Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance.

The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO2 assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R2 ranged from 0.97 to 0.99) and testing (R2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R2 ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.

Prediction model for sap flow in cacao trees under different radiation intensities in the western Colombian Amazon.

In this study, we measured diurnal patterns of sap flow (Vs) in cacao trees growing in three types of agroforestry systems (AFs) that differ in the incident solar radiation they receive. We modeled the relationship of Vs with several microclimatic characteristics of the AFs using mixed linear models. We characterized microclimatic variables that may have an effect on diurnal patterns of sap flow: air relative humidity, air temperature, photosynthetically active radiation and vapor pressure deficit. Overall, our model predicted the differences between cacao Vs in the three different AFs, with cacao plants with dense Musaceae plantation and high mean diurnal incident radiation (HPAR) displaying the highest differences compared to the other agroforestry arrangements. The model was also able to predict situations such as nocturnal transpiration in HPAR and inverse nocturnal sap flows indicative of hydraulic redistribution in the other AFs receiving less incident radiation. Overall, the model we present here can be a useful and cost-effective tool for predicting transpiration and water use in cacao trees, as well as for managing cacao agroforestry systems in the Amazon rainforest.

Comparative Pathogenesis of Generalist AcMNPV and Specific RanuNPV in Larvae of Rachiplusia nu (Lepidoptera: Noctuidae) Following Single and Mixed Inoculations.

The South American soybean pest, Rachiplusia nu (Guenée), is naturally infected by Autographa californica multiple nucleopolyhedrovirus (AcMNPV) and Rachiplusia nu nucleopolyhedrovirus (RanuNPV). We compared their pathogenicity to fourth-instar R. nu larvae, by evaluating time to death and virus spread throughout the tissues in single and mixed infections. Bioassays showed that generalist AcMNPV had a faster speed of kill than specific RanuNPV, while the mixed-virus treatment did not statistically differ from AcMNPV alone. Histopathology evidenced similar tissue tropism for both viruses, but co-inoculation resulted in mostly AcMNPV-infected cells. In sequential inoculations, however, the first virus administered predominated over the second one. Implications on baculovirus interactions and biocontrol potential are discussed.

Biochemical and molecular approach of oxidative damage triggered by water stress and rewatering in sunflower seedlings of two inbred lines with different ability to tolerate water stress.

Water stress accelerates the generation of reactive oxygen species, which trigger a cascade of antioxidative defence mechanisms comprising enzymatic and nonenzymatic antioxidants. The aim of this study was to investigate the oxidative damage and the antioxidative defence systems in seedlings of the water stress-tolerant (B71) and the sensitive (B59) inbred lines of sunflower (Helianthus annuus L.) in response to water stress and rewatering. In addition, we characterised the transcriptomic profile associated with enzymatic antioxidative defence. An elevated electrolyte leakage in B59 indicated increased plasmatic membrane permeability, which correlated with greater sensitivity to water stress. In response to water stress, both lines showed an increase in malondialdehyde and H2O2 content but these increases were more noticeable in the sensitive line. In both lines, an increase in enzymatic activity (e.g. peroxidase and ascorbate peroxidase) was not sufficient to overcome the H2O2 accumulation triggered by water stress. Upon water stress, the overall expression level of genes associated with the enzymatic antioxidant system increased in B71 and decreased in B59, which showed downregulated levels of most genes in the shoots. The general profile of phenolic compounds was clearly different between organs and between inbred lines. The B59 line activated nonenzymatic antioxidant responses to counteract the oxidative stress caused by water stress. The tolerance of B71 to water stress could be associated with compensatory antioxidant mechanisms based on the expression of genes encoding enzyme components of the ascorbate-glutathione and redoxin cycles, which contributed to explaining, at least partly, the response of this line.

Photosynthesis limitations in cacao leaves under different agroforestry systems in the Colombian Amazon.

