Lipoteichoic acid polymer length is determined by competition between free starter units

Carbohydrate polymers exhibit incredible chemical and structural diversity, yet are produced by polymerases without a template to guide length and composition. As the length of carbohydrate polymers is critical for their biological functions, understanding the mechanisms that determine polymer length is an important area of investigation. Most Gram-positive bacteria produce anionic glycopolymers called lipoteichoic acids (LTA) that are synthesized by lipoteichoic acid synthase (LtaS) on a diglucosyl-diacylglycerol (Glc₂DAG) starter unit embedded in the extracellular leaflet of the cell membrane. LtaS can use phosphatidylglycerol (PG) as an alternative starter unit, but PG-anchored LTA polymers are significantly longer, and cells that make these abnormally long polymers exhibit major defects in cell growth and division. To determine how LTA polymer length is controlled, we reconstituted Staphylococcus aureus LtaS in vitro. We show that polymer length is an intrinsic property of LtaS that is directly regulated by the identity and concentration of lipid starter units. Polymerization is processive, and the overall reaction rate is substantially faster for the preferred Glc₂DAG starter unit, yet the use of Glc₂DAG leads to shorter polymers. We propose a simple mechanism to explain this surprising result: free starter units terminate polymerization by displacing the lipid anchor of the growing polymer from its binding site on the enzyme. Because LtaS is conserved across most Gram-positive bacteria and is important for survival, this reconstituted system should be useful for characterizing inhibitors of this key cell envelope enzyme.

All cell surfaces are rich in carbohydrate polymers that act as structural components, scaffolds for other molecules, and participants in signaling processes (1). The biological functions of a carbohydrate polymer are often greatly affected by its length. For example, depending on molecular weight, hyaluronic acid polymers can promote cell migration, differentiation, and inflammation or can inhibit these processes (2, 3). Similarly, the number of repeat units in bacterial O-antigen has a profound effect on complement activation and host cell uptake (4, 5). Unlike protein and nucleic acid polymers, which are assembled on a template that determines both length and composition, carbohydrate polymers are assembled without the use of a template. Template-independent length regulation is not as precise as template-directed polymerization, but physiological lengths of carbohydrate polymers typically fall into a defined range that is important for function (6). How different polymerases achieve length control is a fundamental question in the field.Several mechanisms for carbohydrate polymer length determination have been described. Some polymerases include a “molecular ruler” domain that measures the polymer against a portion of the enzyme (7), some use a dedicated “termination enzyme” to control length (8), and others rely on repeat unit concentration to control polymerization (9). These mechanisms are not mutually exclusive and can act together to control length (10, 11). The degree to which a polymerase is processive also influences product length. Processivity, a fundamental property of polymerases, refers to the number of elongation steps that occur without release of the growing polymer (12). A polymerase may be partially processive, in that more than one monomer addition occurs while the polymer is bound to the enzyme, but the polymer can be released and then rebind to continue elongation. A polymerase may also act in a distributive manner, where the growing polymer is released after each round of monomer addition. While some general mechanisms and aspects of length control for carbohydrate polymerases are known, here we describe a previously unknown mechanism for length regulation of a common type of lipoteichoic acid (LTA), a cell surface polymer that is crucially important to the physiology of most Gram-positive bacteria (13, 14).In the Gram-positive pathogen Staphylococcus aureus (Sa), LTA is a membrane-anchored poly(glycerol-phosphate) polymer involved in virulence (15–19), regulation of cell size and division (20–23), and osmotic stability (24, 25) (Fig. 1A). Sa LTA is assembled by the conserved lipoteichoic acid synthase (LtaS) on the cell surface using glucose(β1,6)-glucose(β1,3)-diacylglycerol (Glc₂DAG) as the membrane-anchored “starter unit” (20, 26). The polymer elongates in a process that involves the repeated transfer of phosphoglycerol units from phosphatidylglycerol (PG) to a catalytic threonine in LtaS (T300) and then to the tip of the growing polymer (Fig. 1B) (27–29). Repeat units may be modified by D-alanyl esters or, less commonly, GlcNAc moieties (24, 30). Because LTA is so important for Sa survival (13, 14, 21, 22), LtaS is a proposed target for antibiotics, and understanding its behavior may facilitate inhibitor development.Open in a separate windowFig. 1.LTA is a lipid-anchored polymer assembled from Glc₂DAG and PG on the bacterial cell surface. (A) Chemical structure of LTA from Sa. Phosphoglycerol repeat units may be modified with D-alanine esters or GlcNAc moieties. (B) Mechanism of LTA synthesis by LtaS. Phosphoglycerol units are transferred from PG to residue T300 to form a covalent intermediate, releasing DAG. Phosphoglycerol is then transferred to a Glc₂DAG starter unit to form GroP-Glc₂DAG. Additional repeat units are added to the glycerol tip of the polymer. (C) In Sa, PgcA and GtaB synthesize UDP-glucose from glucose-6-phosphate. UgtP uses UDP-glucose and DAG to make Glc₂DAG. LtaA exports Glc₂DAG to the cell surface. LtaS transfers phosphoglycerol units derived from PG to T300, releasing DAG for recycling. (D) Anti-LTA Western blot of Sa RN4220 wild-type (wt) or ΔugtP lysates. ΔugtP mutants lack Glc₂DAG, and LTA is instead polymerized directly on PG (20).Glc₂DAG, the starter unit for LTA polymerization, is biosynthesized on the cytoplasmic leaflet of the membrane by the sequential action of three enzymes: the phosphoglucose mutase PgcA, the UTP-glucose-1-phosphate uridylyltransferase GtaB, and the diacylglycerol β-glucosyltransferase UgtP (also called YpfP) (15, 20). Glc₂DAG is exported to the cell surface by the flippase LtaA (Fig. 1C) (15). An interesting feature of LtaS is that it can use PG as an alternative starter unit if Glc₂DAG synthesis or export is blocked (20). However, polymers formed on this alternative starter unit (PG-LTA) are significantly longer than polymers formed on Glc₂DAG (Glc₂DAG-LTA, Fig. 1D) (15, 23), and cells that make these longer polymers have cell division defects (20, 23), are much less virulent (15, 16), and are more sensitive to beta-lactam antibiotics and other cell envelope stresses (23). Whether the shorter polymers assembled on Glc₂DAG reflect the intrinsic behavior of LtaS or the action of other cellular factors is an important question that cannot be definitively answered with genetic approaches.Here we used in vitro reconstitution to test whether the identity of the LTA membrane anchor determines the length of the polymers that LtaS synthesizes. We show that the length differences observed between wild-type and mutant cells lacking Glc₂DAG are recapitulated in a proteoliposome system that contains only purified LtaS, PG, and either Glc₂DAG or an alternative anchor. Based on our studies, we propose a model for how polymer length can be controlled in polymerases that operate without a template.     

A solution to a sex ratio puzzle in Melittobia wasps

The puzzling sex ratio behavior of Melittobia wasps has long posed one of the greatest questions in the field of sex allocation. Laboratory experiments have found that, in contrast to the predictions of theory and the behavior of numerous other organisms, Melittobia females do not produce fewer female-biased offspring sex ratios when more females lay eggs on a patch. We solve this puzzle by showing that, in nature, females of Melittobia australica have a sophisticated sex ratio behavior, in which their strategy also depends on whether they have dispersed from the patch where they emerged. When females have not dispersed, they lay eggs with close relatives, which keeps local mate competition high even with multiple females, and therefore, they are selected to produce consistently female-biased sex ratios. Laboratory experiments mimic these conditions. In contrast, when females disperse, they interact with nonrelatives, and thus adjust their sex ratio depending on the number of females laying eggs. Consequently, females appear to use dispersal status as an indirect cue of relatedness and whether they should adjust their sex ratio in response to the number of females laying eggs on the patch.

Sex allocation has produced many of the greatest success stories in the study of social behaviors (1–4). Time and time again, relatively simple theory has explained variation in how individuals allocate resources to male and female reproduction. Hamilton’s local mate competition (LMC) theory predicts that when n diploid females lay eggs on a patch and the offspring mate before the females disperse, the evolutionary stable proportion of male offspring (sex ratio) is (n − 1)/2n (Fig. 1) (5). A female-biased sex ratio is favored to reduce competition between sons (brothers) for mates and to provide more mates (daughters) for those sons (6–8). Consistent with this prediction, females of >40 species produce female-biased sex ratios and reduce this female bias when multiple females lay eggs on the same patch (higher n; Fig. 1) (9). The fit of data to theory is so good that the sex ratio under LMC has been exploited as a “model trait” to study the factors that can constrain “perfect adaptation” (4, 10–13).Open in a separate windowFig. 1.LMC. The sex ratio (proportion of sons) is plotted versus the number of females laying eggs on a patch. The bright green dashed line shows the LMC theory prediction for the haplodiploid species (5, 39). A more female-biased sex ratio is favored in haplodiploids because inbreeding increases the relative relatedness of mothers to their daughters (7, 32). Females of many species adjust their offspring sex ratio as predicted by theory, such as the parasitoid Nasonia vitripennis (green diamonds) (82). In contrast, the females of several Melittobia species, such as M. australica, continue to produce extremely female-biased sex ratios, irrespective of the number of females laying eggs on a patch (blue squares) (15).In stark contrast, the sex ratio behavior of Melittobia wasps has long been seen as one of the greatest problems for the field of sex allocation (3, 4, 14–21). The life cycle of Melittobia wasps matches the assumptions of Hamilton’s LMC theory (5, 15, 19, 21). Females lay eggs in the larvae or pupae of solitary wasps and bees, and then after emergence, female offspring mate with the short-winged males, who do not disperse. However, laboratory experiments on four Melittobia species have found that females lay extremely female-biased sex ratios (1 to 5% males) and that these extremely female-biased sex ratios change little with increasing number of females laying eggs on a patch (higher n; Fig. 1) (15, 17–20, 22). A number of hypotheses to explain this lack of sex ratio adjustment have been investigated and rejected, including sex ratio distorters, sex differential mortality, asymmetrical male competition, and reciprocal cooperation (15–18, 20, 22–26).We tested whether Melittobia’s unusual sex ratio behavior can be explained by females being related to the other females laying eggs on the same patch. After mating, some females disperse to find new patches, while some may stay at the natal patch to lay eggs on previously unexploited hosts (Fig. 2). If females do not disperse, they can be related to the other females laying eggs on the same host (27–31). If females laying eggs on a host are related, this increases the extent to which relatives are competing for mates and so can favor an even more female-biased sex ratio (28, 32–35). Although most parasitoid species appear unable to directly assess relatedness, dispersal behavior could provide an indirect cue of whether females are with close relatives (36–38). Consequently, we predict that when females do not disperse and so are more likely to be with closer relatives, they should maintain extremely female-biased sex ratios, even when multiple females lay eggs on a patch (28, 35).Open in a separate windowFig. 2.Host nest and dispersal manners of Melittobia. (A) Photograph of the prepupae of the leaf-cutter bee C. sculpturalis nested in a bamboo cane and (B) a diagram showing two ways that Melittobia females find new hosts. The mothers of C. sculpturalis build nursing nests with pine resin consisting of individual cells in which their offspring develop. If Melittobia wasps parasitize a host in a cell, female offspring that mate with males inside the cell find a different host on the same patch (bamboo cane) or disperse by flying to other patches.We tested whether the sex ratio of Melittobia australica can be explained by dispersal status in a natural population. We examined how the sex ratio produced by females varies with the number of females laying eggs on a patch and whether or not they have dispersed before laying eggs. To match our data to the predictions of theory, we developed a mathematical model tailored to the unique population structure of Melittobia, where dispersal can be a cue of relatedness. We then conducted a laboratory experiment to test whether Melittobia females are able to directly access the relatedness to other females and adjust their sex ratio behavior accordingly. Our results suggest that females are adjusting their sex ratio in response to both the number of females laying eggs on a patch and their relatedness to the other females. However, relatedness is assessed indirectly by whether or not they have dispersed. Consequently, the solution to the puzzling behavior reflects a more-refined sex ratio strategy.     

