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Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein–protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection.

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture.Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs.Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods.Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

Objective: Although relatively costly and non-scalable, non-invasive neuromodulation interventions are treatment alternatives for neuropsychiatric disorders. The recent developments of highly-deployable transcranial electric stimulation (tES) systems, combined with mobile-Health technologies, could be incorporated in digital trials to overcome methodological barriers and increase equity of access. The study aims are to discuss the implementation of tES digital trials by performing a systematic scoping review and strategic process mapping, evaluate methodological aspects of tES digital trial designs, and provide Delphi-based recommendations for implementing digital trials using tES. Methods: We convened 61 highly-productive specialists and contacted 8 tES companies to assess 71 issues related to tES digitalization readiness, and processes, barriers, advantages, and opportunities for implementing tES digital trials. Delphi-based recommendations (>60% agreement) were provided. Results: The main strengths/opportunities of tES were: (i) non-pharmacological nature (92% of agreement), safety of these techniques (80%), affordability (88%), and potential scalability (78%). As for weaknesses/threats, we listed insufficient supervision (76%) and unclear regulatory status (69%). Many issues related to methodological biases did not reach consensus. Device appraisal showed moderate digitalization readiness, with high safety and potential for trial implementation, but low connectivity. Conclusions: Panelists recognized the potential of tES for scalability, generalizability, and leverage of digital trials processes; with no consensus about aspects regarding methodological biases. Significance: We further propose and discuss a conceptual framework for exploiting shared aspects between mobile-Health tES technologies with digital trials methodology to drive future efforts for digitizing tES trials.

Plants and microbes have coexisted for hundreds of millions of years and have developed deeply intertwined mutually beneficial relations. Among the many benefits of a selected microbial community, pathogen suppression is a particularly desirable trait for both the plant and agronomy industry. Suppressive soils have been described for many years but technology to develop a deep understanding of this phenomenon was only recently introduced. In this thesis, I applied different metagenomic sequencing approaches to study the biological basis of suppressive soils with particular interest toward the biosynthetic potential of the suppressive rhizosphere community. In the introduction, I describe ecology-inspired solutions for biosynthetic gene cluster (BGC) mining and the existing tools and sequencing technologies that can be used to this end. We take a structured approach to the dissection of the suppressive-associated rhizosphere communities. In the first part of the work, we perform the first large-scale soil survey aiming at establishing a soil collection to identify unique characteristics of suppressive soils. Through a combination of phenotyping and marker gene sequencing, we identify four soils with strong Fusarium culmorum-suppressive characteristics. We compare taxonomy composition and volatile profile of both suppressive and non-suppressive soils to identify features that distinguish suppressive soils. The suppressive soils found in this collection do not share physiochemical or categorical characteristics. In addition, diversity and community structure do not separate suppressive and non-suppressive soils. However, network-based analysis shows a group of acidobacteria which co-occur in a suppressive-soil-associated hub. Then, to better understand the secondary metabolite diversity of the suppressive samples we characterize of nonribosomal peptide synthetase diversity in suppressive and conducive soils with the use of functional amplicon sequencing of NR

Sequences are a crucial concept in the bioinformatics field, as they can represent different kinds of biological data. Apart from the common string representations of DNA and protein sequences, many other kinds of biological data have an inherent sequential order. Thus, algorithms for representing, comparing, exploring, and analyzing biological sequences are numerous and diverse. In this thesis I explore two main kinds of biological sequences - those obtained from optical mapping images, and those from three-dimensional protein structures. The sequence representation allows for adapting and reusing algorithms across these quite distinct fields.

The ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics.

Meiotic recombination is a biological process of key importance in breeding, to generate genetic diversity and develop novel or agronomically relevant haplotypes. In crop tomato, recombination is curtailed as manifested by linkage disequilibrium decay over a longer distance and reduced diversity compared with wild relatives. Here, we compared domesticated and wild populations of tomato and found an overall conserved recombination landscape, with local changes in effective recombination rate in specific genomic regions. We also studied the dynamics of recombination hotspots resulting from domestication and found that loss of such hotspots is associated with selective sweeps, most notably in the pericentromeric heterochromatin. We detected footprints of genetic changes and structural variants, among them associated with transposable elements, linked with hotspot divergence during domestication, likely causing fine-scale alterations to recombination patterns and resulting in linkage drag.