DNA,Flexibly Flexible

Investigators have constructed dsDNA molecules with several different base modifications and have characterized their bending and twisting flexibilities using atomic force microscopy, DNA ring closure, and single-molecule force spectroscopy with optical tweezers. The three methods provide persistence length measurements that agree semiquantitatively, and they show that the persistence length is surprisingly similar for all of the modified DNAs. The circular dichroism spectra of modified DNAs differ substantially. Simple explanations based on base stacking strength, polymer charge, or groove occupancy by functional groups cannot explain the results, which will guide further high-resolution theory and experiments.Real double-stranded DNA molecules differ from the idealized zero-Kelvin A, B, and Z forms. They can adopt deformed average conformations, as in bent A-tract DNA or protein-DNA complexes. The path of the DNA helix axis also varies due to thermal energy, so at very long lengths DNA behaves as a random coil. The term “long lengths” is relative to the persistence length P of the wormlike chain model. P is the average offset of the end of a chain along its initial direction, or alternatively the length over which the unit vectors μ1→ and μ2→ tangent to the helix axis lose colinearity according to〈μ1→⋅μ2→〉=〈cosθ〉=e−d12/P,where d₁₂ is the contour length from point 1 to point 2, as in Fig. 1. P can be measured by hydrodynamics (1), atomic force microscopy (AFM) (2), DNA ring closure (3) or protein-DNA looping (4), tethered particle microscopy (5), or single-molecule optical tweezers experiments (6). The long-range loss of memory of DNA direction grows out of local variations in the helix axis direction specified by roll, tilt, and twist angles that parameterize changes in the helix axis direction. For harmonic bending potentials, the bending persistence length is related to roll and tilt according toσroll2+σtilt2=2ℓ/P,where ℓ = 3.4 Å, so for P ∼ 50 nm (147 bp) the average standard deviations in the roll and tilt angles σ_roll and σ_tilt are ∼4.7°, although in real DNA, roll varies more than tilt. Similar relationships hold for twist flexibility (7).Open in a separate windowFigure 1The base modifications studied by Peters et al. (13,14) affect both Watson-Crick hydrogen bonding and groove occupancy. They used AFM, DNA ring closure, single-molecule force spectroscopy, and circular dichroism spectroscopy (not shown) to characterize the resulting changes in bending and twisting flexibility. DNA molecules are not shown to scale. To see this figure in color, go online.DNA flexibility can be studied at contour length scales from Ångstroms to microns. Flexibility at the atomic scale accessed by nuclear magnetic resonance, x-ray crystallography, cryo-electron microscopy, and molecular dynamics simulations (8) refers to many aspects of conformational variability. One active thread of research at this scale concerns interconversion among helical forms, base flipping, DNA kinking, changes in backbone torsion angles, and the sequence dependence of all of these local properties. Local fluctuations in the basepair roll, tilt, and twist angles do seem to predict the correct long-range behavior (9). A second thread asks whether the wormlike chain model holds at DNA lengths shorter than P (2,10); the active controversy concerning enhanced bendability at short lengths has recently been reviewed by Vologodskii and Frank-Kamenetskii (11). A third thread asks whether we can understand the underlying biophysical causes of long-range DNA flexibility. These presumably include base stacking, electrostatic repulsion along the backbone, changes in the counterion atmosphere (12), occupancy of the major and minor grooves by functional groups, conformational entropy, the strength of Watson-Crick hydrogen bonding, and water structure. Helical polymorphisms and the junctions between polymorphs presumably affect the sequence dependence of the persistence length.Peters et al. (13,14) have attempted to understand bending and twisting flexibility by characterizing a variety of modified nucleic acids using DNA ring closure, AFM, and optical tweezer methods, sketched in Fig. 1. In previous work (13), they used ring closure to show that major groove substituents that alter the charge on the polymer do not have substantial effects on the bending persistence length, and that the effects were not correlated in an obvious way to the stacking propensity of the modified bases. The work described in this issue of the Biophysical Journal (14) uses all three methods to demonstrate that DNA with 2-amino-adenosine (a.k.a., 2,6-diaminopurine) substituted for adenosine has an increased persistence length, whereas inosine substitution for guanosine reduces the persistence length, as would be expected if groove occupancy (or the number of Watson-Crick hydrogen bonds) affects flexibility. However, the authors did one experiment too many—when they measured the effects of the earlier major groove substituents (13) using AFM, the correlation with groove occupancy disappeared. This could be because changes in helical geometry, as evidenced by the circular dichroism spectroscopy also reported in the article, alter the grooves sufficiently to prevent a straightforward connection to flexibility.The magnitude of the effect of base modifications on P is the largest for the optical tweezers and the smallest for DNA ring closure, showing that no more than one of the experiments is perfect. The Supporting Material for both articles (13,14) offers valuable resources for the careful evaluation of experimental results and possible sources of error within and between experiments. For example, the DNA lengths and the ionic conditions required by the different methods differ. Ring closure results depend critically on the purity of the DNA and appropriate ligation conditions. Analysis of AFM results averaged several different statistical measures of decaying angular correlations and end-to-end distance, which did not individually always agree. In force spectroscopy there are variations in the bead attachment for each molecule, errors in the stretch modulus can affect the measured persistence length, force can induce DNA melting, and very few molecules can be observed. Rare kinking events proposed to explain enhanced bendability should affect the cyclization experiment most markedly; no evidence for enhanced flexibility was seen. Finally, Peters et al. (14) have observed that DNA twist and twisting flexibility seem to be more sensitive than the persistence length to base modifications.Taken as a whole, this extremely thorough series of experiments shows that we still do not understand the fundamental origins of the remarkable stiffness of double-stranded DNA. There may be compensating effects that make the dissection difficult. For example, changing the charge on the polymer may induce a corresponding adjustment in the counterion condensation atmosphere, leading to a relatively constant residual charge. Groove substituents that enhance basepair stability could enhance bendability for steric reasons. Stacking thermodynamics may not change very much for the very small bend angles at any individual basepair. Locally stiff regions may introduce nearby junctions that are flexible.The stiffness of DNA relative to other biopolymers inspired the development of DNA nanotechnology (although that field has adopted bridged synthetic constructs that are even more rigid). Further research on the biophysics, and specifically the long-range mechanical properties of DNA, will be essential as we build better models of DNA in the cell, which has evolved many proteins that act to increase apparent flexibility. The various aspects of DNA flexibility influence the protein-DNA complexes that mediate DNA’s informational role, the induction of and responses to supercoiling used for long-range communication among sites (15), and chromosome structure and genome organization.     

Concerted actions of DnaA complexes with DNA-unwinding sequences within and flanking replication origin oriC promote DnaB helicase loading

Unwinding of the replication origin and loading of DNA helicases underlie the initiation of chromosomal replication. In Escherichia coli, the minimal origin oriC contains a duplex unwinding element (DUE) region and three (Left, Middle, and Right) regions that bind the initiator protein DnaA. The Left/Right regions bear a set of DnaA-binding sequences, constituting the Left/Right-DnaA subcomplexes, while the Middle region has a single DnaA-binding site, which stimulates formation of the Left/Right-DnaA subcomplexes. In addition, a DUE-flanking AT-cluster element (TATTAAAAAGAA) is located just outside of the minimal oriC region. The Left-DnaA subcomplex promotes unwinding of the flanking DUE exposing TT[A/G]T(T) sequences that then bind to the Left-DnaA subcomplex, stabilizing the unwound state required for DnaB helicase loading. However, the role of the Right-DnaA subcomplex is largely unclear. Here, we show that DUE unwinding by both the Left/Right-DnaA subcomplexes, but not the Left-DnaA subcomplex only, was stimulated by a DUE-terminal subregion flanking the AT-cluster. Consistently, we found the Right-DnaA subcomplex–bound single-stranded DUE and AT-cluster regions. In addition, the Left/Right-DnaA subcomplexes bound DnaB helicase independently. For only the Left-DnaA subcomplex, we show the AT-cluster was crucial for DnaB loading. The role of unwound DNA binding of the Right-DnaA subcomplex was further supported by in vivo data. Taken together, we propose a model in which the Right-DnaA subcomplex dynamically interacts with the unwound DUE, assisting in DUE unwinding and efficient loading of DnaB helicases, while in the absence of the Right-DnaA subcomplex, the AT-cluster assists in those processes, supporting robustness of replication initiation.

