Assessment of yield enhancement in cashew (Anacardium occidentale L.) by the pollinator sharing effect of magnetic bee-friendly plants in India

The objective of this study was to test the hypothesis that pollinator sharing among cashew (Anacardium occidentale L.) and co-blooming magnetic bee-friendly plants facilitate higher productivity of cashew nuts. We examined the reproductive efficacy of cashew in three sites with distinct vegetation pattern (site I: sparsely distributed individuals, without association of co-blooming magnetic bee-friendly plant; site II: densely distributed individuals, without association of co-blooming magnetic bee-friendly plant; site III: densely distributed individuals, associated with co-blooming magnetic bee-friendly plants). Floral traits (including flowering time, flower architecture, number of flowers per panicle, hermaphrodite- male flower ratio, floral rewards, anthesis time and longevity of flower) does not differ among the study sites. According to the value of relative pollinator service (RPS), Apis dorsata was the primary pollinator of cashew and also shared by the co-blooming bee-friendly plants. The abundance of pollinators was the highest in site III and the lowest in site I. The nut yield was also significantly higher in site III cashew orchard which was in association with magnetic bee-friendly plants. Therefore, we can conclude that the association of co-blooming magnetic plants increased nut yield of cashew through the enhancement of pollinator's services.     

SNP identification and marker assay development for high-throughput selection of soybean cyst nematode resistance

Background

Soybean cyst nematode (SCN) is the most economically devastating pathogen of soybean. Two resistance loci, Rhg1 and Rhg4 primarily contribute resistance to SCN race 3 in soybean. Peking and PI 88788 are the two major sources of SCN resistance with Peking requiring both Rhg1 and Rhg4 alleles and PI 88788 only the Rhg1 allele. Although simple sequence repeat (SSR) markers have been reported for both loci, they are linked markers and limited to be applied in breeding programs due to accuracy, throughput and cost of detection methods. The objectives of this study were to develop robust functional marker assays for high-throughput selection of SCN resistance and to differentiate the sources of resistance.

Results

Based on the genomic DNA sequences of 27 soybean lines with known SCN phenotypes, we have developed Kompetitive Allele Specific PCR (KASP) assays for two Single nucleotide polymorphisms (SNPs) from Glyma08g11490 for the selection of the Rhg4 resistance allele. Moreover, the genomic DNA of Glyma18g02590 at the Rhg1 locus from 11 soybean lines and cDNA of Forrest, Essex, Williams 82 and PI 88788 were fully sequenced. Pairwise sequence alignment revealed seven SNPs/insertion/deletions (InDels), five in the 6th exon and two in the last exon. Using the same 27 soybean lines, we identified one SNP that can be used to select the Rhg1 resistance allele and another SNP that can be employed to differentiate Peking and PI 88788-type resistance. These SNP markers have been validated and a strong correlation was observed between the SNP genotypes and reactions to SCN race 3 using a panel of 153 soybean lines, as well as a bi-parental population, F₅–derived recombinant inbred lines (RILs) from G00-3213 x LG04-6000.

Conclusions

Three functional SNP markers (two for Rhg1 locus and one for Rhg4 locus) were identified that could provide genotype information for the selection of SCN resistance and differentiate Peking from PI 88788 source for most germplasm lines. The robust KASP SNP marker assays were developed. In most contexts, use of one or two of these markers is sufficient for high-throughput marker-assisted selection of plants that will exhibit SCN resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1531-3) contains supplementary material, which is available to authorized users.     

