Mechanisms of leiomodin 2-mediated regulation of actin filament in muscle cells

Leiomodin (Lmod) is a class of potent tandem-G-actin–binding nucleators in muscle cells. Lmod mutations, deletion, or instability are linked to lethal nemaline myopathy. However, the lack of high-resolution structures of Lmod nucleators in action severely hampered our understanding of their essential cellular functions. Here we report the crystal structure of the actin–Lmod2_162–495 nucleus. The structure contains two actin subunits connected by one Lmod2_162–495 molecule in a non–filament-like conformation. Complementary functional studies suggest that the binding of Lmod2 stimulates ATP hydrolysis and accelerates actin nucleation and polymerization. The high level of conservation among Lmod proteins in sequence and functions suggests that the mechanistic insights of human Lmod2 uncovered here may aid in a molecular understanding of other Lmod proteins. Furthermore, our structural and mechanistic studies unraveled a previously unrecognized level of regulation in mammalian signal transduction mediated by certain tandem-G-actin–binding nucleators.In response to environmental or cellular signals, eukaryotic cells use actin nucleators to convert globular actin monomers (G-actin) into actin oligomers (actin nuclei), which then quickly lead to actin filaments (F-actin). Actin-related protein 2/3 (Arp2/3), formins, and tandem-G-actin–binding proteins are the three classes of known actin nucleators in nonmuscle cells (1–7). Arp2/3-mediated actin nucleation produces branched actin networks, whereas formins and tandem-G-actin–binding nucleators result in long, unbranched actin filaments (1–7). In muscle cells, the specific mechanisms for actin nucleation and maintenance in sarcomeres were poorly understood (8). Recent studies have uncovered actin nucleation activities of the nebulin–N-WASP complex (9) and of formin proteins FHOD3 (10–12), mDia2, DAAM, FMNL1, and FMNL2 (13, 14) in sarcomeres. In particular, leiomodin (Lmod) has been identified as a class of potent tandem-G-actin–binding nucleators in muscle cells (15, 16); Lmod1 is found in smooth muscle of many human tissues, and Lmod2 and Lmod3 are found in cardiac and skeletal muscle (17). Lmod2 knockdown severely compromises sarcomere organization and assembly in muscle cells (15), whereas mutations, deletions (18), or instability (19) in Lmod3 underlies severe, often lethal, human nemaline myopathy.Full-length human Lmod2 is predicted to have 547 residues with two regions of low sequence complexity, an acidic region between residues 97–138 and a polyproline (polyP) region between residues 421–448 (Fig. S1A). Probably because low-complexity regions tend to be intrinsically disordered, previous studies of human Lmod2 used a protein construct that deleted residues 99–130 in the acidic region and residues 421–440 in the polyP region, resulting in Lmod2_1–495 (15, 16). Another study on chicken Lmod2 removed 12 residues in the polyP region (20). In all cases, Lmod2 remained fully functional (15, 16, 20). Therefore, in the present study we focused on the human Lmod2_1–495 construct as previously used (Fig. S1A) (15, 16).Open in a separate windowFig. S1.Lmod sequences. (A) Sequence alignment of human Lmod2_1–547 and Lmod2_1–495. (B) Sequence alignment of human Tmod1 (hTmod1), human Lmod (hLmod), and mouse Lmod (mLmod) isoforms 1–3. The human Lmod2_1–495 sequence is at the top, and the residues that were tested by mutagenesis in this study are indicated by arrows.Human Lmod2_1–495 has three actin-binding sites (15). The first ∼340 residues are about 45% identical to the pointed-end capping protein tropomodulin 1 (Tmod1) (21) and contain a tropomyosin-binding helix (TM-h) and two actin-binding sites [an actin-binding helix (A-h) and a leucine-rich repeat (LRR) domain] (Fig. 1A and Figs. S1 and S2A). The C-terminal ∼150-residue extension of Lmod2 includes two predicted short helices (h1 and h2), a basic segment (B) harboring the nuclear localization sequence (16), and a Wiskott–Aldrich syndrome protein-homology 2 (W) domain (Fig. 1A and Figs. S1 and S2A). Thus, Lmod2 has the capacity to bind three actin subunits and one tropomyosin (15). Unexpectedly, tropomyosin promoted Lmod2-mediated actin nucleation only weakly (15). In sharp contrast, tropomyosin substantially enhanced the binding of Lmod2 to the pointed end of preformed actin filament for controlled elongation in cardiac muscle (16, 20). In the absence of high-resolution structures of the actin–Lmod complex, however, rationalization of these seemingly contradictory findings is difficult.Open in a separate windowFig. 1.Structure of actin–Lmod2. (A) Domain organization of human Lmod2_1–495 and constructs used in this study. (B) Pyrene-based activity assay of Lmod2_1–495 and its various constructs. a.u., arbitrary units. (C) The crystal structure of actin–Lmod2_162–495(B-GS). All residues are visualized except an internal flexible region (residues 339–388) between LRR and polyP, the extreme four N-terminal residues (162–165), and five C-terminal residues (491–495). AMPPNP is shown as ball-and-stick models, and the Mg²⁺ ions are shown as purple spheres. (D) The modeled structure of actin–Lmod2_1–495 in which the actin(A-h)–A-h complex structure was borrowed from the Tmod1 structure (PDB ID code: 4PKG) and combined with our crystal structure of actin–Lmod2_162–495(B-GS). See also Movies S1–S3.Open in a separate windowFig. S2.Structure of actin–Lmod2_162–495(B-GS). (A) Comparison of domain organization of human Lmod2_1–495 and Tmod1. (B) The structure of actin–Lmod2_162–495(B-GS) as observed in the asymmetric unit. It contains two actin subunits (in green and cyan) bound with one Lmod2_162–495(B-GS) (in magenta) and one extra LRR domain from degradation (in yellow). (C) The modeled actin–Tmod1 structure is superimposed on subunits n+1 and n of the pointed end of the actin filament. (D) Superposition of our actin–Lmod2_162–495(B-GS) structure with the modeled actin–Tmod1 structure at the actin(LRR)–LRR. Lmod2 is shown in magenta, and the interacting actin(LRR) and actin(W) are in green and cyan, respectively. Tmod1 is in orange, and its interacting actin(A-h) and actin(LRR) are in blue and pink, respectively.Historically study of the crystallographic structure of the complexes of actin nucleators with oligomeric actin or of F-actin–binding proteins with F-actin was difficult because actin dimers and trimers are kinetically unstable, and actin tetramers rapidly polymerize into F-actin that is refractory to crystallization (22). Indeed, although Arp2/3 has been subjected to intensive structural studies (23–25), the crystal structure of the actin–Arp2/3 complex has eluded investigation so far. Before our study (26), the only available crystal structure of this kind was the yeast formin Bni1p FH2 domain that binds to two crystallographically related tetramethylrhodamine-modified actin (TMR–actin) subunits in a pseudo short-pitch fashion (27). However, the large size of TMR likely interferes with its interaction with actin and with actin function, thus limiting the use of TMR–actin in investigations of crystal structure.To enable crystallographic studies of biological actin complexes, our group recently has developed a double-mutant strategy in which actin-binding proteins and two types of nonpolymerizable actin mutants are combined to form stable complexes amenable to crystallization (26). This strategy made possible the rapid determination of the first two crystal structures of oligomeric actin with tandem-G-actin–binding nucleator complexes: a mammalian nucleator Cordon-bleu (Cobl) (26) and a bacterial effector Vibrio parahaemolyticus protein L (VopL) (28). Importantly, the observed non–filament-like conformation in actin–Cobl and the filament-like conformation in actin–VopL together suggest that both types of conformation are fully accessible to an actin complex obtained via the double-mutant strategy; thus the observed structure most likely reflects its native functional state.Here we report the crystal structure of the actin–Lmod2 nucleus and complementary functional studies. Our data not only unraveled the atomic mechanisms of Lmod’s essential functions in muscle cells but also suggested a previously unrecognized level of regulation in mammalian signal transduction mediated by certain tandem-G-actin–binding nucleators.     

