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.     

A 2,300-year-old architectural and astronomical complex in the Chincha Valley,Peru

Recent archaeological research on the south coast of Peru discovered a Late Paracas (ca. 400–100 BCE) mound and geoglyph complex in the middle Chincha Valley. This complex consists of linear geoglyphs, circular rock features, ceremonial mounds, and settlements spread over a 40-km² area. A striking feature of this culturally modified landscape is that the geoglyph lines converge on mounds and habitation sites to form discrete clusters. Likewise, these clusters contain a number of paired line segments and at least two U-shaped structures that marked the setting sun of the June solstice in antiquity. Excavations in three mounds confirm that they were built in Late Paracas times. The Chincha complex therefore predates the better-known Nasca lines to the south by several centuries and provides insight into the development and use of geoglyphs and platform mounds in Paracas society. The data presented here indicate that Paracas peoples engineered a carefully structured, ritualized landscape to demarcate areas and times for key ritual and social activities.The Chincha Valley, located 200 km south of Lima, was one of the largest and most productive regions of southern coastal Peru (Fig. 1). Previous research identified a rich prehispanic history in the valley, beginning at least in the early first millennium BCE and continuing through the Inca period in the 16th century CE (1–3). The earliest settled villages were part of the Paracas culture, a widespread political and social entity that began around 800 BCE and continued up to around 100 BCE. Previous field surveys identified at least 30 major Paracas period sites in the valley (1, 3, 4), making Chincha one of the main centers of development for this early Andean civilization (5). As such, it is an ideal area to test models of social evolution in general and to define the strategies that early peoples used to construct complex social organizations within the opportunities and constraints provided by their environments.Open in a separate windowFig. 1.Map showing location of the Chincha Valley, southern coastal Peru.Previous research demonstrated a dense Paracas settlement in the lower valley that focused on large platform mound complexes (Fig. 2) (4, 6). Three seasons of systematic, intensive survey and excavations by our team confirm the existence of a rich and complex Paracas occupation in the midvalley area as well, including both mound clusters and associated geoglyph features. In short, our data indicate that (i): the Chincha geoglyphs predate the better-known Nasca drainage ones by at least three centuries; (ii) Paracas period peoples created a complex landscape by constructing linear geoglyphs that converge on key settlements; and (iii) solstice marking was one component underlying the logic of geoglyph and platform mound construction and use in the Chincha Valley during the Paracas period.Open in a separate windowFig. 2.Distribution of archaeological sites linked to Paracas period settlement in Chincha, Peru. Redrawn from Canziani (4).     

One-step catalytic asymmetric synthesis of all-syn deoxypropionate motif from propylene: Total synthesis of (2R,4R,6R,8R)-2,4,6,8-tetramethyldecanoic acid

In nature, many complex structures are assembled from simple molecules by a series of tailored enzyme-catalyzed reactions. One representative example is the deoxypropionate motif, an alternately methylated alkyl chain containing multiple stereogenic centers, which is biosynthesized by a series of enzymatic reactions from simple building blocks. In organic synthesis, however, the majority of the reported routes require the syntheses of complex building blocks. Furthermore, multistep reactions with individual purifications are required at each elongation. Here we show the construction of the deoxypropionate structure from propylene in a single step to achieve a three-step synthesis of (2R,4R,6R,8R)-2,4,6,8-tetramethyldecanoic acid, a major acid component of a preen-gland wax of the graylag goose. To realize this strategy, we focused on the coordinative chain transfer polymerization and optimized the reaction condition to afford a stereo-controlled oligomer, which is contrastive to the other synthetic strategies developed to date that require 3–6 steps per unit, with unavoidable byproduct generation. Furthermore, multiple oligomers with different number of deoxypropionate units were isolated from one batch, showing application to the construction of library. Our strategy opens the door for facile synthetic routes toward other natural products that share the deoxypropionate motif.The deoxypropionate motif, an alternately methylated alkyl chain containing multiple stereogenic centers, is a common substructure found in natural products synthesized by bacteria, fungi, and plants (Fig. 1) (1). Because of the range of biological activities and abundance of this motif in natural products, its synthesis has received a great amount of attention (2, 3).Open in a separate windowFig. 1.Selected examples of natural products containing the deoxypropionate motif.In nature, the deoxypropionate motif is synthesized by using propionyl-CoA (or methylmalonyl-CoA) as a C3 building block (Fig. 2A). The deoxypropionate chain propagates by Claisen condensation of propionyl-CoA and acyl-CoA moiety at the chain end. After consecutive reduction of the β-ketone, dehydration, and asymmetric reduction of the carbon–carbon double bond, the deoxypropionate motif is elongated. We predicted that if the preparation of the deoxypropionate motif were possible by the asymmetric oligomerization of propylene, which is one of the simplest C3 building blocks, we could construct the analog of biosynthetic pathway in an even simplified manner (Fig. 2B).Open in a separate windowFig. 2.Synthesis of the deoxypropionate motif. (A) Biosynthetic scheme. (B) Synthesis by asymmetric oligomerization of propylene (current study). (C) Synthesis by iterative asymmetric carboalumination (7). (D) Synthesis by iterative asymmetric conjugate addition (11).To demonstrate our strategy, we chose (2R,4R,6R,8R)-2,4,6,8-tetramethyldecanoic acid 1 as a synthetic target. This carboxylic acid is a natural product containing the deoxypropionate motif, and is a major acid component of preen-gland wax of the graylag goose (4). Its total synthesis has been reported by two groups, both involving the oxidation of (2R,4R,6R,8R)-2,4,6,8-tetramethyldecan-1-ol 2. By using our strategy, this intermediate 2 can be constructed in a single step, significantly shortening the overall synthetic route.Conventionally, the deoxypropionate motif has been synthesized mainly using iterative reactions of complex building blocks or stoichiometric amount of organometallic reagents with unavoidable byproduct generation (e.g., inorganic salts) at each step. Previously reported synthetic routes include enolate alkylation (5, 6), carboalumination (7), organocuprate displacement (8), homologation of boronic esters (9), and conjugate addition (10). In addition, due to their iterative nature, long reaction sequences of 3–6 steps per unit were required to yield the desired products. As for the synthesis of 2, Liang et al. used an asymmetric carboalumination (Fig. 2C) (7), whereas ter Horst et al. used an asymmetric conjugate addition of methylcopper species (Fig. 2D) (11). Due to the iterative nature of methyl-branched chiral center formation, these syntheses required a total of 8–17 steps. Recently, convergent strategies for the synthesis of the deoxypropionate motif have been reported to shorten the synthetic route but generation of byproducts remains unavoidable (12, 13).Notably, propylene polymerization catalysts have rarely been used in the stereoselective oligomerization for synthesis of short oligomers, despite the great effort that has been devoted to the development of both homogeneous and heterogeneous catalysts for the highly isoselective propylene polymerization (14, 15). In 1987, Pino et al. reported an asymmetric propylene oligomerization catalyzed by enantiomerically pure chiral zirconocene in the presence of dihydrogen to afford saturated isotactic oligopropylenes (16). Kaminsky et al. later reported the preparation of moderately stereoregular oligomers with unsaturated chain end, which can be converted to other functional groups (17). To achieve natural product synthesis by the asymmetric oligomerization of propylene, a combination of stereoselectivity, functionalizability, and control over initiating groups is required. We herein report the one-step diastereoselective and enantioselective construction of the deoxypropionate motif by coordination chain transfer polymerization using an alkylmetal species as a chain-transfer agent (CTA) (18, 19). Our objective is to achieve highly stereoselective propylene oligomerization using this method. In addition, it is expected that the resulting oligomers will be end-capped with metals, thus enabling further functionalization.     

