilt as previously described35. Transgenes in these constructs were constructed from BAC CH322–55J23, which contains a 20-kbp Drosophila genomic sequence encompassing the hb gene and its proximal and distal enhancers. A yellow-kanamycin fusion was used to replace the hb CDS in all BACs, while the proximal or distal enhancers in the BACs were substituted with ampicillin. All BACs were integrated on chromosome 2 of Drosophila. The distal-enhancer-removed transgene and its control were integrated into landing site VK37 (Bloomington Stock Center number 24872). The proximal-enhancer-removed transgene and its control were integrated into landing site attP40 (Bloomington Stock Center number 25709).Oregon-R (OreR) strain was used as the wild type. CRISPR mutant strains with P1 and P2 deletions (∆P1C and ∆P2C) were developed previouslyhb P1 5′UTR, 32 probes for hb P1 intron, 48 probes for hb CDS, eight probes for hb P2 3′UTR, 43 probes for yellow, 33 probes for Kr, and 29 probes for kni) were designed . hb P2–3′UTR, yellow, and Kr probes were conjugated with Alexa Fluor™ 647 . kni probes were conjugated with Alexa Fluor™ 488 . hb CDS probes were conjugated with either Alexa Fluor™ 647  or Alexa Fluor™ 488 . In a control experiment, P1–5′UTR probes with Alexa Fluor™ 647 and P2–3′UTR probes with TAMRA were used to label WT embryos. A comparison between signals from the same probe set with different fluorophores revealed that the efficiencies of different fluorescent detectors were comparable  in PBTx  Triton X-100) at room temperature, washed (2 × 10 min) in hybridization wash buffer at 30 °C, and incubated with the probe-containing hybridization buffer  formamide, 0.1% (v/v) Triton X-100) at 30 °C overnight. After hybridization, embryos were washed in hybridization wash buffer at 30 °C (2 × 10 min) and in 2× SSC at room temperature (2 × 10 min). For immunofluorescence (IF), embryos were washed (4 × 10 min) in PBTx and blocked in PBT-B  western blocking reagent , 2 mM ribonucleoside vanadyl complex , 0.1% (v/v) Triton X-100) at room temperature for 1 h. The preabsorbed rabbit anti-Bcd primary antibody  dilution in PBT-B) was incubated with embryos at 4 °C for 20 h. Following primary antibody staining, embryos were further washed (4 × 10 min) in PBTx, blocked in PBT-B at room temperature for 1 h, and incubated with goat anti-rabbit IgG secondary antibody conjugated with Alexa Fluor™ 488  dilution in PBT-B) at room temperature for 1 h. For DNA counterstaining, embryos were washed (4 × 10 min) in PBTx and stained with Hoechst 33342 at room temperature for 10 min. Following additional washes (4 × 10 min) in PBTx, embryos were mounted in Aqua-Poly/Mount . Imaging was performed after the samples were completely solidified.Embryo collection, fixation, and labeling were performed according to a previously published protocolyellow or lacZ loci in some control experiments  were imaged using a Leica TCS SP8 confocal microscope equipped with a GaAsP detector and a 63× oil-immersion objective (1.4 NA). 12-bit image stacks were acquired with a pixel size of 81 × 81 nm2 and a z-step size of 0.3 μm. To image hb transcription, nc11–13 embryos at the mitotic interphase were selected based on the number and shape of the nuclei . Pre-gastrulation nc14 embryos were selected to image Kr and kni activities. Approximately 10 μm of the cortex layer of each embryo was imaged. For promoter mutant lines, each mutant allele was made heterozygous with a TM3 Sb balancer containing a hb-lacZ reporter. To identify homozygous mutant embryos, the lacZ FISH signal (labeled by Alexa Fluor™ 594) was imaged first, and those embryos without active lacZ loci were selected for subsequent imaging.Most embryos were imaged using a Zeiss LSM 880 laser scanning confocal microscope equipped with a GaAsP detector and a 63× oil-immersion objective (1.4 NA). 16-bit image stacks were acquired with a pixel size of 71 × 71 nm32 with updated algorithms to improve accuracy and efficiency. Briefly, raw images were divided by a normalized flat-field image to correct for monochromatic aberrations. Three-dimensional (3D) segmentation of nuclei from the Hoechst image stack was done using a combination of local threshold (to optimize a circularity parameter) and watershed (to separate the merged nuclei). The nuclear cleavage cycle of the embryo was determined by the number of recognized nuclei. Embryo boundary was identified from the averaged Hoechst image by thresholding image pixels outside the nuclear area. This boundary was then used to determine the AP position of each nucleus.Image processing and data analysis followed a previously developed pipelineIpeak) and radius (σ0). The spot intensity was calculated as I = 2πIpeakσ02. By comparing the joint distribution of peak height and radius between the anterior and posterior spots, a 2D threshold was determined to distinguish real mRNA spots from background noise  Gaussian function to extract the peak height , outside which the distribution became flat  intensity of each nucleus was calculated from the central In the preceding steps, nuclear segmentation, embryo boundary identification, and detection of active transcription sites could be further refined and corrected manually using custom MATLAB graphical user interfaces.r) against nuclear position (x) for all nuclei and binned individual data points by x. Within the range of 0.25–0.75 EL, we used a least-squares algorithm (the “nlinfit” function in MATLAB) to fit the binned data to a logistic function:x0 is the boundary position of the expression domain, d is the half-width of the transition region, rmax is the maximal transcription level induced in the anterior region, and r0 denotes the basal activity in the posterior part.We analyzed the expression profile of a promoter using embryos in mid-to-late mitotic interphase to ensure steady-state promoter activity. For each embryo, we plotted the nascent mRNA signal  of nascent mRNA signal  per locus in the anterior expression domain (0.2–0.4 EL)32:r〉 and σ are the mean and standard deviation of the single-locus data, respectively.To characterize the expression variability of different 44:m1 and m2 are nascent mRNA signals at two homologous loci in the same nucleus measured using a given probe set, respectively.We quantified the intrinsic noise of P1 and P2 expression in the anterior expression domain (0.2–0.4 EL) of an embryo using the following formulaρ) of the nascent mRNA signal between the two homologous copies of a given promoter within the same nucleus. Specifically, we divided the single-locus data of the promoter activity (in units of the number of mRNA molecules) in an embryo