Gate-controlled proton diffusion and protonation-induced ratchet motion in the stator of the bacterial flagellar motor

The proton permeation process of the stator complex MotA/B in the flagellar motor of Escherichia coli was investigated. The atomic model structure of the transmembrane part of MotA/B was constructed based on the previously published disulfide cross-linking and tryptophan scanning mutations. The dynamic permeation of hydronium/sodium ions and water molecule through the channel formed in MotA/B was observed using a steered molecular dynamics simulation. During the simulation, Leu46 of MotB acts as the gate for hydronium ion permeation, which induced the formation of water wire that may mediate the proton transfer to Asp32 on MotB. Free energy profiles for permeation were calculated by umbrella sampling. The free energy barrier for H₃O⁺ permeation was consistent with the proton transfer rate deduced from the flagellar rotational speed and number of protons per rotation, which suggests that the gating is the rate-limiting step. Structure and dynamics of the MotA/B with nonprotonated and protonated Asp32, Val43Met, and Val43Leu mutants in MotB were investigated using molecular dynamics simulation. A narrowing of the channel was observed in the mutants, which is consistent with the size-dependent ion selectivity. In MotA/B with the nonprotonated Asp32, the A3 segment in MotA maintained a kink whereas the protonation induced a straighter shape. Assuming that the cytoplasmic domain not included in the atomic model moves as a rigid body, the protonation/deprotonation of Asp32 is inferred to induce a ratchet motion of the cytoplasmic domain, which may be correlated to the motion of the flagellar rotor.Bacterial flagella are multifuel engines that convert ion motive force to molecular motor rotation. Escherichia coli has a few proton-driven flagellar motors with stators (protein MotA/B complex) in the inner membrane that act as proton channels (1–5). In addition, Vibrio alginolyticus has a polar flagellum powered by sodium ions (6). Bacillus alcalophilus has motors driven by rubidium (Rb⁺), potassium (K⁺), and sodium ions (Na⁺) that can be converted to Na⁺-driven motors by a single mutation (7).The proton transfer mechanism in membrane proteins is associated with water wire and/or a hydrogen bond chain (HBC) (8, 9). The water wire comprises water molecules aligned in a protein channel, where protons are transferred by hopping along the wire. Protons are conducted through the hydrogen bonds formed by the polar amino acid residues and water molecules along the proton transfer pathway in the HBC. Protons can also be transferred by diffusion of hydronium ions (H₃O⁺). The diffusion distance in a hydrophilic environment is short in a liquid (the lifetime in water is ca. 1 ps) (10, 11), but it should be longer in a more hydrophobic environment. H₃O⁺ forms a hydrogen bond (H bond) with the nearest neighbor water molecules and the carbonyl groups, and proton hopping along the H bonds is faster than diffusion of Na⁺ and K⁺ in the ion channel of Gramicidin A (12, 13).These flagellar motors can rotate in both clockwise (CW) and counterclockwise (CCW) directions (viewed from the outside of the cell), and the swimming pattern of the bacteria is controlled by reversal of the motor rotation (14). In E. coli, the FliG, FliM, FliN, MotA, and MotB proteins are involved with torque generation (Fig. 1A) (14, 15). FliG, FliM, and FliN constitute the flagellar rotor and are also involved with the CW/CCW switching. Each rotor is typically surrounded by 10 stators that consist of two membrane proteins, MotA and MotB (PomA and PomB in V. alginolyticus). Each stator is composed of four MotA and two MotB proteins, and can independently produce torque for flagellar rotation.Open in a separate windowFig. 1.Overall structure of MotA/B. (A) Schematic views of the flagellar motors of E. coli (Left) and V. alginolyticus (Right), (B) TM regions modeled, and (C) TM helix arrangement in the initial modeling of MotA/B in E. coli. The obtained atomic model structure viewed parallel to the membrane (D) and from the periplasmic side (E). Spheres denote P atoms in the lipid head groups. MotA/B cross sections of the area enclosed by magenta in E around a channel at the levels of Leu46 (F) and Asp32 (G). x, y, and z axes are defined as in D and E.Systematic Cys and Trp mutagenesis (16–20) has provided essential information on the structure and function of the flagellar motor. Each MotA (295 residues) contains four transmembrane (TM) alpha helical segments (A1–A4), two short loops in the periplasm, and two long segments (residues 61–160 and 228–295) in the cytoplasm (Fig. 1B) (3, 21). Arg90 and Glu98 on the MotA cytoplasmic domain interact with the polar residues on the rotor protein, FliG, during the rotation of the motor (22, 23). It has been suggested that Pro173 and Pro222 at the cytoplasmic sides of A3 and A4 regulate the conformational changes required for torque generation (24). MotB (308 residues) is composed of a short N-terminal cytoplasimic segment, one TM helix (B), and a large C-terminal periplasmic domain (Fig. 1B) (4, 5). Asp32, which is situated near the cytoplasmic end of the B segment, is conserved across the species and considered to be the most plausible proton binding site (25). The B segment is expected to form a proton channel together with A3 and A4 (Fig. 1C) (17). Only a few polar residues have been identified in the predicted TM segments of MotA/B (19, 20), which implies that the channel surface should be relatively hydrophobic. The periplasmic region of MotB has a peptidoglycan binding motif, which anchors the stator complex to the peptidoglycan layer around the rotor (5, 26). Deletion of residues 52–65 just after the B segment causes proton leakage and cell growth arrest, which suggests that this fragment acts as a plug to suppress proton leakage (27). The generation of torque is hypothesized to originate from the conformational changes of the MotA cytoplasmic domain upon proton association/dissociation at the carboxyl group of Asp32 on MotB and by the interaction with FliG in the rotor (28–31).In the present study, the mechanism for proton permeation in MotA/B was investigated (Fig. S1). The atomic structure of MotA/B was constructed based on the disulfide cross-linking (16–18) and tryptophan scanning mutations (19, 20). The dynamic permeation of hydronium ions, sodium ions, and water molecules was observed using a steered molecular dynamics (SMD) simulation (32–34), and free energy profiles for ion/water permeation were calculated by umbrella sampling. The effects of amino acid substitutions related to ion selectivity was investigated, and the possible ratchet motion of the cytoplasmic domain induced by protonation/deprotonation cycle of Asp32 was examined.Open in a separate windowFig. S1.Overall scheme of this study.     

