Pictet–Spengler reaction-based biosynthetic machinery in fungi

The Pictet–Spengler (PS) reaction constructs plant alkaloids such as morphine and camptothecin, but it has not yet been noticed in the fungal kingdom. Here, a silent fungal Pictet–Spenglerase (FPS) gene of Chaetomium globosum 1C51 residing in Epinephelus drummondhayi guts is described and ascertained to be activable by 1-methyl-l-tryptophan (1-MT). The activated FPS expression enables the PS reaction between 1-MT and flavipin (fungal aldehyde) to form “unnatural” natural products with unprecedented skeletons, of which chaetoglines B and F are potently antibacterial with the latter inhibiting acetylcholinesterase. A gene-implied enzyme inhibition (GIEI) strategy has been introduced to address the key steps for PS product diversifications. In aggregation, the work designs and validates an innovative approach that can activate the PS reaction-based fungal biosynthetic machinery to produce unpredictable compounds of unusual and novel structure valuable for new biology and biomedicine.Microbes and plants produce a multitude of unpredictably structured organic molecules known as secondary metabolites (natural products), from which more than half of globally marketed drugs have been developed (1–3). Large-scale genomic mining has indicated that microbial secondary metabolites are substantially underestimated because many biosynthetic genes remain silent or less active in the laboratory cultivation conditions (4, 5). Accordingly, there has long been an urgent need to develop a new strategy that enables microorganisms to produce more unforeseeable bioactive compounds, which are important to the drug discovery efforts to combat life-threatening diseases (6, 7), and to the complexity-based driving force for synthetic and material chemistry (8–10).Characterized by forming a piperidine ring through a condensation of β-arylethylamine with an aldehyde, Pictet–Spengler (PS) reaction contributes greatly to the framework diversification of important alkaloidal phytochemicals such as morphine, camptothecin, and reserpine (Fig. 1A), with plant-derived Pictet–Spenglerase (called strictosidine synthetase, STR) mechanistically addressed (11). The PS mechanism has been presumed to involve in the tetrahydroisoquinoline antibiotic biosynthesis in the bacterium Streptomyces lavendulae (12, 13), and likely in the biosynthetic pathway of hyrtioreticulin F in the marine sponge Hyrtios reticulatus (14). However, surprisingly, nothing is known concerning the PS reaction in the fungal kingdom.Open in a separate windowFig. 1.Alkaloids derived from the PS reaction. (A) Representatives for PS reaction-based phytochemicals. (B) 1-MT has been found to be a potent up-regulator for the FPS gene expression, and a suitable FPS substrate for its PS condensation with flavipin to yield unnatural natural products (1−8) with unprecedented skeletons.Most if not all Chaetomium fungi in the Chaetomiaceae family produce l-tryptophan–derived alkaloids, but “refuse” to generate any PS reaction-based secondary metabolite (15–18). However, a comparative genomic analysis has clarified that C. globosum 1C51 does have an FPS gene (CHGG_06703, STR-like) (SI Appendix, Fig. S25), but remains silent or poorly activated in the laboratory cultivations because no PS-derived secondary metabolite has been detected in the fungal culture. Therefore, this C. globosum 1C51 strain was adopted here to test for the activation of its “unworking” PS reaction-based biosynthetic machinery. As a result, 1-methyl-l-tryptophan (1-MT) was demonstrated to be able to up-regulate the FPS expression and condense with the fungal aldehyde flavipin (3,4,5-trihydroxy-6-methyl phthalaldehyde) to form unexpectedly a family of skeletally unprecedented alkaloids, trivially named chaetoglines A−H (1−8) (Fig. 1B). A gene-implied enzyme inhibition (GIEI) strategy, derived from the hypothesis-based enzyme modulation described elsewhere (19, 20), was introduced to identify the key diversification steps for the PS reaction-derived compounds (Figs. 2–4). Chaetoglines B (2) and F (6) have been found to be more antibacterial than tinidazole (a coassayed drug prescribed in clinic for bacterial infections) against pathogenic anaerobes Veillonella parvula, Bacteroides vulgatus, Streptococcus sp., and Peptostreptococcus sp. Moreover, alkaloid 6 is potently inhibitory on acetylcholinesterase (AChE), an effective target enzyme exploited for the treatment of Alzheimer’s disease (21, 22).Open in a separate windowFig. 2.LC-MS profile-based comparisons for the chaetogline production in monooxygenase inhibitor exposed fungal cultures: (A) for 3–6; (B) for 1–2 and 7–8. The ESI-MS spectra (C) of 1−8 (①) displayed the corresponding protonated and Na⁺-liganded molecular ions. Samples were ethyl acetate extracts derived from 1-MT supplemented C. globosum 1C51 cultures without (②) and with the separate exposure to PB, PR, and MMI at 0.1 (③∼⑤) and 1.0 mM (⑥∼⑧), respectively.Open in a separate windowFig. 4.Proposed generation of the fungal PS-derived products. Catalyzed by FPS, 1-MT and flavipin (a fungal aldehyde) undergo PS reaction to form chaetoglines A−H (1−8) in concert with tailing reactions including oxidation, decarboxylation, and Aldol reaction. The Schiff base intermediate 9 tends to tautomerize via 10 and 11 to give chaetogline C (3) that can be methyl-esterified into chaetogline D (4). Intramolecular cyclization of 10 gives 12, which is oxidizable into chaetoglines A (1) and E (5), the latter yielding chaetogline F (6) after the oxidative and decarboxylative aromatization. Chaetogline E (5) can also be oxidized to intermediate 13, which gives 14 after condensing presumably via the decarboxylative Aldol reaction (34, 35) with 1-M-IAA derived from 1-MT by the fungi (SI Appendix, Fig. S24). Intermediate 14 undergoes intramolecular cyclization, monooxygenation, and isomerization to form chaetogline G (7), which after decarboxylation gives chaetogline H (8), a precursor of chaetogline B (2).     

Electrophilic aromatic substitution reactions of compounds with Craig-Möbius aromaticity

Electrophilic aromatic substitution (EAS) reactions are widely regarded as characteristic reactions of aromatic species, but no comparable reaction has been reported for molecules with Craig-Möbius aromaticity. Here, we demonstrate successful EAS reactions of Craig-Möbius aromatics, osmapentalenes, and fused osmapentalenes. The highly reactive nature of osmapentalene makes it susceptible to electrophilic attack by halogens, thus osmapentalene, osmafuran-fused osmapentalene, and osmabenzene-fused osmapentalene can undergo typical EAS reactions. In addition, the selective formation of a series of halogen substituted metalla-aromatics via EAS reactions has revealed an unprecedented approach to otherwise elusive compounds such as the unsaturated cyclic chlorirenium ions. Density functional theory calculations were conducted to study the electronic effect on the regioselectivity of the EAS reactions.

Aromaticity, a core concept in chemistry, was initially introduced to account for the bonding, stability, reactivity, and other properties of many unsaturated organic compounds. There have been many elaborations and extensions of the concept of aromaticity (1, 2). The concepts of Hückel aromaticity and Möbius aromaticity are widely accepted (Fig. 1A). A π-aromatic molecule of the Hückel type is planar and has 4n + 2 conjugated π-electrons (n = 0 or an integer), whereas a Möbius aromatic molecule has one twist of the π-system, similar to that in a Möbius strip, and 4n π-electrons (3, 4). Since the discovery of naphthalene in 1821, aromatic chemistry has developed into a rich field and with a variety of subdisciplines over the course of its 200-y history, and the concept of aromaticity has been extended to other nontraditional structures with “cyclic delocalization of mobile electrons” (5). For example, benzene-like metallacycles—predicted by Hoffmann et al. as metallabenzenes—in which a metal replaces a C–H group in the benzene ring (6), have garnered extensive research interest from both experimentalists and theoreticians (7–12). As paradigms of the metalla-aromatic family, most complexes involving metallabenzene exhibit thermodynamic stability, kinetic persistence, and chemical reactivity associated with the classical aromaticity concept (13–15). Typically, like benzene, metallabenzene can undergo characteristic reactions of aromatics such as electrophilic aromatic substitution (EAS) reactions (16–18) (Fig. 1B, I) and nucleophilic aromatic substitution reactions (19–21).Open in a separate windowFig. 1.Schematic representations of aromaticity classification (A) and EAS reactions (B) of benzene, metallabenzene, and polycyclic metallacycles with Craig-Möbius aromaticity.The incorporation of transition metals has also led to an increase in the variety of the aromatic families (22–25). We have reported that stable and highly unusual bicyclic systems, metallapentalenes (osmapentalenes), benefit from Craig-Möbius aromaticity (26–30). In contrast to other reported Möbius aromatic compounds with twisted topologies, which are known as Heilbronner-Möbius aromatics (31–34), the involvement of transition metal d orbitals in π-conjugation switches the Hückel anti-aromaticity of pentalene into the planar Craig-Möbius aromaticity of metallapentalene (35–38) (Fig. 1A, III). Both the twisted topology and the planar Craig-Möbius aromaticity are well established and have been accepted as reasonable extensions of aromaticity (39–43). There has been no experimental evidence, however, as to whether these Möbius aromatic molecules can undergo classical aromatic substitution reactions, such as EAS reactions, instead of addition reactions. Given the key role of EAS in aromatic chemistry to obtain various derivatives, we sought to extend the understanding of the reactivity paradigm in the metalla-aromatic family.Our recent synthetic efforts associated with the metallapentalene system prompted us to investigate whether typical EAS reactions could proceed in these Craig-Möbius aromatics. If so, how could substitution be achieved in the same way that it is with traditional Hückel aromatics such as benzenes? In this paper, we present EAS reactions, mainly the halogenation of osmapentalene, osmafuran-fused osmapentalene, and osmabenzene-fused osmapentalene, which follow the classic EAS mechanistic scheme (Fig. 1B). With the aid of density functional theory (DFT) calculations, we characterized the effects on EAS reactivity and regioselectivity.     

Promotion of atomic hydrogen recombination as an alternative to electron trapping for the role of metals in the photocatalytic production of H2

The production of hydrogen from water with semiconductor photocatalysts can be promoted by adding small amounts of metals to their surfaces. The resulting enhancement in photocatalytic activity is commonly attributed to a fast transfer of the excited electrons generated by photon absorption from the semiconductor to the metal, a step that prevents deexcitation back to the ground electronic state. Here we provide experimental evidence that suggests an alternative pathway that does not involve electron transfer to the metal but requires it to act as a catalyst for the recombination of the hydrogen atoms made via the reduction of protons on the surface of the semiconductor instead.Photocatalysis is a promising technology to address problems in chemical synthesis (1), environmental remediation (2, 3), and energy utilization (4). The potential for harvesting solar energy and storing it as a chemical fuel using photocatalysts is particularly appealing. Specifically, H₂ may be produced via water splitting this way (5–7). In fact, many photocatalysts have been reported already capable of producing hydrogen out of water, even if none of those are yet commercially viable. Such photocatalysis rely on capturing photon energy via excitation of electrons from the valence band to the conduction band of appropriate semiconductors such as titania, which is often cited as a prototypical example (8–10), creating an excited electron–hole pair that is then used to promote redox reactions (11, 12). However, for the photocatalysts to be useful, the lifetime of the electron–hole pair needs to be long enough to be accessible for chemical conversions. The search for photocatalytic systems that fulfill this and other key requirements continues, and would be greatly facilitated by a clearer understanding of the underlying chemical process.It is well known that the addition of metals such as platinum or gold to semiconductors enhances their activity as photocatalysts, in particular for water splitting to produce H₂ (13–15). This effect is currently explained by a mechanism where the excited electrons produced by absorption of light are transferred from the semiconductor to the metal before they have the opportunity to recombine with their corresponding holes and return to the ground electronic state (Fig. 1A) (16–18). In this scheme, the protons from water are reduced on the surface of the metal, in sites physically separated from those on the semiconductor, where oxygen production takes place. Here we present evidence that challenges this conventional explanation, and offer an alternative mechanism for the promotion of photocatalysis by metals. Specifically, using the Au/TiO₂ metal–semiconductor pair as a model system, we show that electron transfer to the metal may not play a significant role in photocatalysis. Instead, the data support a model where the excited electron promotes the reduction of protons on the surface of the semiconductor, not the metal, and where the reduced atomic hydrogen then migrates to the metal and recombines to yield the final molecular hydrogen product (Fig. 1B).Open in a separate windowFig. 1.Schematic representation of the mechanisms (Upper) and electronic transitions (Lower) proposed to explain the role of metals (Au) in the photocatalytic splitting of water with semiconductors (TiO₂). (A) In the conventional model, the metal acts as an electron trap that physically separates the excited electron used for proton reduction from the oxidation step that occurs on the surface of the semiconductor. (B) Our alternative model proposes that H⁺ reduction occurs at semiconductor sites but that the resulting hydrogen atoms need to migrate to the metal to recombine and produce the final H₂ product.     