into two groups, r1 and r2. Each group corresponded to one of the two homologous loci in the nucleus. In a given region of the embryo, we applied the following formula:r1〉, 〈r2〉, σ1, and σ2 are the mean and standard deviation of each group, respectively. In the anterior expression domain (0.2–0.4 EL), P1, P2, and CDS signals showed little correlation  or different  hb loci in the nucleus using the same formula, where r1 and r2 denote the activities of individual P1 and P2 copies, respectively. Both quantities were at low levels . By locally averaging the signal over the nearest five neighboring nuclei, we plotted the three FISH signals against each other and fitted the data to Eq.  against nuclear position (x) for all nuclei and binned individual data points by x. Within position ranges 0.3–0.8 EL (for Kr) or 0.4–1.0 EL (for kni), we used a least-squares algorithm (the “nlinfit” function in MATLAB) to fit the binned data to a product of two logistic functions:x0A and x0P denote the anterior and posterior boundary positions of the expression domain, respectively.We measured the expression patterns of h is the Hill coefficient, C0 is the concentration threshold for promoter activation, a indicates the maximal level of Bcd-dependent activity, and d denotes the basal activity.To analyze the regulation of a promoter by Bcd, we plotted, for each embryo, the nascent mRNA signal versus Bcd concentration for each nucleus within the position range of 0.25–0.75 EL. To extract the GRF, we binned individual data points by Bcd concentration and fitted them to a Hill function using a least-squares algorithm (the “nlinfit” function in MATLAB):hb promoter, we used active P1 and P2 transcription sites to locate individual (active) promoter loci. Near each locus, a locus-integration region (xy distance ≤3 pixel and z distance = 0 from the locus) and an out-of-locus region  were defined, which covered the nuclear volumes of Vl and Vo, respectively. The nuclear IF signal (in units of the number of Bcd molecules) within these two regions was integrated and denoted as Il and Io, respectively. The enriched Bcd signal was defined as the difference between Il and Io in consideration of the volume difference between the two regions, i.e., Ienrich = Il − Io· Vl / Vo. Data from multiple embryos in the sample nuclear cycle were pooled to increase the sample size. Data in the nuclear position range of 0.2–0.35EL were averaged to estimate the mean Bcd binding level of the anterior expression domain. To plot the Bcd binding curve, we binned the single-locus data by nuclear position and related mean Bcd enrichment with mean nuclear position or Bcd concentration.To quantify Bcd binding at a specific Stochastic modeling and inference of transcriptional kinetics are described in detail in Supplementary Note Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Meiosis requires the formation of programmed DNA double strand breaks (DSBs), essential for fertility and for generating genetic diversity. DSBs are induced by the catalytic activity of the TOPOVIL complex formed by SPO11 and TOPOVIBL. To ensure genomic integrity, DNA cleavage activity is tightly regulated, and several accessory factors  are needed for DSB formation in mice. How and when these proteins act is not understood. Here, we show that REC114 is a direct partner of TOPOVIBL, and identify their conserved interacting domains by structural analysis. We then analyse the role of this interaction by monitoring meiotic DSBs in female and male mice carrying point mutations in TOPOVIBL that decrease or disrupt its binding to REC114. In these mutants, DSB activity is strongly reduced genome-wide in oocytes, and only in sub-telomeric regions in spermatocytes. In addition, in mutant spermatocytes, DSB activity is delayed in autosomes. These results suggest that REC114 is a key member of the TOPOVIL catalytic complex, and that the REC114/TOPOVIBL interaction ensures the efficiency and timing of DSB activity. TOPOVIBL and REC114 are required for meiotic DNA double-strand breaks (DSBs). This study shows that TOPOVIBL forms a complex with REC114 and mice carrying mutations that disrupt the interaction show DSB defects with distinct outcomes in males and females. Meiotic recombination initiates at the onset of prophase I by the formation of hundreds of programmed DNA double-strand breaks (DSBs) at preferred DNA sites, named hotspots2. DSBs are formed by the collective action of a conserved set of proteins: the TOPOVIL complex and its accessory partners6.Sexual reproduction relies on the specialized cell division of meiosis to generate haploid cells that eventually differentiate into gametes. In most taxa, proper segregation of the homologous chromosomes (homologs) depends on homologous recombination, which physically connects homologs through at least one reciprocal exchange (crossover) between each homolog pair7, and is composed of two conserved subunits: the SPO11 catalytic subunit and TOPOVIBL. TOPOVIBL shares partial homology with the GHKL-ATPase domain (thus named GHKL-like), with the central transducer domain and to a lesser extent, with the regulatory C-terminal domain (CTD) of the TopoVIB subunit of archaeal TopoVI11. The TOPOVIL meiosis-specific activity is finely regulated in time  and in space  during meiotic prophase. However, the molecular mechanisms underlying this complex regulation remain largely elusive.The TOPOVIL complex is evolutionarily related to the TopoVI type IIB topoisomeraseSaccharomyces cerevisiae, Rec102, the identified TOPOVIBL homolog, shares only partial similarity with TopoVIB, with the conserved transducer domain but not with the GHKL-like domain5. Interaction and biochemical studies suggest that Rec104, which is also essential for DSB formation in yeast, could replace the GHKL domain and that the Rec102/Rec104 complex fulfils the function of TOPOVIBL14. The S. cerevisiae core complex defined biochemically is composed of Spo11, Rec102, Rec104 and Ski8, a protein directly interacting with Spo1113. In addition, to the core complex, several accessory proteins are required for DSB formation. In S. cerevisiae, these accessory partners are Rec114, Mei4 and Mer2, which interact with DNA that mediates the formation of condensate-like RMM protein clusters15. These condensates might recruit the Spo11/Rec102/Rec104/Ski8 complex to DNA through an interaction of Rec114 with Rec102/Rec10416. The finding that the Rec114 residues involved in this interaction are essential for DSB formation supports this hypothesis15.In M. musculus, SPO11 and TOPOVIBL are evolutionarily conserved18 and the DSB sites are determined by PRDM9 that