De novo production of the plant-derived alkaloid strictosidine in yeast

The monoterpene indole alkaloids are a large group of plant-derived specialized metabolites, many of which have valuable pharmaceutical or biological activity. There are ∼3,000 monoterpene indole alkaloids produced by thousands of plant species in numerous families. The diverse chemical structures found in this metabolite class originate from strictosidine, which is the last common biosynthetic intermediate for all monoterpene indole alkaloid enzymatic pathways. Reconstitution of biosynthetic pathways in a heterologous host is a promising strategy for rapid and inexpensive production of complex molecules that are found in plants. Here, we demonstrate how strictosidine can be produced de novo in a Saccharomyces cerevisiae host from 14 known monoterpene indole alkaloid pathway genes, along with an additional seven genes and three gene deletions that enhance secondary metabolism. This system provides an important resource for developing the production of more complex plant-derived alkaloids, engineering of nonnatural derivatives, identification of bottlenecks in monoterpene indole alkaloid biosynthesis, and discovery of new pathway genes in a convenient yeast host.Monoterpene indole alkaloids (MIAs) are a diverse family of complex nitrogen-containing plant-derived metabolites (1, 2). This metabolite class is found in thousands of plant species from the Apocynaceae, Loganiaceae, Rubiaceae, Icacinaceae, Nyssaceae, and Alangiaceae plant families (2, 3). Many MIAs and MIA derivatives have medicinal properties; for example, vinblastine, vincristine, and vinflunine are approved anticancer therapeutics (4, 5). These structurally complex compounds can be difficult to chemically synthesize (6, 7). Consequently, industrial production relies on extraction from the plant, but these compounds are often produced in small quantities as complex mixtures, making isolation challenging, laborious, and expensive (8–10). Reconstitution of plant pathways in microbial hosts is proving to be a promising approach to access plant-derived compounds as evidenced by the successful production of terpenes, flavonoids, and benzylisoquinoline alkaloids in microorganisms (11–19). Microbial hosts can also be used to construct hybrid biosynthetic pathways to generate modified natural products with potentially enhanced bioactivities (8, 20, 21). Across numerous plant species, strictosidine is believed to be the core scaffold from which all 3,000 known MIAs are derived (1, 2). Strictosidine undergoes a variety of redox reactions and rearrangements to form the thousands of compounds that comprise the MIA natural product family (Fig. 1) (1, 2). Due to the importance of strictosidine, the last common biosynthetic intermediate for all known MIAs, we chose to focus on heterologous production of this complex molecule (1). Therefore, strictosidine reconstitution represents the necessary first step for heterologous production of high-value MIAs.Open in a separate windowFig. 1.Strictosidine, the central intermediate in monoterpene indole alkaloid (MIA) biosynthesis, undergoes a series of reactions to produce over 3,000 known MIAs such as vincristine, quinine, and strychnine.     

Stochastic modeling reveals an evolutionary mechanism underlying elevated rates of childhood leukemia

Young children have higher rates of leukemia than young adults. This fact represents a fundamental conundrum, because hematopoietic cells in young children should have fewer mutations (including oncogenic ones) than such cells in adults. Here, we present the results of stochastic modeling of hematopoietic stem cell (HSC) clonal dynamics, which demonstrated that early HSC pools were permissive to clonal evolution driven by drift. We show that drift-driven clonal expansions cooperate with faster HSC cycling in young children to produce conditions that are permissive for accumulation of multiple driver mutations in a single cell. Later in life, clonal evolution was suppressed by stabilizing selection in the larger young adult pools, and it was driven by positive selection at advanced ages in the presence of microenvironmental decline. Overall, our results indicate that leukemogenesis is driven by distinct evolutionary forces in children and adults.The incidence of leukemia, like most cancers in humans, increases exponentially with age. However, most types of leukemia have an early peak of incidence (at 0–7 y of age), which subsequently decreases before rising again later in life (Fig. S1). Cancer development is generally thought to result from a sequence of cancer driver mutations that promote selection for recipient cells by conferring a positive fitness advantage within competing stem cell (SC) and progenitor cell pools (1–4). The acquisition of oncogenic mutations is thus thought to be rate-limiting for cancer development, leading to increased cancer incidence with age. Within this paradigm, the higher incidence of leukemia in young children compared with young adults is puzzling, because younger tissues should have accumulated fewer mutations.Open in a separate windowFig. S1.Incidence of various types of human leukemia based on Surveillance, Epidemology, and End Results (SEER) database statistics (seer.cancer.gov). Incidence per 100,000 is shown. ALL, acute lymphocytic leukemia; AML, acute myeloid leukemia (all forms); AMoL, acute monocytic leukemia (a form of acute myeloid leukemia); CLL, chronic lymphocytic leukemia; CML, chronic myeloid leukemia.Evolution is driven by multiple forces, including mutation, selection, and drift. Although mutation is necessary for cancer development, a large body of evidence has accumulated indicating that the ability of oncogenic mutations to drive clonal evolution is not universal and depends on external factors (5–12). Carcinogenesis may therefore be driven or suppressed by non–cell-autonomous processes. One factor capable of limiting the ability of selection to influence population dynamics is drift. In evolutionary biology, the power of drift is known to be inversely related to population size (13). This relationship also holds true for mammalian tissues, as shown for intestinal SC pools, which are segregated into small groups within intestinal crypts (10, 14, 15). The number of hematopoietic stem cells (HSCs) per individual has been reported to be conserved across mammals at 11,000–22,000 cells in adults (16, 17), with an initial pool size of ∼300 HSCs at birth (17) (Fig. S2A). Although higher estimates of the pool size exist (18), it is clear that during prenatal development, and perhaps the early postnatal period of life, the number of HSCs is substantially smaller than the number in the adult pool. Because HSCs have been shown to effectively represent one large competing population within the body (19), and with evidence from wild populations and intestinal SCs in mind, the small size of early childhood HSC pools led us to hypothesize that early somatic evolution in HSCs would be affected by drift. We analyzed the rates of somatic evolution by measuring maximal clonal expansions at different ages and show that drift, stabilizing selection, and positive selection have a differential impact on somatic evolution at different ages.Open in a separate windowFig. S2.Nonlinear changes in HSC dynamics and leukemia incidence with age. (A) Modeled changes in HSC pool size (red) and cell division frequency (blue) with age inferred using data from a combination of experimental and modeling studies (16, 17) (for HSC numbers) and data for the dynamics of telomere shortening in leukocytes with age (43) (for cell division rates). (B) Accumulation of neutral mutations in AML genomes with age revealed by whole-genome sequencing, as a proxy for mutation accumulation in HSC (47). (C) Accumulation of DNA methylation changes in hematopoietic cells with age (41). (D) Leukemia incidence with age in humans (seer.cancer.gov).     

Solid-phase peptide synthesis and solid-phase fragment coupling mediated by isonitriles

The synthesis of polypeptides on solid phase via mediation by isonitriles is described. The acyl donor is a thioacid, which presumably reacts with the isonitrile to generate a thio-formimidate carboxylate mixed anhydride intermediate. Applications of this chemistry to reiterative solid-phase peptide synthesis as well as solid-phase fragment coupling are described.Amide bond formations are arguably among the most important constructions in organic chemistry (1, 2). The centrality of the amide linkage, as found in polypeptides and proteins, in the maintenance of life hardly needs restatement. Numerous strategies, resulting in a vast array of protocols to synthesize biologically active polypeptides and proteins, have been demonstrated (3, 4). Central to reiterative polypeptide bond formations was the discovery and remarkable development of solid-phase peptide synthesis (SPPS) (5, 6). The extraordinary impact of SPPS in fostering enhanced access to homogeneous polypeptides is clear to everyone in the field.As we have described elsewhere, by classical, mechanistic reasoning, we were led to conjecture about some hitherto-unexplored possibilities relevant to the chemistry of isonitriles (7–14). It was anticipated that isonitriles might be able to mediate the acylation of amines, thus giving rise to amides (15). Early experiments focused on free carboxylic acids as the acylating agents. As our studies progressed, it was found that the combination of thioacids, amines, and isonitriles leads to the efficient formation of amide bonds under stoichiometric or near-stoichiometric conditions (7–13, 16, 17). Although there remain unresolved issues of detail and nuance, the governing mechanism for amide formation under these conditions involves reaction of the thioacid, 1, with an isonitrile, 2, to generate a thio-formimidate carboxylate mixed anhydride (thio-FCMA), 3, which is intercepted by the “acyl-accepting” amine to generate amide, 5, and thioformamide, 6 (Fig. 1). The efficiency of the amidation was further improved through the use of hydroxybenzotriazole (HOBt) (18), which could well give rise to HOBt ester 7, although this pathway has not been mechanistically proven.Open in a separate windowFig. 1.Isonitrile-mediated amidation; structure of OT.The potentialities of the isonitrile-mediated amidation method were foreshadowed via its application to the synthesis of cyclosporine (19). The power of the method was particularly well demonstrated in the context of our recent total synthesis of oxytocin (OT) (20), wherein isonitrile mediation was used in each of the peptide bond constructions, leading to the synthesis of the hormone in high yield and excellent purity. This nonapeptide is involved in a range of biological functions including parturition and lactation (21, 22). Signaling of OT to its receptor (OTR) is apparently an important factor in quality maintenance of various CNS functions (23). The ability to synthesize such modestly sized, but bio-impactful peptides in both native (wild-type) form, and as strategically modified variants, is one of the current missions of our laboratory, with the objective of possible applications to the very serious problem of autism (24–26).     