Atomic-number (Z)-correlated atomic sizes for deciphering electron microscopic molecular images

With the advent of atomic resolution transmission electron microscopy (AR-TEM) achieving sub-Ångstrom image resolution and submillisecond time resolution, an era of cinematic molecular science where chemists can visually study the time evolution of molecular motions and reactions at atomistic precision has arrived. However, the appearance of experimental TEM images often differs greatly from that of conventional molecular models, and the images are difficult to decipher unless we know in advance the structure of the specimen molecules. The difference arises from the fundamental design of the molecular models that represent atomic connectivity and/or the electronic properties of molecules rather than the nuclear charge of atoms and electrostatic potentials that are felt by the e-beam in TEM imaging. We found a good correlation between the atomic number (Z) and the atomic size seen in TEM images when we consider shot noise in digital images. We propose Z-correlated (ZC) atomic radii for modeling AR-TEM images of single molecules and ultrathin crystals with which we can develop a good estimate of the molecular structure from the TEM image much more easily than with conventional molecular models. Two parameter sets were developed for TEM images recorded under high-noise (ZC_HN) and low-noise (ZC_LN) conditions. The molecular models will stimulate the imaginations of chemists planning to use AR-TEM for their research.

The world of atoms and molecules invisible to human eyes has been illuminated by the development of three-dimensional models in which atoms represented by spheres are connected to each other with appropriate spatial disposition. Here, the choice of the radii of the spheres, in addition to coloring, is the most crucial visual identifier of atoms and atomic ions, and a variety of useful constructions of radii, either empirical or quantum mechanical (1, 2), have been proposed in accord with chemical intuition. Whereas wire and ball-and-stick models are primitive, showing only bond lengths, bond angles, and torsional angles, chemists have implemented further information into molecular models. Representing the van der Waals isosurface, a Corey–Pauling–Koltun (CPK) model was developed to illustrate the molecular surface for analysis of intermolecular interactions (3). Shannon’s ionic model (4) is useful for inorganic chemistry. The atomic radii chosen in these models reflect the electronic properties of atoms and molecules.With the advent of atomic-resolution transmission electron microscopy (AR-TEM) achieving sub-Ångstrom image resolution and submillisecond time resolution (5–8), an era of “cinematic molecular science” has arrived, where chemists can visually study the time evolution of molecular motions and reactions at atomistic precision (9–11). However, experimental TEM images often differ greatly from what chemists expect using their favorite molecular models (Fig. 1), and this discrepancy contributed to reducing the popularity of AR-TEM in molecular science. The difficulty is particularly important when we deal with unknown structures that are mobile and lack structural periodicity. For example, structure assignment of the AR-TEM images of a rotating zinc cluster (Fig. 1A) that forms in the synthesis of a crystal of a metal–organic framework (MOF; MOF-5) (12) was impossible without reducing the number of initial guess structures down to several hundred from a total of 4,096 isomers and their rotamers as to the 2-iodo-1,4-phenylene bridges (Fig. 1 D and E). Here, we found the CPK atomic radii (Fig. 1B) more confusing than revealing, and we became convinced of a need for new atomic radii that better represent the experimental images (compare Fig. 1 B and C). In doing so, we found that the atom size seen in TEM images shows a strong correlation to the atomic number (Z), particularly if we consider the shot noise in digital images. In a noise-free image, we find little correlation, as theory predicts (13). Our idea may be symbolically described as comparing the sizes of mountains by measuring the width of the lowest portion of each mountain that is visible above a sea of clouds. We report here a set of Z-correlated (ZC) atomic radii for deciphering AR-TEM images, with which we can develop a good estimate of the molecular structure from the TEM image much more easily than with conventional molecular models. Two parameter sets are described: one for TEM images under high-noise conditions (ZC_HN, Fig. 2C) and another under low-noise conditions (ZC_LN, Fig. 2D). The former is useful for single-molecule imaging and the latter for an ultrathin film such as UiO-66 MOF (Fig. 1F) (14, 15). The ZC radii reflect the relative sizes of various elements and help chemists develop an initial estimate of the molecular structures. Note, however, that we do not intend the ZC model to reproduce the TEM images exactly. No chemists expect the CPK model to represent the van der Waals properties of the molecule exactly, and molecular models are there to stimulate chemists’ imaginations.Open in a separate windowFig. 1.Experimental AR-TEM image and atomic-number-correlated molecular model (ZC model). (A) Time evolution of TEM image of a MOF-5 precursor, a cubic cluster made of Zn²⁺ and 2-iodoterephthalic acid captured on a carbon nanotube. The molecular images are blurred by rotation and vibration. Time is shown in seconds. Taken from ref. 12 (Copyright 2019, Nature Publishing Group). (B) A CPK model corresponding to the image at 10.0 s. (C) A ZC_HN model also for the image at 10.0 s. (D) Chemical structure of the cubic cluster. (E) Schematic illustration of isomers of the cubic MOF-5 precursor. Four structures out of 4096 possible isomers are shown. The structure seen in Fig. 1A is highlighted by a red square. (F) TEM image of an ultrathin crystal of UiO-66 (adapted from ref. 15). The benzene ring in a benzene dicarboxylate linker is indicated by an arrow (Copyright 2018, The American Association for the Advancement of Science). (G) A ZC_LN model corresponding to (F). (Scale bars: 1 nm.)Open in a separate windowFig. 2.Periodic table of atomic sizes used in molecular models. (A) CPK model. (B) Shannon’s ionic parameters. (C) ZC_HN model. (D) ZC_LN model. (Scale bar: 2 Å.) (E) List of ZC_HN radii for single-molecule imaging and ZC_LN radii for ultrathin crystals (in Å).In Fig. 2, we compare the atomic radii used in the CPK model, Shannon’s ionic model, and the ZC_LN and ZC_HN models. CPK radii are smaller for transition metals and larger for main group elements, and Shannon’s radii decrease with the increasing atomic number within the same period. These atomic radii are unsuitable for deciphering AR-TEM images, where the behavior of the e-beam in the electrostatic potential created by atomic nuclei plays a decisive role (16, 17). In the ZC model proposed here, the atomic radius shows element dependence, changing systematically as Z increases. The ZC_HN atomic radii (Fig. 2C) are smaller in signal size than the ZC_LN radii (Fig. 2D), show large Z-dependence, and are suitable for fast molecular imaging where the level of shot noise is large (Fig. 1 A–C) (18). The ZC_LN radii are suitable for images of ultrathin crystalline films where the level of shot noise is comparatively low. The atomic sizes for the graphical construction of the ZC models are listed in Fig. 2E. We anticipate that the molecular model will stimulate the imaginations of chemists planning to use AR-TEM for their research.     

Rapid seafloor changes associated with the degradation of Arctic submarine permafrost

Repeated high-resolution bathymetric surveys of the shelf edge of the Canadian Beaufort Sea during 2- to 9-y-long survey intervals reveal rapid morphological changes. New steep-sided depressions up to 28 m in depth developed, and lateral retreat along scarp faces occurred at multiple sites. These morphological changes appeared between 120-m and 150-m water depth, near the maximum limit of the submerged glacial-age permafrost, and are attributed to permafrost thawing where ascending groundwater is concentrated along the relict permafrost boundary. The groundwater is produced by the regional thawing of the permafrost base due to the shift in the geothermal gradient as a result of the interglacial transgression of the shelf. In contrast, where groundwater discharge is reduced, sediments freeze at the ambient sea bottom temperature of ∼−1.4 °C. The consequent expansion of freezing sediment creates ice-cored topographic highs or pingos, which are particularly abundant adjacent to the discharge area.