Cacao (Theobroma cacao L.) has traditionally been considered a crop that requires shade, and consequently it is implemented under agroforestry systems (AFs) in order to regulate the level of incident solar radiation. However, optimal shade levels for this tree crop may vary depending on the climate conditions of where it is grown. Here we analyzed the physiological performance of cacao under three different AFs in the Colombian Amazon that differed in solar radiation patterns: high (HPAR), medium (MPAR), or low (LPAR) mean daily incident radiation. The physiological performance was characterized using photosynthetic variables in leaves such as light- and CO2-response curves, chlorophyll a fluorescence parameters, and total chlorophyll and carotenoid contents, in conjunction with other leaf functional traits. Cacao trees exposed to HPAR showed an improved physiological performance as compared to those from the other two AFs. Compared to MPAR and LPAR, cacao trees in HPAR doubled the rate of net carbon assimilation and reached higher maximum rates of RuBisCO carboxylation and RuBP regeneration. Moreover, cacao trees in HPAR presented photoprotection mechanisms that avoided photoinhibition, which was accompanied by a greater non-photochemical quenching coefficient and biochemical and morphological adjustments (low chlorophyll but higher carotenoid contents and low specific leaf area) compared to cacao trees from the other AFs. Overall, our results show that, due to the high cloud cover in the Colombian Amazon, cacao plantations under conditions of sparse shade maximized their carbon use, showing an improved physiological performance as a result of higher photosynthetic rates and energy dissipation mechanisms. If the crop were managed with sparse shade, the paradigm that favors the cultivation of cacao under shade would be called into question in the Colombian Amazon and other regions with similar climatic conditions.

Transcriptional Changes in Mycorrhizal and Nonmycorrhizal Soybean Plants upon Infection with the Fungal Pathogen Macrophomina phaseolina.

Macrophomina phaseolina is a soil-borne fungal pathogen with a wide host range that causes charcoal rot in soybean [Glycine max (L.) Merr.]. Control of the disease is a challenge, due to the absence of genetic resistance and effective chemical control. Alternative or complementary measures are needed, such as the use of biological control agents, in an integrated approach. Several studies have demonstrated the role of arbuscular mycorrhizal fungi (AMF) in enhancing plant resistance or tolerance to biotic stresses, decreasing the symptoms and pressure caused by various pests and diseases, including M. phaseolina in soybean. However, the specific contribution of AMF in the regulation of the plant response to M. phaseolina remains unclear. Therefore, the objective of the present study was to investigate, under strict in-vitro culture conditions, the global transcriptional changes in roots of premycorrhized soybean plantlets challenged by M. phaseolina (+AMF+Mp) as compared with nonmycorrhizal soybean plantlets (-AMF+Mp). MapMan software was used to distinguish transcriptional changes, with special emphasis on those related to plant defense responses. Soybean genes identified as strongly upregulated during infection by the pathogen included pathogenesis-related proteins, disease-resistance proteins, transcription factors, and secondary metabolism-related genes, as well as those encoding for signaling hormones. Remarkably, the +AMF+Mp treatment displayed a lower number of upregulated genes as compared with the -AMF+Mp treatment. AMF seemed to counteract or balance costs upon M. phaseolina infection, which could be associated to a negative impact on biomass and seed production. These detailed insights in soybean-AMF interaction help us to understand the complex underlying mechanisms involved in AMF-mediated biocontrol and support the importance of preserving and stimulating the existing plant-AMF associates, via adequate agricultural practices, to optimize their agro-ecological potential.

First typology of cacao (Theobroma cacao L.) systems in Colombian Amazonia, based on tree species richness, canopy structure and light availability.