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

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

Birds have primate-like numbers of neurons in the forebrain

Some birds achieve primate-like levels of cognition, even though their brains tend to be much smaller in absolute size. This poses a fundamental problem in comparative and computational neuroscience, because small brains are expected to have a lower information-processing capacity. Using the isotropic fractionator to determine numbers of neurons in specific brain regions, here we show that the brains of parrots and songbirds contain on average twice as many neurons as primate brains of the same mass, indicating that avian brains have higher neuron packing densities than mammalian brains. Additionally, corvids and parrots have much higher proportions of brain neurons located in the pallial telencephalon compared with primates or other mammals and birds. Thus, large-brained parrots and corvids have forebrain neuron counts equal to or greater than primates with much larger brains. We suggest that the large numbers of neurons concentrated in high densities in the telencephalon substantially contribute to the neural basis of avian intelligence.Many birds have cognitive abilities that match or surpass those of mammals (1). Corvids and parrots appear to be cognitively superior to other birds, rivalling great apes in many psychological domains (1–3). They manufacture and use tools (4, 5), solve problems insightfully (6), make inferences about causal mechanisms (7), recognize themselves in a mirror (8), plan for future needs (9), and use their own experience to anticipate future behavior of conspecifics (10) or even humans (11), to mention just a few striking abilities. In addition, parrots and songbirds (including corvids) share with humans and a few other animal groups a rare capacity for vocal learning (12), and parrots can learn words and use them to communicate with humans (13).Superficially, the architecture of the avian brain appears very different from that of mammals, but recent work demonstrates that, despite a lack of layered neocortex, large areas of the avian forebrain are homologous to mammalian cortex (14–16), conform to the same organizational principles (15, 17, 18), and play similar roles in higher cognitive functions (14, 19), including executive control (20, 21). However, bird brains are small and the computational mechanisms enabling corvids and parrots to achieve ape-like intelligence with much smaller brains remain unclear. The notion that higher encephalization (relative brain size deviation from brain–body allometry) endows species with improved cognitive abilities has recently been challenged by data suggesting that intelligence instead depends on the absolute number of cerebral neurons and their connections (22–25). This is in line with recent findings that absolute rather than relative brain size is the best predictor of cognitive capacity (26–28). However, although corvids and parrots feature encephalization comparable to that of monkeys and apes, their absolute brain size remains small (29, 30). The largest average brain size in corvids and parrots does not exceed 15.4 g found in the common raven (29) and 24.7 g found in the hyacinth macaw (30), respectively. Do corvids and parrots provide a strong case for reviving encephalization as a valid measure of brain functional capacity? Not necessarily: it has recently been discovered that the relationship between brain mass and number of brain neurons differs starkly between mammalian clades (31). Avian brains seem to consist of small, tightly packed neurons, and it is thus possible that they can accommodate numbers of neurons that are comparable to those found in the much larger primate brains. However, to date, no quantitative data have been available to test this hypothesis.Here, we analyze how numbers of neurons compare across birds and mammals (32–39) of equivalent brain mass, and determine the cellular scaling rules for brains of songbirds and parrots. Using the isotropic fractionator (40), we estimated the total numbers of neuronal and nonneuronal cells in the cerebral hemispheres, cerebellum, diencephalon, tectum, and brainstem in a sample of 11 parrot species, 13 vocal learning songbird species (including 6 corvids), and 4 additional model species representing other avian clades (Figs. S1 and ​andS2).S2). Because most of the cited mammalian studies analyzed cellular composition of only three brain subdivisions, namely the pallium (referred to as the cerebral cortex in those papers), the cerebellum, and rest of brain, we divided the avian brain identically to ensure an accurate comparison of neuronal numbers, densities, and relative distribution of neurons in birds and mammals. Specifically, the avian pallium (comprising the hyperpallium, mesopallium, nidopallium, arcopallium, and hippocampus) was compared with its homolog—the mammalian pallium (comprising the neocortex, hippocampus, olfactory cortices such as piriform and entorhinal cortex, and pallial amygdala) (14–16, 41). The avian subpallium (formed by the striatum, pallidum, and septum), diencephalon, tectum, and brainstem were pooled and compared with the same regions of mammalian brains that are referred to as “the rest of brain.” The cerebellum is directly compared between the two clades. The results of our study reveal that avian brains contain many more pallial neurons than equivalently sized mammalian brains.Open in a separate windowFig. S1.Phylogenetic relationships among the 28 species examined. The tree was constructed using birdtree.org/; its topology follows recent studies (46–49). Note that songbirds and parrots are sister groups and together with the distantly related barn owl belong to the clade core landbirds (Telluraves); the pigeon represents the Columbea, a basal clade of the Neoaves; the red junglefowl represents the Galloanseres, a sister group of Neoaves and the most basal clade of Neognathae; and the emu represents Paleognathae (tinamous and flightless ostriches), the most basal clade of extant birds (48). Also note that all passerine birds examined were vocal learners belonging to the clade Oscines.Open in a separate windowFig. S2.Brain dissection and labeling of neurons and nonneuronal cells. (A and B) Brain of the raven before and after the dissection. (A) Ventral side of the brain showing approximate lines of dissection of the brainstem and tectum. (B) Brain dissected into parts used for isotropic fractionation. (C) NeuN-immunolabeled transverse section of the zebra finch brain depicting the line of dissection of the tectum from the rest of the mesencephalon. (D–F) Dissection of the telencephalon into pallium and subpallium. NeuN-immunolabeled transverse sections of the zebra finch brain at rostral (D), intermediate (E), and caudal (F) telencephalic levels. Lines of dissection follow the pallial-subpallial lamina and divide the telencephalon into pallium (dorsal part) and subpallium (ventral part). Coordinates anterior to the Y point are indicated in millimeters at Bottom Left (64). (G–I) High-power micrographs showing a sample of homogenate from the telencephalon of the Eurasian jay; dissociated nuclei stained with DAPI (G) and immunolabeled with NeuN antibody (H), dual-fluorescence merge image (I). Note that neurons are double-labeled, whereas the nonneuronal cells are devoid of anti-NeuN immunoreactivity. [Scale bars: 10 mm (A and B); 1 mm (C and F); 50 µm (I).]     

