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Aims: According to 'the continuum of resistance model' late respondents can be used as a proxy for non-respondents in estimating non-response bias. In the present study, the validity of this model was explored and tested in three surveys on alcohol consumption. Methods: The three studies collected their data by means of mailed questionnaires on alcohol consumption whereby two studies also performed a non-response follow-up. Results: Comparisons of early respondents, late respondents and non-respondents in one study showed some support for 'the continuum of resistance model', although another study could not confirm this result. Comparison of alcohol consumption between three time response groups showed no significant linear pattern of differences between response waves. Conclusions: The hypothesis that late respondents are more similar to non-respondents than early respondents, could not be confirmed or rejected. Repeated mailings are effective in obtaining a greater sample size, but seem ineffective in improving the representativeness of alcohol consumption surveys. [ABSTRACT FROM AUTHOR]

The addition of the proteasome inhibitor bortezomib to standard chemotherapy did not improve survival in pediatric acute myeloid leukemia (AML) when all patients were analyzed as a group in the Children's Oncology Group phase 3 trial AAML1031 (NCT01371981). Proteasome inhibition influences the chromatin landscape and proteostasis, and we hypothesized that baseline proteomic analysis of histone- and chromatin-modifying enzymes (HMEs) would identify AML subgroups that benefitted from bortezomib addition. A proteomic profile of 483 patients treated with AAML1031 chemotherapy was generated using a reverse-phase protein array. A relatively high expression of 16 HME was associated with lower EFS and higher 3-year relapse risk after AML standard treatment compared to low expressions (52% vs. 29%, p = 0.005). The high-HME profile correlated with more transposase-accessible chromatin, as demonstrated via ATAC-sequencing, and the bortezomib addition improved the 3-year overall survival compared with standard therapy (62% vs. 75%, p = 0.033). These data suggest that there are pediatric AML populations that respond well to bortezomib-containing chemotherapy.

Pediatric acute myeloid leukemia (AML) remains a fatal disease for at least 30% of patients, stressing the need for improved therapies and better risk stratification. As proteins are the unifying feature of (epi)genetic and environmental alterations, and are often targeted by novel chemotherapeutic agents, we studied the proteomic landscape of pediatric AML. Protein expression and activation levels were measured in 500 bulk leukemic patients' samples and 30 control CD34+ cell samples, using reverse phase protein arrays with 296 strictly validated antibodies. The multistep MetaGalaxy analysis methodology was applied and identified nine protein expression signatures (PrSIG), based on strong recurrent protein expression patterns. PrSIG were associated with cytogenetics and mutational state, and with favorable or unfavorable prognosis. Analysis based on treatment (i.e., ADE vs. ADE plus bortezomib) identified three PrSIG that did better with ADE plus bortezomib than with ADE alone. When PrSIG were studied in the context of cytogenetic risk groups, PrSIG were independently prognostic after multivariate analysis, suggesting a potential value for proteomics in combination with current classification systems. Proteins with universally increased (n=7) or decreased (n=17) expression were observed across PrSIG. Certain proteins significantly differentially expressed from normal could be identified, forming a hypothetical platform for personalized medicine.

The chronic lymphocytic leukemia (CLL) armamentarium has evolved significantly, with novel therapies that inhibit Bruton Tyrosine Kinase, PI3K delta and/or the BCL2 protein improving outcomes. Still, the clinical course of CLL patients is highly variable and most previously recognized prognostic features lack the capacity to predict response to modern treatments indicating the need for new prognostic markers. In this study, we identified four epigenetically distinct proteomic signatures of a large cohort of CLL and related diseases derived samples (n = 871) using reverse phase protein array technology. These signatures are associated with clinical features including age, cytogenetic abnormalities [trisomy 12, del(13q) and del(17p)], immunoglobulin heavy-chain locus (IGHV) mutational load, ZAP-70 status, Binet and Rai staging as well as with the outcome measures of time to treatment and overall survival. Protein signature membership was identified as predictive marker for overall survival regardless of other clinical features. Among the analyzed epigenetic proteins, EZH2, HDAC6, and loss of H3K27me3 levels were the most independently associated with poor survival. These findings demonstrate that proteomic based epigenetic biomarkers can be used to better classify CLL patients and provide therapeutic guidance., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Purpose: The addition of the proteasome inhibitor (PI) bortezomib to standard chemotherapy (ADE: cytarabine [Ara-C], daunorubicin, and etoposide) did not improve overall outcome of pediatric AML patients in the Children's Oncology Group AAML1031 phase 3 randomized clinical trial (AAML1031) . Bortezomib prevents protein degradation, including RelA via the intracellular NF-kB pathway. In this study, we hypothesized that subgroups of pediatric AML patients benefitting from standard therapy plus bortezomib (ADEB) could be identified based on pre-treatment RelA expression and phosphorylation status., Experimental Design: RelA-total and phosphorylation at serine 536 (RelA-pSer 536 ) were measured in 483 patient samples using reverse phase protein array technology., Results: In ADEB-treated patients, low-RelA-pSer 536 was favorably prognostic when compared to high-RelA-pSer 536 (3-yr overall survival (OS): 81% vs. 68%, p = 0.032; relapse risk (RR): 30% vs. 49%, p = 0.004). Among low-RelA-pSer 536 patients, RR significantly decreased with ADEB compared to ADE (RR: 30% vs. 44%, p = 0.035). Correlation between RelA-pSer 536 and 295 other assayed proteins identified a strong correlation with HSF1-pSer 326 , another protein previously identified as modifying ADEB response. The combination of low-RelA-pSer 536 and low-HSF1-pSer 326 was a significant predictor of ADEB response (3-yr OS: 86% vs. 67%, p = 0.013)., Conclusion and Clinical Relevance: Bortezomib may improve clinical outcome in a subgroup of AML patients identified by low-RelA-pSer 536 and low-HSF1-pSer 326 ., (© 2021 Wiley-VCH GmbH.)