AIM AND BACKGROUND: We present a typology of cacao agroforest systems in Colombian Amazonia. These systems had yet to be described in the literature, especially their potential in terms of biodiversity conservation. The systems studied are located in a post-conflict area, and a deforestation front in Colombian Amazonia. Cacao cropping systems are of key importance in Colombia: cacao plays a prime role in post conflict resolution, as cacao is a legal crop to replace illegal crops; cacao agroforests are expected to be a sustainable practice, promoting forest-friendly land use. MATERIAL AND METHODS: We worked in 50 x 2000 m2 agroforest plots, in Colombian Amazonia. A cluster analysis was used to build a typology based on 28 variables characterised in each plot, and related to diversity, composition, spatial structure and light availability for the cacao trees. We included variables related to light availability to evaluate the amount of transmitted radiation to the cacao trees in each type, and its suitability for cacao ecophysiological development. MAIN RESULTS: We identified 4 types of cacao agroforests based on differences concerning tree species diversity and the impact of canopy spatial structure on light availability for the cacao trees in the understorey. We found 127 tree species in the dataset, with some exclusive species in each type. We also found that 3 out of the 4 types identified displayed an erosion of tree species diversity. This reduction in shade tree species may have been linked to the desire to reduce shade, but we also found that all the types described were compatible with good ecophysiological development of the cacao trees. MAIN CONCLUSIONS AND PROSPECTS: Cacao agroforest systems may actually be achieving biodiversity conservation goals in Colombian Amazonia. One challenging prospect will be to monitor and encourage the conservation of tree species diversity in cacao agroforest systems during the development of these cropping systems, as a form of forest-friendly management enhancing sustainable peace building in Colombia.

Main and epistatic QTL analyses for Sclerotinia Head Rot resistance in sunflower.

Sclerotinia Head Rot (SHR), a disease caused by Sclerotinia sclerotiorum, is one of the most limiting factors in sunflower production. In this study, we identified genomic loci associated with resistance to SHR to support the development of assisted breeding strategies. We genotyped 114 Recombinant Inbred Lines (RILs) along with their parental lines (PAC2 -partially resistant-and RHA266 -susceptible-) by using a 384 single nucleotide polymorphism (SNP) Illumina Oligo Pool Assay to saturate a sunflower genetic map. Subsequently, we tested these lines for SHR resistance using assisted inoculations with S. sclerotiorum ascospores. We also conducted a randomized complete-block assays with three replicates to visually score disease incidence (DI), disease severity (DS), disease intensity (DInt) and incubation period (IP) through four field trials (2010-2014). We finally assessed main effect quantitative trait loci (M-QTLs) and epistatic QTLs (E-QTLs) by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively. As a result of this study, the improved map incorporates 61 new SNPs over candidate genes. We detected a broad range of narrow sense heritability (h2) values (1.86-59.9%) as well as 36 M-QTLs and 13 E-QTLs along 14 linkage groups (LGs). On LG1, LG10, and LG15, we repeatedly detected QTLs across field trials; which emphasizes their putative effectiveness against SHR. In all selected variables, most of the identified QTLs showed high determination coefficients, associated with moderate to high heritability values. Using markers shared with previous Sclerotinia resistance studies, we compared the QTL locations in LG1, LG2, LG8, LG10, LG11, LG15 and LG16. This study constitutes the largest report of QTLs for SHR resistance in sunflower. Further studies focusing on the regions in LG1, LG10, and LG15 harboring the detected QTLs are necessary to identify causal alleles and contribute to unraveling the complex genetic basis governing the resistance.

Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).

KEY MESSAGE: By integration of transcriptional and metabolic profiles we identified pathways and hubs transcription factors regulated during drought conditions in sunflower, useful for applications in molecular and/or biotechnological breeding. Drought is one of the most important environmental stresses that effects crop productivity in many agricultural regions. Sunflower is tolerant to drought conditions but the mechanisms involved in this tolerance remain unclear at the molecular level. The aim of this study was to characterize and integrate transcriptional and metabolic pathways related to drought stress in sunflower plants, by using a system biology approach. Our results showed a delay in plant senescence with an increase in the expression level of photosynthesis related genes as well as higher levels of sugars, osmoprotectant amino acids and ionic nutrients under drought conditions. In addition, we identified transcription factors that were upregulated during drought conditions and that may act as hubs in the transcriptional network. Many of these transcription factors belong to families implicated in the drought response in model species. The integration of transcriptomic and metabolomic data in this study, together with physiological measurements, has improved our understanding of the biological responses during droughts and contributes to elucidate the molecular mechanisms involved under this environmental condition. These findings will provide useful biotechnological tools to improve stress tolerance while maintaining crop yield under restricted water availability.

Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower.

BACKGROUND: In recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence. RESULTS: Weighted Gene Correlation Network Analysis (WGCNA) and BioSignature Discoverer (BioSD, Gnosis Data Analysis, Heraklion, Greece) were used to integrate transcriptomic and metabolomic data. WGCNA allowed the detection of 10 metabolites and 13 TFs whereas BioSD allowed the detection of 1 metabolite and 6 TFs as potential biomarkers. The comparative analysis demonstrated that three transcription factors were detected through both methodologies, highlighting them as potentially robust biomarkers associated with leaf senescence in sunflower. CONCLUSIONS: The complementary use of network and BioSignature Discoverer analysis of transcriptomic and metabolomic data provided a useful tool for identifying candidate genes and metabolites which may have a role during the triggering and development of the leaf senescence process. The WGCNA tool allowed us to design and test a hypothetical network in order to infer relationships across selected transcription factor and metabolite candidate biomarkers involved in leaf senescence, whereas BioSignature Discoverer selected transcripts and metabolites which discriminate between different ages of sunflower plants. The methodology presented here would help to elucidate and predict novel networks and potential biomarkers of leaf senescence in sunflower.

Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.

Leaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process.

Coexistence and within-host evolution of diversified lineages of hypermutable Pseudomonas aeruginosa in long-term cystic fibrosis infections.

The advent of high-throughput sequencing techniques has made it possible to follow the genomic evolution of pathogenic bacteria by comparing longitudinally collected bacteria sampled from human hosts. Such studies in the context of chronic airway infections by Pseudomonas aeruginosa in cystic fibrosis (CF) patients have indicated high bacterial population diversity. Such diversity may be driven by hypermutability resulting from DNA mismatch repair system (MRS) deficiency, a common trait evolved by P. aeruginosa strains in CF infections. No studies to date have utilized whole-genome sequencing to investigate within-host population diversity or long-term evolution of mutators in CF airways. We sequenced the genomes of 13 and 14 isolates of P. aeruginosa mutator populations from an Argentinian and a Danish CF patient, respectively. Our collection of isolates spanned 6 and 20 years of patient infection history, respectively. We sequenced 11 isolates from a single sample from each patient to allow in-depth analysis of population diversity. Each patient was infected by clonal populations of bacteria that were dominated by mutators. The in vivo mutation rate of the populations was ∼100 SNPs/year-∼40-fold higher than rates in normo-mutable populations. Comparison of the genomes of 11 isolates from the same sample showed extensive within-patient genomic diversification; the populations were composed of different sub-lineages that had coexisted for many years since the initial colonization of the patient. Analysis of the mutations identified genes that underwent convergent evolution across lineages and sub-lineages, suggesting that the genes were targeted by mutation to optimize pathogenic fitness. Parallel evolution was observed in reduction of overall catabolic capacity of the populations. These findings are useful for understanding the evolution of pathogen populations and identifying new targets for control of chronic infections.

Assessment of adrenocortical activity and behavior of the collared anteater (Tamandua tetradactyla) in response to food-based environmental enrichment.

