Biology subjects

Grinberg, N. F.

Publications and source records attributed to Grinberg, N. F..

7 recordsLinked to original sources

bioRxiv2026

Managing the phyllosphere: Agronomic practices influence the ecology and evolution of Pseudomonas syringae in cherry orchards

Bacterial canker, caused the Pseudomonas syringae species complex, is a major constraint on sweet cherry production worldwide. However, the influence of agronomic practices on pathogen ecology, dispersal and evolution under field conditions remains poorly understood. Here, we combined a factorial-design field experiment with whole-genome sequencing to investigate the effects of polytunnel covering and nitrogen fertigation on phyllosphere populations and the dynamics of a key pathogen, P. syringae pathovar syringae 9644 (Pss9644) in young cherry trees. Epiphytic P. syringae populations initially resembled those in surrounding woodland environments. Over time, pathogenic phylogroup 2d lineages became dominant, particularly on uncovered trees. Diversity of P. syringae populations was higher in uncovered treatments. Polytunnel covering markedly altered community composition and limited rain-splash dispersal of Pss9644 from stem cankers to leaves, thereby interrupting a key stage of the disease cycle. By contrast, nitrogen fertigation had no detectable effect on phyllosphere community structure, but enhanced plant growth and reduced lesion expansion following inoculation. Whole-genome sequencing of re-isolated Pss9644 strains revealed limited short-term genomic diversification, with single-nucleotide polymorphisms detected in 22 re-isolates. In total, 36 mutations were identified across the chromosome although no mutation affected virulence or motility. Taken together, our results show that agronomic practices influence both pathogen ecology and disease outcomes through distinct mechanisms: polytunnel covering primarily limits pathogen dispersal and reshapes phyllosphere communities, while nitrogen fertigation enhances plant growth and reduces disease severity. These findings highlight the potential to integrate canopy management and nutrient strategies to mitigate bacterial canker risk in commercial cherry production.

microbiology↗

bioRxiv2025

Hyperspectral image analysis for classification of multiple infections in wheat

Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1,447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions.

pathology↗

bioRxiv2024

Genetic dissection of the tissue-specific roles of type III effectors and phytotoxins in the pathogenicity of Pseudomonas syringae pv. syringae to cherry

When compared with other phylogroups (PGs) of the Pseudomonas syringae (Ps) species complex, Ps pv. syringae strains within PG2 have a reduced repertoire of type III effectors (T3Es) but produce several phytotoxins. Effectors within the cherry pathogen Pss9644 were grouped based on their frequency in strains from Prunus as: the conserved effector locus (CEL) common to most Ps pathogens; a CORE of effectors common to PG2; a set of PRUNUS effectors common to cherry pathogens; and a FLEXIBLE set of T3Es. Pss9644 also contains gene clusters for biosynthesis of toxins syringomycin/syringopeptin and syringolin A. After confirmation of virulence gene expression, mutants with a sequential series of T3E and toxin deletions were pathogenicity tested on wood, leaves and fruits of sweet cherry (Prunus avium) and leaves of ornamental cherry (Prunus incisa). The toxins had a key role in disease development in fruits but were less important in leaves and wood. An effectorless mutant retained some pathogenicity to fruit but not wood or leaves. Striking redundancy was observed amongst effector groups. The CEL effectors have important roles during the early-stages of leaf infection and acted synergistically with toxins in all tissues. Deletion of separate groups of T3Es had much more effect in Prunus incisa than in sweet cherry. Mixed inocula were used to complement the toxin mutations in trans and indicated that strain mixtures may be important in the field. Our results highlight the niche-specific role of toxins in cherry tissues and the complexity of effector redundancy in the pathogen Pss9644.