The initiation of bacterial DNA replication requires local duplex unwinding of the chromosomal replication origin oriC, which is regulated by highly ordered initiation complexes. In Escherichia coli, the initiation complex contains oriC, the ATP-bound form of the DnaA initiator protein (ATP–DnaA), and the DNA-bending protein IHF (Fig. 1, A and B), which promotes local unwinding of oriC (1, 2, 3, 4). Upon this oriC unwinding, two hexamers of DnaB helicases are bidirectionally loaded onto the resultant single-stranded (ss) region with the help of the DnaC helicase loader (Fig. 1B), leading to bidirectional chromosomal replication (5, 6, 7, 8). However, the fundamental mechanism underlying oriC-dependent bidirectional DnaB loading remains elusive.Open in a separate windowFigure 1Schematic structures of oriC, DnaA, and the initiation complexes. A, the overall structure of oriC. The minimal oriC region and the AT-cluster region are indicated. The sequence of the AT-cluster−DUE (duplex-unwinding element) region is also shown below. The DUE region (DUE; pale orange bars) contains three 13-mer repeats: L-DUE, M-DUE, and R-DUE. DnaA-binding motifs in M/R-DUE, TT(A/G)T(T), are indicated by red characters. The AT-cluster region (AT cluster; brown bars) is flanked by DUE outside of the minimal oriC. The DnaA-oligomerization region (DOR) consists of three subregions called Left-, Middle-, and Right-DOR. B, model for replication initiation. DnaA is shown as light brown (for domain I–III) and darkbrown (for domain IV) polygons (right panel). ATP–DnaA forms head-to-tail oligomers on the Left- and Right-DORs (left panel). The Middle-DOR (R2 box)-bound DnaA interacts with DnaA bound to the Left/Right-DORs using domain I, but not domain III, stimulating DnaA assembly. IHF, shown as purple hexagons, bends DNA >160° and supports DUE unwinding by the DnaA complexes. M/R-DUE regions are efficiently unwound. Unwound DUE is recruited to the Left-DnaA subcomplex and mainly binds to R1/R5M-bound DnaA molecules. The sites of ssDUE-binding B/H-motifs V211 and R245 of R1/R5M-bound DnaA molecules are indicated (pink). Two DnaB homohexamer helicases (light green) are recruited and loaded onto the ssDUE regions with the help of the DnaC helicase loader (cyan). ss, single stranded.The minimal oriC region consists of the duplex unwinding element (DUE) and the DnaA oligomerization region (DOR), which contains specific arrays of 9-mer DnaA-binding sites (DnaA boxes) with the consensus sequence TTA[T/A]NCACA (Fig. 1A) (3, 4). The DUE underlies the local unwinding and contains 13-mer AT-rich sequence repeats named L-, M-, and R-DUE (9). The M/R-DUE region includes TT[A/G]T(A) sequences with specific affinity for DnaA (10). In addition, a DUE-flanking AT-cluster (TATTAAAAAGAA) region resides just outside of the minimal oriC (Fig. 1A) (11). The DOR is divided into three subregions, the Left-, Middle-, and Right-DORs, where DnaA forms structurally distinct subcomplexes (Fig. 1A) (8, 12, 13, 14, 15, 16, 17). The Left-DOR contains high-affinity DnaA box R1, low-affinity boxes R5M, τ1−2, and I1-2, and an IHF-binding region (17, 18, 19, 20). The τ1 and IHF-binding regions partly overlap (17).In the presence of IHF, ATP–DnaA molecules cooperatively bind to R1, R5M, τ2, and I1-2 boxes in the Left-DOR, generating the Left-DnaA subcomplex (Fig. 1B) (8, 17). Along with IHF causing sharp DNA bending, the Left-DnaA subcomplex plays a leading role in DUE unwinding and subsequent DnaB loading. The Middle-DOR contains moderate-affinity DnaA box R2. Binding of DnaA to this box stimulates DnaA assembly in the Left- and Right-DORs using interaction by DnaA N-terminal domain (Fig. 1B; also see below) (8, 12, 14, 16, 21). The Right-DOR contains five boxes (C3-R4 boxes) and cooperative binding of ATP–DnaA molecules to these generates the Right-DnaA subcomplex (Fig. 1B) (12, 18). This subcomplex is not essential for DUE unwinding and plays a supportive role in DnaB loading (8, 15, 17). The Left-DnaA subcomplex interacts with DnaB helicase, and the Right-DnaA subcomplex has been suggested to play a similar role (Fig. 1B) (8, 13, 16).In the presence of ATP–DnaA, M- and R-DUE adjacent to the Left-DOR are predominant sites for in vitro DUE unwinding: unwinding of L-DUE is less efficient than unwinding of the other two (Fig. 1B) (9, 22, 23). Deletion of L-DUE or the whole DUE inhibits replication of oriC in vitro moderately or completely, respectively (23). A chromosomal oriC Δ(AT-cluster−L-DUE) mutant with an intact DOR, as well as deletion of Right-DOR, exhibits limited inhibition of replication initiation, whereas the synthetic mutant combining the two deletions exhibits severe inhibition of cell growth (24). These studies suggest that AT-cluster−L-DUE regions stimulate replication initiation in a manner concerted with Right-DOR, although the underlying mechanisms remain elusive.DnaA consists of four functional domains (Fig. 1B) (4, 25). Domain I supports weak domain I–domain I interaction and serves as a hub for interaction with various proteins such as DnaB helicase and DiaA, which stimulates ATP–DnaA assembly at oriC (26, 27, 28, 29, 30). Two or three domain I molecules of the oriC–DnaA subcomplex bind a single DnaB hexamer, forming a stable higher-order complex (7). Domain II is a flexible linker (28, 31). Domain III contains AAA+ (ATPase associated with various cellular activities) motifs essential for ATP/ADP binding, ATP hydrolysis, and DnaA–DnaA interactions in addition to specific sites for ssDUE binding and a second, weak interaction with DnaB helicase (1, 4, 8, 10, 19, 25, 32, 33, 34, 35). Domain IV bears a helix-turn-helix motif with specific affinity for the DnaA box (36).As in typical AAA+ proteins, a head-to-tail interaction underlies formation of ATP–DnaA pentamers on the DOR, where the AAA+ arginine-finger motif Arg285 recognizes ATP bound to the adjacent DnaA protomer, promoting cooperative ATP–DnaA binding (Fig. 1B) (19, 32). DnaA ssDUE-binding H/B-motifs (Val211 and Arg245) in domain III sustain stable unwinding by directly binding to the T-rich (upper) strand sequences TT[A/G]T(A) within the unwound M/R-DUE (Fig. 1B) (8, 10). Val211 residue is included in the initiator-specific motif of the AAA+ protein family (10). For DUE unwinding, ssDUE is recruited to the Left-DnaA subcomplex via DNA bending by IHF and directly interacts with H/B-motifs of DnaA assembled on Left-DOR, resulting in stable DUE unwinding competent for DnaB helicase loading; in particular, DnaA protomers bound to R1 and R5M boxes play a crucial role in the interaction with M/R-ssDUE (Fig. 1B) (8, 10, 17). Collectively, these mechanisms are termed ssDUE recruitment (4, 17, 37).Two DnaB helicases are thought to be loaded onto the upper and lower strands of the region including the AT-cluster and DUE, with the aid of interactions with DnaC and DnaA (Fig. 1B) (25, 38, 39). DnaC binding modulates the closed ring structure of DnaB hexamer into an open spiral form for entry of ssDNA (40, 41, 42, 43). Upon ssDUE loading of DnaB, DnaC is released from DnaB in a manner stimulated by interactions with ssDNA and DnaG primase (44, 45). Also, the Left- and Right-DnaA subcomplexes, which are oriented opposite to each other, could regulate bidirectional loading of DnaB helicases onto the ssDUE (Fig. 1B) (7, 8, 35). Similarly, recent works suggest that the origin complex structure is bidirectionally organized in both archaea and eukaryotes (1, 46). In Saccharomyces cerevisiae, two origin recognition complexes containing AAA+ proteins bind to the replication origin region in opposite orientations; this, in turn, results in efficient loading of two replicative helicases, leading to head-to-head interactions in vitro (46). Consistent with this, origin recognition complex dimerization occurs in the origin region during the late M-G1 phase (47). The fundamental mechanism of bidirectional origin complexes might be widely conserved among species.In this study, we analyzed various mutants of oriC and DnaA in reconstituted systems to reveal the regulatory mechanisms underlying DUE unwinding and DnaB loading. The Right-DnaA subcomplex assisted in the unwinding of oriC, dependent upon an interaction with L-DUE, which is important for efficient loading of DnaB helicases. The AT-cluster region adjacent to the DUE promoted loading of DnaB helicase in the absence of the Right-DnaA subcomplex. Consistently, the ssDNA-binding activity of the Right-DnaA subcomplex sustained timely initiation of growing cells. These results indicate that DUE unwinding and efficient loading of DnaB helicases are sustained by concerted actions of the Left- and Right-DnaA subcomplexes. In addition, loading of DnaB helicases are sustained by multiple mechanisms that ensure robust replication initiation, although the complete mechanisms are required for precise timing of initiation during the cell cycle.     

Mutations in PBP2 from ceftriaxone-resistant Neisseria gonorrhoeae alter the dynamics of the β3–β4 loop to favor a low-affinity drug-binding state

Resistance to the extended-spectrum cephalosporin ceftriaxone in the pathogenic bacteria Neisseria gonorrhoeae is conferred by mutations in penicillin-binding protein 2 (PBP2), the lethal target of the antibiotic, but how these mutations exert their effect at the molecular level is unclear. Using solution NMR, X-ray crystallography, and isothermal titration calorimetry, we report that WT PBP2 exchanges dynamically between a low-affinity state with an extended β3–β4 loop conformation and a high-affinity state with an inward β3–β4 loop conformation. Histidine-514, which is located at the boundary of the β4 strand, plays an important role during the exchange between these two conformational states. We also find that mutations present in PBP2 from H041, a ceftriaxone-resistant strain of N. gonorrhoeae, increase resistance to ceftriaxone by destabilizing the inward β3–β4 loop conformation or stabilizing the extended β3–β4 loop conformation to favor the low-affinity drug-binding state. These observations reveal a unique mechanism for ceftriaxone resistance, whereby mutations in PBP2 lower the proportion of target molecules in the high-affinity drug-binding state and thus reduce inhibition at lower drug concentrations.Keywords: PBP2, Neisseria gonorrhoeae, beta-lactam, conformational dynamics, antibiotic resistance

