Accumulation of Azelaic Acid in Xylella fastidiosa-Infected Olive Trees: A Mobile Metabolite for Health Screening

Monitoring Xylella fastidiosa is critical for eradicating or at least containing this harmful pathogen. New low-cost and rapid methods for early detection capability are very much needed. Metabolomics may play a key role in diagnosis; in fact, mobile metabolites could avoid errors in sampling due to erratically distributed pathogens. Of the various different mobile signals, we studied dicarboxylic azelaic acid (AzA) which is a key molecule for biotic stress plant response but has not yet been associated with pathogens in olive trees. We found that infected Olea europaea L. plants of cultivars Cellina di Nardò (susceptible to X. fastidiosa) and Leccino (resistant to the pathogen) showed an increase in AzA accumulation in leaf petioles and in sprigs by approximately seven- and sixfold, respectively, compared with plants negative to X. fastidiosa or affected by other pathogens. No statistically significant variation was found between the X. fastidiosa population level and the amount of AzA in either of the plant tissues, suggesting that AzA accumulation was almost independent of the amount of pathogen in the sample. Furthermore, the association of AzA with X. fastidiosa seemed to be reliable for samples judged as potentially false-negative by quantitative polymerase chain reaction (cycle threshold [Ct] > 33), considering both the absolute value of AzA concentration and the values normalized on negative samples, which diverged significantly from control plants. The accumulation of AzA in infected plants was partially supported by the differential expression of two genes (named OeLTP1 and OeLTP2) encoding lipid transport proteins (LTPs), which shared a specific domain with the LTPs involved in AzA activity in systemic acquired resistance in other plant species. The expression level of OeLTP1 and OeLTP2 in petiole samples showed significant upregulation in samples positive to X. fastidiosa of both cultivars, with higher expression levels in positive samples of Cellina di Nardò compared with Leccino, whereas the two transcripts had a low expression level (Ct > 40) in negative samples of the susceptible cultivar. Although the results derived from the quantification of AzA cannot confirm the presence of the erratically distributed X. fastidiosa, which can be definitively assessed by traditional methods, we believe they represent a fast and cheap screening method for large-scale monitoring.

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Machine learned calibrations to high-throughput molecular excited state calculations

10.26434/chemrxiv-2022-08jm9 ◽

2022 ◽

Author(s):

Shomik Verma ◽

Miguel Rivera ◽

David O. Scanlon ◽

Aron Walsh

Keyword(s):

Machine Learning ◽

Excited State ◽

High Throughput ◽

High Speed ◽

Large Scale ◽

Chemical Space ◽

False Negative ◽

Screening Method ◽

Calibration Model ◽

Linear Calibration

Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.

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Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

Remote Sensing ◽

10.3390/rs13010014 ◽

2020 ◽

Vol 13 (1) ◽

pp. 14

Author(s):

Annamaria Castrignanò ◽

Antonella Belmonte ◽

Ilaria Antelmi ◽

Ruggiero Quarto ◽

Francesco Quarto ◽

...

Keyword(s):

Discriminant Analysis ◽

Early Detection ◽

Plant Pathogens ◽

Early Stage ◽

Xylella Fastidiosa ◽

Mediterranean Countries ◽

Parametric Classification ◽

Aerial Vehicle ◽

Olive Groves ◽

Olive Trees

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.

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HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of “olive quick decline syndrome”

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03279-7 ◽

2021 ◽

Author(s):

Sabrina Di Masi ◽

Giuseppe E. De Benedetto ◽

Cosimino Malitesta ◽

Maria Saponari ◽

Cinzia Citti ◽

...