Early guenon from the late Miocene Baynunah Formation,Abu Dhabi,with implications for cercopithecoid biogeography and evolution

A newly discovered fossil monkey (AUH 1321) from the Baynunah Formation, Emirate of Abu Dhabi, United Arab Emirates, is important in a number of distinct ways. At ∼6.5–8.0 Ma, it represents the earliest known member of the primate subfamily Cercopithecinae found outside of Africa, and it may also be the earliest cercopithecine in the fossil record. In addition, the fossil appears to represent the earliest member of the cercopithecine tribe Cercopithecini (guenons) to be found anywhere, adding between 2 and 3.5 million y (∼50–70%) to the previous first-appearance datum of the crown guenon clade. It is the only guenon—fossil or extant—known outside the continent of Africa, and it is only the second fossil monkey specimen so far found in the whole of Arabia. This discovery suggests that identifiable crown guenons extend back into the Miocene epoch, thereby refuting hypotheses that they are a recent radiation first appearing in the Pliocene or Pleistocene. Finally, the new monkey is a member of a unique fauna that had dispersed from Africa and southern Asia into Arabia by this time, suggesting that the Arabian Peninsula was a potential filter for cross-continental faunal exchange. Thus, the presence of early cercopithecines on the Arabian Peninsula during the late Miocene reinforces the probability of a cercopithecoid dispersal route out of Africa through southwest Asia before Messinian dispersal routes over the Mediterranean Basin or Straits of Gibraltar.Cercopithecine monkeys (Order Primates, Superfamily Cercopithecoidea, Family Cercopithecidae, Subfamily Cercopithecinae), also known as cheek-pouch monkeys, are the most speciose and widely distributed group of living Old World primates. Recent molecular estimates date the divergence of Cercopithecinae from Colobinae (leaf-eating monkeys) to between 17.6 Ma (range 21.5–13.9 Ma) and 14.5 Ma (range 16.2–12.8 Ma) and the origin of crown Cercopithecinae to around 11.5 Ma (range 13.9–9.2 Ma) (1, 2). However, the earliest known fossil cercopithecines only appear much later, around 7.4 Ma in the Turkana Basin of East Africa (3, 4).Cercopithecine monkeys are divided into two tribes: Cercopithecini, including African guenons (Allenopithecus, Miopithecus, Chlorocebus, Erythrocebus, Allochrocebus, Cercopithecus), and Papionini, which includes African and Eurasian macaques (Macaca) as well as African papionins (Papio, Lophocebus, Rungwecebus, Theropithecus, Mandrillus, Cercocebus). Of the living cercopithecines, only two genera are known outside of the African continent, both of them papionins: Papio (found on the Arabian Peninsula) and Macaca (found throughout Southern and Southeast Asia, and introduced in Gibraltar). The earliest fossil cercopithecines known outside of Africa are attributed to the genus Macaca and appear to be latest Miocene or early Pliocene in age (∼6.0–5.0 Ma) (Fig. 1) (5–9). Until now, no guenons, extant or extinct, have ever been known outside of the African continent.Open in a separate windowFig. 1.Hypothesized cercopithecoid dispersal routes out of Africa in relation to the known late Miocene fossil record. The oldest cercopithecine, Parapapio lothagamensis (light blue circles), is known from ∼7.4–6.1 Ma in the Turkana Basin and Tugen Hills, Kenya (3, 4, 41). An unnamed fossil papionin (purple circle) is known from the late Miocene of Ongoliba, Congo (5, 57). Macaca spp. (dark blue circles) are located throughout North Africa at sites ranging in age from ∼6.5–5.5 Ma (5, 8, 58, 59), and Macaca spp. first appear in Europe ∼6.0–5.3 Ma and in China in the early Pliocene (5–9). The oldest colobine outside of Africa, Mesopithecus (green circles), is known from a number of late Miocene sites securely dated between ∼8.5 and 5.3 Ma in Greece, Macedonia, Italy, Ukraine, Iran, Afghanistan, possibly Pakistan, and China (46–48). Three dispersal routes for cercopithecoids can be hypothesized: route 1 imagines a dispersal event over the Straits of Gibraltar or Mediterranean Basin into Europe and Asia; route 2 postulates a dispersal event through the Arabian Sinai Peninsula; and route 3 suggests a migration over the Arabian Straits of Bab el Mandeb. The discovery of AUH 1321 and AUH 35 in Abu Dhabi at >6.5–8 Ma (red circle), contemporaneous with the first appearance of Mesopithecus sp. in Eurasia and ∼1–2 million y earlier than the appearance of Macaca spp. in Eurasia, suggests scenarios 2 and 3 were possible before scenario 1. None of these scenarios is mutually exclusive and may have occurred in combination or succession.Three possible routes can be reasonably hypothesized for cercopithecine (and cercopithecoid) dispersal out of Africa and into Europe and Asia during the late Miocene: (i) over the Mediterranean Basin or Straits of Gibraltar to the north/northwest, (ii) across the Arabian Sinai Peninsula to the northeast, or (iii) across the Arabian Straits of Bab el Mandeb to the east (Fig. 1). Fossil Macaca specimens from the terminal Miocene of Spain and Italy have been suggested to provide evidence for the use of a route across the Mediterranean Basin or the Straits of Gibraltar via an ephemeral land bridge either immediately before—or perhaps associated with—the drop in Mediterranean sea levels during the Messinian (∼6.0–5.3 Ma) (8–19). Paleontological evidence for an Arabian route has been lacking, but paleogeographic and paleoenvironmental work on circum-Arabia suggests that the region did not present a persistent ecological barrier to some amount of intercontinental exchange during the late Miocene (20). In fact, an established land connection through Sinai was probably present during this time period, and oceanic spreading is not estimated to have begun in the southern Red Sea until around 5 Ma, with progressive development of open marine conditions throughout the Pliocene. Thus, before 6.5 Ma, a southern route in the region of the Straits of Bab el Mandeb was also possible (Fig. 1) (21).Although Arabia is a large area of the earth, fossil monkeys have so far been represented by only a single specimen, an isolated male lower canine (AUH 35), discovered in 1989 by A.H. and Peter Whybrow in the late Miocene Baynunah Formation, Abu Dhabi, United Arab Emirates (22–25). The specimen came from Jebel Dhanna, site JDH-3 (JD-3 in refs. 24 and 26) (Fig. 2), a locality now lost to industrial development. Because male cercopithecid lower canines are not metrically identifiable beyond the Family level of classification (23), AUH 35 was described as a cercopithecid with indeterminate affinities. Here we report the discovery of a second monkey specimen from the Baynunah Formation in Abu Dhabi (AUH 1321), found almost 20 y after the first. AUH 1321 clearly represents a cercopithecine and, because it is dated to between 6.5 and 8.0 Ma, it is the oldest cercopithecine yet known outside of Africa and possibly the oldest cercopithecine in the fossil record. Thus, the discovery of AUH 1321 provides the earliest paleontological evidence of cercopithecine dispersal out of continental Africa and possibly hints at an Arabian cercopithecoid dispersal route into Eurasia during the Late Miocene (Fig. 1). Furthermore, we believe AUH 1321 can be attributed to the Cercopithecini (guenons) and, therefore, it represents the only record of this tribe, living or fossil, yet known outside of Africa.Open in a separate windowFig. 2.Map illustrating the location of the two fossil sites in the Baynunah Formation that have produced fossil monkeys. Top Right Inset shows the location of the SHU 2–2 excavation (kite aerial photography by Nathan Craig).     

Coevolution can reverse predator–prey cycles

A hallmark of Lotka–Volterra models, and other ecological models of predator–prey interactions, is that in predator–prey cycles, peaks in prey abundance precede peaks in predator abundance. Such models typically assume that species life history traits are fixed over ecologically relevant time scales. However, the coevolution of predator and prey traits has been shown to alter the community dynamics of natural systems, leading to novel dynamics including antiphase and cryptic cycles. Here, using an eco-coevolutionary model, we show that predator–prey coevolution can also drive population cycles where the opposite of canonical Lotka–Volterra oscillations occurs: predator peaks precede prey peaks. These reversed cycles arise when selection favors extreme phenotypes, predator offense is costly, and prey defense is effective against low-offense predators. We present multiple datasets from phage–cholera, mink–muskrat, and gyrfalcon–rock ptarmigan systems that exhibit reversed-peak ordering. Our results suggest that such cycles are a potential signature of predator–prey coevolution and reveal unique ways in which predator–prey coevolution can shape, and possibly reverse, community dynamics.Population cycles, e.g., predator–prey cycles, and their ecological drivers have been of interest for the last 90 y (1–4). Classical models of predator–prey systems, developed first by Lotka (5) and Volterra (6), share a common prediction: Prey oscillations precede predator oscillations by up to a quarter of the cycle period (7). When plotted in the predator–prey phase plane, these cycles have a counterclockwise orientation (4). These cycles are driven by density-dependent interactions between the populations. When predators are scarce, prey increase in abundance. As their food source increases, predators increase in abundance. When the predators reach sufficiently high densities, the prey population is driven down to low numbers. With a scarcity of food, the predator population crashes and the cycle repeats.While many cycles, like the classic lynx–hare cycles (Fig. 1A) (3), exhibit the above characteristics, predator–prey cycles with different characteristics have also been observed. For example, antiphase cycles where predator oscillations lag behind prey oscillations by half of the cycle period (Fig. 1B) (8) and cryptic cycles where the predator population oscillates while the prey population remains effectively constant (Fig. 1C) (9) have been observed in experimental systems. This diversity of cycle types motivates the question, “Why do cycle characteristics differ across systems?”Open in a separate windowFig. 1.Examples of different kinds of predator–prey cycles. (A) Counterclockwise lynx–hare cycles (3). (B) Antiphase rotifer–algal cycles (8). (C) Cryptic phage-bacteria cycles (9). In all time series, red and blue correspond to predator and prey, respectively. See SI Text, section C for data sources.In Lotka–Volterra and other ecological models, predator and prey life history traits are assumed to be fixed. However, empirical studies across taxa have shown that prey (9–16) and predators (17–20) can evolve over ecological time scales. That is, changes in allele frequencies (and associated phenotypes) can occur at the same rate as changes in population densities or spatial distributions and alter the ecological processes driving the changes in population densities or distributions; this phenomenon has been termed “eco-evolutionary dynamics” (21, 22). Furthermore, predator–prey coevolution is important for driving community composition and dynamics (16, 19, 20, 23–26). This body of work suggests that the interaction between ecological and evolution processes has the potential to alter the ecological dynamics of communities.Experimental (8, 9, 13, 14) and theoretical studies (13, 27, 28) have shown that prey or predator evolution alone can alter the characteristics of predator–prey cycles and drive antiphase (Fig. 1B) and cryptic (Fig. 1C) cycles. Additional theoretical work has shown that predator–prey coevolution can also drive antiphase and cryptic cycles (29). Thus, evolution in one or both species is one mechanism through which antiphase or cryptic predator–prey cycles can arise. However, it is unclear if coevolution can drive additional kinds of cycles with characteristics different from those in Fig. 1.The main contribution of this study is to show that predator–prey coevolution can drive unique cycles where peaks in predator abundance precede peaks in prey abundance, the opposite of what is predicted by classical ecological models. We refer to these reversed cycles as “clockwise cycles.” The theoretical and empirical finding of clockwise cycles represents an example of how evolution over ecological time scales can alter community-level dynamics.     

Stereospecific cis- and trans-ring fusions arising from common intermediates

Highly concise and stereospecific routes to cis and trans fusion, carrying various functionality at one of the bridgehead carbons, have been accomplished.Our group has been studying the synthetic utility of the Diels–Alder (DA) reaction of the parent cyclobutenone, 2 (1). Recently reported results demonstrate that 2 is a highly reactive, endo-selective dienophile (Fig. 1, Eq. 1) (2). We have also developed a series of intramolecular Diels–Alder (IMDA) reactions, wherein the cyclobutenone component is tethered to various conjugated dienes (compare 4); cycloaddition of these substrates delivers adducts of the type 5, which can be readily converted to trans-fused systems bearing iso-DA patterns (Fig. 1, Eq. 2, 5→6) (3). Additionally, we have described the synthesis and DA cycloaddition of an even more powerful dienophile, 2-bromocyclobutenone (Fig. 1, Eq. 3, 7) (4). The direct adducts of this [4+2] reaction (compare 8) are readily converted to norcarane carboxylic acids (9) through exposure to hydroxide base. The research described herein was initially focused on efforts to add carbon-based nucleophiles to DA cycloadducts of the type 8. It might well have been expected that such reactions would give rise to products such as 10, wherein a ketone is appended to the junction of the norcarane system (Fig. 1, Eq. 4).Open in a separate windowFig. 1.Expanding the scope of the Diels–Alder reaction.     

