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.     

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.     

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.     

Allosteric activation of ADAMTS13 by von Willebrand factor

The metalloprotease ADAMTS13 cleaves von Willebrand factor (VWF) within endovascular platelet aggregates, and ADAMTS13 deficiency causes fatal microvascular thrombosis. The proximal metalloprotease (M), disintegrin-like (D), thrombospondin-1 (T), Cys-rich (C), and spacer (S) domains of ADAMTS13 recognize a cryptic site in VWF that is exposed by tensile force. Another seven T and two complement C1r/C1s, sea urchin epidermal growth factor, and bone morphogenetic protein (CUB) domains of uncertain function are C-terminal to the MDTCS domains. We find that the distal T8-CUB2 domains markedly inhibit substrate cleavage, and binding of VWF or monoclonal antibodies to distal ADAMTS13 domains relieves this autoinhibition. Small angle X-ray scattering data indicate that distal T-CUB domains interact with proximal MDTCS domains. Thus, ADAMTS13 is regulated by substrate-induced allosteric activation, which may optimize VWF cleavage under fluid shear stress in vivo. Distal domains of other ADAMTS proteases may have similar allosteric properties.After vascular injury, platelets adhere to von Willebrand factor (VWF) multimers bound to endothelial cell surfaces and connective tissue. The force of flowing blood on a growing platelet-rich thrombus stretches the central A2 domain of VWF and exposes a Tyr¹⁶⁰⁵-Met¹⁶⁰⁶ cleavage site for ADAMTS13 (Fig. 1A) (1–5), a metalloprotease that severs VWF and releases adherent platelets. Deficiency of ADAMTS13 disrupts this feedback regulatory mechanism and causes thrombotic thrombocytopenic purpura (TTP), which is characterized by life-threatening microvascular thrombosis (3, 6, 7).Open in a separate windowFig. 1.Activation of ADAMTS13 by autoantibodies from a patient with TTP or by low pH. (A) Structure of ADAMTS13. (B) Fluorogenic substrates terminate at VWF residues indicated by arrows. Each substrate has Lys¹⁶¹⁷ replaced with Arg, N-terminal Gly modified with IRDye QC-1 (QC1), and Asn¹⁶¹⁰ replaced by Cys and modified with DyLight 633 (DyL) (22). The arrowhead indicates the cleaved Tyr-Met bond. Secondary structure elements of the VWF A2 domain (11) are indicated below and segments that interact with specific ADAMTS13 domains (13) are indicated above the sequence. (C) BCW49 plasma activated ADAMTS13 with a titer of 9.6 U at pH 7.4 (orange squares), but not at pH 6.0 (orange circle). BCW49 plasma did not activate MDTCS at pH 6 (blue circle) or pH 7.4 (blue circle). (D) Rates of VWF71 cleavage were determined as a function of pH for ADAMTS13 (orange circles) and MDTCS (blue circles). Error bars indicate 95% confidence intervals and if not shown are smaller than the symbols.The recognition and cleavage of VWF is a formidable challenge. VWF and ADAMTS13 occur at ∼10 µg/mL and ∼1 µg/mL, respectively, compared with total plasma protein of ∼80,000 µg/mL. ADAMTS13 is constitutively active and has no known inhibitors in vivo. Nevertheless, VWF is the only identified ADAMTS13 substrate, and VWF is resistant to cleavage until subjected to fluid shear stress (8), adsorbed on a surface (9), or treated with denaturants (8, 10). This specificity depends on structural features of both ADAMTS13 and VWF that have not been characterized fully.The proximal metalloprotease (M), disintegrin-like (D), thrombospondin-1 (T), Cys-rich (C), and spacer (S) domains domains of ADAMTS13 bind to cryptic sites that are uncovered by unfolding VWF domain A2 (11-15) (Fig. 1B), and these interactions are required for efficient cleavage of VWF or peptide substrates. More distal ADAMTS13 domains bind to sites in or near VWF domain D4 that are always available (16–18). Deletion of distal ADAMTS13 domains impairs the cleavage of VWF multimers in vitro (16, 19) and increases VWF-dependent microvascular thrombosis in vivo (20) but accelerates the cleavage of peptide substrates (12, 13). In addition, ADAMTS13 cleaves guanidine hydrochloride-treated VWF multimers with an apparent K_m of ∼15 nM (21), which is 100-fold lower than the K_m of ∼1.6–1.7 µM for peptide substrates that are based on the sequence of VWF domain A2 (12, 14). These striking differences suggest that distal T or complement c1r/c1s, sea urchin epidermal growth factor, and bone morphogenetic protein (CUB) domains regulate ADAMTS13 activity. We have now shown that these distal domains inhibit ADAMTS13, and binding to VWF relieves this autoinhibition.     

