Biology subjects

Lumley, T.

Publications and source records attributed to Lumley, T..

6 recordsLinked to original sources

bioRxiv2025

Using a "one-strain-many-compounds" (OSMAC) approach to screen a collection of diverse fungi from Aotearoa New Zealand for antibacterial activity against Escherichia coli

There is an urgent need to identify new chemical compounds with novel modes of action to help manage the antimicrobial resistance crisis. Fungi are prolific producers of secondary metabolites, including those with antimicrobial properties, and contain biosynthetic gene clusters that awaken only under certain growth conditions. In recent years, a wealth of novel fungal biosynthetic pathways and compounds have been identified, suggesting fungi remain a viable source for developing new antimicrobials. The International Collection of Microorganisms from Plants (ICMP) contains thousands of fungi and bacteria primarily sourced from Aotearoa New Zealand. Here, we report the results of our efforts to screen 32 fungal ICMP isolates for activity against Escherichia coli, a leading cause of deaths attributable to antimicrobial resistance. We used a one strain-many compounds (OSMAC) approach, growing the ICMP isolates on seven different media with different pH and various carbon and nitrogen sources. We also tested the isolates for activity at various ages. Our results indicate that several of the tested fungi possess anti-E. coli activity and are suitable for further study, including the aero-aquatic Ascomycete Hyaloscypha sp. ICMP 16864. Our results also provide further strong evidence for the impact of media on both fungal growth and bioactivity.

microbiology↗

bioRxiv2022

Beyond consensus sequence: a quantitative scheme for inferring transmission using deep sequencing in a bacterial transmission model

Genomic surveillance provides a data source complementary to contact tracing to resolve putative transmission chains. However, the role of within-host diversity in transmission is understudied due to a lack of experimental and clinical datasets that capture within-host diversity in both donors and recipients. Here, we assess the utility of deep-sequenced genomic surveillance within a mouse transmission model where the gastrointestinal pathogen Citrobacter rodentium was controllably spread during co-housing of infected and naive animals. We observed that within-host variants were maintained over multiple transmission steps until fixation or elimination. We present a model for inferring the likelihood that a given pair of samples are linked by transmission, by comparing the allelic frequency at variant genomic loci. Our data affirm that within-host single nucleotide variants (iSNVs) can repeatedly pass from donor to recipient along the transmission chain, and the mere sharing of iSNVs between different transmission pairs offers limited confidence in identifying a transmission pair. Beyond the presence and absence of within-host variants, we show that differences arising in the relative abundance of iSNVs can infer transmission pairs with high precision. An important component of our approach is that the inference is based solely on sequence data, without incorporating epidemiological or demographic data for context. Our model, which substantially reduces the number of comparisons a contact tracer needs to consider, may enhance the accuracy of contact tracing and other epidemiological processes, including early detection of emerging transmission clusters.

genomics↗

bioRxiv2018

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Clinical studies of non-communicable diseases identify multimorbidities that reflect our relatively limited fixed metabolic capacity. Despite the fact that we have [~]24000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. We created a \"multimorbidity atlas\" of traits based on pleiotropy of spatially regulated genes using convex biclustering. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analysed 20,782 variants (p < 5 x 10-6) associated with 1,351 phenotypes, to identify 16,248 putative eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of eGenes that are shared between phenotypes identified complex inter-relationships between nominally different phenotype associated SNPs. Notably, the loci at the centre of these inter-relationships were subject to complex tissue and disease specific regulatory effects. The largest cluster, 40 phenotypes that are related to fat and lipid metabolism, inflammatory disorders, and cancers, is centred on the FADS1-FADS3 locus (chromosome 11). Our novel approach enables the simultaneous elucidation of variant interactions with genes that are drivers of multimorbidity and those that contribute to unique phenotype associated characteristics.

bioinformatics↗

bioRxiv2017

A Comprehensive Evaluation of the Genetic Architecture of Sudden Cardiac Arrest

BackgroundSudden cardiac arrest (SCA) accounts for 10% of adult mortality in Western populations. While several risk factors are observationally associated with SCA, the genetic architecture of SCA in the general population remains unknown. Furthermore, understanding which risk factors are causal may help target prevention strategies.\n\nMethodsWe carried out a large genome-wide association study (GWAS) for SCA (n=3,939 cases, 25,989 non-cases) to examine common variation genome-wide and in candidate arrhythmia genes. We also exploited Mendelian randomization methods using cross-trait multi-variant genetic risk score associations (GRSA) to assess causal relationships of 18 risk factors with SCA.\n\nResultsNo variants were associated with SCA at genome-wide significance, nor were common variants in candidate arrhythmia genes associated with SCA at nominal significance. Using cross-trait GRSA, we established genetic correlation between SCA and (1) coronary artery disease (CAD) and traditional CAD risk factors (blood pressure, lipids, and diabetes), (2) height and BMI, and (3) electrical instability traits (QT and atrial fibrillation), suggesting etiologic roles for these traits in SCA risk.\n\nConclusionsOur findings show that a comprehensive approach to the genetic architecture of SCA can shed light on the determinants of a complex life-threatening condition with multiple influencing factors in the general population. The results of this genetic analysis, both positive and negative findings, have implications for evaluating the genetic architecture of patients with a family history of SCA, and for efforts to prevent SCA in highrisk populations and the general community.

genetics↗

bioRxiv2017

A Bayesian approach to multivariate and multilevel modelling with non-random missingness for hierarchical clinical proteomics data

High throughput mass-spectrometry-based proteomics data from clinical studies brings challenges to statistical analysis. The challenges originate from the hierarchical levels of protein abundance data and interactions between clinical study design and experimental design. The non-random missingness of the measurements from a vast amount of information also adds complexity in data analysis. We propose multivariate multilevel models to analyse protein abundances and to handle abundance-dependent missingness within a Bayesian framework. The proposed model enables the variance decomposition at different levels of the data hierarchy and provides shrinkage of protein-level estimates for a group of proteins. A logistic missingness and censored model with informative prior is used to handle incomplete data. Hamiltonian MC/No-U-Turn Sampling and Gibb MCMC algorithms are created to derive the posterior distribution of study parameters; Hamiltonian MC is demonstrated to gain more efficiency for these high-dimensional correlated data. Improvements of the proposed missing data model is compared to the univariate mixed effect model and the multivariate-multilevel model using complete data in a simulated study and a clinical proteomics study. The proposed model framework can be used in other types of data with similar structure and Non Random Missingness mechanism (MNAR).

bioinformatics↗

bioRxiv2016

Sequence kernel association tests for large sets of markers: tail probabilities for large quadratic forms

The Sequence Kernel Association Test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants, that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n x n matrix, for n subjects) has computational complexity proportional to n3. As SKAT is often used when n > 104, this step becomes a major bottleneck in its use in practice. We therefore propose fastSKAT, a new computationally-inexpensive but accurate approximations to the tail probabilities, in which the k largest eigenvalues of a weighted genotype covariance matrix or the largest singular values of a weighted genotype matrix are extracted, and a single term based on the Satterthwaite approximation is used for the remaining eigenval-ues. While the method is not particularly sensitive to the choice of k, we also describe how to choose its value, and show how fastSKAT can automatically alert users to the rare cases where the choice may affect results. As well as providing faster implementation of SKAT, the new method also enables entirely new applications of SKAT, that were not possible before; we give examples grouping variants by topologically assisted domains, and comparing chromosome-wide association by class of histone marker.

bioinformatics↗