The effects of on-going terrestrial permafrost degradation (1–3) have been appraised by comparison of sequential images of Arctic landscapes that show geomorphic changes attributed primarily to thermokarst activity induced by recent atmospheric warming and ongoing natural periglacial processes (4–8). While the existence of extensive relict submarine permafrost on the continental shelves in the Arctic has been known for years (9, 10), the dynamics of submarine permafrost growth and decay and consequent modifications of seafloor morphology are largely unexplored.Throughout the Pleistocene, much of the vast continental shelf areas of the Arctic Ocean experienced marine transgressions and regressions associated with ∼125-m global sea level changes (11). Extensive terrestrial permafrost formed during sea-level low stands when the mean annual air temperatures of the exposed shelves were less than −15 °C (11, 12). Exploration wells drilled on the continental shelf in the Canadian Beaufort Sea show that relict terrestrial permafrost occurs in places to depths >600 m below seafloor (mbsf) and forms a seaward-thinning wedge beneath the outer shelf (10, 13, 14) (Fig. 1 A and B). The hydrography of the Canadian Beaufort Sea slows the degradation of the relict permafrost because a cold-water layer with temperatures usually near -1.4 °C blankets the seafloor from midshelf depths down to ∼200-m water depth (mwd) (15, 16) (Fig. 1B). As the freezing point temperature of interstitial waters is also controlled by salinity and sediment grain size, partially frozen sediments occur in a zone delimited by the ∼−2 °C and 0 °C isotherms (17, 18) (Fig. 1B).Open in a separate windowFig. 1.Map and cross-section showing the relationship between shelf edge morphology and the subsurface thermal structure along the shelf edge in the Canadian Beaufort Sea. (A) Shows the location of the study area with respect to estimates of submarine permafrost density (see key) and thickness, modified after 14. Thin contours indicate permafrost thickness in meters. Thicker contour is 120-m isobath marking the shelf edge. Area of repeat mapping coverage shown in Fig. 2 is indicated with a red box. (B) Shows a schematic cross-section with contours of selected subsurface isotherms modified after 15 along line x-x’ in A. The dotted blue line illustrates a thermal minimum (T-min) running through the relict permafrost isotherm and Beaufort Sea waters (16). Green shading indicates relict permafrost. Turquoise arrows show inferred flow of water from permafrost thawing along the base of the relict permafrost to the seafloor. The brown area indicates the zone where relict Pleistocene permafrost is predicted to have thawed with consequent movement of liberated groundwater, associated latent heat transfer and thaw consolidation causing surface settlement. Dashed brown lines define the subbottom limits for methane hydrate stability zone (MHSZ) which starts at ∼240 m below the sea surface and extends into the subsurface depending on the pressure and temperature gradient. The red box indicates the area shown in more detail in C with the same color scheme. The area of denuded seafloor in C is flanked by PLFs (dark-blue fill). Red arrows indicate the direction of heat transfer along the seaward edge of relict permafrost wedge.Distinctive surface morphologies characterize terrestrial permafrost areas. Conical hills (3 to 100 m in diameter) called pingos are common in the Arctic (19, 20). Pingos are formed due to freezing of groundwater. They characteristically contain lenses of nearly pure ground ice that cause heaving of the ground surface. Positive relief features with similar dimensions, referred to as pingo-like features (PLFs), are scattered across the Canadian Beaufort shelf (21, 22). On land, permafrost thawing, where there is ground ice in excess of the sediment pore space, can induce sediment consolidation (23), and surface subsidence results in widespread thermokarst landforms. Among the more dramatic occurrences are retrogressive thaw slumps (4–8, 24). These form where ice-rich permafrost experiences surface thaw causing thaw settlement and release of liquified sediment flows. Because of the loss of volume associated with thawing of massive ground ice, thaw slumps can quickly denude permafrost landscapes.During the first systematic multibeam mapping surveys in 2010 covering part of the shelf edge and slope in the Canadian Beaufort Sea, a band of unusually rough seafloor morphology between ∼120 and ∼200 mwds (25) was discovered along a ∼95-km-long stretch of the shelf. Subsequently, three additional multibeam surveys covering small characteristic areas (Fig. 2A) were conducted to understand the processes responsible for the observed morphologies. Here, we document the unique morphologies and seafloor change in this area and explore how the seafloor features may be related to subsea permafrost degradation and formation.Open in a separate windowFig. 2.(A) Shows bathymetry of a small section of the shelf edge indicated in Fig. 1A, with a color scale going from white (128 m) to blue (200 m) and contours at 120, 140, 170, and 200 mbsf. Outlines of areas resurveyed in 2013 (blue), 2017 (turquoise), and 2019 (green) are superimposed on the 2019 and regional 2010 survey. Colored symbols indicate locations of cores with porewater data using same key as Fig. 5B. The red star indicates the location of a temperature tripod deployed in the period 2015 to 2016. The location of ROV dive tracks (blue paths) are indicated. (B) Covers the same area as A with polygons identifying sites where changes were noted between surveys as follows: 2010 to 2013 (green), 2013 to 2017 (purple), 2017 to 2019 (black), and 2010 and 2019 (red). (C) Shows the same area, colored according to the difference in bathymetry between the 2019 survey and an idealized smooth surface extending between the top of the shelf edge scarp and the layered sediments occurring between the numerous PLFs. This is used to estimate the volume of material that eroded assuming the earlier Holocene seafloor corresponded with this idealized surface. Three zones of topography are labeled. Red boxes are locations of Figs. 3 and ​and5.5. (D) Shows Chirp profiles with the position of profiles shown in C and Fig. 5A. Light-green backdrop in X-X’ indicates possible void produced by retrogressive slide retreat used to calculated volume loss. Also indicated are TL, tilted layers; P, pingo-like-feature; and DR, diffuse reflector.     

Universal features in the photoemission spectroscopy of high-temperature superconductors