Tuning a riboswitch response through structural extension of a pseudoknot

Structural and dynamic features of RNA folding landscapes represent critical aspects of RNA function in the cell and are particularly central to riboswitch-mediated control of gene expression. Here, using single-molecule fluorescence energy transfer imaging, we explore the folding dynamics of the preQ₁ class II riboswitch, an upstream mRNA element that regulates downstream encoded modification enzymes of queuosine biosynthesis. For reasons that are not presently understood, the classical pseudoknot fold of this system harbors an extra stem–loop structure within its 3′-terminal region immediately upstream of the Shine–Dalgarno sequence that contributes to formation of the ligand-bound state. By imaging ligand-dependent preQ₁ riboswitch folding from multiple structural perspectives, we reveal that the extra stem–loop strongly influences pseudoknot dynamics in a manner that decreases its propensity to spontaneously fold and increases its responsiveness to ligand binding. We conclude that the extra stem–loop sensitizes this RNA to broaden the dynamic range of the ON/OFF regulatory switch.A variety of small metabolites have been found to regulate gene expression in bacteria, fungi, and plants via direct interactions with distinct mRNA folds (1–4). In this form of regulation, the target mRNA typically undergoes a structural change in response to metabolite binding (5–9). These mRNA elements have thus been termed “riboswitches” and generally include both a metabolite-sensitive aptamer subdomain and an expression platform. For riboswitches that regulate the process of translation, the expression platform minimally consists of a ribosomal recognition site [Shine–Dalgarno (SD)]. In the simplest form, the SD sequence overlaps with the metabolite-sensitive aptamer domain at its downstream end. Representative examples include the S-adenosylmethionine class II (SAM-II) (10) and the S-adenosylhomocysteine (SAH) riboswitches (11, 12), as well as prequeuosine class I (preQ₁-I) and II (preQ₁-II) riboswitches (13, 14). The secondary structures of these four short RNA families contain a pseudoknot fold that is central to their gene regulation capacity. Although the SAM-II and preQ₁-I riboswitches fold into classical pseudoknots (15, 16), the conformations of the SAH (17) and preQ₁-II counterparts are more complex and include a structural extension that contributes to the pseudoknot architecture (14). Importantly, the impact and evolutionary significance of these “extra” stem–loop elements on the function of the SAH and preQ₁-II riboswitches remain unclear.PreQ₁ riboswitches interact with the bacterial metabolite 7-aminomethyl-7-deazaguanine (preQ₁), a precursor molecule in the biosynthetic pathway of queuosine, a modified base encountered at the wobble position of some transfer RNAs (14). The general biological significance of studying the preQ₁-II system stems from the fact that this gene-regulatory element is found almost exclusively in the Streptococcaceae bacterial family. Moreover, the preQ₁ metabolite is not generated in humans and has to be acquired from the environment (14). Correspondingly, the preQ₁-II riboswitch represents a putative target for antibiotic intervention. Although preQ₁ class I (preQ₁-I) riboswitches have been extensively investigated (18–28), preQ₁ class II (preQ₁-II) riboswitches have been largely overlooked despite the fact that a different mode of ligand binding has been postulated (14).The consensus sequence and the secondary structure model for the preQ₁-II motif (COG4708 RNA) (Fig. 1A) comprise ∼80–100 nt (14). The minimal Streptococcus pneumoniae R6 aptamer domain sequence binds preQ₁ with submicromolar affinity and consists of an RNA segment forming two stem–loops, P2 and P4, and a pseudoknot P3 (Fig. 1B). In-line probing studies suggest that the putative SD box (AGGAGA; Fig. 1) is sequestered by pseudoknot formation, which results in translational-dependent gene regulation of the downstream gene (14).Open in a separate windowFig. 1.PreQ₁ class II riboswitch. (A) Chemical structure of 7-aminomethyl-7-deazaguanosine (preQ₁); consensus sequence and secondary structure model for the COG4708 RNA motif (adapted from reference 14). Nucleoside presence and identity as indicated. (B) S. pneumoniae R6 preQ₁-II RNA aptamer investigated in this study. (C) Schematics of an H-type pseudoknot with generally used nomenclature for comparison.Here, we investigated folding and ligand recognition of the S. pneumoniae R6 preQ₁-II riboswitch, using complementary chemical, biochemical, and biophysical methods including selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE), mutational analysis experiments, 2-aminopurine fluorescence, and single-molecule fluorescence resonance energy transfer (smFRET) imaging. In so doing, we explored the structural and functional impact of the additional stem–loop element in the context of its otherwise “classical” H-type pseudoknot fold (29–32) (Fig. 1C). Our results reveal that the unique 3′-stem–loop element in the preQ₁-II riboswitch contributes to the process of SD sequestration, and thus the regulation of gene expression, by modulating both its intrinsic dynamics and its responsiveness to ligand binding.     

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.     

Aggregation propensities of superoxide dismutase G93 hotspot mutants mirror ALS clinical phenotypes