Heterogeneity of functional groups in a metal–organic framework displays magic number ratios

Multiple organic functionalities can now be apportioned into nanoscale domains within a metal-coordinated framework, posing the following question: how do we control the resulting combination of “heterogeneity and order”? Here, we report the creation of a metal–organic framework, MOF-2000, whose two component types are incorporated in a 2:1 ratio, even when the ratio of component types in the starting solution is varied by an order of magnitude. Statistical mechanical modeling suggests that this robust 2:1 ratio has a nonequilibrium origin, resulting from kinetic trapping of component types during framework growth. Our simulations show how other “magic number” ratios of components can be obtained by modulating the topology of a framework and the noncovalent interactions between component types, a finding that may aid the rational design of functional multicomponent materials.The assembly of multiple types of component offers a potential route to the precise control of component heterogeneity within ordered 3D frameworks. Metal–organic frameworks (MOFs) (1–5) possessing well-defined connectivities (6, 7) and tunable pore sizes (8–10) can assemble from a variety of building blocks (11–17). Recently, a MOF harboring two components distributed in a heterogeneous fashion on an ordered framework was demonstrated (1, 2). There exists no framework, however, whose component heterogeneity remains controlled in the face of changes of environment. Here, we report the creation of a material with exactly this property. MOF-2000 is assembled from two types of organic struts, called L_r and L_b (Fig. 1A). These struts have identical rigid backbones but bear either a crown ether (L_r) or [2]catenane (L_b) side chain attached at their center. X-ray diffraction of MOF-2000 single crystals revealed that struts form a twofold interpenetrated cubic framework of pcu-c topology (Fig. 1A, Right, and SI Appendix, section S1.3; structure available in Dataset S1). As a result of optical investigations, we confirmed that the two struts are distributed in the crystalline framework in an isotropic manner (Fig. 1B), suggesting that the two components are not distributed in a simple periodic way throughout the framework (because such arrangements would give rise to optical anisotropy; SI Appendix, section S1.5).Open in a separate windowFig. 1.MOF-2000 is a periodic framework harboring controlled component heterogeneity. (A) Chemical structures of organic struts H₂L_r and H₂L_b⁴⁺ incorporated in MOF-2000 (Methods Summary), and the MOF-2000 crystal structure [key: carbon, black; oxygen, red; Zn₄O(CO₂)₆ polyhedra, blue; all hydrogen atoms have been omitted for clarity]. (B) Optical images of MOF-2000 single crystal: without polarizer (up) and in between crossed polarizers (down). The lack of birefringence indicates that MOF-2000 is optically isotropic. The edge of the crystal is about 0.1 mm. (C) As determined by ¹H-NMR and powder X-ray diffraction measurements (SI Appendix, sections S1.3 and S1.4), MOF-2000 consists of a twofold interpenetrated crystalline framework (of topology pcu-c) of 2:1 L_r:L_b ratio (estimated 5% error), for a wide range of solution ratios (shaded region). (D) MOF-2000 therefore combines the regularity of a covalent framework with controlled irregularity of its components.Even though the arrangement of the two components in MOF-2000 is indiscernible by X-ray crystallography, presumably as a result of the positional disorder of the two strut types within the framework, and the rotational (11, 18) and conformational (11) disorder of the side chains, the presence of the two organic struts can be clearly determined by ¹H-NMR (SI Appendix, section S1.4). Strikingly, MOF-2000 displays a 2:1 ratio of L_r and L_b struts, even when the ratios of components in the parent solution are varied by over an order of magnitude (Fig. 1C). This feature makes MOF-2000 unique among multicomponent extended frameworks, whose struts are usually incorporated in ratios determined largely by initial solution conditions (1, 19). Thus, MOF-2000 incorporates environmentally robust component heterogeneity within a regular covalent framework (Fig. 1D).The robust 2:1 composition does not have an obvious thermodynamic origin, in contrast to common organic cocrystals or ionic systems such as CsCl (20). The latter possess robust compositions as a consequence of charge complementarity. In MOF-2000, the L_b strut bears a charge of +4 (neutralized in the assembly by counterions), whereas the L_r strut is uncharged. Thus, charge complementarity alone does not suggest a 2:1 component ratio. Simple packing arguments suggest that it is reasonable that L_b is not the majority species within the framework (because it is bulky), but likewise do not offer a simple explanation for the 2:1 ratio.     

Insect nicotinic receptor interactions in vivo with neonicotinoid,organophosphorus, and methylcarbamate insecticides and a synergist

The nicotinic acetylcholine (ACh) receptor (nAChR) is the principal insecticide target. Nearly half of the insecticides by number and world market value are neonicotinoids acting as nAChR agonists or organophosphorus (OP) and methylcarbamate (MC) acetylcholinesterase (AChE) inhibitors. There was no previous evidence for in vivo interactions of the nAChR agonists and AChE inhibitors. The nitromethyleneimidazole (NMI) analog of imidacloprid, a highly potent neonicotinoid, was used here as a radioligand, uniquely allowing for direct measurements of house fly (Musca domestica) head nAChR in vivo interactions with various nicotinic agents. Nine neonicotinoids inhibited house fly brain nAChR [³H]NMI binding in vivo, corresponding to their in vitro potency and the poisoning signs or toxicity they produced in intrathoracically treated house flies. Interestingly, nine topically applied OP or MC insecticides or analogs also gave similar results relative to in vivo nAChR binding inhibition and toxicity, but now also correlating with in vivo brain AChE inhibition, indicating that ACh is the ultimate OP- or MC-induced nAChR active agent. These findings on [³H]NMI binding in house fly brain membranes validate the nAChR in vivo target for the neonicotinoids, OPs and MCs. As an exception, the remarkably potent OP neonicotinoid synergist, O-propyl O-(2-propynyl) phenylphosphonate, inhibited nAChR in vivo without the corresponding AChE inhibition, possibly via a reactive ketene metabolite reacting with a critical nucleophile in the cytochrome P450 active site and the nAChR NMI binding site.The nicotinic nervous system has two principal sites of insecticide action, the nicotinic receptor (nAChR) activated by acetylcholine (ACh) and neonicotinoid agonists (1–6), and acetylcholinesterase (AChE) inhibited by organophosphorus (OP) and methylcarbamate (MC) compounds to generate and maintain localized toxic ACh levels (Fig. 1) (7). The nAChR and AChE targets have been identified in insects by multiple techniques but not by direct assays of the ACh binding site in the brain of poisoned insects. Here we use the outstanding insecticidal potency of the nitromethyleneimidazole (NMI) analog of imidacloprid (IMI) (8) as a radioligand (9), designated [³H]NMI, to directly measure the house fly (Musca domestica) nAChR not only in vitro but also in vivo, allowing us to validate by a previously undescribed method the neonicotinoid direct and OP/MC indirect nAChR targets (Fig. 2). This approach also helped solve the intriguing mechanism by which an O-(2-propynyl) phosphorus compound strongly synergizes neonicotinoid insecticidal activity (10) by dual inhibition of cytochrome P450 (CYP) (11–13) and the nAChR agonist site (described herein). Insecticide disruption at the insect nAChR can now be readily studied in vitro and in vivo with a single radioligand allowing better understanding of the action of several principal insecticide chemotypes (Fig. 3).Open in a separate windowFig. 1.The insect nicotinic receptor is the direct or indirect target for neonicotinoids, organophosphorus compounds and methylcarbamates, which make up about 45% of the insecticides by number and world market value (2, 7).Open in a separate windowFig. 2.In this study, Musca nicotinic receptor in vivo interactions with major insecticide chemotypes are revealed by a [³H]NMI radioligand reporter assay. *Position of tritium label.Open in a separate windowFig. 3.Two neonicotinoid nicotinic agonists and two anticholinesterase insecticides.     

An intramolecular lock facilitates folding and stabilizes the tertiary structure of Streptococcus mutans adhesin P1

The cariogenic bacterium Streptococcus mutans uses adhesin P1 to adhere to tooth surfaces, extracellular matrix components, and other bacteria. A composite model of P1 based on partial crystal structures revealed an unusual complex architecture in which the protein forms an elongated hybrid alpha/polyproline type II helical stalk by folding back on itself to display a globular head at the apex and a globular C-terminal region at the base. The structure of P1’s N terminus and the nature of its critical interaction with the C-terminal region remained unknown, however. We have cocrystallized a stable complex of recombinant N- and C-terminal fragments and here describe a previously unidentified topological fold in which these widely discontinuous domains are intimately associated. The structure reveals that the N terminus forms a stabilizing scaffold by wrapping behind the base of P1’s elongated stalk and physically “locking” it into place. The structure is stabilized through a highly favorable ΔG_solvation on complex formation, along with extensive hydrogen bonding. We confirm the functional relevance of this intramolecular interaction using differential scanning calorimetry and circular dichroism to show that disruption of the proper spacing of residues 989–1001 impedes folding and diminishes stability of the full-length molecule, including the stalk. Our findings clarify previously unexplained functional and antigenic properties of P1.Streptococcus mutans is a recognized cause of human dental caries (cavities), the most common infectious disease worldwide (1). Identifying how S. mutans interacts with host components at the molecular level is essential to fully understand its virulence properties. The sucrose-independent adhesin P1 (AgI/II, antigen B, PAc) is localized on the surface of this oral pathogen, along with many other streptococci (2–7). In the oral cavity, S. mutans P1 interacts with the salivary agglutinin glycoprotein complex composed predominantly of scavenger receptor gp340/DMBT1 (2, 3, 5–10). Without a complete structural model, the mechanisms by which P1 binds to host components have not yet been fully characterized.P1’s primary structure (Fig. 1A) contains a 38-residue signal sequence, the heretofore uncharacterized N-terminal region, three alanine-rich repeats (A1–3), a central domain containing a so-called variable (V) region (11), three proline-rich repeats (P1–3), a C-terminal region consisting of three domains (C1–3), an LPxTG sortase-recognition motif, and wall- and membrane-spanning regions (12, 13). Recent partial X-ray crystal structure and velocity centrifugation studies of the intact protein unveiled a unique architecture in which the ∼185-kDa (1,561-aa) protein folds back on itself to form a ∼50-nm elongated hybrid helical stalk that separates two independent adherence domains, with a globular head at the apex and a globular C-terminal region at the base (13–15) (Fig. 1B).Open in a separate windowFig. 1.Schematic representation of the primary and modeled tertiary structure of P1. (A) Primary structure of P1 and location of polypeptides used in this study. (B) Proposed tertiary model of P1 based on velocity centrifugation and crystal structures of A3VP1 and C123 fragments (13). (C) Diagram showing the locations of the two engineered Cla1 sites (circled in red) that added isoleucine and aspartic acid residues to either side of the proline-rich region (20).The crystal structure of the third alanine-rich repeat through the first proline-rich repeat first revealed the unusual interaction between the A and P regions to form a hybrid alpha/polyproline type II helix (14). In this model, a globular β super sandwich domain sits at the apex of the molecule (15). At the other end of the hybrid helix, the three contiguous domains of the C-terminal region each adopt a DE-variant Ig-like (DEv-IgG) fold stabilized by isopeptide bonds (13, 16). Despite this recent progress, the structure of the ∼20-kDa N terminus of P1 remained unknown, however (Fig. 1B). We previously demonstrated that proper folding and function of P1 on the surface of S. mutans requires an interaction between N- and C-terminal segments (17), thus increasing the imperative to elucidate the structure of the N terminus in complex with its intramolecular binding partner.For this, we used the recombinant N-terminal (NA1) and C-terminal (P3C) P1 fragments (Fig. 1A), which have been shown to form a stable high-affinity and functionally active complex (17). The NA1/P3C protein complex was copurified and then cocrystallized for X-ray diffraction data collection to 2.0-Å resolution. We observed that the N terminus adopts a previously unidentified fold that serves as an intramolecular scaffold connecting it to C-terminal portions of the molecule, thus locking P1’s hybrid helical stalk into place. We validated this model experimentally using differential scanning calorimetry (DSC) and circular dichroism to demonstrate decreased thermal stability and altered secondary structure in a P1 mutant containing two extra amino acids within the region that normally reacts with the N-terminal intramolecular scaffold. Our X-ray crystallography model and stability measurements agree well with biophysical data characterizing the NA1/P3C complex (17) and provide mechanistic insight into why the N-terminal segment supports the proper folding, function, and stability of the full-length P1 protein. This information contributes to our ability to interpret data regarding protective and nonprotective immune responses and preventative therapies, and will inform future studies evaluating bacterial adhesion and biofilm formation by S. mutans and related organisms.     

