Biology subjects

Watson, J. A.

Publications and source records attributed to Watson, J. A..

14 recordsLinked to original sources

bioRxiv2025

Trypanosome histone variants H3.V and H4.V promote nucleosome plasticity in repressed chromatin.

Histone variants define distinct chromatin states by modulating the biophysical properties of nucleosomes. Variants play a particularly important role in the parasitic protist Trypanosoma brucei, which has unusual chromatin and lacks a canonical repressive heterochromatin system. Instead, T. brucei utilises specialised divergent histone variants H3.V and H4.V. However, the biochemical basis of their repressive functions is unknown. Here, we determined the structure of the H3.V-H4.V nucleosome core particle and biochemically characterised variant-containing nucleosomes and nucleosome arrays probing their unique properties. We discovered that surprisingly for repressive-state nucleosomes, H3.V promotes pronounced DNA splaying, largely via its N-terminal tail region, whilst retaining overall stability that is comparable to canonical nucleosomes. In contrast, H4.V exhibits near-identical binding to DNA but mediates a slight increase in histone octamer stability. The surface of the H3.V-H4.V nucleosome is altered and provides a differential platform for chromatin binding proteins, linking the variants to parasite pathogenicity.

biochemistry↗

bioRxiv2025

MeCP2 requires interactions with nucleosome linker DNA to read chromatin DNA methylation

Methyl-CpG-binding protein 2 (MeCP2) is an epigenetic reader essential for neuronal function, but how it binds DNA methylation within chromatin is unclear. Using designer nucleosomes we observe that MeCP2 preferentially engages DNA methylation positioned at multiple sites around the nucleosome. Surprisingly, MeCP2 can bind methylated DNA in bent, histone-contacting core nucleosomal DNA. However, this activity requires additional DNA linker interactions, using MeCP2 regions beyond its canonical methyl binding domain. We mapped a novel DNA-binding region in MeCP2 required for this function. Furthermore, histone H1 antagonises the MeCP2-nucleosome interaction by competing for linker DNA. Overall, this reveals that MeCP2 combines nonspecific but essential interactions with linker DNA to aid specific binding to nucleosomal methylated DNA, independent of nucleosome structure. Our findings reveal a novel contribution to full MeCP2 function, and mechanistic insight into how this clinically important protein interacts with chromatin.

biochemistry↗

bioRxiv2024

Transmission-blocking activity of artesunate, chloroquine and methylene blue on Plasmodium vivax gametocytes.

Plasmodium vivax is now the main cause of malaria outside Africa. The gametocytocidal effects of antimalarial drugs are important to reduce malaria transmissibility, particularly in low transmission settings, but they are not well characterized for P. vivax. The transmission-blocking effects of chloroquine, artesunate and methylene blue on P. vivax gametocytes were assessed. Blood specimens were collected from patients presenting with vivax malaria, incubated with or without the tested drugs, and then fed to mosquitos from a laboratory-adapted colony of Anopheles dirus (a major malaria vector in Southeast Asia). The effects on oocyst and sporozoite development were analyzed under a multi-level Bayesian model accounting for assay variability and the heterogeneity of mosquito Plasmodium-infection. Artesunate and methylene blue, but not chloroquine, exhibited potent transmission-blocking effects. Gametocyte exposures to artesunate and methylene blue reduced the mean oocyst count 469 fold (95%CI: 345 to 650) and 1438 fold (95%CI: 970 to 2064) respectively. The corresponding estimates for the sporozoite stage were a 148 fold reduction (95%CI: 61 to 470) and a 536 fold reduction (95%CI: 246 to 1311) in the mean count, respectively. In contrast, high chloroquine exposures reduced the mean oocyst count by only 1.40 fold (95%CI: 1.20 to 1.64) and the mean sporozoite count 1.34 fold (95%CI: 1.12 to 1.66). This suggests that patients with vivax malaria often remain infectious to anopheline mosquitos after treatment with chloroquine. Immediate initiation of primaquine radical cure or use of artemisinin combination therapies would reduce the transmissibility of P. vivax infections.

microbiology↗

bioRxiv2023

The N-terminal region of DNMT3A combines multiple chromatin reading motifs to guide recruitment.