Bortezomib (BTZ) was recently evaluated in a randomized phase 3 clinical trial by the Children's Oncology Group (COG) that compared standard chemotherapy (cytarabine, daunorubicin, and etoposide [ADE]) vs standard therapy with BTZ (ADEB) for de novo pediatric acute myeloid leukemia (AML). Although the study concluded that BTZ did not improve outcome overall, we examined patient subgroups benefiting from BTZ-containing chemotherapy using proteomic analyses. The proteasome inhibitor BTZ disrupts protein homeostasis and activates cytoprotective heat shock responses. Total heat shock factor 1 (HSF1) and phosphorylated HSF1 (HSF1-pSer326) were measured in leukemic cells from 483 pediatric patients using reverse phase protein arrays. HSF1-pSer326 phosphorylation was significantly lower in pediatric AML compared with CD34+ nonmalignant cells. We identified a strong correlation between HSF1-pSer326 expression and BTZ sensitivity. BTZ significantly improved outcome of patients with low-HSF1-pSer326 with a 5-year event-free survival of 44% (ADE) vs 67% for low-HSF1-pSer326 treated with ADEB (P = .019). To determine the effect of HSF1 expression on BTZ potency in vitro, cell viability with HSF1 gene variants that mimicked phosphorylated (S326A) and nonphosphorylated (S326E) HSF1-pSer326 were examined. Those with increased HSF1 phosphorylation showed clear resistance to BTZ vs those with wild-type or reduced HSF1-phosphorylation. We hypothesize that HSF1-pSer326 expression could identify patients who benefit from BTZ-containing chemotherapy., (© 2021 by The American Society of Hematology.)

Background: Acute leukemia is an epigenetically heterogeneous disease. The intensity of treatment is currently guided by cytogenetic and molecular genetic risk classifications; however these incompletely predict outcomes, requiring additional information for more accurate outcome predictions. We aimed to identify potential prognostic implications of epigenetic modification of histone proteins, with a focus on H3K4 and H3K27 methylation marks in relation to mutations in chromatin, splicing and transcriptional regulators in adult-onset acute lymphoblastic and myeloid leukemia., Results: Histone 3 lysine 4 di- and trimethylation (H3K4me2, H3K4me3) and lysine 27 trimethylation (H3K27me3) mark expression was evaluated in 241 acute myeloid leukemia (AML), 114 B-cell acute lymphoblastic leukemia (B-ALL) and 14T-cell ALL (T-ALL) patient samples at time of diagnosis using reverse phase protein array. Expression levels of the marks were significantly lower in AML than in B and T-ALL in both bone marrow and peripheral blood, as well as compared to normal CD34+ cells. In AML, greater loss of H3K27me3 was associated with increased proliferative potential and shorter overall survival in the whole patient population, as well as in subsets with DNA methylation mutations. To study the prognostic impact of H3K27me3 in the context of cytogenetic aberrations and mutations, multivariate analysis was performed and identified lower H3K27me3 level as an independent unfavorable prognostic factor in all, as well as in TP53 mutated patients. AML with decreased H3K27me3 demonstrated an upregulated anti-apoptotic phenotype. In ALL, the relative quantity of histone methylation expression correlated with response to tyrosine kinase inhibitor in patients who carried the Philadelphia cytogenetic aberration and prior smoking behavior., Conclusion: This study shows that proteomic profiling of epigenetic modifications has clinical implications in acute leukemia and supports the idea that epigenetic patterns contribute to a more accurate picture of the leukemic state that complements cytogenetic and molecular genetic subgrouping. A combination of these variables may offer more accurate outcome prediction and we suggest that histone methylation mark measurement at time of diagnosis might be a suitable method to improve patient outcome prediction and subsequent treatment intensity stratification in selected subgroups.

