Biology subjects

Williams, D. M.

Publications and source records attributed to Williams, D. M..

3 recordsLinked to original sources

bioRxiv2018

A neuron-optimized CRISPR/dCas9 activation system for robust and specific gene regulation

Recent developments in CRISPR-based gene editing have provided new avenues to interrogate gene function. However, application of these tools in the central nervous system has been delayed due to difficulties in transgene expression in post-mitotic neurons. Here, we present a highly efficient, neuron-optimized dual lentiviral CRISPR-based transcriptional activation (CRISPRa) system to drive gene expression in primary neuronal cultures and the adult brain of rodent model systems. We demonstrate robust, modular, and tunable induction of endogenous target genes as well as multiplexed gene regulation necessary for investigation of complex transcriptional programs. CRISPRa targeting unique promoters in the complex multi-transcript gene Brain-derived neurotrophic factor (Bdnf) revealed both transcript- and genome-level selectivity of this approach, in addition to highlighting downstream transcriptional and physiological consequences of Bdnf regulation. Finally, we illustrate that CRISPRa is highly efficient in vivo, resulting in increased protein levels of a target gene in diverse brain structures. Taken together, these results demonstrate that CRISPRa is an efficient and selective method to study gene expression programs in brain health and disease.

neuroscience↗

bioRxiv2017

A frailty index for UK Biobank participants

BackgroundFrailty indices (FIs) measure variation in health between aging individuals. Researching FIs in resources with large-scale genetic and phenotypic data will provide insights into the causes and consequences of frailty. Thus, we aimed to develop an FI using UK Biobank data, a cohort study of 500,000 middle-aged and older adults.\n\nMethodsAn FI was calculated using 49 self-reported questionnaire items on traits covering health, presence of diseases and disabilities, and mental wellbeing, according to standard protocol. We used multiple imputation to derive FI values for the entire eligible sample in the presence of missing item data (N =500,336). To validate the measure, we assessed associations of the FI with age, sex, and risk of all-cause mortality (follow-up [≤] 9.7 years) using linear and Cox proportional hazards regression models.\n\nResultsMean FI in the cohort was 0.125 (standard deviation = 0.075), and there was a curvilinear trend towards higher values in older participants. FI values were also marginally higher on average in women than men. In survival models, 10% higher baseline frailty (i.e. a 0.1 FI increment) was associated with higher risk of death (hazard ratio (HR) = 1.65; 95% confidence interval: 1.62, 1.68). Associations were stronger in younger participants than in old, and in men compared to women (HRs: 1.72 vs. 1.56, respectively).\n\nConclusionsThe FI is a valid measure of frailty in UK Biobank. The cohorts data are open-access for researchers to use, and we provide script for deriving this tool to facilitate future studies on frailty.

epidemiology↗

bioRxiv2017

Association of pre-pregnancy body mass index with future offspring metabolic profile: findings from three independent European birth cohorts

BackgroundA high proportion of women start pregnancy overweight/obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives, and, thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial (genetic, lifestyle, socioeconomic) characteristics. We aimed to determine if associations between maternal body mass index (BMI) with offspring systemic metabolite profile are causal via intrauterine mechanisms or familial factors.\n\nMethods and FindingsWe used one and two-stage individual participant data (IPD) metaanalysis, and a negative-control (paternal BMI) to examine the association between maternal prepregnancy BMI and offspring serum metabolome from three European birth cohorts (offspring age at metabolite assessment 16, 17 and 31 years). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from one-stage IPD meta-analysis (N=5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with VLDL-lipoproteins, VLDL-C, VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with HDL-lipoprotein, HDL-diameter, HDL-C, HDL2-C and HDL3-C (all P<0.003). Stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations, however there was no strong statistical evidence for heterogeneity between them (all bootstrap P >0.003, equivalent to 0.05 after accounting for multiple testing). Results were similar in each individual cohort, and in the two-stage analysis. Offspring BMI showed similar patterns of crosssectional association with metabolic profiles as for parental pre-pregnancy BMI associations, but with greater magnitudes. Adjustment of the parent BMI-offspring metabolite associations for offspring BMI suggested the parental associations were largely due to the association of parental BMI measures with offspring BMI.\n\nConclusionOur findings suggest that maternal BMI-offspring metabolome associations are likely to be largely due to shared genetic or familial lifestyle confounding, rather than intrauterine programming mechanisms. They do not support the introduction of measures to reduce maternal BMI in order to prevent adverse offspring cardio-metabolic health.

epidemiology↗