Toxicity,behavior impairment,and repellence of essential oils from pepper‐rosmarin and patchouli to termites

Plant essential oils are potential sources of insecticidal compounds, but have rarely been explored for their effect on termites. In the present study, we assessed the chemical composition of essential oils of Lippia sidoides Cham. (pepper‐rosmarin; Verbenaceae) and Pogostemon cablin (Blanco) Benth. (patchouli; Lamiacaeae) and evaluated their toxicity, behavioral impairment, and repellence to termite species of the genera Amitermes and Microcerotermes (Isoptera: Termitidae: Termitinae). The main components of essential oils of L. sidoides and P. cablin were thymol (44.6%) and patchouli alcohol (36.6%), respectively. The essential oil of P. cablin was most potent against Amitermes cf. amifer Silvestri and had the lowest LD₅₀ (0.63 μg mg^?1). There was no difference in toxicity for Microcerotermes indistinctus Mathews between the essential oils of L. sidoides (LD₅₀ = 1.49 μg mg^?1) and P. cablin (LD₅₀ = 1.67 μg mg^?1). Pogostemon cablin essential oil was the most toxic to M. indistinctus (LC₅₀ = 0.32 μl ml^?1) and A. cf. amifer (LC₅₀ = 0.29 μl ml^?1). The essential oils analyzed exhibited high toxicity and repellence to the termites, in addition to reducing behavioral interactions among individuals, thus constituting potential termiticides.     

Preparation and Enantioselectivity Binding Studies of a New Chiral Cobalt(II)porphyrin‐Tröger's Base Conjugate

A new bis[cobalt(II)porphyrin]‐Tröger's base conjugate was studied as a potential receptor for methyl esters of several amino acids. The conjugate was prepared as racemate, and then resolved via preparative high‐performance liquid chromatography (HPLC) on a chiral column. The high affinity to lysine, histidine, and proline methyl esters was found by complexation studies followed by UV‐Vis spectroscopy. The studies of pure enantiomers, followed by UV‐Vis and electronic circular dichroism spectroscopy, revealed the highest enantioselectivity for lysine methyl ester. Chirality 26:361–367, 2014. © 2014 Wiley Periodicals, Inc.     

Molecular characterization of phytoplasmas in lilies with fasciation in the Czech Republic

Lilium spp. with symptoms of severe fasciation were observed in Southern and central Bohemia during the period 1999-2003. Nucleic acids extracted from symptomatic and asymptomatic plants were used in nested-PCR assays with primers amplifying 16S-23S rRNA sequences specific for phytoplasmas. The subsequent nested-PCR with phytoplasma group-specific primers followed by RFLP analyses and the 16S ribosomal gene sequencing, allowed classification of the detected phytoplasmas in the aster yellows group, subgroups 16SrI-B and 16SrI-C alone, and in mixed infection. Samples infected by 16SrI-C phytoplasmas showed different overlapping RFLP profiles after TruI digestion of R16F2/R2 amplicons. Two of these amplicons were sequenced, one of them directly and the other after cloning; sequence analyses and blast alignment confirmed the presence of two different overlapping patterns in samples studied. The sequences obtained were closely related, respectively, to operon A and operon B ribosomal sequences of the clover phyllody phytoplasma. Direct PCR followed by RFLP analyses of the tuf gene with two restriction enzymes showed no differences from reference strain of subgroup 16SrI-C. Infection with aster yellows phytoplasmas of 16SrI-B subgroup in asymptomatic lilies cv. Sunray was also detected.     

Decimal Place Slope,A Fast and Precise Method for Quantifying 13C Incorporation Levels for Detecting the Metabolic Activity of Microbial Species