Introduction : The biological heterogeneity of acute myeloid leukemia (AML) complicates personalized medicine. Individual prognosis is typically based on the presence of chromosomal and genetic lesions. Nevertheless, these classifications often lack a priori information about response to therapy. Since the protein expression landscape reflects the functional activity state of cells, we hypothesize that analyzing this can be used for the identification of protein activity markers to provide better risk stratification as well as may provide targeted therapeutic guidance in AML. Areas covered : Herein, we review recently new adopted drugs in the treatment for AML and discuss how quantitative proteomic techniques may contribute to better therapeutic selection in AML. Expert commentary : The net functional state of the cell is defined by the activity of protein within all the pathways that are active in the cell. Recognition of the proteomic profile of the leukemic blast could, therefore, complement current classification systems by providing a better a priori description of what pathways are important within a cell as a guide to the selection of therapy for the patient.

Plants exhibit a vast array of sesquiterpenes, C15 hydrocarbons which often function as herbivore-repellents or pollinator-attractants. These in turn are produced by a diverse range of sesquiterpene synthases. A comprehensive analysis of these enzymes in terms of product specificity has been hampered by the lack of a centralized resource of sufficient functionally annotated sequence data. To address this, we have gathered 262 plant sesquiterpene synthase sequences with experimentally characterized products. The annotated enzyme sequences allowed for an analysis of terpene synthase motifs, leading to the extension of one motif and recognition of a variant of another. In addition, putative terpene synthase sequences were obtained from various resources and compared with the annotated sesquiterpene synthases. This analysis indicated regions of terpene synthase sequence space which so far are unexplored experimentally. Finally, we present a case describing mutational studies on residues altering product specificity, for which we analyzed conservation in our database. This demonstrates an application of our database in choosing likely-functional residues for mutagenesis studies aimed at understanding or changing sesquiterpene synthase product specificity., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Posttranslational histone tail modifications are known to play a role in leukemogenesis and are therapeutic targets. A global analysis of the level and patterns of expression of multiple histone-modifying proteins (HMP) in acute myeloid leukemia (AML) and the effect of different patterns of expression on outcome and prognosis has not been investigated in AML patients. Here we analyzed 20 HMP by reverse phase protein array (RPPA) in a cohort of 205 newly diagnosed AML patients. Protein levels were correlated with patient and disease characteristics, including survival and mutational state. We identified different protein clusters characterized by higher (more on) or lower (more off) expression of HMP, relative to normal CD34+ cells. On state of HMP was associated with poorer outcome compared to normal-like and a more off state. FLT3 mutated AML patients were significantly overrepresented in the more on state. DNA methylation related mutations showed no correlation with the different HMP states. In this study, we demonstrate for the first time that HMP form recurrent patterns of expression and that these significantly correlate with survival in newly diagnosed AML patients., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We determined the crossover (CO) distribution, frequency and genomic sequences involved in interspecies meiotic recombination by using parent-assigned variants of 52 F 6 recombinant inbred lines obtained from a cross between tomato, Solanum lycopersicum, and its wild relative, Solanum pimpinellifolium. The interspecific CO frequency was 80% lower than reported for intraspecific tomato crosses. We detected regions showing a relatively high and low CO frequency, so-called hot and cold regions. Cold regions coincide to a large extent with the heterochromatin, although we found a limited number of smaller cold regions in the euchromatin. The CO frequency was higher at the distal ends of chromosomes than in pericentromeric regions and higher in short arm euchromatin. Hot regions of CO were detected in euchromatin, and COs were more often located in non-coding regions near the 5' untranslated region of genes than expected by chance. Besides overrepresented CCN repeats, we detected poly-A/T and AT-rich motifs enriched in 1-kb promoter regions flanking the CO sites. The most abundant sequence motifs at CO sites share weak similarity to transcription factor-binding sites, such as for the C2H2 zinc finger factors class and MADS box factors, while InterPro scans detected enrichment for genes possibly involved in the repair of DNA breaks., (© 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.)

This chapter presents a use case illustrating the search for homologues of a known protein in species-specific genome sequence databases. The results from different species-specific resources are compared to each other and to results obtained from a more general genome sequence database (Phytozome). Various options and settings relevant when searching these databases are discussed. For example, it is shown how the choice of reference sequence set in a given database influences the results one obtains. The provided examples illustrate some problems and pitfalls related to interpreting results obtained from species-specific genome sequence databases.