Atomic-scale evidence for highly selective electrocatalytic N−N coupling on metallic MoS2

Molybdenum sulfide (MoS₂) is the most widely studied transition-metal dichalcogenide (TMDs) and phase engineering can markedly improve its electrocatalytic activity. However, the selectivity toward desired products remains poorly explored, limiting its application in complex chemical reactions. Here we report how phase engineering of MoS₂ significantly improves the selectivity for nitrite reduction to nitrous oxide, a critical process in biological denitrification, using continuous-wave and pulsed electron paramagnetic resonance spectroscopy. We reveal that metallic 1T-MoS₂ has a protonation site with a pK_a of ∼5.5, where the proton is located ∼3.26 Å from redox-active Mo site. This protonation site is unique to 1T-MoS₂ and induces sequential proton−electron transfer which inhibits ammonium formation while promoting nitrous oxide production, as confirmed by the pH-dependent selectivity and deuterium kinetic isotope effect. This is atomic-scale evidence of phase-dependent selectivity on MoS₂, expanding the application of TMDs to selective electrocatalysis.

Transition-metal dichalcogenides (TMDs) have gained considerable attention in recent years due to their variable crystal phases, which allow for precise tuning of their electronic, optical, magnetic, and catalytic properties (1, 2). For example, molybdenum sulfide (MoS₂), which is one of the most extensively studied TMDs, exists as different polymorphs depending on the orientation of sulfur atoms around the molybdenum center. In octahedral coordination (1T phase), MoS₂ exhibits metallic behavior, whereas the material acts as a semiconductor in trigonal prismatic coordination (2H phase) (3–6). In addition to higher conductivity, 1T-MoS₂ has enlarged layer spacing and more electrochemical active sites (7, 8), making it a promising next-generation material for batteries (9, 10), memristors (11, 12), capacitors (13, 14), and numerous other energy-related applications (15–17).In the field of electrocatalysis, phase engineering has mainly been used to enhance catalytic activity. For instance, exchanging 2H-MoS₂ for 1T-MoS₂ results in a marked increase toward the hydrogen evolution reaction (18, 19). Considering the advantage of TMDs being able to control the atomic-scale structure, phase engineering may also open possibilities to control the selectivity of multielectron/proton reactions with multiple possible products, such as CO₂ reduction (20–23), denitrification (NO₃⁻/NO₂⁻ reduction) (24–26), and the electrosynthesis of functional molecules (27–30). Selectivity is a critical requirement for cascade catalysis, one-pot reaction systems, and multistep catalytic processes, and strategies to guide the complex chemical reaction network toward the desired end product are necessary (31, 32). However, to the best of our knowledge, no studies have attempted to exploit the advantages of phase-engineered materials for selective electrocatalysis.One effective approach to explore phase-engineered MoS₂ for selectivity control is to utilize the newly proposed concept of sequential proton−electron transfer (SPET) (off-diagonal pathways, Fig. 1A) (33, 34). In contrast to the extensively studied concerted proton−electron transfer (CPET) pathway, the energy landscape of sequential (decoupled) proton−electron transfer (SPET) pathways is pH-dependent (Fig. 1B). This leads to pH-dependent reaction rates (Fig. 1C), where the maximum reaction rate can be obtained at a pH close to the pK_a of the reaction intermediate (33, 34). This was recently observed experimentally for nitrite reduction to dinitrogen – an artificial analog of biological denitrification – on partially oxygenated molybdenum sulfide (oxo-MoS_x), and the record high selectivity toward dinitrogen was achieved by simple pH optimization (35). In contrast, this pH dependence was absent in the case of crystalline 2H-MoS₂, demonstrating that the SPET pathway is a unique property of oxo-MoS_x and is therefore probably phase-dependent. However, the origin of the SPET behavior on this material remains unclear. Therefore, elucidating the mechanism at the atomic level would help rationalize the relationship between selectivity and crystal phases, thus providing significant insight into the newly proposed SPET mechanism (33, 34) to enhance the selectivity of multistep electrochemical processes.Open in a separate windowFig. 1.Selectivity control of MoS₂ based on SPET theory. (A) Diagram showing the possible pathways for proton−electron transfer on MoS₂. In the blue pathway (CPET), protons and electrons are transferred in a single elementary step. In contrast, stepwise pathways (SPET) generate an intermediate whose charge depends on whether the electron or proton transfers first (red and black pathways, respectively). (B) Diagram showing the energetic landscape of SPET. The landscape depends on the relationship between the pK_a of the reaction intermediate and the solution pH. (C) Influence of pH on reaction selectivity. The rates of SPET reactions (red lines) show a pH dependence with a maximum corresponding to the pK_a of the intermediate. Therefore, the relative rate of one reaction over another can be tuned by changing the pH. In contrast, the rate of CPET reactions are pH-independent, and therefore, their relative rates are also constant with respect to pH.Here, we identified the atomic-scale origin of SPET-driven selectivity on MoS₂ using continuous-wave electron paramagnetic resonance (CW-EPR), Raman, and pulsed ¹H/²H electron−nuclear double-resonance (ENDOR) spectroscopy. Specifically, a proton located at the first coordination sphere (∼3.26 Å) of a redox-active Mo center was found to have a pK_a value matching that involved in the pH-dependent electrocatalytic selectivity and H/D kinetic isotope effect (KIE). The observed pH-dependent behavior is specific to 1T-MoS₂, as oxo-MoS_x was assigned to the 1T phase using high-resolution transmission electron microscopy (HRTEM), Raman- and X-ray photoelectron spectroscopy (XPS). These results not only provide atomic-scale evidence of SPET in heterogeneous catalysis, but also demonstrate how the phase engineering of TMDs can be used to enhance their electrocatalytic selectivity.     

From the Cover: Neurophysiological coordination of duet singing

Coordination of behavior for cooperative performances often relies on linkages mediated by sensory cues exchanged between participants. How neurophysiological responses to sensory information affect motor programs to coordinate behavior between individuals is not known. We investigated how plain-tailed wrens (Pheugopedius euophrys) use acoustic feedback to coordinate extraordinary duet performances in which females and males rapidly take turns singing. We made simultaneous neurophysiological recordings in a song control area “HVC” in pairs of singing wrens at a field site in Ecuador. HVC is a premotor area that integrates auditory feedback and is necessary for song production. We found that spiking activity of HVC neurons in each sex increased for production of its own syllables. In contrast, hearing sensory feedback produced by the bird’s partner decreased HVC activity during duet singing, potentially coordinating HVC premotor activity in each bird through inhibition. When birds sang alone, HVC neurons in females but not males were inhibited by hearing the partner bird. When birds were anesthetized with urethane, which antagonizes GABAergic (γ-aminobutyric acid) transmission, HVC neurons were excited rather than inhibited, suggesting a role for GABA in the coordination of duet singing. These data suggest that HVC integrates information across partners during duets and that rapid turn taking may be mediated, in part, by inhibition.

Animals routinely rely on sensory feedback for the control of their own behavior. In cooperative performances, such sensory feedback can include cues produced by other participants (1–8). For example, in interactive vocal communication, including human speech, individuals take turns vocalizing. This “turn taking” is a consequence of each participant responding to auditory cues from a partner (4–6, 9, 10). The role of such “heterogenous” (other-generated) feedback in the control of vocal turn taking and other cooperative performances is largely unknown.Plain-tailed wrens (Pheugopedius euophrys) are neotropical songbirds that cooperate to produce extraordinary duet performances but also sing by themselves (Fig. 1A) (4, 10, 11). Singing in plain-tailed wrens is performed by both females and males and used for territorial defense and other functions, including mate guarding and attraction (1, 11–16). During duets, female and male plain-tailed wrens take turns, alternating syllables at a rate of between 2 and 5 Hz (Fig. 1A) (4, 11).Open in a separate windowFig. 1.Neural control of solo and duet singing in plain-tailed wrens. (A) Spectrogram of a singing bout that included male solo syllables (blue line, top) followed by a duet. Solo syllables for both sexes (only male solo syllables are shown here) are sung at lower amplitudes than syllables produced in duets. Note that the smeared appearance of wren syllables in spectrograms reflects the acoustic structure of plain-tailed wren singing. (B and C) Each bird has a motor system that is used to produce song and sensory systems that mediate feedback. (B) During solo singing, the bird hears its own song, which is known as autogenous feedback (orange). (C) During duet singing, each bird hears both its own singing and the singing of its partner, known as heterogenous feedback (green). The key difference between solo and duet singing is heterogenous feedback that couples the neural systems of the two birds. This coupling results in changes in syllable amplitude and timing in both birds.There is a categorical difference between solo and duet singing. In solo singing, the singing bird receives only autogenous (hearing its own vocalization) feedback (Fig. 1B). The partner may hear the solo song if it is nearby, a heterogenous (other-generated) cue. In duet singing, birds receive both heterogenous and autogenous feedback as they alternate syllable production (Fig. 1C). Participants use heterogenous feedback during duet singing for precise timing of syllable production (4, 11). For example, when a male temporarily stops participating in a duet, the duration of intersyllable intervals between female syllables increases (4), showing an effect of heterogenous feedback on the timing of syllable production.How does the brain of each wren integrate heterogenous acoustic cues to coordinate the precise timing of syllable production between individuals during duet performances? To address this question, we examined neurophysiological activity in HVC, a nucleus in the nidopallium [an analogue of mammalian cortex (17, 18)]. HVC is necessary for song learning, production, and timing in species of songbirds that do not perform duets (19–24). Neurons in HVC are active during singing and respond to playback of the bird’s own learned song (25–27). In addition, recent work has shown that HVC is also involved in vocal turn taking (19).To examine the role of heterogenous feedback in the control of duet performances, we compared neurophysiological activity in HVC when female or male wrens sang solo syllables with syllables sung during duets. Neurophysiological recordings were made in awake and anesthetized pairs of wrens at the Yanayacu Biological Station and Center for Creative Studies on the slopes of the Antisana volcano in Ecuador. We found that heterogenous cues inhibited HVC activity during duet performances in both females and males, but inhibition was only observed in females during solo singing.     