Protein framework alterations in heritable Cu, Zn superoxide dismutase (SOD) mutants cause misassembly and aggregation in cells affected by the motor neuron disease ALS. However, the mechanistic relationship between superoxide dismutase 1 (SOD1) mutations and human disease is controversial, with many hypotheses postulated for the propensity of specific SOD mutants to cause ALS. Here, we experimentally identify distinguishing attributes of ALS mutant SOD proteins that correlate with clinical severity by applying solution biophysical techniques to six ALS mutants at human SOD hotspot glycine 93. A small-angle X-ray scattering (SAXS) assay and other structural methods assessed aggregation propensity by defining the size and shape of fibrillar SOD aggregates after mild biochemical perturbations. Inductively coupled plasma MS quantified metal ion binding stoichiometry, and pulsed dipolar ESR spectroscopy evaluated the Cu²⁺ binding site and defined cross-dimer copper–copper distance distributions. Importantly, we find that copper deficiency in these mutants promotes aggregation in a manner strikingly consistent with their clinical severities. G93 mutants seem to properly incorporate metal ions under physiological conditions when assisted by the copper chaperone but release copper under destabilizing conditions more readily than the WT enzyme. Altered intradimer flexibility in ALS mutants may cause differential metal retention and promote distinct aggregation trends observed for mutant proteins in vitro and in ALS patients. Combined biophysical and structural results test and link copper retention to the framework destabilization hypothesis as a unifying general mechanism for both SOD aggregation and ALS disease progression, with implications for disease severity and therapeutic intervention strategies.ALS is a lethal degenerative disease of the human motor system (1). Opportunities for improved understanding and clinical intervention arose from the discovery that up to 23.5% of familial ALS cases and 7% of spontaneous cases are caused by mutations in the superoxide dismutase 1 (SOD1) gene encoding human Cu, Zn SOD (2–4). SOD is a highly conserved (5), dimeric, antioxidant metalloenzyme that detoxifies superoxide radicals (6, 7), but overexpression of SOD1 ALS mutants is sufficient to cause disease in mice (8). Misfolded and/or aggregated SOD species are deposited within mouse neuronal and glial inclusions (9, 10), even before symptoms appear (11, 12). Although human familial ALS has a symptomatic phenotype indistinguishable from sporadic cases (13), individual SOD1 mutations can result in highly variable disease progression and penetrance (14, 15).Many nongeneral mechanisms, including loss of activity or gain of function, were postulated to explain the roles of SOD mutants in ALS (3, 16–19). Recently, however, an initial hypothesis proposing that SOD manifests disease symptoms by framework destabilization (protein instability caused by structural defects) and consequent protein misassembly and aggregation has gained renewed support (2, 10, 14, 20–23). Ironically, WT SOD is an unusually stable protein (7, 24–26), and precisely how SOD mutations cause disease remains unclear. For instance, human SOD free cysteine residues C6 and C111 have been implicated in protein aggregation by promoting cross-linking (27, 28) and/or stability changes associated with oxidative modifications (29–33). Mutation of the chemically reactive thiols significantly decreases the irreversible denaturation rate for human and bovine SOD (24, 34). However, ALS mutants in a C6A/C111S SOD (AS-SOD) background (35, 36) maintain the native C57–C146 disulfide bond but can still undergo aggregation, and mutations of the free cysteines can cause ALS (37, 38). These results imply that free cysteines are not strictly required but rather, may alter aggregation kinetics (20). SOD also contains two metal ion cofactors in each subunit: a catalytic copper ion (6) and a structurally stabilizing zinc ion (34, 39, 40) (Fig. 1A). In higher eukaryotes, a copper chaperone for SOD (CCS) plays an important role in catalyzing both the copper incorporation and native disulfide bond formation (41). Structural analyses of apo WT SOD point to greater flexibility or increased solvent accessibility of C6 otherwise buried in the stable dimer interface (42, 43), and molecular dynamics simulations also suggest a critical role for metal ions in protein structure, because SOD’s β-sheet propensity decreases in the absence of metals (44). As a result, apo SOD readily forms protein aggregates (45, 46), but the molecular structures of SOD aggregates are likely polymorphic and represent a controversial topic (23, 47–51). The intertwined effects of the aggregation-enhancing free cysteines, dimer-stabilizing metal ions, and CCS maturation of SOD complicate the study of the ALS-causing SOD mutations themselves, and therefore, a clear cause-and-effect relationship remains obscure and requires deconvolution.Open in a separate windowFig. 1.Comparison of crystallographic and solution structures of WT and G93A SOD. (A) Overall architecture of the WT SOD dimer is displayed in 90° rotated views. G93 (small red spheres) resides on a surface-exposed interstrand loop between the fifth and sixth sequential β-strands of SOD and is expected to be innocuous in facilitating protein stability; however, this site harbors the most substitutions observed to result in ALS. G93 is also distant from both (Upper) the dimer interface and (Lower Left) the SOD active site (gold and silver spheres), which are generally implicated as the major determinants for SOD stability. Small blue spheres denote free cysteines. (Lower Right) The close-up view of the mutation site (boxed region in Lower Left tilted forward) shows high similarity between WT (purple) and G93A (red) SOD crystal structures [Protein Data Bank ID codes 1PU0 (WT) and 2ZKY (G93A)]. Hydrogen bonds characteristic of a β-bulge motif are indicated, whereby G93 (or A93) represents position 1. The main chain carbonyl group of β-barrel cork residue L38 is adjacent to the G93 site. (B) SAXS-derived electron pair P(r) distributions from WT (purple) and G93A (red) SOD samples in solution are compared with the theoretical curve for 1PU0. P(r) plots are normalized to peak height. Ab initio models of WT SOD derived from P(r) data are depicted in purple, with crystal structure docked into mesh envelope. Contributions to major and minor peaks from subunit and dimer dimensions are indicated.To better understand the structural effects of ALS mutations on SOD architecture, we coupled the wealth of crystallographic knowledge on SOD structure (7, 52, 53) with small-angle X-ray scattering (SAXS) experiments to characterize misassembly aggregates of ALS mutant SODs in solution. Over 20 y ago, we solved the first atomic structure of the human WT SOD protein (Fig. 1A) (20, 34) and proposed the framework destabilization hypothesis to explain how diverse mutations located throughout the 153-residue β-barrel enzyme might produce a similar disease phenotype (2), albeit with distinctions in the progression trajectory. Since that time, a staggering number of ALS mutations has been documented in patients [178 (mostly missense) (54)], with a similar phenotype in dogs (55, 56). Solution-based techniques are increasingly being applied to connect structure to biological outcome, for instance, through examination of intermolecular interactions within stress-activated pathways, for instance (57, 58). SAXS, which can probe structures for a wide size range of species, also provides higher resolution insights (59), for instance, over visible light-scattering techniques, readily distinguishing unfolded from folded proteins (60).Here, we monitor the initial events of protein aggregation in a subset of ALS mutants localized to a mutational hotspot site at glycine 93. Specifically, we wished to test a possible structural basis for how G93 mutations (to A, C, D, R, S, or V) modulate age of onset and clinical severity in ALS patients (14, 15). The G93 substitution occurs in a β-bulge region (61) between sequential β-strands of the protein (Fig. 1A) on a protruding loop roughly ∼20 Å from T54, the nearest residue of the opposing subunit, and the metal-containing active site (Fig. S1). A priori, mutation of this outer loop position would not be expected to interfere with active site chemistry or buried molecular interfaces. However, we discovered correlations of aggregation nucleation kinetics of SOD proteins with ALS mutations at this site, the stabilizing effects of metal ion retention, and available data for clinical phenotypes in patients with the same mutation. Furthermore, by measuring and exploiting the dimer geometry to observe intrinsic SOD conformers, we show that G93 mutant proteins natively reveal increased intradimer conformational flexibility in the absence of aggregation, which may reflect an increased tendency for ALS mutants to become metal-deficient and misfolding-prone and further explain the correlation to disease severity. Collective results on G93 mutants, thus, support and extend the framework destabilization hypothesis.     