DNA methyltransferase 3A (DNMT3A) plays a critical role in establishing and maintaining DNA methylation patterns. However, the mechanisms underlying DNMT3A recruitment to and function within different chromatin environments remain unclear. Using a combination of biochemical and structural approaches we find that DNMT3A interacts using multiple interfaces with chromatin; directly binding generic nucleosome features as well as site-specific post-translational histone modifications. The N-terminal region, unique to the DNMT3A1 isoform, is essential for these interactions and stabilises H3K36me2-nucleosome recruitment. Intriguingly, in the same region critical for nucleosome binding we also map a ubiquitylation-dependent recruitment motif (UDR). The UDR binds specifically to ubiquitylated H2AK119, explaining the previously observed recruitment to Polycomb-occupied heterochromatin. A cryo-EM structure of DNMT3A1-DNMT3L with a modified nucleosome reveals that the UDR interacts with the nucleosome surface including the acidic patch. Previously unexplained disease-associated mutations are present in the UDR and ablate nucleosome interactions. This leads to an increased understanding of how DNMT3A1 recruitment occurs in the genome and highlights the importance of multivalent binding of DNMT3A to histone modifications and the nucleosome.

biochemistry↗

bioRxiv2023

Histone divergence in Trypanosoma brucei results in unique alterations to nucleosome structure

Eukaryotes have a multitude of diverse mechanisms for organising and using their genomes, but the histones that make up chromatin are highly conserved. Unusually, histones from kinetoplastids are highly divergent. The structural and functional consequences of this variation are unknown. Here, we have biochemically and structurally characterised nucleosome core particles (NCPs) from the kinetoplastid parasite Trypanosoma brucei. A structure of the T. brucei NCP reveals that global histone architecture is conserved, but specific sequence alterations lead to distinct DNA and protein interaction interfaces. The T. brucei NCP is unstable and has weakened overall DNA binding. However, dramatic changes at the H2A-H2B interface introduce local reinforcement of DNA contacts. The T. brucei acidic patch has altered topology and is refractory to known binders, indicating that the nature of chromatin interactions in T. brucei may be unique. Overall, our results provide a detailed molecular basis for understanding evolutionary divergence in chromatin structure.

biochemistry↗

bioRxiv2022

Structural analysis of SALL4 zinc-finger domain reveals a link between AT-rich DNA binding and Okihiro syndrome

Spalt-like 4 (SALL4) maintains vertebrate embryonic stem cell identity and is required for the development of multiple organs, including limbs. Mutations in SALL4 are associated with Okihiro syndrome and SALL4 is also a known target of thalidomide. SALL4 protein has a distinct preference for AT-rich sequences, recognised by a pair of zinc fingers at the C-terminus. However, unlike many characterised zinc finger proteins, SALL4 shows flexible recognition with many different combinations of AT-rich sequences being targeted. SALL4 interacts with the NuRD corepressor complex which potentially mediates repression of AT-rich genes. We present a crystal structure of SALL4 C-terminal zinc fingers with an AT-rich DNA sequence, which shows that SALL4 uses small hydrophobic and polar side chains to provide flexible recognition in the major groove. Missense mutations reported in patients that lie within the C-terminal zinc fingers reduced overall binding to DNA but not the preference for AT-rich sequences. Furthermore, these mutations altered association of SALL4 with AT-rich genomic sites, providing evidence that these mutations are likely pathogenic.

biochemistry↗

bioRxiv2021

Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

genetics↗

bioRxiv2020

A cautionary note on the use of machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results. Author summaryGenetic epidemiology studies of malaria attempt to characterise what is happening in malaria parasite populations. In particular, they are an important tool to track the spread of drug resistance and to validate genetic makers of drug resistance. To make sense of parasite genetic data, researchers usually characterise the population structure using statistical methods. This is most often done as a two step process. The first is a data reduction step, whereby the data are summarised into a distance matrix (each entry represents the genetic distance between two isolates) and then the distance matrix is input into an unsupervised machine learning algorithm. Principal coordinates analysis and hierarchical agglomerative clustering are the two most popular unsupervised machine learning algorithms used for this purpose in malaria genetic epidemiology. We illustrate that this procedure is sensitive to the choice of genetic distance and to the specification of the algorithms. These unsupervised methods are useful for exploratory data analysis but cannot be used to infer historical events. We provide some guidance on how to make genetic epidemiology analyses more transparent and reproducible.