The energy gap for electronic excitations is one of the most important characteristics of the superconducting state, as it directly reflects the pairing of electrons. In the copper–oxide high-temperature superconductors (HTSCs), a strongly anisotropic energy gap, which vanishes along high-symmetry directions, is a clear manifestation of the d-wave symmetry of the pairing. There is, however, a dramatic change in the form of the gap anisotropy with reduced carrier concentration (underdoping). Although the vanishing of the gap along the diagonal to the square Cu–O bond directions is robust, the doping dependence of the large gap along the Cu–O directions suggests that its origin might be different from pairing. It is thus tempting to associate the large gap with a second-order parameter distinct from superconductivity. We use angle-resolved photoemission spectroscopy to show that the two-gap behavior and the destruction of well-defined electronic excitations are not universal features of HTSCs, and depend sensitively on how the underdoped materials are prepared. Depending on cation substitution, underdoped samples either show two-gap behavior or not. In contrast, many other characteristics of HTSCs, such as the dome-like dependence of on doping, long-lived excitations along the diagonals to the Cu–O bonds, and an energy gap at the Brillouin zone boundary that decreases monotonically with doping while persisting above (the pseudogap), are present in all samples, irrespective of whether they exhibit two-gap behavior or not. Our results imply that universal aspects of high- superconductivity are relatively insensitive to differences in the electronic states along the Cu–O bond directions.Elucidating the mechanism of high-temperature superconductivity in the copper–oxide materials remains one of the most challenging open problems in physics. It has attracted the attention of scientists working in fields as diverse as materials science, condensed matter physics, cold atoms, and string theory. To clearly define the problem of high-temperature superconductors (HTSCs), it is essential to establish which of the plethora of observed features are universal, namely, qualitatively unaffected by material-specific details.An important early result concerns the universality of the symmetry of the order parameter for superconductivity. The order parameter was found to change sign under a 90° rotation (1, 2), which implies that the energy gap must vanish along the diagonal to the Cu–O bonds, i.e., the Brillouin zone diagonal. This sign change is consistent with early spectroscopic studies of near-optimally-doped samples (those with the highest in a given family), where a energy gap (3, 4) was observed (ϕ being the angle from the Cu–O bond direction), the simplest functional form consistent with d-wave pairing. More recently, there is considerable evidence (5–8) that, with underdoping, the anisotropy of the energy gap deviates markedly from the simple form. Although the gap node at is observed at all dopings, the gap near the antinode (near and 90°) is significantly larger than that expected from the simplest d-wave form. Further, the large gap continues to persist in underdoped (UD) materials as the normal-state pseudogap (9–11) above . This suggests that the small (near-nodal) and large (antinodal) gaps are of completely different origin, the former related to superconductivity and the latter to some other competing order parameter.This two-gap picture has attracted much attention (8), raising the possibility that multiple energy scales are involved in the HTSC problem. There is mounting evidence for additional broken symmetries (12–14) in UD cuprates, once superconductivity is weakened upon approaching the Mott insulating state. The central issue is the role of these additional order parameters in impacting the universal properties of high- superconductivity.In this paper we use angle-resolved photoemission (ARPES) to examine the universality of the two-gap scenario in HTSCs by addressing the following questions. To what extent are the observed deviations from a simple d-wave energy gap independent of material details? How does the observed gap anisotropy correlate, as a function of doping, with other spectroscopic features such as the size of the antinodal gap, and the spectral weights of the nodal and antinodal quasiparticle excitations?We systematically examine the electronic spectra of various families of cation-substituted Bi₂Sr₂CaCu₂O_8+δ single crystals as a function of carrier concentration to elucidate which properties are universal and which are not. We present ARPES data on four families of float-zone-grown Bi₂Sr₂CaCu₂O_8+δ single crystals, where was adjusted by both oxygen content and cation doping. As-grown samples, labeled Bi2212, have an optimal of 91 K. These crystals were UD to by varying the oxygen content. Ca-rich crystals (grown from material with a starting composition Bi_2.1Sr_1.4Ca_1.5Cu₂O_8+δ) with an optimal of 82 K are labeled Ca. Two Dy-doped families grown with starting compositions Bi_2.1Sr_1.9Ca_{1 − x}Dy_xCu₂O_8+δ with x = 0.1 and 0.3 are labeled Dy1 and Dy2, respectively. A full list of the samples used and their determined from magnetization measurements are shown in SI Text, where we also show high-resolution X-ray data that give evidence for the excellent structural quality of our samples.Our main result is that the Dy1 and Dy2 samples show clear evidence of a two-gap behavior in the UD regime , with loss of coherent quasiparticles in the antinodal region of k space where the gap deviates from a simple d-wave form. In marked contrast, the UD Bi2212 samples and the Ca samples show a simple d-wave gap in the superconducting state and sharp quasiparticles over the entire Fermi surface in a similar range of the UD regime. We conclude by discussing the implications of the nonuniversality of the two-gap behavior for the phenomenon of high superconductivity.We begin our comparison of the various families of samples by focusing in Fig. 1 on the superconducting state antinodal spectra as a function of underdoping. The antinode is the Fermi momentum k_F on the Brillouin zone boundary, where the energy gap is a maximum and, as we shall see, the differences between the various samples are the most striking. We show data at optimal doping, corresponding to the highest in each family, in Fig. 1A. Increasing Dy leads to a small suppression of the optimal compared with Bi2212, together with an increase in the antinodal gap and a significant reduction of the quasiparticle weight. This trend continues down to moderate underdoping, as seen in Fig. 1B, where we show UD Bi2212 and Dy2 samples with very similar . For more severely UD samples, with , spectral changes in the Dy-substituted samples are far more dramatic. In Fig. 1C, we see that quasiparticle peaks in the Dy samples are no longer visible, even well below , consistent with earlier work on Y-doped Bi2212 and also Bi2201 and La_1.85Sr_0.15CuO₄ (5, 15–18). In contrast, Bi2212 and Ca-doped samples with comparable continue to exhibit quasiparticle peaks. In this respect the latter two are similar to epitaxially grown thin-film samples that exhibit quasiparticle peaks all of the way down to the lowest (19).Open in a separate windowFig. 1.Superconducting state antinodal ARPES spectra. We use the label “Bi2212” for samples without cation doping, “Dy1” for 10% Dy, “Dy2” for 30% Dy, and “Ca” for Ca-doped samples. The temperature is indicated along with . OP denotes optimal doped, UD underdoped, and OD overdoped samples. (A) Antinodal spectra for OP samples of three different families: Bi2212 (blue), Dy1 (green), and Dy2 (red), showing an increase in gap and a decrease in quasiparticle weight with increasing Dy content. (B) Antinodal spectra for UD samples with similar (≃66 K) for Bi2212 (blue) and Dy2 (red). As in A, there is a larger gap and smaller coherent weight in the Dy-substituted sample. (C) Same as in B, but for four UD samples with near 55 K for Bi2212 (dark blue), Ca (light blue), Dy1 (green), and Dy2 (red). The Bi2212 and Ca spectra are very similar to each other and quite different from those of the Dy1 and Dy2 materials. (D) Doping evolution of the antinodal spectra of four Dy1 samples from OP to UD . (E) Doping evolution of the antinodal spectra of four Dy2 samples from OP to UD . We see in D and E the sudden loss of quasiparticle weight for below 60 K. (F) Doping evolution of the antinodal spectra of three Bi2212 samples and three Ca samples, showing well-defined quasiparticle peaks in all cases.A significant feature of the highly UD Dy samples in Fig. 1C is that, in addition to the strong suppression of the quasiparticle peak, there is severe loss of low-energy spectral weight. To clearly highlight this, we show the doping evolution of antinodal spectra for the Dy1 (Fig. 1D) and Dy2 (Fig. 1E) samples. These observations are in striking contrast with the Bi2212 and Ca-doped data in Fig. 1F, where we do see a systematic reduction of the quasiparticle peak with underdoping, but not a complete wipeout of the low-energy spectral weight. To the extent that the superconducting state peak–dip–hump line shape (20, 21) originates from one broad normal-state spectral peak, the changes in spectra of the Dy materials are not simply due to a loss of coherence, but more likely a loss of the entire spectral weight near the chemical potential.The doping evolution of the k-dependent gap is illustrated in Figs. 2 and​and 3. 