recognizes specific DNA motifs and modifies chromatin upon binding to these sites21. In the mouse, the accessory proteins REC114, MEI4, IHO1 , and MEI1 are essential for DSB formation, and localize as foci on chromosome axes at meiotic prophase onset. It has been proposed that IHO1, MEI4 and REC114 directly control TOPOVIL through its recruitment or activation26. MEI4 and REC114 form a stable complex, and structural analyses revealed that REC114 N-terminus forms a Pleckstrin homology (PH) domain27 harbouring exposed conserved residues potentially involved in protein-protein interactions. This suggests that REC114 acts as a regulatory platform. In line with this hypothesis, it was recently reported that ANKRD31 directly interacts with REC114 PH domain and is involved in regulating DSB number and localization28. However, how these accessory proteins participate in TOPOVIL activity remains to be determined.In Here, using structural analysis, we found that the mouse REC114 PH domain directly interacts with a conserved C-terminal peptide of TOPOVIBL, identifying its CTD as a predicted regulatory unit of the TOPOVIL complex. Accordingly, in mice where TOPOVIBL interaction with REC114 was disrupted, we observed meiotic DSB formation defects in both sexes associated with reduced fertility. In females, DSB formation was drastically reduced. In males, DSB formation was delayed, but DSB levels were not reduced except for sub-telomeric regions. On the X and Y chromosomes, which recombine specifically in the distal sub-telomeric pseudo-autosomal region (PAR) region, DSB activity reduction led to chromosome synapsis defects. These results suggest that REC114 is part of the TOPOVIL catalytic complex and acts to regulate the level and timing of DSB formation.1–550) , which includes the PH domain, was sufficient for the interaction with TOPOVIBL452–579  we identified a specific interaction between mouse TOPOVIBL and REC114. The deletion analysis showed that the N-terminal PH domain of REC114 was required for this interaction because TOPOVIBL did not bind to REC114 lacking the first 39 amino acids (40–259) Fig. . This in50) Fig. . TOPOVIBing Fig. . We obtaing Fig. . While iing Fig. , lane 2 ing Fig. , lane 3.579 Fig. , lane 12385 Fig. , lane 11579 Fig. , lane 14phy Fig. .Fig. 1Th7. As our yeast two-hybrid assays suggested that the corresponding C-terminal 29 residues of TOPOVIBL (residues 551 to 579) were important for the interaction with REC114  of 1.2 μM   suggested that TOPOVIBL CTD does not contain any known globular domain and was partially intrinsically disordered. However, it indicated the presence of a putative helix at its C-terminus. Interestingly, within the last C-terminal residues, we detected a high conservation among metazoan species Fig. . An exte114 Fig. , we hypo114 Fig.  might re μM Fig. . MoreoveμM) Fig. . These rQMY9 sugg559–576 peptide by X-ray crystallography   and the TOPOVIBLrea Fig. . The intrea Fig. . In the rea Fig. . Specifirea Fig. . The wel117 Fig. . W572 al117 Fig. .Fig. 2St29 25. In agreement, we could show by size exclusion chromatography the simultaneous interaction of REC114 with MEI4 and TOPOVIBL  is significantly longer than that of TOPOVIBL and covers a larger surface on REC114 (~1800 Å2) packing against both of its β-sheets  Top6blW562A harbouring the point mutation W562A that disrupt the interaction between TOPOVIBL and REC114 in vitro  that appears at chromatin domains around DSB sites upon DSB formation31. In wild-type oocytes, γH2AX was present over large chromatin domains at leptonema and zygonema and mostly disappeared at pachynema , when leptonema and zygonema are predominantema Fig. . In bothtes Fig. . To dete32. In wild-type oocytes, we detected DMC1 and RPA foci that colocalized with the chromosome axis at leptonema and zygonema. The number of DMC1 foci was decreased in both Top6bl mutants at leptonema (3.1-fold in Top6blW562A/W562A and 18.5-fold in Top6blΔ17Ct/Δ17Ct mice) , respectively, compared with 61.9%, 37.7% and 0.4% (n = 496) in Top6blW562A/W562A, and 57.6%, 42.4% and 0% (n = 151) in Top6blΔ17Ct/Δ17Ct ovaries. Overall, these results are consistent with a reduced DSB activity that affects synapsis formation between homologues.We confirmed this hypothesis by quantifying DSB repair through the detection of DMC1 and RPA2. DMC1 binds to resected DSB ends and catalyses homologous strand exchange for DSB repair. RPA is recruited to resected DSB ends before and also after strand exchange during second-end capture for repairce) Fig. . DMC1 lehed Fig. . The redhed Fig. . We obtahed Fig. . In wildhed Fig. 33. In bo34, and because the two Top6bl mutations disrupt the interaction interface with REC114, we assessed REC114 cytological localization in Top6blW562A/W562A and Top6blΔ17Ct/Δ17Ct mice. In wild-type oocytes, REC114, IHO1, MEI4 and ANKRD31 form several hundred foci on chromosome axes at leptonema and they progressively disappear as DSBs form28. The number of REC114 axis-associated foci was significantly higher in Top6blW562A/W562A and particularly in Top6blΔ17Ct/Δ17Ct mice at leptonema and especially at zygonema compared with wild-type oocytes . Conversely, at zygonema and pachynema, the number of DMC1 and RPA2 foci was similar in wild-type and mutant spermatocytes  overlapped with wild-type hotspots  but not from mice expressing the Prdm9Cst variant (RJ2 strain) confirms that they are Prdm9-dependent hotspots  showed that in Top6blΔ17Ct/Δ17Ct mice, most of the hotspots (86%) were of similar intensity compared to wild-type, and that the signal was decreased (by 1.2- to 24-fold) in 12% of hotspots, and increased (by 1.45- to 3.3-fold) in 2% of hotspots  , followed by ssDNA enrichment ots Fig. . This inice Fig. . The averes Fig. , 15. We res Fig. . Second,mes Fig. . Fourth,mes Fig. . In thismes Fig. . We concice Fig.  could inTop6blW562A/W562A, 72% of Top6blΔ17Ct/Δ17Ctvs 18% in wild-type)  Fig. . At metape) Fig.  and a depe) Fig.  was dete38. Indeed, Top6blW562A/W562A and Top6blΔ17Ct/Δ17Ct spermatocytes proceed through prophase like wild-type spermatocytes, but they arrested in metaphase and many cells were apoptotic, particularly in the Top6blΔ17Ct mutant. This metaphase I arrest was correlated with reduced sperm production in both mutants and fertility loss in Top6blΔ17Ct/Δ17Ct mice . We further used AlphaFold229 to model the SPO11/TOPOVIBL complex and combined this model with the crystal structure of the REC114-PH/TOPOVIBL559-576 complex determined in this study  of the Rec114/Mei4 complex in EI4 Fig.  is compaIBL Fig. . Moreove27). TOPOVIBL is found outside from metazoans but is highly divergent and no eukaryotic consensus motif