Genome-wide analysis of lipoxygenase gene family in Arabidopsis and rice

The enzymes called lipoxygenases (LOXs) can dioxygenate unsaturated fatty acids, which leads to lipoperoxidation of biological membranes. This process causes synthesis of signaling molecules and also leads to changes in cellular metabolism. LOXs are known to be involved in apoptotic (programmed cell death) pathway, and biotic and abiotic stress responses in plants. Here, the members of LOX gene family in Arabidopsis and rice are identified. The Arabidopsis and rice genomes encode 6 and 14 LOX proteins, respectively, and interestingly, with more LOX genes in rice. The rice LOXs are validated based on protein alignment studies. This is the first report wherein LOXs are identified in rice which may allow better understanding the initiation, progression and effects of apoptosis, and responses to bitoic and abiotic stresses and signaling cascades in plants.Key words: apoptosis, biotic and abiotic stresses, genomics, jasmonic acid, lipidsLipoxygenases (linoleate:oxygen oxidoreductase, EC 1.13.11.-; LOXs) catalyze the conversion of polyunsaturated fatty acids (lipids) into conjugated hydroperoxides. This process is called hydroperoxidation of lipids. LOXs are monomeric, non-heme and non-sulfur, but iron-containing dioxygenases widely expressed in fungi, animal and plant cells, and are known to be absent in prokaryotes. However, a recent finding suggests the existence of LOX-related genomic sequences in bacteria but not in archaea.¹ The inflammatory conditions in mammals like bronchial asthama, psoriasis and arthritis are a result of LOXs reactions.² Further, several clinical conditions like HIV-1 infection,³ disease of kidneys due to the activation of 5-lipoxygenase,^4,5 aging of the brain due to neuronal 5-lipoxygenase⁶ and atherosclerosis⁷ are mediated by LOXs. In plants, LOXs are involved in response to biotic and abiotic stresses.⁸ They are involved in germination⁹ and also in traumatin and jasmonic acid biochemical pathways.^10,11 Studies on LOX in rice are conducted to develop novel strategies against insect pests¹² in response to wounding and insect attack,¹³ and on rice bran extracts as functional foods and dietary supplements for control of inflammation and joint health.¹⁴ In Arabidopsis, LOXs are studied in response to natural and stress-induced senescence,¹⁵ transition to flowering,¹⁶ regulation of lateral root development and defense response.¹⁷The arachidonic, linoleic and linolenic acids can act as substrates for different LOX isozymes. A hydroperoxy group is added at carbons 5, 12 or 15, when arachidonic acid is the substrate, and so the LOXs are designated as 5-, 12- or 15-lipoxygenases. Sequences are available in the database for plant lipoxygenases (EC:1.13.11.12), mammalian arachidonate 5-lipoxygenase (EC:1.13.11.34), mammalian arachidonate 12-lipoxygenase (EC:1.13.11.31) and mammalian erythroid cell-specific 15-lipoxygenase (EC:1.13.11.33). The prototype member for LOX family, LOX-1 of Glycine max L. (soybean) is a 15-lipoxygenase. The LOX isoforms of soybean (LOX-1, LOX-2, LOX-3a and LOX-3b) are the most characterized of plant LOXs.¹⁸ In addition, five vegetative LOXs (VLX-A, -B, -C, -D, -E) are detected in soybean leaves.¹⁹ The 3-dimensional structure of soybean LOX-1 has been determined.^20,21 LOX-1 was shown to be made of two domains, the N-terminal domain-I which forms a β-barrel of 146 residues, and a C-terminal domain-II of bundle of helices of 693 residues²¹ (Fig. 1). The iron atom was shown to be at the centre of domain-II bound by four coordinating ligands, of which three are histidine residues.²²Open in a separate windowFigure 1Three-dimensional structure of soybean lipoxygenase L-1. The domain I (N-terminal) and domain II (C-terminal) are indicated. The catalytic iron atom is embedded in domain II (PDB ID-1YGE).²¹This article describes identification of LOX genes in Arabidopsis and rice. The Arabidopsis genome encodes for six LOX proteins²³ (www.arabidopsis.org) (

Metabolome-ionome-biomass interactions: What can we learn about salt stress by multiparallel phenotyping?

Long-term exposure of plants to saline soil results in mineral ion imbalance, altered metabolism and reduced growth. Currently, the interaction between ion content and plant metabolism under salt-stress is poorly understood. Here we present a multivariate correlation study on the metabolome, ionome and biomass changes of Lotus japonicus challenged by salt stress. Using latent variable models, we show that increasing salinity leads to reproducible changes of metabolite, ion and nutrient pools. Strong correlations between the metabolome and the ionome or biomass may allow one to estimate the degree of salt stress experienced by a plant based on metabolite profiles. Despite the apparently high predictive power of the models, it remains to be investigated whether such metabolite profiles of non- or moderately-stressed plants can be used by breeding programs as ideal ideotypes for the selection of enhanced salt-tolerant genotypes.Key words: acclimation, ionomic, lotus, metabolic, metabolomic, nutrients, salinity, salt stressAcclimation of plants to saline soils involves changes in the uptake, transport and/or partitioning of mineral ions.^1–3 These responses not only alter ion concentrations but also impair metabolism and growth.⁴ Exactly how metabolism as a whole changes in response to salinity is still unknown because of the complexity of the processes involved. Nevertheless, one might expect plant metabolism to respond in a predictable way to salt stress. With this in mind we carried out a multivariate correlation analysis of 137 metabolome and ionome profiles, and the corresponding biomass measurements, of shoot samples from Lotus japonicus exposed to two different salinity regimes.⁵Metabolome data obtained using GC/EI-TOF-MS technology were analyzed using the TagFinder software,⁶ resulting in a series of discrete metabolic-features. A metabolic-feature may be defined to represent a quantitative signal, measured by any analytical means or technology, which is distinct from analytical signals that arise as artefacts from electronic or chemical noise. A total of 1019 metabolic-features were obtained after filtering for those represented by 3 or more inter-correlated mass fragments.⁶ Corresponding ionomic data were obtained using ICP-AES technology and included measurements of Na and 10 macro- and micro-nutrients (K, Ca, S, P, Mg, B, Mn, Fe, Zn and Mo).⁵To integrate metabolomic and ionomic data, we used the statistical multivariate regression technique called orthogonal projections to latent structures (OPLS^7,8), which performs a regression of two matrices or a matrix versus a single variable and simultaneously corrects the resulting model for systematic, irrelevant variance. The metabolite profile matrix was regressed in three different models against the concentrations of Na, K and a matrix of all nutrients excluding K and Na. These regressions were designated metabolome-[ Na], metabolome-[K] and metabolome [nutrients-K] models, respectively. With OPLS it is possible to estimate how well associated the different metabolites are with the modeled variance of the different ions. The used measure is called the correlation loading and can be interpreted as a multivariate version of the standard Pearson correlation. In order to compare how the metabolic profiles may predict the different matrices, we regressed the correlation loadings vectors of the three different models amongst each other (Fig. 1). Remarkably, the loadings were highly correlated. Despite the high magnitude of change in K content compared to the other nutrients under salinity, the metabolome-[K] and metabolome-[nutrients-K] loadings were nearly identical, highlighting that K levels correlate strongly with the main metabolome correlated variance in the rest of the nutrient matrix. These observations suggest that salinity leads to reproducible changes in metabolite pools which match both the concentration of salt accumulated in the shoot and induced changes in the content of other elements.⁵ Since metabolome profiles have been considered a predictor of plant biomass under non-stressed growth conditions,⁹ a metabolome-biomass model was evaluated for the stress cue of our experimental setup. The metabolome data appeared to be correlated to shoot biomass in a manner similar to the predictability of [Na], [K] and [nutrients-K] (Fig. 2). Presumably, this observation reflects the property of the plant system to integrate in a highly interdependent process the nutritive elements, metabolism and growth.Open in a separate windowFigure 1Regression of the correlation loadings obtained from the models metabolome [Na], metabolome [K] and metabolome [nutrients-K].Open in a separate windowFigure 2Regression of the correlation loadings of the metabolome [Na], metabolome [K] and metabolome [nutrients-K] models with the metabolome biomass model.The correlation loadings of the models allowed a ranking of metabolite-features according to their contribution to the modeled regressions. We used the magnitude of the weight of each metabolic-feature to assess which metabolites may be more characteristic or diagnostic of salt stress, as determined by Na levels (Fig. 3).Open in a separate windowFigure 3Predictive power of the analysis, as revealed by a linear regression between the measured [Na] or biomass and the predicted [Na] or biomass from the models. The predictions were performed based on 10-fold crossvalidation, where in each segment the true values of Na content and biomass were held out and predicted from the corresponding metabolome data using the OPLS model.

