scholarly journals Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

2018 ◽  
Author(s):  
Jana Sperschneider ◽  
Peter N. Dodds ◽  
Donald M. Gardiner ◽  
Karam B. Singh ◽  
Jennifer M. Taylor
Keyword(s):  
Fungal Pathogens ◽  
False Positive Rate ◽  
Pathogenic Fungi ◽  
Effector Proteins ◽  
Ensemble Of Classifiers ◽  
Net Charge ◽  
Protein Size ◽  
Learning Classifier ◽  
Positive Rate ◽  
Size Classifier

AbstractPlant-pathogenic fungi secrete effector proteins to facilitate infection. We describe extensive improvements to EffectorP, the first machine learning classifier for fungal effector prediction. EffectorP 2.0 is now trained on a larger set of effectors and utilizes a different approach based on an ensemble of classifiers trained on different subsets of negative data, offering different views on classification. EffectorP 2.0 achieves accuracy of 89%, compared to 82% for EffectorP 1.0 and 59.8% for a small size classifier. Important features for effector prediction appear to be protein size, protein net charge as well as the amino acids serine and cysteine. EffectorP 2.0 decreases the number of predicted effectors in secretomes of fungal plant symbionts and saprophytes by 40% when compared to EffectorP 1.0. However, EffectorP 1.0 retains value and combining EffectorP 1.0 and 2.0 results in a stringent classifier with low false positive rate of 9%. EffectorP 2.0 predicts significant enrichments of effectors in 12 out of 13 sets of infection-induced proteins from diverse fungal pathogens, whereas a small cysteine-rich classifier detects enrichment only in 7 out of 13. EffectorP 2.0 will fast-track prioritization of high-confidence effector candidates for functional validation and aid in improving our understanding of effector biology. EffectorP 2.0 is available at http://effectorp.csiro.au.

scholarly journals The Ustilago hordei–Barley Interaction is a Versatile System for Characterization of Fungal Effectors

2021 ◽  
Vol 7 (2) ◽  
pp. 86
Author(s):  
Bilal Ökmen ◽  
Daniela Schwammbach ◽  
Guus Bakkeren ◽  
Ulla Neumann ◽  
Gunther Doehlemann
Keyword(s):  
Fungal Pathogens ◽  
Plant Pathogens ◽  
Expression System ◽  
Pathogenic Fungi ◽  
Effector Proteins ◽  
Mating Partner ◽  
Fungal Virulence ◽  
Functional Studies ◽  
Infection Structures ◽  
Ustilago Hordei

Obligate biotrophic fungal pathogens, such as Blumeria graminis and Puccinia graminis, are amongst the most devastating plant pathogens, causing dramatic yield losses in many economically important crops worldwide. However, a lack of reliable tools for the efficient genetic transformation has hampered studies into the molecular basis of their virulence or pathogenicity. In this study, we present the Ustilago hordei–barley pathosystem as a model to characterize effectors from different plant pathogenic fungi. We generate U. hordei solopathogenic strains, which form infectious filaments without the presence of a compatible mating partner. Solopathogenic strains are suitable for heterologous expression system for fungal virulence factors. A highly efficient Crispr/Cas9 gene editing system is made available for U. hordei. In addition, U. hordei infection structures during barley colonization are analyzed using transmission electron microscopy, showing that U. hordei forms intracellular infection structures sharing high similarity to haustoria formed by obligate rust and powdery mildew fungi. Thus, U. hordei has high potential as a fungal expression platform for functional studies of heterologous effector proteins in barley.

scholarly journals Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi

2021 ◽  
Vol 7 (9) ◽  
Author(s):  
Darcy A. B. Jones ◽  
Paula M. Moolhuijzen ◽  
James K. Hane
Keyword(s):  
Fungal Pathogens ◽  
Pathogenic Fungi ◽  
Comparison Method ◽  
Fungal Species ◽  
Effector Proteins ◽  
Plant Diseases ◽  
Plant Host ◽  
Homology Inference ◽  
Developing Area ◽  
Inference Methods

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between ‘effector’ molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

scholarly journals The Ustilago hordei-barley interaction is a versatile system to characterize fungal effectors

2020 ◽  
Author(s):  
Bilal Ökmen ◽  
Daniela Schwammbach ◽  
Guus Bakkeren ◽  
Ulla Neumann ◽  
Gunther Doehlemann
Keyword(s):  
Fungal Pathogens ◽  
Plant Pathogens ◽  
Expression System ◽  
Pathogenic Fungi ◽  
Effector Proteins ◽  
Mating Partner ◽  
Fungal Virulence ◽  
Functional Studies ◽  
Infection Structures ◽  
Expression Platform