Dietary options and behavior suggested by plant biomarker evidence in an early human habitat

The availability of plants and freshwater shapes the diets and social behavior of chimpanzees, our closest living relative. However, limited evidence about the spatial relationships shared between ancestral human (hominin) remains, edible resources, refuge, and freshwater leaves the influence of local resources on our species’ evolution open to debate. Exceptionally well-preserved organic geochemical fossils—biomarkers—preserved in a soil horizon resolve different plant communities at meter scales across a contiguous 25,000 m² archaeological land surface at Olduvai Gorge from about 2 Ma. Biomarkers reveal hominins had access to aquatic plants and protective woods in a patchwork landscape, which included a spring-fed wetland near a woodland that both were surrounded by open grassland. Numerous cut-marked animal bones are located within the wooded area, and within meters of wetland vegetation delineated by biomarkers for ferns and sedges. Taken together, plant biomarkers, clustered bone debris, and hominin remains define a clear spatial pattern that places animal butchery amid the refuge of an isolated forest patch and near freshwater with diverse edible resources.Spatial patterns in archaeological remains provide a glimpse into the lives of our ancestors (1–5). Although many early hominin environments are interpreted as grassy or open woodlands (6–8), fossil bones and plant remains are rarely preserved together in the same settings. As a result, associated landscape reconstructions commonly lack coexisting fossil evidence for hominins and local-scale habitat (microhabitat) that defined the distribution of plant foods, refuge, and water (7). This problem is exacerbated by the discontinuous nature and low time resolution often available across ancient soil (paleosol) horizons, including hominin archaeological localities. One notable exception is well-time-correlated 1.8-million-y-old paleosol horizons exposed at Olduvai Gorge. Associated horizons contain exceptionally preserved plant biomarkers along with many artifacts and fossilized bones. Plant biomarkers, which previously revealed temporal patterns in vegetation and water (8), are well preserved in the paleosol horizon and document plant-type spatial distributions that provide an ecosystem context (9, 10) for resources that likely affected the diets and behavior of hominin inhabitants.Plant biomarkers are delivered by litter to soils and can distinguish plant functional type differences in standing biomass over scales of 1–1,000 m² (11). Trees, grasses, and other terrestrial plants produce leaf waxes that include long-chain n-alkanes such as hentriacontane (nC₃₁), whereas aquatic plants and phytoplankton produce midchain homologs (e.g., nC₂₃) (12, 13). The ratio of shorter- versus long-chain n-alkane abundances distinguish relative organic matter inputs from aquatic versus terrestrial plants to sediments (13):P_aq = (nC₂₃ + nC₂₅)/(nC₂₃ + nC₂₅ + nC₂₉ + nC₃₁).Sedges and ferns are prolific in many tropical ecosystems (14). These plants both have variable and therefore nondiagnostic n-alkane profiles. However, sedges produce distinctive phenolic compounds [e.g., 5-n-tricosylresorcinol (ⁿR₂₃)] and ferns produce distinctive midchain diols [e.g., 1,13-dotriacontanediol (C₃₂-diol)] (SI Discussion).Lignin monomers provide evidence for woody and nonwoody plants. This refractory biopolymer occurs in both leaves and wood, serves as a structural tissue, and accounts for up to half of the total organic carbon in modern vegetation (11). Lignin is composed of three phenolic monomer types that show distinctive distributions in woody and herbaceous plant tissues. Woody tissues from dicotyledonous trees and shrubs contain syringyl (S) and vanillyl (V) phenols (12), whereas cinnamyl (C) phenols are exclusively found in herbaceous tissues (12). The relative abundance of C versus V phenols (C/V) is widely used to distinguish between woody and herbaceous inputs to sedimentary and soil organic matter (15).Plant biomarker ¹³C/¹²C ratios (expressed as δ¹³C values) are sensitive indicators of community composition, ecosystem structure, and climate conditions (8). Most woody plants and forbs in eastern Africa use C₃ photosynthesis (6), whereas arid-adapted grasses use C₄ photosynthesis (8, 14). These two pathways discriminate differently against ¹³C during photosynthesis, resulting in characteristic δ¹³C values for leaf waxes derived from C₃ (about –36.0‰) and C₄ (–21.0‰) plants (16). Carbon isotopic abundances of phenolic monomers of lignin amplify the C₃–C₄ difference and range between ca. –34.0‰ (C₃) and –14.0‰ (C₄) in tropical ecosystems (15). Terrestrial C₃ plant δ¹³C values decrease with increased exposure to water, respired CO₂, and shade (8), with lowest values observed in moist regions with dense canopy (17). Although concentration and δ¹³C values of atmospheric CO₂ can affect C₃ plant δ¹³C values (17), this influence is not relevant to our work here, which focuses on a single time window (SI Discussion). The large differences in leaf-wax δ¹³C values between closed C₃ forest to open C₄ grassland are consistent with soil organic carbon isotope gradients across canopy-shaded ground surfaces (6) and serve as a quantitative proxy for woody cover (f_woody) in savannas (8).As is observed for nonhuman primates, hominin dietary choices were likely shaped by ecosystem characteristics over habitat scales of 1–1,000 m² (3–5). To evaluate plant distributions at this small spatial scale (9), we excavated 71 paleosol samples from close-correlated trenches across a ∼25,000-m² area that included FLK Zinjanthropus Level 22 (FLK Zinj) at Olduvai Gorge (Fig. 1). Recent excavations (18–21) at multiple trenches at four sites (FLK^NN, FLK^N, FLK, and FLK^S, Fig. 1D) exposed a traceable thin (5–50 cm), waxy green to olive-brown clay horizon developed by pedogenic alterations of playa lake margin alluvium (22). Weak stratification and irregular redox stains suggest initial soil development occurred during playa lake regression (18, 22), around 1.848 Ma (ref. 23 and SI Discussion). To date, craniodental remains from at least three hominin individuals (18–20), including preadolescent early Homo and Paranthropus boisei, were recovered from FLK Zinj. Fossils and artifacts embedded in the paleosol horizon often protrude into an overlying airfall tuff (18, 19), which suggests fossil remains were catastrophically buried in situ under volcanic ash. Rapid burial likely fostered the exceptional preservation of both macrofossils (10) and plant biomarkers across the FLK Zinj land surface.Open in a separate windowFig. 1.Location and map of FLK Zinj paleosol excavations. (A and B) Location of FLK Zinj as referenced to reconstructed depositional environments at Olduvai Gorge during the early Pleistocene (18, 22) and the modern gorge walls. The perennial lake contained shallow saline–alkaline waters that frequently flooded the surrounding playa margin (i.e., floodplain) flats. (C) Outline of FLK Zinj paleosol excavation sites used for our spatial biomarker reconstructions. (D) Concentric (5 m) gridded distribution map of FLK Zinj paleosol excavations relative to previous archaeological trenches (18–21). Major aggregate complexes (FLK^NN, FLK^N, FLK, and FLK^S) are color-coded to show excavation-site associations.Plant biomarker signatures reveal distinct types of vegetation juxtaposed across the FLK Zinj land surface (Figs. 2–4 and Fig. S1). In the northwest, FLK^NN trenches show high nC₂₃ δ¹³C values (Fig. 2B) as well as high C/V and P_aq values (Figs. 3 and ​and4A).4A). They indicate floating or submerged aquatic plants (macrophytes) in standing freshwater (13), a finding that is consistent with nearby low-temperature freshwater carbonates (tufa), interpreted to be deposited from spring waters (22). Adjacent FLK^N trenches have lower P_aq values (Fig. 4A) with occurrences of fern-derived C₃₂-diol and sedge-derived ⁿR₂₃ (Fig. 2 C and D). These biomarker distributions indicate an abrupt (around 10 m) transition from aquatic to wetland vegetation. Less than 100 m away (Fig. 1C), low nC₃₁ δ¹³C values (Fig. 2A) and low C/V and very low P_aq values (Figs. 3 and ​and4A)4A) collectively indicate dense woody cover (Fig. 4B). In the farthest southeastern (FLK^S) trenches, high C/V values and high δ¹³C values for C lignin phenols (Fig. 3) indicate open C₄ grassland.Open in a separate windowFig. 2.Spatial distributions and δ¹³C values for plant biomarkers across FLK Zinj. Measured and modeled δ¹³C values (large and smaller circles, respectively) are shown for (A) nC₃₁ from terrestrial plants, (B) nC₂₃ from (semi)aquatic plants, (C) C₃₂-diol from ferns, and (D) ⁿR₂₃ from sedges (see refs. 12 and 13 and SI Discussion). Modeled values [inverse distance-weighted (9)] account for spatial autocorrelation (15-m radius) in standing biomass (35) over scales of soil organic matter accumulation (11). Black dots represent paleosols with insufficient plant biomarker concentrations for isotopic analysis.Open in a separate windowFig. 3.Molecular and isotopic signatures for lignin phenols across FLK Zinj. Bivariate plots are shown for diagnostic lignin compositional parameters (see refs. 12 and 15 and Fig. 1C). Symbols are colored according to respective δ¹³C values for the C lignin phenol, p-coumaric acid. FLK symbols are uncolored due to insufficient p-coumaric acid concentrations for isotopic analysis. Representative lignin compositional parameters (12, 15) are shown for monocotyledonous herbaceous tissues (G), dicotyledonous herbaceous tissues (H), cryptogams (N), and dicotyledonous woody tissues (W).Open in a separate windowFig. 4.Spatial relationships shared between local plant resources and hominin remains. Measured and modeled values (large and smaller circles, respectively) are shown for (A) P_aq (13) and (B) f_woody (8). Modeled values [inverse distance-weighted (9)] account for spatial autocorrelation (15-m radius) in standing biomass (35) over scales of soil organic matter accumulation (11). (C) Kernel density map of cut-marked bones (18–21) across the FLK Zinj land surface (Fig. S4). High estimator values indicate hotspots of hominin butchery (Fig. S5). A shaded rectangle captures the area (ca. 0.68 probability mass) with highest cut-marked bone densities and is shown in A and B for reference.Open in a separate windowFig. S1.Total ion chromatograms for saturated hydrocarbons in representative paleosols at (A) FLK^NN, (B) FLK^N, (C) FLK, and (D) FLK^S. C₂₃, tricosane; C₂₅, pentacosane; C₂₉ nonacosane; C₃₁, hentriacontane.Biomarkers define a heterogeneous landscape at Olduvai and suggest an influence of local resources on hominin diets and behavior. It is recognized (2, 24–26) that early Homo species and P. boisei had similar physiological characteristics. These similarities in physical attributes suggest behavioral differences were what allowed for overlapping ranges and local coexistence (sympatry) of both hominins. For instance, differences in seasonal subsistence strategies or different behavior during periods of drought and limited food could have reduced local hominin competition and fostered diversification via niche specialization (27–29).Physical and isotopic properties of fossil teeth indicate P. boisei was more water-dependent [low enamel δ¹⁸O values (24)] and consumed larger quantities of abrasive, ¹³C-enriched foodstuffs [flat-worn surfaces (25) and high enamel δ¹³C values (26)] than coexisting early Homo species. Although ¹³C-enriched enamel is commonly attributed to consumption of C₄ grasses or meat from grazers (14), this was not likely, because P. boisei craniodental features are inconsistent with contemporary gramnivores (24, 25) or extensive uncooked flesh mastication (26). Numerous scholars have proposed the nutritious underground storage organs (USOs) of C₄ sedges were a staple of hominin diets (14, 24, 26, 27). Consistent with this suggestion, occurrences of ⁿR₂₃ attest to the presence of sedges at FLK^NN and FLK^N (Fig. 2D). However, the low δ¹³C values measured for ⁿR₂₃ at these same sites (Fig. 2D and Fig. S2) indicate C₃ photosynthesis (12, 16), a trait common in modern sedges that grow in alkaline wetlands and lakes (30) (Fig. S3). Thus, biomarker signatures support the presence of C₃ sedges in the wetland area of FLK Zinj.Open in a separate windowFig. S2.Total ion chromatogram [TIC (A)] and selected ion chromatograms for derivatized 5-n-alkylresorcinols [m/z 268 (●)] and midchain diols [m/z 369 (○)] from a representative paleosol at FLK^N. Also shown are δ¹³C values for homologous (B) 5-n-alkylresorcinols and (C) midchain diols. C₃₂-diol, dotriacontanediol; ⁿR₂₃, tricosylresorcinol.Open in a separate windowFig. S3.Summary phyogenetic consensus tree of Cyperaceae (sedges) based on nucleotide (rcbL and ETS1f) sequence data (50–54, 95, 96). Important taxonomic distinctions discussed in SI Discussion, Fern Alkyldiols are shown explicitly. Triangle-enclosed digits represent the number of additional branches at different levels of taxonomic classification. CEFA, Cypereae Eleocharideae Fuireneae Abildgaardieae; CSD, Cariceae Scirpeae Dulichieae.Alternative foodstuffs with abrasive, ¹³C-enriched biomass include seedless vascular plants (cryptogams), such as ferns and lycophytes [e.g., quillworts (27, 30)]. Ferns are widely distributed throughout eastern Africa in moist and shaded microhabitats (31) and are often found near dependable sources of drinking water (32). Today, ferns serve as a dietary resource for humans and nonhuman primates alike (27), and fiddlehead consumption is consistent with the inferred digestive physiology [salivary proteins (33)] and the microwear on molars (34) of P. boisei in eastern Africa (25, 26). Ferns were present at FLK^N, based on measurements of C₃₂-diol (Fig. 2D). Further, the high δ¹³C values measured for these compounds are consistent with significant fern consumption by P. boisei at Olduvai Gorge.Ferns and grasses were not the only plant foods present during the time window documented by FLK Zinj. Further, the exclusive reliance on a couple of dietary resources was improbable for P. boisei, because its fossils occur in diverse localities (24–26). Aquatic plants are an additional candidate substrate, as evidenced by high P_aq values at FLK^NN and FLK^N (Fig. 4A). Floating and submerged plants proliferate in wetlands throughout eastern Africa today (13, 14), and many produce nutritious leaves and rootstock all year long (27, 28). Although C₄ photosynthesis is rare among modern macrophytes (30), they can assimilate bicarbonate under alkaline conditions, which results in C₄-like isotope signatures in their biomass (30). Their leaf waxes, such as nC₂₃ (13), are both present and carry ¹³C-enriched signatures at FLK^NN and FLK^N (Fig. 2B). It is also likely that aquatic macrophytes sustained invertebrates and fish with comparably ¹³C-enriched biomass, as they do in modern systems (14), and we suggest aquatic animal foods could have been important in P. boisei diets (27, 28).Biomarkers across the FLK Zinj soil horizon resolve clear patterns in the distribution of plants and water and suggest critical resources that shaped hominin existence at Olduvai Gorge. The behavioral implications of local conditions require understanding of regional climate and biogeography (3–5, 7), because hominin species likely had home ranges much larger than the extent of excavated sites at FLK Zinj. Lake sediments at Olduvai Gorge include numerous stacked tuffs with precise radiometric age constraints (23). These tephrostratigraphic correlations (21) tie the FLK Zinj landscape horizon to published records of plant biomarkers in lake sediments that record climate cycles and catchment-scale variations in ecology. Correlative lake sediment data indicate the wet and wooded microhabitats of FLK Zinj sat within a catchment dominated by arid C₄ grassland (8). Under similarly arid conditions today, only a small fraction of landscape area (ca. 0.05) occurs within 5 km of either forest or standing freshwater (35). Given a paucity of shaded refuge and potable water in the catchment, the concentration of hominin butchery debris (18–21) exclusively within the forest microhabitat and adjacent to a freshwater wetland (Fig. 4) is notable. We suggest the spatial patterns defined by both macro- and molecular fossils reflect hominins engaged in social transport of resources (1–5), such as bringing animal carcasses and freshwater-sourced foods from surrounding grassy or wetland habitats to a wooded patch that provided both physical protection and access to water.     

