Biology subjects

Calia, G.

Publications and source records attributed to Calia, G..

4 recordsLinked to original sources

bioRxiv2024

Disentangling plant response to biotic and abiotic stress using HIVE, a novel tool to perform unpaired multi-omics integration

All organisms are subjected to multiple stresses usually occurring at the same time, requiring the activation of the appropriate signalling pathways to respond to all or by prioritizing the response to one stress factor. Plants, as sessile organisms, are particularly impacted by the constantly changing environment that is often unfavourable or even hostile. Because of the experimental complexity of studying the response of one organism to multiple stressors simultaneously, usually experiments are conducted considering one individual stress factor at the time. An alternative consists in performing in silico integration of those data on single stress response. Currently used methods to integrate unpaired experiments consist of performing meta-analysis or finding differentially expressed genes for each condition separately and then selecting the commonly regulated ones. Although these approaches allowed to find valuable results, they mainly identify specific signatures in response to one stress and very few signature responding to multiple stresses and lack those modulated differently in each condition. For this purpose, we developed HIVE (Horizontal Integration analysis using Variational AutoEncoders) to integrate multiple single-stress transcriptomics datasets composed of unpaired experiments. Briefly, we coupled a variational autoencoder, that alleviates batch effects, with a random forest regression and the SHAP explainer to select relevant genes modulated specifically in response to one or multiple stresses. We illustrate the functionality of HIVE to study the transcriptional changes of several different plants namely Arabidopsis thaliana, rice, maize, wheat, grapevine and peanut by collecting publicly available experiments on single stress, either biotic and/or abiotic, and jointly analyse them. HIVE performed better than the differential expression analysis, meta-analysis and the state-of-the-art tool for horizontal integration allowing to identify novel promising candidates responsible for triggering effective defence responses to multiple stresses.

plant biology↗

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

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↗

bioRxiv2022

A single-administration therapeutic interfering particle reduces SARS-CoV-2 viral shedding and pathogenesis in hamsters

The high transmissibility of SARS-CoV-2 is a primary driver of the COVID-19 pandemic. While existing interventions prevent severe disease, they exhibit mixed efficacy in preventing transmission, presumably due to their limited antiviral effects in the respiratory mucosa, whereas interventions targeting the sites of viral replication might more effectively limit respiratory virus transmission. Recently, intranasally administered RNA-based therapeutic interfering particles (TIPs) were reported to suppress SARS-CoV-2 replication, exhibit a high barrier to resistance, and prevent serious disease in hamsters. Since TIPs intrinsically target the tissues with the highest viral replication burden (i.e., respiratory tissues for SARS-CoV-2), we tested the potential of TIP intervention to reduce SARS-CoV-2 shedding. Here, we report that a single, post-exposure TIP dose lowers SARS-CoV-2 nasal shedding and at 5 days post-infection infectious virus shed is below detection limits in 4 out of 5 infected animals. Furthermore, TIPs reduce shedding of Delta variant or WA-1 from infected to uninfected hamsters. Co-housed contact animals exposed to infected, TIP-treated, animals exhibited significantly lower viral loads, reduced inflammatory cytokines, no severe lung pathology, and shortened shedding duration compared to animals co-housed with untreated infected animals. TIPs may represent an effective countermeasure to limit SARS-CoV-2 transmission. SignificanceCOVID-19 vaccines are exceptionally effective in preventing severe disease and death, but they have mixed efficacy in preventing virus transmission, consistent with established literature that parenteral vaccines for other viruses fail to prevent mucosal virus shedding or transmission. Likewise, small-molecule antivirals, while effective in reducing viral-disease pathogenesis, also appear to have inconsistent efficacy in preventing respiratory virus transmission including for SARS-CoV-2. Recently, we reported the discovery of a single-administration antiviral Therapeutic Interfering Particle (TIP) against SARS-CoV-2 that prevents severe disease in hamsters and exhibits a high genetic barrier to the evolution of resistance. Here, we report that TIP intervention also reduces SARS-CoV-2 transmission between hamsters.

microbiology↗