3. In Fig. 2 we contrast the optimally doped Dy1 (T_c = 86 K) sample (Fig. 2 A and B) with a severely UD Dy1 (T_c = 38 K) sample (Fig. 2 C and D ), the spectra being particle–hole-symmetrized to better illustrate the gap. The OP 86 K sample shows a well-defined quasiparticle peak over the entire Fermi surface (Fig. 2A) with a simple d-wave gap of the form (blue curve in Fig. 2B). For the UD 38 K sample, we see in Fig. 2C well-defined quasiparticles near the node (red spectra), but not near the antinode (blue spectra). The near-nodal gaps (red triangles in Fig. 2D) are obtained from the energy of quasiparticle peaks and continue to follow a d-wave gap (blue curve in Fig. 2D). However, once the quasiparticle peak is lost closer to the antinode, one has to use some other definition of the gap scale. We identify a break in the slope of the spectrum, by locating the energy scale at which it deviates from the black straight lines (Fig. 2C), which leads to the gap estimates (blue squares) in Fig. 2D.Open in a separate windowFig. 2.Superconducting state spectra and energy gap for OD and highly UD Dy1 samples. (A) Symmetrized spectra at k_F, from the antinode (Upper) to the node (Lower) for an OP 86 K Dy1 sample. (B) Gap as a function of Fermi surface angle (0° is the antinode and 45° the node). The blue curve is a d-wave fit to the data. (C) Same as A for an UD 38 K Dy1 sample. Curves, near the node, with discernible quasiparticle peaks are shown in red; those near the antinode are shown in blue. (D) Gap along the Fermi surface from data of C.Open in a separate windowFig. 3.Energy gap anisotropies of various samples. (A) OD 79 K Ca (where ); (B) UD 54 K Ca; (C) OP 81 K Dy2; and (D) UD 59 K Dy2. The two near-optimal samples in A and C both show a simple d-wave gap. This behavior persists in the UD Ca sample of B, but the UD Dy2 sample of D has a two-gap behavior despite having a similar to the UD Ca sample.Despite the larger error bar associated with gap scale extraction in the absence of quasiparticles, it is nevertheless clear (Fig. 2D) that the UD 38 K Dy1 sample has an energy gap that deviates markedly from the simple d-wave form. This observation is called two-gap in the UD regime, in contrast with a single gap near optimality (Fig. 2B). It is easy to observe from Fig. 2 that the Fermi surface angle at which the energy gap starts to deviate from the form matches the one at which the spectral peak gets washed out. This is very similar to the two-gap behavior demonstrated in refs. 5, 15–18. From this, one might conclude that two-gap behavior is directly correlated with a loss of well-defined quasiparticle excitations in the antinodal region. However, we point to recent ARPES data on Y-doped Bi2212 (6, 7), where two-gap behavior has been observed despite the presence of small antinodal quasiparticle peaks.We next show that the two-gap behavior is not a universal feature of all UD samples. To make this point, we compare in Fig. 3 the gap anisotropies of the Ca-doped samples (Fig. 3 A and B) with the Dy2 samples (Fig. 3 C and D) with essentially identical , where both families have the same optimal . The near-optimal samples, OD 79 K Ca (Fig. 3A) and OP 81 K Dy2 (Fig. 3C) samples, both have a simple d-wave anisotropy (although different maximum gap values at the antinode). However, upon underdoping to similar values, the two have markedly different gap anisotropies. The UD 59 K Dy2 sample (Fig. 3D) shows two-gap behavior, and an absence of quasiparticles near the antinode (similar to the discussion in connection with Fig. 2 above). However, the UD 54 K Ca sample (Fig. 3B) continues to exhibit sharp spectral peaks and a single-gap, despite a very similar as the UD 59 K Dy2.Having established the qualitative differences in the gap anisotropies for various samples as a function of underdoping, we next summarize in Fig. 4 the doping evolution of various spectroscopic features. Instead of estimating the carrier concentration in our samples using an empirical equation (22) (that may or may not be valid for various cation substitutions), we prefer to use the measured to label the doping. In Fig. 4A we show the doping evolution of the antinodal energy gap, which is consistent with the known increase in the gap with underdoping.Open in a separate windowFig. 4.Antinodal gaps and quasiparticle weights. (A) Antinodal energy gap as a function of doping for various samples is seen to grow monotonically with underdoping. Here, and in B and C, the doping is characterized by the measured quantity , with UD samples shown to the left of and OD samples to the right. All results are at temperatures well below . (B) Coherent spectral weight for antinodal quasiparticles as a function of doping. Dy-doped samples exhibit a rapid suppression of this weight to zero for UD , whereas the Ca-doped samples show robust antinodal peaks even for . (C) Coherent spectral weight for nodal quasiparticles as a function of doping, which is seen to be much more robust than the antinodal one.The coherent spectral weight Z for antinodal quasiparticles is plotted in Fig. 4B (for details on the procedure used to estimate this weight, from a ratio of spectral areas, see SI Text). The Dy1 and Dy2 samples both show a sudden and complete loss of Z with underdoping (23), which coincides with the appearance of two-gap behavior. In marked contrast with the Dy samples, the Bi2212 and Ca samples that exhibit a single d-wave gap show a gradual drop in the antinodal Z. On the other hand, we find that the nodal excitations are much less sensitive to how the sample is UD compared with the antinodal ones. Similar sharp nodal excitations have been observed in Dy-doped Bi2212 samples in ref. 7 as well. The nodal quasiparticle weight Z in Fig. 4C decreases smoothly with underdoping for all families of samples, as expected for a doped Mott insulator (24).The two-gap behavior and the attendant loss of quasiparticle weight near the antinode imply a nodal–antinodal dichotomy, aspects of which have been recognized in k space (25–27) and in real space (28–30). Two possible, not mutually exclusive, causes of this behavior are disorder and competing orders.It is known that antinodal states are much more susceptible to impurity scattering, whereas near-nodal excitations are protected (31). However, it is not a priori clear why certain cation substitutions (Dy) should lead to more electronic disorder than others (Ca). As shown by our X-ray studies in SI Text, there is no difference in the structural disorder in Dy and Ca samples. One possibility is that Dy has a local moment, but there is no direct experimental evidence for this.The two-gap behavior in UD materials, with a large antinodal gap that persists above , is suggestive of an order parameter, distinct from d-wave superconductivity, which sets in at the pseudogap temperature . There are several experiments (12–14) that find evidence for a broken symmetry at . However, it is not understood how the observed small, and often subtle, order parameter(s) could lead to large antinodal gaps of , with a loss of spectral weight over a much larger energy range (Fig. 1 D and E).We now discuss the pertinence of competing order parameters based on our measurements. First, in our ARPES data, we have not found any direct evidence for density wave ordering (say, from zone folding). Second, our X-ray data did not provide any signature for additional diffraction peaks expected for long-range density wave ordering. However, none of these null results provide definitive evidence for the absence of a density wave ordering, particularly if it were short range. In contrast, in previously published work (5, 15–18), two-gap behavior has been conjectured to be a direct consequence of phase competition between d-wave superconductivity and some type of density wave ordering. As we have demonstrated, two-gap behavior in and of itself is a sample-specific issue and hence, even if we assume a linkage between competing order and two-gap behavior, it cannot be central to the question of superconductivity in HTSC systems.Whatever the mechanism leading to qualitatively different gap anisotropies for the UD Dy and Ca samples, it only produces relatively small, quantitative changes in key aspects of these materials, such as the dependence of on doping, the presence of sharp nodal quasiparticles, and the pseudogap. We thus conclude that antinodal states do not make a substantial contribution to the universal features of HTSCs. Clearly, two gaps are not necessary for high-temperature superconductivity.     