A family of starch-active polysaccharide monooxygenases

The recently discovered fungal and bacterial polysaccharide monooxygenases (PMOs) are capable of oxidatively cleaving chitin, cellulose, and hemicelluloses that contain β(1→4) linkages between glucose or substituted glucose units. They are also known collectively as lytic PMOs, or LPMOs, and individually as AA9 (formerly GH61), AA10 (formerly CBM33), and AA11 enzymes. PMOs share several conserved features, including a monocopper center coordinated by a bidentate N-terminal histidine residue and another histidine ligand. A bioinformatic analysis using these conserved features suggested several potential new PMO families in the fungus Neurospora crassa that are likely to be active on novel substrates. Herein, we report on NCU08746 that contains a C-terminal starch-binding domain and an N-terminal domain of previously unknown function. Biochemical studies showed that NCU08746 requires copper, oxygen, and a source of electrons to oxidize the C1 position of glycosidic bonds in starch substrates, but not in cellulose or chitin. Starch contains α(1→4) and α(1→6) linkages and exhibits higher order structures compared with chitin and cellulose. Cellobiose dehydrogenase, the biological redox partner of cellulose-active PMOs, can serve as the electron donor for NCU08746. NCU08746 contains one copper atom per protein molecule, which is likely coordinated by two histidine ligands as shown by X-ray absorption spectroscopy and sequence analysis. Results indicate that NCU08746 and homologs are starch-active PMOs, supporting the existence of a PMO superfamily with a much broader range of substrates. Starch-active PMOs provide an expanded perspective on studies of starch metabolism and may have potential in the food and starch-based biofuel industries.Polysaccharide monooxygenases (PMOs) are enzymes secreted by a variety of fungal and bacterial species (1–5). They have recently been found to oxidatively degrade chitin (6–8) and cellulose (8–14). PMOs have been shown to oxidize either the C1 or C4 atom of the β(1→4) glycosidic bond on the surface of chitin (6, 7) or cellulose (10–12, 14), resulting in the cleavage of this bond and the creation of new chain ends that can be subsequently processed by hydrolytic chitinases and cellulases. Several fungal PMOs were shown to significantly enhance the degradation of cellulose by hydrolytic cellulases (9), indicating that these enzymes can be used in the conversion of plant biomass into biofuels and other renewable chemicals.There are three families of PMOs characterized thus far: fungal PMOs that oxidize cellulose (9–12) (also known as GH61 and AA9); bacterial PMOs that are active either on chitin (6, 8) or cellulose (8, 13) (also known as CBM33 and AA10); and fungal PMOs that oxidize chitin (AA11) (7). Sequence homology between these three families is very low. Nevertheless, the available structures of PMOs from all three families reveal a conserved fold, including an antiparallel β-sandwich core and a highly conserved monocopper active site on a flat protein surface (Fig. 1A) (2, 6, 7, 9, 10, 15–17). Two histidine residues in a motif termed the histidine brace coordinate the copper center. The N-terminal histidine ligand binds in a bidentate mode, and its imidazole ring is methylated at the N_ε position in fungal PMOs (Fig. 1A).Open in a separate windowFig. 1.(A) Representative overall and active site structures of fungal PMOs (PDB ID code 2YET) (10). (B) Structure of cellulose (18, 19). Chitin also contains β(1→4) linkages and has similar crystalline higher order structure to cellulose. (C) Model structure of amylopectin (23–25). Hydrogen bonds are shown with green dashed lines.Considering the conserved structural features, it is not surprising that the currently known PMOs act on substrates with similar structures. Cellulose and chitin contain long linear chains of β(1→4) linked glucose units and N-acetylglucosamine units, respectively (Fig. 1B). The polymer chains form extensive hydrogen bonding networks, which result in insoluble and very stable crystalline structures (18–21). PMOs are thought to bind to the substrate with their flat active site surface, which orients the copper center for selective oxidation at the C1 or C4 position (6, 16, 22). Some bacterial chitin-binding proteins are cellulose-active PMOs (8, 13, 14), further suggesting that the set of PMO substrates is restricted to β(1→4) linked polymers of glucose and glucose derivatives.Here, we report on the identification of new families of PMOs that contain several key features of previously characterized PMOs, but act on substrates different from cellulose or chitin. A member of one of these novel families of PMOs, NCU08746, was shown to oxidatively cleave amylose, amylopectin, and starch. We designate the NCU08746 family as starch-active PMOs. Both amylose and amylopectin contain linear chains of α(1→4) linked glucose, whereas the latter also contains α(1→6) glycosidic linkages at branch points in the otherwise α(1→4) linked polymer. Unlike cellulose and chitin, amylose and amylopectin do not form microcrystals; instead, they exist in disordered, single helical, and double helical forms (23–27) (see Fig. 1C for example). Starch exists partially in nanocrystalline form, but lacks the flat molecular surfaces as those found in chitin and cellulose. The discovery of starch-active PMOs shows that this oxidative mechanism of glycosidic bond cleavage is more widespread than initially expected.     