Keyword(s):

Bacterial Infections ◽

High Performance ◽

High Resolution Mass Spectrometry ◽

Xylella Fastidiosa ◽

Long Chain Fatty Acids ◽

Powerful Approach ◽

Diffusible Signal Factor ◽

Profile Changes ◽

Olive Quick Decline Syndrome ◽

Olive Trees

AbstractOlive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants. Graphical abstract

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Controlling the Spatial Spread of a Xylella Epidemic

Bulletin of Mathematical Biology ◽

10.1007/s11538-021-00861-z ◽

2021 ◽

Vol 83 (4) ◽

Author(s):

Sebastian Aniţa ◽

Vincenzo Capasso ◽

Simone Scacchi

Keyword(s):

Spatial Structure ◽

Numerical Simulations ◽

Control Strategies ◽

Xylella Fastidiosa ◽

Cost Effective ◽

Olive Tree ◽

Control Measures ◽

Weed Biomass ◽

Spatial Spread ◽

Olive Trees

AbstractIn a recent paper by one of the authors and collaborators, motivated by the Olive Quick Decline Syndrome (OQDS) outbreak, which has been ongoing in Southern Italy since 2013, a simple epidemiological model describing this epidemic was presented. Beside the bacterium Xylella fastidiosa, the main players considered in the model are its insect vectors, Philaenus spumarius, and the host plants (olive trees and weeds) of the insects and of the bacterium. The model was based on a system of ordinary differential equations, the analysis of which provided interesting results about possible equilibria of the epidemic system and guidelines for its numerical simulations. Although the model presented there was mathematically rather simplified, its analysis has highlighted threshold parameters that could be the target of control strategies within an integrated pest management framework, not requiring the removal of the productive resource represented by the olive trees. Indeed, numerical simulations support the outcomes of the mathematical analysis, according to which the removal of a suitable amount of weed biomass (reservoir of Xylella fastidiosa) from olive orchards and surrounding areas resulted in the most efficient strategy to control the spread of the OQDS. In addition, as expected, the adoption of more resistant olive tree cultivars has been shown to be a good strategy, though less cost-effective, in controlling the pathogen. In this paper for a more realistic description and a clearer interpretation of the proposed control measures, a spatial structure of the epidemic system has been included, but, in order to keep mathematical technicalities to a minimum, only two players have been described in a dynamical way, trees and insects, while the weed biomass is taken to be a given quantity. The control measures have been introduced only on a subregion of the whole habitat, in order to contain costs of intervention. We show that such a practice can lead to the eradication of an epidemic outbreak. Numerical simulations confirm both the results of the previous paper and the theoretical results of the model with a spatial structure, though subject to regional control only.

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Further In Vitro Assessment and Mid-Term Evaluation of Control Strategy of Xylella fastidiosa subsp. pauca in Olive Groves of Salento (Apulia, Italy)

Pathogens ◽

10.3390/pathogens10010085 ◽

2021 ◽

Vol 10 (1) ◽

pp. 85

Author(s):

Giuseppe Tatulli ◽

Vanessa Modesti ◽

Nicoletta Pucci ◽

Valeria Scala ◽

Alessia L’Aurora ◽

...

Keyword(s):

Control Strategy ◽

Xylella Fastidiosa ◽

In Planta ◽

Bacterial Concentration ◽

Infected Area ◽

Olive Groves ◽

Term Evaluation ◽

In Vitro Assessment ◽

Olive Trees

During recent years; Xylella fastidiosa subsp. pauca (Xfp) has spread in Salento causing relevant damage to the olive groves. Measures to contain the spreading of the pathogen include the monitoring of the areas bordering the so-called “infected” zone and the tree eradication in case of positive detection. In order to provide a control strategy aimed to maintain the tree productivity in the infected areas, we further evaluated the in vitro and in planta mid-term effectiveness of a zinc-copper-citric acid biocomplex. The compound showed an in vitro bactericidal activity and inhibited the biofilm formation in representative strains of X. fastidiosa subspecies, including Xfp isolated in Apulia from olive trees. The field mid-term evaluation of the control strategy assessed by quantitative real-time PCR in 41 trees of two olive groves of the “infected” area revealed a low concentration of Xfp over the seasons upon the regular spraying of the biocomplex over 3 or 4 consecutive years. In particular, the bacterial concentration lowered in July and October with respect to March, after six consecutive treatments. The trend was not affected by the cultivar and it was similar either in the Xfp-sensitive cultivars Ogliarola salentina and Cellina di Nardò or in the Xfp-resistant Leccino. Moreover, the scoring of the number of wilted twigs over the seasons confirmed the trend. The efficacy of the treatment in the management of olive groves subjected to a high pathogen pressure is highlighted by the yielded a good oil production

