Biology subjects

Douglas, J.

Publications and source records attributed to Douglas, J..

27 records · Page 2Linked to original sources

bioRxiv2023

Domain Acquisition by Class I Aminoacyl-tRNA Synthetase Urzymes Coordinated the Catalytic Functions of HVGH and KMSKS Motifs

Leucyl-tRNA synthetase (LeuRS) is a Class I aminoacyl-tRNA synthetase (aaRS) that catalyzes synthesis of leucyl-tRNAleu for codon-directed protein synthesis on the ribosome. Class I aaRS, which were key to the evolution of genetic coding, contain two discrete signature sequences, HIGH and KMSKS, that participate in transition-state stabilization by the entire eleven-enzyme Class I aaRS superfamily. Combinatorial mutagenesis and thermodynamic cycle analyses of these catalytic signatures in full-length Pyrococcus horikoshii LeuRS and the 129-residue urzyme ancestral model generated from it (LeuAC) provide quantitative insight into the evolutionary gain of function induced by acquisition of the anticodon-binding (ABD) and multiple insertion modules in the catalytic domain. The free energy coupling terms, {Delta}({Delta}G{ddagger}), are small and unfavorable for LeuAC, but large and favorable for LeuRS. Thus, the ABD and other insertion modules induce strong cooperativity between the two signature sequences, which are uncoupled in LeuAC. These results further substantiate the authenticity of LeuAC urzyme catalysis and implicate domain motion in catalysis by the full-length LeuRS. Most importantly, the implication that backbone elements of secondary structures achieve a major portion of the overall transition-state stabilization by LeuAC is also consistent with coevolution of the genetic code and metabolic pathways necessary to produce histidine and lysine sidechains. Bullet PointsO_LIThe LeuRS HVGH and KMSKS signature motifs are energetically coupled by -1.6 kcal/mole. C_LIO_LIThe same motifs are anti-coupled by +0.8 kcal/mole in the 129 residue urzyme, LeuAC. C_LIO_LIAncestral Class I aaRS did not require either histidine or lysine for catalysis. C_LI

biochemistry↗

bioRxiv2022

Quantitatively defining species boundaries with moreefficiency and more biological realism

We introduce a widely applicable species delimitation method based on the multispecies coalescent model that is more efficient and more biologically realistic than existing methods. We extend the phylogenetic tree collapse model to the Yule-skyline model, allowing the ancestral speciation rate to vary through time as a smooth piecewise function. Furthermore, we introduce the cutting-edge proposal kernels of StarBeast3 to this model, thus enabling rapid species delimitation on large molecular datasets and allowing the use of relaxed molecular clock models. We validate these methods with genomic sequence data and SNP data, and show they are more efficient than existing methods at achieving parameter convergence during Bayesian MCMC. Lastly, we apply these methods to two datasets and find inconsistencies with the published literature. Our methods are powerful for rapid quantitative testing of species boundaries in large multilocus datasets and are implemented as an open source BEAST 2 package called SPEEDEMON.

evolutionary biology↗

bioRxiv2021

StarBeast3: Adaptive Parallelised Bayesian Inference of the Multispecies Coalescent

As genomic sequence data becomes increasingly available, inferring the phylogeny of the species as that of concatenated genomic data can be enticing. However, this approach makes for a biased estimator of branch lengths and substitution rates and an inconsistent estimator of tree topology. Bayesian multispecies coalescent methods address these issues. This is achieved by embedding a set of gene trees within a species tree and jointly inferring both under a Bayesian framework. However, this approach comes at the cost of increased computational demand. Here, we introduce StarBeast3 - a software package for efficient Bayesian inference of the multispecies coalescent model via Markov chain Monte Carlo. We gain efficiency by introducing cutting-edge proposal kernels and adaptive operators, and StarBeast3 is particularly efficient when a relaxed clock model is applied. Furthermore, gene tree inference is parallelised, allowing the software to scale with the size of the problem. We validated our software and benchmarked its performance using three real and two synthetic datasets. Our results indicate that StarBeast3 is up to one-and-a-half orders of magnitude faster than StarBeast2, and therefore more than two orders faster than *BEAST, depending on the dataset and on the parameter, and is suitable for multispecies coalescent inference on large datasets (100+ genes). StarBeast3 is open-source and is easy to set up with a friendly graphical user interface.