One of the current standard approaches to the study of animal welfare is measuring hypothalamic-pituitary-adrenal activity, frequently in association with behavioral assessment. We studied the effects of food-based environmental enrichment on adrenocortical activity and behavior in zoo-housed collared anteaters (Tamandua tetradactyla; n = 5). We successfully validated measurements of fecal cortisol metabolites (FCMs) using an 11-oxoetiocholanolone enzyme immunoassay by stimulating (ACTH injection) and suppressing (dexamethasone administration) adrenocortical activity. Three months later, we subjected animals to an ABA-type experiment (three 6-week periods): pre-enrichment (routine diet: A), enrichment (modified diet: B), and post-enrichment (routine diet: A) periods. We assessed adrenocortical activity by collecting individual feces three times a week (total number of samples: 228), and evaluated behavior by performing 3 days of behavioral observations per period (with a total of 3,600 behavioral data points for the individuals studied). Statistical analysis revealed changes in FCM concentrations (µg/g) over the periods (3.04 ± 0.68, 2.98 ± 0.66, and 4.04 ± 0.90, respectively). Additionally, it showed that the number of FCM peaks was highly reduced during enrichment; meanwhile active natural behaviors were significantly increased. We consider that these changes in response to food-based environmental enrichment improved the welfare of individual zoo-housed collared anteaters. This research might contribute to in situ and ex situ studies on the physiology and behavior of this endemic South American species.

Simple sequence repeats together with mismatch repair deficiency can bias mutagenic pathways in Pseudomonas aeruginosa during chronic lung infection.

Pseudomonas aeruginosa is an opportunistic pathogen that chronically infects the airways of cystic fibrosis (CF) patients and undergoes a process of genetic adaptation based on mutagenesis. We evaluated the role of mononucleotide G:C and A:T simple sequence repeats (SSRs) in this adaptive process. An in silico survey of the genome sequences of 7 P. aeruginosa strains showed that mononucleotide G:C SSRs but not A:T SSRs were greatly under-represented in coding regions, suggesting a strong counterselection process for G:C SSRs with lengths >5 bp but not for A:T SSRs. A meta-analysis of published whole genome sequence data for a P. aeruginosa strain from a CF patient with chronic airway infection showed that G:C SSRs but not A:T SSRs were frequently mutated during the infection process through the insertion or deletion of one or more SSR subunits. The mutation tendency of G:C SSRs was length-dependent and increased exponentially as a function of SSR length. When this strain naturally became a stable Mismatch Repair System (MRS)-deficient mutator, the degree of increase of G:C SSRs mutations (5-fold) was much higher than that of other types of mutation (2.2-fold or less). Sequence analysis of several mutated genes reported for two different collections, both containing mutator and non-mutator strains of P. aeruginosa from CF chronic infections, showed that the proportion of G:C SSR mutations was significantly higher in mutators than in non-mutators, whereas no such difference was observed for A:T SSR mutations. Our findings, taken together, provide genome-scale evidences that under a MRS-deficient background, long G:C SSRs are able to stochastically bias mutagenic pathways by making the genes in which they are harbored more prone to mutation. The combination of MRS deficiency and virulence-related genes that contain long G:C SSRs is therefore a matter of concern in P. aeruginosa CF chronic infection.

Association mapping in sunflower for Sclerotinia Head Rot resistance.

BACKGROUND: Sclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. RESULTS: A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P < 0.01), accounting for a SHR incidence reduction of about 20 %. CONCLUSIONS: These results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding.

Comparison of predictive methods and biological validation for qPCR reference genes in sunflower leaf senescence transcript analysis.