Effects of Familial Mutations on the Monomer Structure of Aβ42

Amyloid beta (Aβ) peptide plays an important role in Alzheimer’s disease. A number of mutations in the Aβ sequence lead to familial Alzheimer’s disease, congophilic amyloid angiopathy, or hereditary cerebral hemorrhage with amyloid. Using molecular dynamics simulations of ∼200 μs for each system, we characterize and contrast the consequences of four pathogenic mutations (Italian, Dutch, Arctic, and Iowa) for the structural ensemble of the Aβ monomer. The four familial mutations are found to have distinct consequences for the monomer structure.Amyloid beta (Aβ) peptides have long been thought to play a central role in Alzheimer’s disease (AD). Usually 40 or 42 residues in length, Aβ peptides are proteolytic products of the Aβ precursor protein and they aggregate to form the fibrillar plaques in AD patients’ brains. Besides fibrillar plaques, Aβ oligomers are also neurotoxic. The significance and nature of Aβ oligomerization has recently become a focus of intensive research studies and debates (1,2). Notably, numerous pathogenic mutations have been identified in the Aβ precursor protein sequence and in the enzymes involved in Aβ processing (3). These mutations generally lead to early onset of AD or cerebral amyloid angiopathy. Understanding how the pathogenic mutations alter Aβ oligomerization/aggregation is essential to our understanding of the disease mechanism.Four of these pathogenic mutations (Italian E22K, Dutch E22Q, Arctic E22G, and Iowa D23N) cluster in the region of E22 and D23 in the Aβ sequence (distal from proteolytic cleavage sites) and they have higher neurotoxicity compared to wild-type (WT) Aβ (4). These mutations are thought to modify the physicochemistry of the peptide. For example, kinetic studies (4) show that the E22K and E22Q mutations lead to faster peptide aggregation, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (although the E22G mutation shows increased protofibril formation (5)). Recent solid-state NMR studies also suggest that rather than the in-register β-sheet conformation adopted by WT Aβ, the Iowa D23N mutant forms amyloid fibrils with antiparallel β-sheet structure (6).To understand how the mutations modify the peptide oligomerization/aggregation it is critical to characterize the starting point of the process, the monomers. Unfortunately, investigating the early phase of the oligomerization process experimentally is a challenging task due to the high aggregation propensity of Aβ and its intrinsic disorder. Therefore, a number of computational approaches have been adopted to investigate the consequences of mutations for the monomer structure (7–16). However, due to the high computational demands of explicit-solvent molecular dynamics (MD) simulations to simulate full-length Aβ peptides, most of these computational studies are either on Aβ fragments (to decrease the system size) using explicit-solvent simulations (8–12) or on full-length Aβ using implicit-solvent simulations (which are less computationally demanding and enable longer simulation times, but lack explicit water molecules in the simulations to fully describe water-peptide interactions) (13–15). In a very recent report, explicit-solvent simulations were used to study the effects of the E22Q mutation on full-length Aβ; however, rather limited data (<10 μs) were collected (16). Thus, characterizing full-length Aβ monomers remains quite a daunting task even with simulations.To characterize the effects of mutations on full-length Aβ monomer using explicit-solvent MD simulations, we employed distributed computing (17) to simulate the WT Aβ₄₂, Aβ₄₂-E22K, Aβ₄₂-E22Q, Aβ₄₂-E22G, and Aβ₄₂-D23N monomers. MD simulations of >200 μs were performed for each system and AMBER ff99sb (18) and the tip3p water model (19) were used for force field parameters. Peptide configurations in the MD trajectories were clustered with the root mean-square deviation metric to identify representative conformations (i.e., states) and transitions between these states were counted. Markov state model analysis was then performed where the master equations were solved and the equilibrium population of each state deduced (20). Details of the MD simulation procedures and Markov state model analysis can be found in the Supporting Material.Each of the five Aβ monomer systems exhibits great structural diversity and can only be characterized in an ensemble fashion (rather than described by a handful of representative configurations). This is in accord with the notion that full-length Aβ peptides are intrinsically disordered (21,22). Using the Dictionary of Secondary Structure of Proteins program (23) to assign secondary structure, it is clear that the five Aβ monomer systems are found overall not well structured, although small β-hairpins and α-helices are observed. In Fig. 1 we plot the residue-dependent extended β propensity and α-helix propensity, in the