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The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8–9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas. [ABSTRACT FROM AUTHOR]

The East/Japan Sea (EJS), a marginal sea of the Northwestern Pacific, is one of the ocean regions showing the most rapid warming and greatest increases in ocean heatwaves over the last several decades. Predictability and skillful prediction of the summer season EJS variability are crucial, given the increasing severity of ocean temperature events impacting fisheries and reinforcing climate conditions like the East Asian rainy season, which in turn affects adjacent high-population density areas over East Asia. We use observations and the Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system to investigate the summertime EJS Sea Surface Temperature (SST) predictability and prediction skill. The observations and seasonal prediction system show that the summer season EJS SST can be closely linked to the previous winter air-sea coupling and predictable 8–9 months in advance. The SPEAR seasonal prediction system demonstrates skillful forecast of EJS SST events from summer to late fall, with added skill for long-lead forecasts initialized in winter. We find that winter large-scale atmospheric circulations linked to Barents Sea variability can induce persistent surface wind anomalies and corresponding northward Ekman heat transport over the East China Sea. The ocean advection anomalies that enter the EJS in prior seasons appear to play a role in developing anomalous SST during summer, along with instantaneous atmospheric forcing, as the source of long-lead predictability. Our findings provide potential applications of large-scale ocean-atmosphere interactions in understanding and predicting seasonal variability of East Asian marginal seas. [ABSTRACT FROM AUTHOR]

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE. [ABSTRACT FROM AUTHOR]

The frequency and intensity of heat extremes over the United States have increased since the mid‐20th century and are projected to increase with additional anthropogenic greenhouse gas forcing. We define heat extremes as summertime (June–August) daily maximum 2m temperatures that exceed historical records. We examine characteristics of historical and near‐future heat extremes using observations and past and future projections using 100 ensemble members from three coupled global climate models large ensemble simulations. We find that the large ensembles capture the trend and variability of heat extremes over the period 2006–2020 relative to the 1991–2005 climatology but overestimate the frequency at which the heat extremes occur. In future warming scenarios, heat extremes continue to increase over the next 30 years, with high amplitude records in the Northwest and Central US. After 2050, we find there is a spread in the frequency of heat extremes that is dependent on the emissions scenario, with a high emissions until mid‐century followed by a high mitigation scenario showing a decrease in heat extremes by the end of the century. Although the frequency of future heat extremes is likely overestimated in the large ensembles, they are still a powerful tool for researching extreme temperatures in the climate system. Plain Language Summary: Heat extremes have been increasing around the world and the United States, driven largely by human‐caused global warming. With additional warming projected, heat extremes are likely to increase. It is important to understand how heat extremes will change in the future since heat has many societal impacts, especially on human health as it causes more fatalities than any other weather‐related hazard in the US. In this study we use 100 ensemble members from three global climate models simulating past and future climate to evaluate characteristics of record breaking maximum daily temperature records from June to August. We find that the models capture the observed trend and variability of past heat extremes but appear to overestimate the frequency that they occur. The models project an increase in heat extremes in the next 30 years, with high amplitude extremes projected in the Northwest and Central US, and high frequency extremes in the Southwest and Mountain West. The amount of warming directly impacts the frequency of heat extremes past year 2050. In a scenario with rapid reduction of greenhouse emissions after 2040 the frequency of heat extremes declines toward the end of the century. We find that while the frequency of future heat extremes is likely overestimated in the models, the ensembles are nevertheless a powerful tool for researching extreme temperatures in the climate system. Key Points: Large ensembles of climate model simulations capture the observed variability and trend of heat extremes but overestimate the frequency and amplitudeHeat extremes are projected to increase in the next 30 years with additional warming due to greenhouse gas forcingThe frequency of heat extremes toward the end of the century is highly dependent on the emissions scenario and extent of warming [ABSTRACT FROM AUTHOR]