From the Cover: Evidence from South Africa for a protracted end-Permian extinction on land

Earth’s largest biotic crisis occurred during the Permo–Triassic Transition (PTT). On land, this event witnessed a turnover from synapsid- to archosauromorph-dominated assemblages and a restructuring of terrestrial ecosystems. However, understanding extinction patterns has been limited by a lack of high-precision fossil occurrence data to resolve events on submillion-year timescales. We analyzed a unique database of 588 fossil tetrapod specimens from South Africa’s Karoo Basin, spanning ∼4 My, and 13 stratigraphic bin intervals averaging 300,000 y each. Using sample-standardized methods, we characterized faunal assemblage dynamics during the PTT. High regional extinction rates occurred through a protracted interval of ∼1 Ma, initially co-occurring with low origination rates. This resulted in declining diversity up to the acme of extinction near the Daptocephalus–Lystrosaurus declivis Assemblage Zone boundary. Regional origination rates increased abruptly above this boundary, co-occurring with high extinction rates to drive rapid turnover and an assemblage of short-lived species symptomatic of ecosystem instability. The “disaster taxon” Lystrosaurus shows a long-term trend of increasing abundance initiated in the latest Permian. Lystrosaurus comprised 54% of all specimens by the onset of mass extinction and 70% in the extinction aftermath. This early Lystrosaurus abundance suggests its expansion was facilitated by environmental changes rather than by ecological opportunity following the extinctions of other species as commonly assumed for disaster taxa. Our findings conservatively place the Karoo extinction interval closer in time, but not coeval with, the more rapid marine event and reveal key differences between the PTT extinctions on land and in the oceans.

Mass extinctions are major perturbations of the biosphere resulting from a wide range of different causes including glaciations and sea level fall (1), large igneous provinces (2), and bolide impacts (3, 4). These events caused permanent changes to Earth’s ecosystems, altering the evolutionary trajectory of life (5). However, links between the broad causal factors of mass extinctions and the biological and ecological disturbances that lead to species extinctions have been difficult to characterize. This is because ecological disturbances unfold on timescales much shorter than the typical resolution of paleontological studies (6), particularly in the terrestrial record (6–8). Coarse-resolution studies have demonstrated key mass extinction phenomena including high extinction rates and lineage turnover (7, 9), changes in species richness (10), ecosystem instability (11), and the occurrence of disaster taxa (12). However, finer time resolutions are central to determining the association and relative timings of these effects, their potential causal factors, and their interrelationships. Achieving these goals represents a key advance in understanding the ecological mechanisms of mass extinctions.The end-Permian mass extinction (ca. 251.9 Ma) was Earth’s largest biotic crisis as measured by taxon last occurrences (13–15). Large outpourings from Siberian Trap volcanism (2) are the likely trigger of calamitous climatic changes, including a runaway greenhouse effect and ocean acidification, which had profound consequences for life on land and in the oceans (16–18). An estimated 81% of marine species (19) and 89% of tetrapod genera became extinct as established Permian ecosystems gave way to those of the Triassic. In the ocean, this included the complete extinction of reef-forming tabulate and rugose corals (20, 21) and significant losses in previously diverse ammonoid, brachiopod, and crinoid families (22). On land, many nonmammalian synapsids became extinct (16), and the glossopterid-dominated floras of Gondwana also disappeared (23). Stratigraphic sequences document a global “coral gap” and “coal gap” (24, 25), suggesting reef and forest ecosystems were rare or absent for up to 5 My after the event (26). Continuous fossil-bearing deposits documenting patterns of turnover across the Permian–Triassic transition (PTT) on land (27) and in the oceans (28) are geographically widespread (29, 30), including marine and continental successions that are known from China (31, 32) and India (33). Continental successions are known from Russia (34), Australia (35), Antarctica (36), and South Africa’s Karoo Basin (Fig. 1 and 37–40), the latter providing arguably the most densely sampled and taxonomically scrutinized (41–43) continental record of the PTT. The main extinction has been proposed to occur at the boundary between two biostratigraphic zones with distinctive faunal assemblages, the Daptocephalus and Lystrosaurus declivis assemblage zones (Fig. 1), which marks the traditional placement of the Permian–Triassic geologic boundary [(37) but see ref. 44]. Considerable research has attempted to understand the anatomy of the PTT in South Africa (38, 39, 45–52) and to place it in the context of biodiversity changes across southern Gondwana (53, 54) and globally (29, 31, 32, 44, 47, 55).Open in a separate windowFig. 1.Map of South Africa depicting the distribution of the four tetrapod fossil assemblage zones (Cistecephalus, Daptocephalus, Lystrosaurus declivis, Cynognathus) and our two study sites where fossils were collected in this study (sites A and B). Regional lithostratigraphy and biostratigraphy within the study interval are shown alongside isotope dilution–thermal ionization mass spectrometry dates retrieved by Rubidge et al., Botha et al., and Gastaldo et al. (37, 44, 80). The traditional (dashed red line) and associated PTB hypotheses for the Karoo Basin (37, 44) are also shown. Although traditionally associated with the PTB, the Daptocephalus–Lystrosaurus declivis Assemblage Zone boundary is defined by first appearances of co-occurring tetrapod assemblages, so its position relative to the three PTB hypotheses is unchanged. The Ripplemead member (*) has yet to be formalized by the South African Committee for Stratigraphy.Decades of research have demonstrated the richness of South Africa’s Karoo Basin fossil record, resulting in hundreds of stratigraphically well-documented tetrapod fossils across the PTT (37, 39, 56). This wealth of data has been used qualitatively to identify three extinction phases and an apparent early postextinction recovery phase (39, 45, 51). Furthermore, studies of Karoo community structure and function have elucidated the potential role of the extinction and subsequent recovery in breaking the incumbency of previously dominant clades, including synapsids (11, 57). Nevertheless, understanding patterns of faunal turnover and recovery during the PTT has been limited by the scarcity of quantitative investigations. Previous quantitative studies used coarsely sampled data (i.e., assemblage zone scale, 2 to 3 Ma time intervals) to identify low species richness immediately after the main extinction, potentially associated with multiple “boom and bust” cycles of primary productivity based on δ¹³C variation during the first 5 My of the Triassic (41, 58). However, many details of faunal dynamics in this interval remain unknown. Here, we investigate the dynamics of this major tetrapod extinction at an unprecedented time resolution (on the order of hundreds of thousands of years), using sample-standardized methods to quantify multiple aspects of regional change across the Cistecephalus, Daptocephalus, and Lystrosaurus declivis assemblage zones.     

A rate threshold mechanism regulates MAPK stress signaling and survival

Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, how cells integrate information conveyed from stimulation rate along with concentration remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast mitogen-activated protein kinase (MAPK) signaling and cell behavior at the single-cell level. We show that signaling depends on a rate threshold that operates in conjunction with stimulus concentration to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold and stimulation rate-dependent cell survival are sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of cell behavior and provide a paradigm to guide the dissection of major stimulation rate dependent mechanisms in other systems.