AbstractObligate biotrophic fungal pathogens, such as Blumeria graminis and Puccinia graminis, are amongst the most devastating plant pathogens, causing dramatic yield losses in many economically important crops worldwide. However, a lack of reliable tools for the efficient genetic transformation has hampered studies into the molecular basis of their virulence/pathogenicity. In this study, we present the U. hordei-barley pathosystem as a model to characterize effectors from different plant pathogenic fungi. We have generated U. hordei solopathogenic strains, which form infectious filaments without presence of compatible mating partner. Solopathogenic strains are suitable as heterologous expression system for fungal virulence factors. A highly efficient Crispr/Cas9 gene editing system is made available for U. hordei. In addition, U. hordei infection structures during barley colonization were analyzed by transmission electron microscopy, which shows that U. hordei forms intracellular infection structures sharing high similarity to haustoria formed by obligate rust and powdery mildew fungi. Thus, U. hordei has high potential as a fungal expression platform for functional studies of heterologous effector proteins in barley.

scholarly journals ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning

2017 ◽  
Author(s):  
Jana Sperschneider ◽  
Peter N. Dodds ◽  
Karam B. Singh ◽  
Jennifer M. Taylor
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
False Positive Rate ◽  
Effector Proteins ◽  
Computational Method ◽  
Host Cells ◽  
Plant Proteins ◽  
Functional Studies ◽  
Positive Rate ◽  
Improving Accuracy

AbstractThe plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting if an effector or plant protein localizes to the apoplast. ApoplastP uncovers features for apoplastic localization common to both effectors and plant proteins, namely an enrichment in small amino acids and cysteines as well as depletion in glutamic acid. ApoplastP predicts apoplastic localization in effectors with sensitivity of 75% and false positive rate of 5%, improving accuracy of cysteine-rich classifiers by over 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted plant and effector proteins. The secretomes of fungal saprophytes, necrotrophic pathogens and extracellular pathogens are enriched for predicted apoplastic proteins. Rust pathogen secretomes have the lowest percentage of apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells. ApoplastP is available at http://apoplastp.csiro.au.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

SOYBEAN AND FLAX SELECTION METHODS

1970 ◽  
pp. 19-23
Author(s):  
V. М. Lukomets ◽  
S. V. Zelentsov
Keyword(s):  
High Resistance ◽  
Fungal Pathogens ◽  
Selection Process ◽  
Pathogenic Fungi ◽  
Cultivated Plants ◽  
Practical Implementation ◽  
Physiological Basis ◽  
Oil Crops ◽  
Soybean Genotypes ◽  
Genetically Determined

To improve the effectiveness of the soybeans and oil flax breeding, research to improve existing and develop new breeding methods are conducting in all-Russia Research institute of Oil Crops (Krasnodar). One of the improved methods for the soybean breeding, based on the use of sources of complexes of compensatory genes, is the CCG technology, which allows to create varieties with an increased yield of a heterotic level transmitted along the progeny for the entire life cycle of the variety. For the purpose of non-transgenic production of new traits, a theory of polyploid recombination of the genome (TPR) was formulated, which models the mechanism of the natural formation of polymorphism in the centers of origin of cultivated plants. On the basis of this theory, a method of breeding (TPR-technology) has been developed, which makes it possible to obtain recombinant reploids of soybeans and oil flax with an extended spectrum of traits. Of these reploids, the soybean lines with increased sucking force of the roots, providing high drought resistance, were distinguished; cold-resistant soybean lines, which stand in the phase of shoots of freezing to minus 5 °С; lines of oil flax with complete resistance to flax sickness of soil and high resistance to Fusarium; winter-hardy flax lines that withstand winter frosts down to minus 20–23 °С and ripen one and a half months earlier than spring sowings. Another original developed method is the ODCS-technology for isolating and selecting soybean genotypes with high resistance to fungal pathogens. The physiological basis of ODCS-technology is the blocking of osmotic nutrition of pathogenic fungi due to genetically determined increased osmotic pressure in the tissues of host plants. The practical implementation of CCG-, TPR- and ODKS-technologies in the selection process, allowed to create a whole series of soybean and oil flax varieties with improved or new traits.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

Forms, Importance, and Ineffability of Factor Interactions to Define Personality Disorders: Comment on Lilienfeld et al. (2019)

2019 ◽  
Author(s):  
Stephen D Benning ◽  
Edward Smith
Keyword(s):  
Personality Disorders ◽  
False Positive Rate ◽  
Empirical Work ◽  
Design Studies ◽  
Linear Threshold ◽  
Positive Rate ◽  
Saturation Effects ◽  
Factor Interactions ◽  
Main Effects ◽  
Criterion Variables

The emergent interpersonal syndrome (EIS) approach conceptualizes personality disorders as the interaction among their constituent traits to predict important criterion variables. We detail the difficulties we have experienced finding such interactive predictors in our empirical work on psychopathy, even when using uncorrelated traits that maximize power. Rather than explaining a large absolute proportion of variance in interpersonal outcomes, EIS interactions might explain small amounts of variance relative to the main effects of each trait. Indeed, these interactions may necessitate samples of almost 1,000 observations for 80% power and a false positive rate of .05. EIS models must describe which specific traits’ interactions constitute a particular EIS, as effect sizes appear to diminish as higher-order trait interactions are analyzed. Considering whether EIS interactions are ordinal with non-crossing slopes, disordinal with crossing slopes, or entail non-linear threshold or saturation effects may help researchers design studies, sampling strategies, and analyses to model their expected effects efficiently.

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