Arabidopsis thaliana response to extracellular dna: Self versus nonself exposure

The inhibitory effect of extracellular DNA (exDNA) on the growth of conspecific individuals was demonstrated in different kingdoms. In plants, the inhibition has been observed on root growth and seed germination, demonstrating its role in plant\u2013soil negative feedback. Several hypotheses have been proposed to explain the early response to exDNA and the inhibitory effect of conspecific exDNA. We here contribute with a whole-plant transcriptome profiling in the model species Arabidopsis thaliana exposed to extracellular self-(conspecific) and nonself-(heterologous) DNA. The results highlight that cells distinguish self-from nonself-DNA. Moreover, confocal microscopy analyses reveal that nonself-DNA enters root tissues and cells, while self-DNA remains outside. Specifically, exposure to self-DNA limits cell permeability, affecting chloroplast functioning and reactive oxygen species (ROS) production, eventually causing cell cycle arrest, consistently with macroscopic observations of root apex necrosis, increased root hair density and leaf chlorosis. In contrast, nonself-DNA enters the cells triggering the activation of a hypersensitive response and evolving into systemic acquired resistance. Complex and different cascades of events emerge from exposure to extracellular selfor nonself-DNA and are discussed in the context of Damage-and Pathogen-Associated Molecular Patterns (DAMP and PAMP, respectively) responses

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

ISOL@: an Italian SOLAnaceae genomics resource

BACKGROUND: Present-day '-omics' technologies produce overwhelming amounts of data which include genome sequences, information on gene expression (transcripts and proteins) and on cell metabolic status. These data represent multiple aspects of a biological system and need to be investigated as a whole to shed light on the mechanisms which underpin the system functionality.The gathering and convergence of data generated by high-throughput technologies, the effective integration of different data-sources and the analysis of the information content based on comparative approaches are key methods for meaningful biological interpretations.In the frame of the International Solanaceae Genome Project, we propose here ISOLA, an Italian SOLAnaceae genomics resource. RESULTS: ISOLA (available at http://biosrv.cab.unina.it/isola) represents a trial platform and it is conceived as a multi-level computational environment.ISOLA currently consists of two main levels: the genome and the expression level. The cornerstone of the genome level is represented by the Solanum lycopersicum genome draft sequences generated by the International Tomato Genome Sequencing Consortium. Instead, the basic element of the expression level is the transcriptome information from different Solanaceae species, mainly in the form of species-specific comprehensive collections of Expressed Sequence Tags (ESTs).The cross-talk between the genome and the expression levels is based on data source sharing and on tools that enhance data quality, that extract information content from the levels' under parts and produce value-added biological knowledge. CONCLUSIONS: ISOLA is the result of a bioinformatics effort that addresses the challenges of the post-genomics era. It is designed to exploit '-omics' data based on effective integration to acquire biological knowledge and to approach a systems biology view. Beyond providing experimental biologists with a preliminary annotation of the tomato genome, this effort aims to produce a trial computational environment where different aspects and details are maintained as they are relevant for the analysis of the organization, the functionality and the evolution of the Solanaceae family

The Proteomic Code: a molecular recognition code for proteins

<p>Abstract</p> <p>Background</p> <p>The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.</p> <p>Review</p> <p>The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.</p> <p>Methods and conclusions</p> <p>A novel cloning method for the design and production of specific, high-affinity-reacting proteins (SHARP) is presented. This method is based on the concept of proteomic codes and is suitable for large-scale, industrial production of specifically interacting peptides.</p

WHOLE-GENOME RE-SEQUENCING OF TWO TOMATO LANDRACES REVEALS SEQUENCE VARIATIONS UNDERPINNING KEY ECONOMICALLY IMPORTANT TRAITS

In the post-genomic era, one of the major challenges is the identification of alleles directly responsible for phenotype variation among different genotypes within the same species. Tomato is a model crop for understanding the development and ripening of climacteric fleshy fruits, and it is also known to be an important source of health-promoting compounds. In addition, cultivated tomato germplasm shows a high phenotypic variation despite its very low genetic diversity. Toward the identification of sequence variations responsible for stress tolerance, high fruit quality and long shelf life, we re-sequenced the genomes of two traditional landraces grown in the Campania region (Southern Italy). Crovarese, belonging to the Corbarino type (COR), and Lucariello (LUC) are typically grown under low water regimes and produce highly appreciated fruits, which can be stored up to 4-8 months. We generated 65.8M and 56.4M of paired-end 30-150 bp reads with an average insert size of 380 bp (± 52bp) and 364 bp (± 49bp) for COR and LUC, respectively. A referenceguided assembly was performed using 'Heinz 1706' as a reference genome. We estimated a mean coverage depth of ~15X for COR and 13X for LUC. Comparing the genomes of COR and LUC with that of 'Heinz 1706' we found a similar distribution of SNPs (68.8% vs. 69.9%, respectively), small deletions (8.9% vs. 8.6%) and small insertions (22.1% vs. 21.3%). Through a de novo assembly of the unmapped reads we identified 29 and 36 new contigs in COR and LUC, respectively. The new contigs could be assigned to the chromosomes thanks to the use of a splitread approach. On average, the contigs inserted in COR were 654bp, whereas those inserted in LUC were 616bp. Using custom RNA-seq data, a total of 43054 and 44576 gene loci were annotated in COR and LUC, corresponding to 62369 and 65094 transcripts, respectively. Among the genes showing a similar structure in COR and LUC compared to 'Heinz 1706', we identified ~2000 and 1700 SNPs causing potentially disruptive effects on the function of 1371 and 1201 genes in COR and LUC, respectively. Interesting GO categories highly represented in genes affected by sequence changes were identified. Major variations were present in stress-responsive genes as well as in fruit quality and development-related genes. From a practical perspective, the identified SNPs and InDels are candidate polymorphisms to track DNA variations associated to key traits of economic interest

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p

Preface of the 31st Italian Symposium on Advanced Database Systems

This volume contains the proceedings of the 31st Italian Symposium on Advanced Database Systems (SEBD - Sistemi Evoluti per Basi di Dati), held in Galzinagno Terme (Padua, Italy) from 2 to 5 July 2023.</p