genetics↗

bioRxiv2020

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. less than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined optimal dose. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation studies. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russells viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Russells viper efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified 3+3 design). The model based design can identify the optimal dose after fewer patients than the rule based design. Open source code for the simulations can be used to calculate sample sizes under a priori beliefs of efficacy and toxicity. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar. Author summarySnakebite envenoming is one of the most neglected tropical diseases relative to its mortality and morbidity. Antivenoms are the only known effective treatment for snake-bite envenoming but are frequently responsible for high rates of adverse reactions. Clinical development of antivenoms rarely follows the iterative phases of clinical development applied to other drugs. Dosing is typically based on pre-clinical testing. Here we propose a Bayesian model based adaptive design for clinical trials aiming to determine the optimal dose of antivenom needed. Optimality is defined using safety and efficacy thresholds contextual to the study. This design can be applied to all antivenoms which have binary efficacy and toxicity endpoints. Our design formally specifies a desired efficacy and a maximum tolerated toxicity. We use simulation studies to characterise the sample size necessary to determine the optimal dose in different scenarios. The simulation studies highlight the advantages of a model based design over simpler rule based alternatives. We intend to use this design to determine an effective and safe dose of the new lyophilised viper antivenom currently in use to treat Russells viper envenoming in Myanmar.

pharmacology and toxicology↗

bioRxiv2019

Graphing and reporting heterogeneous treatment effects through reference classes

BackgroundExploration and modelling of individual treatment effects and treatment heterogeneity is an important aspect of precision medicine in randomized controlled trials (RCTs). The usual approach is to develop a predictive model for individual outcomes and then look for an interaction effect between treatment allocation and important patient covariates. However, such models are prone to overfitting and multiple testing, and typically demand a transformation of the outcome measurement, for example, from the absolute risk in the original RCT to log-odds of risk in the predictive model.\n\nMethodsWe show how reference classes derived from background information can be used to alleviate this problem through a two-stage approach where we first estimate a key aspect of heterogeneity in the trial population and then explore for an interaction with the treatment effect along this axis of variation. This bypasses the search for interactions, protecting against multiple testing, and allows for exploration of heterogeneous treatment effects on the original outcome scale of the RCT. This would typically be applied to multivariate models of baseline risk to assess the stability of average treatment effects with respect to the distribution of risk in the population studied. We show how local and tilting schemes based on ranking patients by baseline risk can be used as a general approach for exploring heterogeneity of treatment effect.\n\nResultsWe illustrate this approach using the single largest randomised treatment trial in severe falciparum malaria and show how the estimated treatment effect in terms of absolute mortality risk reduction increases considerably for higher risk strata.

bioinformatics↗

bioRxiv2018

Estimating the probable cause of recurrence in Plasmodium vivax malaria: relapse, reinfection or recrudescence?