All cells employ signal transduction pathways to respond to physiologically relevant changes in extracellular stressors, nutrient levels, hormones, morphogens, and other stimuli that vary as functions of both concentration and rate in healthy and diseased states (1–7). Switch-like “instantaneous” changes in the concentrations of stimuli in the extracellular environment have been widely used to show that the strength of signaling and overall cellular response are dependent on the stimulus concentration, which in many cases needs to exceed a certain threshold (8, 9). Previous studies have shown that the rate of stimulation can also influence signaling output in a variety of pathways (10–17) and that stimulation profiles of varying rates can be used to probe underlying signaling pathway circuitry (4, 18, 19). However, it is still not clear how cells integrate information conveyed by changes in both the stimulation rate and concentration in determining signaling output. It is also not clear if cells require stimulation gradients to exceed a certain rate in order to commence signaling.Recent investigations have demonstrated that stimulation rate can be a determining factor in signal transduction. In contrast to switch-like perturbations, which trigger a broad set of stress-response pathways, slow stimulation rates activate a specific response to the stress applied in Bacillus subtilis cells (10). Meanwhile, shallow morphogen gradient stimulation fails to activate developmental pathways in mouse myoblast cells in culture, even when concentrations sufficient for activation during pulsed treatment are delivered (12). These observations raise the possibility that stimulation profiles must exceed a set minimum rate or rate threshold to achieve signaling activation. Although such rate thresholds would help cells decide if and how to respond to dynamic changes in stimulus concentration, the possibility of signaling regulation by a rate threshold has never been directly investigated in any system. Further, no study has experimentally examined how stimulation rate requirements impact cell phenotype or how cells molecularly regulate the stimulation rate required for signaling activation. As such, the biological significance of any existing rate threshold regulation of signaling remains unknown.The budding yeast Saccharomyces cerevisiae high osmolarity glycerol (HOG) pathway provides an ideal model system for addressing these issues (Fig. 1A). The evolutionarily conserved mitogen-activated protein kinase (MAPK) Hog1 serves as the central signaling mediator of this pathway (20–22). It is well established that instantaneous increases in osmotic stress concentration induce Hog1 phosphorylation, activation, and translocation to the nucleus (18, 21, 23–30). Activated Hog1 governs the majority of the cellular osmoadaptation response that enables cells to survive (23, 31, 32). Multiple apparently redundant MAPK phosphatases dephosphorylate and inactivate Hog1, which, along with the termination of upstream signaling after adaptation, results in its return to the cytosol (Fig. 1A) (23, 25, 26, 33–39). Because of this behavior, time-lapse analysis of Hog1 nuclear enrichment in single cells has proven an excellent and sensitive way to monitor signaling responses to dynamic stimulation patterns in real time (18, 27–30, 40, 41). Further, such assays have been readily combined with traditional growth and molecular genetic approaches to link observed signaling responses with cell behavior and signaling pathway architecture (27–29).Open in a separate windowFig. 1.Hog1 signaling and cell survival are sensitive to the rate of preconditioning osmotic stress application. (A) Schematic of the budding yeast HOG response. (B) Preconditioning protection assay workflow indicating the first stress treatments to a final concentration of 0.4 M NaCl (Left), high-stress exposure (Middle), and colony formation readout (Right). (C) High-stress survival as a function of each first treatment relative to the untreated first stress condition. Bars and errors are means and SD from three biological replicates. *Statistically significant by Kolmogorov–Smirnov test (P < 0.05). NS = not significant. (D) Treatment concentration over time. (E) Treatment rate over time for quadratic and pulse treatment. The rate for the pulse is briefly infinite (blue vertical line) before it drops to 0. (F) Hog1 nuclear localization during the treatments depicted in D and E. (Inset) Localization pattern in the quadratic-treated sample. Lines represent means and shaded error represents the SD from three to four biological replicates.Here, we use systematically designed osmotic stress treatments imposed at varying rates of increase to show that a rate threshold condition regulates yeast high-stress survival and Hog1 MAPK signaling. We demonstrate that only stimulus profiles that satisfy both this rate threshold condition and a concentration threshold condition result in robust signaling. We go on to show that the protein tyrosine phosphatase Ptp2, but not the related Ptp3 phosphatase, serves as a major rate threshold regulator. By expressing PTP2 under the control of a series of different enhancer–promoter DNA constructs, we demonstrate that changes in the level of Ptp2 expression can alter the stimulation rate required for signaling induction and survival. These findings establish rate thresholds as a critical and regulated component of signaling biology akin to concentration thresholds.     

Capillary equilibrium of bubbles in porous media

In geologic, biologic, and engineering porous media, bubbles (or droplets, ganglia) emerge in the aftermath of flow, phase change, or chemical reactions, where capillary equilibrium of bubbles significantly impacts the hydraulic, transport, and reactive processes. There has previously been great progress in general understanding of capillarity in porous media, but specific investigation into bubbles is lacking. Here, we propose a conceptual model of a bubble’s capillary equilibrium associated with free energy inside a porous medium. We quantify the multistability and hysteretic behaviors of a bubble induced by multiple state variables and study the impacts of pore geometry and wettability. Surprisingly, our model provides a compact explanation of counterintuitive observations that bubble populations within porous media can be thermodynamically stable despite their large specific area by analyzing the relationship between free energy and bubble volume. This work provides a perspective for understanding dispersed fluids in porous media that is relevant to CO₂ sequestration, petroleum recovery, and fuel cells, among other applications.

Bubbles are generated, trapped, and mobilized within porous media as a consequence of incomplete fluid–fluid displacements (1, 2), phase changes (3, 4), chemical and biochemical reactions (5, 6), or injection of emulsified fluids and foams (7, 8). Compared to continuously connected phases, the behavior of dispersed bubbles, or ganglia, are far less understood. In particular, the thermodynamic stability of bubbles, despite their large specific surface area, remains a puzzle. The difficulty comes from the fact that each bubble can attain a volume (V), topology, and capillary pressure (P_c) that is distinct from other bubbles in the medium (9). The variability poses challenges to understanding the transport and trapping mechanisms of bubbles in geologic CO₂ sequestration (10, 11), hydrocarbon recovery (12, 13), fuel cell water management (14, 15), and vadose zone oxygen supply (16, 17).The dominant factor controlling a bubble’s behavior in a porous medium is capillarity, which is typically much larger than either viscous, gravitational, or inertial forces (18, 19). Capillary pressure, P_c, allows a closure relationship for two-phase Darcy Eqs. (20–22) and influences thermodynamic properties like phase partition (23). Capillary pressure is derived from the Young–Laplace equation P_c = γκ, where γ is the interfacial tension and κ is the surface curvature. In an open space without obstacles, a bubble spontaneously evolves into a sphere to minimize its total interfacial energy. Thus, P_c is a continuous and monotonically decreasing function of V (Fig. 1A). However, in a porous medium, bubble’s P_c–V relation is more complicated due to the geometric confinement imposed by the porous structure and topological evolution (24). A bubble can no longer remain spherical as it grows in size but must conform to the geometry of the pore(s) it occupies. Therefore, a bubble’s P_c is a function of not only its volume and interfacial tension but also its topology as dictated by the confining porous medium, as confirmed by recent laboratory experiments and numerical simulations (25–29). The mere presence of confinement therefore engenders a host of phenomena that would otherwise be absent, such as capillary trapping (30, 31), anticoarsening of bubble populations (32, 33), and complex ganglion dynamics (11, 18). Furthermore, theoretical studies in mathematical topology (28, 34, 35) prove that immiscible fluids can be fully characterized by d+1 Minkowski functionals, where d is the problem dimension. Such characterizations remove the path-dependent (or hysteretic) behavior common to these systems (34, 35).Open in a separate windowFig. 1.(A) Spherical bubbles inside a bulk fluid. (B) Micromodel observations show that bubbles are nonspherical in porous media and may occupy multiple pores. This image is from SI Appendix, Movie S1. (C) A 2D porous medium comprised of an ordered array of identical circular grains. A bubble occupying multiple pores including a zoom-in to a portion of it. (D) Illustration of the full state. (E) Illustration of the critical state. (F) Decomposition of a bubble into four distinct parts: minor arc menisci shown by dark blue cap-shaped regions, throats shown by light blue diamond-shaped regions, inner bulk bodies shown by red star-shaped regions, and major arc menisci shown by dark green cap-shaped regions.Recent developments in microfluidics and micro computed tomography imaging allow detailed pore-scale visualizations of fluids inside porous media, including the morphology of bubbles and ganglia (25, 36–39). Garing et al. (25) experimentally measured the equilibrium capillary pressure of trapped air bubbles inside sandstone and bead-pack samples. They found that, unlike bubbles within a bulk fluid, the P_c of trapped bubbles shows no clear dependence on V and seems to fall within a bounded interval, except for vanishingly small V. Xu et al. (40) proposed an empirical correlation for the P_c trapped bubbles based on microfluidic observations. In this correlation, as V increases, P_c decreases until a minimum is reached and then increases linearly. In the first stage, the bubble is unconfined, whereas in the second, it is reshaped by the surrounding solid walls. The proposed correlation, however, is only valid for bubbles in a single pore and not bubbles that span multiple pores. The latter seems to be rather common in nature as evidenced by recent direct observations (Fig. 1B) (2, 25).Here, we propose a simple conceptual model to describe the equilibrium states of a bubble with arbitrary size trapped inside a porous medium. The model accounts for the bubble’s morphology, the geometry of the solid matrix, and the wettability between the two. We derive all metastable configurations of the bubble analytically and highlight the thermodynamic states the bubble assumes when it is static, growing, or shrinking. We also show that the relationship between surface free energy (F) and volume (V) of large bubbles is approximately linear, which explains the previously counterintuitive observation that such bubbles are thermodynamically stable despite having large surface areas. Our work provides a step toward understanding the capillary state, stability, and evolution of dispersed immiscible fluids in porous media.     

Stable metal anodes enabled by a labile organic molecule bonded to a reduced graphene oxide aerogel

Metallic anodes (lithium, sodium, and zinc) are attractive for rechargeable battery technologies but are plagued by an unfavorable metal–electrolyte interface that leads to nonuniform metal deposition and an unstable solid–electrolyte interphase (SEI). Here we report the use of electrochemically labile molecules to regulate the electrochemical interface and guide even lithium deposition and a stable SEI. The molecule, benzenesulfonyl fluoride, was bonded to the surface of a reduced graphene oxide aerogel. During metal deposition, this labile molecule not only generates a metal-coordinating benzenesulfonate anion that guides homogeneous metal deposition but also contributes lithium fluoride to the SEI to improve Li surface passivation. Consequently, high-efficiency lithium deposition with a low nucleation overpotential was achieved at a high current density of 6.0 mA cm⁻². A Li|LiCoO₂ cell had a capacity retention of 85.3% after 400 cycles, and the cell also tolerated low-temperature (−10 °C) operation without additional capacity fading. This strategy was applied to sodium and zinc anodes as well.