Single-molecule spectroscopy reveals how calmodulin activates NO synthase by controlling its conformational fluctuation dynamics

Mechanisms that regulate the nitric oxide synthase enzymes (NOS) are of interest in biology and medicine. Although NOS catalysis relies on domain motions, and is activated by calmodulin binding, the relationships are unclear. We used single-molecule fluorescence resonance energy transfer (FRET) spectroscopy to elucidate the conformational states distribution and associated conformational fluctuation dynamics of the two electron transfer domains in a FRET dye-labeled neuronal NOS reductase domain, and to understand how calmodulin affects the dynamics to regulate catalysis. We found that calmodulin alters NOS conformational behaviors in several ways: It changes the distance distribution between the NOS domains, shortens the lifetimes of the individual conformational states, and instills conformational discipline by greatly narrowing the distributions of the conformational states and fluctuation rates. This information was specifically obtainable only by single-molecule spectroscopic measurements, and reveals how calmodulin promotes catalysis by shaping the physical and temporal conformational behaviors of NOS.Although proteins adopt structures determined by their amino acid sequences, they are not static objects and fluctuate among ensembles of conformations (1). Transitions between these states can occur on a variety of length scales (Å to nm) and time scales (ps to s) and have been linked to functionally relevant phenomena such as allosteric signaling, enzyme catalysis, and protein–protein interactions (2–4). Indeed, protein conformational fluctuations and dynamics, often associated with static and dynamic inhomogeneity, are thought to play a crucial role in biomolecular functions (5–11). It is difficult to characterize such spatially and temporally inhomogeneous dynamics in bulk solution by an ensemble-averaged measurement, especially in proteins that undergo multiple-conformation transformations. In such cases, single-molecule spectroscopy is a powerful approach to analyze protein conformational states and dynamics under physiological conditions, and can provide a molecular-level perspective on how a protein’s structural dynamics link to its functional mechanisms (12–21).A case in point is the nitric oxide synthase (NOS) enzymes (22–24), whose nitric oxide (NO) biosynthesis involves electron transfer reactions that are associated with relatively large-scale movement (tens of Å) of the enzyme domains (Fig. 1A). During catalysis, NADPH-derived electrons first transfer into an FAD domain and an FMN domain in NOS that together comprise the NOS reductase domain (NOSr), and then transfer from the FMN domain to a heme group that is bound in a separate attached “oxygenase” domain, which then enables NO synthesis to begin (22, 25–27). The electron transfers into and out of the FMN domain are the key steps for catalysis, and they appear to rely on the FMN domain cycling between electron-accepting and electron-donating conformational states (28, 29) (Fig. 1B). In this model, the FMN domain is suggested to be highly dynamic and flexible due to a connecting hinge that allows it to alternate between its electron-accepting (FAD→FMN) or closed conformation and electron-donating (FMN→heme) or open conformation (Fig. 1 A and B) (28, 30–36). In the electron-accepting closed conformation, the FMN domain interacts with the NADPH/FAD domain (FNR domain) to receive electrons, whereas in the electron donating open conformation the FMN domain has moved away to expose the bound FMN cofactor so that it may transfer electrons to a protein acceptor like the NOS oxygenase domain, or to a generic protein acceptor like cytochrome c. In this way, the reductase domain structure cycles between closed and open conformations to deliver electrons, according to a conformational equilibrium that determines the movements and thus the electron flux capacity of the FMN domain (25, 28, 32, 34, 35, 37). A similar conformational switching mechanism is thought to enable electron transfer through the FMN domain in the related flavoproteins NADPH-cytochrome P450 reductase and methionine synthase reductase (38–42).Open in a separate windowFig. 1.(A) The nNOSr ribbon structure (from PDB: 1TLL) showing bound FAD (yellow) in FNR domain (green), FMN (orange) in FMN domain (yellow), connecting hinge (blue), and the Cy3–Cy5 label positions (pink) and distance (42 Å, dashed line). (B) Cartoon of an equilibrium between the FMN-closed and FMN-open states, with Cy dye label positions indicated. (C) Cytochrome c reductase activity of nNOSr proteins in their CaM-bound and CaM-free states. Color scheme of bar graphs: Black, WT nNOSr unlabeled; Red, Cys-lite (CL) nNOSr unlabeled; Blue, E827C/Q1268C CL nNOSr unlabeled; and Dark cyan, E827C/Q1268C CL nNOSr labeled.NOS enzymes also contain a calmodulin (CaM) binding domain that is located just before the N terminus of the FMN domain (Fig. 1B), and this provides an important layer of regulation (25, 27). CaM binding to NOS enzymes increases electron transfer from NADPH through the reductase domain and also triggers electron transfer from the FMN domain to the NOS heme as is required for NO synthesis (31, 32). The ability of CaM, or similar signaling proteins, to regulate electron transfer reactions in enzymes is unusual, and the mechanism is a topic of interest and intensive study. It has long been known that CaM binding alters NOSr structure such that, on average, it populates a more open conformation (43, 44). Recent equilibrium studies have detected a buildup of between two to four discreet conformational populations in NOS enzymes and in related flavoproteins, and in some cases, have also estimated the distances between the bound FAD and FMN cofactors in the different species (26, 36, 37, 39, 40), and furthermore, have confirmed that CaM shifts the NOS population distribution toward more open conformations (34, 36, 45). Although valuable, such ensemble-averaged results about conformational states cannot explain how electrons transfer through these enzymes, or how CaM increases the electron flux in NOS, because answering these questions requires a coordinate understanding of the dynamics of the conformational fluctuations. Indeed, computer modeling has indicated that a shift toward more open conformations as is induced by CaM binding to nNOS should, on its own, actually diminish electron flux through nNOS and through certain related flavoproteins (38). Despite its importance, measuring enzyme conformational fluctuation dynamics is highly challenging, and as far as we know, there have been no direct measures on the NOS enzymes or on related flavoproteins, nor studies on how CaM binding might influence the conformational fluctuation dynamics in NOS.To address this gap, we used single-molecule fluorescence energy resonance transfer (FRET) spectroscopy to characterize individual molecules of nNOSr that had been labeled at two specific positions with Cyanine 3 (Cy3) donor and Cyanine 5 (Cy5) acceptor dye molecules, regarding their conformational states distribution and the associated conformational fluctuation dynamics, which in turn enabled us to determine how CaM binding impacts both parameters. This work provides a unique perspective and a novel study of the NOS enzymes and within the broader flavoprotein family, which includes the mammalian enzymes methionine synthase reductase (MSR) and cytochrome P450 reductase (CPR), and reveals how CaM’s control of the conformational behaviors may regulate the electron transfer reactions of NOS catalysis.     

Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping

DNA polymorphisms are important markers in genetic analyses and are increasingly detected by using genome resequencing. However, the presence of repetitive sequences and structural variants can lead to false positives in the identification of polymorphic alleles. Here, we describe an analysis strategy that minimizes false positives in allelic detection and present analyses of recently published resequencing data from Arabidopsis meiotic products and individual humans. Our analysis enables the accurate detection of sequencing errors, small insertions and deletions (indels), and structural variants, including large reciprocal indels and copy number variants, from comparisons between the resequenced and reference genomes. We offer an alternative interpretation of the sequencing data of meiotic products, including the number and type of recombination events, to illustrate the potential for mistakes in single-nucleotide polymorphism calling. Using these examples, we propose that the detection of DNA polymorphisms using resequencing data needs to account for nonallelic homologous sequences.DNA polymorphisms are ubiquitous genetic variations among individuals and include single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and other larger rearrangements (1–3) (Fig. 1 A and B). They can have phenotypic consequences and also serve as molecular markers for genetic analyses, facilitating linkage and association studies of genetic diseases, and other traits in humans (4–6), animals, plants, (7–10) and other organisms. Using DNA polymorphisms for modern genetic applications requires low-error, high-throughput analytical strategies. Here, we illustrate the use of short-read next-generation sequencing (NGS) data to detect DNA polymorphisms in the context of whole-genome analysis of meiotic products.Open in a separate windowFig. 1.(A) SNPs and small indels between two ecotype genomes. (B) Possible types of SVs. Col genotypes are marked in blue and Ler in red. Arrows indicate DNA segments involved in SVs between the two ecotypes. (C) Meiotic recombination events including a CO and a GC (NCO). Centromeres are denoted by yellow dots.There are many methods for detecting SNPs (11–14) and structural variants (SVs) (15–25), including NGS, which can capture nearly all DNA polymorphisms (26–28). This approach has been widely used to analyze markers in crop species such as rice (29), genes associated with diseases (6, 26), and meiotic recombination in yeast and plants (30, 31). However, accurate identification of DNA polymorphisms can be challenging, in part because short-read sequencing data have limited information for inferring chromosomal context.Genomes usually contain repetitive sequences that can differ in copy number between individuals (26–28, 31); therefore, resequencing analyses must account for chromosomal context to avoid mistaking highly similar paralogous sequences for polymorphisms. Here, we use recently published datasets to describe several DNA sequence features that can be mistaken as allelic (32, 33) and describe a strategy for differentiating between repetitive sequences and polymorphic alleles. We illustrate the effectiveness of these analyses by examining the reported polymorphisms from the published datasets.Meiotic recombination is initiated by DNA double-strand breaks (DSBs) catalyzed by the topoisomerase-like SPORULATION 11 (SPO11). DSBs are repaired as either crossovers (COs) between chromosomes (Fig. 1C), or noncrossovers (NCOs). Both COs and NCOs can be accompanied by gene conversion (GC) events, which are the nonreciprocal transfer of sequence information due to the repair of heteroduplex DNA during meiotic recombination. Understanding the control of frequency and distribution of CO and NCO (including GC) events has important implications for human health (including cancer and aneuploidy), crop breeding, and the potential for use in genome engineering. COs can be detected relatively easily by using polymorphic markers in the flanking sequences, but NCO products can only be detected if they are accompanied by a GC event. Because GCs associated with NCO result in allelic changes at polymorphic sites without exchange of flanking sequences, they are more difficult to detect. Recent advances in DNA sequencing have made the analysis of meiotic NCOs more feasible (30–32, 34); however, SVs present a challenge in these analyses. We recommend a set of guidelines for detection of DNA polymorphisms by using genomic resequencing short-read datasets. These measures improve the accuracy of a wide range of analyses by using genomic resequencing, including estimation of COs, NCOs, and GCs.     