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging., Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2., Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Recent methodological developments in plant phenotyping, as well as the growing importance of its applications in plant science and breeding, are resulting in a fast accumulation of multidimensional data. There is great potential for expediting both discovery and application if these data are made publicly available for analysis. However, collection and storage of phenotypic observations is not yet sufficiently governed by standards that would ensure interoperability among data providers and precisely link specific phenotypes and associated genomic sequence information. This lack of standards is mainly a result of a large variability of phenotyping protocols, the multitude of phenotypic traits that are measured, and the dependence of these traits on the environment. This paper discusses the current situation of standardization in the area of phenomics, points out the problems and shortages, and presents the areas that would benefit from improvement in this field. In addition, the foundations of the work that could revise the situation are proposed, and practical solutions developed by the authors are introduced., (© The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Various environmental signals integrate into a network of floral regulatory genes leading to the final decision on when to flower. Although a wealth of qualitative knowledge is available on how flowering time genes regulate each other, only a few studies incorporated this knowledge into predictive models. Such models are invaluable as they enable to investigate how various types of inputs are combined to give a quantitative readout. To investigate the effect of gene expression disturbances on flowering time, we developed a dynamic model for the regulation of flowering time in Arabidopsis thaliana. Model parameters were estimated based on expression time-courses for relevant genes, and a consistent set of flowering times for plants of various genetic backgrounds. Validation was performed by predicting changes in expression level in mutant backgrounds and comparing these predictions with independent expression data, and by comparison of predicted and experimental flowering times for several double mutants. Remarkably, the model predicts that a disturbance in a particular gene has not necessarily the largest impact on directly connected genes. For example, the model predicts that SUPPRESSOR OF OVEREXPRESSION OF CONSTANS (SOC1) mutation has a larger impact on APETALA1 (AP1), which is not directly regulated by SOC1, compared to its effect on LEAFY (LFY) which is under direct control of SOC1. This was confirmed by expression data. Another model prediction involves the importance of cooperativity in the regulation of APETALA1 (AP1) by LFY, a prediction supported by experimental evidence. Concluding, our model for flowering time gene regulation enables to address how different quantitative inputs are combined into one quantitative output, flowering time.

Background: Elucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait., Results: The prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait., Conclusions: Our approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.

Strigolactones (SLs) are a class of phytohormones and rhizosphere signaling compounds with high structural diversity. Three enzymes, carotenoid isomerase DWARF27 and carotenoid cleavage dioxygenases CCD7 and CCD8, were previously shown to convert all-trans-β-carotene to carlactone (CL), the SL precursor. However, how CL is metabolized to SLs has remained elusive. Here, by reconstituting the SL biosynthetic pathway in Nicotiana benthamiana, we show that a rice homolog of Arabidopsis More Axillary Growth 1 (MAX1), encodes a cytochrome P450 CYP711 subfamily member that acts as a CL oxidase to stereoselectively convert CL into ent-2'-epi-5-deoxystrigol (B-C lactone ring formation), the presumed precursor of rice SLs. A protein encoded by a second rice MAX1 homolog then catalyzes the conversion of ent-2'-epi-5-deoxystrigol to orobanchol. We therefore report that two members of CYP711 enzymes can catalyze two distinct steps in SL biosynthesis, identifying the first enzymes involved in B-C ring closure and a subsequent structural diversification step of SLs.

The architecture of tomato inflorescence strongly affects flower production and subsequent crop yield. To understand the genetic activities involved, insight into the underlying network of genes that initiate and control the sympodial growth in the tomato is essential. In this paper, we show how the structure of this network can be derived from available data of the expressions of the involved genes. Our approach starts from employing biological expert knowledge to select the most probable gene candidates behind branching behavior. To find how these genes interact, we develop a stepwise procedure for computational inference of the network structure. Our data consists of expression levels from primary shoot meristems, measured at different developmental stages on three different genotypes of tomato. With the network inferred by our algorithm, we can explain the dynamics corresponding to all three genotypes simultaneously, despite their apparent dissimilarities. We also correctly predict the chronological order of expression peaks for the main hubs in the network. Based on the inferred network, using optimal experimental design criteria, we are able to suggest an informative set of experiments for further investigation of the mechanisms underlying branching behavior.

Plant MADS-domain transcription factors act as key regulators of many developmental processes. Despite the wealth of information that exists about these factors, the mechanisms by which they recognize their cognate DNA-binding site, called CArG-box (consensus CCW6GG), and how different MADS-domain proteins achieve DNA-binding specificity, are still largely unknown. We used information from in vivo ChIP-seq experiments, in vitro DNA-binding data and evolutionary conservation to address these important questions. We found that structural characteristics of the DNA play an important role in the DNA binding of plant MADS-domain proteins. The central region of the CArG-box largely resembles a structural motif called 'A-tract', which is characterized by a narrow minor groove and may assist bending of the DNA by MADS-domain proteins. Periodically spaced A-tracts outside the CArG-box suggest additional roles for this structure in the process of DNA binding of these transcription factors. Structural characteristics of the CArG-box not only play an important role in DNA-binding site recognition of MADS-domain proteins, but also partly explain differences in DNA-binding specificity of different members of this transcription factor family and their heteromeric complexes.

Nootkatone is one of the major terpenes in the heartwood of the Nootka cypress Callitropsis nootkatensis. It is an oxidized sesquiterpene, which has been postulated to be derived from valencene. Both valencene and nootkatone are used for flavouring citrus beverages and are considered among the most valuable terpenes used at commercial scale. Functional evaluation of putative terpene synthase genes sourced by large-scale EST sequencing from Nootka cypress wood revealed a valencene synthase gene (CnVS). CnVS expression in different tissues from the tree correlates well with nootkatone content, suggesting that CnVS represents the first dedicated gene in the nootkatone biosynthetic pathway in C. nootkatensis The gene belongs to the gymnosperm-specific TPS-d subfamily of terpenes synthases and its protein sequence has low similarity to known citrus valencene synthases. In vitro, CnVS displays high robustness under different pH and temperature regimes, potentially beneficial properties for application in different host and physiological conditions. Biotechnological production of sesquiterpenes has been shown to be feasible, but productivity of microbial strains expressing valencene synthase from Citrus is low, indicating that optimization of valencene synthase activity is needed. Indeed, expression of CnVS in Saccharomyces cerevisiae indicated potential for higher yields. In an optimized Rhodobacter sphaeroides strain, expression of CnVS increased valencene yields 14-fold to 352 mg/L, bringing production to levels with industrial potential., (© 2013 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.)

