Spatio-temporal expression patterns of Arabidopsis thaliana and Medicago truncatula defensin-like genes

Plant genomes contain several hundred defensin-like (DEFL) genes that encode short cysteine-rich proteins resembling defensins, which are well known antimicrobial polypeptides. Little is known about the expression patterns or functions of many DEFLs because most were discovered recently and hence are not well represented on standard microarrays. We designed a custom Affymetrix chip consisting of probe sets for 317 and 684 DEFLs from Arabidopsis thaliana and Medicago truncatula, respectively for cataloging DEFL expression in a variety of plant organs at different developmental stages and during symbiotic and pathogenic associations. The microarray analysis provided evidence for the transcription of 71% and 90% of the DEFLs identified in Arabidopsis and Medicago, respectively, including many of the recently annotated DEFL genes that previously lacked expression information. Both model plants contain a subset of DEFLs specifically expressed in seeds or fruits. A few DEFLs, including some plant defensins, were significantly up-regulated in Arabidopsis leaves inoculated with Alternaria brassicicola or Pseudomonas syringae pathogens. Among these, some were dependent on jasmonic acid signaling or were associated with specific types of immune responses. There were notable differences in DEFL gene expression patterns between Arabidopsis and Medicago, as the majority of Arabidopsis DEFLs were expressed in inflorescences, while only a few exhibited root-enhanced expression. By contrast, Medicago DEFLs were most prominently expressed in nitrogen-fixing root nodules. Thus, our data document salient differences in DEFL temporal and spatial expression between Arabidopsis and Medicago, suggesting distinct signaling routes and distinct roles for these proteins in the two plant species

Parasitic weed incidence and related economic losses in rice in Africa

Parasitic weeds pose increasing threats to rain-fed rice production in Africa. Most important species are Striga asiatica, S. aspera and S. hermonthica in rain-fed uplands, and Rhamphicarpa fistulosa in rain-fed lowlands. Information on the regional spread and economic importance of parasitic weeds in cereal production systems is scant. This article presents the first multi-species, multi-country, single-crop impact assessment of parasitic weeds in Africa. A systematic search of public international and national herbaria and the scientific literature was conducted to collect all available data on the regional distribution, incidences and related yield losses of the most important parasitic weeds in rice. Herbaria specimens were geo-referenced and these coordinates were overlapped with rain-fed rice areas. Probabilistic diffusion waves of parasitic weeds were generated to derive most likely incidence values. Estimates from this spatial analysis were then combined with secondary data from the literature into a stochastic impact assessment model to generate a confidence interval of the likely economic impact per country and for sub-Saharan Africa as a whole. Rhamphicarpa fistulosa occurs in at least 36 African countries, 28 of which produce rice in rain-fed lowlands where this species thrives. Striga hermonthica is found in at least 32 countries, Striga asiatica in at least 44 and S. aspera in at least 17. A total of 50 countries have at least one of these three species of Striga, 31 of which produce rice in the rain-fed uplands where these species can be encountered. An estimated 1.34 million ha of rain-fed rice is infested with at least one species of a parasitic weed in Africa. Our stochastic model estimates that annual economic losses inflicted by all parasitic weeds exceeds, with 95% certainty, a minimum value of US $111 million and most likely reaches roughly US$200 million and increases by US $30 million annually. To reverse this trend and support small-holder rice farmers in Africa with effective, sustainable and affordable solutions for control, targeted investments in research, development and capacity building are required. The top-10 priority countries where such investments would probably have the highest return are Nigeria, Guinea, Mali, Côte d’Ivoire, Cameroon, Tanzania, Madagascar, Uganda, Sierra Leone and Burkina Faso

Arabidopsis Cytochrome P450 Monooxygenase 71A13 Catalyzes the Conversion of Indole-3-Acetaldoxime in Camalexin Synthesis[W]