Little evidence for enhanced phenotypic evolution in early teleosts relative to their living fossil sister group

Since Darwin, biologists have been struck by the extraordinary diversity of teleost fishes, particularly in contrast to their closest “living fossil” holostean relatives. Hypothesized drivers of teleost success include innovations in jaw mechanics, reproductive biology and, particularly at present, genomic architecture, yet all scenarios presuppose enhanced phenotypic diversification in teleosts. We test this key assumption by quantifying evolutionary rate and capacity for innovation in size and shape for the first 160 million y (Permian–Early Cretaceous) of evolution in neopterygian fishes (the more extensive clade containing teleosts and holosteans). We find that early teleosts do not show enhanced phenotypic evolution relative to holosteans. Instead, holostean rates and innovation often match or can even exceed those of stem-, crown-, and total-group teleosts, belying the living fossil reputation of their extant representatives. In addition, we find some evidence for heterogeneity within the teleost lineage. Although stem teleosts excel at discovering new body shapes, early crown-group taxa commonly display higher rates of shape evolution. However, the latter reflects low rates of shape evolution in stem teleosts relative to all other neopterygian taxa, rather than an exceptional feature of early crown teleosts. These results complement those emerging from studies of both extant teleosts as a whole and their sublineages, which generally fail to detect an association between genome duplication and significant shifts in rates of lineage diversification.Numbering ∼29,000 species, teleost fishes account for half of modern vertebrate richness. In contrast, their holostean sister group, consisting of gars and the bowfin, represents a mere eight species restricted to the freshwaters of eastern North America (1). This stark contrast between teleosts and Darwin''s original “living fossils” (2) provides the basis for assertions of teleost evolutionary superiority that are central to textbook scenarios (3, 4). Classic explanations for teleost success include key innovations in feeding (3, 5) (e.g., protrusible jaws and pharyngeal jaws) and reproduction (6, 7). More recent work implicates the duplicate genomes of teleosts (8–10) as the driver of their prolific phenotypic diversification (8, 11–13), concordant with the more general hypothesis that increased morphological complexity and innovation is an expected consequence of genome duplication (14, 15).Most arguments for enhanced phenotypic evolution in teleosts have been asserted rather than demonstrated (8, 11, 12, 15, 16; but see ref. 17), and draw heavily on the snapshot of taxonomic and phenotypic imbalance apparent between living holosteans and teleosts. The fossil record challenges this neontological narrative by revealing the remarkable taxonomic richness and morphological diversity of extinct holosteans (Fig. 1) (18, 19) and highlights geological intervals when holostean taxonomic richness exceeded that of teleosts (20). This paleontological view has an extensive pedigree. Darwin (2) invoked a long interval of cryptic teleost evolution preceding the late Mesozoic diversification of the modern radiation, a view subsequently supported by the implicit (18) or explicit (19) association of Triassic–Jurassic species previously recognized as “holostean ganoids” with the base of teleost phylogeny. This perspective became enshrined in mid-20th century treatments of actinopterygian evolution, which recognized an early-mid Mesozoic phase dominated by holosteans sensu lato and a later interval, extending to the modern day, dominated by teleosts (4, 20, 21). Contemporary paleontological accounts echo the classic interpretation of modest teleost origins (22–24), despite a systematic framework that substantially revises the classifications upon which older scenarios were based (22–25). Identification of explosive lineage diversification in nested teleost subclades like otophysans and percomorphs, rather than across the group as a whole, provides some circumstantial neontological support for this narrative (26).Open in a separate windowFig. 1.Phenotypic variation in early crown neopterygians. (A) Total-group holosteans. (B) Stem-group teleosts. (C) Crown-group teleosts. Taxa illustrated to scale.In contrast to quantified taxonomic patterns (20, 23, 24, 27), phenotypic evolution in early neopterygians has only been discussed in qualitative terms. The implicit paleontological model of morphological conservatism among early teleosts contrasts with the observation that clades aligned with the teleost stem lineage include some of the most divergent early neopterygians in terms of both size and shape (Fig. 1) (see, for example, refs. 28 and 29). These discrepancies point to considerable ambiguity in initial patterns of phenotypic diversification that lead to a striking contrast in the vertebrate tree of life, and underpins one of the most successful radiations of backboned animals.Here we tackle this uncertainty by quantifying rates of phenotypic evolution and capacity for evolutionary innovation for the first 160 million y of the crown neopterygian radiation. This late Permian (Wuchiapingian, ca. 260 Ma) to Cretaceous (Albian, ca. 100 Ma) sampling interval permits incorporation of diverse fossil holosteans and stem teleosts alongside early diverging crown teleost taxa (Figs. 1 and ​and2A2A and Figs. S1 and ​andS2),S2), resulting in a dataset of 483 nominal species-level lineages roughly divided between the holostean and teleost total groups (Fig. 2B and Fig. S2). Although genera are widely used as the currency in paleobiological studies of fossil fishes (30; but see ref. 31), we sampled at the species level to circumvent problems associated with representing geological age and morphology for multiple congeneric lineages. We gathered size [both log-transformed standard length (SL) and centroid size (CS); results from both are highly comparable (Figs. S3 and ​andS4);S4); SL results are reported in the main text] and shape data (the first three morphospace axes arising from a geometric morphometric analysis) (Fig. 2A and Figs. S1) from species where possible. To place these data within a phylogenetic context, we assembled a supertree based on published hypotheses of relationships. We assigned branch durations to a collection of trees under two scenarios for the timescale of neopterygian diversification based on molecular clock and paleontological estimates. Together, these scenarios bracket a range of plausible evolutionary timelines for this radiation (Fig. 2B). We used the samples of trees in conjunction with our morphological datasets to test for contrasts in rates of, and capacity for, phenotypic change between different partitions of the neopterygian Tree of Life (crown-, total-, and stem-group teleosts, total-group holosteans, and neopterygians minus crown-group teleosts), and the sensitivity of these conclusions to uncertainty in both relationships and evolutionary timescale. Critically, these include comparisons of phenotypic evolution in early crown-group teleosts—those species that are known with certainty to possess duplicate genomes—with rates in taxa characterized largely (neopterygians minus crown teleosts) or exclusively (holosteans) by unduplicated genomes. By restricting our scope to early diverging crown teleost lineages, we avoid potentially confounding signals from highly nested radiations that substantially postdate both genome duplication and the origin of crown teleosts (26, 32). This approach provides a test of widely held assumptions about the nature of morphological evolution in teleosts and their holostean sister lineage.Open in a separate windowFig. 2.(A) Morphospace of Permian–Early Cretaceous crown Neopterygii. (B) One supertree subjected to our paleontological (Upper) and molecular (Lower) timescaling procedures to illustrate contrasts in the range of evolutionary timescales considered. Colors of points (A) and branches (B) indicate membership in major partitions of neopterygian phylogeny. Topologies are given in Datasets S4 and S5. See Dataset S6 for source trees.Open in a separate windowFig. S1.Morphospace of 398 Permian–Early Cretaceous Neopterygii. Three major axes of shape variation are presented. Silhouettes and accompanying arrows illustrate the main anatomical correlates of these principal axes, as described in Open in a separate windowFig. S2.Morphospace of 398 Permian–Early Cretaceous Neopterygii, illustrating the major clades of (A) teleosts and (B) holosteans.Open in a separate windowFig. S3.Comparisons of size rates between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Identical taxon sampling leads the CS and pruned SL datasets to yield near identical results. Although the larger SL dataset results often differ slightly, the overall conclusion from each pairwise comparison (i.e., which outcome is the most likely in an overall majority of trees) is identical in all but one comparison (E, under molecular timescales).Open in a separate windowFig. S4.Comparisons of size innovation between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Comparisons of size innovation are presented for K value distributions of the three datasets resemble each other closely.     