The selection and validation of reference genes constitute a key point for gene expression analysis based on qPCR, requiring efficient normalization approaches. In this work, the expression profiles of eight genes were evaluated to identify novel reference genes for transcriptional studies associated to the senescence process in sunflower. Three alternative strategies were applied for the evaluation of gene expression stability in leaves of different ages and exposed to different treatments affecting the senescence process: algorithms implemented in geNorm, BestKeeper software, and the fitting of a statistical linear mixed model (LMModel). The results show that geNorm suggested the use of all combined genes, although identifying α-TUB1 as the most stable expressing gene. BestKeeper revealed α-TUB and ß-TUB as stable genes, scoring ß-TUB as the most stable one. The statistical LMModel identified α-TUB, actin, PEP, and EF-1α as stable genes in this order. The model-based approximation allows not only the estimation of systematic changes in gene expression, but also the identification of sources of random variation through the estimation of variance components, considering the experimental design applied. Validation of α-TUB and EF-1α as reference genes for expression studies of three sunflower senescence associated genes showed that the first one was more stable for the assayed conditions. We conclude that, when biological replicates are available, LMModel allows a more reliable selection under the assayed conditions. This study represents the first analysis of identification and validation of genuine reference genes for use as internal control in qPCR expression studies in sunflower, experimentally validated throughout six different controlled leaf senescence conditions.

How to Group Genes according to Expression Profiles?

The most commonly applied strategies for identifying genes with a common response profile are based on clustering algorithms. These methods have no explicit rules to define the appropriate number of groups of genes. Usually the number of clusters is decided on heuristic criteria or through the application of different methods proposed to assess the number of clusters in a data set. The purpose of this paper is to compare the performance of seven of these techniques, including traditional ones, and some recently proposed. All of them produce underestimations of the true number of clusters. However, within this limitation, the gDGC algorithm appears to be the best. It is the only one that explicitly states a rule for cutting a dendrogram on the basis of a testing hypothesis framework, allowing the user to calibrate the sensitivity, adjusting the significance level.

Genome-wide expression profiling Arabidopsis at the stage of Golovinomyces cichoracearum haustorium formation.

Compatibility between plants and obligate biotrophic fungi requires fungal mechanisms for efficiently obtaining nutrients and counteracting plant defenses under conditions that are expected to induce changes in the host transcriptome. A key step in the proliferation of biotrophic fungi is haustorium differentiation. Here we analyzed global gene expression patterns in Arabidopsis thaliana leaves during the formation of haustoria by Golovinomyces cichoracearum. At this time, the endogenous levels of salicylic acid (SA) and jasmonic acid (JA) were found to be enhanced. The responses of wild-type, npr1-1, and jar1-1 plants were used to categorize the sensitivity of gene expression changes to NPR1 and JAR1, which are components of the SA and JA signaling pathways, respectively. We found that the infection process was the major source of variation, with 70 genes identified as having similarly altered expression patterns regardless of plant genotype. In addition, principal component analysis (PCA) identified genes responding both to infection and to lack of functional JAR1 (17 genes) or NPR1 (18 genes), indicating that the JA and SA signaling pathways function as secondary sources of variation. Participation of these genes in the SA or JA pathways had not been described previously. We found that some of these genes may be sensitive to the balance between the SA and JA pathways, representing novel markers for the elucidation of cross-talk points between these signaling cascades. Conserved putative regulatory motifs were found in the promoter regions of each subset of genes. Collectively, our results indicate that gene expression changes in response to infection by obligate biotrophic fungi may support fungal nutrition by promoting alterations in host metabolism. In addition, these studies provide novel markers for the characterization of defense pathways and susceptibility features under this infection condition.

Existence of infective juveniles in the offspring of first- and second-generation adults of Steinernema rarum (OLI strain): evaluation of their virulence.

Descriptions of the life cycle of the genus Steinernema do not consider the production of infective juveniles (IJs) by the first-generation developed within the insect host when more than one generation develops. We demonstrated IJ production by first- and second-generation adults of Steinernema rarum (OLI strain), evaluated their virulence and compared virulence and morphometric characters between the two IJ forms. Our results demonstrated not only the presence of IJs in the offspring of first- and second-generation adults but also a greater virulence of first-generation IJs. Both types of IJs also differed in five morphometric characters. According to our results, a population of IJs emerging from a host cadaver has individuals of two generations with different characteristics; hence, they should not be considered the same. These generational differences may be exploited, for example, for biocontrol purposes, by using a specific generation of IJs for inoculative release.