can be identified at its C-terminal end7. With the contribution of genetics, both TOPOVIBL and REC114 homologs have also been identified in fungi and green plants23. In S. cerevisiae, TOPOVIBL appears to be split into two proteins Rec102 and Rec104. Phylogenetic and biochemical analysis identified Rec102 as homologous to the transducer domain of TOPOVIBL, while Rec104 is predicted to be at the position of the GHKL domain15. S. cerevisiae Rec114 has the conserved PH domain interacting with both, Rec102 and Rec104 in yeast-two hybrid assays and the Rec114 residues involved in the interaction are essential for DSB formation16. According to the AlphaFold2 model of S. cerevisiae Rec114 (AF-A0A6V8S448), many of the residues of the mouse REC114 PH domain required for the interaction with mouse TOPOVIBL are not conserved in S. cerevisiae. These include mouse REC114 V97 and L104, shown in this study as essential for binding to TOPOVIBL  dependent phosphorylation of Rec114 and its consequence on down-regulating DSB activity46, a modification and regulation also observed for C. elegans DSB-142. In A. thaliana, both the TOPOVIBL ortholog, MTOPVIB6, and REC11423 have been identified. Although the PH domain of REC114 is conserved27, the metazoan C-terminal motif is absent in MTOPVIB. It is therefore possible that the function of these proteins is mediated by distinct molecular interactions as suggested by interaction and protein localization assays in A. thaliana47. These divergences are correlated with a distinct role for A. thaliana REC114 which is dispensable for DSB formation47. AtREC114 may however regulate DSB activity at a distinct level, and it would be interesting to know if it plays a role in controlling the timing of DSB activity, since this is one of the REC114 roles we have uncovered in male mice.The interaction between the C-terminal motif of TOPOVIBL and the PH domain of REC114 is predicted to be conserved in many metazoans based on the presence of the motif and the PH domain  could be explained by a lack of interaction between TOPOVIBL Δ17Ct and REC114 and a remaining weak interaction between TOPOVIBL W562A and REC114. Such differential effect of the two mutations on the interaction is however not supported by in vitro analysis since no interaction between TOPOVIBL W562A and REC114 PH domain was detected in vitro. It remains possible that in vivo TOPOVIBL W562A can interact weakly with REC114. In vivo interaction assays would be required to test this hypothesis. Alternatively, the stronger phenotype observed in Top6blΔ17Ct mice may be due to additional interactions or properties mediated by the last 17 amino acids of TOPOVIBL. The different phenotypes of Top6bl mutants, show that REC114 is a regulatory subunit of the activity, and not just an accessory factor of the TOPOVIL complex. The reduced DSB activity in oocytes fits exactly the simple interpretation that REC114 binding to TOPOVIBL is required for the catalytic activity. The male phenotype highlights REC114 double role in fine tuning DSB activity by regulating the timing of DSB formation genome-wide and DSB formation at sub-telomeric regions. It is remarkable that the delayed DSB formation with respect to axis formation in Top6blΔ17Ct/Δ17Ct mice does not alter homologous synapsis and seems to allow weak DSB sites to be more active than in wild-type. This could be explained if DSB sites can be turned off upon synapsis, as shown in S. cerevisiae48. Delaying synapsis would thus allow weak DSB sites to be active. The sub-telomeric effect in Top6blΔ17Ct/Δ17Ct mice potentially applies to all chromosomes and is not restricted to the four chromosomes  shown to accumulate REC114, MEI4, IHO1 and ANKRD31 aggregates28. The property we detected is therefore not strictly linked to those aggregates, which is also consistent with the observation that Top6bl mutants do not have any detectable defect in the formation of those aggregates  that may influence the control of SPO11/TOPOVIBL activity. These regions are known to behave differently from chromosome arms for meiotic DSBs, based on their sensitivity to the expression of a GAL4BD-SPO11 fusion protein on DSB activity51, to a lower DMC1/SPO11-oligonucleotides ratio52 and to differential activities in male and female meiosis53. It is also possible that mice expressing SPO11β-only (the long isoform of SPO11) and shown to have decreased DSB activity in the PAR, have a specific decrease of DSB activity near autosome ends38. This would imply a specific regulation of SPO11 near chromosome ends, as the phenotypes due to the Top6blΔ17Ct mutation suggest. As SPO11α (the short isoform of SPO11) is lacking the interaction domain with TOPOVIBL, it is not expected to be catalytically active. SPO11α may rather repress an inhibitor of TOPOVIL. One possibility is that this inhibitor interferes with the REC114-TOPOVIBL interaction. Such scenario, although certainly others can be envisioned, would fit with the potential similarity of phenotypes between Spo11β-only and Top6blΔ17Ct/Δ17Ct male mice, including the XY synapsis defect. Overall, these observations imply that i) the TOPOVIBL-REC114 interaction is not essential for DSB activity in all genomic contexts; ii) REC114 activity senses or responds to specific chromosomal features. Several studies have highlighted differences of recombination and/or chromosome organization between sexes as well along chromosomes but which links to DSB activity remain to be determined54.The observation of meiotic DSB defects in female and male mice harbouring ans Fig. 7. The stProgramming hundreds of DSBs in the genome is a challenge for the cell, and the current knowledge that the catalytic complex of SPO11/TOPOVIBL requires several other proteins is coherent with the need to regulate these events. Here, we described the central role of REC114 in SPO11/TOPOVIBL activity through its direct interaction with TOPOVIBL, thus highlighting a first level of this regulation. Moreover, the DSB program must be executed in two very different cell types (oocytes and spermatocytes), and our findings show that the REC114-TOPOVIBL interaction is sensitive to these differences. Other components of the meiotic DSB machinery and their potential multiple interactions should contribute to SPO11/TOPOVIBL regulation. Additional directed-mutagenesis studies will unravel them and will identify the complex(es) active in vivo.Top6blW562A/W562A and Top6blΔ17Ct/Δ17Ct, respectively. Top6bl−/− mice carry the Gm960em2Arte allele, a null allele due to a 5 bp deletion in Top6bl5. Mice housing conditions were: temperature 22 °C, humidity 55%, dark/light cycle 12/12 corresponding to 8 