Camalexin (3-thiazol-2-yl-indole) is an indole alkaloid phytoalexin produced by Arabidopsis thaliana that is thought to be important for resistance to necrotrophic fungal pathogens, such as Alternaria brassicicola and Botrytis cinerea. It is produced from Trp, which is converted to indole acetaldoxime (IAOx) by the action of cytochrome P450 monooxygenases CYP79B2 and CYP79B3. The remaining biosynthetic steps are unknown except for the last step, which is conversion of dihydrocamalexic acid to camalexin by CYP71B15 (PAD3). This article reports characterization of CYP71A13. Plants carrying cyp71A13 mutations produce greatly reduced amounts of camalexin after infection by Pseudomonas syringae or A. brassicicola and are susceptible to A. brassicicola, as are pad3 and cyp79B2 cyp79B3 mutants. Expression levels of CYP71A13 and PAD3 are coregulated. CYP71A13 expressed in Escherichia coli converted IAOx to indole-3-acetonitrile (IAN). Expression of CYP79B2 and CYP71A13 in Nicotiana benthamiana resulted in conversion of Trp to IAN. Exogenously supplied IAN restored camalexin production in cyp71A13 mutant plants. Together, these results lead to the conclusion that CYP71A13 catalyzes the conversion of IAOx to IAN in camalexin synthesis and provide further support for the role of camalexin in resistance to A. brassicicola

Dual Regulation of Gene Expression Mediated by Extended MAPK Activation and Salicylic Acid Contributes to Robust Innate Immunity in <i>Arabidopsis thaliana</i>

<div><p>Network robustness is a crucial property of the plant immune signaling network because pathogens are under a strong selection pressure to perturb plant network components to dampen plant immune responses. Nevertheless, modulation of network robustness is an area of network biology that has rarely been explored. While two modes of plant immunity, Effector-Triggered Immunity (ETI) and Pattern-Triggered Immunity (PTI), extensively share signaling machinery, the network output is much more robust against perturbations during ETI than PTI, suggesting modulation of network robustness. Here, we report a molecular mechanism underlying the modulation of the network robustness in <i>Arabidopsis thaliana</i>. The salicylic acid (SA) signaling sector regulates a major portion of the plant immune response and is important in immunity against biotrophic and hemibiotrophic pathogens. In <i>Arabidopsis</i>, SA signaling was required for the proper regulation of the vast majority of SA-responsive genes during PTI. However, during ETI, regulation of most SA-responsive genes, including the canonical SA marker gene <i>PR1</i>, could be controlled by SA-independent mechanisms as well as by SA. The activation of the two immune-related MAPKs, MPK3 and MPK6, persisted for several hours during ETI but less than one hour during PTI. Sustained MAPK activation was sufficient to confer SA-independent regulation of most SA-responsive genes. Furthermore, the MPK3 and SA signaling sectors were compensatory to each other for inhibition of bacterial growth as well as for <i>PR1</i> expression during ETI. These results indicate that the duration of the MAPK activation is a critical determinant for modulation of robustness of the immune signaling network. Our findings with the plant immune signaling network imply that the robustness level of a biological network can be modulated by the activities of network components.</p></div

SA-independent regulation of <i>PR1</i> during ETI.

<p>(A) The <i>PR1</i> expression level in leaves at 6 or 24 hpi with <i>Pto</i> strains (OD₆₀₀ = 0.001) or mock was determined by qRT-PCR. Bars represent means and standard errors of two biological replicates calculated using a mixed linear model. The vertical axis shows the log₂ expression level relative to <i>Actin2</i> (At2g18780). (B) The free SA levels in leaf samples corresponding to those in (A) were determined. Bars represent means and standard errors of two biological replicates calculated using a mixed linear model. The SA level is shown on a log₁₀ scale. Asterisks indicate significant differences from mock (<i>P</i><0.01, two-tailed <i>t</i>-tests).</p

Sustained MAPK activation is sufficient for <i>PR1</i> induction.

<p>The <i>PR1</i> (A) or <i>FRK1</i> (B) expression levels in <i>DEX-MKK4DD</i> (MKK4DD) or <i>-MKK5DD</i> (MKK5DD) at the indicated times after treatment with 2 µM DEX were determined by qRT-PCR. Bars represent means and standard errors of three biological replicates calculated using a mixed linear model. The vertical axis shows the log₂ expression level relative to <i>Actin2</i> (At2g18780). Asterisks indicate significant differences from untreated samples (0 h) (<i>P</i><0.01, two-tailed <i>t</i>-tests).</p

MAPK activation is sustained in ETI but transient in non-ETI conditions.