Skillful prediction of wintertime cold extremes on seasonal time scales is beneficial for multiple sectors. This study demonstrates that North American cold extremes, measured by the frequency of cold days in winter, are predictable several months in advance in the Geophysical Fluid Dynamics Laboratory's SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Three predictable components of cold extremes over the North American continent are found to be skillfully predicted on seasonal scales. One is a trend-like component, which shows a continent-wide decrease in the frequency of cold extremes and is primarily attributable to external radiative forcing. This trend-like component is predictable at least 9 months ahead. The second predictable component displays a dipole structure over North America, with negative signs in the northwest and positive signs in the southeast. This dipole component is predictable with significant correlation skill for 2 months and is a response to the central Pacific ENSO (El Niño-Southern Oscillation) as revealed from SPEAR AMIP-style simulations. The third component with the largest loadings over Canada and the northern US shows significant correlations with snow anomalies over mid-to-high latitudes of the North American continent. Predictions using only the three predictable components yield higher/comparable skill relative to the SPEAR raw forecasts. [ABSTRACT FROM AUTHOR]

Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems. [ABSTRACT FROM AUTHOR]

This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed. [ABSTRACT FROM AUTHOR]

The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model's SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022. [ABSTRACT FROM AUTHOR]

Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium‐to‐high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50‐km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next‐generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR‐day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time‐independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first‐order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member. Plain Language Summary: An "Atmospheric River" (AR) is a weather phenomenon characterized by strong, narrow moisture transport that brings heavy rainfall to land. They serve as a critical water resource but also can cause damaging flash floods and high winds. Thus, knowing how AR activity will change in the future climate can help us to mitigate potential AR‐related disasters and promote effective water resource management. However, this task is challenging due to uncertainty in how fast the climate will warm and in how much the noise of natural climate variability can obscure the signal from global warming. In addition, several different definitions of AR exist, which raises questions about the sensitivity of AR changes to AR definition. In this study, we use a next‐generation global climate model to evaluate the influence of these uncertainties and to determine the time when humanity will notice a discernible change in AR activity. We find that the response to global warming can be robustly identified by 2060 across all explored methods of computation. We further examine a less expensive approach, which enables us to quantify the uncertainty in warming signals and estimate the time of signal emergence in a single realization of nature. Key Points: Increases in atmospheric river (AR) frequency emerge from the noise on internal variability by 2060 in simulations with a global climate modelA computationally efficient method is developed for determining the time of emergence (ToE) of AR responses to global warmingThe AR ToE is not sensitive to the criteria of AR detection algorithm or shared Socioeconomic pathways [ABSTRACT FROM AUTHOR]

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables. [ABSTRACT FROM AUTHOR]

Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (≤5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (≥3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal‐to‐decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction. Plain Language Summary: Atmospheric rivers (ARs), narrow corridors of intense moisture transport and heavy precipitation, are an important water resource but also a cause of flooding‐related disasters for western North America. Consequently, predictions of AR frequency several seasons in advance potentially would be of great value, but such operational forecasts are currently lacking due to the challenges in simulating such intense, small‐scale weather phenomena and their predictability sources on seasonal timescales. In this study, we examine the forecast skill of AR frequency on seasonal‐to‐multiseasonal timescales (≥3 months) in a new generation seasonal‐to‐decadal prediction system developed at the Geophysical Fluid Dynamics Laboratory. We find that AR frequency can be skillfully forecast at least 9 months in advance over certain regions of the west coast of North America, such as California and Alaska, while the forecasts are only reliable for the first season in other regions. This regional variability can be further explained by the large‐scale climate variability pattern that is responsible for much of the skill, which is strongly modulated by slowly varying sea surface temperature (SST) variations. A prototype probabilistic seasonal AR forecast product is proposed. Key Points: Atmospheric river frequency can be skillfully forecast at least 9 months in advanceThe skills are significant over certain regions of western North America such as Alaska and CaliforniaEl Nino‐Southern Oscillation and Interdecadal Pacific Oscillation are important predictability sources [ABSTRACT FROM AUTHOR]

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales. [ABSTRACT FROM AUTHOR]