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein-protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein-protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family.

Most secreted proteins in eukaryotes are modified on the amino acid consensus sequence NxS/T by an N-glycan through the process of N-glycosylation. The N-glycans on glycoproteins are processed in the endoplasmic reticulum (ER) to different mannose-type N-glycans or, when the protein passes through the Golgi apparatus, to different complex glycan forms. Here we describe the capturing of N-glycopeptides from a trypsin digest of total protein extracts of Arabidopsis plants and release of these captured peptides following Peptide N-glycosidase (PNGase) treatment for analysis of N-glycan site-occupancy. The mixture of peptides released as a consequence of the PNGase treatment was analyzed by two dimensional nano-LC-MS. As the PNGase treatment of glycopeptides results in the deamidation of the asparagine (N) in the NxS/T site of the released peptide, this asparagine (N) to aspartic acid (D) conversion is used as a glycosylation 'signature'. The efficiency of PNGase F and PNGase A in peptide release is discussed. The identification of proteins with a single glycopeptide was limited by the used search algorithm but could be improved using a reference database including deamidated peptide sequences. Additional stringency settings were used for filtering results to minimize false discovery. This resulted in identification of 330 glycopeptides on 173 glycoproteins from Arabidopsis, of which 28 putative glycoproteins, that were previously not annotated as secreted protein in The Arabidopsis Information Resource database (TAIR). Furthermore, the identified glycosylation site occupancy helped to determine the correct topology for membrane proteins. A quantitative comparison of peptide signal was made between wild type and complex-glycan-less (cgl) mutant Arabidopsis from three replicate leaf samples using a label-free MS peak comparison. As an example, the identified membrane protein SKU5 (AT4G12420) showed differential glycopeptide intensity ratios between WT and cgl indicating heterogeneous glycan modification on single protein., Biological Significance: Proteins that enter the secretory pathway are mostly modified by N-glycans. The function of N-glycosylation has been well studied in mammals. However, in plants the function of N-glycosylation is still unclear, because glycosylation mutants in plants often do not have a clear phenotype. Here we analyzed which proteins are modified by N-glycans in plants by developing a glycopeptide enrichment method for plant proteins. Subsequently, label free comparative proteomics was employed using protein fractions from wild type and from a mutant which is blocked in modification of the N-glycan into complex glycans. The results provide new information on N-glycosylation sites on numerous secreted proteins. Results allow for specific mapping of multiple glycosylation site occupancy on proteins, which provides information on which glycosylation sites are protected or non-used from downstream processing and thus presumably are buried into the protein structure. Glycoproteomics can therefore contribute to protein structure analysis. Indeed, mapping the glycosylation sites on membrane proteins gives information on the topology of protein folds over the membrane. We thus were able to correct the topology prediction of three membrane proteins. Besides, these studies also identified limitations in the software that is used to identify single modified peptide per protein. This article is part of a Special Issue entitled: Translational Plant Proteomics., (© 2013 Elsevier B.V. All rights reserved.)

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

HADDOCK is one of the few docking programs that can explicitly account for water molecules in the docking process. Its solvated docking protocol starts from hydrated molecules and a fraction of the resulting interfacial waters is subsequently removed in a biased Monte Carlo procedure based on water-mediated contact probabilities. The latter were derived from an analysis of water contact frequencies from high-resolution crystal structures. Here, we introduce a simple water-mediated amino acid-amino acid contact probability scale derived from the Kyte-Doolittle hydrophobicity scale and assess its performance on the largest high-resolution dataset developed to date for solvated docking. Both scales yield high-quality docking results. The novel and simple hydrophobicity scale, which should reflect better the physicochemical principles underlying contact propensities, leads to a performance improvement of around 10% in ranking, cluster quality and water recovery at the interface compared with the statistics-based original solvated docking protocol., (Copyright © 2012 Wiley Periodicals, Inc.)

Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible. In other words, a perfect solution for CMC would be a minimal set of motif pairs that describes the interaction behavior perfectly in the sense that two proteins from the network interact if and only if their sequences match a motif pair in the minimal set. In this paper, we introduce and formally define CMC and show that it is closely related to the red-blue set cover (RBSC) problem and its weighted version (WRBSC)--both well-known NP-hard problems for that there exist several algorithms with known approximation factor guarantees. We prove the hardness of approximation of CMC by providing an approximation factor preserving reduction from RBSC to CMC. We show the existence of a theoretical approximation algorithm for CMC by providing an approximation factor preserving reduction from CMC to WRBSC. We adapt the latter algorithm into a functional heuristic for CMC, called CMC-approx, and experimentally assess its performance and biological relevance. The implementation in Java can be found at >http://bioinformatics.uhasselt.be.

