Biology subjects

Holmes, M.

Publications and source records attributed to Holmes, M..

4 recordsLinked to original sources

bioRxiv2018

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.\n\nSummaryResting State functional magnetic resonance imaging (Rs-fMRI), previously utilized to predict lateralization of seizure foci in temporal lobe epilepsy (TLE), is utilized to determine potential mechanisms and biomarkers for treatment-resistant and well-controlled unilateral TLE. We found significant differences in thalamo-hippocampal functional connectivity between treatment-resistant and well-controlled TLE patients. Differences in functional connectivity were focused to thalamo-hippocampal connections and more pronounced in the hemisphere ipsilateral to seizure foci. Aberrant functional connectivity patterns as measured by Rs-fMRI could thus serve as biomarkers for treatment response in TLE.

neuroscience↗

bioRxiv2018

PCSK9 genetic variants, life-long lowering of LDL-cholesterol and cognition: a large-scale Mendelian randomization study

AimsPCSK9 inhibitors lower LDL cholesterol and are efficacious at reducing risk of vascular disease, however questions remain about potential adverse effects on cognitive function. We examined the association of LDL cholesterol-lowering genetic variants in PCSK9 with continuous measures of cognitive ability\n\nMethods and ResultsSix independent SNPs in PCSK9 were used in up to 337,348 individuals from the UK Biobank who underwent measures of cognitive ability (fluid reasoning, reaction time, trial making test and digit symbol coding. Scaled to a 50mg/dL lower LDL cholesterol, the PCSK9 allele score was associated with a lower risk of CHD (odds ratio 0.73; 95% CI: 0.60 to 0.90, P = 0.003). The scaled PCSK9 allele score nominally associated with worse log reaction time (0.04 standard deviations; 95%CI: 0.00, 0.08; P=0.038). Although no strong associations of the PCSK9 allele score were identified with any cognitive trait, the imprecision around the estimates meant that we could not exclude a similar magnitude of effect of genetic inhibition of PCSK9 to that seen with established risk factors, including APOEe4 or smoking status for any of the individual cognition traits. Point estimates for the PCSK9 allele score and cognition traits were all on the harmful side of unity.\n\nConclusionsUsing currently available data in UK Biobank, we are not able to rule out meaningful associations of PCSK9 genetic variants with cognition traits. These data highlight the need for further large-scale genetic analyses and, in parallel, continued pharmacovigilance for patients currently treated with PCSK9 inhibitors.

epidemiology↗

bioRxiv2017

High fidelity detection of crop biomass QTL from low-cost imaging in the field

Above-ground biomass production is a key target for studies of crop abiotic stress tolerance, disease resistance and yield improvement. However, biomass is slow and laborious to evaluate in the field using traditional destructive methods. High-throughput phenotyping (HTP) is widely promoted as a potential solution that can rapidly and non-destructively assess plant traits by exploiting advances in sensor and computing technology. A key potential application of HTP is for quantitative genetics studies that identify loci where allelic variation is associated with variation in crop production. And, the value of performing such studies in the field, where environmental conditions match that of production farming, is recognized. To date, HTP of biomass productivity in field trials has largely focused on expensive and complex methods, which - even if successful - will limit their use to a subset of wealthy research institutions and companies with extensive research infrastructure and highly-trained personnel. Even with investment in ground vehicles, aerial vehicles and gantry systems ranging from thousands to millions of dollars, there are very few examples where Quantitative trait loci (QTLs) detected by HTP of biomass production in a field-grown crop are shown to match QTLs detected by direct measures of biomass traits by destructive harvest techniques. Until such proof of concept for HTP proxies is generated it is unlikely to replace existing technology and be widely adopted. Therefore, there is a need for methods that can be used to assess crop performance by small teams with limited training and at field sites that are remote or have limited infrastructure. Here we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population. This provides a case study of a HTP technology that can deliver results for QTL mapping without high costs or complexity.

plant biology↗

bioRxiv2016

Cannabis use and risk of schizophrenia:a Mendelian randomization study

Cannabis use is observationally associated with an increased risk of schizophrenia, however whether the relationship is causal is not known. To determine the nature of the association between cannabis use on risk of schizophrenia using Mendelian randomization (MR) analysis, we used ten genetic variants previously identified to associate with cannabis use in 32,330 individuals. Genetic variants were used in a MR analyses of the association of genetically determined cannabis on risk of schizophrenia in 34,241 cases and 45,604 controls from predominantly European descent. Estimates from MR were compared to a metaanalysis of observational studies reporting effect estimates for ever use of cannabis and risk of schizophrenia or related disorders. Genetically determined use of cannabis was associated with increased risk of schizophrenia (OR of schizophrenia for users vs. non-users of cannabis: 1.37; 95%CI, 1.09 to 1.67; P-value=0.007). The corresponding estimate from observational analysis was 1.50 (95% CI, 1.10 to 2.00; P-value for heterogeneity = 0.88). The genetic instrument did not show evidence of pleiotropy on MR-Egger (Egger test, P-value=0.292) nor on multivariable MR accounting for tobacco exposure (OR of schizophrenia for users vs. nonusers of cannabis, adjusted for ever vs. never smoker: 1.41; 95% CI, 1.09-1.83). Furthermore, the causal estimate remained robust to sensitivity analyses. These findings strongly support a causal association between genetically determined use of cannabis and risk of schizophrenia. Such robust evidence may inform public health message about the risks of cannabis use, especially regarding its potential mental health consequences.

epidemiology↗