Biology subjects

Morgan, S.

Publications and source records attributed to Morgan, S..

3 recordsLinked to original sources

bioRxiv2026

MIND the gap: methodological considerations and guidance for structural MRI similarity network analysis with MIND

Structural similarity networks quantify the similarity of structural properties across cortical regions, providing a macroscopic window onto the organisation of cortical architecture. Morphometric inverse divergence (MIND) is a multivariate metric of similarity between cortical areas, based on the Kullback-Leibler (KL) divergence between areal distributions of multiple MRI features or morphometric variables locally measured at voxel or vertex resolution. MIND has demonstrated technical robustness and biological validity and is increasingly widely used as a measure of cortico-cortical similarity in clinical and developmental network neuroscience. Here we provide in-depth methodological background on KL divergence and MIND, highlighting possible sources of bias, critical user decision points in the design of a MIND processing pipeline, and recommendations for technical risk mitigation in using MIND as a metric of cortical similarity. We use simulated data and observational MRI datasets from adults (UK Biobank, N = 500 T1-weighted and diffusion scans) and neonates (Developing Human Connectome Project, N = 752 T2-weighted scans), to show how the estimator of KL divergence implemented in MIND is potentially influenced or biased by five properties of input MRI feature maps: (i) their smoothness; (ii) the proportion of identical values; (iii) analysis in native or common space and the choice of vertex mesh resolution; (iv) parcellation choice; and (v) covariance between input features. We offer principled and practical guidance for investigators wanting to specify and implement the MIND processing pipeline that is best suited to the constraints and opportunities of the MRI data available to them. These recommendations outline which pipeline steps should be used sparingly, such as vertex map smoothing; which should be used with informed caution, such as parcellation choice or vertex mesh resampling; and which could be newly implemented for more robust estimation of MIND, such as the use of principal component analysis to preprocess multivariate MRI features. To support further development of structural MRI similarity network analysis, and wider adoption of robust MIND methods, we also publish the code used to generate the results in this paper as an open resource.

neuroscience↗

bioRxiv2018

A fungal ribonuclease-like effector protein inhibits plant host ribosomal RNA degradation

The biotrophic fungal pathogen Blumeria graminis causes the powdery mildew disease of cereals and grasses. Proteins with a predicted ribonuclease (RNase)-like fold (termed RALPHs) comprise the largest set of secreted effector candidates within the B. graminis f. sp. hordei genome. Their exceptional abundance suggests they play crucial functions during pathogenesis. We show that transgenic expression of RALPH CSEP0064/BEC1054 increases susceptibility to infection in monocotyledenous and dicotyledonous plants. CSEP0064/BEC1054 interacts in planta with five host proteins: two translation elongation factors (eEF1 and eEF1{gamma}), two pathogenesis-related proteins (PR5 and PR10) and a glutathione-S-transferase. We present the first crystal structure of a RALPH, CSEP0064/BEC1054, demonstrating it has an RNase-like fold. The protein interacts with total RNA and weakly with DNA. Methyl jasmonate levels modulate susceptibility to aniline-induced host RNA fragmentation. In planta expression of CSEP0064/BEC1054 reduces the formation of this RNA fragment. We propose that CSEP0064/BEC1054 is a pseudoenzyme that binds to host ribosomes, thereby inhibiting the action of plant ribosome-inactivating proteins that would otherwise lead to host cell death, an unviable interaction and demise of the fungus.

plant biology↗

bioRxiv2016

Detection of long repeat expansions from PCR-free whole-genome sequence data

Identifying large repeat expansions such as those that cause amyotrophic lateral sclerosis (ALS) and Fragile X syndrome is challenging for short-read (100-150 bp) whole genome sequencing (WGS) data. A solution to this problem is an important step towards integrating WGS into precision medicine. We have developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3,001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Taking the RP-PCR calls as the ground truth, our WGS-based method identified pathogenic repeat expansions with 98.1% sensitivity and 99.7% specificity. Further inspection identified that all 11 conflicts were resolved as errors in the original RP-PCR results. Compared against this updated result, ExpansionHunter correctly classified all (212/212) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2,786/2,789) of the wild type samples were correctly classified as wild type by this method with the remaining two identified as possible expansions. We further applied our algorithm to a set of 144 samples where every sample had one of eight different pathogenic repeat expansions including examples associated with fragile X syndrome, Friedreichs ataxia and Huntingtons disease and correctly flagged all of the known repeat expansions. Finally, we tested the accuracy of our method for short repeats by comparing our genotypes with results from 860 samples sized using fragment length analysis and determined that our calls were >95% accurate. ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

bioinformatics↗