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Identification of Streptococcus pyogenes in a Pediatric Outpatient Department: A Practical System Designed for Rapid Results and Resident Teaching

PEDIATRICS ◽

10.1542/peds.54.6.718 ◽

1974 ◽

Vol 54 (6) ◽

pp. 718-723

Author(s):

Katherine Sprunt ◽

Dorothea Vail ◽

Russell S. Asnes

Keyword(s):

False Positive ◽

Fluorescent Antibody ◽

False Negative ◽

Screening Method ◽

Rapid Screening ◽

Resident Teaching ◽

Antibody Uptake ◽

Busy Clinic ◽

Group A ◽

Laboratory Tool

A rapid screening method for identification of clinic patients with pharyngitis who are carrying group A beta-hemolytic streptococci and for teaching residents the values and limitations of the culture-disk approach to identification has been reviewed as developed for a busy clinic and a busy hospital laboratory. Identification of positive cultures in less than 24 hours, using Taxos A disk and specific fluorescent antibody uptake, resulted in 12% apparent false-positive and 3.6% false-negative reports. However, when viewed in the light of the techniques used for verifying results, there were probably 3% false-positive and 3% false-negative reports. The screening method is considered acceptably reliable and practical as a laboratory tool and a resident teaching device.

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Hysteroscopy is Gold standard in Uterine evaluation for Infertility, but HSG still has a place

Asian Journal of Medical Sciences ◽

10.3126/ajms.v12i4.35199 ◽

2021 ◽

Vol 12 (4) ◽

pp. 98-104

Author(s):

Manisha Bajaj ◽

Rajib Roy ◽

Motiur Rahman ◽

Joydeb Roychowdhury

Keyword(s):

Predictive Value ◽

False Negative ◽

Screening Method ◽

Uterine Cavity ◽

High Specificity ◽

Filling Defect ◽

Endometrial Polyp ◽

Care Hospital ◽

Saline Infusion Sonography ◽

First Choice

Background: Uterine abnormalities, congenital or acquired are implicated as causal factor in 10%-15% of infertile couplesreporting for treatment. Hysteroscopy, hysterosalpingography (HSG), saline-infusion-sonography and USG are available for evaluation of uterine cavity. HSG helps in initial evaluation of a sub-fertile woman, but hysteroscopy is gold standardas itallows direct visualisation ofintrauterine pathology and treatment in same-setting, if required. Aims and Objective: To describe hysteroscopic findings of infertile patients and compare the observations with their respective HSG findings. Materials and Methods: It’s a prospective analysis of 105 women with infertility who attendedtertiary-care hospital during 18 monthsfulfilling pre-defined inclusion and exclusion criteria. All cases were evaluated with both HSG and hysteroscopy, observations were recorded and co-related with each other. Results: Among 105 cases, maximum (76.19%) were 25-35 years of age. The primary infertility accounted for 68.57% cases.Abnormal HSG findings observed in 19 cases (20%), most common being filling-defect.Hysteroscopy detected abnormalities in 39 cases (37.14%), commonest being endometrial polyp. Out of 39 cases of abnormal uterine cavity detected on hysteroscopy only 19 were picked-up by HSG, rest 20 cases failed to be identified. The strength of agreement between hysteroscopy and HSG calculated is moderate (Kappa=0.505). Conclusion: As HSG hadlow false positivity (03%), high positive-predictive-value (90.48%) and negative-predictive-value (76.19%) and high specificity (96.96%) it is still considered as a first-choice screening method of uterine cavity. However, high false-negative-value (51.28%)of HSG makes Hysteroscopy a better diagnostic test. HSG couldn’t differentiate endometrial polyp, adhesions and submucous fibroid, shown them as filling defect only.