The transition from vegetative to reproductive development is one of the most important phase changes in the plant life cycle. This step is controlled by various environmental signals that are integrated at the molecular level by so-called floral integrators. One such floral integrator in Arabidopsis (Arabidopsis thaliana) is the MADS domain transcription factor SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1). Despite extensive genetic studies, little is known about the transcriptional control of SOC1, and we are just starting to explore the network of genes under the direct control of SOC1 transcription factor complexes. Here, we show that several MADS domain proteins, including SOC1 heterodimers, are able to bind SOC1 regulatory sequences. Genome-wide target gene analysis by ChIP-seq confirmed the binding of SOC1 to its own locus and shows that it also binds to a plethora of flowering-time regulatory and floral homeotic genes. In turn, the encoded floral homeotic MADS domain proteins appear to bind SOC1 regulatory sequences. Subsequent in planta analyses revealed SOC1 repression by several floral homeotic MADS domain proteins, and we show that, mechanistically, this depends on the presence of the SOC1 protein. Together, our data show that SOC1 constitutes a major hub in the regulatory networks underlying floral timing and flower development and that these networks are composed of many positive and negative autoregulatory and feedback loops. The latter seems to be crucial for the generation of a robust flower-inducing signal, followed shortly after by repression of the SOC1 floral integrator.

TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR1 (TCP) transcription factors control developmental processes in plants. The 24 TCP transcription factors encoded in the Arabidopsis (Arabidopsis thaliana) genome are divided into two classes, class I and class II TCPs, which are proposed to act antagonistically. We performed a detailed phenotypic analysis of the class I tcp20 mutant, showing an increase in leaf pavement cell sizes in 10-d-old seedlings. Subsequently, a glucocorticoid receptor induction assay was performed, aiming to identify potential target genes of the TCP20 protein during leaf development. The LIPOXYGENASE2 (LOX2) and class I TCP9 genes were identified as TCP20 targets, and binding of TCP20 to their regulatory sequences could be confirmed by chromatin immunoprecipitation analyses. LOX2 encodes for a jasmonate biosynthesis gene, which is also targeted by class II TCP proteins that are under the control of the microRNA JAGGED AND WAVY (JAW), although in an antagonistic manner. Mutation of TCP9, the second identified TCP20 target, resulted in increased pavement cell sizes during early leaf developmental stages. Analysis of senescence in the single tcp9 and tcp20 mutants and the tcp9tcp20 double mutants showed an earlier onset of this process in comparison with wild-type control plants in the double mutant only. Both the cell size and senescence phenotypes are opposite to the known class II TCP mutant phenotype in JAW plants. Altogether, these results point to an antagonistic function of class I and class II TCP proteins in the control of leaf development via the jasmonate signaling pathway.

Spatial organ arrangement plays an important role in flower development. The position and the identity of floral organs is influenced by various processes, in particular the expression of MADS-box transcription factors for identity and dynamics of the plant hormone auxin for positioning. We are currently integrating patterning processes of MADS and auxin into our computational models, based on interactions that are known from experiments, in order to get insight in how these define the floral body plan. The resulting computational model will help to explore hypothetical interactions between the MADS and auxin regulation networks in floral organ patterning.

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.

Water molecules are active components in, literally, every biochemical event, forming hydrogen bonds, filling cavities, and mediating interactions with other (bio)molecules. Therefore, solvent drastically affects the kinetics and thermodynamics of numerous cellular events, including protein-protein interactions. While docking techniques are becoming successful in predicting the three-dimensional structure of protein-protein complexes, they are still limited in accounting explicitly for water in the binding process. HADDOCK is one of the few programs so far that can explicitly deal with water molecules during docking. Its solvated docking protocol starts from hydrated molecules, and a fraction of the interfacial water is subsequently removed from the docked models in a biased Monte Carlo procedure. The Monte Carlo-based removal is based on interfacial amino acid-water contact propensities derived from a dataset of high-resolution crystal structures of protein-protein complexes. In this chapter, this solvated docking protocol is described and associated methodological aspects are illustrated through an application example. It is shown that, although docking results do not always improve when the solvated docking protocol is applied, scoring is improved and the positions of buried water molecules in an interface are correctly predicted. Therefore, by identifying important water molecules, solvated docking can aid the development of novel inhibitors of protein-protein complexes that might be better accommodated at an interface.