Mutant cycle analysis with modified saxitoxins reveals specific interactions critical to attaining high-affinity inhibition of hNaV1.7

Improper function of voltage-gated sodium channels (Na_Vs), obligatory membrane proteins for bioelectrical signaling, has been linked to a number of human pathologies. Small-molecule agents that target Na_Vs hold considerable promise for treatment of chronic disease. Absent a comprehensive understanding of channel structure, the challenge of designing selective agents to modulate the activity of Na_V subtypes is formidable. We have endeavored to gain insight into the 3D architecture of the outer vestibule of Na_V through a systematic structure–activity relationship (SAR) study involving the bis-guanidinium toxin saxitoxin (STX), modified saxitoxins, and protein mutagenesis. Mutant cycle analysis has led to the identification of an acetylated variant of STX with unprecedented, low-nanomolar affinity for human Na_V1.7 (hNa_V1.7), a channel subtype that has been implicated in pain perception. A revised toxin-receptor binding model is presented, which is consistent with the large body of SAR data that we have obtained. This new model is expected to facilitate subsequent efforts to design isoform-selective Na_V inhibitors.Modulation of action potentials in electrically excitable cells is controlled by tight regulation of ion channel expression and distribution. Voltage-gated sodium ion channels (Na_Vs) constitute one such family of essential membrane proteins, encoded in 10 unique genes (Na_V1.1–Na_V1.9, Na_x) and further processed through RNA splicing, editing, and posttranslational modification. Sodium channels are comprised of a large (∼260 kDa) pore-forming α-subunit coexpressed with ancillary β-subunits. Misregulation and/or mutation of Na_Vs have been ascribed to a number of human diseases including neuropathic pain, epilepsy, and cardiac arrhythmias. A desire to understand the role of individual Na_V subtypes in normal and aberrant signaling motivates the development of small-molecule probes for regulating the function of specific channel isoforms (1–4).Nature has provided a collection of small-molecule toxins, including (+)-saxitoxin (STX, 1) and (−)-tetrodotoxin (TTX), which bind to a subset of mammalian Na_V isoforms with nanomolar affinity (5–7). Guanidinium toxins inhibit Na⁺ influx through Na_Vs by occluding the outer pore above the ion selectivity filter (site 1). This proposed mechanism for toxin block follows from a large body of electrophysiological and site-directed mutagenesis studies (Fig. 1A and refs. 8–10). The detailed view of toxin binding, however, is unsupported by structural biology, as no high-resolution structure of a eukaryotic Na_V has been solved to date (11–16). Na_V homology models, constructed based on X-ray analyses of prokaryotic Na⁺ and K⁺ voltage-gated channels, do not sufficiently account for experimental structure–activity relationship (SAR) data (6, 17–20), and the molecular details underlying distinct differences in toxin potencies toward individual Na_V subtypes remain undefined (5, 6, 21–23). The lack of structural information motivates a comprehensive, systematic study of toxin–protein interactions.Open in a separate windowFig. 1.(A) Schematic drawing of 1 bound in the Na_V outer pore as suggested by previous electrophysiology and mutagenesis experiments. Each of the four domains (I, orange; II, red; III, gray; and IV, teal) is represented by a separate panel. (B) Schematic representation of double-mutant cycle analysis and mathematical definition of coupling energy (ΔΔE_Ω). X¹ = IC₅₀(WT⋅STX)/IC₅₀(MutNa_V⋅STX), X² = IC₅₀(WT⋅MeSTX)/IC₅₀(MutNa_V⋅MeSTX), Y¹ = IC₅₀(MutNa_V⋅STX)/IC₅₀(MutNa_V⋅MeSTX), and Y² = IC₅₀(WT⋅STX)/IC₅₀(WT⋅MeSTX).Double-mutant cycle analysis has proven an invaluable experimental method for assessing protein–protein, protein–peptide, and protein–small-molecule interactions in the absence of crystallographic data (Fig. 1B and Fig. S1 and refs. 9, 10, and 24–31). Herein, we describe mutant cycle analysis with Na_Vs using STX and synthetically modified forms thereof. Our results are suggestive of a toxin–Na_V binding pose distinct from previously published views. Our studies have resulted in the identification of a natural variant of STX that is potent against the STX-resistant human Na_V1.7 isoform (hNa_V1.7). Structural insights gained from these studies provide a foundation for engineering guanidinium toxins with Na_V isoform selectivity.Open in a separate windowFig. S1.Mutant cycle analysis definition and examples. (A) Schematic of a single mutant cycle with mathematical expressions for coupling energy ΔΔE_Ω. R is the ideal gas constant and T is temperature. Each IC₅₀ is the half maximal inhibition concentration determined by whole-cell voltage-clamp electrophysiology. When the separation between IC₅₀ values for the reference compound and the modified compound is different with a mutant than with the WT protein, a nonzero value for ΔΔE_Ω is obtained (B), but when the separation is the same (C), ΔΔE_Ω is equal to 0. In B, the difference in the relative affinity of 1 and 4 with Y401A is smaller than the difference with the WT channel, indicating a positive coupling (ΔΔE_Ω > 0). In C, the relative affinities of 1 and 8 against WT rNa_V1.4 and Y401A are similar, and ΔΔE_Ω ∼0 kcal/mol.     