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Deletion of two genes improves heterologous secretion of fungal lignin-degrading heme peroxidases in S. cerevisiae

10.1101/2021.11.27.470220 ◽

2021 ◽

Author(s):

Nikita A. Khlystov

Keyword(s):

Large Scale ◽

Screening Method ◽

Strain Engineering ◽

Scale Production ◽

Heterologous Production ◽

Yeast Saccharomyces Cerevisiae ◽

Double Deletion ◽

Large Scale Production ◽

Crucial Component ◽

Heme Peroxidases

Efficient, large-scale heterologous production of enzymes is a crucial component of the biomass valorization industry. Whereas cellulose utilization has been successful in applications such as bioethanol, its counterpart lignin remains significantly underutilized despite being an abundant potential source of aromatic commodity chemicals. Fungal lignin-degrading heme peroxidases are thought to be the major agents responsible for lignin depolymerization in nature, but their large-scale production remains inaccessible due to the genetic intractability of basidiomycete fungi and the challenges in the heterologous production of these enzymes. In this study, we employ a strain engineering approach based on functional genomics to identify mutants of the model yeast Saccharomyces cerevisiae with enhanced heterologous production of lignin-degrading heme peroxidases. We show that our screening method coupling an activity-based readout with fluorescence-assisted cell sorting enables identification of two single null mutants of S. cerevisiae, pmt2 and cyt2, with up to 11-fold improved secretion of a versatile peroxidase from the lignin-degrading fungus Pleurotus eryngii. We demonstrate that the double deletion strain pmt2cyt2 displays positive epistasis, improving and even enabling production of members from all three classes of lignin-degrading fungal peroxidases. We anticipate that these mutant strains will be broadly applicable for improved heterologous production of this biotechnologically important class of enzymes.

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A Deep Boosting Based Approach for Capturing the Sequence Binding Preferences of RNA-Binding Proteins from High-Throughput CLIP-Seq Data

10.1101/086421 ◽

2016 ◽

Cited By ~ 1

Author(s):

Shuya Li ◽

Fanghong Dong ◽

Yuexin Wu ◽

Sai Zhang ◽

Chen Zhang ◽

...

Keyword(s):

High Throughput ◽

Large Scale ◽

Binding Proteins ◽

Rna Binding ◽

Gene Expression Regulation ◽

Rna Binding Proteins ◽

False Negative ◽

Functional Roles ◽

Potential Applications ◽

Validation Tests

AbstractCharacterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false positive and false negative issues. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data. Comprehensive validation tests have shown that DeBooster can outperform other state-of-the-art approaches in predicting RBP targets and recover false negatives that are common in current CLIP-seq data. In addition, we have demonstrated several new potential applications of DeBooster in understanding the regulatory functions of RBPs, including the binding effects of the RNA helicase MOV10 on mRNA degradation, the influence of different binding behaviors of the ADAR proteins on RNA editing, as well as the antagonizing effect of RBP binding on miRNA repression. Moreover, DeBooster may provide an effective index to investigate the effect of pathogenic mutations in RBP binding sites, especially those related to splicing events. We expect that DeBooster will be widely applied to analyze large-scale CLIP-seq experimental data and can provide a practically useful tool for novel biological discoveries in understanding the regulatory mechanisms of RBPs.

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Large-scale randomized evidence: Trials and meta-analyses of trials

Oxford Textbook of Medicine ◽

10.1093/med/9780198746690.003.0010 ◽

2020 ◽

pp. 51-66

Author(s):

Colin Baigent ◽

Richard Peto ◽

Richard Gray ◽

Natalie Staplin ◽

Sarah Parish ◽

...

Keyword(s):

Clinical Trials ◽

Large Scale ◽

Meta Analysis ◽

False Negative ◽

Premature Deaths ◽

Common Causes ◽

The Common ◽

Meta Analyses ◽

Positive Results

Clinical trials generally need to be able to detect or to refute realistically moderate (but still worthwhile) differences between treatments in long-term disease outcome. Large-scale randomized evidence should be able to detect such effects, but medium-sized trials or medium-sized meta-analyses can, and often do, yield false-negative or exaggeratedly positive results. Hundreds of thousands of premature deaths each year could be avoided by seeking appropriately large-scale randomized evidence about various widely practicable treatments for the common causes of death, and by disseminating this evidence appropriately. This chapter takes a look at the use of large-scale randomized evidence—produced from trials and meta-analysis of trials—and how this data should be handled in order to produce accurate result.

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