An intriguing phenomenon in plant development is the timing and positioning of lateral organ initiation, which is a fundamental aspect of plant architecture. Although important progress has been made in elucidating the role of auxin transport in the vegetative shoot to explain the phyllotaxis of leaf formation in a spiral fashion, a model study of the role of auxin transport in whorled organ patterning in the expanding floral meristem is not available yet. We present an initial simulation approach to study the mechanisms that are expected to play an important role. Starting point is a confocal imaging study of Arabidopsis floral meristems at consecutive time points during flower development. These images reveal auxin accumulation patterns at the positions of the organs, which strongly suggests that the role of auxin in the floral meristem is similar to the role it plays in the shoot apical meristem. This is the basis for a simulation study of auxin transport through a growing floral meristem, which may answer the question whether auxin transport can in itself be responsible for the typical whorled floral pattern. We combined a cellular growth model for the meristem with a polar auxin transport model. The model predicts that sepals are initiated by auxin maxima arising early during meristem outgrowth. These form a pre-pattern relative to which a series of smaller auxin maxima are positioned, which partially overlap with the anlagen of petals, stamens, and carpels. We adjusted the model parameters corresponding to properties of floral mutants and found that the model predictions agree with the observed mutant patterns. The predicted timing of the primordia outgrowth and the timing and positioning of the sepal primordia show remarkable similarities with a developing flower in nature.

Several genome-wide studies demonstrated that alternative splicing (AS) significantly increases the transcriptome complexity in plants. However, the impact of AS on the functional diversity of proteins is difficult to assess using genome-wide approaches. The availability of detailed sequence annotations for specific genes and gene families allows for a more detailed assessment of the potential effect of AS on their function. One example is the plant MADS-domain transcription factor family, members of which interact to form protein complexes that function in transcription regulation. Here, we perform an in silico analysis of the potential impact of AS on the protein-protein interaction capabilities of MIKC-type MADS-domain proteins. We first confirmed the expression of transcript isoforms resulting from predicted AS events. Expressed transcript isoforms were considered functional if they were likely to be translated and if their corresponding AS events either had an effect on predicted dimerisation motifs or occurred in regions known to be involved in multimeric complex formation, or otherwise, if their effect was conserved in different species. Nine out of twelve MIKC MADS-box genes predicted to produce multiple protein isoforms harbored putative functional AS events according to those criteria. AS events with conserved effects were only found at the borders of or within the K-box domain. We illustrate how AS can contribute to the evolution of interaction networks through an example of selective inclusion of a recently evolved interaction motif in the MADS AFFECTING FLOWERING1-3 (MAF1-3) subclade. Furthermore, we demonstrate the potential effect of an AS event in SHORT VEGETATIVE PHASE (SVP), resulting in the deletion of a short sequence stretch including a predicted interaction motif, by overexpression of the fully spliced and the alternatively spliced SVP transcripts. For most of the AS events we were able to formulate hypotheses about the potential impact on the interaction capabilities of the encoded MIKC proteins.

Background: In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies., Results: We propose RMRCM, a method for Regularized Multinomial Regression in order to obtain Correlated Mutations from protein multiple sequence alignments. Importantly, our method is not restricted to pairwise (column-column) comparisons only, but takes into account the network nature of relationships between protein residues in order to predict residue-residue contacts. The use of regularization ensures that the number of predicted links between columns in the multiple sequence alignment remains limited, preventing overprediction. Using simulated datasets we analyzed the performance of our approach in predicting residue-residue contacts, and studied how it is influenced by various types of noise. For various biological datasets, validation with protein structure data indicates a good performance of the proposed algorithm for the prediction of residue-residue contacts, in comparison to previous results. RMRCM can also be applied to predict interactions (in addition to only predicting interaction sites or contact sites), as demonstrated by predicting PDZ-peptide interactions., Conclusions: A novel method is presented, which uses regularized multinomial regression in order to obtain correlated mutations from protein multiple sequence alignments., Availability: R-code of our implementation is available via http://www.ab.wur.nl/rmrcm.

Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that cmm is an np-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the generic metaheuristic slider which uses steepest ascent with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that slider outperforms existing motif-driven cmm methods and scales to large protein-protein interaction networks. The slider-implementation and the data used in the experiments are available on http://bioinformatics.uhasselt.be.

Although several tools for the analysis of ChIP-seq data have been published recently, there is a growing demand, in particular in the plant research community, for computational resources with which such data can be processed, analyzed, stored, visualized and integrated within a single, user-friendly environment. To accommodate this demand, we have developed PRI-CAT (Plant Research International ChIP-seq analysis tool), a web-based workflow tool for the management and analysis of ChIP-seq experiments. PRI-CAT is currently focused on Arabidopsis, but will be extended with other plant species in the near future. Users can directly submit their sequencing data to PRI-CAT for automated analysis. A QuickLoad server compatible with genome browsers is implemented for the storage and visualization of DNA-binding maps. Submitted datasets and results can be made publicly available through PRI-CAT, a feature that will enable community-based integrative analysis and visualization of ChIP-seq experiments. Secondary analysis of data can be performed with the aid of GALAXY, an external framework for tool and data integration. PRI-CAT is freely available at http://www.ab.wur.nl/pricat. No login is required.