Rechargeable batteries based on metal anodes including lithium (Li), sodium (Na), and zinc (Zn) show great promise in achieving high energy density (1–3). Unfortunately, the electrochemical interface of the metal anodes is not favorable for metal deposition. Metal nucleation is inhomogeneous at the surface, leading to the growth of metal dendrites (4–7) and the formation of an unstable solid–electrolyte interphase (SEI) that is incapable of protecting metals from the side reactions with the electrolyte (8–12).Substantial efforts have been devoted to stabilizing the interface of metal anodes, especially for Li metal. These include the design of artificial protective layers (13–17), alternative electrolytes (18–24), and sacrificial additives (25–30) to stabilize the metal–electrolyte interface, the development of mechanically robust coatings (31–34) to block Li dendrite growth, and the use of structured scaffolds to host dendrite-free Li deposition by reducing local current densities (35–43). However, the performance of metal anodes remains poor under high-current or low-temperature conditions. This is because the inhomogeneous Li nucleation and unstable SEI problems have not been well addressed, and these problems at the interface are even exacerbated under critical operating conditions, especially high-current densities and low temperatures (5, 6, 44).Toward this end, we report a simple molecular approach for regulating the electrochemical interface of metal anodes, which enables even Li deposition and stable SEI formation in a conventional electrolyte. This was realized by bonding a labile organic molecule, benzenesulfonyl fluoride (BSF), to a reduced graphene oxide (rGO) aerogel surface as the Li anode host (Fig. 1A). During Li deposition, BSF molecules electrochemically decompose at the interface and generate benzenesulfonate anions bonded to the rGO aerogel (Fig. 1B). The conjugated anions have a strong binding affinity for Li, serving as lithiophilic sites on the rGO surface to synergistically induce homogeneous Li nucleation of Li on the rGO surface. At the same time, BSF molecules contribute LiF to the SEI layer, which facilitates Li surface passivation (Fig. 1C). As a result, high-efficiency (99.2%) Li deposition was achieved at a Li deposition amount of 6.0 mAh cm⁻² and a current density of 6.0 mA cm⁻²; the barrier to Li nucleation was markedly reduced, as evidenced by the low nucleation overpotentials at high-current density (6.0 mA cm⁻²) or at a low temperature (−10 °C). A 400-cycle life with a capacity retention of 83.6% was achieved for a Li|LiCoO₂ (LCO) cell in a conventional carbonate electrolyte. Moreover, with the organic molecule-tuned interface, the Li|LCO cell can be stably cycled at a low operating temperature (−10 °C). This approach was applied to Na and Zn metal anodes as well.Open in a separate windowFig. 1.Illustration of a stable interface for Li deposition using a labile organic molecule, benzenesulfonyl fluoride (BSF). (A) Covalently bonded BSF on the rGO aerogel surface. (B) In situ generation of a lithiophilic conjugated anion (benzenesulfonate) and LiF on the surface during Li deposition. (C) Li nucleation preferentially occurs at the conjugated anion sites owing to the strong Li binding affinity, which leads to uniform Li deposition. In addition, the LiF that is formed is in the SEI layer and passivates the Li surface.     

“Soft” oxidative coupling of methane to ethylene: Mechanistic insights from combined experiment and theory

The oxidative coupling of methane to ethylene using gaseous disulfur (2CH₄ + S₂ → C₂H₄ + 2H₂S) as an oxidant (SOCM) proceeds with promising selectivity. Here, we report detailed experimental and theoretical studies that examine the mechanism for the conversion of CH₄ to C₂H₄ over an Fe₃O₄-derived FeS₂ catalyst achieving a promising ethylene selectivity of 33%. We compare and contrast these results with those for the highly exothermic oxidative coupling of methane (OCM) using O₂ (2CH₄ + O₂ → C₂H₄ + 2H₂O). SOCM kinetic/mechanistic analysis, along with density functional theory results, indicate that ethylene is produced as a primary product of methane activation, proceeding predominantly via CH₂ coupling over dimeric S–S moieties that bridge Fe surface sites, and to a lesser degree, on heavily sulfided mononuclear sites. In contrast to and unlike OCM, the overoxidized CS₂ by-product forms predominantly via CH₄ oxidation, rather than from C₂ products, through a series of C–H activation and S-addition steps at adsorbed sulfur sites on the FeS₂ surface. The experimental rates for methane conversion are first order in both CH₄ and S₂, consistent with the involvement of two S sites in the rate-determining methane C–H activation step, with a CD₄/CH₄ kinetic isotope effect of 1.78. The experimental apparent activation energy for methane conversion is 66 ± 8 kJ/mol, significantly lower than for CH₄ oxidative coupling with O₂. The computed methane activation barrier, rate orders, and kinetic isotope values are consistent with experiment. All evidence indicates that SOCM proceeds via a very different pathway than that of OCM.

The oxidative coupling of methane (OCM) with O₂ would seem to be a concise, direct route to convert methane, one of the most Earth-abundant carbon sources (1), to ethylene (2CH₄ + O₂ → C₂H₄ + 2H₂O), a key chemical intermediate (2, 3), and this process has been extensively studied (1, 4–19) since 1982 (20). Nevertheless, the widespread use of OCM is challenged by methane overoxidation to CO₂ and other oxygenates. Furthermore, the severe reaction conditions of nonoxidative pathways (2, 21–28) typically risk carbon deposition and catalyst deactivation (2, 21–26). In preliminary studies, we reported a 2CH₄ + S₂ → C₂H₄ + 2H₂S coupling process that moderates the methane overoxidation driving force using gaseous disulfur (S₂) as a “soft” oxidant (SOCM; Fig. 1A) (29). S₂ is isoelectronic with O₂, the major sulfur vapor species at 700 to 925 °C (30–32), and is a less aggressive oxidant than O₂ (33). In this scenario, elemental sulfur is recovered from the H₂S coproduct via the known Claus process (Fig. 1B) (30), in a cycle where sulfur mediates/moderates the high nonselective O₂ reactivity. SOCM achieved promising ethylene selectivity, raising intriguing mechanistic questions and the possibility of higher selectivity. Methane + S_2(g) ethylene selectivities near ∼20% are achieved over a PdS/ZrO₂ catalyst (29), and oxide precatalysts give selectivities near 33% (34).Open in a separate windowFig. 1.Energetic comparison between the oxidative coupling of methane with O₂ (OCM) and with S₂ (SOCM) and the pathway to recover elemental sulfur from H₂S. (A) Gibbs free energy of desired and overoxidation processes in OCM and SOCM at 800 and 1,050 °C. (B) Industrialized catalytic Claus process used to recover elemental sulfur from H₂S.Nevertheless, in contrast to extensive OCM (17, 35–39) and nonoxidative CH₄ coupling studies (40), far less is known about the SOCM reaction pathway. Post-SOCM X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and elemental analysis (29, 34) indicate that the oxide precatalysts are predominantly sulfided. Density functional theory (DFT) analyses of molybdenum sulfide catalysts suggest that methane is activated at M–S or S–S sites to form surface-bound CH₃* species which dehydrogenate to form CH₂* (methylidene) species, which then couple to produce C₂H₄. It was proposed that CH₃* species can also desorb as methyl radicals which couple to form ethane (29). The overoxidation product, CS₂, was suggested to form via sulfur addition to methylidene surface intermediates (29).Kinetic, mechanistic, and theoretical analyses are needed to better understand the CH₄ conversion pathways to C₂H₄ and other products. In principle, there are two plausible pathways following methane activation: 1) H abstraction from adsorbed methyl species forms methylidene (CH₂*) and methylidyne (CH*) species then couple to C₂ products or undergo oxidation to CS₂ or 2) coupling of surface or gas phase methyl species form ethane, which then dehydrogenates to form ethylene or oxidizes to CS₂. For further SOCM optimization it is important to determine which pathways are operative, their relative rates, and the C₂ and CS₂ formation sites.Here we investigate SOCM pathways over a sulfided Fe₃O₄ precatalyst which affords C₂H₄ selectivities near 33%, complete oxide to sulfide conversion, minimal carbon deposition (coking), and 48-h SOCM stability at 950 °C (34). We first summarize SOCM phenomenology, followed by analysis of the Fe phases during sulfurization and SOCM. Next, kinetic/mechanistic studies focus on the methane and S₂ reaction orders, activation energetics, and isotope effects and probe the pathways governing C₂ vs. CS₂ formation. Complementary DFT calculations focus on reaction mechanisms, the active sites, and their role in product formation. The results are used in a microkinetic model to simulate reaction rates, apparent activation barriers, and reaction rate orders and to compare with experiment. Finally, SOCM and OCM are compared, revealing that they follow distinctly different pathways.     

Developmental influence on evolutionary rates and the origin of placental mammal tooth complexity

Development has often been viewed as a constraining force on morphological adaptation, but its precise influence, especially on evolutionary rates, is poorly understood. Placental mammals provide a classic example of adaptive radiation, but the debate around rate and drivers of early placental evolution remains contentious. A hallmark of early dental evolution in many placental lineages was a transition from a triangular upper molar to a more complex upper molar with a rectangular cusp pattern better specialized for crushing. To examine how development influenced this transition, we simulated dental evolution on “landscapes” built from different parameters of a computational model of tooth morphogenesis. Among the parameters examined, we find that increases in the number of enamel knots, the developmental precursors of the tooth cusps, were primarily influenced by increased self-regulation of the molecular activator (activation), whereas the pattern of knots resulted from changes in both activation and biases in tooth bud growth. In simulations, increased activation facilitated accelerated evolutionary increases in knot number, creating a lateral knot arrangement that evolved at least ten times on placental upper molars. Relatively small increases in activation, superimposed on an ancestral tritubercular molar growth pattern, could recreate key changes leading to a rectangular upper molar cusp pattern. Tinkering with tooth bud geometry varied the way cusps initiated along the posterolingual molar margin, suggesting that small spatial variations in ancestral molar growth may have influenced how placental lineages acquired a hypocone cusp. We suggest that development could have enabled relatively fast higher-level divergence of the placental molar dentition.