Robust efficiency and actuator saturation explain healthy heart rate control and variability

The correlation of healthy states with heart rate variability (HRV) using time series analyses is well documented. Whereas these studies note the accepted proximal role of autonomic nervous system balance in HRV patterns, the responsible deeper physiological, clinically relevant mechanisms have not been fully explained. Using mathematical tools from control theory, we combine mechanistic models of basic physiology with experimental exercise data from healthy human subjects to explain causal relationships among states of stress vs. health, HR control, and HRV, and more importantly, the physiologic requirements and constraints underlying these relationships. Nonlinear dynamics play an important explanatory role––most fundamentally in the actuator saturations arising from unavoidable tradeoffs in robust homeostasis and metabolic efficiency. These results are grounded in domain-specific mechanisms, tradeoffs, and constraints, but they also illustrate important, universal properties of complex systems. We show that the study of complex biological phenomena like HRV requires a framework which facilitates inclusion of diverse domain specifics (e.g., due to physiology, evolution, and measurement technology) in addition to general theories of efficiency, robustness, feedback, dynamics, and supporting mathematical tools.Biological systems display a variety of well-known rhythms in physiological signals (1–6), with particular patterns of variability associated with a healthy state (2–6). Decades of research demonstrate that heart rate (HR) in healthy humans has high variability, and loss of this high HR variability (HRV) is correlated with adverse states such as stress, fatigue, physiologic senescence, or disease (6–13). The dominant approach to analysis of HRV has been to focus on statistics and patterns in HR time series that have been interpreted as fractal, chaotic, scale-free, critical, etc. (6–17). The appeal of time series analysis is understandable as it puts HRV in the context of a broad and popular approach to complex systems (5, 18), all while requiring minimal attention to domain-specific (e.g., physiological) details. However, despite intense research activity in this area, there is limited consensus regarding causation or mechanism and minimal clinical application of the observed phenomena (10). This paper takes a completely different approach, aiming for more fundamental rigor (19–24) and methods that have the potential for clinical relevance. Here we use and model data from experimental studies of exercising healthy athletes, to add simple physiological explanations for the largest source of HRV and its changes during exercise. We also present methods that can be used to systematically pursue further explanations about HRV that can generalize to less healthy subjects.Fig. 1 shows the type of HR data analyzed, collected from healthy young athletes (n = 5). The data display responses to changes in muscle work rate on a stationary bicycle during mostly aerobic exercise. Fig. 1A shows three separate exercise sessions with identical workload fluctuations about three different means. With proper sleep, hydration, nutrition, and prevention from overheating, trained athletes can maintain the highest workload in Fig. 1 for hours and the lower and middle levels almost indefinitely. This ability requires robust efficiency: High workloads are sustained while robustly maintaining metabolic homeostasis, a particularly challenging goal in the case of the relatively large, metabolically demanding, and fragile human brain.Open in a separate windowFig. 1.HR responses to simple changes in muscle work rate on a stationary bicycle: Each experimental subject performed separate stationary cycle exercises of ∼10 min for each workload profile, with different means but nearly identical square wave fluctuations around the mean. A typical result is shown from subject 1 for three workload profiles with time on the horizontal axis (zoomed in to focus on a 6-min window). (A) HR (red) and workload (blue); linear local piecewise static fits (black) with different parameters for each exercise. The workload units (most strenuous exercise on top of graph) are shifted and scaled so that the blue curves are also the best global linear fit. (B) Corresponding dynamics fits, either local piecewise linear (black) or global linear (blue). Note that, on all time scales, mean HR increases and variability (HRV) goes down with the increasing workload. Breathing was spontaneous (not controlled).Whereas mean HR in Fig. 1A increases monotonically with workloads, both slow and fast fluctuations (i.e., HRV) in HR are saturating nonlinear functions of workloads, meaning that both high- and low-frequency HRV component goes down. Results from all subjects showed qualitatively similar nonlinearities (SI Appendix). We will argue that this saturating nonlinearity is the simplest and most fundamental example of change in HRV in response to stressors (11, 12, 25) [exercise in the experimental case, but in general also fatigue, dehydration, trauma, infection, even fear and anxiety (6–9, 11, 12, 25)].Physiologists have correlated HRV and autonomic tone (7, 11, 12, 14), and the (im)balance between sympathetic stimulation and parasympathetic withdrawal (12, 26–28). The alternation in autonomic control of HR (more sympathetic and less parasympathetic tone during exercise) serves as an obvious proximate cause for how the HRV changes as shown in Fig. 1, but the ultimate question remains as to why the system is implemented this way. It could be an evolutionary accident, or could follow from hard physiologic tradeoff requirements on cardiovascular control, as work in other systems suggests (1). Here, the explanation of HRV similarly involves hard physiological tradeoffs in robust efficiency and employs the mathematical tools necessary to make this explanation rigorous in the context of large measurement and modeling uncertainties.     

