Transcriptome Profiling of Citrus Fruit Response to Huanglongbing Disease

Huanglongbing (HLB) or “citrus greening” is the most destructive citrus disease worldwide. In this work, we studied host responses of citrus to infection with Candidatus Liberibacter asiaticus (CaLas) using next-generation sequencing technologies. A deep mRNA profile was obtained from peel of healthy and HLB-affected fruit. It was followed by pathway and protein-protein network analysis and quantitative real time PCR analysis of highly regulated genes. We identified differentially regulated pathways and constructed networks that provide a deep insight into the metabolism of affected fruit. Data mining revealed that HLB enhanced transcription of genes involved in the light reactions of photosynthesis and in ATP synthesis. Activation of protein degradation and misfolding processes were observed at the transcriptomic level. Transcripts for heat shock proteins were down-regulated at all disease stages, resulting in further protein misfolding. HLB strongly affected pathways involved in source-sink communication, including sucrose and starch metabolism and hormone synthesis and signaling. Transcription of several genes involved in the synthesis and signal transduction of cytokinins and gibberellins was repressed while that of genes involved in ethylene pathways was induced. CaLas infection triggered a response via both the salicylic acid and jasmonic acid pathways and increased the transcript abundance of several members of the WRKY family of transcription factors. Findings focused on the fruit provide valuable insight to understanding the mechanisms of the HLB-induced fruit disorder and eventually developing methods based on small molecule applications to mitigate its devastating effects on fruit production

Comparative Genomics Used to Predict Virulence Factors and Metabolic Genes among Monilinia Species

Brown rot, caused by Monilinia spp., is among the most important diseases in stonefruits, and some pome fruits (mainly apples). This disease is responsible for significant yield losses,particularly in stone fruits, when weather conditions favorable for disease development appear.To achieve future sustainable strategies to control brown rot on fruit, one potential approach will be to characterize genomic variation among Monilinia spp. to define, among others, the capacity to infect fruit in this genus. In the present work, we performed genomic and phylogenomic comparisons of five Monilinia species and inferred differences in numbers of secreted proteins, including CAZy proteins and other proteins important for virulence. Duplications specific to Monilinia were sparse and, overall, more genes have been lost than gained. Among Monilinia spp., low variability in the CAZome was observed. Interestingly, we identified several secondary metabolism clusters based on similarity to known clusters, and among them was a cluster with homology to pyriculol that could be responsible for the synthesis of chloromonilicin. Furthermore, we compared sequences of all strains available from NCBI of these species to assess their MAT loci and heterokaryon Compatibility systems. Our comparative analyses provide the basis for future studies into understanding how these genomic differences underlie common or differential abilities to interact with the host plant.info:eu-repo/semantics/publishedVersio

The Role and Application of Bioinformatics in Plant Disease Management

In recent years, rapid developments in genomics and proteomics have generated a large amount of biological data. Drawing conclusions from these data requires sophisticated computational analyses. Bioinformatics is the interdisciplinary science of interpreting biological data using information technology and computer science. The importance of this new field of inquiry will grow as we continue to generate and integrate large quantities of genomic, proteomic, and other data. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling and prediction. Bioinformatics plays an essential role in today’s plant pathology with regards to the development of new plant diagnostic tools. Pathogen is among the traits considered in the primary interest of plant bioinformatics. The contribution of bioinformatics advances made possible the mapping of the entire genomes of many organisms in just over a decade. These discoveries, along with current efforts to determine gene and protein functions, have improved the ability to understand the root causes of plant diseases and find new cures. Furthermore, many future bioinformatics innovations will likely be spurred by the data and analysis demands of the life sciences. Bioinformatics have many practical applications in current plant disease management with respect to the study of host-pathogen interactions, understanding the disease genetics and pathogencity factor of a pathogen which ultimately help in designing best management options. This review paper describes some of the key concepts and databases used in bioinformatics, with an emphasis on those relevant to plant science. It also covers some aspects with regards to the role application of this endeavor science in today’s plant disease management strategies. Keywords: Bioinformatics, Database, Diagnostic tool, Pathogen, Plant disease

INVESTIGATION OF BIOTIC STRESS RESPONSES IN FRUIT TREE CROPS USING META-ANALYTICAL TECHNIQUES.