The next‐generation seasonal prediction system is built as part of the Seamless System for Prediction and EArth System Research (SPEAR) at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). SPEAR is an effort to develop a seamless system for prediction and research across time scales. The ensemble‐based ocean data assimilation (ODA) system is updated for Modular Ocean Model Version 6 (MOM6), the ocean component of SPEAR. Ocean initial conditions for seasonal predictions, as well as an ocean state estimation, are produced by the MOM6 ODA system in coupled SPEAR models. Initial conditions of the atmosphere, land, and sea ice components for seasonal predictions are constructed through additional nudging experiments in the same coupled SPEAR models. A bias correction scheme called ocean tendency adjustment (OTA) is applied to coupled model seasonal predictions to reduce model drift. OTA applies the climatological temperature and salinity increments obtained from ODA as three‐dimensional tendency terms to the MOM6 ocean component of the coupled SPEAR models. Based on preliminary retrospective seasonal forecasts, we demonstrate that OTA reduces model drift—especially sea surface temperature (SST) forecast drift—in coupled model predictions and improves seasonal prediction skill for applications such as El Niño–Southern Oscillation (ENSO). Plain Language Summary: Dynamic seasonal prediction systems employ global climate models to predict climate variations on monthly to seasonal time scales. A new state‐of‐the‐art seasonal prediction system has been developed at the Geophysical Fluid Dynamics Laboratory (GFDL) of the National Oceanic and Atmospheric Administration (NOAA). The prediction models are based on GFDL's new component models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The new seasonal prediction system includes new ways to apply observational information to initialize the model simulations, including an updated data assimilation system for the Modular Ocean Model Version 6 (MOM6). Bias correction is applied to the coupled dynamic models to reduce prediction bias, namely, the gap between model‐simulated and real‐world climatology. Preliminary seasonal prediction experiments demonstrate reduced errors in the predicted climate state globally, as well as improved prediction skill for applications such as El Niño–Southern Oscillation (ENSO). Key Points: The next‐generation SPEAR seasonal prediction system uses the recently developed coupled general circulation model at GFDLThe updated ocean data assimilation system for the MOM6 ocean model produces ocean state estimation and initial conditions for predictionsThe new coupled model and ocean tendency adjustment (OTA) reduce model forecast drift and improve seasonal prediction skill [ABSTRACT FROM AUTHOR]

We document the configuration and emergent simulation features from the Geophysical Fluid Dynamics Laboratory (GFDL) OM4.0 ocean/sea ice model. OM4 serves as the ocean/sea ice component for the GFDL climate and Earth system models. It is also used for climate science research and is contributing to the Coupled Model Intercomparison Project version 6 Ocean Model Intercomparison Project. The ocean component of OM4 uses version 6 of the Modular Ocean Model and the sea ice component uses version 2 of the Sea Ice Simulator, which have identical horizontal grid layouts (Arakawa C-grid). We follow the Coordinated Ocean-sea ice Reference Experiments protocol to assess simulation quality across a broad suite of climate-relevant features. We present results from two versions differing by horizontal grid spacing and physical parameterizations: OM4p5 has nominal 0.5° spacing and includes mesoscale eddy parameterizations and OM4p25 has nominal 0.25° spacing with no mesoscale eddy parameterization. Modular Ocean Model version 6 makes use of a vertical Lagrangian-remap algorithm that enables general vertical coordinates. We show that use of a hybrid depth-isopycnal coordinate reduces the middepth ocean warming drift commonly found in pure z* vertical coordinate ocean models. To test the need for the mesoscale eddy parameterization used in OM4p5, we examine the results from a simulation that removes the eddy parameterization. The water mass structure and model drift are physically degraded relative to OM4p5, thus supporting the key role for a mesoscale closure at this resolution. [ABSTRACT FROM AUTHOR]

