Biology subjects

Cestaro, A.

Publications and source records attributed to Cestaro, A..

8 recordsLinked to original sources

bioRxiv2024

Prostaglandin D2 synthase controls Schwann cells metabolism

We previously reported that in the absence of Prostaglandin D2 synthase (L-PGDS) peripheral nerves are hypomyelinated in development and that with aging they present aberrant myelin sheaths. We now demonstrate that L-PGDS expressed in Schwann cells is part of a coordinated program aiming at preserving myelin integrity. In vivo and in vitro lipidomic, metabolomic and transcriptomic analyses confirmed that myelin lipids composition, Schwann cells energetic metabolism and key enzymes controlling these processes are altered in the absence of L-PGDS. Moreover, Schwann cells undergo a metabolic rewiring and turn to acetate as the main energetic source. Further, they produce ketone bodies to ensure glial cell and neuronal survival. Importantly, we demonstrate that all these changes correlate with morphological myelin alterations and describe the first physiological pathway implicated in preserving PNS myelin. Collectively, we posit that myelin lipids serve as a reservoir to provide ketone bodies, which together with acetate represent the adaptive substrates Schwann cells can rely on to sustain the axo-glial unit and preserve the integrity of the PNS.

neuroscience↗

bioRxiv2023

Definition of the effector landscape across 13 Phytoplasma proteomes with LEAPH and EffectorComb

BackgroundCrop pathogens are a major threat to plants health, reducing the yield and quality of agricultural production. Among them, the Candidatus Phytoplasma genus, a group of fastidious phloem-restricted bacteria, can parasite a wide variety of both ornamental and agro-economically important plants. Several aspects of the interaction with the plant host are still unclear but it was discovered that phytoplasmas secrete certain proteins (effectors) responsible for the symptoms associated with the disease. Identifying and characterizing these proteins is of prime importance for globally improving plant health in an environmentally friendly context. ResultsWe challenged the identification of phytoplasmas effectors by developing LEAPH, a novel machine-learning ensemble predictor for phytoplasmas pathogenicity proteins. The prediction core is composed of four models: Random Forest, XGBoost, Gaussian, and Multinomial Naive Bayes. The consensus prediction is achieved by a novel consensus prediction score. LEAPH was trained on 479 proteins from 53 phytoplasmas species, described by 30 features accounting for the biological complexity of these protein sequences. LEAPH achieved 97.49% accuracy, 95.26% precision, and 98.37% recall, ensuring a low false-positive rate and outperforming available state-of-the-art methods for putative effector prediction. The application of LEAPH to 13 phytoplasma proteomes yields a comprehensive landscape of 2089 putative pathogenicity proteins. We identified three classes of these proteins according to different secretion models: "classical", presenting a signal peptide, "classically-like" and "non-classical", lacking the canonical secretion signal. Importantly, LEAPH was able to identify 15 out of 17 known experimentally validated effectors belonging to the three classes. Furthermore, to help the selection of novel candidates for biological validation, we applied the Self-Organizing Maps algorithm and developed a shiny app called EffectorComb. Both tools would be a valuable resource to improve our understanding of effectors in plant-phytoplasmas interactions. ConclusionsLEAPH and EffectorComb app can be used to boost the characterization of putative effectors at both computational and experimental levels and can be employed in other phytopathological models. Both tools are available at https://github.com/Plant-Net/LEAPH-EffectorComb.git.

bioinformatics↗

bioRxiv2023

Species-level classification provides new insights into the biogeographical patterns of microbial communities in shallow saline lakes

Saline lakes are rapidly drying out across the globe, particularly in Central Asia, due to climate change and anthropogenic activities. We present the results of a long-read next generation sequencing analysis of the 16S rRNA-based taxonomic structure of bacteriomes of the Tengiz-Korgalzhyn lakes system. We found that the shallow endorheic, mostly saline lakes of the system show unusually low bacterioplankton dispersal rates at species-level taxonomic resolution. The major environmental factor structuring the lakes microbial communities was salinity. The dominant bacterial phyla of the lakes with high salinity included a significant proportion of marine and halophilic species. In sum, these results, which can be applied to other lake systems of the semi-arid regions, improve our understanding of the factors influencing lake microbiomes undergoing salinization in response to climate change and other anthropogenic factors. Our results show that finer taxonomic classification can provide new insights and improve our understanding of the environmental factors influencing the microbiomes of lakes undergoing salinization in response to climate change and other anthropogenic factors.

ecology↗

bioRxiv2023

Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER.

Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification in protein sequences predicted from genomes is challenging and current methods generate too many candidates without indication for prioritizing further experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Although bacterial, fungal and oomycetes effectors have been studied extensively and conserved characteristic motifs have been identified, research on plant-parasitic nematode effectors (PPN) identified some enriched degenerate motifs in only one species so far. The different lifestyles of PPNs might reflect effectors with different functions according to the nematodes specific needs, thus presenting a high variety of characteristic motifs. To circumvent these limitations, we have developed MOnSTER a novel tool that identifies clusters of motifs of protein sequences (CLUMPs). MOnSTER can be fed with motifs identified by de novo tools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in candidate parasitism proteins of plant-pathogenic oomycetes. We showed the reliability of MOnSTER by identifying five CLUMPs that correspond to the known motifs: RxLR, -dEER and LxLFLAK-HVLVxxP. Consequently, we applied MOnSTER on PPN candidate parasitism proteins and identified peculiar motifs in their sequences. We identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found that specific co-occurrences of at least two CLUMPs are present in PPN candidate parasitism protein sequences bearing protein domains important for invasion and pathogenicity. The potentiality of this tool goes beyond the candidate parasitism proteins and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences. Authors summaryPopulation growth, environmental degradation and climate change are already bringing harm to human communities and the natural world that needs to be addressed rapidly. Ensuring food security for a population that will exceed 9 billion people by 2050 while preserving the environment and biodiversity is a major challenge. Agricultural pathogens, to cause the infection, secrete effector proteins that promote colonization of the host plant. Identifying and characterizing pathogens effectors is crucial towards understanding how they manipulate the plant and better combat them. Because of their poor sequence conservation, effector identification in protein sequences predicted from genomes is challenging and current methods generate too many candidates without indication for prioritizing further experimental studies. To address these challenges, we have developed a novel tool called MOnSTER, that identifies and score clusters of motifs of protein sequences (CLUMPs). MOnSTER is an easy to use tool that can be included in any pipeline needing motif calling and will be of great use to accelerate both computational and experimental studies relating to protein motif discovery. Altogether our findings provide improvements in the understanding of the mechanisms set up by the pathogens to infect the plant and can elucidate important signatures to block the development of plant-pathogen interactions and allow to engineer of durable disease resistance.