genetics↗

bioRxiv2020

Evolutionary history of cotranscriptional editing inthe paramyxoviral phosphoprotein gene

The phosphoprotein gene of the paramyxoviruses encodes multiple protein products. The P, V, and W proteins are generated by transcriptional slippage. This process results in the insertion of non-templated guanosine nucleosides into the mRNA at a conserved edit site. The P protein is an essential component of the viral RNA polymerase, and is encoded by a direct copy of the gene in the majority of paramyxoviruses. However, in some cases the non-essential V protein is encoded by default and guanosines must be inserted into the mRNA in order to encode P. The number of guanosines inserted can be described by a probability distribution which varies between viruses. In this article we review the nature of these distributions, which can be inferred from mRNA sequencing data, and reconstruct the evolutionary history of cotranscriptional editing in the paramyxovirus family. Our model suggests that, throughout known history of the family, the system has switched from a P default to a V default mode four times; complete loss of the editing system has occurred twice, the canonical zinc finger domain of the V protein has been deleted or heavily mutated a further two times, and the W protein has independently evolved a novel function three times. Finally, we review the physical mechanisms of cotranscriptional editing via slippage of the viral RNA polymerase.

genetics↗

bioRxiv2020

Adaptive dating and fast proposals: revisiting the phylogenetic relaxed clock model

Uncorrelated relaxed clock models enable estimation of molecular substitution rates across lineages and are widely used in phylogenetics for dating evolutionary divergence times. In this article we delved into the internal complexities of the relaxed clock model in order to develop efficient MCMC operators for Bayesian phylogenetic inference. We compared three substitution rate parameterisations, introduced an adaptive operator which learns the weights of other operators during MCMC, and we explored how relaxed clock model estimation can benefit from two cutting-edge proposal kernels: the AVMVN and Bactrian kernels. This work has produced an operator scheme that is up to 65 times more efficient at exploring continuous relaxed clock parameters compared with previous setups, depending on the dataset. Finally, we explored variants of the standard narrow exchange operator which are specifically designed for the relaxed clock model. In the most extreme case, this new operator traversed tree space 40% more efficiently than narrow exchange. The methodologies introduced are adaptive and highly effective on short as well as long alignments. The results are available via the open source optimised relaxed clock (ORC) package for BEAST 2 under a GNU licence (https://github.com/jordandouglas/ORC). Author summaryBiological sequences, such as DNA, accumulate mutations over generations. By comparing such sequences in a phylogenetic framework, the evolutionary tree of lifeforms can be inferred. With the overwhelming availability of biological sequence data, and the increasing affordability of collecting new data, the development of fast and efficient phylogenetic algorithms is more important than ever. In this article we focus on the relaxed clock model, which is very popular in phylogenetics. We explored how a range of optimisations can improve the statistical inference of the relaxed clock. This work has produced a phylogenetic setup which can infer parameters related to the relaxed clock up to 65 times faster than previous setups, depending on the dataset. The methods introduced adapt to the dataset during computation and are highly efficient when processing long biological sequences.

genetics↗

bioRxiv2019

Intensive infection control responses and whole genome sequencing to interrupt and resolve widespread transmission of OXA-181 Escherichia coli in a hospital setting