top and bottom panels, respectively, for each Aβ monomer system. Although we are reasonably confident of the convergence behavior of the α-helix propensity, we note that the convergence of the extended β-propensity might be more challenging and demand a much longer sampling time than the current aggregate simulation time of ∼200 μs (24).Open in a separate windowFigure 1Ensemble-averaged %population of β-strand (top) and α-helix (bottom) propensity for all five monomer systems. The sequence of the WT Aβ₄₂ is given on the x axis.We observe in Fig. 1 that all five Aβ monomer systems share a rather similar residue-dependent tendency to form an extended β-structure, although minor differences are present. On the other hand, these pathogenic mutations alter the α-helix propensity quite significantly. The E22K and E22Q mutations increase the α-helix propensity in the region of residues 20–23. All four mutations (E22K, E22Q, E22G, and D23N) decrease the α-helix propensity in the region of residues 33–36.Notably, we find that in all five systems only short stretches of α-helices are formed. That is, when a residue is involved in α-helix formation, it participates in forming mostly short helical segments (consisting of only four helical residues). To provide more insight into the changes of α-helix propensity due to the mutations, in Fig. S1 we plot the tendency of forming short α-helices along the sequence for all five systems. Each data point in Fig. S1 represents the propensity to form an α-helix of four residues in length, ending at the specific residue. For example, in the structural ensemble adopted by the WT peptide, ∼5.5% of the conformations have a short α-helix of size four, involving residues 15–18. We see from Fig. S1 that the E22K and E22Q mutations induce the formation of two short helices in residues 19–22 and 20–23. The higher α-helix propensity in this region for the E22K mutant compared to the WT was previously attributed to the elimination of the electrostatic repulsion between E22 and D23 in the WT by the mutation and the longer aliphatic chain of K22 in the mutant compared to E22 in the WT (9,22). This is consistent with the observation that the E22Q mutation also induces helix formation in this region (by eliminating the electrostatic repulsion between E22 and D23 in the WT) but to a lesser extent, possibly due to the shorter aliphatic chain of Q22 compared to K22.In the E22G mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, glycine is known to be a helix breaker (25), leading to diminished α-helix propensity in the region around residue G22 seen in Fig. S1.In the D23N mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, it does not induce (or rather even slightly decreases) helix formation around residue 23. This may be due to the short aliphatic chain of N23 but it is possible that the mutation induces some nonlocal effects on the peptide structure, disfavoring helix formation in this region.It is worth noting that all four mutations (E22K, E22Q, E22G, and D23N) virtually eliminate the α-helix propensity in the region of residues 33–36. This region is rather far away from the mutation sites in sequence but its α-helix propensity is nonetheless affected. The origin of such a nonlocal effect is less straightforward to explain and further analysis will aid untangling this behavior. Nonetheless, the diminished α-helix propensity in the region of residues 33–36 appears to be a consistent feature across all four mutants.The four mutations studied here (E22K, E22Q, E22G, and D23N) have been thought to modify the physicochemistry of the peptide and alter the oligomerization/aggregation process, leading to higher neurotoxicity. In predicting intrinsic aggregation propensities using peptide sequences, all four mutants are suggested to be more aggregation prone (26). On the other hand, kinetic studies show that only the E22K and E22Q mutants aggregate more quickly, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (4). Our simulation results suggest these pathogenic mutations have complicated effects on the monomer structure—all four mutations decrease helix propensity in residues 33–36, whereas only the E22K and E22Q mutations increase helix propensity in residues 20–23. It is interesting to note that α-helix propensity is generally thought to anticorrelate with aggregation propensity; however, recent studies have suggested an important role of α-helical intermediates in amyloid oligomerization (27–29). Our studies suggest that it would be of great value to investigate how the distinct patterns of α-helix propensity in these five systems may propagate to give rise to different oligomerization kinetics or even mechanisms. The pathogenic mutations studied here have complex effects on the oligomerization of the peptide. The characterization of the monomer structural ensembles reported here should aid understanding of such an important and complicated process.     

