Ulrich, W. | Search bioRxiv

bioRxiv2023

Colonization of axenic beet by rhizosphere bacteria takes place in discreet phases regardless of bioinculation with next generation bioinoculant

Bioinoculation can increase crop yields under environmental stress. Plant colonization by microbes is an example of succession, with its distinct phases differing in community structure and diversity. This process needs to be studied to determine the optimal timing for bioinoculation and its effects. Haere, we show that, regardless of bio-inoculation, soil type and plant genotype, bacteria colonize the rhizosphere of axenic beets and tissues in two phases, differing in bacterial load, nestedness, community structure, diversity and assembly mechanism, and associated with taproot development. Communities remained stable after five weeks of growth in soil. The alpha diversity was greater and the bacterial load was lower in the late samples than in the early ones. Time, soil type and genotype determined community structure but not alpha diversity, bacterial load, nestedness or assembly mechanisms both in the rhizosphere and in the endosphere. Inoculation changed the community structure and members of Pseudomonadota and Bacillota of low abundance in the inoculant were recruited by beets. Axenic beet colonization occurs through phases similar to other instances of microbial succession, and bacteria are recruited mostly randomly. The transition from the early to late phase involves a decrease in the bacterial load in plant tissues, which may be linked to plant growth and the arrest of bacterial cell division. Therefore, early inoculation seems to be favourable. Five weeks of growth in soil enabled formation of stable bacterial communities in both the rhizosphere and the endosphere. The influence of inoculation seems to be indirect, probably due to microbe-microbe interactions.

microbiology↗

bioRxiv2021

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Phenotypes of Disease

AO_SCPLOWBSTRACTC_SCPLOWWe propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.

bioinformatics↗