BackgroundOXA-48-like carbapenemases have become increasingly prevalent in healthcare settings worldwide. Their low-level activity against carbapenems makes them difficult to identify, causing problems for infection control. Here we present an outbreak of Escherichia coli producing OXA-181 (part of the OXA-48 family of carbapenemases) in a Queensland Hospital, and describe how we used whole genome sequencing (WGS) to identify the outbreak strain, determine the extent of transmission within the hospital and support infection control responses. Methods116 isolates were collected and sequenced on an Illumina NextSeq to determine species, sequence type (ST) and presence of resistance genes. Core single nucleotide polymorphisms were used to determine strain relatedness. Three isolates were also sequenced on an Oxford Nanopore MinION to determine the context of the resistance genes. ResultsOf 116 isolates, 85 (84 E. coli and one K. pneumoniae) from 78 patients (and two environmental sources) were related to the ongoing outbreak. The outbreak E. coli strain was found to be ST38 and carried blaOXA-181, blaCTX-M-15 and qnrS1 genes. Long read sequencing revealed blaOXA-181 to be carried on an IncX3 plasmid with qnrS1. blaCTX-M-15 was chromosomally integrated (via ISEcp1 insertion) in close proximity to a second qnrS1 gene. A search of the laboratory database identified an isolate with an identical unusual antibiogram from a patient recently admitted to a hospital in Vietnam, suggesting that the strain was introduced to the hospital. This conclusion was supported by WGS, as comparison of the strain to public data identified a close match to an E. coli recovered from Vietnam in 2011. ConclusionA blaOXA-181-carrying E. coli ST38 strain was introduced to a Brisbane hospital and spread undetected throughout multiple wards over several months. Using WGS, we characterized the outbreak strain and unambiguously detected its presence throughout the hospital. We show how both WGS and infection control measures can be utilized to effectively terminate widespread transmission of an elusory pathogen.

microbiology↗

bioRxiv2019

Prospective evaluation of beta-lactamase detection in penicillin susceptible Staphylococcus aureus by interpretation of the penicillin disc edge

Penicillin susceptible Staphylococcus aureus (PSSA) may occasionally be encountered as a cause of complicated S. aureus infection, such as endocarditis or bloodstream infections. Clinicians may choose to treat these patients with penicillin over a semi-synthetic penicillin derivative, such as flucloxacillin or oxacillin, due to a favourable Pk/Pd profile. In this study, we prospectively evaluated the penicillin disc (1-IU) method for detection of blaZ, with interpretation of the penicillin edge according to EUCAST recommendations. 472 PSSA isolates were collected between September 2014 to December 2015 from three clinical microbiology laboratories in Queensland, Australia. Initial antimicrobial susceptibility testing was performed by the Vitek 2 system. Real-time PCR for blaZ was performed following phenotypic testing with the 1-IU penicillin disc and the PCR used as the gold standard for detection of penicillinase. The prevalence of blaZ amongst the isolates was 7%. The sensitivity, specificity, positive predictive value and negative predictive value of the penicillin disc method was 97%, 95%, 61% and 100% when compared to blaZ PCR. In summary, the penicillin disc zone size and edge interpretation is a reliable method for detection of blaZ in S. aureus isolates that otherwise test susceptible to penicillin by Vitek 2 AST.