Application of sensitive enzymeimmunoassays for oxytocin and prolactin determination in blood plasma of yaks (Poephagus grunniens L.) during milk let down and cyclicity

Highly sensitive and specific enzymeimmunoassays for oxytocin and prolactin determination in yak plasma using the biotin-streptavidin amplification system and the second antibody coating technique were validated and applied for determining their profiles during milk let down and cyclicity in yaks. Oxytocin EIA was conducted taking duplicate 200 microl of unknown plasma samples and standards per well. The lowest detection limit was 0.2 pg/well, which corresponded to 1pg/ml plasma. Prolactin EIA was carried out directly in 50 microl of yak plasma. The sensitivity of EIA procedure was 5 pg/well prolactin, which corresponded to 0.1 ng/ml plasma. Mean plasma prolactin concentrations although high at estrus were not statistically different (P > 0.05) from the hormone concentrations on other days. Mean plasma prolactin concentrations during non-breeding season were significantly higher (P < 0.001) than that recorded in breeding season. Oxytocin and prolactin profiles were also obtained in two yaks before, during and after milking. A sharp release of oxytocin and prolactin shortly after udder stimulation was observed. High levels of oxytocin and prolactin were maintained during milking, falling sharply thereafter.     

Human DNA polymerase iota incorporates dCTP opposite template G via a G.C + Hoogsteen base pair

Human DNA polymerase iota (hPoliota), a member of the Y family of DNA polymerases, differs in remarkable ways from other DNA polymerases, incorporating correct nucleotides opposite template purines with a much higher efficiency and fidelity than opposite template pyrimidines. We present here the crystal structure of hPoliota bound to template G and incoming dCTP, which reveals a G.C + Hoogsteen base pair in a DNA polymerase active site. We show that the hPoliota active site has evolved to favor Hoogsteen base pairing, wherein the template sugar is fixed in a cavity that reduces the C1'-C1' distance across the nascent base pair from approximately 10.5 A in other DNA polymerases to 8.6 A in hPoliota. The rotation of G from anti to syn is then largely in response to this curtailed C1'-C1' distance. A G.C+ Hoogsteen base pair suggests a specific mechanism for hPoliota's ability to bypass N(2)-adducted guanines that obstruct replication.     

Evaluation of a diospyrin derivative as antileishmanial agent and potential modulator of ornithine decarboxylase of Leishmania donovani

World health organization has called for academic research and development of new chemotherapeutic strategies to overcome the emerging resistance and side effects exhibited by the drugs currently used against leishmaniasis. Diospyrin, a bis-naphthoquinone isolated from Diospyros montana Roxb., and its semi-synthetic derivatives, were reported for inhibitory activity against protozoan parasites including Leishmania. Presently, we have investigated the antileishmanial effect of a di-epoxide derivative of diospyrin (D17), both in vitro and in vivo. Further, the safety profile of D17 was established by testing its toxicity against normal macrophage cells (IC₅₀ ∼ 20.7 μM), and also against normal BALB/c mice in vivo. The compound showed enhanced activity (IC₅₀ ∼ 7.2 μM) as compared to diospyrin (IC₅₀ ∼ 12.6 μM) against Leishmania donovani promastigotes. Again, D17 was tested on L. donovani BHU1216 isolated from a sodium stibogluconate-unresponsive patient, and exhibited selective inhibition of the intracellular amastigotes (IC₅₀ ∼ 0.18 μM). Also, treatment of infected BALB/c mice with D17 at 2 mg/kg/day reduced the hepatic parasite load by about 38%. Subsequently, computational docking studies were undertaken on selected enzymes of trypanothione metabolism, viz. trypanothione reductase (TryR) and ornithine decarboxylase (ODC), followed by the enzyme kinetics, where D17 demonstrated non-competitive inhibition of the L. donovani ODC, but could not inhibit TryR.     

