Madlen Vetter

Madlen Vetter
University of Chicago | UC · Department of Ecology & Evolution

PhD, Botany

About

21
Publications
1,299
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
346
Citations
Additional affiliations
January 2007 - June 2010
Max Planck Institute for Plant Breeding Research
Position
  • PhD Student
Education
October 2001 - October 2006
Universität Potsdam
Field of study
  • Molecular Biology, Ecology

Publications

Publications (21)
Article
Full-text available
A first line of defense against pathogen attack for both plants and animals involves the detection of microbe-associated molecular patterns (MAMPs), followed by the induction of a complex immune response. Plants, like animals, encode several receptors that recognize different MAMPs. While these receptors are thought to function largely redundantly,...
Data
Experimental validation of GWA candidate loci. We tested plants carrying a non-functional allele (mutant) versus plants carrying the WT allele (Col-0 except fls2-24 that has a Ler genetic background). Column N indicates the number of tested mutants / WT plants, and effect (Eff) indicates an increase (+) or decrease (-) in SGI. Column ‘p-value’ indi...
Data
The EFR haplotype group strongly influences elf18DC-induced SGI. The three panels shows SGI induced by elf18DC, elf18Ps and elf18Pv in 186 genotypes of A. thaliana. Above each panel we indicate results of a t-test testing the effect of EFR haplotype group on SGI. We labeled the two outliers (i.e., elf18-insensitive genotypes) Pro-0 and Alc-0 in the...
Data
Genomic region 18500000 to 19600000 of chromosome 5 is densely populated with highly associated SNPs. The genomic region 18500000 to 19600000 of chromosome 5 has several peaks that are associated with flg22-induced SGI. These peaks do not co-localize with known a priori candidate genes such as flagellin receptor FLS2. None of the 69 SNPs within 15...
Data
Number of shared genomic regions. Many genomic regions were mapped by more than one MAMP variant. SGI induced by peptides of the same MAMP class (elf18 or flg22 variants) share a larger number of genomic regions than between MAMP classes. As a result of linkage disequilibrium and SNP density, a highly associated SNP can be located several kb away f...
Data
Sequences of Flagellin in P. syringae and P. viridiflava. Fasta nucleotide sequences of region surrounding flagellin gene in 20 P. syringae strains and two P. viridiflava strains. Strains are labeled with name first then species designation. (TXT)
Data
Sequences of EF-Tu in P. syringae and P. viridiflava. Fasta nucleotide sequences of region surrounding EF-Tu gene in 20 P. syringae strains and two P. viridiflava strains. Strains are labeled with name first then species designation. (TXT)
Data
The relationship between receptor expression level and SGI. Expression data on the x-axis for FLS2 (a) and EFR (b) are taken from [38]. Plants were grown at 16°C. Receptor mRNA expression level is plotted against SGI induced by the respective MAMP. Expression data is presented in the units of reads per kilobase per million of mapped reads (RPKM). T...
Data
The number of shared peaks within a MAMP class is higher than expected by chance. Each histogram shows the distribution of the number of shared peaks for 100 GWA runs that were generated with randomized phenotypic values. The red vertical line represents the number of peaks that were found in the actual mapping. Given is also the empirical p-value...
Data
Analysis of variance determining the effect of MAMP class (MAMPclass), host genotype (genotype) and MAMP peptide (MAMP, nested within MAMP class) on seedling growth inhibition (SGI). (PDF)
Data
Correlation of SGI is high within MAMP classes (elf18 or flg22) but not among MAMP classes. The table indicates Pearson’s correlation coefficients for genotype means of seedling growth inhibition. Genotypes that did not exhibit seedling growth inhibitionin response to elf18 or flg22 were excluded prior analysis. Significant correlations are indicat...
Data
Heritability estimates and confidence intervals. Marker-assisted heritability was estimated for seedling growth inhibition (SGI), fresh mass in control conditions (CFM) and fresh mass after MAMP treatment (TFM). SGI is calculated by [(CFM—TFM) / CFM] * 100. (PDF)
Data
Summary statistics of GWA mapping. We conducted GWA mapping on SGI, induced by seven diverse MAMPs. We considered the 0.1% tail of strongest associated p-values for further analysis, which corresponds to 203 SNPs out of 203,498 for which genotype data were available. Column “MAMP” indicates the MAMP used to induce SGI, “p-value” indicates the p-val...
Data
flg22-insensitivity is associated with nucleotide deletions within the FLS2 Serine-Threonine kinase catalytic domain. Shown are the 100 bp surrounding the start site of the Serine/Threonine kinase catalytic domain of FLS2 in twenty two genotypes that exhibit SGI in response to flg22 (SGI+) and nine genotypes that do not (SGI-). Pseudogenome data wa...
Article
Full-text available
The 16S rRNA gene (16S) is an accepted marker of bacterial taxonomic diversity, even though differences in copy number obscure the relationship between amplicon and organismal abundances. Ancestral state reconstruction methods can predict 16S copy numbers through comparisons with closely related reference genomes; however, the database of closed ge...
Article
Full-text available
Identifying the factors that influence the outcome of host-microbial interactions is critical to protecting biodiversity, minimizing agricultural losses and improving human health. A few genes that determine symbiosis or resistance to infectious disease have been identified in model species, but a comprehensive examination of how a host genotype in...
Article
Much is known about the evolution of plant immunity components directed against specific pathogen strains: They show pervasive functional variation and have the potential to coevolve with pathogen populations. However, plants are effectively protected against most microbes by generalist immunity components that detect conserved pathogen-associated...
Article
Zusammenfassung Pflanzen erkennen Mikroorganismen anhand konservierter Strukturen, was eine aktive Immunabwehr auslöst. Pathogene müssen dies umgehen, doch Pflanzen können ihr Immunsystem anpassen, so kommt es zu einem Wettrüsten zwischen Pflanze und Erreger. Pflanzen müssen sich durch ihre sessile Lebensform an unterschiedliche Bedingungen wie So...
Article
Plants sense microorganisms and initiate immune responses. A continuous adaptation leads to an arms race between plants and pathogens.

Network

Cited By