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.     

Conjugated polymers based on metalla-aromatic building blocks

Conjugated polymers usually require strategies to expand the range of wavelengths absorbed and increase solubility. Developing effective strategies to enhance both properties remains challenging. Herein, we report syntheses of conjugated polymers based on a family of metalla-aromatic building blocks via a polymerization method involving consecutive carbyne shuttling processes. The involvement of metal d orbitals in aromatic systems efficiently reduces band gaps and enriches the electron transition pathways of the chromogenic repeat unit. These enable metalla-aromatic conjugated polymers to exhibit broad and strong ultraviolet–visible (UV–Vis) absorption bands. Bulky ligands on the metal suppress π–π stacking of polymer chains and thus increase solubility. These conjugated polymers show robust stability toward light, heat, water, and air. Kinetic studies using NMR experiments and UV–Vis spectroscopy, coupled with the isolation of well-defined model oligomers, revealed the polymerization mechanism.

Conjugated polymers are macromolecules usually featuring a backbone chain with alternating double and single bonds (1–3). These characteristics allow the overlapping p-orbitals to form a system with highly delocalized π-electrons, thereby giving rise to intriguing chemical and physical properties (4–6). They have exhibited many applications in organic light-emitting diodes, organic thin film transistors, organic photovoltaic cells, chemical sensors, bioimaging and therapies, photocatalysis, and other technologies (7–10). To facilitate the use of solar energy, tremendous efforts have been devoted in recent decades to developing previously unidentified conjugated polymers exhibiting broad and strong absorption bands (11–13). The common strategies for increasing absorption involve extending π-conjugation by incorporating conjugated cyclic moieties, especially fused rings; modulating the strength of intramolecular charge transfer between donor and acceptor units (D–A effect); increasing the coplanarity of π conjugation through weak intramolecular interactions (e.g., hydrogen bonds); and introducing heteroatoms or heavy atoms into the repeat units of conjugated polymers (11–16). Additionally, appropriate solubility is a prerequisite for processing and using polymers and is usually achieved with the aid of long alkyl or alkoxy side chains (12, 17).Aromatic rings are among the most important building blocks for conjugated polymers. In addition to aromatic hydrocarbons, a variety of aromatic heterocycles composed of main-group elements have been used as fundamental components. These heteroatom-containing conjugated polymers show unique optical and electronic properties (4–10). However, while metalla-aromatic systems bearing a transition metal have been known since 1979 due to the pioneering work by Thorn and Hoffmann (18), none of them have been used as building blocks for conjugated polymers. The HOMO–LUMO gaps (E_g) of metalla-aromatics are generally narrower (Fig. 1) than those of their organic counterparts (19–22). We reasoned that this feature should broaden the absorption window if polymers stemming from metalla-aromatics are achievable.Open in a separate windowFig. 1.Comparison of traditional organic skeletons with metalla-aromatic building blocks (the computed energies are in eV). (A) HOMO–LUMO gaps of classic aromatic skeletons. (B) Carbolong frameworks as potential building blocks for novel conjugated polymers with broad absorption bands and improved solubility.In recent years, we have reported a series of readily accessible metal-bridged bicyclic/polycyclic aromatics, namely carbolong complexes, which are stable in air and moisture (23–25). The addition of osmium carbynes (in carbolong complexes) and alkynes gave rise to an intriguing family of d_π–p_π conjugated systems, which function as excellent electron transport layer materials in organic solar cells (26, 27). These observations raised the following question: Can this efficient addition reaction be used to access metalla-aromatic conjugated polymers? It is noteworthy that incorporation of metalla-aromatic units into conjugated polymers is hitherto unknown. In this contribution, we disclose a polymerization reaction involving M≡C analogs of C≡C bonds, which involves a unique carbyne shuttling strategy (Fig. 2A). This led to examples of metalla-aromatic conjugated polymers (polycarbolongs) featuring metal carbyne units in the main chain. On the other hand, the development of polymerization reactions plays a crucial role in involving certain building blocks in conjugated polymers (28–32). These efficient, specific, and feasible polymerizations could open an avenue for the synthesis of conjugated polymers.Open in a separate windowFig. 2.Design of polymers and synthesis of monomers. (A) Schematic illustration of the polymerization strategy. (B) Preparation of carbolong monomers. Insert: X-ray molecular structure for the cations of complex 3. Ellipsoids are shown at the 50% probability level; phenyl groups in PPh₃ are omitted for clarity.     

Probing large conformational rearrangements in wild-type and mutant spectrin using structural mass spectrometry

Conformational changes of macromolecular complexes play key mechanistic roles in many biological processes, but large, highly flexible proteins and protein complexes usually cannot be analyzed by crystallography or NMR. Here, structures and conformational changes of the highly flexible, dynamic red cell spectrin and effects of a common mutation that disrupts red cell membranes were elucidated using chemical cross-linking coupled with mass spectrometry. Interconversion of spectrin between closed dimers, open dimers, and tetramers plays a key role in maintaining red cell shape and membrane integrity, and spectrins in other cell types serve these as well as more diverse functions. Using a minispectrin construct, experimentally verified structures of closed dimers and tetramers were determined by combining distance constraints from zero-length cross-links with molecular models and biophysical data. Subsequent biophysical and structural mass spectrometry characterization of a common hereditary elliptocytosis-related mutation of α-spectrin, L207P, showed that cell membranes were destabilized by a shift of the dimer–tetramer equilibrium toward closed dimers. The structure of αL207P mutant closed dimers provided previously unidentified mechanistic insight into how this mutation, which is located a large distance from the tetramerization site, destabilizes spectrin tetramers and cell membrane integrity.Solving static structures of protein complexes and probing dynamic conformational rearrangements have frequently provided mechanistic insights into macromolecular functions as well as effects of disease-related mutations. However, high-resolution structural techniques such as X-ray crystallography and NMR usually cannot be applied to large proteins that are highly flexible, intrinsically disordered, or undergo large conformational changes. Chemical cross-linking coupled with mass spectrometry (CX-MS) is a powerful tool that identifies proximal amino acid residues of proteins in solution. These spatial constraints can greatly enhance and experimentally validate molecular modeling to result in reliable medium-resolution structures. This approach has been effectively used in numerous studies (1–5), although homobifunctional lysine-specific cross-linkers with relatively long spacer arms were mostly used. Such reagents have been preferred because they enable introduction of isotope labels and other functional sites that facilitate cross-linked peptide identifications (6–9). In contrast, zero-length cross-linkers such as 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide form a covalent bond between reactive amines (N-terminal amine or lysine side chain) and carboxyls (C-terminal carboxyl or aspartic or glutamic acid side chains) without inserting extra atoms (Fig. 1). Hence, reactive groups have to be within salt bridge distances to react. This results in tighter distance constraints that outperform those from longer cross-links when these data are used to refine structural models, and a much lower density of cross-links is needed to achieve high-quality structures (7). In this study we used a recently developed strategy for in-depth identification of zero-length cross-links, as summarized in Fig. 1, to probe structures and conformational changes in spectrin, a highly flexible, dynamic protein with multiple functions that is typically associated with cell membranes. In red cells, spectrin is the central component of a highly specialized, 2D, net-like submembraneous complex that confers both structural integrity and elasticity to the cell membrane. The 1,052-kDa spectrin tetramer is a long, highly flexible, worm-like protein composed primarily of many tandem, homologous, “spectrin-type” domains (Fig. 2A). In all reported crystal structures, these domains are approximately 50-Å-long three-helix bundles with helical connectors (Fig. 2 B and C). To date, crystallization of more than four spectrin-type domains has not been feasible, presumably owing to spectrin’s highly flexible nature. In the membrane skeleton, spectrin tetramers bridge short actin oligomers, and membrane stability is highly dependent upon the dimer–tetramer equilibrium because conversion to dimers breaks the spectrin bridges between actin oligomers (10, 11).Open in a separate windowFig. 1.Schematic for the zero-length CX-MS data acquisition and data analysis pipeline.Open in a separate windowFig. 2.Spectrin topography and minispectrin tetramer structure. (A) Schematic showing spectrin domains, the dimer–tetramer equilibria, and minispectrin. The spectrin-type domains that constitute most of the molecule are represented as rounded rectangles. Red asterisks in the minispectrin cartoon indicate the approximate location of the αL207P mutation. (B) Superimposition of the four crystal structures of spectrin-type domains [Protein Data Bank (PBD) ID: 1CUN, 1U5P, 3FB2, and 1S35] used as template building blocks for homology modeling. (C) Crystal structure for the spectrin tetramerization interface (PDB ID: 3LBX). (D) Locations of interdomain cross-links used to model minispectrin tetramer; blue lines, cross-links identified previously (21); red lines, previously unidentified cross-links; dashed lines, the same cross-links repeated in the second half of the tetramer. (E) Superimposition of present and previous tetramer structures. (F) Space-filling representations of tetramer models. β-Spectrin domains are colored in bright or pale cyan, and α-spectrin domains are colored in bright or pale orange to distinguish the two strands.The spectrin dimer–tetramer equilibrium actually involves three states, including closed dimers, open dimers, and bivalent head-to-head tetramers (Fig. 2A). Conversion from open to closed dimers involves a large conformational rearrangement of the longer α-subunit, where it folds back upon itself and forms a head-to-head association analogous to the head-to-head associations in tetramers (Fig. 2A). Closed dimers play a critical but poorly understood role in this equilibrium and are responsible for a high-energy threshold that regulates kinetics of the dimer–tetramer equilibrium (12). Furthermore, hereditary elliptocytosis (HE) and hereditary pyropoikilocytosis (HPP) are common human clinical disorders characterized by reduced spectrin tetramerization resulting in abnormal red cell shape, increased membrane fragility, and in some cases, severe anemia that is transfusion dependent (13, 14). Many HE and HPP mutations are located within the tetramerization site (13), although a number of interesting mutations are located large distances from this binding domain and destabilize tetramer formation through unknown mechanisms (15–19). For example, the very common αL207P mutation is located in the middle of the α2 domain, which is ∼75 Å from the tetramerization site. However, it seemed likely that this region could undergo important conformational changes during the closed–open dimer transition (Fig. 2A). We previously developed a 90 kDa fused minispectrin dimer (Fig. 2A) to further study the dimer–tetramer equilibrium (20) and used it here for the CX-MS experiments because it is substantially simpler than the 526-kDa full-length spectrin dimer and retains physiological tetramer binding properties.     