Whether developmental processes bias or constrain morphological adaptation is a long-standing question in evolutionary biology (1–4). Many of the distinctive features of a species derive from pattern formation processes that establish the position and number of anatomical structures (5). If developmental processes like pattern formation are biased toward generating only particular kinds of variation, adaptive radiations may often be directed along developmental–genetic “lines of least resistance” (2, 4, 6, 7). Generally, the evolutionary consequences of this developmental bias have been considered largely in terms of how it might influence the pattern of character evolution (e.g., refs. 1, 2, 8–10). But development could also influence evolutionary rates by controlling how much variation is accessible to natural selection in a given generation (11).For mammals, the dentition is often the only morphological system linking living and extinct species (12). Correspondingly, tooth morphology plays a crucial role in elucidating evolutionary relationships, time calibrating phylogenetic trees, and reconstructing adaptive responses to past environmental change (e.g., refs. 13–15). One of the most pervasive features of dental evolution among mammals is an increase in the complexity of the tooth occlusal surface, primarily through the addition of new tooth cusps (16, 17). These increases in tooth complexity are functionally and ecologically significant because they enable more efficient mechanical breakdown of lower-quality foods like plant leaves (18).Placental mammals are the most diverse extant mammalian group, comprising more than 6,000 living species spread across 19 extant orders, and this taxonomic diversity is reflected in their range of tooth shapes and dietary ecologies (12). Many extant placental orders, especially those with omnivorous or herbivorous ecologies (e.g., artiodactyls, proboscideans, rodents, and primates), convergently evolved a rectangular upper molar cusp pattern from a placental ancestor with a more triangular cusp pattern (19–21). This resulted from separate additions in each lineage of a novel posterolingual cusp, the "hypocone'''' [sensu (19)], to the tritubercular upper molar (Fig. 1), either through modification of a posterolingual cingulum (“true” hypocone) or another posterolingual structure, like a metaconule (pseudohypocone) (19). The fossil record suggests that many of the basic steps in the origin of this rectangular cusp pattern occurred during an enigmatic early diversification window associated with the divergence and early radiation of several placental orders (20, 21; Fig. 1). However, there remains debate about the rate and pattern of early placental divergence (22–24). On the one hand, most molecular phylogenies suggest that higher-level placental divergence occurred largely during the Late Cretaceous (25, 26), whereas other molecular phylogenies and paleontological analyses suggest more rapid divergence near the Cretaceous–Paleogene (K–Pg) boundary (21, 24, 27–29). Most studies agree that ecological opportunity created in the aftermath of the K–Pg extinction probably played an important role in ecomorphological diversification within the placental orders (30, 31). But exactly how early placentals acquired the innovations needed to capitalize on ecological opportunity remains unclear. Dental innovations, especially those which facilitated increases in tooth complexity, may have been important because they would have promoted expansion into plant-based dietary ecologies left largely vacant after the K–Pg extinction event (32).Open in a separate windowFig. 1.Placental mammal lineages separately evolved complex upper molar teeth with a rectangular cusp pattern composed of two lateral pairs of cusps from a common ancestor with a simpler, triangular cusp pattern. Many early relatives of the extant placental orders, such as Eritherium, possessed a hypocone cusp and a more rectangular primary cusp pattern. Examples of complex upper molars are the following: Proboscidea, the gomphothere Anancus; Rodentia, the wood mouse Apodemus; and Artiodactyla, the suid Nyanzachoerus.Mammalian tooth cusps form primarily during the “cap” and “bell” stage of dental development, when signaling centers called enamel knots establish the future sites of cusp formation within the inner dental epithelium (33, 34). The enamel knots secrete molecules that promote proliferation and changes in cell–cell adhesion, which facilitates invagination of the dental epithelium into an underlying layer of mesenchymal cells (34, 35). Although a range of genes are involved in tooth cusp patterning (36–38), the basic dynamics can be effectively modeled using reaction–diffusion models with just three diffusible morphogens: an activator, an inhibitor, and a growth factor (39–41). Candidate activator genes in mammalian tooth development include Bmp4, Activin A, Fgf20, and Wnt genes, whereas potential inhibitors include Shh and Sostdc, and Fgf4 and Bmp2 have been hypothesized to act as growth factors (38, 40–43). In computer models of tooth development, activator molecules up-regulated in the underlying mesenchyme stimulate differentiation of overlying epithelium into nondividing enamel knot cells. These in turn secrete molecules that inhibit further differentiation of epithelium into knot cells, while also promoting cell proliferation that creates the topographic relief of the cusp (40). Although many molecular, cellular, and physical processes have the potential to influence cusp formation, and thereby tooth complexity (35, 37), parameters that control the strength and conductance of the activator and inhibitor signals, the core components of the reaction–diffusion cusp patterning mechanism (39, 40) are likely to be especially important.Here, we integrate a previous computer model of tooth morphogenesis called ToothMaker (41), with simulations of trait evolution and data from the fossil record (Fig. 2), to examine the developmental origins of tooth complexity in placental mammals. Specifically, we ask the following: 1) What developmental processes can influence how many cusps form? 2) How might these developmental processes influence the evolution of tooth cusp number, especially rates? And 3) what developmental changes may have been important in the origins of the fourth upper molar cusp, the hypocone, in placental mammal evolution?Open in a separate windowFig. 2.Workflow for simulations of tooth complexity evolution. (A) Tooth shape is varied for five signaling and growth parameters in ToothMaker. (B) From an ancestral state, each parameter is varied in 2.5% increments up to a maximum of ± 50% of the ancestral state. (C) Tooth complexity and enamel knot (EK) pattern were quantified for each parameter combination. Tooth complexity was measured using cusp number/EK number and OPC. ToothMaker and placental upper second molars were classified into categories based on EK/cusp pattern. (D) The parameter space was populated with pattern and tooth complexity datums to build a developmental landscape. (E) Tooth complexity evolution was simulated on each developmental landscape. (F) Resulting diversity and pattern of tooth complexity was compared with placental mammal molar diversity.     

Modulation of immune cell reactivity with cis-binding Siglec agonists

Inflammatory pathologies caused by phagocytes lead to numerous debilitating conditions, including chronic pain and blindness due to age-related macular degeneration. Many members of the sialic acid-binding immunoglobulin-like lectin (Siglec) family are immunoinhibitory receptors whose agonism is an attractive approach for antiinflammatory therapy. Here, we show that synthetic lipid-conjugated glycopolypeptides can insert into cell membranes and engage Siglec receptors in cis, leading to inhibitory signaling. Specifically, we construct a cis-binding agonist of Siglec-9 and show that it modulates mitogen-activated protein kinase (MAPK) signaling in reporter cell lines, immortalized macrophage and microglial cell lines, and primary human macrophages. Thus, these cis-binding agonists of Siglecs present a method for therapeutic suppression of immune cell reactivity.

Sialic acid-binding immunoglobulin (IgG)-like lectins (Siglecs) are a family of immune checkpoint receptors that are on all classes of immune cells (1–5). Siglecs bind various sialoglycan ligands and deliver signals to the immune cells that report on whether the target is healthy or damaged, “self” or “nonself.” Of the 14 human Siglecs, 9 contain cytosolic inhibitory signaling domains. Accordingly, engagement of these inhibitory Siglecs by sialoglycans suppresses the activity of the immune cell, leading to an antiinflammatory effect. In this regard, inhibitory Siglecs have functional parallels with the T cell checkpoint receptors CTLA-4 and PD-1 (6–9). As with these clinically established targets for cancer immune therapy, there has been a recent surge of interest in antagonizing Siglecs to potentiate immune cell reactivity toward cancer (10). Conversely, engagement of Siglecs with agonist antibodies can suppress immune cell reactivity in the context of antiinflammatory therapy. This approach has been explored to achieve B cell suppression in lupus patients by agonism of CD22 (Siglec-2) (11, 12), and to deplete eosinophils for treatment of eosinophilic gastroenteritis by agonism of Siglec-8 (13). Similarly, a CD24 fusion protein has been investigated clinically as a Siglec-10 agonist for both graft-versus-host disease and viral infection (14, 15).Traditionally, Siglec ligands have been studied as functioning in trans, that is, on an adjacent cell (16–18), or as soluble clustering agents (9, 19). In contrast to these mechanisms of action, a growing body of work suggests that cis ligands for Siglecs (i.e., sialoglycans that reside on the same cell membrane) cluster these receptors and maintain a basal level of inhibitory signaling that increases the threshold for immune cell activation. Both Bassik and coworkers (20) and Wyss-Coray and coworkers (21) have linked the depletion of cis Siglec ligands with increased activity of macrophages and microglia, and other studies have shown that a metabolic blockade of sialic acid renders phagocytes more prone to activation (22).Synthetic ligands are a promising class of Siglec agonists (17, 23, 24). Many examples rely on clustering architectures (e.g., sialopolymers, nanoparticles, liposomes) to induce their effect (19, 23–26). Indeed, we have previously used glycopolymers to study the effects of Siglec engagement in trans on natural killer (NK) cell activity (16). We and other researchers have employed glycopolymers (16, 23), glycan-remodeling enzymes (27, 28), chemical inhibitors of glycan biosynthesis (22), and mucin overexpression constructs (29, 30) to modulate the cell-surface levels of Siglec ligands. However, current approaches lack specificity for a given Siglec.We hypothesized that Siglec-specific cis-binding sialoglycans displayed on immune cell surfaces could dampen immune cell activity with potential therapeutic applications. Here we test this notion with the synthesis of membrane-tethered cis-binding agonists of Siglec-9 (Fig. 1). Macrophages and microglia widely express Siglec-9 and are responsible for numerous pathologies including age-related inflammation (31), macular degeneration (32), neural inflammation (33), and chronic obstructive pulmonary disease (34). We designed and developed a lipid-linked glycopolypeptide scaffold bearing glycans that are selective Siglec-9 ligands (pS9L-lipid). We show that pS9L-lipid inserts into macrophage membranes, binds Siglec-9 specifically and in cis, and induces Siglec-9 signaling to suppress macrophage activity. By contrast, a lipid-free soluble analog (pS9L-sol) binds Siglec-9 but does not agonize Siglec-9 or modulate macrophage activity. Membrane-tethered glycopolypeptides are thus a potential therapeutic modality for inhibiting phagocyte activity.Open in a separate windowFig. 1.Lipid-tethered glycopolypeptides cluster and agonize Siglecs in cis on effector cells. (A) Immune cells express activating receptors that stimulate inflammatory signaling. (B) Clustering of Siglec-9 by cis-binding agonists stimulates inhibitory signaling that quenches activation.     

Cryo-EM structure and kinetics reveal electron transfer by 2D diffusion of cytochrome c in the yeast III-IV respiratory supercomplex

Energy conversion in aerobic organisms involves an electron current from low-potential donors, such as NADH and succinate, to dioxygen through the membrane-bound respiratory chain. Electron transfer is coupled to transmembrane proton transport, which maintains the electrochemical proton gradient used to produce ATP and drive other cellular processes. Electrons are transferred from respiratory complexes III to IV (CIII and CIV) by water-soluble cytochrome (cyt.) c. In Saccharomyces cerevisiae and some other organisms, these complexes assemble into larger CIII₂CIV_1/2 supercomplexes, the functional significance of which has remained enigmatic. In this work, we measured the kinetics of the S. cerevisiae supercomplex cyt. c-mediated QH₂:O₂ oxidoreductase activity under various conditions. The data indicate that the electronic link between CIII and CIV is confined to the surface of the supercomplex. Single-particle electron cryomicroscopy (cryo-EM) structures of the supercomplex with cyt. c show the positively charged cyt. c bound to either CIII or CIV or along a continuum of intermediate positions. Collectively, the structural and kinetic data indicate that cyt. c travels along a negatively charged patch on the supercomplex surface. Thus, rather than enhancing electron transfer rates by decreasing the distance that cyt. c must diffuse in three dimensions, formation of the CIII₂CIV_1/2 supercomplex facilitates electron transfer by two-dimensional (2D) diffusion of cyt. c. This mechanism enables the CIII₂CIV_1/2 supercomplex to increase QH₂:O₂ oxidoreductase activity and suggests a possible regulatory role for supercomplex formation in the respiratory chain.