Table 1

The top-most positively and negatively correlated metabolites of the metabolite [Na] regression model

Genome-wide analysis of thioredoxin fold superfamily peroxiredoxins in Arabidopsis and rice

A broad range of peroxides generated in subcellular compartments, including chloroplasts, are detoxified with peroxidases called peroxiredoxins (Prx). The Prx are ubiquitously distributed in all organisms including bacteria, fungi, animals and also in cyanobacteria and plants. Recently, the Prx have emerged as new molecules in antioxidant defense in plants. Here, the members which belong to Prx gene family in Arabidopsis and rice are been identified. Overall, the Prx members constitute a small family with 10 and 11 genes in Arabidopsis and rice respectively. The prx genes from rice are assigned to their functional groups based on homology search against Arabidopsis protein database. Deciphering the Prx functions in rice will add novel information to the mechanism of antioxidant defense in plants. Further, the Prx also forms the part of redox signaling cascade. Here, the Prx gene family has been described for rice.Key words: antioxidant defense, chloroplast, gene family, oxidative stress, reactive oxygen speciesThe formation of free radicals and reactive oxygen species (ROS) occur in several enzymatic and non-enzymatic reactions during cellular metabolism. The accumulation of these reactive and deleterious intermediates is suppressed by antioxidant defense mechanism comprised of low molecular weight antioxidants and enzymes. In photosynthetic organisms, the defense against the damage from free radicals and oxidative stress is crucial. For instance, the ROS production occurs in photosystem II with generation of singlet oxygen (¹O₂) and hydrogen peroxide (H₂O₂),^1,2 photosystem I from superoxide anion radicals (O₂⁻),³ and during photorespiration with generation of H₂O₂.⁴ ROS production may exceed under environmental stress conditions like excess light, low temperature and drought.⁵The antioxidant defense mechanism is activated by antioxidant metabolities and enzymes which detoxify ROS and lipid peroxides. The detoxification of ROS can occur in various cellular compartments such as chloroplasts, mitochondria, peroxisomes and cytosol.⁶ The enzymes like ascorbate peroxidase, catalase, glutathione peroxidase and superoxide dismutase are prominent antioxidant enzymes.⁶ The peroxiredoxins (Prx) emerged as new components in the antioxidant defense network of barley.^7,8 Later, Prx were studied in other plants.^9–14Prx can be classified into four different functional groups, PrxQ, 1-Cys Prx, 2-Cys Prx and Type-2 Prx.^15,16 They are members of the thioredoxin fold superfamily.^17,18 In this study, the prx genes found in Arabidopsis and rice genomes are been identified. The Arabidopsis genome encodes 10 prx genes classified into four functional categories, 1-Cys Prx, 2-Cys Prx, PrxQ and Type-2 Prx.¹³ Of these, one each of 1-Cys Prx and PrxQ, two of 2-Cys Prx (2-Cys PrxA and 2-Cys PrxB) and six Type-2 Prx (PrxA–F) are identified¹³ (LocusAnnotationSynonymA^*B^*C^*AT1G481301-Cysteine peroxiredoxin 1 (ATPER1)1-Cys Prx21624081.36.603AT1G60740Peroxiredoxin type 2Type-2 PrxD16217471.95.2297AT1G65970Thioredoxin-dependent peroxidase 2 (TPX2)Type-2 PrxC16217413.95.2297AT1G65980Thioredoxin-dependent peroxidase 1 (TPX1)Type-2 PrxB16217427.84.9977AT1G65990Type 2 peroxiredoxin-relatedType-2 PrxA55362653.66.4368AT3G06050Peroxiredoxin IIF (PRXIIF)Type-2 PrxF20121445.29.3905AT3G116302-Cys Peroxiredoxin A (2CPA, 2-Cys PrxA)2-Cys PrxA26629091.77.5686AT3G26060ATPRX Q, periredoxin QPrxQ21623677.810.0565AT3G52960Peroxiredoxin type 2Type-2 PrxE23424684.09.572AT5G062902-Cysteine Peroxiredoxin B (2CPB, 2-Cys PrxB)2-Cys PrxB27329779.55.414Open in a separate window^*A, amino acids; B, molecular weight; C, isoelectric point.In rice (rice.plantbiology.msu.edu/), there are 11 genomic loci which encode for Prx proteins (​and33). Interestingly, a new prx gene (LOC_Os07g15670) annotated as “peroxiredoxin, putative, expressed” is identified making the tally of prx genes to eleven in rice as compared to ten in Arabidopsis (​and22). The BLAST search has identified its counterpart in Arabidopsis which has been annotated as “antioxidant/oxidoreductase” (AT1G21350) in the TAIR database (www.arabidopsis.org). The rice LOC_Os07g15670 and Arabidopsis AT1G21350 share protein homology %68/78 for 236 amino acids (ChromosomeLocus IdPutative function/AnnotationA^*B^*C^*1LOC_Os01g16152peroxiredoxin, putative, expressed19920873.68.22091LOC_Os01g24740peroxiredoxin-2E-1, chloroplast precursor, putative10711591.56.79061LOC_Os01g48420peroxiredoxin, putative, expressed16317290.85.68282LOC_Os02g09940peroxiredoxin, putative, expressed22623179.56.5352LOC_Os02g33450peroxiredoxin, putative, expressed26228096.95.77094LOC_Os04g339702-Cys peroxiredoxin BAS1, chloroplast precursor, putative, expressed12213410.24.37056LOC_Os06g09610peroxiredoxin, putative, expressed2662892610.50976LOC_Os06g42000peroxiredoxin, putative, expressed23323688.39.20597LOC_Os07g15670peroxiredoxin, putative, expressed25327684.69.85457LOC_Os07g44440peroxiredoxin, putative, expressed22124232.65.36187LOC_Os07g44430peroxiredoxin, putative25627785.36.8544Open in a separate window^*A, amino acids; B, molecular weight; C, isoelectric point.

Table 3

Identification of rice homologs of peroxiredoxins in A. thaliana

Cell cycle phosphorylation of mitotic exit network (MEN) proteins

Phosphorylation of proteins is an important mechanism used to regulate most cellular processes. Recently, we completed an extensive phosphoproteomic analysis of the core proteins that constitute the Saccharomyces cerevisiae centrosome. Here, we present a study of phosphorylation sites found on the mitotic exit network (MEN) proteins, most of which are associated with the cytoplasmic face of the centrosome. We identified 55 sites on Bfa1, Cdc5, Cdc14 and Cdc15. Eight sites lie in cyclin-dependent kinase motifs (Cdk, S/T-P), and 22 sites are completely conserved within fungi. More than half of the sites were found in centrosomes from mitotic cells, possibly in preparation for their roles in mitotic exit. Finally, we report phosphorylation site information for other important cell cycle and regulatory proteins.Key words: in vivo phosphorylation, yeast centrosome, mitotic exit network (MEN), cell cycle, protein kinase, Cdk (cyclin-dependent kinase)/Cdc28, Plk1 (polo-like kinase)/Cdc5Reversible protein phosphorylation leads to changes in targeting, structure and stability of proteins and is used widely to modulate biochemical reactions in the cell. We are interested in phosphoregulation of centrosome duplication and function in the yeast Saccharomyces cerevisiae. Centrosomes nucleate microtubules and, upon duplication during the cell cycle, form the two poles of the bipolar mitotic spindle used to segregate replicated chromosomes into the two daughter cells. Timing and spatial cues are highly regulated to ensure that elongation of the mitotic spindle and separation of sister chromatids occur prior to progression into late telophase and initiation of mitotic exit. The mitotic exit network (MEN) regulates this timing through a complex signaling cascade activated at the centrosome that triggers the end of mitosis, ultimately through mitotic cyclin-dependent kinase (Cdk) inactivation (reviewed in ref. ¹).The major components of the MEN pathway (Fig. 1) are a Ras-like GTPase (Tem1), an activator (Lte1) with homology to nucleotide exchange factors, a GTPase-activating protein (GAP) complex (Bfa1/Bub2), several protein kinases [Cdc5 (Plk1 in humans), Cdc15 and Dbf2/Mob1] and Cdc14 phosphatase (reviewed in ref. ^2–5). Tem1 initiates the signal for the MEN pathway when switched to a GTP-active state. Prior to activation at anaphase, it is held at the centrosome in an inactive GDP-bound state by an inhibiting GAP complex, Bfa1/Bub2.⁶ The Bfa1/Bub2 complex and the inactive Tem1 are localized at the mother centrosome destined to move into the budded cell upon chromosome segregation, whereas the activator Lte1 is localized at the tip of the budded cell. These separate localizations ensure that Lte1 and Tem1 only interact in late anaphase, when the mitotic spindle elongates.^7,8 Lte1 has been thought to activate Tem1 as a nucleotide exchange factor, although more recent evidence suggests that it may instead affect Bfa1 localization.⁹ In addition, full activation of Tem1 is achieved through Cdc5 phosphorylation of the negative regulator Bfa1 ¹⁰ and potentially through phosphorylation of Lte1. GTP-bound Tem1 is then able to recruit Cdc15 to the centrosome, allowing for Dbf2 activation.³ The final step in the MEN pathway is release of Cdc14 from the nucleolus, which is at least partially due to phosphorylation by Dbf2¹¹ an leads to mitotic cyclin degradation and inactivation of the mitotic kinase.²Open in a separate windowFigure 1Schematic representation of the MEN proteins and pathway. MEN protein localization is shown within a metaphase cell when mitotic exit is inhibited and in a late anaphase cell when mitotic exit is initiated. Primary inhibition and activation events are described below the cells.Recently, we performed a large-scale analysis of phosphorylation sites on the 18 core yeast centrosomal proteins present in enriched centrosomal preparations.¹² In total, we mapped 297 sites on 17 of the 18 proteins and described their cell cycle regulation, levels of conservation and demonstrated defects in centrosome assembly and function resulting from mutating selected sites. MEN proteins were also identified in the centrosome preparations. This was expected, because Nud1, one of the 18 core centrosome components, is known to recruit several MEN proteins to the centrosome¹³ as part of its function in mitotic exit.^14,15 As phosphorylation is essential to several steps in the MEN pathway, beginning with recruitment of Bfa1/Bub2 by phosphorylated Nud1,¹⁵ we were interested in mapping in vivo phosphorylation sites on the MEN proteins associated with centrosomes and identifying when they occur during the cell cycle.We combined centrosome enrichment with mass spectrometry analysis to examine phosphorylation from asynchronously growing cells.¹² Centrosomes were also prepared from cells arrested in G₁ and mitosis¹² to monitor potentially cell cycle-regulated sites. We obtained significant coverage of a number of the MEN proteins, several of which have human homologs (​and33, column 1), of which eight sites lie within Cdk/Cdc28 motifs [S/T(P)], (23 Mob1 and Dbf2 are known phosphoproteins²⁴ for which we observed peptide coverage but no phosphorylation. Surprisingly, we did not detect phosphorylation on Bub2 despite the high peptide coverage; it is possible that the mitotic centrosome preparations (using a Cdc20 depletion protocol) affect the phosphorylation state of Bub2, as Bub2 is required for mitotic exit arrest in cdc20 mutants.²⁵ Additionally, specific phosphorylation sites have not been mapped on Bub2, suggesting that modifications on this protein may be difficult to observe by mass spectrometry. Lte1 does not localize to the centrosome, and we did not recover Lte1 peptides in our preparations. Many phosphorylation events on MEN proteins were observed in mitotic centrosomal preparations, most likely in preparation for their subsequent role in exit from mitosis (MEN ProteinSequence CoverageTotal SitesS/T (P) SitesHuman HomologsBfa198%352N/ACdc1480%102CDC14A, 14B2Cdc1512%31MST1, STK4Cdc541%73PLK1, PLK2, PLK3Bub267%--N/ATem118%--RAB22, RAB22AMob113%--MOB1B, 1A, 2A, 2BDbf22%--STK38, LATS1TOTAL558Open in a separate window

