Biology subjects

French, L.

Publications and source records attributed to French, L..

22 records · Page 2Linked to original sources

bioRxiv2017

Transcriptomic characterization of MRI contrast, focused on the T1-w/T2-w ratio in the cerebral cortex

Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, at the macromolecular scale, we lack a comprehensive understanding of what determines MR signal contrast. To address this gap, we used genome-wide measurements to correlate gene expression with MR signal intensity across the cortex in the Allen Human Brain Atlas. We focused on the ratio of T1-weighted and T2-weighted intensities (T1-w/T2-w) which is considered to be a useful proxy for myelin content. Positive correlations between myelin-associated genes and the ratio supported its use as a myelin marker. However, stronger correlations were observed for neurofilaments, and genes linked to the production of formaldehyde (which cross-links protein to create larger molecules). There was also an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1-w/T2-w values. Oligodendrocyte gene markers were strongly correlated but this was not driven by myelin-associated genes, suggesting this signal is from non-myelinating satellite oligodendrocytes. We find the strongest support for the previous finding of high aerobic glycolysis in regions with low T1-w/T2-w ratio. Specifically, many mitochondrial genes were negatively correlated with T1-w/T2-w ratio. Genes up-regulated by pH in the brain were also highly correlated with the ratio, suggesting the pH gradient in mitochondria may explain the aerobic glycolysis association. Expression of protease subunit genes was also inversely associated with the ratio, in agreement with the protein mass correlation. While we corroborate associations with myelin and synaptic plasticity, differences in the T1-w/T2-w ratio appear to be more attributable to molecule size, satellite oligodendrocyte proportion, mitochondrial number, alkalinity, and axon caliber. Using disease-associated gene lists, we observed an enrichment of negative T1-w/T2-w ratio correlations with human immunodeficiency virus (HIV) associated genes. Expanding our analysis to the whole brain results in strong positive T1-w/T2-w associations for immune system, inflammatory disease, and microglial genes. In contrast, neuron markers and synaptic plasticity genes are negatively enriched. Lastly, our results vary little when our analysis is performed on T1-w or inverted T2-w intensities alone, possibly because the noise reduction properties of the ratio are not needed for postmortem brain scans. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain.

neuroscience↗

bioRxiv2017

Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

An organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.\n\nHere, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.\n\nWe found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

neuroscience↗

bioRxiv2017

A New Mechanism for Cost Savings in NHS Prescribing: Minimising "Price-Per-Unit"

BackgroundMinimising prescription costs while maintaining quality is a core element of delivering high value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost; and compare these with potential savings from an established method (generic prescribing).\n\nMethodsWe used English NHS Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price-per-unit (e.g. pill, ml) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile, to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching; and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable.\n\nResultsWe identified a theoretical maximum of {pound}410M of savings over 12 months. {pound}273M of these savings were for individual prescribing changes worth over {pound}50 per practice per month; this compares favorably with generic switching, where only {pound}35M of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents ({pound}12M), fluticasone propionate ({pound}9M) and venlafaxine ({pound}8M). Approximately half of all savings were deemed practically achievable.\n\nDiscussionWe have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form. However savings could potentially be achieved more simply through national policy change.\n\nAbbreviations

epidemiology↗

bioRxiv2016

Mapping Autosomal Recessive Intellectual Disability: Combined Microarray and Exome Sequencing Identifies 26 Novel Candidate Genes in 192 Consanguineous Families

Approximately 1% of the global population is affected by intellectual disability (ID), and the majority receive no molecular diagnosis. Previous studies have indicated high levels of genetic heterogeneity, with estimates of more than 2500 autosomal ID genes, the majority of which are autosomal recessive (AR). Here, we combined microarray genotyping, homozygosity-by-descent (HBD) mapping, copy number variation (CNV) analysis, and whole exome sequencing (WES) to identify disease genes/mutations in 192 multiplex Pakistani and Iranian consanguineous families with non-syndromic ID. We identified definite or candidate mutations (or CNVs) in 51% of families in 72 different genes, including 26 not previously reported for ARID. The new ARID genes include nine with loss-of-function mutations (ABI2, MAPK8, MPDZ, PIDD1, SLAIN1, TBC1D23, TRAPPC6B, UBA7, and USP44), and missense mutations include the first reports of variants in BDNF or TET1 associated with ID. The genes identified also showed overlap with de novo gene sets for other neuropsychiatric disorders. Transcriptional studies showed prominent expression in the prenatal brain. The high yield of AR mutations for ID indicated that this approach has excellent clinical potential and should inform clinical diagnostics, including clinical whole exome and genome sequencing, for populations in which consanguinity is common. As with other AR disorders, the relevance will also apply to outbred populations.

genetics↗