Biology subjects

Gibson, G.

Publications and source records attributed to Gibson, G..

27 records · Page 2Linked to original sources

bioRxiv2019

Improving Probabilistic Infectious Disease Forecasting Through Coherence

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 90% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting systems geographical hierarchy. Author SummarySeasonal influenza causes a significant public health burden nationwide. Accurate influenza forecasting may help public health officials allocate resources and plan responses to emerging outbreaks. The U.S. Centers for Disease Control and Prevention (CDC) reports influenza data at multiple geographical units, including regionally and nationally, where the national data are by construction a weighted sum of the regional data. In an effort to improve influenza forecast accuracy across all models submitted to the CDCs annual flu forecasting challenge, we examined the effect of imposing this geographical constraint on the set of independent forecasts, made publicly available by the CDC. We developed a novel method to transform forecast densities to obey the geographical constraint that respects the correlation structure between geographical units. This method showed consistent improvement across 90% of models and that held when stratified by targets and test seasons. Our method can be applied to other forecasting systems both within and outside an infectious disease context that have a geographical hierarchy.

bioinformatics↗

bioRxiv2019

Succinylation Links Metabolic Reductions to Amyloid and Tau Pathology

Abnormalities in glucose metabolism and misfolded protein deposits composed of the amyloid-{beta} peptide (A{beta}) and tau are the three most common neuropathological hallmarks of Alzheimers disease (AD), but their relationship(s) to the disease process or to each other largely remains unclear. In this report, the first human brain quantitative lysine succinylome together with a global proteome analysis from controls and patients reveals that lysine succinylation contributes to these three key AD-related pathologies. Succinylation, a newly discovered protein post-translational modification (PTM), of multiple proteins, particularly mitochondrial proteins, declines with the progression of AD. In contrast, amyloid precursor protein (APP) and tau consistently exhibit the largest AD-related increases in succinylation, occurring at specific sites in AD brains but never in controls. Transgenic mouse studies demonstrate that succinylated APP and succinylated tau are detectable in the hippocampus concurrent with A{beta} assemblies in the oligomer and insoluble fiber assembly states. Multiple biochemical approaches revealed that succinylation of APP alters APP processing so as to promote A{beta} accumulation, while succinylation of tau promotes its aggregation and impairs its microtubule binding ability. Succinylation, therefore, is the first single PTM that can be added in parallel to multiple substrates, thereby promoting amyloidosis, tauopathy, and glucose hypometabolism. These data raise the possibility that, in order to show meaningful clinical benefit, any therapeutic and/or preventative measures destined for success must have an activity to either prevent or reverse the molecular pathologies attributable to excess succinylation.

neuroscience↗

bioRxiv2018

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

The transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool \"TIGAR\" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

genetics↗

bioRxiv2018

Profiling gene expression of the host response to a Plasmodium vivax irradiated sporozoite immunization and infectious challenge