Table 2

Cell cycle regulators of MEN proteins

Sources of floral scent variation: Can environment define floral scent phenotype?

Studies of floral scent generally assume that genetic adaptation due to pollinator-mediated natural selection explains a significant amount of phenotypic variance, ignoring the potential for phenotypic plasticity in this trait. In this paper, we assess this latter possibility, looking first at previous studies of floral scent variation in relation to abiotic environmental factors. We then present data from our own research that suggests among-population floral scent variation is determined, in part, by environmental conditions and thus displays phenotypic plasticity. Such an outcome has strong ramifications for the study of floral scent variation; we conclude by presenting some fundamental questions that should lead to greater insight into our understanding of the evolution of this trait, which is important to plant-animal interactions.Key words: abiotic factors, aromatics, floral scent, GxE interaction, phenotypic plasticity, pollination, terpenoids, volatilesFloral scent is thought to function as a major non-visual attractive cue for many pollinators in a large number of plant systems^1,2 and therefore most research on this plant trait has proceeded in the context of pollination ecology. Such studies have revealed the physiological and behavioral responses of pollinators to various floral volatiles (reviewed in refs. ³ and ⁴), convergent evolution of odor phenotypes attractive to specific pollinator classes (reviewed in refs. ⁵ and ⁶), reproductive isolation of plant species due to differences in pollinator attraction by scent,⁷ and instances of deception in which flowers mimic insect pheromones to effect pollination.⁸ Together, this body of evidence suggests that specific floral scent profiles can have important implications for the reproductive potential of many plant species.This pollinator-centered viewpoint has carried through to research on floral scent variation, including our most recent work on the insect-pollinated species Hesperis matronalis (Brassicaceae).⁹ Such studies usually suggest that the floral scent variation commonly found within and among individuals, populations and species (reviewed in ref. ²) is due to genetic differentiation as a result of selection by pollinators over time (reviewed in ref. ¹⁰). But an organism''s genes are only one factor determining phenotype. Both biotic (living) and abiotic (non-living) environmental conditions can profoundly affect phenotype expression, leading to significant variation. For plants, abiotic factors such as climate and soil chemistry can have particularly strong effects on phenotypes. When these environmental conditions cause changes in phenotype, we would say that a trait displays phenotypic plasticity.^11–13 A number of studies have uncovered phenotypic plasticity for many different plant traits.¹² However, while phenotypic variation in floral scent has been well-documented^1,2 and correlated with variation in biotic factors like pollinator behavior,^14–17 these studies were decidedly focused on natural selection, rather than phenotypic plasticity, as an organizational framework.However, in examining the scientific literature on floral scent, we found four studies in which the effects of naturally variable abiotic factors on floral scent profiles were examined, three of which were performed by the same research group (18^–21 (21). Moreover, these studies are decidedly not analyzed and interpreted using standard protocols for phenotypic plasticity studies.¹³

Table 1

A survey of previous studies examining changes in floral scent phenotype due to abiotic factors

Aluminum induced proteome changes in tomato cotyledons

Cotyledons of tomato seedlings that germinated in a 20 µM AlK(SO₄)₂ solution remained chlorotic while those germinated in an aluminum free medium were normal (green) in color. Previously, we have reported the effect of aluminum toxicity on root proteome in tomato seedlings (Zhou et al.¹). Two dimensional DIGE protein analysis demonstrated that Al stress affected three major processes in the chlorotic cotyledons: antioxidant and detoxification metabolism (induced), glyoxylate and glycolytic processes (enhanced), and the photosynthetic and carbon fixation machinery (suppressed).Key words: aluminum, cotyledons, proteome, tomatoDifferent biochemical processes occur depending on the developmental stages of cotyledons. During early seed germination, before the greening of the cotyledons, glyoxysomes enzymes are very active. Fatty acids are converted to glucose via the gluconeogenesis pathway.^2,3 In greening cotyledons, chloroplast proteins for photosynthesis and leaf peroxisomal enzymes in the glycolate pathway for photorespiration are metabolized.^2–4 Enzymes involved in regulatory mechanisms such as protein kinases, protein phosphatases, and mitochondrial enzymes are highly expressed.^3,5,6The chlorotic cotyledons are similar to other chlorotic counterparts in that both contains lower levels of chlorophyll, thus the photosynthetic activities are not as active. In order to understand the impact of Al on tomato cotyledon development, a comparative proteome analysis was performed using 2D-DIGE following the as previously described procedure.¹ Some proteins accumulated differentially in Al-treated (chlorotic) and untreated cotyledons (Fig. 1). Mass spectrometry of tryptic digestion fragments of the proteins followed by database search has identified some of the differentially expressed proteins (Open in a separate windowFigure 1Image of protein spots generated by Samspot analysis of Al treated and untreated tomato cotyledons proteomes separated on 2D-DIGE.

Table 1

Proteins identified from tomato cotyledons of seeds germinating in Al-solution

Prion interference with multiple prion isolates

Co-inoculation of prion strains into the same host can result in interference, where replication of one strain hinders the ability of another strain to cause disease. The drowsy (DY) strain of hamster-adapted transmissible mink encephalopathy (TME) extends the incubation period or completely blocks the hyper (HY) strain of TME following intracerebral, intraperitoneal or sciatic nerve routes of inoculation. However, it is not known if the interfering effect of the DY TME agent is exclusive to the HY TME agent by these experimental routes of infection. To address this issue, we show that the DY TME agent can block hamster-adapted chronic wasting disease (HaCWD) and the 263K scrapie agent from causing disease following sciatic nerve inoculation. Additionally, per os inoculation of DY TME agent slightly extends the incubation period of per os superinfected HY TME agent. These studies suggest that prion strain interference can occur by a natural route of infection and may be a more generalized phenomenon of prion strains.Key words: prion diseases, prion interference, prion strainsPrion diseases are fatal neurodegenerative diseases that are caused by an abnormal isoform of the prion protein, PrP^Sc.¹ Prion strains are hypothesized to be encoded by strain-specific conformations of PrP^Sc resulting in strain-specific differences in clinical signs, incubation periods and neuropathology.^2–7 However, a universally agreed upon definition of prion strains does not exist. Interspecies transmission and adaptation of prions to a new host species leads to the emergence of a dominant prion strain, which can be due to selection of strains from a mixture present in the inoculum, or produced upon interspecies transmission.^8,9 Prion strains, when present in the same host, can interfere with each other.Prion interference was first described in mice where a long incubation period strain 22C extended the incubation period of a short incubation period strain 22A following intracerebral inoculation.¹⁰ Interference between other prion strains has been described in mice and hamsters using rodent-adapted strains of scrapie, TME, Creutzfeldt-Jacob disease and Gerstmannn-Sträussler-Scheinker syndrome following intracerebral, intraperitoneal, intravenous and sciatic nerve routes of inoculation.^10–15 We previously demonstrated the detection of PrP^Sc from the long incubation period DY TME agent correlated with its ability to extend the incubation period or completely block the superinfecting short incubation period HY TME agent from causing disease and results in a reduction of HY PrP^Sc levels following sciatic nerve inoculation.¹² However, it is not known if a single long incubation period agent (e.g., DY TME) can interfere with more than one short incubation period agent or if interference can occur by a natural route of infection.To examine the question if one long incubation period agent can extend the incubation period of additional short incubation period agents, hamsters were first inoculated in the sciatic nerve with the DY TME agent 120 days prior to superinfection with the short-incubation period agents HY TME, 263K scrapie and HaCWD.^16–18 The HY TME and 263K scrapie agents have been biologically cloned and have distinct PrP^Sc properties.^19,20 The HaCWD agent used in this study is seventh hamster passage that has not been biologically cloned and therefore will be referred to as a prion isolate. Sciatic nerve inoculations were performed as previously described.^11,12 Briefly, hamsters were inoculated with 103.0 i.c. LD₅₀ of the DY TME agent or equal volume (2 µl of a 1% w/v brain homogenate) of uninfected brain homogenate 120 days prior to superinfection of the same sciatic nerve with either 10^4.6 i.c. LD₅₀ of the HY TME agent, 10^5.2 i.c. LD₅₀ of the HaCWD agent or 10^4.6 i.c. LD₅₀/g 263K scrapie agent (Bartz J, unpublished data).^16,18,21 Animals were observed three times per week for the onset of clinical signs of HY TME, 263K and HaCWD based on the presence of ataxia and hyperexcitability, while the clinical diagnosis of DY TME was based on the appearance of progressive lethargy.^16–18 The incubation period was calculated as the number of days between the onset of clinical signs of the agent strain that caused disease and the inoculation of that strain. The Student''s t-test was used to compare incubation periods.¹² We found that sciatic nerve inoculation of both the HaCWD agent and 263K scrapie agent caused disease with a similar incubation period to animals infected with the HY TME agent (12 In hamsters inoculated with the DY TME agent 120 days prior to superinfection with the HaCWD or 263K agents, the animals developed clinical signs of DY TME with an incubation period that was not different from the DY TME agent control group (12 The PrP^Sc migration properties were consistent with the clinical diagnosis and all co-infected animals had PrP^Sc that migrated similar to PrP^Sc from the DY TME agent infected control animal (Fig. 1, lanes 1–10). This data indicates that the DY TME agent can interfere with more than one isolate and that interference in the CNS may be a more generalized phenomenon of prion strains.Open in a separate windowFigure 1The strain-specific properties of PrP^Sc correspond to the clinical diagnosis of disease. Western blot analysis of 250 µg brain equivalents of proteinase K digested brain homogenate from prion-infected hamsters following intracerebral (i.c.), sciatic nerve (i.sc.) or per os inoculation with either the HY TME (HY), DY TME (DY), 263K scrapie (263K), hamster-adapted CWD (CWD) agents or mock-infected (UN). The unglycoyslated PrP^Sc glycoform of HY TME, 263K scrapie and hamster-adapted CWD migrates at 21 kDa. The unglycosylated PrP^Sc glycoform of DY PrP^Sc migrates at 19 kDa. Migration of 19 and 21 kDa PrP^Sc are indicated by the arrows on the left of the figure. n.a., not applicable.