Neisseria gonorrhoeae is the causative agent of the sexually transmitted infection gonorrhea, with nearly 80 million cases worldwide each year (1). Without antibiotic treatment, infections persist as a chronic disease and can cause serious sequelae, including pelvic inflammatory disease, infertility, arthritis, and disseminated infections (2). For many years, N. gonorrhoeae was treated with a single dose of penicillin, and more recently, ceftriaxone. In 2012, the emergence of several high-level ceftriaxone-resistant strains led the Centers for Disease Control and Prevention to change its recommended treatment for gonorrhea from monotherapy to dual therapy with ceftriaxone and azithromycin (3, 4, 5). However, treatment failures have been reported for both agents, and in 2018, a strain with high-level resistance to both ceftriaxone and azithromycin was identified (6, 7). Concern about azithromycin resistance led the Centers for Disease Control and Prevention recently to drop the recommendation of dual therapy in favor of an increased dose (500 mg) of ceftriaxone alone (8). Both penicillin and ceftriaxone inhibit cell wall biosynthesis in N. gonorrhoeae by targeting penicillin-binding protein 2 (PBP2).PBP2 is an essential peptidoglycan transpeptidase (TPase) that crosslinks the peptide chains from adjacent peptidoglycan strands during cell-wall synthesis (9). β-lactam antibiotics, including the extended-spectrum cephalosporin (ESC) ceftriaxone, are analogs of the d-Ala-d-Ala C terminus of the peptidoglycan substrate and as such target PBP2 by binding to and reacting with the active-site serine nucleophile (Ser310 in N. gonorrhoeae PBP2) to form a covalently acylated complex (10, 11). The acylation reaction (Equation 1) proceeds first through formation of a noncovalent complex with the β-lactam (defined by the equilibrium constant, Ks), which is then attacked by the serine nucleophile to form a covalent acyl-enzyme complex (k₂). For PBPs, hydrolysis of the acyl-enzyme (k₃) is very slow compared with its formation, and the enzyme is essentially irreversibly inactivated. The acylation of PBPs by β-lactam antibiotics is therefore defined by a second-order rate constant, k₂/Ks (M⁻¹ s⁻¹), which reflects both the noncovalent binding affinity (Ks) and the first-order acylation rate (k₂):E+S↔KsE⋅S→k2E−S→k3E+P(1)The emergence of resistance to penicillin and ceftriaxone in N. gonorrhoeae occurs primarily via the acquisition of mutant alleles of the penA gene encoding PBP2 (12). These alleles are referred to as mosaic because they arise through multiple homologous recombination events with DNA released by commensal Neisseria species. PBP2 from the high-level ceftriaxone-resistant strain, H041, contains 61 mutations compared with PBP2 from the antibiotic-susceptible strain, FA19 (13, 14). Determining how these mutations lower the k₂/Ks of ceftriaxone for PBP2 by over 10,000-fold while still preserving essential TPase activity is fundamental for understanding the evolution of antibiotic resistance.Toward this goal, we have identified a subset of these mutations that, when incorporated into the penA gene from FA19, confer ∼80% of the increase in minimum inhibitory concentration for ceftriaxone relative to that of the penA gene from H041 (penA41) (15, 16). We recently reported the structures of apo and ceftriaxone-acylated PBP2 at high resolution and have detailed conformational changes in β3 and the β3–β4 loop involved in antibiotic binding and acylation (17). Intriguingly, although present in the active site region, most of the mutations conferring resistance are not in direct contact with ceftriaxone in the crystal structure of acylated PBP2 (17, 18). We have proposed that these mutations alter the binding and acylation kinetics of PBP2 with ceftriaxone by restricting protein dynamics (18).To understand further the structural and biochemical mechanisms by which these mutations lower the acylation rates of β-lactam antibiotics, we utilized a combination of solution ¹⁹F NMR, X-ray crystallography, and biochemical approaches to investigate PBP2. We report that the β3–β4 loop in the TPase domain of WT PBP2, which is known to adopt markedly different conformations in the apo versus acylated crystal structures (17), samples two major conformational states in solution. Substitutions of WT PBP2 residues with mutations in H041 that confer ceftriaxone resistance alter the conformational landscape of PBP2 by destabilizing the high-affinity state containing the inward conformation of the β3–β4 loop and stabilizing a low-affinity conformation containing an extended β3–β4 loop conformation, thereby restricting access to the inward conformation required for high-affinity drug binding. Our combined solution NMR and crystallographic analyses of PBP2 and its preacylation drug complexes further support the notion that mutations in PBP2 from ceftriaxone-resistant strains of N. gonorrhoeae confer antibiotic resistance by hindering conformational changes required to form a productive drug-binding state (18).     

Indolicidin Binding Induces Thinning of a Lipid Bilayer

We use all-atom molecular dynamics simulations on a massive scale to compute the standard binding free energy of the 13-residue antimicrobial peptide indolicidin to a lipid bilayer. The analysis of statistical convergence reveals systematic sampling errors that correlate with reorganization of the bilayer on the microsecond timescale and persist throughout a total of 1.4 ms of sampling. Consistent with experimental observations, indolicidin induces membrane thinning, although the simulations significantly overestimate the lipophilicity of the peptide.Antimicrobial peptides are a component of the innate immune system of eukaryotes (1). As such, they must interact with pathogenic membranes, either during translocation or by disrupting their structural integrity (2). Here we examine the binding of the 13-residue cationic antimicrobial peptide indolicidin (3) (ILPWKWPWWPWRR-NH₂) to a lipid membrane as a first step towards elucidating its mechanism of action.Molecular solutes interact with lipid membranes in many cellular processes (4). Computational approaches such as molecular dynamics simulations have been widely used to characterize these interactions (5). However, molecular dynamics simulations can require unfeasibly long times to reach equilibrium (6). Therefore, it is common to compute equilibrium properties of solute insertion into lipid bilayers using umbrella sampling (7) simulations in which the solute is restrained along the bilayer normal using harmonic restraining potentials, or umbrellas, centered at zi0 values distributed between bulk water and the bilayer center.It is often assumed that equilibrium properties rapidly attain convergence in umbrella sampling simulations; accordingly, convergence measures are rarely published (8). However, we have recently shown that umbrella sampling simulations require up to 100 ns per umbrella (3 μs in total) to eliminate systematic sampling errors in the standard free energy of binding, ΔGbind0, of an arginine side-chain analog from bulk water to a lipid bilayer (8). The fact that umbrella sampling has been used to investigate the bilayer insertion of substantially larger solutes (9) motivates a systematic evaluation of statistical sampling convergence of ΔGbind0 for indolicidin in a lipid bilayer.To estimate ΔGbind0 of indolicidin to a lipid bilayer, we conducted 60 sets of umbrella-sampling simulations while systematically varying the initial conformation. In each umbrella sampling simulation, each umbrella was simulated for 1.5 μs, yielding a total simulation time of 1.4 ms and 60 independent free energy or potential of mean force (PMF) profiles from bulk water to the center of a POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine) lipid bilayer.The PMF profiles indicate that indolicidin strongly binds to the bilayer, partitioning inside the lipid headgroups (Fig. 1, A and E). Importantly, the mean estimate of ΔGbind0 decays exponentially with equilibration time t_eq, indicating that systematic sampling errors in individual simulations continued to decrease throughout the 1.5-μs interval as rare events led to more favorable states (Fig. 1 B). The low frequency of transitions to more favorable states exacerbates the requirement for massive sampling using multiple independent simulations.Open in a separate windowFigure 1PMF for indolicidin partitioning into a POPC bilayer. (A) Average PMF from 60 independent umbrella-sampling simulations based on 1 < t ≤ 1.5 μs/umbrella. (B) Average ΔGbind0 from 50-ns time intervals per umbrella (t_eq < t ≤ t_eq+50 ns) as a function of equilibration time, t_eq. (Solid line) Single exponential fit to the mean over 0.5 < t_eq ≤ 1.5 μs. (C) Mean values of ΔGbind0 from the 10-μs/umbrella simulations (crosses) together with the mean values of ΔGbind0 (triangles) and exponential fit from panel B. PMF and ΔGbind0 profiles obtained from each of the 60 independent simulations are shown in Fig. S1 in the Supporting Material. (D–F) Representative conformations after 1.5 μs of simulation at zi0 = (D) 3 nm, (E) 1.2 nm, and (F) 0.0 nm. To see this figure in color, go online.Computational limitations precluded extending all 60 sets of umbrella-sampling simulations to even longer times. Instead, we identified the two simulations at each umbrella that appeared to be most representative of equilibrium and extended each to 10 μs per umbrella (see Methods in the Supporting Material). The resulting estimates of ΔGbind0 continued to decrease until t_eq = 4 μs (68 μs in total), after which they stabilized at the asymptotic limit of the exponential fit of the shorter simulation data (ΔGbind0 = −26 ± 5 kcal/mol; Fig. 1 C).As indolicidin approaches the bilayer, it is drawn closer (Fig. 2 A) as salt bridges form between the peptide and the phospholipid headgroups (Fig. 2 B), inducing their protrusion (Figs. 1 D and ​and22 C). At large separation distances, this state is attained only when the peptide becomes highly extended (Fig. 2, D and E). As indolicidin is inserted more deeply, the surface of the lipid bilayer invaginates (Figs. 1 E and ​and22 C), maintaining peptide-lipid salt bridges (Fig. 2 B) and leading to the formation of a pore when the solute is near the bilayer center (Figs. 1 F and ​and22 C, and see Fig. S2, Fig. S3, Fig. S4, and Fig. S5 in the Supporting Material). These Boltzmann-weighted ensemble averages may not be mechanistically representative of nonequilibrium binding events (8,10).Open in a separate windowFigure 2Slow equilibration of bilayer and peptide. (A–D) Color quantifies conformational reorganization for t_eq < t ≤ t_eq + 100 ns as a function of t_eq and |zi0|. (A) Deviation of insertion depth, z, from zi0, Δz ≡ z − zi0; (B) number of peptide-lipid salt bridges, N_SB; (C) volume change of the bilayer’s proximal leaflet in the radial vicinity of the solute, V_ε; and (D) peptide end-to-end distance (EED). There is no sampling for t > 0.5 μs at |zi0| ≥ 4.5 nm. (E) Representative time-series of a trajectory at zi0 = 3.9 nm. (F) Representative conformation at 10 μs for |zi0| = 1.2 nm. To see this figure in color, go online.The reorganization of the peptide, the bilayer, and the ionic interactions between them became more pronounced with increasing simulation time at peptide insertion depths shallower than the global free energy minimum (|zi0| >1.4 nm; Fig. 1 A and Fig. 2, B–D). These conformational transitions are likely the source of the systematic drift of ΔGbind0. Reorganization of the bilayer also controls the rate of equilibration during membrane insertion of an arginine side chain (8,9) and a cyclic arginine nonamer (11), suggesting that slow reorganization of lipids around cationic solutes presents a general impediment to simulation convergence.Consistent with the perturbation of membrane thickness observed by in situ atomic force microscopy (12), our results suggest that indolicidin insertion induces local thinning of the bilayer (Fig. 1, E and F, and Fig. 2, C and F). The different conformational ensembles sampled by the peptide in water and in the lipid bilayer (Fig. 2 D) are consistent with the observations that indolicidin is disordered in solution (13) and adopts stable conformations in the presence of detergent (14). Although the peptide’s conformation continued to change when it was deeply inserted (Fig. 2 D), the amount of water in the bilayer’s hydrophobic core converged relatively rapidly (see Fig. S2). Indolicidin can induce the formation of hydrated, porelike defects (see Fig. S2, Fig. S3, Fig. S4, and Fig. S5) but does not act as a chloride carrier (see Fig. S6 and Fig. S7). Future studies of the mechanism of indolicidin action will examine the effect of multiple peptide binding.The PMF profile presented in this Letter is strikingly different from that computed by Yeh et al. (15) using different force field parameters for indolicidin partitioning into a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphatidylcholine) bilayer, from which the binding free energy was estimated to be 0 kcal/mol (15). However, that study comprised only 25 ns per umbrella and likely suffers from systematic sampling errors induced by initial conditions (see Fig. S8).Our estimate of the binding affinity is much larger than the values obtained for indolicidin and large unilamellar POPC vesicles using isothermal titration calorimetry, −7.4 kcal/mol (16), and equilibrium dialysis, −8.8 kcal/mol (13). Such a discrepancy suggests that the relative accuracy of binding free energies for amino-acid side-chain analogs (8,9,17) does not necessarily extend to polypeptides. Although more work is needed to elucidate the source of this discrepancy, this study underlines the importance of attaining convergence before evaluating force-field accuracy.Importantly, this work also highlights the extensive sampling required to remove systematic errors induced by initial conditions in atomistic simulations of peptides in membranes. Slow equilibration of the system is due to rare transitions across hidden free energy barriers involving reorganization of the membrane. Two simple recommendations are 1), evaluating the time-dependence of ensemble averages, and 2), conducting multiple simulations with different initial conditions. We have recently shown that by using enhanced sampling techniques it is possible to identify the locations of hidden free energy barriers without a priori knowledge (9). Future research will examine strategies for speeding up the crossing of these barriers, such as optimized order parameters including bilayer reorganization and enhanced sampling techniques including a random walk along the order parameter (9).     