The development of vaccines that provide sterile protection against human malaria is a major global public health priority requiring a better understanding of the mechanisms involved in natural and vaccine-induced sterile immunity. RNAseq was used to profile gene expression of peripheral blood samples from 12 Duffy positive (Fy+) (Plasmodium vivax susceptible) volunteers enrolled in a phase 2 trial who were vaccinated with radiation attenuated P. vivax sporozoites (RAS; 5 were protected, 7 not) and from 5 Fy- (P. vivax resistant) volunteers exposed to mosquitoes harboring live non-attenuated sporozoites. Blood samples were obtained before and after immunization as well as after controlled infection with live P vivax sporozoites. The most profound changes in gene expression were observed between baseline and post-challenge, with 97 distinct signatures differentiating protected and not protected Fy+ individuals. Differentiation was also observed between Fy- and Fy+ protected individuals, notably with downregulation of multiple inflammatory responses as well as extracellular matrix-related gene activity. Analysis of transcriptional modules shows that both B-cell and T-cell signaling are reduced while cell cycle regulation, interferon response, and other informative signatures are elevated in individuals who are not protected against malaria. An asymptomatic individual had an intermediate profile indicative of differential transcription associated with pathology and symptomology. Systems biology thus provides insight into how whole malaria-attenuated sporozoites prime the immune system to protect against malaria, as well as the transcription responses that are associated with sterile protection.\n\nAUTHOR SUMMARYTranscriptomic analysis has shed considerable light on the molecular mechanisms of vaccine efficacy against a variety of viral pathogens, but has not yet been used in relation to parasite vaccinology. Here we describe whole peripheral blood gene expression profiling to contrast the contributions of myeloid and lymphoid immune responses to vaccine-induced and natural protection against Plasmodium vivax- mediated malaria. Two modes of modular bioinformatic analyses are applied to RNAseq data from pre- and post-vaccination samples from 19 volunteer participants in a clinical trial investigating the efficacy of an irradiated sporozoite vaccine. During active infection, both B- and T-cell activity are reduced in individuals who are not protected by vaccination relative to those who are protected. Lack of the Fy antigen prevents entry of parasite into red blood cells, thereby providing natural clinical immunity, but we also document differences in T-cell and neutrophil activity in these individuals relative to those protected by irradiation of the parasite. One individual who was asymptomatic for malaria but had blood parasite despite vaccination showed an intermediate response indicating how systems biology approaches can distinguish gene expression associated with pathology and infection.

genomics↗

bioRxiv2018

The Medical Genome Reference Bank: Whole genomes and phenotype of 2,570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2,570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analysed the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. Individuals in the MGRB had fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK BioBank cohorts, consistent with risk depletion. Pervasive age-related somatic changes were correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. This research has been conducted using the UK Biobank Resource under Application Number 17984.

genomics↗

bioRxiv2018

The effect of X-linked dosage compensation on complex trait variation

Quantitative genetics theory predicts that X-chromosome dosage compensation between sexes will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of dosage compensation in complex trait variation in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and in 1,600 gene expression traits from a sample of 2,000 individuals as well as across-tissue gene expression from the GTEx resource. We find, on average, twice as much genetic variation for complex traits due to X-linked loci in males compared to females, consistent with a negligible effect of predicted escape from X-inactivation on complex trait variation across traits and also detect biologically relevant X-linked heterogeneity between the sexes for a number of complex traits.

genetics↗

bioRxiv2018

Holographic optical trapping Raman micro-spectroscopy for interacting live cells

We present a combined holographic optical tweezers and confocal Raman spectroscopy instrument which allows fast, flexible, and interactive manipulation with molecular measurement of interacting live cell systems. Multiple laser foci created using a spatial light modulator are simultaneously used for optical trapping and spontaneous Raman spectroscopy. To enable confocal Raman measurements with high spectral resolution, a digital micro-mirror device was used to generate reflective pinholes which are matched to each laser trap. We demonstrate this unique capability by initiating complex interactions between multiple live cells whilst non-invasively acquiring Raman spectra of the processes with high spatial, spectral, and temporal resolution.

biophysics↗

bioRxiv2017

Widespread signatures of negative selection in the genetic architecture of human complex traits

Estimation of the joint distribution of effect size and minor allele frequency (MAF) for genetic variants is important for understanding the genetic basis of complex trait variation and can be used to detect signature of natural selection. We develop a Bayesian mixed linear model that simultaneously estimates SNP-based heritability, polygenicity (i.e. the proportion of SNPs with nonzero effects) and the relationship between effect size and MAF for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752), and show that on average across 28 traits, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (p < 0.05/28 =1.8x10-3) signatures of natural selection for 23 out of 28 traits including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. We further apply the method to 27,869 gene expression traits (N = 1,748), and identify 30 genes that show significant (p < 2.3x10-6) evidence of natural selection. All the significant estimates of the relationship between effect size and MAF in either complex traits or gene expression traits are consistent with a model of negative selection, as confirmed by forward simulation. We conclude that natural selection acts pervasively on human complex traits shaping genetic variation in the form of negative selection.

genetics↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