TY - JOUR A1 - Stoessel, Daniel A1 - Stellmann, Jan-Patrick A1 - Willing, Anne A1 - Behrens, Birte A1 - Rosenkranz, Sina C. A1 - Hodecker, Sibylle C. A1 - Stuerner, Klarissa H. A1 - Reinhardt, Stefanie A1 - Fleischer, Sabine A1 - Deuschle, Christian A1 - Maetzler, Walter A1 - Berg, Daniela A1 - Heesen, Christoph A1 - Walther, Dirk A1 - Schauer, Nicolas A1 - Friese, Manuel A. A1 - Pless, Ole T1 - Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring JF - Frontiers in human neuroscienc N2 - Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson’s disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies. KW - untargeted metabolomics KW - biomarker KW - PPMS KW - MS neurodegeneration KW - LysoPC(20:0) Y1 - 2018 U6 - https://doi.org/10.3389/fnhum.2018.00226 SN - 1662-5161 VL - 12 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Stoessel, Daniel A1 - Schulte, Claudia A1 - dos Santos, Marcia C. Teixeira A1 - Scheller, Dieter A1 - Rebollo-Mesa, Irene A1 - Deuschle, Christian A1 - Walther, Dirk A1 - Schauer, Nicolas A1 - Berg, Daniela A1 - da Costa, Andre Nogueira A1 - Maetzler, Walter T1 - Promising Metabolite Profiles in the Plasma and CSF of Early Clinical JF - Frontiers in Aging Neuroscience N2 - Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (<= 1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex-and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. The semetabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD KW - biomarker KW - untargeted metabolomics KW - neurodegeneration KW - plasma KW - CSF KW - machinelearning Y1 - 2018 U6 - https://doi.org/10.3389/fnagi.2018.00051 SN - 1663-4365 VL - 10 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Sprenger, Heike A1 - Rudack, Katharina A1 - Schudoma, Christian A1 - Neumann, Arne A1 - Seddig, Sylvia A1 - Peters, Rolf A1 - Zuther, Ellen A1 - Kopka, Joachim A1 - Hincha, Dirk K. A1 - Walther, Dirk A1 - Koehl, Karin T1 - Assessment of drought tolerance and its potential yield penalty in potato JF - Functional plant biology : an international journal of plant function N2 - Climate models predict an increased likelihood of seasonal droughts for many areas of the world. Breeding for drought tolerance could be accelerated by marker-assisted selection. As a basis for marker identification, we studied the genetic variance, predictability of field performance and potential costs of tolerance in potato (Solanum tuberosum L.). Potato produces high calories per unit of water invested, but is drought-sensitive. In 14 independent pot or field trials, 34 potato cultivars were grown under optimal and reduced water supply to determine starch yield. In an artificial dataset, we tested several stress indices for their power to distinguish tolerant and sensitive genotypes independent of their yield potential. We identified the deviation of relative starch yield from the experimental median (DRYM) as the most efficient index. DRYM corresponded qualitatively to the partial least square model-based metric of drought stress tolerance in a stress effect model. The DRYM identified significant tolerance variation in the European potato cultivar population to allow tolerance breeding and marker identification. Tolerance results from pot trials correlated with those from field trials but predicted field performance worse than field growth parameters. Drought tolerance correlated negatively with yield under optimal conditions in the field. The distribution of yield data versus DRYM indicated that tolerance can be combined with average yield potentials, thus circumventing potential yield penalties in tolerance breeding. KW - performance prediction KW - Solanum tuberosum KW - tolerance index KW - target environment Y1 - 2015 U6 - https://doi.org/10.1071/FP15013 SN - 1445-4408 SN - 1445-4416 VL - 42 IS - 7 SP - 655 EP - 667 PB - CSIRO CY - Clayton ER - TY - JOUR A1 - Sprenger, Heike A1 - Erban, Alexander A1 - Seddig, Sylvia A1 - Rudack, Katharina A1 - Thalhammer, Anja A1 - Le, Mai Q. A1 - Walther, Dirk A1 - Zuther, Ellen A1 - Koehl, Karin I. A1 - Kopka, Joachim A1 - Hincha, Dirk K. T1 - Metabolite and transcript markers for the prediction of potato drought tolerance JF - Plant Biotechnology Journal N2 - Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6% and 9%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions. KW - drought tolerance KW - machine learning KW - metabolite markers KW - potato (Solanum tuberosum) KW - prediction models KW - transcript markers Y1 - 2017 U6 - https://doi.org/10.1111/pbi.12840 SN - 1467-7644 SN - 1467-7652 VL - 16 IS - 4 SP - 939 EP - 950 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Schudoma, Christian A1 - Larhlimi, Abdelhalim A1 - Walther, Dirk T1 - The