Table 1

Clinical signs and incubation periods of hamsters inoculated in the sciatic nerve with either the HY TME, HaCWD or 263K scrapie agents, or co-infected with the DY TME agent 120 days prior to superinfection of hamsters with the HY TME, HaCWD or 263K agents

Stress-induced flowering

Many plant species can be induced to flower by responding to stress factors. The short-day plants Pharbitis nil and Perilla frutescens var. crispa flower under long days in response to the stress of poor nutrition or low-intensity light. Grafting experiments using two varieties of P. nil revealed that a transmissible flowering stimulus is involved in stress-induced flowering. The P. nil and P. frutescens plants that were induced to flower by stress reached anthesis, fruited and produced seeds. These seeds germinated, and the progeny of the stressed plants developed normally. Phenylalanine ammonialyase inhibitors inhibited this stress-induced flowering, and the inhibition was overcome by salicylic acid (SA), suggesting that there is an involvement of SA in stress-induced flowering. PnFT2, a P. nil ortholog of the flowering gene FLOWERING LOCUS T (FT) of Arabidopsis thaliana, was expressed when the P. nil plants were induced to flower under poor-nutrition stress conditions, but expression of PnFT1, another ortholog of FT, was not induced, suggesting that PnFT2 is involved in stress-induced flowering.Key words: flowering, stress, phenylalanine ammonia-lyase, salicylic acid, FLOWERING LOCUS T, Pharbitis nil, Perilla frutescensFlowering in many plant species is regulated by environmental factors, such as night-length in photoperiodic flowering and temperature in vernalization. On the other hand, a short-day (SD) plant such as Pharbitis nil (synonym Ipomoea nil) can be induced to flower under long days (LD) when grown under poor-nutrition, low-temperature or high-intensity light conditions.^1–9 The flowering induced by these conditions is accompanied by an increase in phenylalanine ammonia-lyase (PAL) activity.¹⁰ Taken together, these facts suggest that the flowering induced by these conditions might be regulated by a common mechanism. Poor nutrition, low temperature and high-intensity light can be regarded as stress factors, and PAL activity increases under these stress conditions.¹¹ Accordingly, we assumed that such LD flowering in P. nil might be induced by stress. Non-photoperiodic flowering has also been sporadically reported in several plant species other than P. nil, and a review of these studies suggested that most of the factors responsible for flowering could be regarded as stress. Some examples of these factors are summarized in 12^–14

Table 1

Some cases of stress-induced flowering

Arabidopsis thaliana overexpressing glycolate oxidase in chloroplasts: H2O2-induced changes in primary metabolic pathways