Phenotypic Plasticity in Photosynthetic Temperature Acclimation among Crop Species with Different Cold Tolerances

While interspecific variation in the temperature response of photosynthesis is well documented, the underlying physiological mechanisms remain unknown. Moreover, mechanisms related to species-dependent differences in photosynthetic temperature acclimation are unclear. We compared photosynthetic temperature acclimation in 11 crop species differing in their cold tolerance, which were grown at 15°C or 30°C. Cold-tolerant species exhibited a large decrease in optimum temperature for the photosynthetic rate at 360 μL L⁻¹ CO₂ concentration [Opt (A₃₆₀)] when growth temperature decreased from 30°C to 15°C, whereas cold-sensitive species were less plastic in Opt (A₃₆₀). Analysis using the C₃ photosynthesis model shows that the limiting step of A₃₆₀ at the optimum temperature differed between cold-tolerant and cold-sensitive species; ribulose 1,5-bisphosphate carboxylation rate was limiting in cold-tolerant species, while ribulose 1,5-bisphosphate regeneration rate was limiting in cold-sensitive species. Alterations in parameters related to photosynthetic temperature acclimation, including the limiting step of A₃₆₀, leaf nitrogen, and Rubisco contents, were more plastic to growth temperature in cold-tolerant species than in cold-sensitive species. These plastic alterations contributed to the noted growth temperature-dependent changes in Opt (A₃₆₀) in cold-tolerant species. Consequently, cold-tolerant species were able to maintain high A₃₆₀ at 15°C or 30°C, whereas cold-sensitive species were not. We conclude that differences in the plasticity of photosynthetic parameters with respect to growth temperature were responsible for the noted interspecific differences in photosynthetic temperature acclimation between cold-tolerant and cold-sensitive species.The temperature dependence of leaf photosynthetic rate shows considerable variation between plant species and with growth temperature (Berry and Björkman, 1980; Cunningham and Read, 2002; Hikosaka et al., 2006). Plants native to low-temperature environments and those grown at low temperatures generally exhibit higher photosynthetic rates at low temperatures and lower optimum temperatures, compared with plants native to high-temperature environments and those grown at high temperatures (Mooney and Billings, 1961; Slatyer, 1977; Berry and Björkman, 1980; Sage, 2002; Salvucci and Crafts-Brandner, 2004b). For example, the optimum temperature for photosynthesis differs between temperate evergreen species and tropical evergreen species (Hill et al., 1988; Read, 1990; Cunningham and Read, 2002). Such differences have been observed even among ecotypes of the same species (Björkman et al., 1975; Pearcy, 1977; Slatyer, 1977).Temperature dependence of the photosynthetic rate has been analyzed using the biochemical model proposed by Farquhar et al. (1980). This model assumes that the photosynthetic rate (A) is limited by either ribulose 1,5-bisphosphate (RuBP) carboxylation (A_c) or RuBP regeneration (A_r). The optimum temperature for photosynthetic rate in C₃ plants is thus potentially determined by (1) the temperature dependence of A_c, (2) the temperature dependence of A_r, or (3) both, at the colimitation point of A_c and A_r (Fig. 1; Farquhar and von Caemmerer, 1982; Hikosaka et al., 2006).Open in a separate windowFigure 1.A scheme illustrating the shift in the optimum temperature for photosynthesis depending on growth temperature. Based on the C₃ photosynthesis model, the A₃₆₀ (white and black circles) is limited by A_c (solid line) or A_r (broken line). The optimum temperature for the photosynthetic rate is potentially determined by temperature dependence of A_c (A), temperature dependence of A_r (B), or the intersection of the temperature dependences of A_c and A_r (C). When the optimum temperature for the photosynthetic rate shifts to a higher temperature, there are also three possibilities determining the optimum temperature: temperature dependence of A_c (D), temperature dependence of A_r (E), or the intersection of the temperature dependences of A_c and A_r (F). Especially in the case that the optimum temperature is determined by the intersection of the temperature dependences of A_c and A_r, the optimum temperature can shift by changes in the balance between A_c and A_r even when the optimum temperatures for these two partial reactions do not change.In many cases, the photosynthetic rate around the optimum temperature is limited by A_c, and thus the temperature dependence of A_c determines the optimum temperature for the photosynthetic rate (Hikosaka et al., 1999, 2006; Yamori et al., 2005, 2006a, 2006b, 2008; Sage and Kubien, 2007; Sage et al., 2008). As the temperature increases above the optimum, A_c is decreased by increases in photorespiration (Berry and Björkman, 1980; Jordan and Ogren, 1984; von Caemmerer, 2000). Furthermore, it has been suggested that the heat-induced deactivation of Rubisco is involved in the decrease in A_c at high temperature (Law and Crafts-Brandner, 1999; Crafts-Brandner and Salvucci, 2000; Salvucci and Crafts-Brandner, 2004a; Yamori et al., 2006b). Numerous previous studies have shown changes in the temperature dependence of A_c with growth temperature (Hikosaka et al., 1999; Bunce, 2000; Yamori et al., 2005). Also, the temperature sensitivity of Rubisco deactivation may differ between plant species (Salvucci and Crafts-Brandner, 2004b) and with growth temperature (Yamori et al., 2006b), which may explain variation in the optimum temperature for photosynthesis (Fig. 1, A and D).A_r is more responsive to temperature than A_c and often limits photosynthesis at low temperatures (Hikosaka et al., 1999, 2006; Sage and Kubien, 2007; Sage et al., 2008). Recently, several researchers indicated that A_r limits the photosynthetic rate at high temperature (Schrader et al., 2004; Wise et al., 2004; Cen and Sage, 2005; Makino and Sage, 2007). They suggested that the deactivation of Rubisco at high temperatures is not the cause of decreased A_c but a result of limitation by A_r. However, it remains unclear whether limitation by A_r is involved in the variation in the optimum temperature for the photosynthetic rate (Fig. 1, B and E).A shift in the optimum temperature for photosynthesis can result from changes in the balance between A_r and A_c, even when the optimum temperatures for these two partial reactions do not change (Fig. 1, C and F; Farquhar and von Caemmerer, 1982). The balance between A_r and A_c has been shown to change depending on growth temperature (Hikosaka et al., 1999; Hikosaka, 2005; Onoda et al., 2005a; Yamori et al., 2005) and often brings about a shift in the colimitation temperature of A_r and A_c. Furthermore, recent studies have shown that plasticity in this balance differs among species or ecotypes (Onoda et al., 2005b; Atkin et al., 2006; Ishikawa et al., 2007). Plasticity in this balance could explain interspecific variation in the plasticity of photosynthetic temperature dependence (Farquhar and von Caemmerer, 1982; Hikosaka et al., 2006), although there has been no evidence in the previous studies that the optimum temperature for photosynthesis occurs at the colimitation point of A_r and A_c.Temperature tolerance differs between species and, with growth temperature, even within species from the same functional group (Long and Woodward, 1989). Bunce (2000) indicated that the temperature dependences of A_r and A_c to growth temperature were different between species from cool and warm climates and that the balance between A_r and A_c was independent of growth temperature for a given plant species. However, it was not clarified what limited the photosynthetic rate or what parameters were important in temperature acclimation of photosynthesis. Recently, we reported that the extent of temperature homeostasis of leaf respiration and photosynthesis, which is assessed as a ratio of rates measured at their respective growth temperatures, differed depending on the extent of the cold tolerance of the species (Yamori et al., 2009b). Therefore, comparisons of several species with different cold tolerances would provide a new insight into interspecific variation of photosynthetic temperature acclimation and their underlying mechanisms. In this study, we selected 11 herbaceous crop species that differ in their cold tolerance (Yamori et al., 2009b) and grew them at two contrasting temperatures, conducting gas-exchange analyses based on the C₃ photosynthesis model (Farquhar et al., 1980). Based on these results, we addressed the following key questions. (1) Does the plasticity in photosynthetic temperature acclimation differ between cold-sensitive and cold-tolerant species? (2) Does the limiting step of photosynthesis at several leaf temperatures differ between plant species and with growth temperature? (3) What determines the optimum temperature for the photosynthetic rate among A_c, A_r, and the intersection of the temperature dependences of A_c and A_r?     