Many long non-coding RNAs (lncRNAs) affect gene expression, but the mechanisms by which they act are still largely unknown. One of the best-studied lncRNAs is Xist, which is required for transcriptional silencing of one X chromosome during development in female mammals. Despite extensive efforts to define the mechanism of Xist-mediated transcriptional silencing, we still do not know any proteins required for this role. The main challenge is that there are currently no methods to comprehensively define the proteins that directly interact with a lncRNA in the cell. Here we develop a method to purify a lncRNA from cells and identify proteins interacting with it directly using quantitative mass spectrometry. We identify ten proteins that specifically associate with Xist, three of these proteins-SHARP, SAF-A and LBR-are required for Xist-mediated transcriptional silencing. We show that SHARP, which interacts with the SMRT co-repressor that activates HDAC3, is not only essential for silencing, but is also required for the exclusion of RNA polymerase II (Pol II) from the inactive X. Both SMRT and HDAC3 are also required for silencing and Pol II exclusion. In addition to silencing transcription, SHARP and HDAC3 are required for Xist-mediated recruitment of the polycomb repressive complex 2 (PRC2) across the X chromosome. Our results suggest that Xist silences transcription by directly interacting with SHARP, recruiting SMRT, activating HDAC3, and deacetylating histones to exclude Pol II across the X chromosome. [ABSTRACT FROM AUTHOR]

Living cells compartmentalize materials and enzymatic reactions to increase metabolic efficiency. While eukaryotes use membrane-bound organelles, bacteria and archaea rely primarily on protein-bound nanocompartments. Encapsulins constitute a class of nanocompartments widespread in bacteria and archaea whose functions have hitherto been unclear. Here, we characterize the encapsulin nanocompartment from Myxococcus xanthus, which consists of a shell protein (EncA, 32.5 kDa) and three internal proteins (EncB, 17 kDa; EncC, 13 kDa; EncD, 11 kDa). Using cryo-electron microscopy, we determined that EncA self-assembles into an icosahedral shell 32 nm in diameter (26 nm internal diameter), built from 180 subunits with the fold first observed in bacteriophage HK97 capsid. The internal proteins, of which EncB and EncC have ferritin-like domains, attach to its inner surface. Native nanocompartments have dense iron-rich cores. Functionally, they resemble ferritins, cage-like iron storage proteins, but with a massively greater capacity (~30,000 iron atoms versus ~3,000 in ferritin). Physiological data reveal that few nanocompartments are assembled during vegetative growth, but they increase fivefold upon starvation, protecting cells from oxidative stress through iron sequestration. [ABSTRACT FROM AUTHOR]

Bactofilins are fibre-forming bacterial cytoskeletal proteins. Here, we report the structural and biochemical characterization of MXAN_7475 (BacM), one of the four bactofilins of Myxococcus xanthus. Absence of BacM leads to a characteristic 'crooked' cell morphology and an increased sensitivity to antibiotics targeting cell wall biosynthesis. The absence of the other three bactofilins MXAN_4637-4635 (BacN-P) has no obvious phenotype. In M. xanthus, BacM exists as a 150-amino-acid full-length version and as a version cleaved before Ser28. In the cell, native BacM forms 3 nm wide fibres, which assemble into bundles forming helix-like cytoplasmic cables throughout the cell, and in a subset of cells additionally a polarly arranged lateral rod-like structure. Isolated fibres consist almost completely of the N-terminally truncated version, suggesting that the proteolytic cleavage occurs before or during fibre formation. Fusion of BacM to mCherry perturbs BacM function and cellular fibre arrangement, resulting for example in the formation of one prominent polar corkscrew-like structure per cell. Immunofluorescence staining of BacM and MreB shows that their cellular distributions are not matching. Taken together, these data suggest that rod-shaped bacteria like M. xanthus use bactofilin fibres to achieve and maintain their characteristic cell morphology and cell wall stability. [ABSTRACT FROM AUTHOR]

Cell differentiation is widespread during the development of multicellular organisms, but rarely observed in prokaryotes. One example of prokaryotic differentiation is the Gram-negative bacterium Myxococcus xanthus. In response to starvation, this gliding bacterium initiates a complex developmental programme that results in the formation of spore-filled fruiting bodies. How the cells metabolically support the necessary complex cellular differentiation from rod-shaped vegetative cells into spherical spores is unknown. Here, we present evidence that intracellular lipid bodies provide the necessary metabolic fuel for the development of spores. Formed at the onset of starvation, these lipid bodies gradually disappear until they are completely used up by the time the cells have become mature spores. Moreover, it appears that lipid body formation in M. xanthus is an important initial step indicating cell fate during differentiation. Upon starvation, two subpopulations of cells occur: cells that form lipid bodies invariably develop into spores, while cells that do not form lipid bodies end up becoming peripheral rods, which are cells that lack signs of morphological differentiation and stay in a vegetative-like state. These data indicate that lipid bodies not only fuel cellular differentiation but that their formation represents the first known morphological sign indicating cell fate during differentiation. [ABSTRACT FROM AUTHOR]