Role of protein dynamics in ion selectivity and allosteric coupling in the NaK channel

Flux-dependent inactivation that arises from functional coupling between the inner gate and the selectivity filter is widespread in ion channels. The structural basis of this coupling has only been well characterized in KcsA. Here we present NMR data demonstrating structural and dynamic coupling between the selectivity filter and intracellular constriction point in the bacterial nonselective cation channel, NaK. This transmembrane allosteric communication must be structurally different from KcsA because the NaK selectivity filter does not collapse under low-cation conditions. Comparison of NMR spectra of the nonselective NaK and potassium-selective NaK2K indicates that the number of ion binding sites in the selectivity filter shifts the equilibrium distribution of structural states throughout the channel. This finding was unexpected given the nearly identical crystal structure of NaK and NaK2K outside the immediate vicinity of the selectivity filter. Our results highlight the tight structural and dynamic coupling between the selectivity filter and the channel scaffold, which has significant implications for channel function. NaK offers a distinct model to study the physiologically essential connection between ion conduction and channel gating.Ion conduction through the pore domain of cation channels is regulated by two gates: an inner gate at the bundle crossing of the pore-lining transmembrane helices and an outer gate located at the selectivity filter (Fig. 1 B and C). These two gates are functionally coupled as demonstrated by C-type inactivation, in which channel opening triggers loss of conduction at the selectivity filter (1–4). A structural model for C-type inactivation has been developed for KcsA, with selectivity filter collapse occurring upon channel opening (4–10). In the reverse pathway, inactivation of the selectivity filter has been linked to changes at the inner gate (5–14). However, flux-dependent inactivation occurs in Na⁺ and Ca²⁺ channels as well and would likely require a structurally different mechanism to explain coupling between the selectivity filter and inner gate (7, 13–18).Open in a separate windowFig. 1.Crystal structures of the nonselective cation channel NaK and the potassium-selective NaK2K mutant show structural changes restricted to the area of the selectivity filter. Alignment of the WT NaK (gray; PDB 3E8H) and NaK2K (light blue; PDB 3OUF) selectivity filters shows a KcsA-like four-ion-binding-site selectivity filter is created by the NaK2K mutations (D66Y and N68D) (A), but no structural changes occur outside the vicinity of the selectivity filter (B). (C) Full-length NaK (green; PDB 2AHZ) represents a closed conformation. Alignment of this structure with NaK (gray) highlights the changes in the M2 hinge (arrow), hydrophobic cluster (residues F24, F28, and F94 shown as sticks), and constriction point (arrow; residue Q103 shown as sticks) upon channel opening. Two (A) or three monomers (B and C) from the tetramer are shown for clarity.This study provides experimental evidence of structural and dynamic coupling between the inner gate and selectivity filter in the NaK channel, a nonselective cation channel from Bacillus cereus (19). These results were entirely unexpected given the available high-resolution crystal structures (20, 21). The NaK channel has the same basic pore architecture as K⁺ channels (Fig. 1 B and C) and has become a second model system for investigating ion selectivity and gating due to its distinct selectivity filter sequence (₆₃TVGDGN₆₈) and structure (19–23). Most strikingly, there are only two ion binding sites in the selectivity filter of the nonselective NaK channel (Fig. 1A) (21, 24). However, mutation of two residues in the selectivity filter sequence converts the NaK selectivity filter to the canonical KcsA sequence (₆₃TVGYGD₆₈; Fig. 1 A and B), leading to K⁺ selectivity and a KcsA-like selectivity filter structure with four ion binding sites (21, 23). This K⁺-selective mutant of NaK is called NaK2K. Outside of the immediate vicinity of the two mutations in the selectivity filter, high-resolution crystal structures of NaK and NaK2K are essentially identical (Fig. 1B) with an all-atom rmsd of only 0.24 Å.NaK offers a distinct model to study the physiologically essential connection between ion conduction and channel gating because there is no evidence for any collapse or structural change in the selectivity filter. The NaK selectivity filter structure is identical in Na⁺ or K⁺ (22) and even in low-ion conditions (25), consistent with its nonselective behavior. Even the selective NaK2K filter appears structurally stable in all available crystal structures (25). Here we use NMR spectroscopy to study bicelle-solubilized NaK. Surprisingly, we find significant differences in the NMR spectra of NaK and NaK2K that extend throughout the protein and are not localized to the selectivity filter region. This, combined with NMR dynamics studies of NaK, suggests a dynamic pathway for transmembrane coupling between the inner gate and selectivity filter of NaK.     

Increasing aridity reduces soil microbial diversity and abundance in global drylands

Soil bacteria and fungi play key roles in the functioning of terrestrial ecosystems, yet our understanding of their responses to climate change lags significantly behind that of other organisms. This gap in our understanding is particularly true for drylands, which occupy ∼41% of Earth´s surface, because no global, systematic assessments of the joint diversity of soil bacteria and fungi have been conducted in these environments to date. Here we present results from a study conducted across 80 dryland sites from all continents, except Antarctica, to assess how changes in aridity affect the composition, abundance, and diversity of soil bacteria and fungi. The diversity and abundance of soil bacteria and fungi was reduced as aridity increased. These results were largely driven by the negative impacts of aridity on soil organic carbon content, which positively affected the abundance and diversity of both bacteria and fungi. Aridity promoted shifts in the composition of soil bacteria, with increases in the relative abundance of Chloroflexi and α-Proteobacteria and decreases in Acidobacteria and Verrucomicrobia. Contrary to what has been reported by previous continental and global-scale studies, soil pH was not a major driver of bacterial diversity, and fungal communities were dominated by Ascomycota. Our results fill a critical gap in our understanding of soil microbial communities in terrestrial ecosystems. They suggest that changes in aridity, such as those predicted by climate-change models, may reduce microbial abundance and diversity, a response that will likely impact the provision of key ecosystem services by global drylands.Climate change is a major driver of biodiversity loss from local to global scales, in both terrestrial and aquatic ecosystems (1, 2). Given the dependence of crucial ecosystem processes and services on biodiversity (3–5), climate-change-driven biodiversity losses will dramatically alter the functioning of natural ecosystems (4, 6). Key ecosystem processes—such as nutrient cycling, carbon (C) sequestration, and organic matter decomposition—depend on soil bacteria and fungi (7–9). However, we have limited knowledge of the role of climatic factors as drivers of their abundance and diversity at regional and global scales (10–12). This gap in our understanding is particularly true for drylands, areas with an aridity index (precipitation/potential evapotranspiration ratio) below 0.65 (13), which are among the most sensitive ecosystems to climate change (14). Drylands are expected to expand in global area by 11–23% by 2100 (15), experiencing increased aridity and reduced soil moisture (16). Land degradation and desertification already affect ∼250 million people in the developing world (17). Altered climate and the growth of human populations will almost inevitably exacerbate these problems in drylands (14, 17). Because the provisioning of ecosystem services essential for human development (e.g., soil fertility, food, and biomass production) heavily relies on the abundance, composition, and diversity of soil fungi and bacteria (18, 19), it is crucial to understand how changes in aridity affect soil microbial communities. Drylands, however, are poorly represented in global soil bacteria and fungi databases (10–12, 20), and no field study has simultaneously examined how the abundance, composition, and diversity of these organisms vary along aridity gradients in drylands worldwide.Here, we present a global field study conducted across 80 dryland sites from all continents, except Antarctica (Fig. S1), to assess how changes in aridity, as defined by the aridity index, affect the total abundance and diversity of soil bacteria and fungi and the relative abundance of major bacterial and fungal taxa. The studied ecosystems encompass a wide variety of the climatic, edaphic, and vegetation conditions found in drylands worldwide (Materials and Methods). We predict that increases in aridity should reduce the abundance and diversity of soil bacteria and fungi due to the negative relationships typically found between aridity and the availability of resources such as water and C (21), which largely drive soil microbial abundance and activity in drylands (22–24). To test this hypothesis, we characterized bacterial and fungal communities in the soil surface (top 7.5 cm) along natural aridity gradients by using Illumina Miseq profiling of ribosomal genes and internal transcribed spacer (ITS) markers, quantified bacterial and fungal abundances with quantitative PCR (qPCR), and gathered information on multiple biotic and abiotic factors known to influence soil microbes (Fig. S2).Open in a separate windowFig. S1.Location of the 80 sites used in this study. Some of them overlap and are thus indistinguishable. Exact locations and additional site characteristics are provided in figshare (DOI 10.6084/m9.figshare.1487693).Open in a separate windowFig. S2.A priori SEM used in this study. Spatial is a composite variable formed by latitude and longitude. MDR, mean diurnal temperature range (mean of monthly differences between maximum and minimum temperature). The numbers in the arrows denote example references used to support our predictions, which can be found in the reference list.