High thermoelectric performance by resonant dopant indium in nanostructured SnTe

From an environmental perspective, lead-free SnTe would be preferable for solid-state waste heat recovery if its thermoelectric figure-of-merit could be brought close to that of the lead-containing chalcogenides. In this work, we studied the thermoelectric properties of nanostructured SnTe with different dopants, and found indium-doped SnTe showed extraordinarily large Seebeck coefficients that cannot be explained properly by the conventional two-valence band model. We attributed this enhancement of Seebeck coefficients to resonant levels created by the indium impurities inside the valence band, supported by the first-principles simulations. This, together with the lower thermal conductivity resulting from the decreased grain size by ball milling and hot pressing, improved both the peak and average nondimensional figure-of-merit (ZT) significantly. A peak ZT of ∼1.1 was obtained in 0.25 atom % In-doped SnTe at about 873 K.Good thermoelectric (TE) materials should not only have high figure-of-merit (Z), but also be environmentally friendly and cost-effective (1–5). The nondimensional figure-of-merit (ZT) is defined as ZT = [S²σ/(κ_L+κ_e)]T, where S is the Seebeck coefficient, σ the electrical conductivity, κ_L the lattice thermal conductivity, κ_e the electronic thermal conductivity, and T the absolute temperature. Lead chalcogenides and their alloys can be engineered to exhibit high ZTs; however, environmental concern regarding Pb prevents their deployment in large-scale applications (6–10). Tin telluride (SnTe), a lead-free IV–VI narrow band-gap semiconductor has not been considered favorably as a good thermoelectric material because of its low ZT due to the relatively low Seebeck coefficient and high electronic thermal conductivity caused by intrinsic Sn vacancies (11–13), although SnTe has been used to alloy with other tellurides for better TE properties (14–26). Even though there has been no real success in achieving good TE properties of lead-free SnTe, the similarity between the electronic band structure of SnTe and that of PbTe and PbSe (27–31) suggests it has the potential to be a good TE material, especially given the two valence bands (light-hole and heavy-hole bands) that contribute to the hole density of states. The main difficulty here, however, is the fact that the separation between the light-hole and heavy-hole band edges in SnTe is estimated to be in the range of ∼0.3 to ∼0.4 eV (27, 29), larger than those of PbTe or PbSe (9), rendering the benefit of the heavier mass for the Seebeck coefficient less significant.In this paper, we prepared In-doped SnTe by high-energy ball milling and hot pressing and measured the samples up to 873 K without experiencing any mechanical strength issues. We show, based on both experiments and first-principles simulation, that a small amount of In-doping helps create resonant states around the Fermi level inside the valence band, which increases the Seebeck coefficient, especially at room temperature, leading to improvements in both average ZT and peak ZT, combined with the decreased lattice thermal conductivity due to the increased density of grain boundaries (32–34). Peak ZT value reaches ∼1.1 at about 873 K for SnTe doped with 0.25 atom % In.Single-phased In-doped SnTe was obtained by ball milling and hot pressing. Fig. 1 presents the X-ray diffraction (XRD) patterns of In_xSn_1-xTe (x = 0, 0.0025, 0.005, and 0.01). All the peaks can be indexed to the face-centered structure (space group Fmm). No impurity phase was found, despite the increasing content of In. First-principles calculations (Table S1) indicated it is energetically favorable for In to substitute for Sn, which is consistent with the case in In-doped PbTe and PbSe. In previous work, we found In substitutes for Pb in PbTe and PbSe, which is the same with In-doped SnTe, but it is n-type doping in In_xPb_1-xTe and In_xPb_1-xSe, which is different from p-type doping by In in SnTe, as we are reporting in this work (35, 36).Open in a separate windowFig. 1.XRD patterns for In_xSn_1-xTe (x = 0, 0.0025, 0.005, and 0.01) prepared by ball milling and hot pressing.The electrical conductivities decrease with increasing temperature, as shown in Fig. 2A, showing the typical behavior of degenerate semiconductors. With increasing content of In, the electrical conductivity decreases, especially at room temperature, from ∼7 × 10⁵ S⋅m⁻¹ to ∼2 ×10⁵ S⋅m⁻¹. The hole concentration indicated by the Hall measurement, however, changes in an interesting way with increasing In content: it drops below the intrinsic value at the beginning and starts to rise after x ≥ 0.0025 (as shown in Fig. 3A). Based on this observation, we conclude In atoms should be p-type dopants and explain the change of the carrier concentration as follows. The intrinsic SnTe is p-type because of the Sn vacancies (19). Those vacancies create empty electronic states and behave like p-type dopants. If we dope SnTe with In, In atoms first fill the Sn vacancies. Despite being p-type dopants, they are not as “strong” as the vacancies, in the sense that they induce fewer holes (examined by the simulation shown in Table S1); thus, at low doping levels, the p-type charge concentration decreases. However, as the doping level is increased, at some point all the Sn vacancies are filled with In, and beyond that point, excessive In atoms substitute for Sn, and the p-type charge concentration increases again (Fig. 3A). However, when In is more than the solubility limit in SnTe, the extra In atoms act as donors, which decreases the hole carrier concentration (x = 0.01) (37). The fact that the electrical conductivity decreases all the way indicates that the In dopants affected the hole mobility significantly (shown in Fig. 3B), as the result of both increased effective mass and impurity scattering. The Seebeck coefficients increase with temperature in the whole temperature range and also increase with In content, as shown in Fig. 2B. No bipolar effect is evident, even up to 873 K, in all the compositions despite the small band gap ∼0.18 eV for SnTe (29, 31). All the measured Seebeck coefficients are positive, consistent with the density of states (DOS) calculation presented in Fig. 4 and different from In-doped PbTe and PbSe, in which In turned out to be an n-type dopant (36, 38). Fig. 2C shows the power factors for undoped and In-doped SnTe. The highest power factor reaches ∼2.0 × 10⁻³ W⋅m⁻¹⋅K⁻² at about 873 K, higher than all the reported power factors of doped PbTe and PbSe at this temperature (9, 39–41). Most importantly, the average power factor is increased a great deal by In doping. Compared with the undoped SnTe prepared by melting and hand milling (M+HM) (broken line), the electrical properties of the ball-milled samples are not different.Open in a separate windowFig. 2.Temperature dependence of (A) electrical conductivity, (B) the Seebeck coefficient, and (C) the power factor for In_xSn_1-xTe (x = 0, 0.0025, 0.005, and 0.01). The undoped SnTe prepared by melting, hand milling, and hot pressing (M+HM) is shown for comparison (broken line).Open in a separate windowFig. 3.Hall carrier concentration (A) and Hall mobility (B) at room temperature with respect to the doping content x. ○, undoped SnTe; ●, In-doped SnTe.Open in a separate windowFig. 4.Comparison of DOS for undoped SnTe (broken line), Bi-doped SnTe (solid line), and In-doped SnTe (bold solid line). Sharp features are observed in the DOS of In-doped SnTe near the band edge, to which the abnormal Seebeck coefficient might be attributed. The simulated supercell configuration corresponds to 3 atom % In concentration, which is higher than that achieved in the experiment. The Fermi level in the simulation resides at 6.207 eV, slightly below the DOS hump. With the experimental In concentration, the Fermi level is expected to sit around the DOS peak.Fig. 5 shows variation of the Seebeck coefficient vs. carrier concentration for both pure SnTe and In-doped SnTe. The Seebeck coefficients of undoped SnTe with different hole concentrations (2 × 10²⁰ to 1.8 × 10²¹ cm⁻³) were obtained previously by annealing under different conditions (open circles) (27). The carrier concentration obtained in this work is ∼2.35 × 10²⁰ cm⁻³ (filled circle). Unlike PbTe and PbSe (7, 9, 36, 39, 40), the Seebeck coefficient of SnTe shows abnormal variation with increasing carrier concentration, which was qualitatively explained previously by two parabolic band models (27) and density functional theory (DFT) calculations (31). The valence band model (VBM), which takes into account the nonparabolicity of the light-hole band (solid line), provides a quantitative fit to all the Seebeck coefficient data, except for those of In-doped samples, and thus is expected to best depict the contribution from the intrinsic band structure of SnTe (29). The model details for TE transport of p-type SnTe may be found in SI Text. Compared with the same model we used for PbTe and PbSe (9, 36), two major differences should be stated. The L point energy gap, Eg, is smaller for SnTe, making the nonparabolicity larger. This makes the Seebeck coefficient drop faster with increasing concentration, as seen in Fig. S1. The light-hole–heavy-hole band edge energy difference is 0.12 eV for PbTe, 0.26 eV for PbSe, and 0.35 eV for SnTe (9, 29, 36); thus, the heavy-hole contribution is relatively weaker for SnTe. This may be seen from the fact that there is not much difference between the predictions of VBM and those of the two-band Kane model (which ignores the heavy-hole band contribution) at room temperature for SnTe, until 10 × 10¹⁹ cm⁻³. However, the contribution from the heavy-hole band gradually increases at higher temperatures (Fig. S2) as for PbSe (9, 36), helping improve the Seebeck coefficient at high temperature and suppress the bipolar effect. Although the Seebeck coefficients of bismuth- (Bi-) and Cu-doped samples agree well with the VBM model, as shown in Fig. 5, indicating pure doping effects, the deviation of the In-doped samples from the VBM model implies that there must be mechanisms through which In dopants significantly alter the band structure of pure SnTe near the band edge. One of the possible mechanisms is the introduction of resonant levels (6, 42–44) into the valence band. Fig. 4 shows the DOS of pure SnTe, Bi-doped SnTe, and In-doped SnTe near the top of the valence band. A well-defined peak is observed in the DOS of In-doped SnTe that may contribute to the large deviation of the Seebeck coefficient from the VBM model. One may question whether the observed features are a result of the limited size of the supercell and thus the artificial interactions between In atoms. Similar features, however, are not observed in Bi-doped SnTe with the same supercell size. Therefore, we believe the added feature originates from the interactions of the In atoms with the host atoms. Because of the limitation of computing resources, a sufficiently dense k-mesh for calculating transport properties for the supercells is not possible at this stage; also, the simulated supercells are too small to represent a realistic doping concentration. [The simulated supercell corresponds to 3% In concentration, with a Fermi level located slightly below the DOS “hump.” With the doping concentration achieved in the experiments, the Fermi level is expected to reside close to the DOS peak. An alternative simulation method, such as a Korringa–Kohn–Rostoker coherent-potential-approximation (KKR-CPA) calculation (44), is required in cases of more dilute doping concentrations.] Thus, a direct evaluation of the effect of the features in DOS on the Seebeck coefficient is not available for now. However, the rich features introduced by In atoms are speculated to play an important role in the enhanced TE properties.Open in a separate windowFig. 5.Room temperature Pisarenko plot for ball-milled and hot-pressed In_xSn_1-xTe (x = 0, shown by ●; x = 0.001, 0.0015, 0.0025, 0.005, 0.0075, and 0.01, shown by ▲) in comparison with reported data on undoped SnTe (○), Bi-doped SnTe (□), and Cu-doped SnTe (♢) by Brebrick and Strauss (27). The solid curve is based on the VBM (light nonparabolic band and heavy parabolic band) with the heavy-hole effective mass of SnTe m*/m_e = 1.92.The other problem we should resolve is the high thermal conductivity induced by intrinsic Sn vacancies, causing very high electrical conductivity. By In doping, the decreased electrical conductivity results in a reduced electronic part of the thermal conductivity determined by the Wiedemann–Franz law (κ_e = LσT), where L is the Lorenz number. The Lorenz number is calculated using the VBM in a way similar to that of the Seebeck coefficient, including contributions from both nonparabolic light-hole and parabolic heavy-hole bands. The detailed expressions used are included in SI Text. Fig. 6 A–C gives the temperature dependences of the thermal diffusivity, specific heat, total thermal conductivity, and lattice thermal conductivity (obtained by subtracting the electronic contribution from the total thermal conductivity) of the undoped and In-doped SnTe, respectively. With increasing temperature, the total thermal conductivity decreases rapidly without showing any bipolar effect, consistent with the behavior of the Seebeck coefficient in Fig. 2B. The total thermal conductivities of all In-doped SnTe are lower than the undoped sample. Compared with the undoped SnTe prepared by melting and hot pressing (dotted line), the samples prepared by ball milling and hot pressing exhibit lower lattice thermal conductivity, which may be attributed to the increased density of grain boundaries by ball milling. In Fig. 7, the representative microstructure of ball-milled and hot-pressed In-doped SnTe is presented. Scanning electron microscopic (SEM) images shown in Fig. 7A indicate that the In_0.0025Sn_0.9975Te samples consist of both big grains with diameters of several tens of microns and small grains. The observed small cavities may contribute to the lower lattice thermal conductivity. The densities of all the samples are listed in Table S2. The size of the small grains is about 1 μm, as shown in Fig. 7B, less than one tenth that of the big grains. Nanograins in the samples also are observed via transmission electron microscopy (TEM). Fig. 7C shows a typical bright-field TEM image of the nanograins, with sizes around 100 nm. As a result, the lattice thermal conductivity of the samples is greatly reduced by significantly enhanced boundary scatterings of the phonons, as shown in Fig. 6C. Selected area electron diffraction and high-resolution TEM (HRTEM) images show that all the grains, whether in microns or nanometers, are single crystals with clean boundaries and good crystallinity, as shown in Fig. 7D. The crystalline grains and boundaries would benefit the transport of charge carriers, as observed in nanograined Bi_xSb_2-xTe₃ bulks (45), without degrading the electronic properties (Fig. 2).Open in a separate windowFig. 6.Temperature dependence of (A) thermal diffusivity (the undoped SnTe prepared by melting and hot pressing is shown by the broken line), (B) specific heat (the specific heat of sample x = 0 is used for the undoped SnTe prepared by melting and hot pressing), and (C) total thermal conductivity and lattice thermal conductivity for In_xSn_1-xTe (x = 0, 0.0025, 0.005, and 0.01) (the undoped SnTe prepared by melting and hot pressing is shown by the broken line).Open in a separate windowFig. 7.Representative SEM (A and B), TEM (C), and HRTEM (D) images for as-prepared In_0.0025Sn_0.9975Te samples by ball milling and hot pressing.Fig. 8 summarizes the ZT values of different samples. The two intrinsic valence bands contribute to the peak ZT value ∼0.7 at about 873 K for the undoped SnTe. The decreased lattice thermal conductivity by ball milling further boosts the peak ZT value to ∼0.8. However, the ZT values in both cases are quite low, below 600 K, resulting in low average ZTs. The enhanced Seebeck coefficient by resonant states increased both the peak and average ZTs in the In-doped nanostructured SnTe. A peak ZT ∼1.1 is obtained at about 873 K in In_0.0025Sn_0.9975Te.Open in a separate windowFig. 8.Temperature dependence of ZT for In_xSn_1-xTe (x = 0, 0.0025, 0.005, and 0.01) compared with the reported data on undoped SnTe (0.5–2.0 atom % Te excess) (■) by Vedeneev et al. (25) and codoped SnTe (0.5–2.0 atom % Te excess) with 1 atom % In and 1 atom % Ag (●) by Vedeneev et al. (25). The undoped SnTe prepared by melting and hot pressing is included for comparison (broken line).In summary, nanostructured In-doped SnTe with a ZT >1 has been prepared by ball milling and hot pressing. The improved ZT (peaked around 1.1 at about 873 K in 0.25 atom % In-doped SnTe) incorporates both the high Seebeck coefficient resulting from the two valence bands and the local resonant states around Fermi level created by In-doping and the lowered lattice thermal conductivity owing to the increased phonon interface scattering. This lead-free TE material is a potential candidate to replace lead chalcogenides used at medium to high temperatures for waste heat recovery applications. Further improvement is expected by adding suitable nanoinclusions or alloying with SnSe and SnS to decrease the thermal conductivity and increase the Seebeck coefficient.     