Background: Large-scale analyses of genomics and transcriptomics data have revealed that alternative splicing (AS) substantially increases the complexity of the transcriptome in higher eukaryotes. However, the extent to which this complexity is reflected at the level of the proteome remains unclear. On the basis of a lack of conservation of AS between species, we previously concluded that AS does not frequently serve as a mechanism that enables the production of multiple functional proteins from a single gene. Following this conclusion, we hypothesized that the extent to which AS events contribute to the proteome diversity in Arabidopsis thaliana would be lower than expected on the basis of transcriptomics data. Here, we test this hypothesis by analyzing two large-scale proteomics datasets from Arabidopsis thaliana., Results: A total of only 60 AS events could be confirmed using the proteomics data. However, for about 60% of the loci that, based on transcriptomics data, were predicted to produce multiple protein isoforms through AS, no isoform-specific peptides were found. We therefore performed in silico AS detection experiments to assess how well AS events were represented in the experimental datasets. The results of these in silico experiments indicated that the low number of confirmed AS events was the consequence of a limited sampling depth rather than in vivo under-representation of AS events in these datasets., Conclusion: Although the impact of AS on the functional properties of the proteome remains to be uncovered, the results of this study indicate that AS-induced diversity at the transcriptome level is also expressed at the proteome level.

Although Arabidopsis (Arabidopsis thaliana) is the best studied plant species, the biological role of one-third of its proteins is still unknown. We developed a probabilistic protein function prediction method that integrates information from sequences, protein-protein interactions, and gene expression. The method was applied to proteins from Arabidopsis. Evaluation of prediction performance showed that our method has improved performance compared with single source-based prediction approaches and two existing integration approaches. An innovative feature of our method is that it enables transfer of functional information between proteins that are not directly associated with each other. We provide novel function predictions for 5,807 proteins. Recent experimental studies confirmed several of the predictions. We highlight these in detail for proteins predicted to be involved in flowering and floral organ development.

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.

Background: Plant MADS domain proteins are involved in a variety of developmental processes for which their ability to form various interactions is a key requisite. However, not much is known about the structure of these proteins or their complexes, whereas such knowledge would be valuable for a better understanding of their function. Here, we analyze those proteins and the complexes they form using a correlated mutation approach in combination with available structural, bioinformatics and experimental data., Results: Correlated mutations are affected by several types of noise, which is difficult to disentangle from the real signal. In our analysis of the MADS domain proteins, we apply for the first time a correlated mutation analysis to a family of interacting proteins. This provides a unique way to investigate the amount of signal that is present in correlated mutations because it allows direct comparison of mutations in various family members and assessing their conservation. We show that correlated mutations in general are conserved within the various family members, and if not, the variability at the respective positions is less in the proteins in which the correlated mutation does not occur. Also, intermolecular correlated mutation signals for interacting pairs of proteins display clear overlap with other bioinformatics data, which is not the case for non-interacting protein pairs, an observation which validates the intermolecular correlated mutations. Having validated the correlated mutation results, we apply them to infer the structural organization of the MADS domain proteins., Conclusion: Our analysis enables understanding of the structural organization of the MADS domain proteins, including support for predicted helices based on correlated mutation patterns, and evidence for a specific interaction site in those proteins.

Background: The genetic control of floral organ specification is currently being investigated by various approaches, both experimentally and through modeling. Models and simulations have mostly involved boolean or related methods, and so far a quantitative, continuous-time approach has not been explored., Results: We propose an ordinary differential equation (ODE) model that describes the gene expression dynamics of a gene regulatory network that controls floral organ formation in the model plant Arabidopsis thaliana. In this model, the dimerization of MADS-box transcription factors is incorporated explicitly. The unknown parameters are estimated from (known) experimental expression data. The model is validated by simulation studies of known mutant plants., Conclusions: The proposed model gives realistic predictions with respect to independent mutation data. A simulation study is carried out to predict the effects of a new type of mutation that has so far not been made in Arabidopsis, but that could be used as a severe test of the validity of the model. According to our predictions, the role of dimers is surprisingly important. Moreover, the functional loss of any dimer leads to one or more phenotypic alterations.

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S. cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.

Background: In autonomic failure (AF), blood pressure (BP) falls upon standing which is commonly ascribed to defective vasoconstriction and excessive pooling. Observations on the amount of pooling in AF are contradictory., Methods: We evaluated pooling using strain-gauge plethysmography (SGP) during head-up tilt (HUT) with a parachute harness fixed to the tilt table to avoid muscle tension in the lower limbs and thus to maximise pooling. 23 healthy subjects and 12 patients with AF were tilted for 5 min. BP and calf volume changes, as measured by SGP, were measured continuously. Multiple regression analysis was used to examine the effect of AF on orthostatic fluid shifts after adjustment for potential confounders., Results: Patients did not differ from controls with respect to the increase of calf volume after 5 min HUT. The acute (0-1 min) and the prolonged (1-5 min) phases of calf volume responses to HUT were also similar between patients and controls. No correlation was found between the degree of orthostatic hypotension and the orthostatic calf volume change in AF. In one patient an additional measurement was made before rising from bed in the early morning demonstrating a greater albeit small increase of calf volume upon HUT., Conclusion: Orthostatic fluid shifts at the level of the calf in AF are not augmented during the course of the day despite marked hypotension. However, a small increase of pooling may be expected when the patient first gets out of bed in the morning probably due to the absence of oedema.