In recent years, RNA sequencing and analysis using Next Generation Sequencing (NGS) methods have enabled to understand the gene expression pertaining to plant biotic and abiotic stress conditions in both quantitative and qualitative manner. The large number of transcriptomic works published in plants requires more meta-analysis studies that would identify common and specific features in relation of the high number of objective studies performed at different developmental and environmental conditions. Meta-analysis of transcriptomic data will identify commonalities and differences between differentially regulated gene lists and will allow screen which genes are key players in gene-gene and protein-protein interaction networks. These analyses will allow delivering important information on how a specific environmental factor affects plant molecular responses and how plants activate general stress responses to environmental stresses. The identification of common genes between different biotic stress will allow to gain insight into these general responses and help the diagnosis of an early “stress state” of the plants. These analyses help in monitoring stressed plants to start early specific management procedures for each disease or disorder. In this meta-analysis study, I considered all transcriptomic data related to biotic stresses in fruit tree crops, which are already published. The aim was to determine which genes, pathways, gene set categories and predicted protein-protein interaction networks may play key roles in specific responses to pathogen infections

A genome survey of Moniliophthora perniciosa gives new insights into Witches' Broom Disease of cacao

<p>Abstract</p> <p>Background</p> <p>The basidiomycete fungus <it>Moniliophthora perniciosa </it>is the causal agent of Witches' Broom Disease (WBD) in cacao (<it>Theobroma cacao</it>). It is a hemibiotrophic pathogen that colonizes the apoplast of cacao's meristematic tissues as a biotrophic pathogen, switching to a saprotrophic lifestyle during later stages of infection. <it>M. perniciosa</it>, together with the related species <it>M. roreri</it>, are pathogens of aerial parts of the plant, an uncommon characteristic in the order Agaricales. A genome survey (1.9× coverage) of <it>M. perniciosa </it>was analyzed to evaluate the overall gene content of this phytopathogen.</p> <p>Results</p> <p>Genes encoding proteins involved in retrotransposition, reactive oxygen species (ROS) resistance, drug efflux transport and cell wall degradation were identified. The great number of genes encoding cytochrome P450 monooxygenases (1.15% of gene models) indicates that <it>M. perniciosa </it>has a great potential for detoxification, production of toxins and hormones; which may confer a high adaptive ability to the fungus. We have also discovered new genes encoding putative secreted polypeptides rich in cysteine, as well as genes related to methylotrophy and plant hormone biosynthesis (gibberellin and auxin). Analysis of gene families indicated that <it>M. perniciosa </it>have similar amounts of carboxylesterases and repertoires of plant cell wall degrading enzymes as other hemibiotrophic fungi. In addition, an approach for normalization of gene family data using incomplete genome data was developed and applied in <it>M. perniciosa </it>genome survey.</p> <p>Conclusion</p> <p>This genome survey gives an overview of the <it>M. perniciosa </it>genome, and reveals that a significant portion is involved in stress adaptation and plant necrosis, two necessary characteristics for a hemibiotrophic fungus to fulfill its infection cycle. Our analysis provides new evidence revealing potential adaptive traits that may play major roles in the mechanisms of pathogenicity in the <it>M. perniciosa</it>/cacao pathosystem.</p

MELOGEN: an EST database for melon functional genomics

<p>Abstract</p> <p>Background</p> <p>Melon (<it>Cucumis melo </it>L.) is one of the most important fleshy fruits for fresh consumption. Despite this, few genomic resources exist for this species. To facilitate the discovery of genes involved in essential traits, such as fruit development, fruit maturation and disease resistance, and to speed up the process of breeding new and better adapted melon varieties, we have produced a large collection of expressed sequence tags (ESTs) from eight normalized cDNA libraries from different tissues in different physiological conditions.</p> <p>Results</p> <p>We determined over 30,000 ESTs that were clustered into 16,637 non-redundant sequences or unigenes, comprising 6,023 tentative consensus sequences (contigs) and 10,614 unclustered sequences (singletons). Many potential molecular markers were identified in the melon dataset: 1,052 potential simple sequence repeats (SSRs) and 356 single nucleotide polymorphisms (SNPs) were found. Sixty-nine percent of the melon unigenes showed a significant similarity with proteins in databases. Functional classification of the unigenes was carried out following the Gene Ontology scheme. In total, 9,402 unigenes were mapped to one or more ontology. Remarkably, the distributions of melon and Arabidopsis unigenes followed similar tendencies, suggesting that the melon dataset is representative of the whole melon transcriptome. Bioinformatic analyses primarily focused on potential precursors of melon micro RNAs (miRNAs) in the melon dataset, but many other genes potentially controlling disease resistance and fruit quality traits were also identified. Patterns of transcript accumulation were characterised by Real-Time-qPCR for 20 of these genes.</p> <p>Conclusion</p> <p>The collection of ESTs characterised here represents a substantial increase on the genetic information available for melon. A database (MELOGEN) which contains all EST sequences, contig images and several tools for analysis and data mining has been created. This set of sequences constitutes also the basis for an oligo-based microarray for melon that is being used in experiments to further analyse the melon transcriptome.</p