bioinformatics↗

bioRxiv2021

The haplotype-resolved reference genome of lemon (Citrus limon L. Burm f.)

Lemon (Citrus limon (L.) Burm. f.) is an evergreen tree belonging to the genus Citrus. The fruits are particularly prized for their organoleptic and nutraceutical properties of the juice. Herein we report, for the first time, the release of a high-quality reference genome of the two haplotypes of lemon. The sequencing has been carried out coupling Illumina short reads and Oxford Nanopore data leading to the definition of a primary and an alternative assembly characterized by a genome size of 312.8 Mb and 324.74 Mb respectively. The analysis of the long terminal repeat (LTR) allowed the identification of 1921 regions on the primary and 1911 on the alternative assembly distributed across the nine chromosomes. Furthermore, an in-silico analysis of the microRNA genes was carried out using 246 mature miRNA and the respective pre-miRNA hairpin sequences of C. sinensis. Such analysis highlighted a high conservation between the two species with 233 mature miRNAs and 51 pre-miRNA stem-loops aligning with perfect match on the lemon genome. In parallel, total RNA was extracted from fruit, flower, leaf and root enabling the detection of 38,205 and 37,753 predicted transcripts on primary and alternative assemblies respectively. Among those, the highest and lowest number of tissue-specific transcripts were detected in flower (2.73% and 2.71% in primary and alternative assemblies respectively) and leaf (0.7% and 0.68%) while gene ontology analysis enables a more precise characterization of the expressed genes based on their function. The availability of a reference genome is an important prerequisite both for the set-up of high-throughput genotyping analysis and for functional genomic approaches toward the characterization of the genetic determinism of traits of agronomic interest.

genomics↗

bioRxiv2021

Stability of microbiota in vineyard soils across consecutive years.

Agricultural soils harbor rich and diverse microbial communities that have a deep influence on soil properties and productivity. Large scale studies have shown the impact of environmental parameters like climate or chemical composition on the distribution of bacterial and fungal species. Comparatively, little data exists documenting how soil microbial communities change between different years. Quantifying the temporal stability of soil microbial communities will allow us to better understand the relevance of the differences between environments and their impact on ecological processes on the global and local scale. We characterized the bacterial and fungal components of the soil microbiota in ten vineyards in two consecutive years. Despite differences of species richness and diversity between the two years, we found a general stability of the taxonomic structure of the soil microbiota. Temporal differences were smaller than differences due to geographical location, vineyard land management or differences between sampling sites within the same vineyard. Using machine learning, we demonstrated that each site was characterized by a distinctive microbiota, and we identified a reduced set of indicator species that could classify samples according to their geographic origin across different years with high accuracy. ImportanceThe temporal stability of the soil microbiota is important to understand the relevance of the differences that are found in response to a variety of environmental factors. By comparing fungal and bacterial microbiota from samples collected in the same sites in two consecutive years, we found a remarkable stability of both components, with characteristic differences between bacteria and fungi. Our work fills an important gap toward the definition of a microbial cartography of agricultural soils.

microbiology↗

bioRxiv2019

Pyrus Communis chromosome scale assembly

We report an improved assembly and scaffolding of the European pear (Pyrus communis L.) genome (referred to as BartlettDHv2.0), obtained using a combination of Pacific Biosciences RSII Long read sequencing (PacBio), Bionano optical mapping, chromatin interaction capture (Hi-C), and genetic mapping. A total of 496.9 million bases (Mb) corresponding to 97% of the estimated genome size were assembled into 494 scaffolds. Hi-C data and a high-density genetic map allowed us to anchor and orient 87% of the sequence on the 17 chromosomes of the pear genome. About 50% (247 Mb) of the genome consists of repetitive sequences. Comparison with previous assemblies of Pyrus communis and Pyrus x bretschneideri confirmed the presence of 37,445 protein-coding genes, which is 13% fewer than previously predicted.

bioinformatics↗

bioRxiv2019

Pseudo-chromosome length genome assembly of a double haploid ‘Bartlett’ pear (Pyrus communis L.)

We report an improved assembly and scaffolding of the European pear (Pyrus communis L.) genome (referred to as BartlettDHv2.0), obtained using a combination of Pacific Biosciences RSII Long read sequencing (PacBio), Bionano optical mapping, chromatin interaction capture (Hi-C), and genetic mapping. A total of 496.9 million bases (Mb) corresponding to 97% of the estimated genome size were assembled into 494 scaffolds. Hi-C data and a high-density genetic map allowed us to anchor and orient 87% of the sequence on the 17 chromosomes of the pear genome. About 50% (247 Mb) of the genome consists of repetitive sequences. Comparison with previous assemblies of Pyrus communis. and Pyrus x bretschneideri confirmed the presence of 37,445 protein-coding genes, which is 13% fewer than previously predicted.

plant biology↗