Type-3 BRET,an Improved Competition-Based Bioluminescence Resonance Energy Transfer Assay

The heart adjusts its power output to meet specific physiological needs through the coordination of several mechanisms, including force-induced changes in contractility of the molecular motor, the β-cardiac myosin (βCM). Despite its importance in driving and regulating cardiac power output, the effect of force on the contractility of a single βCM has not been measured. Using single molecule optical-trapping techniques, we found that βCM has a two-step working stroke. Forces that resist the power stroke slow the myosin-driven contraction by slowing the rate of ADP release, which is the kinetic step that limits fiber shortening. The kinetic properties of βCM are affected by load, suggesting that the properties of myosin contribute to the force-velocity relationship in intact muscle and play an important role in the regulation of cardiac power output.The cardiac cycle is a tightly regulated process in which the heart generates power during systole and relaxes during diastole. Appropriate power must be generated to effectively pump blood against cardiac afterload. Dysfunction of this cycle has devastating consequences for affected individuals.Cardiac power output is regulated by several feedback mechanisms (e.g., neuronal, hormonal, mechanical) that ultimately lead to changes in the force and power output of the molecular motor, β-cardiac myosin (βCM). In isolated cardiac fibers and cardiomyocytes, loading the muscle during systole slows contraction and alters power output. It is widely believed that this slowing is partially due to force-induced inhibition of myosin ATPase kinetics, similar to the Fenn Effect in skeletal muscle. However, this hypothesis has not been directly tested at the molecular level. Much of our contemporary view of how power is generated in cardiac muscle is due to in vivo and isolated muscle-fiber studies (1). Substantial progress has been made in understanding the actomyosin interactions required for power generation, but resolving the molecular effects of mechanical load on the ATPase properties of βCM in intact muscle has been challenging. Nevertheless, determining the biophysical parameters that define βCM contractility is key to understanding cardiac regulation and the etiology of several muscle diseases (1).In vitro assays using isolated contractile proteins have been central to advancing our understanding of contractility, although most experiments have been conducted at low resisting loads that do not mimic working conditions. Elegant optical trapping experiments have imposed loads on small ensembles of murine α-cardiac myosin at subsaturating [ATP] (2), and these experiments suggest that force slows α-cardiac myosin kinetics. The kinetic properties of α-cardiac myosin are substantially different from βCM, the primary isoform in the adult human myocardium (3). Thus, experiments using βCM must be performed to determine the unitary force-dependent kinetic parameters of this key molecular motor. We used optical trapping to measure the working-stroke displacement and force dependence of actin-detachment kinetics of single porcine βCM molecules at saturating ATP concentrations. These experiments allow direct measurement of the force-velocity relationship for single βCM molecules and reveal the mechanism of how loads regulate βCM-driven power output.Using the three-bead geometry (4) in which an actin filament is strung between two optically-trapped beads and then lowered over a bead that is sparsely coated with purified full-length porcine ventricular βCM, interactions between single βCM molecules and actin were recorded at 10 μM ATP (Fig. 1 A) (5, 6). Ensemble averages of these interactions were constructed to determine the size and kinetics of the working stroke (7, 8). βCM has an average displacement (6.8 ± 0.04 nm) that is similar to previously characterized muscle myosins (9, 10). Similar to skeletal muscle myosin (10), ensemble averages clearly show that the βCM working stroke is composed of two substeps with average displacements of 4.7 ± 0.05 nm and 1.9 ± 0.05 nm (Fig. 1 B). A single exponential function was fit to the rising-phase of the time-forward ensemble averages, yielding a rate (74 ± 2 s⁻¹) for the transition from state 1 to state 2 (Fig. 1 C). This rate is similar to the biochemical rate of ADP release measured for βCM (64 s⁻¹) (3), indicating that this structural transition is associated with the release of ADP. The rate of the rising phase of the time-reversed ensemble averages (22 ± 0.7 s⁻¹) reports the rate of exit from state 2 and is consistent with the biochemical rate of ATP binding and actomyosin detachment at 10 μM ATP (16 s⁻¹) (3) (Fig. 1 C).Open in a separate windowFigure 1(A) Representative data trace showing actomyosin displacements generated by βCM at 10 μM ATP. (Blue lines) Individual binding events. (B) Ensemble averages of the βCM working stroke generated from averaging 1295 binding interactions collected at 10 μM ATP. Single exponential functions were fit to the data (red lines) and the reported errors are the standard errors from the fit. (C) Cartoon showing an idealized actomyosin interaction with the corresponding mechanical and biochemical states. To see this figure in color, go online.To examine actomyosin detachment kinetics under working conditions, a positional feedback optical clamp was used to apply a dynamic load to the myosin, keeping the myosin at an isometric position during its working stroke (11). We measured the effect of force on the actin-attachment duration at 4 mM Mg.ATP to ensure that the rate of ATP binding is not rate-limiting for detachment. Increases in attachment durations are observed as the force on the myosin is increased (Fig. 2 A, inset). Assuming a two-state model (12), we expect the attachment durations to be exponentially distributed at each force with the force-dependent actin detachment rate, k(F), given by (13)k(F)=k0∗e−F·ddetkB∗T,(1)where k₀ is the rate of the primary force-sensitive transition in the absence of force, F is the force on the myosin, d_det is the distance to the transition state (a measurement of force sensitivity), k_B is Boltzmann’s constant, and T is the temperature. Maximum likelihood estimation (MLE) fitting Eq. 1 to the data yields a detachment rate (k₀ = 71 (−1.0/+0.8 s⁻¹)) that is similar to the rate of ADP release measured for βCM (64 s⁻¹) (3) and the rate of the time-forward ensemble averages (74 ± 2 s⁻¹). Thus, the ADP release step (and the accompanying state-1 to state-2 mechanical transition) is force-sensitive (d_det = 0.97 (−0.014/+0.011) nm). The value of d_det indicates that the ADP release step slows with increasing force, but less than some other characterized myosins (14). Using the values determined from the MLE fitting and the measured size of the working stroke, it is possible to calculate a force-velocity relationship for βCM, assuming the rate of ADP release limits actin motility (Fig. 2 A).Open in a separate windowFigure 2(A, Inset) Single molecule actomyosin interactions were collected in the presence of the isometric optical clamp. The scatter plot shows 262 binding events. Attachment durations are exponentially distributed at each force. (A) The detachment rate as a function of force as determined by MLE fitting. (Black line) Best fit; (small gray shaded area) 95% confidence interval. (Right axis) Velocity, calculated by multiplying the displacement of the working stroke by the detachment rate. (B) The calculated mean detachment rate as a function of force. Attachment durations were binned according to the average force experienced by the myosin during the binding event. Error bars were calculated via bootstrapping simulations of each force bin. (Blue line) Expected mean detachment rate based on the MLE fitting and the limited temporal resolution of our experiment (see the Supporting Material for details). (C) Proposed model for how force slows shortening velocity. Force inhibits the mechanical transition associated with ADP release, slowing the rate of actomyosin detachment. To see this figure in color, go online.The MLE fitting of Eq. 1 assumes an exponential distribution of attachment durations at every force. As such, the MLE fitting of the raw data should yield correct values of the parameters k₀ and d_det, despite limitations of the temporal resolution of our experiment (see Supporting Material for detailed discussion of MLE fitting). Frequently, groups report the mean attachment duration as a function of force. However, the mean attachment duration at each force will be overestimated because some shorter binding events cannot be resolved. We provide a method for calculating the expected mean detachment rate based on the parameters determined from the MLE fitting, given the limited temporal resolution of the experiment, and verify the robustness of the MLE fitting (see the Supporting Material). For demonstration purposes only, Fig. 2 B shows that the measured mean detachment rate agrees well with the expected mean detachment rate based on the MLE fitting and the temporal resolution of the experiment. It should be emphasized that the relevant dissociation values are obtained from the MLE fitting in Fig. 2 A (see also Figs. S1–S3).Our data demonstrate that at saturating [ATP], the detachment rate is limited by the ADP release step, which is the same transition that limits fiber shortening velocity (15). We propose that resisting loads slow ADP release and actin detachment by slowing the mechanical transition that accompanies ADP release (Fig. 2 C), thereby reducing the shortening velocity of muscle fibers. Thus, our data demonstrate that the intrinsic force-dependent properties of βCM contribute to the force-velocity relationship in the heart. It is important to note that our proposed mechanism does not rule out additional mechanisms by which force could directly modulate the activity of actomyosin such as force-induced reversal of the power stroke (11) or population of branched pathways (16, 17).Are the loads in our experiments physiologically relevant to contracting muscle? Modeling of the force per cross-bridge generated in isometric soleus muscle, which contains the βCM isoform, suggests a load of 2–4 pN per myosin (18). At these loads, we expect actin-detachment to slow up to threefold. Interestingly, βCM is substantially less force-sensitive than smooth muscle myosin (d_det = 2.7), suggesting that βCM can generate more power (the product of force and velocity) under load.In conclusion, our data show that cardiac power output can be directly modulated by force at the level of single myosin molecules. These data will enable the comparison of how molecular changes, such as light-chain phosphorylation, pharmacological treatments, or mutations associated with cardiomyopathies, affect the ability of the myosin to generate power against the afterload.     