influence of the local sequence environment on RNA loop structures JF - RNA : a publication of the RNA Society N2 - RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized eta/theta-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55%, thus significantly better than expected by chance (17%-25%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure. KW - RNA KW - 3D structure KW - structure prediction KW - Random Forests KW - machine learning KW - backbone conformation Y1 - 2011 U6 - https://doi.org/10.1261/rna.2550211 SN - 1355-8382 VL - 17 IS - 7 SP - 1247 EP - 1257 PB - Cold Spring Harbor Laboratory Press CY - Cold Spring Harbor, NY ER - TY - JOUR A1 - Riaño-Pachón, Diego Mauricio A1 - Kleessen, Sabrina A1 - Neigenfind, Jost A1 - Durek, Pawel A1 - Weber, Elke A1 - Engelsberger, Wolfgang R. A1 - Walther, Dirk A1 - Selbig, Joachim A1 - Schulze, Waltraud X. A1 - Kersten, Birgit T1 - Proteome-wide survey of phosphorylation patterns affected by nuclear DNA polymorphisms in Arabidopsis thaliana JF - BMC Genomics N2 - Background: Protein phosphorylation is an important post-translational modification influencing many aspects of dynamic cellular behavior. Site-specific phosphorylation of amino acid residues serine, threonine, and tyrosine can have profound effects on protein structure, activity, stability, and interaction with other biomolecules. Phosphorylation sites can be affected in diverse ways in members of any species, one such way is through single nucleotide polymorphisms (SNPs). The availability of large numbers of experimentally identified phosphorylation sites, and of natural variation datasets in Arabidopsis thaliana prompted us to analyze the effect of non-synonymous SNPs (nsSNPs) onto phosphorylation sites. Results: From the analyses of 7,178 experimentally identified phosphorylation sites we found that: (i) Proteins with multiple phosphorylation sites occur more often than expected by chance. (ii) Phosphorylation hotspots show a preference to be located outside conserved domains. (iii) nsSNPs affected experimental phosphorylation sites as much as the corresponding non-phosphorylated amino acid residues. (iv) Losses of experimental phosphorylation sites by nsSNPs were identified in 86 A. thaliana proteins, among them receptor proteins were overrepresented. These results were confirmed by similar analyses of predicted phosphorylation sites in A. thaliana. In addition, predicted threonine phosphorylation sites showed a significant enrichment of nsSNPs towards asparagines and a significant depletion of the synonymous substitution. Proteins in which predicted phosphorylation sites were affected by nsSNPs (loss and gain), were determined to be mainly receptor proteins, stress response proteins and proteins involved in nucleotide and protein binding. Proteins involved in metabolism, catalytic activity and biosynthesis were less affected. Conclusions: We analyzed more than 7,100 experimentally identified phosphorylation sites in almost 4,300 protein-coding loci in silico, thus constituting the largest phosphoproteomics dataset for A. thaliana available to date. Our findings suggest a relatively high variability in the presence or absence of phosphorylation sites between different natural accessions in receptor and other proteins involved in signal transduction. Elucidating the effect of phosphorylation sites affected by nsSNPs on adaptive responses represents an exciting research goal for the future. KW - Gene Ontology KW - Phosphorylation Site KW - phosphorylated amino acid KW - slim term KW - single nucleotide polymorphism mapping Y1 - 2010 U6 - https://doi.org/10.1186/1471-2164-11-411 SN - 1471-2164 VL - 11 PB - Biomed Central CY - London ER - TY - JOUR A1 - Knox-Brown, Patrick A1 - Rindfleisch, Tobias A1 - Günther, Anne A1 - Balow, Kim A1 - Bremer, Anne A1 - Walther, Dirk A1 - Miettinen, Markus S. A1 - Hincha, Dirk K. A1 - Thalhammer, Anja T1 - Similar Yet Different BT - Structural and Functional Diversity among Arabidopsis thaliana LEA_4 Proteins JF - International Journal of Molecular Sciences N2 - The importance of intrinsically disordered late embryogenesis abundant (LEA) proteins in the tolerance to abiotic stresses involving cellular dehydration is undisputed. While structural transitions of LEA proteins in response to changes in water availability are commonly observed and several molecular functions have been suggested, a systematic, comprehensive and comparative study of possible underlying sequence-structure-function relationships is still lacking. We performed molecular dynamics (MD) simulations as well as spectroscopic and light scattering experiments to characterize six members of two distinct, lowly homologous clades