Aerobic organisms obtain energy by linking oxidation of food to the synthesis of adenosine triphosphate (ATP). An intermediate step in this process is translocation of protons across a membrane by a series of integral membrane proteins collectively known as the respiratory chain. In eukaryotes, the respiratory chain is found in the inner mitochondrial membrane. Transmembrane proton translocation is driven by electron transfer from NADH and succinate to O₂, which renders the mitochondrial matrix more negative (n side) and the intermembrane space more positive (p side). The resulting electrochemical proton gradient is used for ATP production by ATP synthase and for transmembrane transport processes.In Saccharomyces cerevisiae, NADH donates electrons to dehydrogenases Nde1, Nde2, and Ndi1 (1), which reduce membrane-bound quinone (Q) to quinol (QH₂). Succinate dehydrogenase also contributes to the pool of reduced QH₂ in the membrane. QH₂ diffuses in the hydrophobic core of the lipid bilayer to donate electrons to cytochrome (cyt.) c reductase (also known as cyt. bc₁ or complex III), which forms an obligate homodimer, CIII₂ (reviewed in refs. 2–5). Within CIII, electrons are transferred from QH₂ to cyt. c₁, which is a component of CIII, in a series of reactions known as the proton-motive Q-cycle (Fig. 1A) (3, 5). In brief, binding of QH₂ in a site near the p side, called Q_P (or Q_o), is followed by transfer of one of its electrons, first to an FeS center and then to cyt. c₁. This oxidation of QH₂ is linked to release of its two protons to the p side of the membrane. The second electron from the resulting semiquinone (SQ^•-) in the Q_P site is transferred via hemes b_L and b_H to bound Q in the Q_N (or Q_i) site, reducing it to SQ^•-. Repetition of this sequence of events leads to reduction of the SQ^•- in the Q_N site to QH₂, which abstracts two protons from the n side of the membrane and subsequently equilibrates with the reduced QH₂ pool.Open in a separate windowFig. 1.Reactions catalyzed by CIII₂ and CIV and possible models for electron transfer between them. (A) Q_N and Q_P indicate the two quinol/quinone-binding sites. The black and blue arrows indicate electron-transfer and proton-transfer reactions, respectively. In CIV, H⁺_p and H⁺_s indicate protons that are pumped or used as substrate for reduction of O₂ to H₂O, respectively. Only reactions in one-half of the CIII₂ dimer are indicated. These reactions occur independently in the two halves of the dimer. Transfer of electrons between CIII₂ and CIV via the 3D diffusion of cyt. c is depicted. (B) Electron transfer from CIII₂ to CIV via 2D diffusion of a cyt. c associated with the CIII₂CIV₂ supercomplex. (C) Electron transfer from CIII₂ to CIV via a bridge formed by more than one bound cyt. c.Electrons from cyt. c₁ within CIII are transferred to the water-soluble mobile one-electron carrier cyt. c, which resides in the mitochondrial intermembrane space (Fig. 1A). Reduced cyt. c binds to the p side surface of cytochrome c oxidase (also known as cyt. aa₃, or complex IV), where its electron is transferred first to CIV’s dinuclear copper site, Cu_A, then to heme a, and then to the binuclear heme a₃ and Cu_B catalytic site. Transfer of a second electron from a second cyt. c to CIV leads to reduction of both heme a₃ and Cu_B, allowing O₂ to bind to the heme a₃ iron. This O₂ is reduced to water after transfer of four electrons from four cyt. c molecules to the catalytic site. Each electron transfer to the catalytic site is linked to uptake of one proton from the n side of the membrane and pumping of one proton across the membrane from the n side to the p side (Fig. 1A) (reviewed in refs. 6, 7).Each respiratory complex can function independently of the others, and early studies supported a model in which the complexes diffuse independently in the mitochondrial inner membrane (8) (Fig. 1A). However, more recently, variable fractions of the respiratory complexes have been shown to form larger supercomplexes consisting of two or more components of the respiratory chain (9–13). These supercomplexes can be isolated with preserved enzymatic activity (14, 15), and the overall arrangement of complexes within supercomplexes has been determined in cells from mammals, yeast, and plants (16). In addition, respiratory supercomplexes with different compositions and stoichiometries of components have been isolated using mild detergents, and their high-resolution structures have been determined by single-particle electron cryomicroscopy (cryo-EM) (reviewed in ref. 17).In S. cerevisiae, essentially all CIVs are part of supercomplexes (18) composed of CIII₂ flanked by either one copy or two copies of CIV (9, 10, 18–26). Recent studies showed only minor structural changes in the individual complexes on association (27–29), which suggests that CIII-CIV binding does not result in functional differences in the components, and that supercomplex formation alters only their proximity.Electron transfer from CIII to CIV within the supercomplex requires cyt. c (28). The cyt. c docking sites in CIII and CIV are 60 to 70 Å apart, which is too far to allow direct electron transfer through a single stationary cyt. c on the supercomplex surface. Thus, electron transfer between the two complexes must occur via one of three scenarios (Fig. 1 A–C): 1) diffusion of cyt. c between CIII and CIV via the bulk solvent, referred to as three-dimensional (3D) diffusion (8); 2) lateral diffusion of cyt. c along the supercomplex surface (26, 30–33; also see 34), referred to as two-dimensional (2D) diffusion; or 3) two or more cyt. c molecules that bind simultaneously on the supercomplex surface to bridge CIII and CIV. Here we explored these possibilities to address the functional significance of supercomplex formation with combined structural and functional studies of the S. cerevisiae supercomplex with added cyt. c. The kinetic data exclude the possibility that electron transfer between CIII and CIV by cyt. c involves 3D diffusion of reduced cyt. c between its CIII and CIV binding sites. The structural data show an ensemble of states in which cyt. c is bound to CIII, to CIV, or at intermediate positions between the two on the supercomplex surface. Collectively, these results indicate that electron transfer between CIII and CIV occurs along the surface of the supercomplex, suggesting a mechanism to regulate the redox state of the cyt. c pool by altering the CIII:CIV ratio and through association/dissociation of supercomplexes on changing environmental conditions.     

High-throughput developability assays enable library-scale identification of producible protein scaffold variants

Proteins require high developability—quantified by expression, solubility, and stability—for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low throughput in nature, often slowing the developmental pipeline. We evaluated the ability of 10 variations of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 10⁵ variants was calculated via deep sequencing of populations sorted by proxied developability. We identified the most informative assay combination via cross-validation accuracy and correlation feature selection and demonstrated the ability of machine learning models to exploit nonlinear mutual information to increase the assays’ predictive utility. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a site-wise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

A common constraint across diagnostic, therapeutic, and industrial proteins is the ability to manufacture, store, and use intact and active molecules. These protein properties, collectively termed developability, are often associated to quantitative metrics such as recombinant yield, stability (chemical, thermal, and proteolytic), and solubility (1–5). Despite this universal importance, developability studies are performed late in the commercialization pipeline (2, 4) and limited by traditional experimental capacity (6). This is problematic because 1) proteins with poor developability limit practical assay capacity for measuring primary function, 2) optimal developability is often not observed with proteins originally found in alternative formats [such as display or two-hybrid technologies (7)], and 3) engineering efforts are limited by the large gap between observation size (∼10²) and theoretical mutational diversity (∼10²⁰). Thus, efficient methods to measure developability would alleviate a significant bottleneck in the lead selection process and accelerate protein discovery and engineering.Prior advances to determine developability have focused on calculating hypothesized proxy metrics from existing sequence and structural data or developing material- and time-efficient experiments. Computational sequence-developability models based on experimental antibody data have predicted posttranslational modifications (8, 9), solubility (10, 11), viscosity (12), and overall developability (13). Structural approaches have informed stability (14) and solubility (10, 15). However, many in silico models require an experimentally solved structure or suffer from computational structure prediction inaccuracies (16). Additionally, limited developability information allows for limited predictive model accuracy (17). In vitro methods have identified several experimental protocols to mimic practical developability requirements [e.g., affinity-capture self-interaction nanoparticle spectroscopy (18) and chemical precipitation (19) as metrics for solubility]. However, traditional developability quantification requires significant amounts of purified protein. Noted in both fronts are numerous in silico and/or in vitro metrics to fully quantify developability (1, 5).We sought a protein variant library that would benefit from isolation of proteins with increased developability and demonstrate the broad applicability of the process. Antibodies and other binding scaffolds, comprising a conserved framework and diversified paratope residues, are effective molecular targeting agents (20–24). While significant progress has been achieved with regards to identifying paratopes for optimal binding strength and specificity (25, 26), isolating highly developable variants remains plagued. One particular protein scaffold, Gp2, has been evolved into specific binding variants toward multiple targets (27–29). Continued study improved charge distribution (30), hydrophobicity (31), and stability (28). While these studies have suggested improvements for future framework and paratope residues (including a disulfide-stabilized loop), a poor developability distribution is still observed (32) (Fig. 1 A and B). Assuming the randomized paratope library will lack similar primary functionality, the Gp2 library will simulate the universal applicability of the proposed high-throughput (HT) developability assays.Open in a separate windowFig. 1.HT assays were evaluated for the ability to identify protein scaffold variants with increased developability. (A and B) Gp2 variant expression, commonly measured via low-throughput techniques such as the dot blot shown, highlights the rarity of ideal developability. (C and D) The HT on-yeast protease assay measures the stability of the POI by proteolytic extent. (E and F) The HT split-GFP assay measures POI expression via recombination of a genetically fused GFP fragment. (G and H) The HT split β-lactamase assay measures the POI stability by observing the change in cell-growth rates when grown at various antibiotic concentrations. (I and J) Assay scores, assigned to each unique sequence via deep sequencing, were evaluated by predicting expression (Fig. 3). (K and L) HT assay capacity enables large-scale developability evaluation and can be used to identify beneficial mutations (Fig. 4).We sought HT assays that allow protein developability differentiation via cellular properties to improve throughput. Variations of three primary assays were examined: 1) on-yeast stability (Fig. 1 C and D)—previously validated to improve the stability of de novo proteins (33), antimicrobial lysins (34), and immune proteins (35)—measures proteolytic cleavage of the protein of interest (POI) on the yeast cell surface via fluorescence-activated cell sorting (FACS). We extend the assay by performing the proteolysis at various denaturing combinations to determine if different stability attributes (thermal, chemical, and protease specificity) can be resolved; 2) Split green fluorescent protein (GFP, Fig. 1 E and F)—previously used to determine soluble protein concentrations (36)—measures the assembled GFP fluorescence emerging from a 16–amino acid fragment (GFP₁₁) fused to the POI after recombining with the separably expressed GFP_1-10. We extend the assay by utilizing FACS to separate cells with differential POI expression to increase throughput over the plate-based assay; and 3) Split β-lactamase (Fig. 1 G and H)—previously used to improve thermodynamic stability (37) and solubility (38)—measures cell growth inhibition via ampicillin to determine functional lactamase activity achieved from reconstitution of two enzyme fragments flanking the POI. We expand assay capacity by deep sequencing populations grown at various antibiotic concentrations to relate change in cell frequency to functional enzyme concentration.In this paper, we determined the HT assays’ abilities to predict Gp2 variant developability. We deep sequenced the stratified populations and calculated assay scores (correlating to hypothesized developability) for ∼10⁵ Gp2 variants (Fig. 1I). We then converted the assay scores into a traditional developability metric by building a model that predicts recombinant yield (Fig. 1J). The assays’ capacity enabled yield evaluations for >100-fold traditional assay capacity (Fig. 1K, compared to Fig. 1B) and provide an introductory analysis of factors driving protein developability by observing beneficial mutations via predicted developable proteins (Fig. 1L).     