Reactive oxygen species (ROS) represent both toxic by-products of aerobic metabolism as well as signaling molecules in processes like growth regulation and defense pathways. The study of signaling and oxidative-damage effects can be separated in plants expressing glycolate oxidase in the plastids (GO plants), where the production of H₂O₂ in the chloroplasts is inducible and sustained perturbations can reproducibly be provoked by exposing the plants to different ambient conditions. Thus, GO plants represent an ideal non-invasive model to study events related to the perception and responses to H₂O₂ accumulation. Metabolic profiling of GO plants indicated that under high light a sustained production of H₂O₂ imposes coordinate changes on central metabolic pathways. The overall metabolic scenario is consistent with decreased carbon assimilation, which results in lower abundance of glycolytic and tricarboxylic acid cycle intermediates, while simultaneously amino acid metabolism routes are specifically modulated. The GO plants, although retarded in growth and flowering, can complete their life cycle indicating that the reconfiguration of the central metabolic pathways is part of a response to survive and thus, to adapt to stress conditions imposed by the accumulation of H₂O₂ during the light period.Key words: Arabidopsis thaliana, H₂O₂, oxidative stress, reactive oxygen species, signalingReactive oxygen species (ROS) are key molecules in the regulation of plant development, stress responses and programmed cell death. Depending on the identity of ROS species or its subcellular production site, different cellular responses are provoked.¹ To assess the effects of metabolically generated H₂O₂ in chloroplasts, we have recently generated Arabidopsis plants in which the peroxisomal GO was targeted to chloroplasts.² The GO overexpressing plants (GO plants) show retardation in growth and flowering time, features also observed in catalase, ascorbate peroxidase and MnSOD deficient mutants.^3–5 The analysis of GO plants indicated that H₂O₂ is responsible for the observed phenotype. GO plants represent an ideal non-invasive model system to study the effects of H₂O₂ directly in the chloroplasts because H₂O₂ accumulation can be modulated by growing the plants under different ambient conditions. By this, growth under low light or high CO₂ concentrations minimizes the oxygenase activity of RubisCO and thus the flux through GO whereas the exposition to high light intensities enhances photorespiration and thus the flux through GO.Here, we explored the impact of H₂O₂ production on the primary metabolism of GO plants by assessing the relative levels of various metabolites by gas chromatography coupled to mass spectrometry (GC-MS)⁶ in rosettes of plants grown at low light (30 µmol quanta m⁻² s⁻¹) and after exposing the plants for 7 h to high light (600 µmol quanta m⁻² s⁻¹). The results obtained for the GO5 line are shown in After 1 h at 30 µEAfter 7 h at 600 µEAlanine0.88 ± 0.052.83 ± 0.68Asparagine1.39 ± 0.123.64 ± 0.21Aspartate0.88 ± 0.031.65 ± 0.10GABA1.14 ± 0.051.13 ± 0.05Glutamate0.97 ± 0.041.51 ± 0.07Glutamine1.06 ± 0.111.87 ± 0.06Glycine1.23 ± 0.070.30 ± 0.02Isoleucine3.52 ± 0.403.00 ± 0.15Leucine1.36 ± 0.220.57 ± 0.06Lysine1.49 ± 0.130.38 ± 0.02Methionine0.96 ± 0.054.54 ± 0.51Phenylalanine0.95 ± 0.030.94 ± 0.04Proline1.32 ± 0.221.60 ± 0.13Serine1.05 ± 0.041.49 ± 0.15Threonine4.74 ± 0.175.51 ± 0.34Valine0.91 ± 0.130.29 ± 0.02Citrate/Isocitrate0.65 ± 0.020.64 ± 0.022-oxoglutarate0.95 ± 0.110.76 ± 0.05Succinate0.78 ± 0.040.72 ± 0.02Fumarate0.64 ± 0.030.31 ± 0.01Malate0.74 ± 0.030.60 ± 0.02Pyruvate1.19 ± 0.280.79 ± 0.04Ascorbate1.13 ± 0.142.44 ± 0.45Galactonate-γ-lactone1.81 ± 0.401.62 ± 0.28Fructose1.20 ± 0.130.37 ± 0.01Glucose1.38 ± 0.170.30 ± 0.01Mannose0.90 ± 0.271.34 ± 0.28Sucrose1.04 ± 0.070.49 ± 0.02Fructose-6P0.82 ± 0.151.20 ± 0.15Glucose-6P0.87 ± 0.061.25 ± 0.183-PGA1.13 ± 0.110.35 ± 0.02DHAP1.38 ± 0.091.26 ± 0.08Glycerate0.99 ± 0.040.67 ± 0.01Glycerol1.07 ± 0.041.12 ± 0.05Shikimate1.18 ± 0.040.35 ± 0.01Salicylic acid1.04 ± 0.180.66 ± 0.18Open in a separate windowPlants were grown at 30 µmol m⁻² sec⁻¹ (30 µE). The samples were collected 1 h after the onset of the light period and after 7 h of exposure to 600 µmol m⁻² sec⁻¹ (600 µE), respectively. The values are relative to the respective wild-type (each metabolite = 1) and represent means ± SE of four determinations of eight plants. (*) indicates the value is significantly different from the respective wild-type as determined by the Student''s t test (p < 0.05).At the beginning of the light period in low light conditions, some significant deviations in the levels of metabolites tested were observed in GO plants when compared to the wild-type (2 the transgenic GO activity is sufficient to induce a characteristic metabolic phenotype (Fig. 1). The levels of the tricarboxylic acid (TCA) cycle intermediates, citrate/isocitrate, succinate, fumarate and malate were lower in the GO plants (7 In consequence, OAA might not freely enter the TCA cycle and is redirected to the synthesis of Lys, Thr and Ile, which accumulate in the GO plants (Open in a separate windowFigure 1Simplified scheme of the primary metabolism showing the qualitative variations in metabolite abundance in GO plants obtained by GC-MS analysis (2 Blue boxes indicate a significant increase in the content of the particular metabolite compared to the wild-type, while red boxes indicate a significant decrease. Metabolites without boxes have not been determined. The arrows do not always indicate single steps. Adapted from Baxter et al., 2007.High light treatment induced massive changes in the metabolic profile of GO plants (Fig. 1). The OAA-derived amino acids Asp, Asn, Thr, Ile and Met as well as the 2-oxoglutarate-derived amino acids Glu and Gln accumulated. On the contrary, the levels of the Pyr-derived amino acids Val and Leu and the OAA-derived amino acid Lys decreased. A rational explanation for these metabolic changes is difficult to assess, but these changes could be a consequence of a metabolic reconfiguration in response to high light leading to required physiological functions and thus ensuring continued cellular function and survival, e.g., production of secondary metabolites to mitigate photooxidative damage. The higher levels of Glu observed in the GO plants could be attributed to alternative pathways of glyoxylate metabolism that may occur during photorespiration.⁸ It has been shown earlier that isocitrate derived from glyoxylate and succinate is decarboxylated by cytosolic isocitrate dehydrogenase producing 2-oxoglutarate and further glutamate.⁸In GO plants grown under low light conditions (minimized photorespiratory conditions), the levels of Gly were similar to those of the wild-type whereas, after exposure to high light (photorespiratory conditions), the Gly levels were extremely low, indicating that the GO activity diverts a significant portion of flux from the photorespiratory pathway (7 and also the levels of the lipoic acid-containing subunits of the pyruvate- and 2-oxoglutarate dehydrogenases were shown to be significantly reduced under oxidative stress conditions.^9,10 Similarly, the contents of the soluble sugars sucrose, fructose and glucose and those of 3-PGA and glycerate were lower. In addition, the GO plants showed an impairment in the accumulation of starch under high light conditions, a feature that was not observed if the plants were grown under non-photorespiratory conditions.²Together, these results indicate that the low photosynthetic carbon assimilation in the GO plants exposed to high light is most probably due to enhanced photoinhibition,² the repression of genes encoding photosynthetic components by H₂O₂,^11–13 and the direct damage or inhibition of enzyme activities involved in CO₂ assimilation and energy metabolism by H₂O₂.^7,10,14,15 Moreover, Scarpeci and Valle¹³ showed that in plants treated with the superoxid anion radical producing methylviologen (MV) most of the genes involved in phosphorylytic starch degradation, e.g., the trioseP/Pi translocator and genes involved in starch and sucrose synthesis were repressed, while genes involved in hydrolytic starch breakdown and those involved in sucrose degradation were induced. In line with this, the contents of carbohydrates were also lower in MV-treated plants. Together, these observations can also explain the lower growth rates of the GO plants in conditions where the oxygenase activity of RubisCO becomes important and thus, the flux through GO increases.²The levels of shikimate were lower in GO plants (2^,16 and the low levels of substrates available, as anthocyanins are ultimately synthesized from photosynthates and the GO plants showed a diminished photosynthetic performance.²As expected, the levels of ascorbate and its precursor, galactonate-γ-lactone, were enhanced in the GO plants clearly showing the activation of the cellular antioxidant machinery (10 described the metabolic response to oxidative stress of heterotrophic Arabidopsis cells treated with menadione, which also generates superoxide anion radicals. This oxidative stress was shown to induce metabolic inhibition of flux through the TCA cycle and sectors of amino acid metabolism together with a diversion of carbon into the oxidative pentose phosphate pathway.Signaling and oxidative-damage effects are difficult to separate by manipulating the enzymes of antioxidant systems. In this regard, the GO plants represent a challenging inducible model that avoid acclimatory and adaptative effects. Moreover, it is possible to control the H₂O₂ production in the chloroplasts of GO plants without inducing oxidative damage by changing the conditions of growth.² Further exploration of metabolic changes imposed by different ROS at the cellular and whole organ levels will allow to address many intriguing questions on how plants can rearrange metabolism to cope with oxidative stresses.     

Dementia screening, biomarkers and protein misfolding: Implications for public health and diagnosis