of LEA_4 family proteins from Arabidopsis thaliana. We compared structural and functional characteristics to elucidate to what degree structure and function are encoded in LEA protein sequences and complemented these findings with physicochemical properties identified in a systematic bioinformatics study of the entire Arabidopsis thaliana LEA_4 family. Our results demonstrate that although the six experimentally characterized LEA_4 proteins have similar structural and functional characteristics, differences concerning their folding propensity and membrane stabilization capacity during a freeze/thaw cycle are obvious. These differences cannot be easily attributed to sequence conservation, simple physicochemical characteristics or the abundance of sequence motifs. Moreover, the folding propensity does not appear to be correlated with membrane stabilization capacity. Therefore, the refinement of LEA_4 structural and functional properties is likely encoded in specific patterns of their physicochemical characteristics. KW - IDP KW - LEA protein KW - abiotic stress KW - dehydration KW - conformational rearrangement KW - membrane stabilization KW - sequence-structure-function relationship Y1 - 2020 U6 - https://doi.org/10.3390/ijms21082794 SN - 1422-0067 VL - 21 IS - 8 PB - Molecular Diversity Preservation International CY - Basel ER - TY - JOUR A1 - Christian, Jan-Ole A1 - Braginets, Rostyslav A1 - Schulze, Waltraud X. A1 - Walther, Dirk T1 - Characterization and prediction of protein phosphorylation hotspots in Arabidopsis thaliana JF - Frontiers in plant science N2 - The regulation of protein function by modulating the surface charge status via sequence-locally enriched phosphorylation sites (P-sites) in so called phosphorylation "hotspots" has gained increased attention in recent years. We set out to identify P-hotspots in the model plant Arabidopsis thaliana. We analyzed the spacing of experimentally detected P-sites within peptide-covered regions along Arabidopsis protein sequences as available from the PhosPhAt database. Confirming earlier reports (Schweiger and Lanial, 2010), we found that, indeed, P-sites tend to cluster and that distributions between serine and threonine P-sites to their respected closest next P-site differ significantly from those for tyrosine P-sites. The ability to predict P-hotspots by applying available computational P-site prediction programs that focus on identifying single P-sites was observed to be severely compromised by the inevitable interference of nearby P-sites. We devised a new approach, named HotSPotter, for the prediction of phosphorylation hotspots. HotSPotter is based primarily on local amino acid compositional preferences rather than sequence position-specific motifs and uses support vector machines as the underlying classification engine. HotSPotter correctly identified experimentally determined phosphorylation hotspots in A. thaliana with high accuracy. Applied to the Arabidopsis proteome, HotSPotter-predicted 13,677 candidate P-hotspots in 9,599 proteins corresponding to 7,847 unique genes. Hotspot containing proteins are involved predominantly in signaling processes confirming the surmised modulating role of hotspots in signaling and interaction events. Our study provides new bioinformatics means to identify phosphorylation hotspots and lays the basis for further investigating novel candidate P-hotspots. All phosphorylation hotspot annotations and predictions have been made available as part of the PhosPhAt database at http://phosphat.mpimp-golm.mpg.de. KW - protein phosphorylation KW - hotspots KW - Arabidopsis thaliana KW - support vector machines KW - regulation Y1 - 2012 U6 - https://doi.org/10.3389/fpls.2012.00207 SN - 1664-462X VL - 3 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Childs, Liam H. A1 - Witucka-Wall, Hanna A1 - Guenther, Torsten A1 - Sulpice, Ronan A1 - Korff, Maria V. A1 - Stitt, Mark A1 - Walther, Dirk A1 - Schmid, Karl J. A1 - Altmann, Thomas T1 - Single feature polymorphism (SFP)-based selective sweep identification and association mapping of growth- related metabolic traits in Arabidopsis thaliana N2 - Background: Natural accessions of Arabidopsis thaliana are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 A. thaliana natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits. Results: A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non- repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait. Conclusions: We generated a high density polymorphism map for 54 A. thaliana accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation. Y1 - 2010 UR - http://www.biomedcentral.com/1471-2164/ U6 - https://doi.org/10.1186/1471-2164-11-188 SN - 1471-2164 ER -