Biology subjects

Hussain, U.

Publications and source records attributed to Hussain, U..

6 recordsLinked to original sources

bioRxiv2025

Large-scale culturing of the tree microbiome enables targeted disease suppression

The tree microbiome is essential for host health and pathogen suppression. Synthetic microbial communities (SynComs) are emerging as important tools to understand microbiome dynamics and engineer microbiomes to harness beneficial properties. However, while the rational design, assembly and application of SynComs requires representative microbiota isolate collections combined with functional information, microbial culture collections from tree species such as oak (Quercus) are critically lacking. Here, we generated an oak microbiota culture collection comprising >30,000 isolates from 150 oak trees across Britain, belonging to key bacterial and fungal taxa that represented 61% of the total bacterial sequences and 87% of total fungal sequences as determined by culture-independent sequencing. Over 22,000 isolates were screened for suppression of bacterial species associated with degradation of live stem tissue in trees affected by Acute Oak Decline (AOD), identifying 341 bacterial isolates that suppressed oak pathogens. In vitro screening of 40 randomly assembled SynComs demonstrated that oak microbiota SynComs can suppress oak pathogenic bacteria associated with AOD. Inoculation of a disease-suppressive SynCom into the stem of oak seedlings and logs prior to pathogen challenge reduced the quantities of the bacteria, Brenneria goodwinii and Gibbsiella quercinecans, by 56% and 87%, respectively, in seedlings, and 71% and 95% in logs. This work demonstrates that the tree microbiome can be engineered using disease suppressive SynComs.

microbiology↗

bioRxiv2025

Environment and disease have tissue-specific effects on the tree microbiome

Trees are essential for ecosystem function, but due to their long lifespan, are disproportionately impacted by climate change and disease. Tree-associated microbiota are critical for tree health and resilience, but the composition and function of tree microbiomes across different tissue types, and how environmental factors and disease impact tree microbiomes, is poorly understood. Oak trees are major constituents of forests of the Northern hemisphere, but are increasingly impacted by climate and disease. Here, we studied the oak microbiome across Britain, combining 16S rRNA gene and ITS microbial community profiling and shotgun metagenomics of leaf, stem and root/rhizosphere samples, and developed a three-level occupancy model to describe microbiota distribution across the landscape. We show that oak leaf, stem and root/rhizosphere tissues harbour taxonomically and functionally distinct microbiota and identified differences in tissue-specific effects of environmental variables (e.g. temperature, rainfall, ion deposition) on microbiome composition and function. We generated 1657 bacterial, archaeal and fungal metagenome-assembled genomes representing key members of the oak microbiome. Furthermore, the stem microbiome of oak trees with symptoms of Acute Oak Decline, a complex decline disease driven by abiotic and biotic stressors, exhibited reduced bacterial and fungal richness and altered microbiome function. This work represents the most comprehensive microbiome study of a tree species to date. Understanding how tree-associated microbiota respond to environmental change and disease across different tissues is crucial to predict future climate and disease impacts on tree microbiome function, and inform translational approaches to modulate tree microbiomes for plant health.

microbiology↗

bioRxiv2023

Chemical cues from beetle larvae trigger proliferation and virulence of a plant pathogen

Agricultural crop productivity and global forest biomes are coming under increasing threat from insect pests and microbial pathogens. This impact is worsened by inter- kingdom insect-microbe interactions that can increase transmission and disease severity in affected plants. Whilst bacterial chemical cues have been shown to directly influence insect behaviour, the impact of insect-derived compounds on phytopathogens is poorly understood. Here, we investigated the chemical basis for interactions between beetle larvae and bacteria in acute oak decline (AOD), a disease characterised by inner bark necrosis and involving a polymicrobial consortium including Brenneria goodwinii and larval galleries of Agrilus biguttatus. We found that A. biguttatus larval extracts contain chemical elicitors that increase bacterial growth rate and final cell density during in vitro culture, and stimulate the differential expression of [~]600 genes, including the Type III Secretion System and its effectors, which are major virulence factors in plant pathogens. Chemical compounds from closely related insect species did not have this effect. These findings highlight the importance of inter-kingdom interactions in plant disease and suggests a novel mode-of-action for insect-derived chemical elicitors in facilitating the virulence of phytopathogens.

ecology↗

bioRxiv2023

Gauge equivariant convolutional neural networks for diffusion mri

Diffusion MRI (dMRI) is an imaging technique widely used in neuroimaging research, where the signal carries directional information of underlying neuronal fibres based on the diffusivity of water molecules. One of the shortcomings of dMRI is that numerous images, sampled at gradient directions on a sphere, must be acquired to achieve a reliable angular resolution for model-fitting, which translates to longer scan times, higher costs, and barriers to clinical adoption. In this work we introduce gauge equivariant convolutional neural network (gCNN) layers for dMRI that overcome the challenges associated with the signal being acquired on a sphere with antipodal points identified. This is done by noting that the domain is equivalent to the real projective plane, [R]P2, which is a non-euclidean and a non-orientable manifold. This is in stark contrast to a rectangular grid which typical convolutional neural networks (CNNs) are designed for. We apply our method to upsample angular resolution for predicting diffusion tensor imaging (DTI) parameters from just six diffusion gradient directions. The symmetries introduced allow gCNNs the ability to train with fewer subjects as compared to a baseline model that involves only 3D convolutions.

neuroscience↗

bioRxiv2022

Investigating the in vivo Spatial Distribution of Hippocampal Microstructure and Macrostructure

The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current MR based neuroimaging techniques. In this work, we used diffusion MRI and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the Neurite Orientation Dispersion and Density Imaging (NODDI) model optimized for gray matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the Cornu Ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibers, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitecture differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures provided good separability across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fiber pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo diffusion MRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.

neuroscience↗

bioRxiv2020

DIFFUSION MRI OF THE UNFOLDED HIPPOCAMPUS

The hippocampus is implicated in numerous neurological disorders and the ability to detect subtle or focal hippocampal abnormalities earlier in disease progression could significantly improve the treatment of patients. Ex vivo studies with ultra-high field have revealed that diffusion MRI (dMRI) can reveal microstructural variations within the hippocampal subfields and lamina, and may also be sensitive to intra-hippocampal pathways. However, translation to lower resolution in vivo dMRI studies of the hippocampus is challenging due to its complicated geometry. One novel way to overcome some of these obstacles is by transforming the usual Cartesian coordinates in an MRI image to coordinates that are crafted to curve themselves according to the complicated geometry of the hippocampus. This procedure allows us to virtually unfold the hippocampus into a thin sheet. In this work, we introduce an algorithm to map diffusion MRI data to this sheet, allowing us to overcome the difficulties associated with the hippocampus complicated geometry. We demonstrate how our method can be readily integrated into existing implementations of traditional tractography methods and how it leads to enhancements in the resulting tracts. Further, our results on high quality in vivo dMRI acquisitions show that unfolding the hippocampus leads to a more anatomically plausible modelling of the connectivity of the hippocampus as probed by probabilistic tractography, revealing key elements of the polysynaptic pathway and anterior-posterior connectivity gradients.

neuroscience↗