Misfolded proteins are at the core of many neurodegenerative diseases, nearly all of them associated with cognitive impairment. For example, Creutzfeldt-Jacob disease is associated with aggregation of prion protein,^1,2 Lewy body dementia and Parkinson disease with α-synuclein^3,4 and forms of frontotemporal dementia with tau, TDP43 and a host of other proteins.^5,6 Alzheimer disease (AD), the most common cause of dementia,⁷ and its prodromal syndrome mild cognitive impairment (MCI)⁸ are an increasing public health problem and a diagnostic challenge to many clinicians. AD is characterized pathologically by the accumulation of amyloid β-protein (Aβ)^9,10 as senile plaques and in the walls of blood vessels as amyloid angiopathy.^11,12 Additionally, there are accumulations of tau-protein as neurofibrillary tangles and dystrophic neurites.^11,12 Biological markers of AD and MCI can serve as in vivo diagnostic indicators of underlying pathology, particularly when clinical symptoms are mild^13–15 and are likely present years before the onset of clinical symptoms.^16–19 Research to discover and refine fluid and imaging biomarkers of protein aggregation has undergone a rapid evolution^20–22 and combined analysis of different modalities may further increase diagnostic sensitivity and specificity.^23–26 Multi-center trials are now investigating whether imaging and/or cerebrospinal fluid (CSF) biomarker candidates can be used as outcome measures for use in phase III clinical trials for AD.^27–29Key words: dementia, screening, biomarkers, amyloid, tau, Alzheimer disease, preclinical, presymptomaticCurrently, the diagnosis of AD is based on exclusion of other forms of impairment with definitive diagnosis requiring autopsy confirmation.³⁰ Thus, there is a strong need to find easily measurable in vivo AD biomarkers that could facilitate early and accurate diagnosis³¹ as well as prognostic data to assist in monitoring therapeutic efficacy.³² Although biological markers such as MRI, PET scans and CSF increase the diagnostic likelihood that AD is present,^{9,18–20,33,34} biomarkers are invasive, uncomfortable, expensive and may not be readily available to rural areas, underserved communities, underinsured individuals or developing countries, making them impractical for broad use. However, the lessons learned from biomarkers can be applied to increase the likelihood that clinicians will be able to detect disease at earlier stages in the form of dementia screening.Public health may be best defined as the organized efforts of society to improve health, often framed in terms of primary, secondary and tertiary prevention. Prevention encompasses an understanding of causation, alteration of natural history of disease and understanding of pathophysiological mechanisms.³⁵ The clearest application of this from a public health perspective is in the setting of secondary prevention (i.e., screening)—early detection as a core element, coupled with treatments or preventative actions to reduce the burden of disease.³⁵ In this instance we seek to identify individuals in whom a disease has already begun and who may be experiencing very mild clinical symptoms but have not yet sought out medical care. The objective of effective screening is to detect the disease earlier than it would have been detected with usual care. Recent healthcare reform (Accountable Care Act)³⁶ proposes a Personalized Prevention Plan including screening for cognitive disorders, reimbursable through Medicare. Thus tying knowledge about dementia screening with underlying biology of protein misfolding associated with neurodegenerative disease can have enormous implications.A review of the natural history of dementia illustrates this point (Fig. 1). The timeline of disease from presumptive start to the patient demise is plotted. Stage I marks the biologic onset of disease; however this point often cannot be identified and may begin years to decades before any evidence is apparent (represented by dashed lines). As this stage is subclinical, it is difficult to study in humans but lends itself nicely to animal models. At some point in the progression of the biology, stage II begins heralding the first pathologic evidence of disease could be obtained—in the case of AD this could include CSF measurements of amyloid and tau^22,26,27 or PET imaging with amyloid ligands.^18,37 Subsequently, the first signs and symptoms of disease develop (stage III). Till this point, the disease process has been entirely presymptomatic. Beginning with the onset of symptoms, the patient may seek medical care (stage IV) and eventually be diagnosed (stage V). From stage III onwards, the patient enters the symptomatic phase of disease. From this point, the patient is typically treated with various pharmacologic and nonpharmacologic approaches towards some outcome. Another way to envision the disease spectrum is from the biological onset to the seeking of medical attention as the preclinical phase of disease with the clinical phase beginning with the initial clinical investigations into the cause of the patients'' symptoms.Open in a separate windowFigure 1Model of the natural history of AD. Timeline from presumptive start of AD through patient diagnosis is plotted. The initiation of biological changes (stage I) marks the onset of disease and begins years to decades before any evidence is apparent (represented by dashed lines). At some point the first pathologic evidence of disease (stage II) begins and in theory can be detected with biomarkers such as CSF measurements of amyloid and tau or PET imaging with amyloid ligands. Subsequently, the first signs and symptoms of disease develop (stage III) followed by the patient seeking medical attention (stage IV) and finally a diagnosis is established (stage V). This timeline can be clustered into a presymptomatic phase (stages I–III) and a symptomatic phase (stages III–V). An alternative way to envision the disease spectrum is from the biological onset to the seeking of medical attention (stages I–IV) as the preclinical phase of disease with the clinical phase beginning with the initial clinical investigations into the cause of the patients'' symptoms (stages IV and V). Stage III is the ideal time for dementia screening.What is the value of thinking about disease in this fashion? Such models allow researchers and clinicians to model the approach to finding and applying new diagnostics and offering new interventions. From stage I to stage III, the patient is the presymptomatic, preclinical phase of disease. The only means of detection would be with a biological marker that reflected protein misfolding or some proxy marker of these events. Although longitudinal evidence of cognitive change exist from 1–3 years before clinical diagnosis, raw scores on neuropsychological testing during this time remains in the normal range.³⁸ After stage IV, the patient is in the symptomatic, clinical phase of disease. Testing here is centered on confirming the suspected diagnosis, correctly staging the disease and initiating the appropriate therapies. Basic scientific approaches focusing on the presymptomatic, preclinical phase and clinical care approaches focusing on the symptomatic, clinical phase are well established and will continue to benefit from additional research.However, if we focus only on these two phases, an opportunity will be missed to make a decidedly important impact in the patient''s well-being. From stage III to stage IV, the patient enters symptomatic, preclinical phase of disease; symptomatic because the patient or family is beginning to detect some aspect of change, but preclinical because these signs and symptoms have not yet been brought to medical attention. In the case of AD (and the other forms of dementia) this period may go for an extended length of time as patients, families and clinicians dismiss early cognitive symptoms as part of the normal aging process. Thus, the rationale for screening is that if we can identify disease earlier in its natural history than would ordinarily occur, intervention measures (those currently available and those that are being developed) would be more effective. Dementia screening therefore would be best suited to detect cognitive impairment at the beginning of disease signs (stage III), particularly if these screening measures reflect what is known about the symptomatic, clinical phase of disease and correlate with the pathologic changes occurring in the brain during the pre-symptomatic, preclinical phase of disease.In a recent paper, we evaluated the relationship between several dementia screening tests and biomarkers of AD.⁴⁰ We tested whether a reliable and validated informant-based dementia screening test (the AD8)^41,42 correlates with changes in AD biomarkers and, if positive, screening with the AD8 clinically supports an AD clinical phenotype, superior to a commonly used performance-based screening tests including the Mini Mental State Exam (MMSE)⁴³ and the Short Blessed Test (SBT).⁴⁴ A total of 257 participants were evaluated, administered a comprehensive clinical and cognitive evaluation with the Clinical Dementia Rating scale (CDR)⁴⁵ used as the gold standard. Participants consented to and completed a variety of biomarker studies including MRI, amyloid imaging using the Pittsburgh Compound B (PiB)^37,46 and CSF studies of Aβ₄₂, tau and phosphorylated tau at Serine 181 (p-tau₁₈₁).^23,24 The sample had a mean age of 75.4 ± 7.3 years with 15.1 ± 3.2 years of education. The sample was 88.7% Caucasian and 45.5% male with a mean MMSE score of 27.2 ± 3.6. The formal diagnoses of the sample was 156 CDR 0 cognitively normal, 23 CDR 0.5 MCI, 53 CDR 0.5 very mild AD and 25 CDR 1 mild AD. Participants with positive AD8 scores (graded as a score of 2 or greater) exhibited the typical AD fluid biomarker phenotype characterized by significantly lower mean levels of CSF Aβ₄₂, greater CSF tau, p-tau₁₈₁ and the tau(s)/Aβ₄₂ ratios.^26,27 They also exhibited smaller temporal lobe volumes and increased mean cortical binding potential (MCBP) for PiB imaging similar to studies of individuals with AD.^18,19 These findings support that informant-based assessments may be superior to performance-based screening measures such as the MMSE or SBT in corresponding to underlying AD pathology, particularly at the earliest stages of decline. The use of a brief test such as the AD8 may improve strategies for detecting dementia in community settings where biomarkers may not be readily available and also may enrich clinical trial recruitment by increasing the likelihood that participants have underlying biomarker abnormalities.⁴⁰To gain a better understanding of changes in biomarkers in the symptomatic, preclinical phase, a post hoc evaluation of the 156 individuals who were rated as CDR 0 no dementia at the time of their Gold Standard assessment was completed. Some of these nondemented individuals have abnormal AD biomarkers, but in the absence of performing lumbar punctures or PET scans, is it possible to detect evidence of change? AD8 scores for 132 individuals were less than 2; thus their screening test suggests no impairment (mean AD8 score = 0.30 ± 0.46). However 25 of these individuals had AD8 scores (≥2) suggesting impairment (mean AD8 score = 2.4 ± 0.91). Applying the model described in Figure 1, some of these individuals are hypothesized to be in the symptomatic, preclinical phase of disease. No difference in age, education, gender or brief performance tests (MMSE or SBT) were detected between groups (45 is increased in the individuals with higher AD8 scores supporting that informants were noticing and reporting changes in the participants cognitive function. A review of the individual AD8 questions that were first reported to change suggest that informants endorsement of subtle changes in memory (repeats questions, forgets appointments) and executive ability (trouble with judgment, appliances, finances) are valuable early signs. This is consistent with previous reports that changes in memory and judgment/problem solving CDR boxscores in nondemented individuals correlate with findings of AD pathology at autopsy.¹⁷ Although biomarkers do not reach significance in this small sample, the direction of change in favor of “Alzheimerization” of this group suggests that some of these individuals may be in the symptomatic, preclinical phase of disease. More research with larger sample sizes and longitudinal follow-up is needed to confirm this hypothesis. It should be also noted that not all individuals with an AD8 score of 2 or greater have AD. The AD8 was designed to detect cognitive impairment from all causes, and as such, these mildly affected individuals may have other causes for their cognitive change such as depression, Lewy body dementia or vascular cognitive impairment.^41,42

Table 1

Characteristics of nondemented CDR 0 individuals stratified by AD8 scores

Multi-element fingerprinting and high throughput sequencing identify multiple elements that affect fungal communities in Quercus macrocarpa foliage