Potent growth suppressive activity of curcumin in human breast cancer cells: Modulation of Wnt/β-catenin signaling

Abnormal activation of the Wnt/β-catenin signaling pathway and subsequent upregulation of β-catenin driven downstream targets—c-Myc and cyclin D1 is associated with development of breast cancer. The objective of our study was to determine if curcumin could modulate the key elements of Wnt pathway in breast cancer cells; an effect that might underscore its usefulness for chemoprevention/treatment of this malignancy. Curcumin showed a cytotoxic effect on MCF-7 cells with 50% inhibitory concentration (IC₅₀) of 35 μM; while IC₅₀ for MDA-MB-231 cells was 30 μM. Treatment with low cytostatic dose of 20 μM curcumin showed G₂/M arrest in both breast cancer cells. The effect of curcumin (20 μM) treatment on expression of Wnt/β-catenin pathway components in breast cancer cells (MCF-7 and MDA-MB-231) was analyzed by immunofluorescence and Western blotting. Curcumin was found to effectively inhibit the expression of several Wnt/β-catenin pathway components—disheveled, β-catenin, cyclin D1 and slug in both MCF-7 and MDA-MB-231. Immunofluorescence analysis showed that curcumin markedly reduced the nuclear expression of disheveled and β-catenin proteins. Further, the protein levels of the positively regulated β-catenin targets—cyclin D1 and slug, were downregulated by curcumin treatment. The expression levels of two integral proteins of Wnt signaling, GSK3β and E-cadherin were also altered by curcumin treatment. In conclusion, our data demonstrated that the efficacy of curcumin in inhibition of cell proliferation and induction of apoptosis might occur through modulation of β-catenin pathway in human breast cancer cells.     

An antisense RNA regulates the bidirectional silencing property of the Kcnq1 imprinting control region

The Kcnq1 imprinting control region (ICR) located in intron 10 of the Kcnq1 gene is unmethylated on the paternal chromosome and methylated on the maternal chromosome and has been implicated in the manifestation of parent-of-origin-specific expression of six neighboring genes. The unmethylated Kcnq1 ICR harbors bidirectional silencer activity and drives expression of an antisense RNA, Kcnq1ot1, which overlaps the Kcnq1 coding region. To elucidate whether the Kcnq1ot1 RNA plays a role in the bidirectional silencing activity of the Kcnq1 ICR, we have characterized factor binding sites by genomic footprinting and tested the functional consequence of various deletions of these binding sites in an episome-based system. Deletion of the elements necessary for Kcnq1ot1 promoter function resulted in the loss of silencing activity. Furthermore, interruption of Kcnq1ot1 RNA production by the insertion of a polyadenylation sequence downstream of the promoter also caused a loss of both silencing activity and methylation spreading. Thus, the antisense RNA plays a key role in the silencing function of the ICR. Double-stranded RNA (dsRNA)-mediated RNA interference is unlikely to be involved, as the ICR is active irrespective of the simultaneous production of dsRNA from the genes it silences.     

Genomewide patterns of variation in genetic diversity are shared among populations,species and higher‐order taxa

Genomewide screens of genetic variation within and between populations can reveal signatures of selection implicated in adaptation and speciation. Genomic regions with low genetic diversity and elevated differentiation reflective of locally reduced effective population sizes (N_e) are candidates for barrier loci contributing to population divergence. Yet, such candidate genomic regions need not arise as a result of selection promoting adaptation or advancing reproductive isolation. Linked selection unrelated to lineage‐specific adaptation or population divergence can generate comparable signatures. It is challenging to distinguish between these processes, particularly when diverging populations share ancestral genetic variation. In this study, we took a comparative approach using population assemblages from distant clades assessing genomic parallelism of variation in N_e. Utilizing population‐level polymorphism data from 444 resequenced genomes of three avian clades spanning 50 million years of evolution, we tested whether population genetic summary statistics reflecting genomewide variation in N_e would covary among populations within clades, and importantly, also among clades where lineage sorting has been completed. All statistics including population‐scaled recombination rate (ρ), nucleotide diversity (π) and measures of genetic differentiation between populations (F_ST, PBS, d_xy) were significantly correlated across all phylogenetic distances. Moreover, genomic regions with elevated levels of genetic differentiation were associated with inferred pericentromeric and subtelomeric regions. The phylogenetic stability of diversity landscapes and stable association with genomic features support a role of linked selection not necessarily associated with adaptation and speciation in shaping patterns of genomewide heterogeneity in genetic diversity.