Alleviation of chronic pain following rat spinal cord compression injury with multimodal actions of huperzine A

Diverse mechanisms including activation of NMDA receptors, microglial activation, reactive astrogliosis, loss of descending inhibition, and spasticity are responsible for ∼40% of cases of intractable neuropathic pain after spinal cord injury (SCI). Because conventional treatments blocking individual mechanisms elicit only short-term effectiveness, a multimodal approach with simultaneous actions against major pain-related pathways may have value for clinical management of chronic pain. We hypothesize that [-]-huperzine A (HUP-A), an alkaloid isolated from the club moss Huperzia serrata, that is a potent reversible inhibitor of acetylcholinesterase and NMDA receptors, could mitigate pain without invoking drug tolerance or dependence by stimulating cholinergic interneurons to impede pain signaling, inhibiting inflammation via microglial cholinergic activation, and blocking NMDA-mediated central hypersensitization. We tested our hypothesis by administering HUP-A i.p. or intrathecally to female Sprague–Dawley rats (200–235 g body weight) after moderate static compression (35 g for 5 min) of T10 spinal cord. Compared with controls, HUP-A treatment demonstrates significant analgesic effects in both regimens. SCI rats manifested no drug tolerance following repeated bolus i.p. or chronic intrathecal HUP-A dosing. The pain-ameliorating effect of HUP-A is cholinergic dependent. Relative to vehicle treatment, HUP-A administration also reduced neural inflammation, retained higher numbers of calcium-impermeable GluR2-containing AMPA receptors, and prevented Homer1a up-regulation in dorsal horn sensory neurons. Therefore, HUP-A may provide safe and effective management for chronic postneurotrauma pain by reestablishing homeostasis of sensory circuits.Neuropathic pain is one of the most debilitating sequelae of neurotrauma and is an unmet clinical need for at least 40% of patients with spinal cord injury (SCI) (1). Administration of conventional drugs has shown only various degrees of short-term efficacy in reducing at- and/or below-injury-level hypersensitivity after SCI by acting on individual pathways to inhibit descending facilitation of pain-transmission neurons (2), activate inhibitory interneurons in the spinal cord (2, 3), mitigate firing of pain-transmission neurons (3–7), or impede inflammation triggered by the activation of astrocytes and microglial cells (8). The diversity of mechanisms and potential targets for intervention may be partially responsible for the difficulty in developing therapeutics that can provide long-term efficacy. Therefore we postulated that devising a multimodal treatment with potent, defined simultaneous effects on multiple pain-related pathways might be an effective concept for clinical management of neuropathic pain.Given the potential roles of cholinergic agonists (9, 10) and antagonists of NMDA-subtype glutamate receptors (10, 11) in the treatment of neuropathic pain, we hypothesized that [-]-huperzine A (HUP-A) (Fig. 1A), a naturally occurring Lycopodium alkaloid isolated from the Chinese club moss, Huperzia serrata (Fig. 1B) that has potent reversible inhibitory action on acetylcholinesterase (AChE) (Fig. 1C) (10, 12) and NMDA receptors (Fig. 1D) (13), might be an exceptional prospect for multimodal treatment of SCI-induced neuropathic pain. We tested whether HUP-A–derived NMDA blockade and the overall HUP-A–derived augmentation of cholinergic neurotransmission arising from AChE inhibition might be harnessed specifically to stage a multifocal intervention in post-SCI pain pathways because (i) antagonism of NMDA receptor prevents the post-SCI hyperexcitability of neurons in the dorsal horn (DH) of the spinal cord that feature a wide, dynamic range of pain transmission (7); (ii) activation of presynaptic α3β2 nicotinic ACh receptors (nAChR) minimizes release of glutamate from C-fiber terminals in the DH that are involved in nociceptive neurotransmission (14); (iii) activation of GABAergic interneurons via stimulation of their M2 and M4 muscarinic ACh receptors (mAChR), in turn, inhibits presynaptic release of glutamate from primary afferent axons (15, 16); (iv) activation of the α4β2 nAChR on GABAergic inhibitory interneurons mitigates the firing of secondary spinothalamic pain transmission neurons (17); and (v) stimulation of the α7 nAChR on microglial cells blocks their activation, ameliorating neuroinflammation (18, 19). The primary goal of our study is to develop a class of therapeutics for the management of chronic neuropathic pain that aims to restore homeostasis of the sensory neurocircuitry by simultaneous multimodal mechanisms without invoking drug tolerance and dependence or respiratory suppression. HUP-A has been used for centuries in herbal medicine to treat inflammatory and other diseases (10, 12), has demonstrated effectiveness in inhibiting hypersensitivity triggered by peripheral neuropathy (20), and has been studied in phase II clinical trials for Alzheimer’s disease in the United States (11). We tested whether HUP-A regimens might translate into an effective and safe therapy for neuropathic pain following SCI in adult female rats (21–23).Open in a separate windowFig. 1.HUP-A is a natural alkaloid extract. HUP-A''s molecular structure is presented in (A) from the club moss Huperzia serrata (B) that is a potent, highly specific, and reversible inhibitor of AChE (C) as well as a noncompetitive partial antagonist of the NMDA receptor (D). A reproducible moderate static-compression SCI model maintaining 35 g of static weight on the dorsal surface of T10 spinal cord for 5 min was used for the present study (E).     