A new sea-level record for the Neogene/Quaternary boundary reveals transition to a more stable East Antarctic Ice Sheet

Sea-level rise resulting from the instability of polar continental ice sheets represents a major socioeconomic hazard arising from anthropogenic warming, but the response of the largest component of Earth’s cryosphere, the East Antarctic Ice Sheet (EAIS), to global warming is poorly understood. Here we present a detailed record of North Atlantic deep-ocean temperature, global sea-level, and ice-volume change for ∼2.75 to 2.4 Ma ago, when atmospheric partial pressure of carbon dioxide (pCO₂) ranged from present-day (>400 parts per million volume, ppmv) to preindustrial (<280 ppmv) values. Our data reveal clear glacial–interglacial cycles in global ice volume and sea level largely driven by the growth and decay of ice sheets in the Northern Hemisphere. Yet, sea-level values during Marine Isotope Stage (MIS) 101 (∼2.55 Ma) also signal substantial melting of the EAIS, and peak sea levels during MIS G7 (∼2.75 Ma) and, perhaps, MIS G1 (∼2.63 Ma) are also suggestive of EAIS instability. During the succeeding glacial–interglacial cycles (MIS 100 to 95), sea levels were distinctly lower than before, strongly suggesting a link between greater stability of the EAIS and increased land-ice volumes in the Northern Hemisphere. We propose that lower sea levels driven by ice-sheet growth in the Northern Hemisphere decreased EAIS susceptibility to ocean melting. Our findings have implications for future EAIS vulnerability to a rapidly warming world.

The instability of polar continental ice sheets in a warmer future is an issue of major societal concern (1–5). Based on linear extrapolation of recent sea-level rise (2), mean global sea level could increase by 65 ± 12 cm by 2100 relative to the 2005 baseline, consistent with Intergovernmental Panel on Climate Change projections (1) of a ∼30- to 100-cm increase by 2100. Further, satellite observations (4) document substantial mass loss of both the Greenland Ice Sheet (GIS) and the West Antarctic Ice Sheet (WAIS) over the past decade—the two ice sheets that are most susceptible to global warming because of rapidly rising Arctic air temperatures (1) (GIS) and vulnerability to ocean-atmospheric warming (5, 6) (WAIS). The mass balance of the much larger EAIS and its contribution to ongoing sea-level change, however, remain poorly constrained (1).The role of atmospheric partial pressure of carbon dioxide (pCO₂) as a driver of long-term changes in ice volume and sea level over the Cenozoic Era (past ∼66 My) is widely documented (7–9) and there is compelling evidence (6, 10–12) of East Antarctic Ice Sheet (EAIS) retreat during warm intervals of the Pliocene epoch between ∼5.3 and 3.3 Ma when pCO₂ levels (13, 14) last reached values close to the present day (∼400 parts per million volume [ppmv]; Fig. 1 A and B and see SI Appendix, section S1). However, there is disagreement over EAIS behavior under pCO₂ levels (13) similar to those of preindustrial Quaternary times (<280 ppmv). A compilation of marine geochemical paleo-sea-level and pCO₂ records suggests that the EAIS was stable under these conditions (7). In contrast, while the amplitudes of change are controversial (15) (SI Appendix, section S2), sea-level reconstructions from paleoshorelines (16) and benthic geochemical data (9, 17, 18) (Fig. 2) imply EAIS melting during the Quaternary “super-interglacials” of Marine Isotope Stage (MIS) 11 (∼400 ka) and 31 (∼1.07 Ma) under relatively low pCO₂ conditions. Supporting evidence for EAIS retreat during the most recent “super-interglacial” MIS 11 comes from isotope measurements in mineral deposits recording past changes in subglacial East Antarctic waters (19), as well as records of ice-rafted debris (IRD) and detrital sediment neodymium isotopes from offshore the Wilkes Subglacial Basin (20). The latter records (20) also indicate EAIS retreat during the last interglacial MIS 5e (∼120 ka). Melting of the EAIS as inferred in the late Quaternary was likely driven by ocean–atmosphere warming around Antarctica and grounding-line retreat in response to ice–ocean interactions (19, 20).Open in a separate windowFig. 1.Neogene to Quaternary climate and sea-level evolution. (A) LR04 stack (21) for the past 5 My; arrow indicates the iNHG (∼3.6 to 2.4 Ma) and its culmination (thick-arrowed interval) (22); green line indicates the benthic δ¹⁸O level associated with MIS 101. (B) Atmospheric pCO₂ estimates of refs. 13 (blue) and 23 (purple) for the past 5 My; the late Quaternary glacial–interglacial pCO₂ range (1) is indicated as preindustrial pCO₂ band. Yellow shading in A and B highlights the study interval (∼2.75 to 2.4 Ma). (C and D) Site U1313 benthic δ¹⁸O and Mg/Ca raw data, respectively. (E) Site U1313 deep-sea temperature. (F) Site U1313 δ¹⁸O_sw-based sea level relative to present (black line); blue shading: 95% probability interval from Monte Carlo simulations (2σ); red line: threshold (11.6 msle) above which a smaller-than-present EAIS is signaled (24–26); m = marine part of EAIS, t = terrestrial part of EAIS. Glacials are highlighted in gray.Open in a separate windowFig. 2.Implication of different sea-level-δ¹⁸O_sw conversions for estimates of interglacial ice-volume loss. y axis shows lower-than-modern δ¹⁸O_sw values (∆δ¹⁸O_sw) and the x axis (log-scale) the corresponding sea-level increase for commonly used conversion factors (27–29) (0.011 [black], 0.010 [purple], and 0.008 ‰⋅m⁻¹ [red]) and those for Antarctica only (11) (0.014 ‰⋅m⁻¹) ignoring (yellow) and incorporating (brown) the impact of its marine-based ice sheets. Stars mark ∆δ¹⁸O_sw for interglacials of this study and corresponding sea-level equivalents in dependence of the conversion applied. Orange, blue, and purple diamonds show the same for MIS 31, 11 (18), and 5e (17), respectively. Vertical lines indicate the sea-level increase resulting from complete melting of the GIS (+7.3 m), WAIS (+4.3 m), and EAIS (+53.3 m) (24–26).To further investigate past EAIS response to climate forcing we studied the Neogene/Quaternary transition when mean pCO₂ (13, 23) fell from levels similar to the anthropogenically perturbed values of today into the Quaternary range, leading to progressive high-latitude cooling and the intensification of Northern Hemisphere Glaciation (21, 30–33) (iNHG; Fig. 1 A and B). Our approach is based on a simple approximation that, once estimated past global sea level exceeds 11.6 m sea-level equivalent (msle) above modern, which corresponds to the complete melting of the present-day GIS [7.3 msle (24, 25)] and the marine- and land-based WAIS [3.4 and 0.9 msle (25, 26), respectively], EAIS instability (i.e., a retreat from its present-day size) can be inferred (see SI Appendix, section S4.1 for details). We quantified sea-level and ice-volume changes for the interval ∼2.75 to 2.4 Ma (MIS G7 to 95) by measuring the oxygen-isotope composition (δ¹⁸O) and Mg/Ca ratio in well-preserved benthic foraminiferal calcite (Oridorsalis umbonatus) from Integrated Ocean Drilling Program (IODP) Site U1313 [41°0′N, 32°57′W; 3,426-m water depth (34)] in the North Atlantic Ocean (Fig. 1 C and D). Using this approach we reconstructed changes in seawater δ¹⁸O (δ¹⁸O_sw), a proxy for global sea level and continental ice volume (35). This was done by 1) calculating bottom-water temperatures (BWT) derived from Mg/Ca (36) (Fig. 1E), 2) combining Mg/Ca-derived BWTs with δ¹⁸O to determine δ¹⁸O_sw (37) (Fig. 1F), and 3) converting δ¹⁸O_sw to sea level using a relationship between changes in sea level and δ¹⁸O_sw of 0.011 ‰⋅m⁻¹ (27) (Materials and Methods and Fig. 1F). Ninety-five percent probability intervals calculated through Monte Carlo simulations for individual sea-level data points yield an average uncertainty for our sea-level estimates of ± 28 m (∼2σ [SD]) (Materials and Methods and Fig. 1F), roughly equivalent to the decay/growth of ice four times greater than the GIS. Our approach was validated by reconstructing δ¹⁸O_sw for the recent (∼0 to 7 ka) at IODP Site U1313 and for late Holocene core-top (multicorer) samples from a neighboring site (MSM58) which are indistinguishable from the observed modern-day values (see Materials and Methods and SI Appendix, section S4.2.8 for details).