Background: Ricin is a potent toxin and known bioterrorism threat with no available antidote. The ricin A-chain (RTA) acts enzymatically to cleave a specific adenine base from ribosomal RNA, thereby blocking translation. To understand better the relationship between ligand binding and RTA active site conformational change, we used a fragment-based approach to find a minimal set of bonding interactions able to induce rearrangements in critical side-chain positions. Results: We found that the smallest ligand stabilizing an open conformer of the RTA active site pocket was an amide group, bound weakly by only a few hydrogen bonds to the protein. Complexes with small amide-containing molecules also revealed a switch in geometry from a parallel towards a splayed arrangement of an arginine-tryptophan cation-pi interaction that was associated with an increase and red-shift in tryptophan fluorescence upon ligand binding. Using the observed fluorescence signal, we determined the thermodynamic changes of adenine binding to the RTA active site, as well as the site-specific binding of urea. Urea binding had a favorable enthalpy change and unfavorable entropy change, with a ΔH of -13 ± 2 kJ/mol and a ΔS of -0.04 ± 0.01 kJ/(K*mol). The side-chain position of residue Tyr80 in a complex with adenine was found not to involve as large an overlap of rings with the purine as previously considered, suggesting a smaller role for aromatic stacking at the RTA active site. Conclusion: We found that amide ligands can bind weakly but specifically to the ricin active site, producing significant shifts in positions of the critical active site residues Arg180 and Tyr80. These results indicate that fragment-based drug discovery methods are capable of identifying minimal bonding determinants of active-site side-chain rearrangements and the mechanistic origins of spectroscopic shifts. Our results suggest that tryptophan fluorescence provides a sensitive probe for the geometric relationship of arginine-tryptophan pairs, which often have significant roles in protein function. Using the unusual characteristics of the RTA system, we measured the still controversial thermodynamic changes of site-specific urea binding to a protein, results that are relevant to understanding the physical mechanisms of protein denaturation. [ABSTRACT FROM AUTHOR]

Ricin is a potent toxin presenting a threat as a biological weapon. The holotoxin consists of two disulfide-linked polypeptides: an enzymatically active A chain (RTA) and a galactose/ N-acetylgalactosamine-binding B chain. Efforts to develop an inactivated version of the A chain as a vaccine have been hampered by limitations of stability and solubility. Previously, recombinant truncated versions of the 267-amino-acid A chain consisting of residues 1-33/44-198 or 1-198 were designed by protein engineering to overcome these limits and were shown to be effective and nontoxic as vaccines in mice. Herein we used CD, dynamic light scattering, fluorescence, and Fourier-transform infrared spectroscopy to examine the biophysical properties of these proteins. Although others have found that recombinant RTA (rRTA) adopts a partially unfolded, molten globule-like state at 45°C, rRTA 1-33/44-198 and 1-198 are significantly more thermostable, remaining completely folded at temperatures up to 53°C and 51°C, respectively. Deleting both an exposed loop region (amino acids 34-43) and the C-terminal domain (199-267) contributed to increased thermostability. We found that chemically induced denaturation of rRTA, but not the truncated variants, proceeds through at least a three-state mechanism. The intermediate state in rRTA unfolding has a hydrophobic core accessible to ANS and an unfolded C-terminal domain. Removing the C-terminal domain changed the mechanism of rRTA unfolding, eliminating a tendency to adopt a partially unfolded state. Our results support the conclusion that these derivatives are superior candidates for development as vaccines against ricin and suggest an approach of reduction to minimum essential domains for design of more thermostable recombinant antigens. [ABSTRACT FROM AUTHOR]