Networks are sets of nodes connected by links in various ways (Box 1). Although the properties of random networks have already been systematically investigated in the 1960s, a growing body of literature is now using networks in a range of ecological applications, including the study and management of human, animal, and plant diseases (29,49,58,80,131) (Fig. 1). Given the generality and flexibility of the approach, network representations can be used at a variety of levels in plant pathology, from gene expression during host-pathogen interactions, to the development of plant epidemics among fields, farms, and landscapes and to trade movement of plants infected by pathogens or infested by insects among regions and countries.Network structure has profound effects on the dynamics of an epidemic within a population (51,61,127). In today's globally connected world, social and transportation networks play a crucial role in the spread of human infectious diseases (21,53,83). A network approach provides insights into the transmission of infectious diseases also in animals more generally (40,45,78,137). Although there is an increasing interdisciplinary application of networks in epidemiology, relatively little attention has been paid to these analytical approaches in plant sciences. Hence the need for this review, which aims to summarize recent progress in this rapidly developing field and to highlight research challenges specific to plant pathology.In today's plant pathology, as in other fields, there is a need for integrating investigations at the molecular, mycelium, plant, regional and international scale (9,48,102,111,118,120,132). Networks can provide such a unifying framework. They can be (i) perceived at an abstract level (e.g., fungal species occurring on the same plant species host), (ii) materialized by a physical structure (e.g., the root system of plant individuals connected by mycorrhiza, or vice versa), and (iii) underlying flows of energy, matter or information (e.g., the exchanges of knowledge, equipment and money among farmers, plant health consultants, researchers and phytopharmaceutical companies). There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of superconnected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pat...
Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep.
Post-translational modification of protein cysteine residues is emerging as an important regulatory and signaling mechanism. We have identified numerous putative targets of redox regulation in the unicellular green alga Chlamydomonas reinhardtii. One enzyme, isocitrate lyase (ICL), was identified both as a putative thioredoxin target and as an S-thiolated protein in vivo. ICL is a key enzyme of the glyoxylate cycle that allows growth on acetate as a sole source of carbon. The aim of the present study was to clarify the molecular mechanism of the redox regulation of Chlamydomonas ICL using a combination of biochemical and biophysical methods. The results clearly show that purified C. reinhardtii ICL can be inactivated by glutathionylation and reactivated by glutaredoxin, whereas thioredoxin does not appear to regulate ICL activity, and no inter-or intramolecular disulfide bond could be formed under any of the conditions tested. Glutathionylation of the protein was investigated by mass spectrometry analysis, Western blotting, and site-directed mutagenesis. The enzyme was found to be protected from irreversible oxidative inactivation by glutathionylation of its catalytic Cys 178 , whereas a second residue, Cys 247 , becomes artifactually glutathionylated after prolonged incubation with GSSG. The possible functional significance of this post-translational modification of ICL in Chlamydomonas and other organisms is discussed.
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