Glacial reduction and millennial-scale variations in Drake Passage throughflow

The Drake Passage (DP) is the major geographic constriction for the Antarctic Circumpolar Current (ACC) and exerts a strong control on the exchange of physical, chemical, and biological properties between the Atlantic, Pacific, and Indian Ocean basins. Resolving changes in the flow of circumpolar water masses through this gateway is, therefore, crucial for advancing our understanding of the Southern Ocean’s role in global ocean and climate variability. Here, we reconstruct changes in DP throughflow dynamics over the past 65,000 y based on grain size and geochemical properties of sediment records from the southernmost continental margin of South America. Combined with published sediment records from the Scotia Sea, we argue for a considerable total reduction of DP transport and reveal an up to ∼40% decrease in flow speed along the northernmost ACC pathway entering the DP during glacial times. Superimposed on this long-term decrease are high-amplitude, millennial-scale variations, which parallel Southern Ocean and Antarctic temperature patterns. The glacial intervals of strong weakening of the ACC entering the DP imply an enhanced export of northern ACC surface and intermediate waters into the South Pacific Gyre and reduced Pacific–Atlantic exchange through the DP (“cold water route”). We conclude that changes in DP throughflow play a critical role for the global meridional overturning circulation and interbasin exchange in the Southern Ocean, most likely regulated by variations in the westerly wind field and changes in Antarctic sea ice extent.The Antarctic Circumpolar Current (ACC) is the world’s largest current system. Through inducing pronounced upwelling and formation of new water masses, the ACC fundamentally affects the global meridional overturning circulation (1) and the stability of Antarctica’s ice sheets. The flow of the ACC is largely driven by strong westerly winds and constricted to its narrowest extent in the Drake Passage (DP). This so-called “cold water route” transport through the DP is one important pathway for the return of fresh and cold waters to the Atlantic, which strongly affects the strength of the Atlantic meridional overturning circulation, in concert with the “warm water route” inflow of warm and salty Indian Ocean water masses through the Agulhas Current system (2, 3).The DP is ∼800-km wide and located between Cape Horn and the western Antarctic Peninsula (Fig. 1). Numerous hydrographic surveys across the DP since the 1970s have contributed to the understanding of yearlong and interannual variability in ACC transport through the DP and the mechanisms forcing physical and biological changes within the Southern Ocean (4, 5). However, even with hydrographic time series reaching back 20 y (6) in this well-constrained region of the ACC and more sophisticated model simulations (7), important issues such as the role of zonal winds in forcing ACC transport remain controversial.Open in a separate windowFig. 1.Schematic view of the DP region with major surface and intermediate water circulation and location of the sediment cores discussed (labeled red dots refer to our data, and unlabeled black dots indicate locations of cores in the Scotia Sea) (13). The red line across the DP shows the oceanographic Jason Track 104 (6). Approximate locations are based on the works by Orsi et al. (8) and Comiso et al. (11). AAIW, Antarctic Intermediate Water; HCS, Humboldt Current System; SPC, South Pacific Current; WSI, winter sea ice.Compared with other parts of the Southern Ocean, the modern oceanography of the ACC within the DP is well-monitored. The three major ACC oceanographic fronts (8) [the sub-Antarctic Front (SAF), the Polar Front (PF), and the Southern ACC Front (SACCF)] can be identified from north to south within the DP. The exact location and occurrence of subsidiary fronts change from year to year and strongly affect the current velocity pattern across the DP (6, 9, 10) (Fig. 2B). The modern winter sea ice margin presently does not extend significantly north into the DP (11). The total ACC volume transported through the DP is estimated between ∼130 and 150 Sv (4–6, 9). The many oceanographic studies robustly show that more than one-half of the total DP transport occurs at and north of the SAF (i.e., in the sub-Antarctic Zone) followed by the Polar Frontal Zone (between the SAF and the PF) (Fig. 2A). The relative contribution of DP transport south of the PF is generally less than 20%.Open in a separate windowFig. 2.DP throughflow during the LGM compared with the modern setting. (A) Modern DP cumulative volume transport above 3,000-m water depth. Values are cumulated along DP Jason Track 104 (Fig. 1 and Fig. S1) from north to south. (B) The volume transport is calculated from across-track surface geostrophic velocities (6). Front positions based on the geostrophic velocities. (C) Holocene and glacial (LGM sensu lato, 18–28 ka) mean SS¯ data across the DP, including data south of the SAF in the Scotia Sea (13). Note that the Holocene pattern reflects the northward increase in ACC flow across the DP. The location of each core was projected on the oceanographic Jason Track 104 (SI Methods). Error estimates (2σ/√n) are shown in 12, 13). Downstream of the DP, a meridional section of low-resolution records from the Scotia Sea revealed little overall bottom current speed variations between the Last Glacial Maximum (LGM) sensu lato (18–28 ka) and the Holocene (13). In contrast, a 500,000-y record from the southern Indian Ocean (eastward from the Crozet–Kerguelen Plateau) suggests that the ACC was weak during warm stages and strong during glacial epochs (14). Likewise, in the Southwest Pacific, the deep western boundary current east of New Zealand intensified during glacial periods over the past 1.2 My (15), which has been related to a northward extension of the ACC along the New Zealand continental margin (15, 16). Evidently, disagreement exists for ACC flow intensity and its potential impact on changes in DP throughflow in response to glacial and interglacial cycles.     