Diverse fungal mutualists, pathogens and saprobes colonize plant leaves. These fungi face a complex environment, in which stochastic dispersal interplays with abiotic and biotic filters. However, identification of the specific factors that drive the community assembly seems unattainable. We mined two broad data sets and identified chemical elements, to which dominant molecular operational taxonomic units (OTUs) in the foliage of a native tree respond most extremely. While many associations could be identified, potential complicating issues emerged. Those were related to unevenly distributed OTU frequency data, a large number of potentially explanatory variables and the disproportionate effects of outlier observations.Key words: community assembly, environmental filter, fungi, heavy metal enrichment, nutrient enrichment, oak, Quercus macrocarpaHyperdiverse fungal communities inhabit the foliage of most plants^1,2 and these fungal communities have been reported for virtually every plant that has been examined.³ Baas-Becking hypothesis states that environment selects microbial communities from the abundant and possibly globally distributed propagule pools.⁴ Although the foliage-associated communities—like other microbial communities—are suspected to be sensitive to environmental drivers, determination of the mechanisms that control the assembly of these foliar communities has remained difficult and elusive. Some of the proposed mechanisms include distance limitations to propagule dispersal,^5–7 volume limitations to propagule loads,⁷ or limitations set by the environmental conditions either on the scale of the site of fungal colonization⁸ or more broadly on a landscape level.^6,9 The forces that may control the fungal community assembly are overlaid by additional biotic controls that include compatibilities between the fungi and host species^10,11 or genotypes^6,12 and the competitive or facilitative interactions among the component fungal genotypes.^6,10–13 Although a variety of potential controls for the foliage-associated fungal communities have been speculated, very little consensus exists on the relative importance of the different drivers. For example, while macronutrient and heavy metal enrichment may have an influence on the composition fungal communities¹⁴ and populations,¹⁵ relative importance of various chemical elements in the foliage remains yet to be investigated.To evaluate the use of multi-element fingerprinting data produced by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in combination with high throughput 454-pyrosequencing for determining influential chemical elements in structuring of the leaf-associated fungal communities, we mined a recent dataset¹⁶ that explored the effects of urbanization on the diversity and composition of the fungal communities associated with a native tree Quercus macrocarpa. From a total list of more than 700 non-singleton fungal OTUs, we selected fifty with highest overall frequency to provide an observationrich dataset for elemental effect assessment; these OTUs accounted for 84.5% of all sequences. Even so, many of these OTUs had a number of zero frequencies (Fig. 1), highlighting one of the difficulties in the use of environmental sequencing data. We omitted one OTU (OTU630 with a likely affinity to Trimmatostroma cordae [Mycosphaerellaceae]) that was strongly affected by the original land use design (urbanization; Wilcoxon rank sum test with a Bonferroni adjustment) and therefore unlikely to be representative for the present analyses of elemental drivers. This OTU was replaced with one with the next highest frequency. The frequencies of these 50 OTUs were investigated in the context of concentrations for 29 elements after the omission of five (Ag, Au, C, δ¹³C, δ¹⁵N) in the final analyses because of their strong association with the land use or the difficulty of finding a biological relevance. Of the remaining elements three (Fe, Cr and Ni) had pairwise correlations exceeding 0.98 between the three pairings; others showed no similar high correlations. To allow comparable evaluation across the broad array of elements, all concentrations were standardized to have a mean equal to zero and a standard deviation equal to one.Open in a separate windowFigure 1Rank-ordered distribution of observed frequencies for those OTU s whose frequency had an extreme slope when associated with the concentrations of one or more chemical elements in the mixed effects model. The asterisk denotes one extreme frequency for OTU 313 with a value 0.8636. Numbers in parentheses indicate the number of observations with a frequency equal to zero. The OTU s were assigned to approximate taxa using BLAST:²⁰ 425: Alternaria alternata (Pleosporaceae); 46: Phoma glomerata (Pleosporaceae); 686: Aureobasidium pullulans (Dothioraceae); 520: Davidiella tassiana (Davidiellaceae); 567: Cladosporioum tenuissimum (Davidiellaceae); 313 Oidium heveae (mitosporic Erysiphaceae); 586: Erysiphe hypogena (Erysiphaceae); 671: Mycosphaerella microsora (Mycosphaerellaceae); 555: Pestalotiopsis sp. (Amphisphaeriaceae); 607: Pleiochaeta setosa (incertae sedis).To rank elements according to their magnitude of association with the abundance of each OTU, a total of 1,450 models (50 OTUs times 29 elements) relating element concentration to OTU abundance were fit to the data. For each model, OTU frequency was the dependent variable, element concentration and time (a factor with three levels) were fixed effects, and—to account for the spatial arrangement of the experimental units—random effects associated with tree nested within site were included in the error structure. Time by element interactions were also investigated and tested using a likelihood ratio test. These mixed effect models were fit using R and the package lme4 (www.rproject.org).Statistical “tests of significance” that produce p-values can be sensitive to assumptions or outliers. Because of this and the fact that our analyses evaluated a total of 1,450 models, p-values themselves were not considered a reliable measure of importance when associating elements with OTU frequency. Instead, we emphasized metrics that highlight extraordinary findings rather than rely on tests of statistical significance. This approach facilitates finding few elements that have the strongest effect on OTU frequency. Note that the use of standardized element concentrations (above) provided slope coefficients that are comparable across all models. “Extreme slopes”, i.e., models where the OTU response to element concentration was strongest, were identified as those with estimated slope coefficients in the lower or upper 2.5 percentile, i.e., those farther than 1.8 standard deviations from the mean across all estimated slopes (Fig. 2). Using this approach, we identified a total of 69 models with extreme slopes (Open in a separate windowFigure 2Distribution of estimated slopes (i.e., the slope for element concentration) for a model relating OTU frequency to element concentration, time and a concentration by time interaction, including a tree-nested-within-site random effect. The mean across all 1,450 OTU s is approximately zero; the two vertical lines identify upper and lower 2.5 percentiles, beyond which the slopes were considered extreme (large black symbols). The horizontal line identifies the cut off maximum leverage (0.24), above which the slopes were considered to have observations with high leverage. Models with observations with a high leverage were tested for extreme slopes by refitting without those observations. Models are ranked from bottom to top in order of increasing leverage and the element for which the high-leverage observations and extreme slopes were recorded are identified on the right y-axis.

Table 1

Slopes identified as extreme in our analyses

Niemann-Pick disease,type B with TRAP-positive storage cells and secondary sea blue histiocytosis

We present 2 cases of Niemann Pick disease, type B with secondary sea-blue histiocytosis. Strikingly, in both cases the Pick cells were positive for tartrate resistant acid phosphatase, a finding hitherto described only in Gaucher cells. This report highlights the importance of this finding as a potential cytochemical diagnostic pitfall in the diagnosis of Niemann Pick disease.Key words: Niemann pick disease, Gaucher disease, tartrate resistant acid phosphatase, sea blue histiocytosis.We present two unrelated patients who were referred to the Hematology OPD from Gastroenterology during work-up of long-standing splenomegaly 2 years apart and whose details are presented in Patient 1Patient 2Age, sex14 yr/F18 yr/FPresenting complaintsPain, awareness of mass in left upper abdomen ×12 yearsLow grade fever on and off, abdominal discom fort ×2 yrsHb (gm%), TLC (/µL), platelets (/µL)7.3, 4500, 15300012, 6900, 47000Liver / SpleenNot palpable / 14 cm below costal marginNot palpable / massive enlargement (span 20 cm)Ultrasound abdomenMassive splenomegaly, multiple hyperechoeic foci, no evidence of EHPVO or HVOTOSplenomegaly, mesenteric lymphadenopathyCECT abdomenNot doneSplenomegaly, pre-aortic lymphadenopathy (? lymphoma infiltration)total protein, Albumin, urea, creatinine, sodium, potassium Serum bilirubin, alkaline phosphatase, SGOT, SGPT,Normal rangesNormal rangesHemoglobin HPLC, direct and indirect antiglobulin tests, 24-hour incubated osmotic fragility test, G6PD deficiency screeningNormalNormalRK-39 antigen test for Leishmaniasis, HBsAg, anti HCV, anti HIV 1 & 2NegativeNegativeHDL Cholesterol (normal 40–50 mg%)12 mg%23 mg%Fundoscopic examinationNormalBilateral cherry red spotsAcid phosphatase (normal >6.5 U/L)5.5 U/L4.2 U/LBone marrow examinationAspirate: Cellular smears with normal marrowDiluted marrow with many foamyelements, foamy histiocytes present along with numerous sea blue histiocytes, some foamy histiocytes show haemophagocytosis Biopsy: hypercellular, foamy cells and other histiocytes prominenthistiocytes and sea blue histiocytes, normal marrow elements seen Biopsy: normocellular, foamy cells and other histiocytes presentCytochemistryFoamy cells positive for Sudan Black B, acid phosphatase (AP), tartrate resistant acid phosphatase (TRAP), weak hue with periodic acid Schiff (PAS), sea blue histiocytes strongly positive for PAS and APFoamy cells positive for Sudan Black B, acid Phosphatase, TRAP, weak positive with PAS; sea blue histiocytes positive for PAS, APEnzyme assayNormal beta-glucocerebrosidase level, sphingomyelinase- not doneNormal beta-glucocerebrosidase level, sphingomyelinase- 9 nmol/17 hr/mg protein (normal 10–47 nmol/17 hr/mg protein)Open in a separate window The marrow smears in both patients showed abundant classical Niemann Pick cells (foamy cytoplasm positive for the lipid stain Sudan black B, small central to eccentric nucleoli) with many sea-blue histiocytes, a well-recognized secondary phenomenon (Golde et al., 1975) (Figures 1 and ​and2).2). Diagnostic uncertainty arose when enzyme cytochemistry on the marrow smears showed intense tartrate resistant acid phosphatase (TRAP) activity in the foamy cells, periodic acid Schiff positive material and haemophagocytosis in the sea-blue histiocytes, findings hitherto described only in Gaucher cells (Weisberger et al., 2004) (Figures 3 and ​and4).4). No classical Gaucher cells were seen in multiple Romanowsky stained smears.Open in a separate windowFigure 1The bone marrow aspirate shows numerous Niemann Pick cells with abundant foamy cytoplasm and fewer and smaller sea-blue histiocytes (Jenner-Giemsa).Open in a separate windowFigure 2The multi-vacuolated Niemann Pick cells are positive for the lipid stain Sudan Black B. (Giemsa counterstaining).Open in a separate windowFigure 3The Niemann Pick cells variably measure 20–50 micrometers in greatest diameter. They are uniformly and intensely positive for tartrate resistant acid phosphatase. (Methyl green counterstaining). The sea blue histiocytes'' acid phosphatase is inhibited by tartaric acid (image not shown).Open in a separate windowFigure 4The Niemann Pick cells are only weakly positive for periodic acid Schiff stain. Gaucher cells would be expected to be brilliantly positive. (Haematoxylin counterstaining). The diagnostic puzzle was resolved when both patients showed normal beta-glucocerebrosidase levels, very low levels of HDL cholesterol with low acid phosphatase, and in the one patient where it could be performed, a reduced but recordable level of sphingomyelinase activity (as seen in type B form), thus confirming clinico-pathologically the diagnosis of Niemann Pick disease, type B.A literature search reveals that although serum TRAP levels may be mildly elevated in patients with Niemann Pick disease, the enzyme has not been localized cytochemically to these cells previously (Chambers et al., 1977). Interestingly, a recent publication using sequence profiling and fold recognition methods suggests a remote evolutionary relationship between the phosphoesterase domain of acid sphingomyelinase (deficient in Niemann Pick disease) and purple acid phosphatases (mammalian form of which is TRAP) (Seto et al., 2004). The importance of this relationship is unclear but it is interesting to speculate whether there could be an upregulation of a related enzyme in face of congenital deficiency of acid sphingomyelinase in our cases.The iron content and the haemo-phagocytosis were possibly simply pointers towards the intrinsic histiocytic nature of the sea-blue histiocytes.The major lesson from these cases is to alert the pathologist to the possibility of TRAP and iron positive histiocytic and storage cells other than Gaucher cells that may display haemophagocytosis. This is especially relevant to avoid incorrect diagnosis in resource-restricted settings in India where specialized diagnostic tests may be inaccessible or omitted if the morphological and cytochemical findings are felt to be characteristic of Gaucher disease.     

