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Knott, S.

Publications and source records attributed to Knott, S..

7 recordsLinked to original sources

Tryptophan degradation by intestinal Bacteroides induces anti-tumor immunity and limits melanoma growth

Defining mechanisms used by gut microbiota to control anti-tumor immunity may offer novel therapeutic modalities. Here, we demonstrate that Bacteroides rodentium and closely related Bacteroides uniformis species induce anti-tumor immunity and limit melanoma development when colonized in either germ-free (GF) mice, mice with a complex microbiome, or WT mice. Enhanced CD8+ T cell infiltration seen in tumors of mice harboring B. rodentium coincided with increased expression of immune-stimulating pathways and activation of bone marrow-derived dendritic cells that were co-cultured with the B. rodentium secretome. Metabolomic analyses of cecal samples from GF mice colonized with Altered Shedlar Flora (ASF) plus B. rodentium revealed lower tryptophan levels than in ASF-colonized controls, and WT mice fed a tryptophan-deficient diet exhibited inhibition of melanoma development. In silico genomic reconstruction of metabolic pathways revealed that both B. rodentium and B. uniformis harbor tryptophanase A (TnaA) and aromatic amino transferase (ArAT) genes, both of which function in tryptophan degradation. Administration of a B. uniformis harboring TnaA mutant failed to inhibit melanoma growth in gnotobiotic mice. Notably, administration of indoles, but not kynurenines, also effectively inhibited melanoma development, increasing immune cell infiltration into the tumors. Correspondingly, levels of bacterially encoded tryptophan-degrading enzymes were higher in cohorts of melanoma patients responding to immune checkpoint blockade. These findings highlight a novel mechanism of anti-tumor immunity and tumor growth inhibition dependent on the tryptophan degradation products, indoles, produced by intestinal Bacteroides species.

cancer biology↗

Evaluating regional heritability mapping methods for identifying QTLs in a wild population of Soay sheep

Regional heritability mapping (RHM) is a method that estimates the heritability of genomic segments that may contain both common and rare variants affecting a complex trait. We compared three RHM methods: SNP-RHM, which uses genomic relationship matrices (GRMs) based on SNP genotypes; Hap-RHM, which uses GRMs based on haplotypes; and SNHap-RHM, which uses both SNP-based and haplotype-based GRMs jointly. We applied these methods to data from a wild population of sheep, analysed eleven polygenic morphometric traits and compared the results with previous genome wide association analyses (GWAS). We found that whilst the inclusion of the regional matrix did not explain significant variation for all regions that were associated with trait variation using GWAS, it did for several regions that were not previously associated with trait variation.

genomics↗

Investigating pedigree- and SNP-associated components of heritability in a wild population of Soay sheep

Estimates of narrow sense heritability derived from genomic data that contain related individuals may be biased due to the within-family effects such as dominance, epistasis and common environmental factors. However, for many wild populations, removal of related individuals from the data would result in small sample sizes. In 2013, Zaitlen et al. proposed a method to estimate heritability in populations that include close relatives by simultaneously fitting an identity-by-state genomic relatedness matrix (GRM) and an identity-by-descent GRM. The IBD GRM is identical to the IBS GRM, except relatedness estimates below a specified threshold are set to 0. We applied this method to a sample of 8557 wild Soay sheep from St. Kilda, with genotypic information for 419,281 single nucleotide polymorphisms to investigate polygenic and monogenic traits. We also implemented a variant of the model in which the IBD GRM was replaced by a GRM constructed from SNPs with low minor allele frequency to examine whether any additive genetic variance is captured by rare alleles. Each model was compared to an animal model with a single GRM based on all genotyped markers (the IBS GRM) using a log likelihood ratio test. Whilst the inclusion of the IBD GRM did not significantly improve the fit of the model for the monogenic traits, it improved the fit for some of the polygenic traits, suggesting that dominance, epistasis and/or common environment not already captured by the non-genetic random effects fitted in our models may influence these traits.

genetics↗

The impact of SNP density on quantitative genetic analyses of body size traits in a wild population of Soay sheep

Understanding the genetic architecture underpinning quantitative traits in wild populations is pivotal to understanding the processes behind trait evolution. The animal model is a popular method for estimating quantitative genetic parameters such as heritability and genetic correlation and involves fitting an estimate of relatedness between individuals in the study population. Genotypes at genome-wide markers can be used to estimate relatedness; however, relatedness estimates vary with marker density, potentially affecting results. Increasing density of markers is also expected to increase the power to detect quantitative trait loci (QTL). In order to understand how the density of genetic markers affects the results of quantitative genetic analyses, we estimated heritability and performed genome-wide association studies (GWAS) on five body size traits in an unmanaged population of Soay sheep using two different SNP densities: a dataset of 37,037 genotyped SNPs, and an imputed dataset of 417,373 SNPs. Heritability estimates did not differ between the two SNP densities, but the high-density imputed SNP dataset revealed four new SNP-trait associations that were not found with the lower density dataset, as well as confirming all previously-found QTL. We also demonstrated that fitting fixed and random effects in the same step as performing GWAS is a more powerful approach than pre-correcting for covariates in a separate model.