Quantitative Analysis of Three-Dimensional Fluorescence Localization Microscopy Data

Identifying the three-dimensional molecular organization of subcellular organelles in intact cells has been challenging to date. Here we present an analysis approach for three-dimensional localization microscopy that can not only identify subcellular objects below the diffraction limit but also quantify their shape and volume. This approach is particularly useful to map the topography of the plasma membrane and measure protein distribution within an undulating membrane.Single molecule localization microscopy (SMLM) (1–3) is a superresolution fluorescence microscopy technique that produces coordinate data for single molecule localizations with a precision of tens of nanometers in live and fixed cells. These methods have mainly been performed with total internal reflectance fluorescence microscopy and therefore have generated two-dimensional molecular coordinates. Such two-dimensional data sets have revealed nanosized clusters of membrane proteins at the cell surface (4–7). This was achieved with analysis routines based on pair-correlation analysis (8), Ripley’s K function (9), and related techniques. While three-dimensional localization microscopy techniques such as biplane imaging (10), astigmatic spot analysis (11), and depth-encoding point-spread functions (12) have now been developed, quantitative analysis approaches of three-dimensional coordinate patterns have not.Here, we describe an approach based on Getis and Franklin''s local point pattern analysis to quantitatively analyze three-dimensional subcellular structures and map plasma membrane topography. The latter can also be used to account for topography-induced clustering of membrane proteins in an undulating membrane. To illustrate the approach, we generated three-dimensional SMLM data of the membrane dye DiI and the protein Linker for Activation of T cells (LAT) fused to the photoswitchable fluorescent protein mEos2 in T cells. It has been previously shown that LAT resides within the plasma membrane as well as membrane-proximal vesicles (5,13). The data were acquired using the biplane SMLM technique and highly inclined and laminated optical sheet illumination (14). Three-dimensional molecular coordinates were calculated by fitting a three-dimensional theoretical point-spread-function to the acquired data.As previously described for two-dimensional SMLM data analysis (5), Ripley’s K-function is calculated according to Eq. 1 where V is the analyzed volume, n is the total number of points, and r is the radius of a sphere (a circle for the two-dimensional case) centered on each point. The value K(r) is thus a measure of how many points are encircled within a sphere of radius r:K(r)=V∑i=1n∑j=1n(δij/n2);δij=1ifd(pointi,pointj)<r,0else.(1)For completely spatially random (CSR) data, K(r) scales with the volume of the sphere. We therefore linearize the K-function such that it scales with radius (the L-function) using:L(r)=(3K(r)4π)1/3.(2)The value of L(r)−r is then zero for the CSR case. Values of L(r)−r above zero indicate clustering at the length scale, r.Next we used the related Getis and Franklin''s local point pattern analysis to generate a clustering value (L(r) at r = 50 nm; L(50)) for each point, j, based on the local three-dimensional molecular density. This was calculated using:Lj(50)=((3V4π)∑i=1n(δijn))1/3;δij=1ifd(pointi,pointj)<50,0else.(3)These values can then be interpolated such that every voxel in a volume is assigned a cluster value based on the number of encircled points, relative to the expected CSR case. This allows construction of isosurfaces where all points on the surface have an identical L(50) value. A high threshold imparts a strict criterion for cluster detection compared to a lower one, and this allows users to, for example, determine the efficiency of sequestration into clusters by quantifying the cluster number and size as a function of the threshold.To illustrate the identification of subcellular structures, Lat-mEos2 was imaged by three-dimensional SMLM in activated T cells at the immunological synapse (Fig. 1 A). Three-dimensional projections of isosurfaces (for L(50) = 200) clearly identified intracellular LAT vesicles at varying depths within the synapse (Fig. 1, B and C). Cluster statistics were extracted from this data set to quantify the distribution of clusters in the z direction as well as the volume and sphericity of the LAT objects themselves (Fig. 1, D–F).Open in a separate windowFigure 1Identification of subcellular objects in three dimensions by isosurface rendering of molecular distribution. (A) Schematic of a T cell synapse formed against an activating coverslip where subsynaptic LAT vesicles (red dots) can be imaged with three-dimensional SMLM. (B and C) Isosurfaces, shown in x,z view (B) and as projection (C), identify T cell vesicles as LAT objects with L(50) > 200 (Eq. 3). (D–F) Distribution of LAT objects in z direction (D), volume (E), and sphericity (F) of LAT objects in T cells.Membrane undulations can cause clustering artifacts when the distribution of membrane proteins is recorded as a two-dimensional projection (15) (Fig. 2 A), as is the case in two-dimensional SMLM under total internal reflectance fluorescence illumination. To illustrate a solution to this problem, we obtained three-dimensional SMLM data sets of the membrane dye DiI (16) in resting T cells adhered onto nonactivating coverslips. With appropriately short labeling times to prevent dye internalization, it can be assumed that all DiI molecules reside in the plasma membrane. In this case, as is the case for plasma membrane proteins, neither two-dimensional nor three-dimensional analysis is appropriate, as it is a priori known the points must be derived from a two-dimensional membrane folded in three-dimensional space. To correct for membrane undulations, the plasma membrane topography must first be mapped so that molecular coordinates of membrane molecules can be appropriately corrected in two-dimensional projections. The position of the plasma membrane in three dimensions, i.e., the membrane topography, was determined by averaging the z position of all DiI molecules within a 100-nm radius in x-y at each point. The averaged z-position of DiI molecules was then displayed as a map, which exhibits a smooth, undulating profile (Fig. 2 B). The selection of this radius determines the accuracy of the assigned z position but also causes smoothing of the membrane profile.Open in a separate windowFigure 2Mapping of membrane topography and correction of molecular distributions in undulating membranes. (A) Two-dimensional projections can cause cluster artifacts, for example in membrane ruffles. Molecules (red rectangles) in the upper image are equally spaced along the membrane but appear as clusters in two-dimensional projections in areas with high gradient. (B) Three-dimensional membrane topography of a 2 × 2 μm plasma membrane area of a resting T cell obtained from averaged z positions of DiI molecules. Note that membrane undulation is ∼100 nm. (C) Map of membrane gradient, corresponding to the topography map shown in panel B, with an area of high gradient highlighted (dashed red box). (D) Correction of the circle radii in the Getis and Franklin cluster map calculations to account for projection artifacts. (E and F) Cluster map of data shown in panel C before (E) and after (F) correction for membrane gradient. Boxes in panels C, E, and F highlight the regions with high membrane gradient.Next, the gradient at the position of each DiI molecule was determined and interpolated into a gradient map (Fig. 2 C). Here, blue represents horizontal, i.e., flat membrane areas, whereas red regions indicate areas of high gradient. The information from the gradient map was then used to ensure that the two-dimensional circles in the Getis and Franklin cluster map calculations each correspond to an identical area of membrane, hence accounting for two-dimensional projection artifacts. To do this, the size of the circle (r) used to calculate the L value for each molecule was modified using Eq. 4, where c is calculated for the surface, S, using Eq. 5:r(corr)=r(uncorr)(1+c2)1/4,(4)c=((∂S∂x)2+(∂S∂y)2)1/2.(5)This operation is shown schematically in Fig. 2. The comparison of Getis and Franklin cluster maps before (Fig. 2 E) and after (Fig. 2 F) correction for the gradient shows that cluster values for DiI molecules were substantially reduced by up to 5–10% at sites where the plasma membrane had a high gradient (area highlighted in red box), and where the two-dimensional projection of three-dimensional structures caused an overestimation of clustering.In conclusion, we demonstrated that three-dimensional superresolution localization microscopy data can be used to identify and quantify subcellular structures. The approach has the distinct advantage that subcellular structures are solely identified by the distribution of the fluorescent marker so that no a priori knowledge of the structure is necessary. How precisely the subcellular structures are identified only depends on how efficiently the fluorescent maker is recruited to the structure, and hence does not depend on the resolution limits of optical microscopy. We applied the methods to two very different structures in T cells: small intracellular vesicles and the undulating plasma membrane. Importantly, the topography of plasma membrane can also be used to correct clustering artifacts in two-dimensional projections, which may be useful for distribution analysis within membranes.     

Comparison of Isoflurane and α-Chloralose in an Anesthetized Swine Model of Acute Pulmonary Embolism Producing Right Ventricular Dysfunction

Pulmonary embolism (PE) is a leading cause of sudden cardiac death, and a model is needed for testing potential treatments. In developing a model, we compared the hemodynamic effects of isoflurane and α-chloralose in an acute swine model of PE because the choice of anesthesia will likely affect the cardiovascular responses of an animal to PE. At baseline, swine that received α-chloralose (n = 6) had a lower heart rate and cardiac output and higher SpO_2, end-tidal CO_2, and mean arterial pressure than did those given isoflurane (n = 9). After PE induction, swine given α-chloralose compared with isoflurane exhibited a lower heart rate (63 ± 10 compared with 116 ± 15 bpm) and peripheral arterial pressure (52 ± 12 compared with 61 ± 12 mm Hg); higher SpO₂ (98% ± 3% compared with 95% ± 1%), end-tidal CO₂ (35 ± 4 compared with 32 ± 5), and systolic blood pressure (121 ± 8 compared with 104 ± 20 mm Hg); and equivalent right ventricular:left ventricular ratios (1.32 ± 0.50 compared with 1.23 ± 0.19) and troponin I mean values (0.09 ± 0.07 ng/mL compared with 0.09 ± 0.06 ng/mL). Isoflurane was associated with widely variable fibrinogen and activated partial thromboplastin time. Intraexperiment mortality was 0 of 6 animals for α-chloralose and 2 of 9 swine for isoflurane. All swine anesthetized with α-chloralose survived with sustained pulmonary hypertension, RV-dilation-associated cardiac injury without the confounding vasodilatory or coagulatory effects of isoflurane. These data demonstrate the physiologic advantages of α-chloralose over isoflurane for anesthesia in a swine model of severe submassive PE.Abbreviations: LV, left ventricle; PAP, pulmonary arterial pressure; PE, pulmonary embolism; RV, right ventriclePulmonary embolism (PE) is one of the leading causes of noncardiac sudden death in Western nations and is the third most common cause of cardiovascular morbidity.^4,6,7,18 In survivors, severe PE damages the right heart, leading to a clinical course complicated by hypotension and circulatory shock, suggesting acute right heart failure in about 10% of patients and followed by persistent pulmonary hypertension or right ventricular dysfunction and dyspnea in at least 15% of patients.^{9,15,16,23,29} To test treatments to reduce right heart failure, a standardized model that is repeatable, accurate, and precise and that mimics the gross pathologic, cardiovascular, pulmonary, autonomic, hematologic, biochemical, and cellular characteristics of PE in humans with disease is needed.⁸Three lines of rationale favor domestic pigs as a model for PE. Several publications, using different methods of anesthesia, have found that swine manifest hemodynamic responses similar to those of humans in the presence of autologous PE, including elevated heart rate, decreased cardiac output, and reduced oxygen saturation.^2,12,30 Swine have similar platelet concentrations, and their coagulation profile on thromboelastography has been shown to be similar to humans, with the exception of higher fibrin crosslinking but less fibrin, leading to resistance to plasmin.^5,11,19,34 Market swine, which would otherwise be destined for slaughter, are relatively cost effective compared with other large animals and are of sufficient size for placement of an adult pulmonary arterial catheter for measurement of pulmonary vascular resistance in a closed-chest preparation.In view of the differences in the hemodynamic effects of different anesthetic agents, the choice of anesthesia will likely affect the cardiovascular responses of an animal to PE. However, current literature lacks a methodologic publication that compares the cardiovascular, right ventricular, pulmonary, and hematologic responses to PE in closed-chest swine models incorporating different anesthetic regimens.Figure 1 presents features of an ideal animal model for the purpose of testing treatments for PE. To develop a swine model of PE that closely resembles this physiologic ideal model, we induced PE in swine maintained in a surgical plane of anesthesia with either isoflurane or α-chloralose. Each of these agents has potential advantages and disadvantages. Isoflurane can be titrated minute by minute but causes undesirable vasodilation, whereas α-chloralose is believed to preserve cardiovascular reflexes but requires heating to dissolve and continuous infusion or repeated boluses.^26,35 We hypothesized that, compared with isoflurane, α-chloralose would meet more of the features described in Figure 1.Open in a separate windowFigure 1.Desirable features of large animal model of severe submassive PE designed for translational research.     