Epidemiology and sustainable control of Podosphaera aphanis (strawberry powdery mildew)

Until recently strawberries grown in the United Kingdom were grown in open fields, the plants and fruit were exposed to the British weather. This resulted in a short 6 week harvest period where the fruit was often damaged by rain and infected by Botrytis cinerea. Strawberry growers started to use polythene tunnels to extend the cropping season, protect the fruit from rain damage and reduce the incidence of infection by B. cinerea. However the conditions produced by the polythene tunnels were ideal for the growth and development of Podosphaera aphanis (strawberry powdery mildew). Growers are now under pressure from the retailers to reduce the amount of fungicides that they use to control P. aphanis. The symptoms related to P. aphanis infection have been identified (leaf cupping, visible mycelium and red blotches) and a progression has been established: From the symptom progression two new s~ring methods for the identification of P. aphanis infections were developed wmch have greater relevance to current cultivation methods than the previous method. The source of initial inoculum for newly planted and established sites was identified. The inoculum was planted into new sites on the plants coming from the propagators and overwintering on plants within established sites. This was contrary to what the growers believed. They were basing their early season tunnel management on keeping the perceived air borne infection out of their tunnels. A rule based prediction system has been developed that has the potential to reduce the number of fungicide applications applied by the growers. The prediction system ensures that fungicide applications are not applied too close together. Potassium Bicarbonate has been shown to provide comparable control of P. aphanis to that achieved with Systhane (Myclobutanil). Significantly better control of P. aphanis was achieved using a new (at the time) product, Fortress (Quinoxyfen). There were significant differences in the resistance °to infection by P. aphanis displayed by different cultivars of strawberry. Elsanta, the cultivar favoured by the retailers was not one of the most resistant. Control of inoculum . already present on plants as they are being planted could be achieved by dipping the plant in Systhane. Growers are under considerable pressure from the retailers to reduce the amount of fungicides used to control P. aphanis. Growers could achieve this by implementing the recommendations made in this report

Rekayasa perangkat lunak pada data mining penyakit: Suatu tinjauan literatur sistematis

Saat ini sedang terjadi wabah penyakit virus corona yang dideteksi berasal dari Wuhan China dan telah menyebar ke seluruh dunia, telah banyak database tentang penyakit Covid-19 yang bisa digunakan untuk melakukan data mining penyakit. Pada artikel ini melakukan tinjauan literatur secara sistematis untuk memberikan gambaran tentang data mining pada penyakit. Artikel yang dipublikasikan pada tahun 2015 sampai dengan 2020 dari tiga database terpilih (IEEE, ACM, Sciencedirect). Artikel yang ada dianalisis, dan area yang diteliti tentang rekayasa perangkat lunak untuk data mining penyakit. Metode yang digunakan dalam penelitian ini adalah tinjauan literatur sistematis. Berdasarkan temuan kajian literatur data mining penyakit terdapat banyak ragam penyakit yang diteliti, penyakit yang banyak diteliti yaitu tentang penyakit jantung, serta metode data mining yang banyak digunakan adalah Naive Bayes sedangkan akurasi metode data mining yang paling tinggi yaitu Artificial Neural Networks yang diterapkan pada penyakit Talasemia yaitu sebesar 99,73%, sedangkan negara yang paling banyak melakukan penelitian data mining penyakit yaitu India dan Turki