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing ‟grind and find” model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.Notwithstanding the historical importance of natural products in drug discovery (1) the field continues to face a number of challenges that affect the relevance of natural products research in modern biomedical science (2). Among these are the increasing rates of rediscovery of known classes of natural products (3–6) and the high rates of attrition of bioactive natural products in secondary assays due to limited information about compound modes of action in primary whole-cell assays (7). Although pharmaceutical companies recognize that natural products are an important component of drug discovery programs because of the different pharmacologies of natural products and synthetic compounds (8), there is a reluctance to return to “grind and find” discovery methods (9). Therefore, there is a strong need for technologies that address these issues and provide new strategies for the prioritization of lead compounds with unique structural and/or biological properties (10).Natural product drug discovery is challenging in any assay system because extract libraries are typically complex mixtures of small molecules in varying titers, making it difficult to distinguish biological outcomes (11). This is compounded by issues of additive effects of multiple bioactive compounds and the presence of nuisance compounds that cause false positives in assay systems (12). To address these issues, our laboratory has recently developed several image-based screening platforms that are optimized for natural product discovery (13–16). The cytological profiling platform optimized by Schulze and coworkers characterizes the biological activities of extracts using untargeted phenotypic profiling. These phenotypic profiles are compared with natural products extracts and a training set of compounds with known modes of action to characterize the bioactivity landscape of the screening library (17, 18). This cytological profiling tool forms the basis of the biological characterization component of the Compound Activity Mapping platform, as described below.In the area of chemical characterization of natural product libraries, untargeted metabolomics is gaining attention as a method for evaluating chemical constitution (3, 19–22). Modern “genes-to-molecules” and untargeted metabolomics approaches taking advantage of principal component analysis and MS² spectral comparisons have also been developed to quickly dereplicate complex extracts and distinguish noise and nuisance compounds from new molecules (23–27). Unfortunately, although these techniques are well suited to the discovery of new chemical scaffolds, they are unable to describe the function or biological activities of the compounds they identify. Therefore, there is still a need for new approaches to systematically identify novel bioactive scaffolds from complex mixtures.To overcome some of these outstanding challenges we have developed the Compound Activity Mapping platform to integrate phenotypic screening information from the cytological profiling assay with untargeted metabolomics data from the extract library (Fig. 1). By correlating individual mass signals with specific phenotypes from the high-content cell-based screen (Fig. 2), Compound Activity Mapping allows the prediction of the identities and modes of action of biologically active molecules directly from complex mixtures, providing a mechanism for rational lead selection based on desirable biological and/or chemical properties. To evaluate this platform for natural products discovery we examined a 234-member extract library, from which we derived 58,032 biological measurements (Fig. 1C) and 10,977 mass spectral features (Fig. 1A). By integrating and visualizing these data we created a Compound Activity Map for this library composed of 13 clusters containing 16 compounds from 11 compound classes (Fig. 3). This integrated data network enabled the discovery of four new compounds, quinocinnolinomycins A–D (1–4, Fig. 4), which are the first examples to our knowledge of microbial natural products containing the unusual cinnoline core (Fig. 5). Clustering the cytological profiles of the quinocinnolinomycins with those of the Enzo library training set suggests that these compounds induce endoplasmic reticulum (ER) stress and the protein unfolding response.Open in a separate windowFig. 1.Overview of Compound Activity Mapping. (A) Representation of the chemical space in the tested extract library. The network displays extracts (light blue) connected by edges to all m/z features (red) observed from the metabolomics analysis, illustrating the chemical complexity of even small natural product libraries. (B) Histograms of activity and cluster scores for all m/z features with cutoffs indicated as red lines (for full-size histograms see SI Appendix, Fig. S5). (C) Compound Activity Map, with the network displaying only the m/z features predicted to be associated with consistent bioactivity, and their connectivity to extracts within the library. (D) Expansion of the staurosporine cluster (dotted box in C) with extract numbers and relevant m/z features labeled.Open in a separate windowFig. 2.Determination of synthetic fingerprints and cluster and activity scores. (A) Table of Pearson correlations for the cytological profiles between all extracts containing a specific m/z feature (m/z of 489.1896, rt of 1.59). In each cytological profile, yellow stripes correspond to positive perturbations in the observed cytological attribute and blue stripes correspond to negatively perturbed attributes. The cluster score is determined by calculating the average of the Pearson correlation scores for all relevant extracts. (B) Calculated synthetic fingerprint and activity score for feature (m/z of 489.1896, rt of 1.59). Synthetic fingerprints are calculated as the averages of the values for each cytological attribute to give a predicted cytological profile for each bioactive m/z feature in the screening set.Open in a separate windowFig. 3.Annotated Compound Activity Map. An expanded view of the Compound Activity Map from Fig. 1C, with the extracts and m/z features separated into subclusters and colored coded using the Gephi modularity function. Each bioactive subcluster is composed of extracts containing a family of compounds with a defined biological activity. The Compound Activity Map is annotated with a representative molecule from each of the families of compounds that have been independently confirmed by purification and chemical analysis.Open in a separate windowFig. 4.The prioritization, isolation, and confirmation of the quinocinnolinomycins A–D (1–4). (A) Bioactive m/z features plotted on a graph of activity score vs. cluster score. The color of the dot corresponds to the retention time of the m/z feature with the color bar and scale below in minutes. (B) Isolated cluster from Fig. 1C and Fig. 3 containing both the relevant extracts (blue) and bioactive m/z features (red). (C) HPLC trace of extract RLPA-2003E and the isolation of quinocinnolinomycins A–D (highlighted with blue boxes on HPLC trace). (D) Cell images of pure compounds screened as a twofold dilution series for quinocinnolinomycins A and B in both stain sets compared with images of vehicle (DMSO) wells. (E) Comparison of the synthetic and actual cytological fingerprints of the pure compounds is presented below the relevant images, demonstrating the relationship between experimental and calculated cytological profiles for these two metabolites.Open in a separate windowFig. 5.Structure elucidation of quinocinnolinomycins A–D (1–4). (A) Structures of quinocinnolinomycins A–D. (B) Key NMR correlations used in the structure elucidation of quinocinnolinomycin A. COSY correlations are indicated by bold lines. Heteronuclear multiple-bond correlations are indicated by curved arrows. (C) ∆δ^SR values for the Mosher’s α-methoxy-α-trifluoromethylphenylacetic acid (MTPA) ester analysis of the secondary alcohol in quinocinnolinomycin A (1) to assign the absolute configuration at position C11.     

Bacterial lipids activate,synergize, and inhibit a developmental switch in choanoflagellates

In choanoflagellates, the closest living relatives of animals, multicellular rosette development is regulated by environmental bacteria. The simplicity of this evolutionarily relevant interaction provides an opportunity to identify the molecules and regulatory logic underpinning bacterial regulation of development. We find that the rosette-inducing bacterium Algoriphagus machipongonensis produces three structurally divergent classes of bioactive lipids that, together, activate, enhance, and inhibit rosette development in the choanoflagellate Salpingoeca rosetta. One class of molecules, the lysophosphatidylethanolamines (LPEs), elicits no response on its own but synergizes with activating sulfonolipid rosette-inducing factors (RIFs) to recapitulate the full bioactivity of live Algoriphagus. LPEs, although ubiquitous in bacteria and eukaryotes, have not previously been implicated in the regulation of a host–microbe interaction. This study reveals that multiple bacterially produced lipids converge to activate, enhance, and inhibit multicellular development in a choanoflagellate.The foundational event in animal origins—the transition to multicellularity (1–3)—occurred in oceans filled with diverse bacteria (4–7). There is a growing appreciation that specific bacteria direct diverse animal developmental processes, including light organ development in the Hawaiian bobtail squid and immune system development and maturation in organisms as diverse as cnidaria and mammals (8–20). However, the multicellularity of animals and the complex communities of bacteria with which they often interact hinder the complete characterization of many host–microbe dialogues.Choanoflagellates, a group of microbial eukaryotes that are the closest living relatives of animals (21–24), promise to help illuminate the mechanisms by which bacteria influence animal development. As did cells in the first animals, choanoflagellates use a distinctive collar of actin-filled microvilli surrounding a flow-generating apical flagellum to capture bacteria as prey (25–27). Indeed, choanoflagellate-like cells likely formed the basis for the evolution of animal epithelial cells that today provide a selective barrier for mediating interactions with bacteria (27–29).In many choanoflagellates, including Salpingoeca rosetta, a developmental program can be initiated such that single cells develop into multicellular rosettes. Importantly, rosette development does not occur through cell aggregation. Instead, as in the development of an animal from a zygote, rosettes develop from a single founding cell that undergoes serial rounds of oriented cell division, with the sister cells remaining stably adherent (Fig. 1). The orientation of the nascently divided cells around a central focus, the production of extracellular matrix, and the activity of a C-type lectin called Rosetteless, ultimately result in the formation of spherical, multicellular rosettes (30–32). Rosettes resemble morula-stage embryos, and the transition to multicellularity in S. rosetta evokes ancestral events that spawned the first animals (26, 27, 33).Open in a separate windowFig. 1.Stages of rosette development in S. rosetta. During rosette development, a single founding cell undergoes serial rounds of cell division, resulting in a structurally integrated rosette. Importantly, rosette development does not involve cell aggregation. Shown are a single cell (A), a pair of cells (B), a 4-cell rosette (C), an 8-cell rosette (D) and a 16-cell rosette (E).The initiation of rosette development was recently found to be induced by a coisolated environmental bacterium, Algoriphagus machipongonensis (phylum Bacteroidetes) (34, 35). The ecological relevance of the interaction between A. machipongonensis (hereafter, Algoriphagus) and S. rosetta is evidenced by the coexistence of these organisms in nature (35) and the predator–prey relationship between choanoflagellates and bacteria (25, 36). Indeed, rosettes likely have a fitness advantage over single cells in some environments, as multicellular choanoflagellates are predicted to produce increased flux of water past each cell (37), and prey capture studies reveal that rosettes collect more bacterial prey/cell/unit time than do single cells (38). However, in other environments, rosette development would likely reduce fitness as rosettes have reduced motility relative to single cells. Therefore, we hypothesize that choanoflagellates use bacterially produced molecules to identify environments in which rosette development might provide a fitness advantage.The simplicity of the interaction between S. rosetta and Algoriphagus, in which both members can be cultured together or independently, offers a biochemically tractable model for investigating the molecular bases of bacteria–eukaryote interactions. Using rosette development as a bioassay, the first rosette-inducing molecule, Rosette Inducing Factor-1 (RIF-1), was isolated from Algoriphagus. The observation that RIF-1 fails to fully recapitulate the bioactivity of the live bacterium (Fig. 2 A and C) raised the possibility that additional molecules might be required (35). To gain a more complete understanding of the molecules and regulatory logic by which bacteria regulate rosette development, we set out to identify the minimal suite of molecules produced by Algoriphagus that are necessary and sufficient to regulate rosette development in S. rosetta.Open in a separate windowFig. 2.Maximal rosette development requires lipid cofactor interactions. (A) When treated with media that lack necessary bacterial signals (Media Control), S. rosetta does not produce rosettes. In contrast, when treated with live Algoriphagus, Algoriphagus-conditioned media, OMVs from Algoriphagus, or bulk lipids extracted from Algoriphagus, rosettes develop at maximal (∼90% cells in rosettes) or near-maximal levels. (B) A heat map depicts the rosette-inducing activity of Algoriphagus lipid fractions used to treat SrEpac, either in isolation or in combination, at a final lipid concentration of 2 μg/mL Sulfonolipid-enriched fraction 11 was the only fraction sufficient to induce rosette development when tested alone (30% of cells in rosettes). Tests of each of the lipid fractions in combination revealed previously unidentified inhibitory and enhancing activity. Fractions 4 and 5 decreased rosette development (to 12% and 8%, respectively) in fraction 11-treated cells, whereas fraction 7 increased rosette development to 65%. (C) The RIF mix (solid square) and purified RIF-2 (solid circle) induced rosette development at micromolar concentrations. (Inset) RIF-1 (open circle) is active at femtomolar to nanomolar concentrations, but induces 10-fold lower levels of rosette development than RIF-2. The long gray box in the main graph indicates the range of concentrations at which RIF-1 is active and the range of its rosette-inducing activity. Rosette development was quantified 24 h after induction. Minor ticks on x axis are log-spaced.     