Pharmacological targeting of guanosine monophosphate synthase suppresses melanoma cell invasion and tumorigenicity

Malignant melanoma possesses one of the highest metastatic potentials among human cancers. Acquisition of invasive phenotypes is a prerequisite for melanoma metastases. Elucidation of the molecular mechanisms underlying melanoma invasion will greatly enhance the design of novel agents for melanoma therapeutic intervention. Here, we report that guanosine monophosphate synthase (GMPS), an enzyme required for the de novo biosynthesis of GMP, has a major role in invasion and tumorigenicity of cells derived from either BRAF^V600E or NRAS^Q61R human metastatic melanomas. Moreover, GMPS levels are increased in metastatic human melanoma specimens compared with primary melanomas arguing that GMPS is an attractive candidate for anti-melanoma therapy. Accordingly, for the first time we demonstrate that angustmycin A, a nucleoside-analog inhibitor of GMPS produced by Streptomyces hygroscopius efficiently suppresses melanoma cell invasion in vitro and tumorigenicity in immunocompromised mice. Our data identify GMPS as a powerful driver of melanoma cell invasion and warrant further investigation of angustmycin A as a novel anti-melanoma agent.Malignant melanoma is one of the most aggressive types of human cancers. Its ability to metastasize in combination with resistance to conventional anticancer chemotherapy makes melanoma extremely difficult to cure, and the median survival of patients with metastatic melanoma is 8.5 months.^{1, 2, 3} A better understanding of the biology behind melanoma aggressiveness is imperative to facilitate the development of novel anti-melanoma strategies.Melanoma and other cancers cells have been shown to strongly rely on de novo nucleotide biosynthesis^{4, 5} and often overexpress several biosynthetic enzymes involved in these pathways.^{6, 7, 8, 9} Recently, we have identified a fundamental connection between melanoma invasion and biosynthesis of guanylates,⁸ suggesting that distortion of the guanylate metabolism facilitates melanoma progression.Guanosine monophosphate reductase (GMPR) reduces GMP to one of its precursors, inosine monophosphate (IMP), and depletes intracellular GTP pools (Figure 1a). We have recently demonstrated that GMPR suppresses melanoma cell invasion and growth of human melanoma cell xenografts. These findings tightly linked guanylate production to the invasive potential of melanoma cells.⁸Open in a separate windowFigure 1GMPS contributes to the invasive capability of melanoma cells. (a) Simplified schematic of the metabolic pathway for guanylates production. (b) SK-Mel-103 and SK-Mel-28 cells were transduced with a control vector or two independent shRNAs to GMPS and tested for invasion through Matrigel (left panel). Where indicated, cells were incubated with 100 μM guanosine for 24 h before the assay and guanosine supplementation was maintained throughout the experimental procedure. The data represent the average ± S.E.M. of at least two independent experiments. GMPS suppression was verified by immunoblotting (right panel). (c) Cells transduced as in (a) were plated on coverslips coated with FITC-conjugated gelatin. After 16 h cells were fixed with 4% PFA and stained for actin (rhodamine-conjugated phallodin) and nuclei (Hoechst). Where indicated, cells were incubated with 100 μM guanosine for 24 h before the assay and guanosine supplementation was maintained throughout the experimental procedure. At least 25 cells/sample were imaged to assess the number of cells with gelatin degradation. The data represent the average ± S.E.M. of two independent experiments. *P<0.05, **P<0.001 compared with control; ^#P<0.05, ^##P<0.001 compared with untreated cells. Statistics performed with Student''s t-Test. See also Supplementary Figure S1Of the several enzymes involved in guanylate biosynthesis, inositol monophosphate dehydrogenases 1 and 2 (IMPDH-1, -2), functional antagonists of GMPR (Figure 1a), have been targeted clinically with several drugs including the most specific one, mycophenolic acid (MPA) and its salt, mycophenolate mofetil (MMF).^{10, 11, 12, 13} Nonetheless, prior studies demonstrated that MPA possesses poor anti-tumor activity,^{14, 15} and it is primarily used as an immunosuppressing agent in organ transplantation.^{10, 11, 12}GMP synthase (GMPS) is the other functional antagonist of GMPR. GMPS catalyzes the amination of xanitol monophosphate (XMP) to GMP to promote GTP synthesis (Figure 1a).^{16, 17} Most of the studies on GMPS have been performed in bacteria, yeast, and insects, where GMPS has been shown to have a key role in sporulation, pathogenicity, and axon guidance, respectively.^{18, 19, 20} Mammalian GMPS has been the subject of several studies addressing its unconventional (GMP-unrelated) roles in the regulation of activity of ubiquitin-specific protease 7 (USP7).^{21, 22, 23, 24} However, because of the newly revealed importance of guanylate metabolism in control of melanoma cell invasion and tumorigenicity,⁸ GMPS emerges as an attractive target for anti-cancer therapy.In the late 1950s, a specific inhibitor of bacterial GMPS, angustmycin A (also known as decoyinine), has been isolated from Streptomyces hygroscopius as a potential antibiotic with sporulation-inducing activity in Bacillus subtilis.^{25, 26, 27, 28, 29} Its anti-tumor activity has never been experimentally explored. In the current study, we investigated the role of GMPS in regulation of melanoma invasion and tumorigenicity, and explored the possibility of targeting GMPS by angustmycin A as a novel anti-melanoma strategy.     

The nuclease activity of DNA2 promotes exonuclease 1–independent mismatch repair

The DNA mismatch repair (MMR) system is a major DNA repair system that corrects DNA replication errors. In eukaryotes, the MMR system functions via mechanisms both dependent on and independent of exonuclease 1 (EXO1), an enzyme that has multiple roles in DNA metabolism. Although the mechanism of EXO1-dependent MMR is well understood, less is known about EXO1-independent MMR. Here, we provide genetic and biochemical evidence that the DNA2 nuclease/helicase has a role in EXO1-independent MMR. Biochemical reactions reconstituted with purified human proteins demonstrated that the nuclease activity of DNA2 promotes an EXO1-independent MMR reaction via a mismatch excision-independent mechanism that involves DNA polymerase δ. We show that DNA polymerase ε is not able to replace DNA polymerase δ in the DNA2-promoted MMR reaction. Unlike its nuclease activity, the helicase activity of DNA2 is dispensable for the ability of the protein to enhance the MMR reaction. Further examination established that DNA2 acts in the EXO1-independent MMR reaction by increasing the strand-displacement activity of DNA polymerase δ. These data reveal a mechanism for EXO1-independent mismatch repair.

The mismatch repair (MMR) system has been conserved from bacteria to humans (1, 2). It promotes genome stability by suppressing spontaneous and DNA damage-induced mutations (1, 3, 4, 5, 6, 7, 8, 9, 10, 11). The key function of the MMR system is the correction of DNA replication errors that escape the proofreading activities of replicative DNA polymerases (1, 4, 5, 6, 7, 8, 9, 10, 12). In addition, the MMR system removes mismatches formed during strand exchange in homologous recombination, suppresses homeologous recombination, initiates apoptosis in response to irreparable DNA damage caused by several anticancer drugs, and contributes to instability of triplet repeats and alternative DNA structures (1, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18). The principal components of the eukaryotic MMR system are MutSα (MSH2-MSH6 heterodimer), MutLα (MLH1-PMS2 heterodimer in humans and Mlh1-Pms1 heterodimer in yeast), MutSβ (MSH2-MSH3 heterodimer), proliferating cell nuclear antigen (PCNA), replication factor C (RFC), exonuclease 1 (EXO1), RPA, and DNA polymerase δ (Pol δ). Loss-of-function mutations in the MSH2, MLH1, MSH6, and PMS2 genes of the human MMR system cause Lynch and Turcot syndromes, and hypermethylation of the MLH1 promoter is responsible for ∼15% of sporadic cancers in several organs (19, 20). MMR deficiency leads to cancer initiation and progression via a multistage process that involves the inactivation of tumor suppressor genes and action of oncogenes (21).MMR occurs behind the replication fork (22, 23) and is a major determinant of the replication fidelity (24). The correction of DNA replication errors by the MMR system increases the replication fidelity by ∼100 fold (25). Strand breaks in leading and lagging strands as well as ribonucleotides in leading strands serve as signals that direct the eukaryotic MMR system to remove DNA replication errors (26, 27, 28, 29, 30). MMR is more efficient on the lagging than the leading strand (31). The substrates for MMR are all six base–base mismatches and 1 to 13-nt insertion/deletion loops (25, 32, 33, 34). Eukaryotic MMR commences with recognition of the mismatch by MutSα or MutSβ (32, 34, 35, 36). MutSα is the primary mismatch-recognition factor that recognizes both base–base mismatches and small insertion/deletion loops whereas MutSβ recognizes small insertion/deletion loops (32, 34, 35, 36, 37). After recognizing the mismatch, MutSα or MutSβ cooperates with RFC-loaded PCNA to activate MutLα endonuclease (38, 39, 40, 41, 42, 43). The activated MutLα endonuclease incises the discontinuous daughter strand 5′ and 3′ to the mismatch. A 5'' strand break formed by MutLα endonuclease is utilized by EXO1 to enter the DNA and excise a discontinuous strand portion encompassing the mismatch in a 5''→3′ excision reaction stimulated by MutSα/MutSβ (38, 44, 45). The generated gap is filled in by the Pol δ holoenzyme, and the nick is ligated by a DNA ligase (44, 46, 47). DNA polymerase ε (Pol ε) can substitute for Pol δ in the EXO1-dependent MMR reaction, but its activity in this reaction is much lower than that of Pol δ (48). Although MutLα endonuclease is essential for MMR in vivo, 5′ nick-dependent MMR reactions reconstituted in the presence of EXO1 are MutLα-independent (44, 47, 49).EXO1 deficiency in humans does not seem to cause significant cancer predisposition (19). Nevertheless, it is known that Exo1^-/- mice are susceptible to the development of lymphomas (50). Genetic studies in yeast and mice demonstrated that EXO1 inactivation causes only a modest defect in MMR (50, 51, 52, 53). In agreement with these genetic studies, a defined human EXO1-independent MMR reaction that depends on the strand-displacement DNA synthesis activity of Pol δ holoenzyme to remove the mismatch was reconstituted (54). Furthermore, an EXO1-independent MMR reaction that occurred in a mammalian cell extract system without the formation of a gapped excision intermediate was observed (54). Together, these findings implicated the strand-displacement activity of Pol δ holoenzyme in EXO1-independent MMR.In this study, we investigated DNA2 in the context of MMR. DNA2 is an essential multifunctional protein that has nuclease, ATPase, and 5''→3′ helicase activities (55, 56, 57). Previous research ascertained that DNA2 removes long flaps during Okazaki fragment maturation (58, 59, 60), participates in the resection step of double-strand break repair (61, 62, 63), initiates the replication checkpoint (64), and suppresses the expansions of GAA repeats (65). We have found in vivo and in vitro evidence that DNA2 promotes EXO1-independent MMR. Our data have indicated that the nuclease activity of DNA2 enhances the strand-displacement activity of Pol δ holoenzyme in an EXO1-independent MMR reaction.     