<p>Leaves of Col (A, B) and <i>rpm1 rps2</i> (C) plants were infiltrated with <i>Pto hrcC</i>, <i>Pto</i> EV, <i>Pto</i> AvrRpt2 (OD₆₀₀ = 0.01) or water (mock) and samples were collected at the indicated time points. Activated MAPKs were detected by immunoblot using anti-p44/42 MAPK antibody. Ponceau S stained blots are shown for loading controls. Experiments were conducted three times, yielding similar results.</p

A model of signaling activated by sustained MAPK activation or SA signaling that regulates the common genes during AvrRpt2-ETI, resulting in robust immunity.

<p>During non-ETI, such as PTI, MAPK activation is transient. Transient MAPK activation is not sufficient for regulating the SA-responsive genes. However, during AvrRpt2-ETI, sustained MAPK activation can regulate the SA-responsive genes independently of SA. The differential duration of the MAPK activation can modulate the network property of robustness.</p

Compensation between MPK3 and SA contributes to the robust ETI levels.

<p>(A) The <i>PR1</i> expression level in leaves of the indicated genotypes at 24 hpi with <i>Pto</i> AvrRpt2 (blue bars) or AvrRpm1 (red bars) (OD₆₀₀ = 0.001) was determined by qRT-PCR. Bars represent means and standard errors of three biological replicates calculated using a mixed linear model. The vertical axis shows the log₂ expression level relative to <i>Actin2</i> (At2g18780). Statistically significant differences are indicated by different letters (<i>P</i><0.01, two-tailed <i>t</i>-tests). (B) The signaling allocations for the <i>PR1</i> expression level shown in (A) were estimated for <i>MPK3</i> and <i>SID2</i> (upper panel) or <i>MPK6</i> and <i>SID2</i> (lower panel). (C) The bacterial counts of <i>Pto</i> EV (left panel) or AvrRpt2 (right panel) (inoculation dose, OD₆₀₀ = 0.0001) at 0 or 2 dpi in leaves of the indicated genotypes were measured. Bars represent means and standard errors of three independent experiments with at least 4 or 12 biological replicates for 0 dpi or 2 dpi, respectively. Statistically significant differences are indicated by different letters per strain per dpi (<i>P</i><0.01, two-tailed <i>t</i>-tests). (D) The signaling allocations for AvrRpt2-ETI shown in (C, 2 dpi) were estimated for <i>MPK3</i> and <i>SID2</i> (left panel) or <i>MPK6</i> and <i>SID2</i> (right panel). (B,D) Bars represent means and standard errors determined using a mixed linear model. Asterisks indicate significant effects or interaction (<i>P</i><0.01).</p

Sustained MAPK activation supports transcriptional regulation of a majority of SA-responsive genes without SA.

<p>(A). A heatmap of the SA-responsive genes. Leaves were collected at 24 hpi with the indicated <i>Pto</i> strains (OD₆₀₀ = 0.001) or mock. Independently, leaves of <i>DEX-MKK4DD</i> plants were collected at 24 hpi with 2 µM DEX or mock and subjected to mRNA profiling analysis using a whole genome DNA microarray. SA-responsive genes were selected for reproducible SID2-dependent responsiveness to the <i>Pto</i> strains as described in Experimental Procedures. The log₂ ratios compared to mock for 187 SA-responsive genes were subjected to agglomerative hierarchical clustering analysis. The log₂ ratio of DEX/mock for the <i>DEX-MKK4DD sid2</i> plant (MKK4DD/<i>sid2</i>) samples was weighted by a factor of 0.5 to reduce its effects on the clustering pattern. The log₂ ratios used were averaged from three independent experiments. Green indicates negative values, red indicates positive values and black indicates zero: see the color scale. The arrow indicates the position of <i>PR1</i>. Means and standard errors of (B) Cluster I, 85 genes; (C) Cluster II, 20 genes; (D) Cluster III, 25 genes are shown.</p