am/8 pm in summer and 7 am/7 pm in winter. All experiments were carried out according to the CNRS guidelines and were approved by the ethics committee on live animals .Mice were in the C57BL/6 J background. Mice carrying the homozygous mutant alleles Top6bl < em1(W562A)BdM> and Top6bl < em2(delta17)BdM> were named PstI restriction site. The Top6bl < em1(W562A)BdM> allele, named Top6blW562A, is the result of homologous recombination. The Top6bl < em2(delta17)BdM> allele, named Top6blΔ17Ct, is the result of non-homologous repair and has a 4 bp deletion  and anti-GBD  antibodies.All plasmids used in yeast two-hybrid assays were cloned with the Gateway® Gene Cloning Technology (Invitrogen) and transformed in the AH109 and Y187 haploid yeast strains (Clontech). AH109 and Y187 cells were transformed with Gal4 DNA binding domain (GBD) fusion plasmids derived from pAS2 and Gal4 activation domain (GAD) fusion plasmids that were obtained from pGAD. Purified colonies of diploid strains were streaked on synthetic medium (SD) plates lacking leucine and tryptophan (-LW), or leucine, tryptophan and histidine (-LWH), or leucine, tryptophan and histidine with 5 mM amino-triazole (-LWH + 3AT), or leucine, tryptophan, histidine and adenine (-LWHA). Dilution assays were performed by spotting cells on -LW, -LWH, -LWH + 3AT and -LWHA plates that were incubated at 30 °C for 3 days. For verification of protein expression, protein extracts were prepared and analysed by western blotting, as previously describedE. coli BL21-Gold (DE3) cells (Agilent) from the pProEXHTb expression vector (Invitrogen). The protein was first purified by affinity chromatography using the Ni2+ resin . After His-tag cleavage with the TEV protease, it was purified through a second Ni2+ column and size-exclusion chromatography on Superdex 200 . The pure protein was concentrated to 20 mg ml–1 in a buffer containing 20 mM Tris, pH 8.0, 200 mM NaCl and 5 mM β-mercaptoethanol, and supplemented with a three-fold molar excess of TOPOVIBL peptide (559-EDLWLQEVSNLSEWLNPG-576). The complex was crystallized using the hanging drop vapor diffusion method at 20 °C. The best diffracting crystals grew within seven days in a solution containing 1.6 M MgSO4, 100 mM MES (pH 6.5), and 10% (v/v) dioxane. For data collection at 100 K, crystals were snap-frozen in liquid nitrogen with a solution containing mother liquor and 25% (vol/vol) glycerol.His-tagged mouse REC114 15-159 was expressed in P6122 with the unit cell dimensions a, b = 108.7 Å and c = 83. Å. The asymmetric unit contains one REC114-TOPOVIBL dimer and has a solvent content of 68%. A complete native dataset was collected to a 2.5 Å resolution, partially extending to 2.26 Å on the ESRF beamline ID30B using the MXcuBE3 software (ESRF). The data were processed using autoPROC55. Phases were obtained by molecular replacement using PHASER56 with the crystal structure of the REC114 PH domain (PDB code: 6HFG) as search model. The initial map was improved using the prime-and-switch density modification option of RESOLVE57. After manual model rebuilding with COOT58, the structure was refined using Refmac559 to a final R-factor of 22.6% and Rfree of 24.9%  cells. All proteins were first purified by affinity chromatography (Strep-Tactin XT resin (IBA), Ni-Chelating Sepharose, GE Healthcare) and gel filtration on Superdex 200 . Mixtures of REC114 and TOPOVIBL were loaded onto Strep-Tactin XT (IBA) resin columns. Columns were then extensively washed with a buffer containing 100 mM Tris pH 8, 100 mM NaCl, 5 mM β-mercaptoethanol. Bound proteins were eluted by addition of 50 mM of D-Biotin, and analysed on SDS-PAGE. ANKRD311808–1857 was cloned as 6xHis-MBP fusion in pETM41. Strep-REC1141–159 and ANKRD311808–1857 were individually expressed in E. coli BL21Gold (DE3) cells. Following cell disruption, supernatants containing soluble Strep-REC1141–159 and ANKRD311808–1857 were mixed. The complex was further purified by Superdex 200  gel filtration column. The pull-down with increasing amounts of TOPOVIBL452–579 on Strep-Tactin XT (IBA) resin column was performed as above. For pull-down presented in Fig. Full-length REC114 and its variants were cloned as Strep-tag fusions (using a single Strep-tag: WSHPQFEK) into pRSFDuet-1 (Novagen). TOPOVIBL452–579, TOPOVIBL452–562,W562A,563–579, TOPOVIBL559–576 or TOPOVIBL559–562,W562A,563–576 into the sample cell that contained 30-40 µM REC11415–159 or Strep-REC1141–159 in 20 mM Tris (pH 8.0), 100 mM NaCl, 5% glycerol and 5 mM β-mercaptoethanol. The initial data point was deleted from the data sets. Binding isotherms were fitted with a one-site binding model by nonlinear regression using the Origin software, version 7.0 .ITC experiments were performed at 25 °C using an ITC200 microcalorimeter . Experiments included one 0.5 µl injection and 18-20 injections of 1.5-2 µL of 0.3-1.8 mM TOPOVIBL ), followed by sonication, benzonase (250 U) was added at 4 °C for 1 h. After centrifugation  to remove debris, immunoprecipitation was performed with 5 µg of homemade anti-TOPOVIBL antibody. For each immunoprecipitation, 3.5 mg of whole cell protein extract and 50 µl of Protein A Dynabeads (Invitrogen 10001D) were used. Then, immunoprecipitates were resuspended in 40 µl of Laemmli buffer and TOPOVIBL immunoprecipitation was assessed by western blotting with a homemade affinity-purified anti-TOPOVIBL (1/1000) antibody followed by an anti-rabbit LC mouse monoclonal secondary antibody .18 (Ambion), SuperScriptIII (Invitrogen), and total RNA (1-2 µg) from 16dpc ovaries.Total RNA was extracted with the miRNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. For RT-PCR, first-strand DNA was synthesized using oligo d(T)Top6bl and Spo11 were amplified using standard PCR conditions and the primer pairs Oli63/Oli70 and Spo11:116U22/Spo11:655L22, respectively (Supplementary Table Top6bl ORF was then digested with the EciI enzyme.The open reading frames of Mouse testes or ovaries were fixed in Bouin’s solution for Periodic Acid Schiff (PAS) staining of testes and haematoxylin eosin staining of ovaries. Fixation was in 4% paraformaldehyde/1X PBS for immunostaining and TUNEL assay. Testes and ovaries were embedded in paraffin and cut in 3µm-thick sections. Sections were scanned using the automated tissue slide-scanning tool of a Hamamatsu NanoZoomer Digital Pathology system. TUNEL assay was performed with the DeadEnd Fluorometric TUNEL System (Promega), according to the manufacturer’s protocol.After dissection of the epididymis caudal part from adult testes (2 month-old), spermatozoids were extracted from the epididymis by smashing or crushing the tissue in PBS. After homogenization