Mechanical constraints imposed by 3D cellular geometry and arrangement modulate growth patterns in the Arabidopsis embryo

Morphogenesis occurs in 3D space over time and is guided by coordinated gene expression programs. Here we use postembryonic development in Arabidopsis plants to investigate the genetic control of growth. We demonstrate that gene expression driving the production of the growth-stimulating hormone gibberellic acid and downstream growth factors is first induced within the radicle tip of the embryo. The center of cell expansion is, however, spatially displaced from the center of gene expression. Because the rapidly growing cells have very different geometry from that of those at the tip, we hypothesized that mechanical factors may contribute to this growth displacement. To this end we developed 3D finite-element method models of growing custom-designed digital embryos at cellular resolution. We used this framework to conceptualize how cell size, shape, and topology influence tissue growth and to explore the interplay of geometrical and genetic inputs into growth distribution. Our simulations showed that mechanical constraints are sufficient to explain the disconnect between the experimentally observed spatiotemporal patterns of gene expression and early postembryonic growth. The center of cell expansion is the position where genetic and mechanical facilitators of growth converge. We have thus uncovered a mechanism whereby 3D cellular geometry helps direct where genetically specified growth takes place.Central to developmental biology is the question of how gene expression leads to morphogenesis and the creation of form (1, 2). However, there are few studies that link genes directly with shape change in a mechanistic way (3–5). In plants, where cells do not move, nearly all shape change and morphogenesis occur through the tightly regulated control over the mechanical properties of the cell wall. Mathematical models of plant cell growth are based on the turgor-driven Lockhart model and its derivatives (6, 7) that link the rate of cell wall expansion to the stress experienced by the wall. This model fits well with the biochemistry of the cell wall, which is composed of a strong cellulose microfibril network embedded in a pectin matrix with cross-links of hemicellulose, structural proteins, and other polysaccharides (8). Stress on the cell wall from turgor pressure causes elastic expansion, which becomes plastic as remodeling enzymes rearrange the network and incorporate new material (8). Thus, the physical manifestation of growth, cell expansion, results from a balance between genetically controlled enzymatic activity and the mechanical forces experienced by the cell wall.A common simplifying assumption is that gene expression associated with cell wall modification directly specifies the rate of growth of cells. This assumption is, however, limited as growth-promoting gene expression rarely correlates well with gradients of active cell expansion (9, 10). This suggests that gene expression patterns alone are not sufficient to predict the influence of genes on shape generation. Evidence is accumulating that additional unidentified nongenetic mechanisms influence multicellular morphogenesis, such as the feedback of mechanical stresses on growth (11).In plants, several spatially distinct cellular organizing centers that coordinate and organize organ development programs have been identified (3, 12, 13), as have genes that promote cell expansion through the loosening of cell walls (8). However, efforts to uncover growth regulatory mechanisms in plants are complicated by asynchronous cell division, in addition to variable gradients of spatial differentiation across complex and dynamically growing organs such as roots, meristems, and leaves (3, 14, 15). The induction of growth of the Arabidopsis embryo (Fig. 1A) during seed germination avoids many of these confounding factors. This developmental transition from seed to seedling is driven exclusively by cell shape change in the absence of cell division (16, 17). Following a largely environmentally determined switch that terminates dormancy, a discrete induction of cell expansion is invoked by the hormone gibberellic acid (GA) (18, 19). This binary growth switch represents an ideal system for examining the relationship between the induction of growth-promoting gene expression and the morphogenesis within a multicellular organ.Open in a separate windowFig. 1.Quantification of volumetric increases at specific cell positions. (A) Cellular anatomy of the mature dormant Arabidopsis embryo. (B) Maximum-intensity projection of a confocal z stack of a complete Arabidopsis embryo. (C) Three-dimensional segmentation of the embryo in B. Different colors are used to illustrate unique labels. (D) Cross section through the segmented embryo in C. (E) Virtual isolation of outer cortical cells each labeled along their linear cell files. Cortical cells at a defined position are selected (red cells) and the origin of cutting planes is positioned upon the quiescent center. (F) Plot of cortical cell volume by cell number along the embryo axis, using aggregated data from four individual samples. Cell 1 corresponds to the first cortical cell within the embryo radicle as indicated in A. The 95% confidence interval is indicated in green. (G) False-colored heat map upon the cortical cells of an embryo axis illustrating the output within the graph in F. (Scale bars: G, in μm³; B, 100 μm.)Mechanical models of growth controlled by genetics provide insight into morphogenesis in a way that is not possible within other frameworks. Because shape is an emergent property of the models, it is possible to understand the often unintuitive relationships between gene expression patterns and the resulting organ shapes (1, 20–23). Using computational models, we demonstrate that the 3D shape of cells and their arrangement within multicellular plant organs can profoundly affect their growth response to gradients of expansion-promoting gene expression. This represents an additional mechanism through which the shape and geometry of cells influence the response of plant organs to growth-promoting gene expression.     