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.     

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.     

Pulque production from fermented agave sap as a dietary supplement in Prehispanic Mesoamerica

Although in modern societies fermented beverages are associated with socializing, celebration, and ritual, in ancient times they were also importa`nt sources of essential nutrients and potable water. In Mesoamerica, pulque, an alcoholic beverage produced from the fermented sap of several species of maguey plants (Agavaceae; Fig. 1) is hypothesized to have been used as a dietary supplement and risk-buffering food in ancient Teotihuacan (150 B.C. to A.D. 650). Although direct archaeological evidence of pulque production is lacking, organic residue analysis of pottery vessels offers a new avenue of investigation. However, the chemical components of alcoholic beverages are water-soluble, greatly limiting their survival over archaeological timescales compared with hydrophobic lipids widely preserved in food residues. Hence, we apply a novel lipid biomarker approach that considers detection of bacteriohopanoids derived from the ethanol-producing bacterium Zymomonas mobilis for identifying pulque production/consumption in pottery vessels. Gas chromatography–mass spectrometry selected ion monitoring (m/z 191) of lipid extracts of >300 potsherds revealed characteristic bacteriohopanoid distributions in a subset of 14 potsherds. This hopanoid biomarker approach offers a new means of identifying commonly occurring bacterially fermented alcoholic beverages worldwide, including palm wine, beer, cider, perry, and other plant sap- or fruit-derived beverages [Swings J, De Ley J (1977) Bacteriol Rev 41(1):1–46].Open in a separate windowFig. 1.(A) Maguey plant (Agave salmiana), (B) maguey sap (aguamiel) pooled within a maguey in the process of sap extraction, and (C) man transferring pulque to a 2.5-L container for sale in Apan, Mexico.Teotihuacan, located in the semiarid highlands of Central Mexico, is one of the largest urban centers of prehistory. Founded around 150 B.C., the city grew very rapidly, in part by absorbing a high proportion of the regional population. The city eventually covered around 20 km² and may have reached a population level of 100,000 inhabitants. Teotihuacan was the capital of a state that controlled the Basin of Mexico, and probably much of the adjacent parts of the Central Mexican Highlands, and maintained extensive trade relationships that in specific places and times involved military incursion. It survived as an important economic, political, and cultural power in Mesoamerica until roughly A.D. 650 (1, 2).High altitude, low rainfall, and limited groundwater resources make most of the Teotihuacan Valley a high-risk area for cultivating maize, which was nevertheless a key crop for traditional farmers in the region and was thought to have been important in the diet of residents of ancient Teotihuacan (3–5). Although maize is a food of high caloric value, it contains only low concentrations of several important micronutrients and essential amino acids (e.g., B-complex vitamins, ascorbic acid, iron, calcium, lysine, tryptophan, methionine, and isoleucine) (2, 6). However, a maize-based diet supplemented with beans, which are especially rich in protein, can provide a reasonably balanced diet; although if the components are not consumed in the correct proportions, serious nutritional deficiencies occur (3, 6, 7). Indeed, human osteoarchaeological investigations provide clear evidence of nutritional stress within urban Teotihuacan, especially among lower-status households (8).At Teotihuacan, mural paintings (depicting maguey plants and scenes of possible pulque consumption) and artifactual remains (amphorae resembling vessels used to contain pulque during the later Aztec period and modern times, and types of stone endscrapers thought to have been used in maguey sap extraction) have led to the suggestion that pulque might also have been important there (9). The maguey (Fig. 1A) withstands frost and drought that seriously affect less-hardy plants such as maize, with its sap (Fig. 1B) providing significant calories at times of shortfall in other crops (10). Because of its high viscosity (Fig. 1C), conferred by dextran-producing bacteria, consuming pulque at such times would have naturally helped satiate sensations of hunger. Pulque would also have helped alleviate more chronic nutritional deficiencies. Apart from the ethanol content of ca. 4.5% and pH of 3–4, pulque contains concentrations of most macro/micronutrients, which, together with lactic acid bacteria, confer probiotic properties (11–13). In addition, the presence of phytase mediates dephosphorylation of plant phytates (myo-inositol hexakisphosphate), an indigestible form of phosphorus occurring in high abundance in grains, thereby increasing the bioavailability of iron and zinc, the most deficient minerals in maize (14). Furthermore, the high concentration of ascorbic acid in pulque enhances the absorption of iron, and possibly zinc, into the digestive system (14).Despite the significant challenges involved in identifying organic residues from alcoholic beverages in the archaeological record (15), the fermentation process in pulque production offers a potentially specific means of detecting this activity. The major bacterium involved in pulque fermentation is Zymomonas mobilis, which, together with yeast, produces alcohol (11, 12, 16). To be able to resist ethanol stress and low pH, Z. mobilis has evolved a membrane containing the highest concentration of hopanoids currently known in bacteria (i.e., ca. 30 mg g⁻¹ dry cell mass), mainly as tetrahydroxybacteriohopane and its ether and glycosidic derivatives (16) (Fig. 2A). Hopanoids are extensively used as biomarkers in studies of extinct and extant bacteria in sediments (17–19); however, the use of hopanoids as biomarkers of alcoholic beverage production in archaeology is untested until now.Open in a separate windowFig. 2.(A) Major complex hopanoids in ethanol producer bacterium Z. mobilis (16). (B) Possible diagenetic products and transformations of complex hopanoids (17).     