by pipetting, 10 µl of the soluble part was diluted in 1 mL of water, and spermatozoids were counted.61: Briefly, a suspension of testis cells was prepared in PBS, and then incubated in a hypotonic solution for 8 min at room temperature. Cells were centrifuged, resuspended in a solution of 66 mM sucrose and spread on slides with 1% paraformaldehyde, 0.05% Triton. Nuclei were dried for 1 to 2 h in a humid chamber. Immunostaining was performed using a milk-based blocking buffer 62. Primary antibodies were incubated overnight at room temperature. Secondary antibodies were incubated at 37 °C for 1.5 h. Nuclei were stained with DAPI  during the final washing step.Spread from spermatocytes and oocytes were prepared with the dry down technique, as described62 (1/500), rabbit anti-SYCP1 , rabbit anti-DMC1  anti-RPA2 , anti-MEI423 (1/100), anti-REC11425 (1/50), anti-REC114 , anti-ANKRD3128 (1/400), anti-IHO126 (1/2000) and mouse monoclonal anti-phospho-histone H2AX (Ser139) (γH2AX)  antibodies were used for immunostaining. Homemade affinity purified anti-TOPOVIBL antibody: rabbits were injected with full-length mouse His-TOPOVIBL protein prepared from E. coli inclusion bodies. Rabbit serum was purified by affinity using His-TOPOVIBL purified from inclusion bodies.Guinea pig anti-SYCP3Tubules from decapsulated testes were pulled apart in 1% trisodium citrate and transferred into a 15 ml tube. After pipette homogenization and 1 min sedimentation, the cell-containing supernatant was transferred in a new 15 ml tube. Following the same procedure, the tubule pieces were rinsed twice with 3 ml of 1% trisodium citrate. The cell solution was centrifuged at 180 g for 10 min, and the pellet resuspended in 100 µl of supernatant. Then, 3 ml of methanol: acetic acid: chloroform (3:1:0.05) solution was added drop by drop to the cell solution (rolling the first drops down the sides of the tube while flicking the tube). Cells were then centrifuged at 180 g for 10 min and resuspended in 100 µl of supernatant, and 3 ml of methanol: acetic acid (3:1) was added to the tube. After 10 min of incubation at room temperature, cells were centrifuged again (180 g for 10 min) and resuspended in ~1 ml methanol: acetic acid (3:1). To prepare the slides, 40 µl of the cell suspension was dropped from a height of ~40 cm onto a slide that was held titled at 45°. Slides were dried in a humid chamber.X  and Y  chromosome-specific probes were used according to the manufacturer (Metasystems Probes). 10 µl of probe mixture were added onto slides with metaphase spreads, covered with coverslips and sealed. The samples were then denatured, hybridized, washed and stained with DAPI as recommended by the manufacturer (Metasystems Probes).For focus quantification, all images were deconvoluted using the Huygens software. Image J was used to quantify foci that colocalized with the chromosome axis defined by SYCP3 staining.For γH2AX quantification in oocytes, signal intensity was obtained using Cell Profiler on non-deconvoluted images. Integrated intensity was used for the analysis. For spermatocytes, both Cell Profiler and Image J quantifications were performed and gave similar results. The output of Image J integrated intensity is presented.Staging criteria were as follows. Pre-leptotene nuclei had weak SYCP3 nuclear signal and no or very weak γH2AX signal; early leptotene nuclei were γH2AX-positive and with only short SYCP3 fragments; mid leptotene nuclei were γH2AX-positive and with short and long SYCP3 fragments; late leptotene were γH2AX-positive and with only long SYCP3 fragments; zygotene nuclei had partially synapsed homologs; and pachytene cells had all 19 autosomes fully synapsed . Six Top6blΔ17Ct/Δ17Ct testes and two wild-type testes from 12 to 25-week-old mice were used for each replicate. Sequencing was performed on a HiSeq 2500 instrument in paired-end mode (2x150bp).DMC1 ChIP-seq was performed as described in35 was used for DMC1-SSDS read mapping . A filtering step was performed on the aligned reads to keep only non-duplicated and high-quality uniquely mapped reads with no more than one mismatch per read. To identify meiotic hotspots from biologically replicated samples in DMC1-SSDS, the Irreproducible Discovery Rate (IDR) method was used, as done in our previous studies. This method was developed for ChIP-seq analysis and extensively used by the ENCODE and modENCODE projects64. The framework developed by Qunhua Li and Peter Bickel’s group (https://sites.google.com/site/anshulkundaje/projects/idr) was followed. Briefly, this method allows testing the reproducibility within and between replicates by using IDR statistics. Following their pipeline, peak calling was performed using MACS version 2.0.10 with relaxed conditions  for each of the two replicates, the pooled dataset, and pseudo-replicates that were artificially generated by randomly sampling half of the reads twice for each replicate and the pooled dataset. Then IDR analyses were performed, and reproducibility was checked. Final peak sets were built by selecting the top N peaks from pooled datasets , with N defined as the highest value between N1  and N2 . Hotspot centring and strength calculation were computed following the method described by Khil et al.35. All read distributions and signal intensities presented in this work were calculated after pooling reads from both replicates, if not otherwise stated. When DSB maps were compared between mouse genotypes, the 1bp-overlaps were restricted to the central 400 bp of hotspots (+/− 200 bp around the peak centre). For correlation plots, the type 1 single-strand DNA signal was library-normalized (fragment per million).After quality control and read trimming to remove adapter sequences and low-quality reads, DMC1 ChIP-SSDS reads were mapped to the UCSC mouse genome assembly build GRCm38/mm10. The previously published methodTop6blΔ17Ct/Δ17Ct and wild-type mice, DMC1-SSDS signal intensity was compared at the 13562 wild-type hotspots using DESeq265. Among these hotspots, 4207 hotspots (31%) were filtered out with the default independent filtering option using the mean of normalized counts as filter statistic. The aim was to remove sites with too low counts (mean count below 22) that have zero or low chance of showing significant differences to increase the detection power for the other sites. For the 9355 tested hotspots, log fold change shrinkage was performed to correct data dispersion using the apeglm method66. p-value were adjusted for multiple testing within DESeq2 using the procedure of Benjamini and Hochberg. The hotspots with increased or decreased DMC1-SSDS signal intensity were then determined