Biology subjects

Molinari, R.

Publications and source records attributed to Molinari, R..

3 recordsLinked to original sources

bioRxiv2024

Climatic fluctuations modulate eco-evolutionary processes associated with pathogen dynamics and continue to fuel bacterial spot epidemics in tomato

Recurring outbreaks caused by endemic pathogens continue to pose problems in managing plant, wildlife, livestock and human health. Understanding how these outbreaks unfold and what drives the variability in disease epidemics across space and time is less understood, especially in the agricultural settings. In this study, we investigated the contribution of pathogen genetic diversity, climatic variation and their interaction towards disease dynamics, with an integrative approach grounded on multitype, high resolution sequencing data and analysis techniques. This investigation was carried out for bacterial spot disease epidemic by surveying tomato fields for bacterial pathogen (Xanthomonas perforans) across southeastern US over a span of three years. The strength of epidemic severity varied across space and time in the agricultural fields. Disease severity was positively associated with strain diversity, and was linked to environmental fluctuations, specifically, large variation and extreme changes in certain climatic factors. Strain-resolved metagenomics approach revealed that co-existence of multiple pathogen lineages was common in the individual fields, although accompanied by differential lineage dynamics. The co-occurring lineages displayed environmentally dependent fitness contributions. By tracing allelic frequencies in pathogen populations across temporal scales, we find evidence for asynchronous allele cycling across seasons, hinting at the presence of adaptive single-nucleotide polymorphisms (SNPs) being polymorphic in space in response to seasonality. Despite this pathogen heterogeneity, we identified positively selected loci under parallel evolution, which may explain the nature of selection pressures experienced by the pathogen. While single pathogen lineage is assumed to dominate the host in the agricultural settings, our findings challenge this notion by demonstrating genetic diversity in the pathogen population observed within a single field and linking it to the disease dynamics. Our results explain the role of pathogen genetic diversity, climate-dependent compositional dynamics, and differential fitness contributions in dictating the variability of disease epidemics in the agricultural settings. Such findings will be invaluable for building predictive models in disease epidemiology. Our high-resolution combinatorial approach exploiting high- resolution sequence data, metadata types and analysis tools, is general enough to finely investigate disease epidemics at large scales in diverse case-studies concerning plant, animal and human health.

microbiology↗

bioRxiv2020

Chameleon microRNAs in breast cancer: their elusive role as regulatory factors in cancer progression

Breast cancer is one of the most frequent cancers affecting women. Non-coding micro RNAs (miRNAs) seem to play an important role in the regulation of pathways involved in tumor occurrence and progression. Extending on the research in Haakensen et al., where significant miRNAs were selected as being associated with the progression from normal breast tissue to breast cancer, in this work we put forward 112 sets of miRNA combinations, each including at most 5 expressions with high accuracy in discriminating healthy breast tissue from breast carcinoma. Our results are based on a recently developed machine learning technique which, instead of selecting a single model (or combination of features), delivers a set of models with equivalent predictive capabilities that allow to interpret and visualize the interaction of these features. These results shed new light on the biological action of the selected miRNAs which can behave in different ways according to the miRNA network with which they interact. Indeed, these revealed connections may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA. It is therefore possible to understand how the role of a genomic variable may change when considered in interaction with other sets of variables, as opposed to only considering its effect when it is evaluated within a unique combination of features. The approach proposed in this work provides a statistical basis for the notion of chameleon miRNAs and is inspired by the emerging field of systems biology. Author SummaryO_LIThe notion of a single predictive genomic (statistical) model is replaced by that of a set of models that can be considered as exchangeable due to their indistinguishable (optimal) predictive abilities; C_LIO_LIOur results indicate that the role of miRNAs cannot be interpreted independently from the combination of features with which they interact and can therefore vary considerably when considered in a network of different combinations. Some miRNAs may act as chameleons and behave in opposite manners thereby showing some kind of antagonistic duality; C_LIO_LISome miRNAs are exchangeable inside models with equivalent predictive ability and seem to point to latent biological functions. C_LI

systems biology↗

bioRxiv2020

Granger-Causal Testing for Irregularly Sampled Time Series with Application to Nitrogen Signaling in Arabidopsis

Motivation Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signaling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal.Results This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model’s dependence structure to the specific sampling time. Using Maximum-Likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the Arabidopsis thaliana data, the proposed approach produced 3,078 significant interactions, in which 2,012 interactions have root causal genes and 1,066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signaling, including genes encoding transcription factors, hormones, and signaling peptides. Of the 1,007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signaling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signaling.Availability The method was developed with the R statistical software and is made available thorugh the R package “irg” hosted on the GitHub repository https://github.com/SMAC-Group/irg. A sample data set is made available as an example to apply the method and the complete Arabidopsis thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500.Contact amymc{at}illinois.eduCompeting Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