genomics↗

SNP and Haplotype Regional Heritability Mapping (SNHap-RHM): joint mapping of common and rare variation affecting complex traits

We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold (p-value < 1 x 10-5) for MDD. These significant regions have genes mapped to within 400kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability. Author SummaryIn untangling the genetic contribution to observed phenotype differences, situations can arise where causative variants might be tagged by haplotypes and not in linkage disequilibrium with individual SNPs. This scenario is likely for relatively newly arisen and rarer variants. Here, we propose a regional heritability method, SNHap-RHM, that jointly fits haplotype-based and SNP-based genomic relationship matrices (GRMs) to capture genomic regions harbouring rare variants that the SNP-based GRMs might miss. By analysing ~20,000 Scottish individuals, we show by simulation that the two GRMs are very specific to the type of variant effects they can capture; - the haplotype-based GRMs specifically target haplotype effects which are mostly missed by SNP-based GRMs and vice versa. Applying the method to height and major depressive disorder led to the uncovering of regions in the genome that harbour genes associated with those traits. These results are uniquely important because first they confirm that effects tagged by haplotypes may be missed by conventional SNP-based methods. Secondly, our method, SNHap-RHM, presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability.

genomics↗

SmartCodes : functionalized barcodes that enable targeted retrieval of clonal lineages from a heterogeneous population

Molecular barcoding has provided means to link genotype to phenotype, to individuate cells in single-cell analyses, to enable the tracking of evolving lineages, and to facilitate the analysis of complex mixtures containing phenotypically distinct lineages. To date, all existing approaches enable retrospective associations to be made between characteristics and the lineage harbouring them, but provide no path toward isolating or manipulating those lineages within the complex mixture. Here, we describe a strategy for creating functionalized barcodes that enable straightforward manipulation of lineages within complex populations of cells, either marking and retrieval of selected lineages, or modification of their phenotype within the population, including their elimination. These \"SmartCodes\" rely on a simple CRISPR-based, molecular barcode reader that can switch measurable, or selectable markers, on or off in a binary fashion. While this approach could have broad impact, we envision initial approaches to the study of tumour heterogeneity, focused on issues of tumour progression, metastasis, and drug resistance.

molecular biology↗

Psychosis and the level of mood incongruence in Bipolar Disorder are related to genetic liability for Schizophrenia

ImportanceBipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes/mechanisms.\n\nObjectivesTo investigate the relationship between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRS) and psychotic presentations of BD, using clinical descriptions which consider both occurrence and level of mood-incongruent psychotic features.\n\nDesignCase-control design: using multinomial logistic regression, to estimate differential associations of PRS across categories of cases and controls.\n\nSettings & Participants4399 BDcases, mean [sd] age-at-interview 46[12] years, of which 2966 were woman (67%) from the BD Research Network (BDRN) were included in the final analyses, with data for 4976 schizophrenia cases and 9012 controls from the Type-1 diabetes genetics consortium and Generation Scotland included for comparison.\n\nExposureStandardised PRS, calculated using alleles with an association p-value threshold < 0.05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, adjusted for the first 10 population principal components and genotyping-platform.\n\nMain outcome measureMultinomial logit models estimated PRS associations with BD stratified by (1) Research Diagnostic Criteria (RDC) BD subtypes (2) Lifetime occurrence of psychosis.(3) Lifetime mood-incongruent psychotic features and (4) ordinal logistic regression examined PRS associations across levels of mood-incongruence. Ratings were derived from the Schedule for Clinical Assessment in Neuropsychiatry interview (SCAN) and the Bipolar Affective Disorder Dimension Scale (BADDS).\n\nResultsAcross clinical phenotypes, there was an exposure-response gradient with the strongest PRS association for schizophrenia (RR=1.94, (95% C.1.1.86, 2.01)), then schizoaffective BD (RR=1.37, (95% C.I. 1.22, 1.54)), BD I (RR= 1.30, (95% C.I. 1.24, 1.36)) and BD II (RR=1.04, (95% C.1. 0.97, 1.11)). Within BD cases, there was an effect gradient, indexed by the nature of psychosis, with prominent mood-incongruent psychotic features having the strongest association (RR=1.46, (95% C.1.1.36, 1.57)), followed by mood-congruent psychosis (RR= 1.24, (95% C.1. 1.17, 1.33)) and lastly, BD cases with no history of psychosis (RR= 1.09, (95% C.1. 1.04, 1.15)).\n\nConclusionWe show for the first time a polygenic-risk gradient, across schizophrenia and bipolar disorder, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

genetics↗