using an adjusted p-value threshold of 0.1 and a log2 fold change value below or above zero, respectively. This led to the identification of 214 increased , 1098 decreased , and 8043 unchanged hotspots in Top6blΔ17Ct/Δ17Ct mice . Note that as the genomic DNA sequence of p-arms has not been assembled, it is immediately flanked by centromeric and q-arm DNA sequences, where the signal can be mapped and quantified. To test whether this biased distribution was significant, each hotspot was annotated as sub-telomeric when within the sub-telomeric region defined with a variable size from 1 to 10 Mb. For each sub-telomeric region, the numbers of unchanged, decreased and increased hotspots in the sub-telomeric versus the non-sub-telomeric region  were counted. Pearson’s Chi-square tests were computed (by taking into account or not the increased hotspots) and p-values were adjusted for multiple testing using the Benjamini & Yekutieli method. Megabase-normalized counts (hotspot density) were measured and plotted . Then, the ratio of these two mean values was computed. The procedure was repeated 10 times, each time with a different random set of 10 non-sub-telomeric hotspots. The sub-telomeric effects are presented in Fig. Visual inspection of DMC1-SSDS signal intensity along chromosomes and the localization of hotspots with decreased DMC1-SSDS signal intensity in ted Fig. . Alternahttp://vassarstats.net/ and https://www.quantitativeskills.com/sisa/index.htm. Box plots (25–75 percentiles) show the median and 5-95 percentiles. Statistical tests for DMC1-SSDS data were done using R version 4.0.3. All tests and p values  are provided in the corresponding legends and/or figures.The statistical analyses of cytological observations were done with GraphPad Prism 9. The nonparametric Mann-Whitney test was used to compare the number of foci, and the Pearson’s Chi square test to compare distributions, as indicated in the figure legends. The Chi square tests were performed at The pull-down assays shown in Figs. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Extreme East Asian summer monsoon (EASM) rainfall frequently induces floods that threaten millions of people, and has been generally attributed to internal climate variability. In contrast to the hydrological weakening theory of volcanic eruptions, here we present convergent empirical and modeling evidence for significant intensification of EASM rainfall in response to strong tropical volcanic eruptions. Our multi-proxy analyses show a significantly increased EASM in the first summer after tropical eruptions from 1470 AD to the present, and the more frequent occurrence of El Niños in the first boreal winter after eruptions is necessary for the enhanced EASM. Model simulation ensembles show that a volcano-induced El Niño and the associated stronger than non-volcanic El Niño warm pool air-sea interaction intensify EASM precipitation, overwhelming volcanic-induced moisture deficiency. This work sheds light on the intertwined relationship between external forcing and internal climate variability and potential flood disasters resulting from tropical volcanic eruptions. The probability of an El Niño in the winter after large tropical volcanic eruptions increases. When this happens, summer monsoon precipitation over East Asia is enhanced, overwhelming thermodynamic precipitation reduction from volcanic cooling. EASM rainfall is usually enhanced by moisture advection and convergence due to southwesterly anomaly of an enhanced western North Pacific subtropical high, following an El Niño peak in the previous winter6. A recent study also proposed that El-Niño-related tropical tropospheric warming could shift the midlatitude westerlies southward, which would impinge on the Tibetan Plateau and induce northerlies downstream of the plateau to intensify the East Asian early summer rainband7.The record-breaking EASM rainfall in 2020 caused a humanitarian disaster, affecting 45.4 million people in China and resulting in over 142 human casualties and more than 16 billion US dollars in economic loss9. The EASM is found to be suppressed after volcanic eruptions in model simulations12 due to the reduction of surface shortwave radiation and the associated slowdown of the global hydrological cycle16. Volcanic eruptions have been suggested as the cause of some historical drought events19. However, no significant drought was recorded in the Chinese chronicles following the large 1815 Tambora eruption20, nor in observations following the 1982 El Chichón and 1991 Pinatubo eruptions21. These case studies raise a question about the perception of EASM suppression by volcanic perturbations.Large volcanic eruptions are one of the major natural external forcings of Earth’s climate variability from interannual to centennial time scales28. Two main mechanisms have been proposed using state-of-the-art model simulations to explain this connection: the ocean dynamic thermostat mechanism31 and the land-sea thermal contrast mechanism34.Previous studies of the effects of volcanic eruptions on EASM commonly overlook the role of El Niño, although it has been suggested by a number of proxy-reconstructed El Niño/Southern Oscillation (ENSO) indices that the likelihood of an El Niño increases after tropical eruptionsThis work investigates the response of the EASM-tropical Pacific system to tropical volcanism to understand the interaction and relative roles of internal climate feedback and external forcing. We analyze long-term multi-proxy data and multi-model simulations and find that a volcano-induced El Niño and the associated warm pool air-sea interaction can intensify the EASM precipitation, overshadowing the hydrological weakening by volcanic eruptions.35 for the period from 1470 to 1999 AD using the gridded precipitation proxy data36 and the ensemble mean of 11 sets of ENSO paleoclimate reconstructions (see “Methods”).We first examine the EASM-tropical Pacific response to 22 precisely dated tropical large volcanic eruptions5. Enhanced EASM precipitation in the first post-eruption summer is also observed in two other well-known precipitation reconstructions over China37 and over all of Asia38  positive precipitation anomalies centered around the Yangtze River basin, northeast China, and Kyushu of Japan, accompanied by negative, albeit not significant, anomalies over the Indo-China Peninsula and the Philippines Fig. . This meThe ensemble mean of 11 available ENSO reconstructions indicates a significant post-eruption precipitation increase following an El Niño Fig. , in contNext, we investigate the last-millennium climate simulation results of 13 different models