Enzymatic Formation of β-Citraurin from β-Cryptoxanthin and Zeaxanthin by Carotenoid Cleavage Dioxygenase4 in the Flavedo of Citrus Fruit

In this study, the pathway of β-citraurin biosynthesis, carotenoid contents and the expression of genes related to carotenoid metabolism were investigated in two varieties of Satsuma mandarin (Citrus unshiu), Yamashitabeni-wase, which accumulates β-citraurin predominantly, and Miyagawa-wase, which does not accumulate β-citraurin. The results suggested that CitCCD4 (for Carotenoid Cleavage Dioxygenase4) was a key gene contributing to the biosynthesis of β-citraurin. In the flavedo of Yamashitabeni-wase, the expression of CitCCD4 increased rapidly from September, which was consistent with the accumulation of β-citraurin. In the flavedo of Miyagawa-wase, the expression of CitCCD4 remained at an extremely low level during the ripening process, which was consistent with the absence of β-citraurin. Functional analysis showed that the CitCCD4 enzyme exhibited substrate specificity. It cleaved β-cryptoxanthin and zeaxanthin at the 7,8 or 7′,8′ position. But other carotenoids tested in this study (lycopene, α-carotene, β-carotene, all-trans-violaxanthin, and 9-cis-violaxanthin) were not cleaved by the CitCCD4 enzyme. The cleavage of β-cryptoxanthin and zeaxanthin by CitCCD4 led to the formation of β-citraurin. Additionally, with ethylene and red light-emitting diode light treatments, the gene expression of CitCCD4 was up-regulated in the flavedo of Yamashitabeni-wase. These increases in the expression of CitCCD4 were consistent with the accumulation of β-citraurin in the two treatments. These results might provide new strategies to improve the carotenoid contents and compositions of citrus fruits.Carotenoids, a diverse group of pigments widely distributed in nature, fulfill a variety of important functions in plants and play a critical role in human nutrition and health (Schwartz et al., 1997; Cunningham and Gantt, 1998; Havaux, 1998; Krinsky et al., 2003; Ledford and Niyogi, 2005). The pathway of carotenoid biosynthesis has been well documented in various plant species, including Arabidopsis (Arabidopsis thaliana; Park et al., 2002), tomato (Lycopersicon esculentum; Isaacson et al., 2002), pepper (Capsicum annuum; Bouvier et al., 1998), citrus (Citrus spp.; Kato et al., 2004, 2006; Rodrigo et al., 2004; Rodrigo and Zacarías, 2007; Kato, 2012; Zhang et al., 2012a), and apricot (Prunus armenaica; Kita et al., 2007). Genes encoding the enzymes in the carotenoid biosynthetic pathway have been cloned, and their expression profiles have also been characterized (Fig. 1). As carotenoids contain a series of conjugated double bonds in the central chain, they can be oxidatively cleaved in a site-specific manner (Mein et al., 2011). The oxidative cleavage of carotenoids not only regulates their accumulation but also produces a range of apocarotenoids (Walter et al., 2010). In higher plants, many different apocarotenoids derive from the cleavage of carotenoids and have important metabolic functions, such as plant hormones, pigments, aroma and scent compounds, as well as signaling compounds (Fig. 1). A well-known example is abscisic acid, which is a C₁₅ compound derived from the cleavage of the 11,12 double bond of 9-cis-violaxanthin and 9′-cis-neoxanthin (Schwartz et al., 1997; Tan et al., 1997; Cutler and Krochko, 1999; Chernys and Zeevaart, 2000; Giuliano et al., 2003).Open in a separate windowFigure 1.Carotenoid and apocarotenoid metabolic pathway in plants. GGPP, Geranylgeranyl diphosphate. Enzymes, listed here from top to bottom, are named according to the designation of their genes: PSY, phytoene synthase; PDS, Phytoene desaturase; ZDS, ζ-carotene desaturase; ZISO, 15-cis-ζ-carotene isomerase; CRTISO, carotenoid isomerase; LCYb, lycopene β-cyclase; LCYe, lycopene ε-cyclase; HYe, ε-ring hydroxylase; HYb, β-ring hydroxylase; ZEP, zeaxanthin epoxidase; VDE, violaxanthin deepoxidase; NCED, 9-cis-epoxycarotenoid dioxygenase.Carotenoid cleavage dioxygenases (CCDs) are a group of enzymes that catalyze the oxidative cleavage of carotenoids (Ryle and Hausinger, 2002). CCDs are nonheme iron enzymes present in plants, bacteria, and animals. In plants, CCDs belong to an ancient and highly heterogenous family (CCD1, CCD4, CCD7, CCD8, and 9-cis-epoxycarotenoid dioxygenases [NCEDs]). The similarity among the different members is very low apart from four strictly conserved His residues and a few Glu residues (Kloer and Schulz, 2006; Walter et al., 2010). In Arabidopsis, the CCD family contains nine members (CCD1, NCED2, NCED3, CCD4, NCED5, NCED6, CCD7, CCD8, and NCED9), and orthologs in other plant species are typically named according to their homology with an Arabidopsis CCD (Huang et al., 2009). In our previous study, the functions of CitCCD1, CitNCED2, and CitNCED3 were investigated in citrus fruits (Kato et al., 2006). The recombinant CitCCD1 protein cleaved β-cryptoxanthin, zeaxanthin, and all-trans-violaxanthin at the 9,10 and 9′,10′ positions and 9-cis-violaxanthin at the 9′,10′ position. The recombinant CitNCED2 and CitNCED3 proteins cleaved 9-cis-violaxanthin at the 11,12 position to form xanthoxin, a precursor of abscisic acid (Kato et al., 2006). To date, information on the functions of other CCDs in citrus fruits remains limited, while the functions of CCD7 and CCD8, as well as NCED5, NCED6, and NCED9, in Arabidopsis have been characterized (Kloer and Schulz, 2006; Walter et al., 2010). In Arabidopsis, CCD7 cleaves all-trans-β-carotene at the 9′,10′ position to form all-trans-β-apo-10′-carotenal. All-trans-β-apo-10′-carotenal is further shortened by AtCCD8 at the 13,14 position to produce β-apo-13-carotenone (Alder et al., 2012). NCED5, NCED6, and NCED9 cleave 9-cis-violaxanthin at the 11,12 position to form xanthoxin (Tan et al., 2003). Compared with other CCDs, the function of CCD4 is poorly understood. In Chrysanthemum morifolium, CmCCD4a contributed to the white color formation by cleaving carotenoids into colorless compounds (Ohmiya et al., 2006). Recently, it has been reported that CsCCD4, CmCCD4a, and MdCCD4 could cleave β-carotene to yield β-ionone (Rubio et al., 2008; Huang et al., 2009).β-Citraurin, a C₃₀ apocarotenoid, is a color-imparting pigment responsible for the reddish color of citrus fruits (Farin et al., 1983). In 1936, it was first discovered in Sicilian oranges (Cual, 1965). In citrus fruits, the accumulation of β-citraurin is not a common event; it is only observed in the flavedos of some varieties during fruit ripening. The citrus varieties accumulating β-citraurin are considered more attractive because of their red-orange color (Ríos et al., 2010). Although more than 70 years have passed since β-citraurin was first identified, the pathway of its biosynthesis is still unknown. As its structure is similar to that of β-cryptoxanthin and zeaxanthin, β-citraurin was presumed to be a degradation product of β-cryptoxanthin or zeaxanthin (Oberholster et al., 2001; Rodrigo et al., 2004; Ríos et al., 2010; Fig. 1). To date, however, the specific cleavage reaction producing β-citraurin has not been elucidated. In this study, we found that the CitCCD4 gene was involved in the synthesis of β-citraurin, using two citrus varieties of Satsuma mandarin (Citrus unshiu), Yamashitabeni-wase, which accumulates β-citraurin predominantly, and Miyagawa-wase, which does not accumulate β-citraurin. To confirm the role of the CitCCD4 gene further, functional analyses of the CitCCD4 enzyme were performed in vivo and in vitro. Additionally, the regulation of β-citraurin content and CitCCD4 gene expression in response to ethylene and red light-emitting diode (LED) light treatments was also examined. This study, to our knowledge, is the first to investigate the biosynthesis of β-citraurin in citrus fruits. The results might provide new strategies to enhance the nutritional and commercial qualities of citrus fruits.