microbiology↗

bioRxiv2019

Approximate Bayesian computation of transcriptional pausing mechanisms

At a transcriptional pause site, RNA polymerase (RNAP) takes significantly longer than average to transcribe the nucleotide before moving on to the next position. At the single-molecule level this process is stochastic, while at the ensemble level it plays a variety of important roles in biological systems. The pause signal is complex and invokes interplay between a range of mechanisms. Among these factors are: non-canonical transcription events - such as backtracking and hypertranslocation; the catalytically inactive intermediate state hypothesised to act as a precursor to backtracking; the energetic configuration of basepairing within the DNA/RNA hybrid and of those flanking the transcription bubble; and the structure of the nascent mRNA. There are a variety of plausible models and hypotheses but it is unclear which explanations are better.\n\nWe performed a systematic comparison of 128 kinetic models of transcription using approximate Bayesian computation. Under this Bayesian framework, models and their parameters were assessed by their ability to predict the locations of pause sites in the E. coli genome.\n\nThese results suggest that the structural parameters governing the transcription bubble, and the dynamics of the transcription bubble during translocation, play significant roles in pausing. This is consistent with a model where the relative Gibbs energies between the pre and posttranslocated positions, and the rate of translocation between the two, is the primary factor behind invoking transcriptional pausing. Whereas, hypertranslocation, backtracking, and the intermediate state are not required to predict the locations of transcriptional pause sites. Finally, we compared the predictive power of these kinetic models to that of a non-explanatory statistical model. The latter approach has significantly greater predictive power (AUC = 0.89 cf. 0.73), suggesting that, while current models of transcription contain a moderate degree of predictive power, a much greater quantitative understanding of transcriptional pausing is required to rival that of a sequence motif.\n\nAuthor summaryTranscription involves the copying of a DNA template into messenger RNA (mRNA). This reaction is implemented by RNA polymerase (RNAP) successively incorporating nucleotides onto the mRNA. At a transcriptional pause site, RNAP takes significantly longer than average to incorporate the nucleotide. A model which can not only predict the locations of pause sites in a DNA template, but also explain how or why they are pause sites, is sought after.\n\nTranscriptional pausing emerges from cooperation between several mechanisms. These mechanisms include non-canonical RNAP reactions; and the thermodynamic properties of DNA and mRNA. There are many hypotheses and kinetic models of transcription but it is unclear which hypotheses and models are required to predict and explain transcriptional pausing.\n\nWe have developed a rigorous statistical framework for inferring model parameters and comparing hypotheses. By applying this framework to published pause-site data, we compared 128 kinetic models of transcription with the aim of finding the best models for predicting the locations of pause sites. This analysis offered insights into mechanisms of transcriptional pausing. However, the predictive power of these models lacks compared with non-explanatory statistical models - suggesting the data contains more information than can be satisfied by current quantitative understandings of transcriptional pausing.

systems biology↗

bioRxiv2018

Bayesian inference and comparison of stochastic transcription elongation models

Transcription elongation can be modelled as a three step process, involving polymerase translocation, NTP binding, and nucleotide incorporation into the nascent mRNA. This cycle of events can be simulated at the single-molecule level as a continuous-time Markov process using parameters derived from single-molecule experiments. Previously developed models differ in the way they are parameterised, and in their incorporation of partial equilibrium approximations.\n\nWe have formulated a hierarchical network comprised of 12 sequence-dependent transcription elongation models. The simplest model has two parameters and assumes that both translocation and NTP binding can be modelled as equilibrium processes. The most complex model has six parameters makes no partial equilibrium assumptions. We systematically compared the ability of these models to explain published force-velocity data, using approximate Bayesian computation. This analysis was performed using data for the RNA polymerase complexes of E. coli, S. cerevisiae and Bacteriophage T7.\n\nOur analysis indicates that the polymerases differ significantly in their translocation rates, with the rates in T7 pol being fast compared to E. coli RNAP and S. cerevisiae pol II. Different models are applicable in different cases. We also show that all three RNA polymerases have an energetic preference for the posttranslocated state over the pretranslocated state. A Bayesian inference and model selection framework, like the one presented in this publication, should be routinely applicable to the interrogation of single-molecule datasets.\n\nAuthor summaryTranscription is a critical biological process which occurs in all living organisms. It involves copying the organisms genetic material into messenger RNA (mRNA) which directs protein synthesis on the ribosome. Transcription is performed by RNA polymerases which have been extensively studied using both ensemble and single-molecule techniques (see reviews: [1, 2]). Single-molecule data provides unique insights into the molecular behaviour of RNA polymerases. Transcription at the single-molecule level can be computationally simulated as a continuous-time Markov process and the model outputs compared with experimental data. In this study we use Bayesian techniques to perform a systematic comparison of 12 stochastic models of transcriptional elongation. We demonstrate how equilibrium approximations can strengthen or weaken the model, and show how Bayesian techniques can identify necessary or unnecessary model parameters. We describe a framework to a) simulate, b) perform inference on, and c) compare models of transcription elongation.

molecular biology↗