Drought,agricultural adaptation,and sociopolitical collapse in the Maya Lowlands

Paleoclimate records indicate a series of severe droughts was associated with societal collapse of the Classic Maya during the Terminal Classic period (∼800–950 C.E.). Evidence for drought largely derives from the drier, less populated northern Maya Lowlands but does not explain more pronounced and earlier societal disruption in the relatively humid southern Maya Lowlands. Here we apply hydrogen and carbon isotope compositions of plant wax lipids in two lake sediment cores to assess changes in water availability and land use in both the northern and southern Maya lowlands. We show that relatively more intense drying occurred in the southern lowlands than in the northern lowlands during the Terminal Classic period, consistent with earlier and more persistent societal decline in the south. Our results also indicate a period of substantial drying in the southern Maya Lowlands from ∼200 C.E. to 500 C.E., during the Terminal Preclassic and Early Classic periods. Plant wax carbon isotope records indicate a decline in C₄ plants in both lake catchments during the Early Classic period, interpreted to reflect a shift from extensive agriculture to intensive, water-conservative maize cultivation that was motivated by a drying climate. Our results imply that agricultural adaptations developed in response to earlier droughts were initially successful, but failed under the more severe droughts of the Terminal Classic period.The decline of the lowland Classic Maya during the Terminal Classic period (800–900/1000 C.E.) is a preeminent example of societal collapse (1), but its causes have been vigorously debated (2–5). Paleoclimate inferences from lake sediment and cave deposits (6–11) indicate that the Terminal Classic was marked by a series of major droughts, suggesting that climate change destabilized lowland Maya society. Most evidence for drought during the Terminal Classic comes from the northern Maya Lowlands (Fig. 1) (6–8, 10), where societal disruption was less severe than in the southern Maya Lowlands (12, 13). There are fewer paleoclimate records from the southern Maya Lowlands, and they are equivocal with respect to the relative magnitude of drought impacts during the Terminal Classic (9, 11, 14). Further, the supposition that hydrological impacts were a primary cause for societal change is often challenged by archaeologists, who stress spatial variability in societal disruption across the region and the complexity of human responses to environmental change (2, 3, 12). The available paleoclimate data, however, do not constrain possible spatial variability in drought impacts (6–11). Arguments for drought as a principal cause for societal collapse have also not considered the potential resilience of the ancient Maya during earlier intervals of climate change (15).Open in a separate windowFig. 1.Map of the Maya Lowlands indicating the distribution of annual precipitation (64) and the location of paleoclimate archives discussed in the text. The locations of modern lake sediment and soil samples (Fig. 2) are indicated by diamonds.For this study, we analyzed coupled proxy records of climate change and ancient land use derived from stable hydrogen and carbon isotope analyses of higher-plant leaf wax lipids (long-chain n-alkanoic acids) in sediment cores from Lakes Chichancanab and Salpeten, in the northern and southern Maya Lowlands, respectively (Fig. 1). Hydrogen isotope compositions of n-alkanoic acids (δD_wax) are primarily influenced by the isotopic composition of precipitation and isotopic fractionation associated with evapotranspiration (16). In the modern Maya Lowlands, δD_wax is well correlated with precipitation amount and varies by 60‰ across an annual precipitation gradient of 2,500 mm (Fig. 2). This modern variability in δD_wax is strongly influenced by soil water evaporation (17), and it is possible that changes in potential evapotranspiration could also impact paleo records. Accordingly, we interpret δD_wax values as qualitative records of water availability influenced by both precipitation amount and potential evapotranspiration. These two effects are complementary, since less rainfall and increased evapotranspiration would lead to both increased δD_wax values and reduced water resources, and vice versa.Open in a separate windowFig. 2.Scatter plot showing the negative relationship between annual precipitation and δD_wax-corr measured in modern lake sediment and soil samples (Fig. 1). Results from Lake Chichancanab (CH) and Salpeten (SP) are indicated. The black line indicates a linear regression fit to these data, with regression statistics reported at the bottom of the plot. Large squares indicate mean values for each sampling region, with error bars indicating SEM in both δD_wax-corr and annual precipitation. The black error bar indicates the 1σ error for δD_wax-corr values (SI Text). Original δD_wax data from ref. 17. VSMOW, Vienna Standard Mean Ocean Water.Plant wax carbon isotope signatures (δ¹³C_wax) in sediments from low-elevation tropical environments, including the Maya lowlands, are primarily controlled by the relative abundance of C₃ and C₄ plants (18–20). Ancient Maya land use was the dominant influence on the relative abundance of C₃ and C₄ plants during the late Holocene, because Maya farmers cleared C₃ plant-dominated forests and promoted C₄ grasses, in particular, maize (21–24). Thus, we apply δ¹³C_wax records as an indicator of the relative abundance of C₄ and C₃ plants that reflects past land use change (SI Text). Physiological differences between plant groups also result in differing δD_wax values between C₃ trees and shrubs and C₄ grasses (16), and we use δ¹³C_wax records to correct for the influence of vegetation change on δD_wax values (25) (δD_wax-corr, SI Text and Fig. S1).Plant waxes have been shown to have long residence times in soils in the Maya Lowlands (26). Therefore, age−depth models for our plant wax isotope records are based on compound-specific radiocarbon ages (Fig. 3), which align our δD_wax records temporally with nearby hydroclimate records derived from other methodologies (26) (SI Text and Fig. S2). The mean 95% confidence range for the compound-specific age−depth models is 230 y at Lake Chichancanab and 250 y at Lake Salpeten. Given these age uncertainties, we focus our interpretation on centennial-scale variability (26). The temporal resolution of our plant wax isotope records is lower than speleothem-derived climate records (8, 9), but combining plant wax records from multiple sites allows comparisons of climate change and land use in the northern and southern Maya Lowlands, which would otherwise not be possible. In addition, plant wax isotope records extend to the Early Preclassic/Late Archaic period (1500–2000 B.C.E.), providing a longer perspective on climate change in the Maya Lowlands than most other regional records (6, 8–11).Open in a separate windowFig. 3.Plant wax (green; left) and terrigenous macrofossil (red; right) age−depth models for (A) Lake Chichancanab and (B) Lake Salpeten. The age probability density of individual radiocarbon analyses is shown. The black lines indicate the best age model based on the weighted mean of 1,000 age model iterations (62). Colored envelopes indicate 95% confidence intervals. Cal, calendar.     

Three-dimensionally preserved minute larva of a great-appendage arthropod from the early Cambrian Chengjiang biota

A three-dimensionally preserved 2-mm-long larva of the arthropod Leanchoilia illecebrosa from the 520-million-year-old early Cambrian Chengjiang biota of China represents the first evidence, to our knowledge, of such an early developmental stage in a short-great-appendage (SGA) arthropod. The larva possesses a pair of three-fingered great appendages, a hypostome, and four pairs of well-developed biramous appendages. More posteriorly, a series of rudimentary limb Anlagen revealed by X-ray microcomputed tomography shows a gradient of decreasing differentiation toward the rear. This, and postembryonic segment addition at the putative growth zone, are features of late-stage metanauplii of eucrustaceans. L. illecebrosa and other SGA arthropods, however, are considered representative of early chelicerates or part of the stem lineage of all euarthropods. The larva of an early Cambrian SGA arthropod with a small number of anterior segments and their respective appendages suggests that posthatching segment addition occurred in the ancestor of Euarthropoda.Evolutionary developmental biology (evo-devo) explains evolutionary changes in different organisms by investigating their developmental processes (1). Paleontology contributes to evo-devo by providing information that is only available in fossil organisms (2). Studies of evolutionary development in fossil arthropods, which have dominated faunas from the early Cambrian (∼520 million years ago) to the present, have focused on trilobites (3), “Orsten”-type fossil crustaceans (4–6), and Mesozoic malacostracan crustaceans (7). Due to their small size and low preservation potential, fossil evidence of the appendages of early developmental stages of arthropods are rare, and known mainly from those with the special “Orsten” type of preservation (8), i.e., with the cuticle secondarily phosphatized, from the mid-Cambrian (500–497 million years ago) (9).Here we describe an exceptionally preserved early developmental stage of a Cambrian arthropod from the Chengjiang biota of China. The specimen is only 2 mm long and is three-dimensionally preserved (Fig. 1, Insets). We interpret this specimen as a representative of the short-great-appendage (SGA) arthropod Leanchoilia illecebrosa—the most abundant SGA arthropod from this biota (10). SGA arthropods form a distinct early group characterized by prominent anteriormost appendages specialized for sensory (11) or feeding purposes (11, 12). Thus far, knowledge of L. illecebrosa is based mainly on adult specimens with a body length ranging from 20 to 46 mm (13) (Fig. 1). Specimens smaller than 20 mm are rare—only two examples, both 8 mm long, have been reported (8, 12) (Fig. S1B).Open in a separate windowFig. 1.L. illecebrosa from the Chengjiang biota. Macrophotographs of an adult (specimen YKLP 11087) and the minute larva (Insets; specimen YKLP 11088a, b). cs, cephalic shield; rs, rostrum; sga, short great appendage; ts1 and ts11, trunk segments 1 and 11; te, telson. Insets are to the same scale as main image. (Scale bar: 5 mm.)Open in a separate windowFig. S1.Two larval stages of L. illecebrosa. (A) The 2-mm-long larva described here (specimen YKLP 11088a, b). (B) An 8-mm-long larva previously reported in ref. 12 (specimen YKLP 11084a, b; reprinted with permission from ref. 12). (Scale bar: 2 mm.)     

Inside-out regulation of E-cadherin conformation and adhesion

Cadherin cell–cell adhesion proteins play key roles in tissue morphogenesis and wound healing. Cadherin ectodomains bind in two conformations, X-dimers and strand-swap dimers, with different adhesive properties. However, the mechanisms by which cells regulate ectodomain conformation are unknown. Cadherin intracellular regions associate with several actin-binding proteins including vinculin, which are believed to tune cell–cell adhesion by remodeling the actin cytoskeleton. Here, we show at the single-molecule level, that vinculin association with the cadherin cytoplasmic region allosterically converts weak X-dimers into strong strand-swap dimers and that this process is mediated by myosin II–dependent changes in cytoskeletal tension. We also show that in epithelial cells, ∼70% of apical cadherins exist as strand-swap dimers while the remaining form X-dimers, providing two cadherin pools with different adhesive properties. Our results demonstrate the inside-out regulation of cadherin conformation and establish a mechanistic role for vinculin in this process.

E-cadherins (Ecads) are essential, calcium-dependent cell–cell adhesion proteins that play key roles in the formation of epithelial tissue and in the maintenance of tissue integrity. Ecad adhesion is highly plastic and carefully regulated to orchestrate complex movement of epithelial cells, and dysregulation of adhesion is a hallmark of numerous cancers (1). However, little is known about how cells dynamically regulate the biophysical properties of individual Ecads.The extracellular region of Ecads from opposing cells bind in two distinct trans orientations: strand-swap dimers and X-dimers (Fig. 1 A and B). Strand-swap dimers are the stronger cadherin adhesive conformation and are formed by the exchange of conserved tryptophan (Trp) residues between the outermost domains of opposing Ecads (2–4). In contrast, X-dimers, which are formed by extensive surface interactions between opposing Ecads, are a weaker adhesive structure and serve as an intermediate during the formation and rupture of strand-swap dimers (5–7). Using cell-free, single-molecule experiments we previously showed that X-dimers and strand-swap dimers can be distinguished based on their distinctly different response to mechanical force. When a strand-swap dimer is pulled, its lifetime decreases with increasing force, resulting in the formation of a slip bond (8, 9) (Fig. 1B). In contrast, an X-dimer responds to pulling force by forming a catch bond, where bond lifetime initially increases up to a threshold force and then subsequently decreases (8, 10) (Fig. 1B). It has also been shown that wild-type Ecad ectodomains in solution can interconvert between X-dimer and strand-swap dimer conformations (9, 11). However, the biophysical mechanisms by which Ecad conformations (and adhesion) are regulated on the cell surface are unknown.Open in a separate windowFig. 1.Overview of experiment. (A) The extracellular region of Ecad from opposing cells mediates adhesion. The cytoplasmic region of Ecad associates either directly or indirectly with p120 catenin, β-catenin, α-catenin, vinculin, and F-actin. (B) Strand-swap dimers form slip bonds (blue) and X-dimers form catch bonds (red). Ecads interconvert between these two dimer conformations. Structures were generated from the crystal structure of mouse Ecad (PDB ID code 3Q2V); the X-dimer was formed by alignment to an X-dimer crystal structure (PDB ID code 3LNH). (C) Graphics showing the cell lines used in experiments and Western blot analysis of corresponding cell lysates.The cytoplasmic region of Ecad associates with the catenin family of proteins, namely, p120-catenin, β-catenin, and α-catenin. The Ecad–catenin complex, in turn, links to filamentous actin (F-actin) either by the direct binding of α-catenin and F-actin or by the indirect association of α-catenin and F-actin via vinculin (12) (Fig. 1A). Adhesive forces transmitted across intercellular junctions by Ecad induce conformational changes in α-catenin (13, 14), strengthen F-actin binding (15), and recruit vinculin to the sites of force application (16, 17). However, vinculin and α-catenin do not merely serve as passive cytoskeletal linkers; they also dynamically modulate cytoskeletal rearrangement and recruit myosin to cell–cell junctions (13, 18–20). Studies show that α-catenin and vinculin play important roles in strengthening and stabilizing Ecad adhesion: bead-twisting experiments show force-induced stiffening of Ecad-based junctions and cell doublet stretching experiments demonstrate reinforcement of cell–cell adhesion in vinculin- and α-catenin–dependent manners (18, 19, 21).Currently, actin anchorage and cytoskeletal remodeling are assumed to be the exclusive mechanisms by which α-catenin and vinculin strengthen Ecad adhesion (22–24). Here, we directly map the allosteric effects of cytoplasmic proteins on Ecad ectodomain conformation and demonstrate, at the single-molecule level, that vinculin association with the Ecad cytoplasmic region switches X-dimers to strand-swap dimers. We show that cytoskeletal tension, due to vinculin-mediated recruitment of myosin II, regulates Ecad ectodomain structure and adhesion. Finally, we demonstrate that only ∼50% of Ecads are linked to the underlying cytoskeleton and that while about 70% of Ecads form strand-swap dimers the remaining form X-dimers, which provides cells with two Ecad pools with different adhesive properties.