microbiology↗

bioRxiv2020

Multi-tissue transcriptome-wide association studies

Many genetic mutations affecting phenotypes are presumed to do so via altering gene expression in particular cells or tissues, but identifying the specific genes involved has been challenging. A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by first learning a predictive model on an eQTL dataset and then imputing gene expression levels into a larger genome-wide association study (GWAS). Finally, associations between predicted gene expressions and GWAS phenotype are identified. Here, we compared tree-based machine learning (ML) method of random forests (RF) with more widely used linear methods of lasso, ridge, and elastic net regression, for prediction of gene expression. We also developed a multi-task learning extension to RF which simultaneously makes use of information from multiple tissues (RF-MTL) and compared it to a multi-dataset version of lasso, the joint lasso, and to a single tissue RF. We found that for prediction of gene expression, RF, in general, outperformed linear approaches on our chosen eQTL dataset and that multi-tissue methods generally outperformed their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally propagated to the next steps of the analysis, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed a disease signal for that gene in another. We tested all four methods on type 1 diabetes (T1D) GWAS and expression data for several immune cells and found that 46 genes were identified by at least one method, though only 7 by all methods. Joint lasso discovered the most T1D-associated genes, including 15 unique to that method, but this may reflect its higher false positive rate due to "overborrowing" information across tissues. RF-MTL found more unique associated genes than RF for 3 out 5 tissues. Compared to lasso-based analysis, the RF gene list was more likely to relate to T1D in an analysis of independent data types. We conclude that RF, both single- and multi-task version, is competitive and, for some cell types, superior to linear models conventionally used in the TWAS studies. Author summaryA transcriptome-wide association study (TWAS) is a way of integrating expression data and genome-wide association studies (GWAS), which allows for discovery of genes, rather than mutations, associated to traits of interest. In the TWAS framework, we first train predictive models on an eQTL dataset, then use these models to impute gene expression into a GWAS dataset. Finally, we look for significant associations between predicted gene expression and a GWAS trait. In this work, we compare non-linear method of random forests (RF) to linear models, customarily used in TWAS. Furthermore, we demonstrate that TWAS framework can naturally be extended to, and potentially benefit from, a multi-tissue setting, thereby taking advantage of the correlation between gene expression in different tissue types. We applied the RF, a selection of linear models, and the multi-tissue approaches to an eQTL dataset of monocytes and B cells and a large T1D GWAS. We found that RF outperform lasso in terms of predictive accuracy and the number of differentially expressed genes found, and that multi-dataset version of lasso discovered the most T1D-associated genes. Analysis of the gene lists produced for each method in independent data types (excluding genetic association data) showed all related to T1D, but that the RF methods ranked T1D higher in their lists than the linear methods. We conclude that RF is a useful addition to the TWAS tool box.

genetics↗

bioRxiv2019

Sharing information between related diseases using Bayesian joint fine mapping increases accuracy and identifies novel associations in six immune mediated diseases

Thousands of genetic variants have been associated with human disease risk, but linkage disequilibrium (LD) hinders fine-mapping the causal variants. We show that stepwise regression, and, to a lesser extent, stochastic search fine mapping can mis-identify as causal, SNPs which jointly tag distinct causal variants. Frequent sharing of causal variants between immune-mediated diseases (IMD) motivated us to develop a computationally efficient multinomial fine-mapping (MFM) approach that borrows information between diseases in a Bayesian framework. We show that MFM has greater accuracy than single disease analysis when shared causal variants exist, and negligible loss of precision otherwise. Applying MFM to data from six IMD revealed causal variants undetected in individual disease analysis, including in IL2RA where we confirm functional effects of multiple causal variants using allele-specific expression in sorted CD4+ T cells from genotype-selected individuals. MFM has the potential to increase fine-mapping resolution in related diseases enabling the identification of associated cellular and molecular phenotypes.

genomics↗

bioRxiv2019

Multivariate genome-wide association analysis of a cytokine network reveals variants with widespread immune, haematological and cardiometabolic pleiotropy

Cytokines are essential regulatory components of the immune system and their aberrant levels have been linked to many disease states. Despite increasing evidence that cytokines operate in concert, many of the physiological interactions between cytokines, and the shared genetic architecture that underlie them, remain unknown. Here we aimed to identify and characterise genetic variants with pleiotropic effects on cytokines - to do this we performed a multivariate genome-wide association study on a correlation network of 11 circulating cytokines measured in 9,263 individuals. Meta-analysis identified a total of 8 loci significantly associated with the cytokine network, of which two (PDGFRB and ABO) had not been detected previously. Bayesian colocalisation analysis revealed shared causal variants between the eight cytokine loci and other traits; in particular, cytokine network variants at the ABO, SERPINE2, and ZFPM2 loci showed pleiotropic effects on the production of immune-related proteins; on metabolic traits such as lipoprotein and lipid levels; on blood-cell related traits such as platelet count; and on disease traits such as coronary artery disease and type 2 diabetes.

genomics↗

bioRxiv2017

An Evaluation of Machine-learning for Predicting Phenotype: studies in yeast and wheat

In phenotype prediction, the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods (elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM)), with two state-of-the-art classical statistical genetics methods (including genomic BLUP). Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all phenotypes considered standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. When applied to the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure, which suggests one way to improve standard machine learning methods when population structure is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise.

bioinformatics↗