From the Cover: Presynaptic mitochondrial morphology in monkey prefrontal cortex correlates with working memory and is improved with estrogen treatment

Humans and nonhuman primates are vulnerable to age- and menopause-related decline in working memory, a cognitive function reliant on the energy-demanding recurrent excitation of neurons within Brodmann’s Area 46 of the dorsolateral prefrontal cortex (dlPFC). Here, we tested the hypothesis that the number and morphology (straight, curved, or donut-shaped) of mitochondria in dlPFC presynaptic boutons are altered with aging and menopause in rhesus monkeys (Macaca mulatta) and that these metrics correlate with delayed response (DR) accuracy, a well-characterized measure of dlPFC-dependent working memory. Although presynaptic bouton density or size was not significantly different across groups distinguished by age or menses status, DR accuracy correlated positively with the number of total and straight mitochondria per dlPFC bouton. In contrast, DR accuracy correlated inversely with the frequency of boutons containing donut-shaped mitochondria, which exhibited smaller active zone areas and fewer docked synaptic vesicles than those with straight or curved mitochondria. We then examined the effects of estrogen administration to test whether a treatment known to improve working memory influences mitochondrial morphology. Aged ovariectomized monkeys treated with vehicle displayed significant working memory impairment and a concomitant 44% increase in presynaptic donut-shaped mitochondria, both of which were reversed with cyclic estradiol treatment. Together, our data suggest that hormone replacement therapy may benefit cognitive aging, in part by promoting mitochondrial and synaptic health in the dlPFC.Working memory is a type of executive function that involves the storage, organization, and update of information which together guide decision making and goal-directed behavior (1, 2). This complex function is highly vulnerable to age- and menopause-related decline in humans and nonhuman primates and can be assessed in rhesus monkeys using the well-characterized delayed response (DR) test of visuospatial working memory (3–6). Rhesus monkeys are exceptionally valuable models of human aging, menopause, and related cognitive decline, because their brain anatomy, neuronal gene expression, reproductive physiology, and patterns of endocrine senescence closely resemble those of humans (4, 7–10). Importantly, they fail to develop the histopathological features of Alzheimer’s disease (11–13). Thus, we can investigate the neurobiological parameters that are coupled to age- and menopause-related cognitive dysfunction in the absence of confounding factors inherent to pathology.Performance on DR is mediated in part by layer III neurons of the dorsolateral prefrontal cortex (dlPFC) Brodmann’s Area 46, which exhibit persistent spatially tuned firing during the delay period of the DR when the spatial position is held in working memory (1, 14). A recent electrophysiological study showed that firing of these “delay cells” in Brodmann’s Area 46 is markedly decreased in aged monkeys (15, 16). This loss of firing can be accounted for partly by structural changes that occur with aging and surgical menopause (ovariectomy), both of which are associated with a dramatic loss of the plastic, thin dendritic spines on dlPFC neurons (17, 18). Intriguingly, cyclic estradiol treatment improves cognitive function in aged ovariectomized monkeys while concurrently restoring these thin spines (18). There is evidence for postsynaptic structural and molecular features that promote working memory (15, 18–20), but the contribution of presynaptic morphology has received less attention.Because the recurrent firing necessary for working memory is highly energy demanding, the prefrontal cortex (PFC) contains more mitochondria than other cortical regions (15, 21). Mitochondria are very dynamic organelles that perform various metabolic functions, including energy production and facilitation of synaptic transmission (22, 23). In neurons, mitochondria are trafficked throughout the dendritic and axonal extents and are more stationary at synaptic sites, where energy demand is especially high (24, 25). Although postsynaptic dendritic spines lack mitochondria, many presynaptic boutons contain them and promote neurotransmission by accelerating recovery from synaptic depression following activity (26). In cell cultures across many tissue types, mitochondrial stressors such as rotenone (an inhibitor of mitochondrial complex I) or hypoxia can induce the formation of donut-shaped mitochondria, which generate increased levels of reactive oxygen species (ROS) (27, 28).Here, we tested the hypothesis that mitochondrial number and morphology (Fig. 1) in dlPFC presynaptic boutons of female rhesus monkeys are altered with aging and menopause and correlate with DR accuracy. In addition, we used a cyclic estradiol manipulation known to improve working memory to test whether mitochondrial morphology is regulated in relation to the treatment.Open in a separate windowFig. 1.3D reconstructions of serial electron micrographs displaying straight (A), curved (B), and donut-shaped (C) mitochondria within monkey dlPFC axonal boutons. (Scale bar: 1 μm.)