from the Paleoclimate Modeling Intercomparison Project phases 3 and 4 (PMIP3 & PMIP4) to interpret the monsoon-ocean response to tropical large volcanic eruptions in the context of the current state-of-the-art models. Based on the actual volcanic forcing used in each model, the 13 simulations amount to 92 eruption events for the common pre-industrial period from 1470 to 1849 AD, and they are used to construct a superposed epoch analysis (see “Methods”). The average of these 92 eruption simulations shows a relative El Niño signal, i.e., a reduced zonal gradient of Pacific equatorial sea surface temperature (SST), in the first post-eruption boreal winter  two seasons before, and the correlation is higher for the EASM circulation  than for the precipitation. This is because the EASM circulation index, i.e., the Pacific High, is better captured by numerical models than precipitation45. The enhanced Pacific High is tied to the SST cooling to its southeast . After a volcanic eruption, dynamic-related precipitation is increased while the thermodynamic-related precipitation is decreased, demonstrating the volcanic-induced cooling and drying 55 to volcanic eruptions with asymmetric hemispheric distributions further complicate the volcano-EASM relationship quantification. Future study expanding to Northern or Southern Hemispheric eruptions should correct for SST variations associated with such ITCZ shifts.Although our ensemble analysis shows that a tropical eruption can increase the likelihood of an El Niño, individual paleoclimate reconstructions exhibit divergent responses34 or the northern Eurasia winter warming responses to stratospheric soot and sulfate aerosols injections during volcanic scenarios56. Results obtained from this work demonstrate the important role and complexity of the coupled atmosphere-ocean dynamics in Pacific, and shed light on the potential impact of volcanic eruptions on EASM hydroclimate.To conclude, this work finds an east-west asymmetric monsoonal-ocean response to the zonally symmetric volcanic forcing, highlighting the delayed oceanic responses to volcanic eruptions in exciting EASM precipitation changes. A precipitation increase due to wind convergence induced by the enhanced western North Pacific high overwrites the precipitation reduction due to the thermodynamically induced moisture deficiency. The tropical Pacific plays a critical role in modulating the climate system response to external volcanic forcing, i.e., increasing the likelihood of an El Niño and enhanced post-El Niño warm pool air-sea interaction. Similar cases are found in the Nuclear Niño36 and 11 reconstructed ENSO indices (Supplementary Table 36 data are a gridded (0.5° × 0.5°) reconstruction of annual May–September precipitation from 1470 to 1999 AD over the whole Asian continent and are mainly based on 500-year historical documentary records, tree-ring data, ice-core records, and a few long-term instrumental data series. A regional empirical orthogonal function method is utilized to increase the signal-to-noise ratio. Two other gridded reconstructions of precipitation based on point-by-point regression methods are also analyzed in parallel, serving as confirmation of the results: a 530-yr multi-proxy May–September precipitation reconstruction over China (0.5° × 0.5°) based on the tree-ring chronologies and drought/flood indices from 1470 to 2000 AD37, and an annual June-August rainfall reconstruction over the Asian land region (2° × 2°) generated by merging 453 tree-ring width chronologies and 71 historical documentary records from 1470 to 201338.The proxy data we analyzed mainly include a long-term reconstruction of Asian summer precipitation26 except adding one new proxy49. All of the 11 ENSO indices first go through a 9-yr Lanczos high-pass filter57 to isolate the ENSO signal, and then are normalized according to their own standard deviations. The ensemble mean is utilized to remove proxy uncertainty. Since these 11 indices cover different periods, with the longest extending from 900 to 2002 AD and the shortest from 1706 to 1997 AD, the ensemble mean for each year from 1470 to 1999 AD is calculated as the average of available indices. The ensemble mean with any individual index removed is still highly correlated (above the 99% confidence level), with a minimum correlation coefficient of 0.95, to the ensemble mean of all 11 indices. The resulting ensemble mean has a high correlation with the instrumental December–February Niño3.4 index of 0.83 (at the 99% confidence level) for the period of 1871–1999, based on the averaged  SST anomaly from Hadley Centre Ice and SST version 1 (HadISST1)58.To test the potential role of a preceding El Niño on volcanic-induced EASM rainfall change, the ensemble mean of 11 available ENSO indices is reconstructed and applied, following the method used before35 volcanic reconstructions during the period of 1470–1999 AD, when Asian precipitation proxies are available, are used for the composite analysis. We define the peak aerosol loading year as the eruption year 0 47. Since the PMIP simulations only cover the pre-industrial period before 1850, we have 92 eruption events in the 13 PMIP model simulations for a period of 1470–1849 AD and 228 events for the whole last millennium of 850–1849 AD, listed in Supplementary Table All of the 10 last-millennium climate simulations in the PMIP3 and three simulations in the PMIP4 are analyzed to understand the underlying mechanisms of observed EASM response to large volcanic eruptions  study period22.A superposed epoch analysis62.The Niño3.4 index is defined as the area-averaged SST anomaly over the area . To use the same standard for selecting an El Niño event list among reconstructions with different scales, an El Niño event is defined when the boreal winter Niño3.4 index is >0.5 standard deviations in both reconstructions and simulations. A non-volcanic El Niño is defined as when it occurs in a normal year without an eruption. Using different thresholds to select El Niño events does not change our results qualitatively. Boreal summer is defined as May–September, and boreal winter, as December–February, consistent with those used for reconstructions. The EASM region is defined as the region over  in both reconstructions and simulations. Based on the simulated western North Pacific subtropical high, the EASM circulation index is defined as the 850-hPa zonal wind difference between the northern  and southern  regions, a little different from the original index between northern  and southern  regions from instrumental observations63:The monsoon precipitation index Supplementary InformationPeer Review File