The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful tool for qualitative and quantitative proteome studies. We recently introduced a method that monitors heavy isotope incorporation into proteins and presented data revealing the metabolic activity of various species in a microbial consortium using this technique. To further develop our method using an liquid chromatography (LC)-mass spectrometry (MS)-based approach, we present here a novel approach for calculating the incorporation level of ¹³C into peptides by using the information given in the decimal places of peptide masses obtained by modern high-resolution MS. In the present study, the applicability of this approach is demonstrated using Pseudomonas putida ML2 proteins uniformly labeled via the consumption of [¹³C₆]benzene present in the medium at concentrations of 0, 10, 25, 50, and 100 atom %. The incorporation of ¹³C was calculated on the basis of several labeled peptides derived from one band on an SDS-PAGE gel. The accuracy of the calculated incorporation level depended upon the number of peptide masses included in the analysis, and it was observed that at least 100 peptide masses were required to reduce the deviation below 4 atom %. This accuracy was comparable with calculations of incorporation based on the isotope envelope. Furthermore, this method can be extended to the calculation of the labeling efficiency for a wide range of biomolecules, including RNA and DNA. The technique will therefore allow a highly accurate determination of the carbon flux in microbial consortia with a direct approach based solely on LC-MS.The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful component of qualitative and quantitative proteome studies (1). Incorporation of heavy isotopes can be used to analyze microbial processes such as turnover rates and also to help to establish structure-function relationships within microbial communities. Stable isotope probing (SIP¹) techniques based on DNA-SIP (2) and RNA-SIP (3) have been used for this purpose previously. With the introduction of protein-SIP (4), the need for an accurate alternative method for calculating label incorporation into biomolecules arose. Protein-SIP has several advantages compared with DNA/RNA-SIP, the most important being its capacity to detect dynamic levels of incorporation, whereas only labeled or unlabeled states can be categorized by means of DNA/RNA-SIP because of the need to separate ¹³C-DNA/RNA by density gradient centrifugation. Quantitative analysis of ¹³C incorporation levels is of the utmost importance, especially when unraveling carbon fluxes through either microbial communities or food webs with different trophic levels.In contrast to the incorporation of isotopically labeled amino acids, which is often used in quantitative proteomics (5), metabolic labeling by growth substrates and nutrients (e.g. salts) is often imperfect and makes the processing of mass spectrometry (MS) data difficult. For example, when the incorporation of ¹³C exceeds ∼2 atom %, common database search algorithms fail to identify peptides and proteins. The problem can only be managed successfully if a stable, known degree of ¹³C incorporation can be achieved during the experiment (6). Using a low labeling efficiency of roughly 5 atom %, Huttlin et al. (6) chose the altered envelope chain for calculating the incorporation and simultaneously used the signal intensity for a quantitative comparison with the sample that had a natural abundance of ¹³C. Database approaches for peptide identification can cope only with the natural abundance of carbon isotopes; they fail if the incorporation of ¹³C significantly exceeds the natural isotope abundance or if incorporation patterns occur in unpredictable ways (7).The simplest method for determining the incorporation level is to compare the unlabeled average mass of the monoisotopic peptide with the mass of the labeled protein, as estimated by matrix-assisted laser desorption/ionization or electrospray ionization MS (8, 9). A more advanced approach for determining the isotopic mass distribution of peptides is based on the isotopic distribution of the peaks of a peptide envelope (10, 11). Here, for a given isotopomer, the incorporation efficiency is defined as the percentage of incorporated ¹³C atoms with relation to the total number of carbon atoms with the natural isotope abundance (approximately 1.01 atom % ¹³C). As a reference, the theoretical isotopic distribution of a peptide is calculated based upon an algorithm described elsewhere (12). The isotope distribution of both unlabeled and labeled peptides can subsequently be used to calculate the incorporation level. For this method, an Excel spreadsheet (ProSIPQuant.xls) was developed (4). A similar approach, also based on the calculation of isotopic distributions, has been used in other studies (7). In these studies, however, the identification of the peptides is limited to those that have unlabeled counterparts; in addition, an exact calculation can be hampered by overlapping signals coming from additional peaks with similar masses.In the present study, we describe a new way of determining the isotope incorporation level. Our method makes use of characteristic patterns in the digits after the decimal point of the peptide masses generated by high-accuracy instruments such as the linear ion trap LTQ-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). For tryptic peptides, typical regularities in the decimal places of the monoisotopic masses have been observed (13, 14). These observations have been explored in detail for theoretical and experimental data of proteins originating from Helicobacter pylori (15). As a result, a rule called the “half decimal place rule” (HDPR) was defined; it states that the decimal place is nearly half of the first digit for tryptic peptides with masses in the range of 500–1,000 Da. In other words, the exact mass of a peptide is equal to its nominal mass times ∼1.005. Because the difference between ¹²C and ¹³C is slightly greater than 1 Da, exactly 1.0033548378, the decimal places of a tryptic peptide''s mass are shifted in a regular manner by the incorporation level and lead to a significantly increased slope for the digits in the third and fourth place after the decimal point. This shift can be used to estimate the incorporation level of heavy isotopes into the protein. Detecting such shifts requires the highly accurate measurement possible with modern mass spectrometers such as the LTQ-Orbitrap, the Fourier transform ion cyclotron resonance, or the quadrupole time of flight. In this communication, we demonstrate the applicability of this approach using Pseudomonas putida ML2 proteins labeled uniformly via the consumption of [¹³C₆]benzene with five different substrate concentrations (0, 10, 25, 50, and 100 atom % of ¹³C). The ¹³C incorporation was calculated based on several labeled peptides derived from different proteins in one SDS-PAGE band. By these means, we have established a method that allows the determination of ¹³C incorporation into proteins and can be used to assess the metabolic activity of a given species within a mixed community.     

Fungal antagonists of the plant pathogen Rhizoctonia solani: selection, control efficacy and influence on the indigenous microbial community

A broad spectrum of fungal antagonists was evaluated as potential biocontrol agents (BCAs) against the soil-borne pathogen Rhizoctonia solani using a new combination of in vitro and in vivo assays. The in vitro characterisation of diverse parameters including the ability to parasitise mycelium and to inhibit the germination of Rhizoctonia sclerotia at different temperatures resulted in the selection of six potential fungal antagonists. These were genotypically characterised by their BOX-PCR fingerprints, and identified as Trichoderma reesei and T. viride by partial 18S rDNA sequencing. When potato sprouts were treated with Trichoderma, all isolates significantly reduced the incidence of Rhizoctonia symptoms. Evaluated under growth chamber conditions, the selected Trichoderma isolates either partly or completely controlled the dry mass loss of lettuce caused by R. solani. Furthermore, the antagonistic Trichoderma strains were active under field conditions. To analyse the effect of Trichoderma treatment on indigenous root-associated microbial communities, we performed a DNA-dependent SSCP (Single-Strand Conformation Polymorphism) analysis of 16S rDNA/ITS sequences. In this first assessment study for Trichoderma it was shown that the pathogen and the vegetation time had much more influence on the composition of the microbiota than the BCA treatment. After evaluation of all results, three Trichoderma strains originally isolated from Rhizoctonia sclerotia were selected as promising BCAs.     

Methods to account for spatial autocorrelation in the analysis of species distributional data: a review

Species distributional or trait data based on range map (extent‐of‐occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix.     

Involvement of the Adhesion GPCRs Latrophilins in the Regulation of Insulin Release

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