Continental crust beneath southeast Iceland

The magmatic activity (0–16 Ma) in Iceland is linked to a deep mantle plume that has been active for the past 62 My. Icelandic and northeast Atlantic basalts contain variable proportions of two enriched components, interpreted as recycled oceanic crust supplied by the plume, and subcontinental lithospheric mantle derived from the nearby continental margins. A restricted area in southeast Iceland—and especially the Öræfajökull volcano—is characterized by a unique enriched-mantle component (EM2-like) with elevated ⁸⁷Sr/⁸⁶Sr and ²⁰⁷Pb/²⁰⁴Pb. Here, we demonstrate through modeling of Sr–Nd–Pb abundances and isotope ratios that the primitive Öræfajökull melts could have assimilated 2–6% of underlying continental crust before differentiating to more evolved melts. From inversion of gravity anomaly data (crustal thickness), analysis of regional magnetic data, and plate reconstructions, we propose that continental crust beneath southeast Iceland is part of ∼350-km-long and 70-km-wide extension of the Jan Mayen Microcontinent (JMM). The extended JMM was marginal to East Greenland but detached in the Early Eocene (between 52 and 47 Mya); by the Oligocene (27 Mya), all parts of the JMM permanently became part of the Eurasian plate following a westward ridge jump in the direction of the Iceland plume.The North Atlantic Igneous Province covers vast areas in Baffin Island, Greenland, United Kingdom, Ireland, the Faroe Islands, and offshore regions (Fig. 1). Volcanic activity commenced ∼62 Mya (1) and is attributed to the impingement of a mantle plume head on the lithosphere (2). Enriched and depleted geochemical signatures in the Paleogene to Recent basalts from Iceland reflect a combination of plume-derived and shallow asthenospheric material, representing a classic case of plume–ridge interaction. The volcanic products of the Iceland plume have signatures of recycled oceanic crust and primordial-like material with high ³He/⁴He ratios (up to 50 R_A, where R_A is the ³He/⁴He ratio of air), spanning a range of refractory to fertile compositions (3, 4). Additional shallow-level contributions include the depleted mid-ocean ridge basalt–type asthenosphere, variably mixed with subcontinental lithospheric mantle material (5–8). The proximity of Iceland to the Jan Mayen Microcontinent (JMM) (Fig. 1) raises the question of whether Iceland includes underlying fragments of continental crust (9, 10). There are unconfirmed reports of both Precambrian and Mesozoic zircons in young basalts from Iceland, but only in abstract form (9, 11). The recovery of a 1.8-Ga-old grain from the most primitive Öræfajökull basalts (9) in southeast Iceland (Figs. 1 and ​and2A)2A) is potentially very interesting, although two Jurassic zircon grains (160 Ma) uncovered in the same separation are now suspected to be due to laboratory contamination.Open in a separate windowFig. 1.Crustal thickness map based on gravity inversion and revised location of the Iceland plume (white star symbols in 10-My intervals) relative to Greenland back to 60 Ma (see Fig. 6A for details). Transition between continental and oceanic crust (COB, black lines), plate boundaries (blue lines), site locations for dated North Atlantic Igneous Province magmatism (yellow circles with black fill), and the estimated size of the “classic” Jan Mayen Microcontinent (JMM) and the extended JMM (JMM-E) are also shown. The classic JMM is ∼500 km long (200 km at its widest and shown with four continental basement ridges), and crustal thicknesses are about 18–20 km (scale at Upper Left), which suggests considerable continental stretching before drifting off Greenland. We extend JMM 350 km southwestward (∼70 km wide) beneath southeast Iceland where we calculate maximum crustal thicknesses of ∼32 km (Fig. 2B). The size of JMM-E is a conservative estimate and could be as large as the white-stippled area. Öræfajökull location is shown as a yellow star.Open in a separate windowFig. 2.(A) Simplified geological map of Iceland outlining the major rift zones (RZ), rift zone central volcanoes (black outline), flank zone central volcanoes (colored outlines), the location of the Öræfajökull central volcano, and the position of the current plume axis of Shorttle et al. (25). Yellow areas are fissure swarms. WRZ, ERZ, NRZ, and MIB: Western, Eastern, and Northern Rift Zone and Mid-Iceland Belt, respectively. The stippled west-northwest–trending line through the plume axis depicts the position of the cross sections in Fig. 9. (B) Enlarged crustal thickness map (Fig. 1) with superimposed earthquake locations (earthquake.usgs.gov/earthquakes) and contour intervals (in meters) showing that the anomalously thick crust under southeast Iceland extends offshore to the northeast suggesting that it is a southerly fragment of the Jan Mayen Microcontinent (JMM) rather than an extension of the southeast-northwest–orientated Iceland-Faroes Ridge.     

A neural surveyor to map touch on the body

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

In the 18th century, surveyors in France completed the world’s first topographically accurate map of an entire country. To do so, they relied on the computation of triangulation; given a precisely known distance between two baseline landmarks, the location of a third landmark could be computed from its angles of intersection with the baseline landmarks. Countries could utilize this simple geometric computation to accurately map the location of all landmarks in their borders (Fig. 1A). This is also possible using multilateration (or trilateration, more specifically), where the known distance between multiple baseline landmarks is used to compute the location of another landmark. These computations are simple yet robust ways to localize objects and therefore still used in modern surveying and global position systems.Open in a separate windowFig. 1.Examples of geometric computations. (A) Idealized example of how multi/trilateration can be used to localize an object within a country. Given the distance between two baseline landmarks whose locations are known (d₁), an object (e.g., the Eiffel tower) can be localized by calculating its distance from each landmark individually (d₂ and d₃). (B) Path integration: By computing over distances and angles traveled (d₁ and d₂), a rat can calculate how much it needs to travel (d₃) to return to its starting position. (C) Visuomotor reaching: The distance between the hand and object (d₃) can be computed by evaluating over the eye-centered hand distance (d₁) and object distance (d₂). (D) Egocentric tactile localization: The vector (black arrow) for reaching to a touch (black dot) on the arm may be computed using trilateration. First, an arm-centered touch location is trilaterated (see next section; see Fig. 2A) by computing distance between the touch and the elbow (d₁) and wrist (d₂). An egocentric representation of touch could be derived by further taking into account the distance between the reaching hand and the elbow (d₃) and wrist (d₄).Geometric computations involving manipulating distances and angles are also employed by the nervous system of animals to localize and interact with objects in the environment. During spatial navigation (Fig. 1B), mammals can readily return to their starting location by taking into account all computed distances and heading directions traveled (1), a phenomenon known as path integration. Reaching to grasp a visible target is another behavior involving geometric computations (Fig. 1C). To do so, the brain must compute a reach vector from distances derived from hand and target position signals (2, 3), involving transformations that take place in the frontal and parietal cortices (4, 5).Equally crucial to localizing objects in the environment is localizing objects on the personal space of the body. In the 180 y since Weber’s seminal investigations on the sense of touch (6), researchers have extensively characterized tactile localization at both the behavioral (7–18) and neural (19–25) level. However, despite this progress, the computations underlying tactile localization remain largely unknown. Recent accounts have suggested that tactile localization requires two computational steps (26, 27). First, afferent input must be localized within a topographic map in somatosensory cortex (19). However, an activation within this map is not sufficient for localization since it alone is meaningless without a reference frame that is centered on the body. Localizing touch on the body therefore requires a neural decoder (28) that can “read” the topographic landscape of the population response within the map (22) and reference this information to stored spatial representations of the body (29). However, given that the nature of these computations—and how they might be implemented by neural circuits—remains largely unknown, it is unclear whether the brain uses geometric computations to localize objects touching the body.We propose that, like a surveyor, the human brain employs multi/trilateration to localize an object in body-centered coordinates (Fig. 1D). To do so, this “neural surveyor” uses simple arithmetic to calculate the relative distance between the location of the afferent input and the boundaries of the body part (the baseline landmarks, e.g., elbow and wrist of the forearm in Fig. 2A). This body-centered coordinate system is tied to a neural architecture that represents the body’s geometry (29) and is influenced by proprioceptive feedback about limb size (30). In the present study, we provide multiple lines of evidence that the brain may use multi/trilateration to localize touch on a limb. We first develop a Bayesian formulation of this computation in the nervous system. We then develop a population-based neural network model that implements trilateration, thus allowing us to identify its computational signatures. Simulations revealed that trilateration produces a unique pattern of localization variability across a limb. Finally, we identify this pattern in actual psychophysical experiments and provide further predictions of the model to be tested in future work.Open in a separate windowFig. 2.Neural network implementing trilateration. (A) Trilateral computation for tactile localization: The location of touch on the arm is computed by integrating two estimates (d₁ and d₂) of the distance between each joint (x₁ and x₂; elbow and wrist) and the sensory input x₃. (B) Neural network implementation of trilateration: (Lower) the encoding layer is composed of homogenous tuning curves across the space of the sensory surface (in percent); (Upper) the decoding layer is composed of two subpopulations of neurons with distance-dependent gradients in tuning properties (shown: firing rate and tuning width). The distance of a tuning curve from its “anchor” is coded by the luminance, with darker colors corresponding to neurons that are closer to the limb boundary. (C) Activations for each layer of the network averaged over 5,000 simulations. Each circle corresponds to a unit of the neural network. (Lower) Encoding layer; (Middle) decoding layer; (Upper) posterior probabilities of localization for each decoding subpopulation (blue and red) and their integration by the Bayesian decoder (purple).