Influence of polyploidy on insect herbivores of native and invasive genotypes of Solidago gigantea (Asteraceae)

Herbivores are sensitive to the genetic structure of plant populations, as genetics underlies plant phenotype and host quality. Polyploidy is a widespread feature of angiosperm genomes, yet few studies have examined how polyploidy influences herbivores. Introduction to new ranges, with consequent changes in selective regimes, can lead to evolution of changes in plant defensive characteristics and also affect herbivores. Here, we examine how insect herbivores respond to polyploidy in Solidago gigantea, using plants derived from both the native range (USA) and introduced range (Europe). S. gigantea has three cytotypes in the US, with two of these present in Europe. We performed bioassays with generalist (Spodoptera exigua) and specialist (Trirhabda virgata) leaf-feeding insects. Insects were reared on detached leaves (Spodoptera) or potted host plants (Trirhabda) and mortality and mass were measured. Trirhabda larvae showed little variation in survival or pupal mass attributable to either cytotype or plant origin. Spodoptera larvae were more sensitive to both cytotype and plant origin: they grew best on European tetraploids and poorly on US diploids (high mortality) and US tetraploids (low larval mass). These results show that both cytotype and plant origin influence insect herbivores, but that generalist and specialist insects may respond differently.Key words: polyploidy, cytotype, Solidago gigantea, insect herbivore, herbivory, invasive plant, introduced plantPolyploidy, or the possession of more than two sets of homologous chromosomes, is a fundamental force in angiosperm evolution.^1,2 Many plant species or species complexes consist of multiple cytotypes that may occur sympatrically;³ this is an important source of genetic structure in plant populations that is often overlooked.⁴ Possession of multiple genomes may confer advantages to polyploid plants such as increased heterozygosity, a decreased probability of inbreeding depression, or a greater gene pool available for selection; these traits contribute to the widespread success of polyploids and may make them prone to invasiveness.^5,6 In a recent article,⁷ we examined the functional consequences of polyploidy for different cytotypes of Solidago gigantea Ait. (Asteraceae), collected from both its native range (North America) and its introduced range (Europe). In this addendum, we show how cytotype and continent of origin influence interactions of S. gigantea with insect herbivores. Interactions with herbivores are expected to vary with cytotype because of phenotypic changes associated with polyploidy, but this area has received little study (reviewed in refs. ^8–11). Plant origin, from either the native range or an introduced range, should also influence herbivores. Plants may escape from their specialist natural enemies in the introduced range, thereby experiencing reduced herbivore pressure from an insect community dominated by generalists.^12,13 Given sufficient time, plants from the introduced range may evolve to decrease investment in anti-herbivore defenses, particularly those effective against specialists.¹⁴ While a growing body of research has addressed whether plant defenses against herbivory are lower in the introduced range,^12,15,16 few of these studies have also examined the influence of cytotype.¹⁷Three cytotypes of S. gigantea can be found in its native range in North America (diploid, tetraploid and hexaploid, 2n = 18, 36 and 54 respectively). These are morphologically indistinguishable and not generally treated as separate species.¹⁸ In Europe, where S. gigantea was introduced in the mid 18^th century,¹⁹ tetraploids are the dominant cytotype but diploids also occur. S. gigantea supports a diverse array of insect herbivores in its native range, but has few natural enemies in its introduced range.²⁰ We report here on experiments using both a generalist and a specialist leaf-chewing insect. The generalist, Spodoptera exigua (Lepidoptera: Noctuidae) is widely distributed and highly polyphagous, while the specialist Trirhabda virgata (Coleoptera: Chrysomelidae) feeds only on closely-related species within the genus Solidago. T. virgata is an outbreak insect that can be a major defoliator of S. gigantea and related species in North America.²¹ We grew plants originating from 10 populations in the US and 20 populations in Europe in common gardens at the University of Wisconsin-Milwaukee Field Station in Saukville, Wisconsin. There were five plant origin-cytotype combinations: three cytotypes from the US and two from Europe. Insects were reared on detached leaves from a single plant (Spodoptera) or on potted host plants (Trirhabda), for a set period of 21 d (Spodoptera) or until pupation (Trirhabda). We recorded insect survival and mass at the end of 21 d (Spodoptera) or at pupation (Trirhabda) (reviewed in ref. ²²).Overall survival was much better for the specialist Trirhabda than for the generalist Spodoptera (91% vs. 72%). Spodoptera larvae are not generally found on S. gigantea in the field, and while they are able to complete development, we found that this plant was not an ideal host. Spodoptera larvae were more sensitive to differences among cytotype and plant origin than were Trirhabda larvae. Percent survival was particularly poor for Spodoptera larvae reared on diploids from the US, where slightly more than half of the caterpillars survived for 21 days (Fig. 1). Trirhabda pupal mass was remarkably consistent across the five ploidy-plant origin combinations. In contrast, Spodoptera larvae responded to both cytotype and continent of origin. Surviving Spodoptera larvae did particularly well on tetraploid plants from the introduced range (Europe), and particularly poorly on tetraploids from the US (Fig. 1). We have previously reported that Spodoptera grow better on plants from Europe;²² our current results reveal that this difference is due exclusively to better growth on tetraploid plants. However, our results also show that both diploids and tetraploids from the US were poor hosts for Spodoptera: diploids because they caused high mortality and tetraploids because they resulted in poor growth. These results indicate that plants from the introduced range have reduced defenses against herbivores, even when accounting for polyploidy.Open in a separate windowFigure 1Mass ± se of S. exigua (A) and T. virgata (B) larvae reared on host plants of different cytotypes of Solidago gigantea originating from the US (native range) or europe (introduced range). Means in A followed by different letters are significantly different at p < 0.05 (ANOVA followed by multiple Student''s t-tests with Bonferroni correction). There were no significant differences in (B). Sample sizes for (A and B) shown in SpodopteraTrirhabdaNo. SurvivingInitial No.% SurvivalNo. SurvivingInitial No.% SurvivalUS-Diploid213954373995US-Tetraploid709375829289US-Hexaploid162467232496EU-Diploid152365232496EU-Tetraploid1011297811412988Open in a separate windowInsects were reared on a single genotype of each cytotype-origin combination for 21 days (Spodoptera) or until pupation (Trirhabda). Sample sizes for each cytotype-origin combination vary because cytotypes were not known at the time plants were collected; these distributions represent frequencies of cytotypes in our collections.Effects of the host plant on Spodoptera were probably driven, at least in part, by changes in secondary chemistry. We have previously shown that foliar terpenoids, chemicals known to influence insect herbivores,^23,24 are affected by both cytotype and continent of origin.⁷ It is surprising that Trirhabda larvae were not more sensitive to these differences in secondary chemistry among the five ploidy-origin combinations, given that Trirhabda is known to respond to host-plant chemistry.²³ We have previously reported that Trirhabda growth does not differ on European and US plants²² and show here that accounting for cytotype does not change this conclusion. In a recent study on the closely-related Solidago altissima, Halverson et al.¹¹ reported that the effects of plant cytotype on 5 gall-making herbivores were complex and not easily characterized. All five herbivores responded to plant cytotype, but for four of the five insects the most preferred cytotype was not consistent across sites. It is possible in our study that Trirhabda were responding to cytotype at a finer scale than that examined here. There may be differences due to cytotype that shift among the populations that we sampled, and that are averaged out when examined at the continental scale. We lack sufficient replication of cytotypes within populations to test this possibility. Even so, our results reported here reveal that plant cytotype can be an important source of variation affecting insect herbivores, but that generalist and specialist insects may respond differently.     

De novo mammalian prion synthesis

Prions are responsible for a heterogeneous group of fatal neurodegenerative diseases. They can be sporadic, genetic, or infectious disorders involving post-translational modifications of the cellular prion protein (PrP^C). Prions (PrP^Sc) are characterized by their infectious property and intrinsic ability to convert the physiological PrP^C into the pathological form, acting as a template. The “protein-only” hypothesis, postulated by Stanley B. Prusiner, implies the possibility to generate de novo prions in vivo and in vitro. Here we describe major milestones towards proving this hypothesis, taking into account physiological environment/s, biochemical properties and interactors of the PrP^C.Key words: prion protein (PrP), prions, amyloid, recombinant prion protein, transgenic mouse, protein misfolding cyclic amplification (PMCA), synthethic prionPrions are responsible for a heterogeneous group of fatal neurodegenerative diseases (1 They can be sporadic, genetic or infectious disorders involving post-translational modifications of the cellular prion protein (PrP^C).² Prions are characterized by their infectious properties and by their intrinsic ability to encipher distinct biochemical properties through their secondary, tertiary and quaternary protein structures. In particular, the transmission of the disease is due to the ability of a prion to convert the physiological PrP^C into the pathological form (PrP^Sc), acting as a template.³ The two isoforms of PrP appear to be different in terms of protein structures, as revealed by optical spectroscopy experiments such as Fourier-transform infrared and circular dichroism.⁴ PrP^C contains 40% α-helix and 3% β-sheet, while the pathological isoform, PrP^Sc, presents approximately 30% α-helix and 45% β-sheet.^4,5 PrP^Sc differs from PrP^C because of its altered physical-chemical properties such as insolubility in non-denaturing detergents and proteinases resistance.^2,6,7

Table 1

The prion diseases