BackgroundRelapse of Plasmodium vivax infection is the main cause of vivax malaria in many parts of Asia. However at the individual patient level, recurrence of a blood stage infection following treatment within the endemic area can be either a relapse (from dormant liver-stage parasites), a recrudescence (blood-stage treatment failure), or a reinfection (following a new mosquito inoculation). Each requires a different prevention strategy, but previously they could not be distinguished reliably. Time-of-event and genetic data provide complimentary information about the cause of P. vivax recurrence, but the optimum approach to genotyping and analysis remains uncertain.\n\nMethodsIndividual-level data from two large drug trials in acute vivax malaria patients (Vivax History: VHX; Best Primaquine Dose: BPD) conducted on the Thailand-Myanmar border with follow-up of one year were pooled (n=1299). A total of 710 isolates from both acute and recurrent P. vivax episodes were genotyped using 3-9 highly polymorphic microsatellite markers. These pooled data were analyzed using a novel population statistical model incorporating an assessment of genetic relatedness, treatment drug administered, and the time-to-recurrence.\n\nResults99% of genotyped recurrences in individuals who did not receive primaquine (n=365) were estimated to be relapses. In comparison, 14% of genotyped recurrences (n=121) were estimated to be relapses following high-dose supervised primaquine. By comparing episodes across individuals (90194 comparisons), the false-positive rate of relapse identification using genetic data alone was estimated to be 2.2%. We estimated the true failure rate after high-dose primaquine (7mg/kg total dose) to be 2.6% in this epidemiological context, substantially lower the reinfection unadjusted estimate of 12%. Simulation studies show that 9 highly polymorphic microsatellite markers suffice to discriminate between recurrence states. Drug exposures reflected by plasma carboxy-primaquine concentrations were not predictive of treatment failure, but did identify non-adherence.\n\nConclusionUsing this novel statistical model, relapse of P. vivax malaria could be distinguished reliably from reinfection. This showed that in this population supervised high-dose primaquine could avert up to 99% of relapses. In low transmission settings, microsatellite genotyping combined with time-to-event data can accurately discriminate between the different causes of recurrent P. vivax malaria.\n\nAuthor summaryOne hundred years ago, Plasmodium vivax, the most globally diverse cause of human malaria, was present across most of the old-world, throughout tropical and temperate climes. Its main evolutionary advantage over Plasmodium falciparum, responsible for the most deadly type of human malaria, is its ability to stay dormant in the liver, emerging weeks to years later causing recurring illness and continuing transmission. The dormant liver-stage parasites are called hypnozoites. A recurrent infection can either be hypnozoite-derived (a relapse), a blood-stage treatment failure (recrudescence), or from a new mosquito bite (a reinfection). At a population level, each requires a different preventative strategy but no widely applicable methodology existed to discriminate between the three possible states: relapse, recrudescence and reinfection. Parasite genetic data alone cannot resolve the different possibilities. We developed a novel probabilistic framework which uses both epidemiological and genetic data to determine the most likely cause of vivax recurrence at the individual level. We applied this method to the largest available pooled clinical trial data from Southeast Asia incorporating information from highly polymorphic microsatellite markers, drug treatments received and time-to-recurrent illness. This analysis provides a tool for estimating probabilities of relapse, recrudescence and reinfection and, importantly, shows that hypnozoiticidal high-dose primaquine radical cure is much more effective than previously thought.

epidemiology↗

bioRxiv2018

Nessys: a novel method for accurate nuclear segmentation in 3D

Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However current methods for segmenting nuclei in 3D tissues are not designed for situations where nuclei are densely packed, non-spherical, heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture.\n\nHere we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree structured ridge tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computers memory. We consistently obtain accurate segmentation rates of >90% even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation.\n\nWe have also generated a live reporter of the NE that can be used to track live cells in three dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation.\n\nWe provide these tools in an open-access user-friendly format.

developmental biology↗

bioRxiv2018

A pharmacokinetic-pharmacodynamic assessment of the hepatic and bone-marrow toxicities of the new trypanoside fexinidazole

Fexinidazole is a novel oral treatment for Trypanosoma brucei gambiense human African trypanosomiasis: g-HAT. Fexinidazole is also active against other kinetoplastid parasites, notably T. cruzi the causative agent of Chagas disease.During the course of a dose ranging assessment in chronic indeterminate Chagas disease, delayed neutropenia and significant increases in hepatic transaminases were observed and clinical investigations were suspended. We retrospectively analyzed all available pharmacokinetic and pharmacodynamic data on fexinidazole in normal healthy volunteers and in patients with chronic Chagas disease and g-HAT, in order to assess the determinants of toxicity.\n\nA population pharmacokinetic model was fitted to plasma concentration data on the bioactive fexinidazole sulfone metabolite from three phase 1 studies, two g-HAT phase 2/3 field trials and one Chagas phase 2 field trial (462 individuals in total). Bayesian exposure-response models were then fitted to hematological and liver related pharmacodynamic outcomes in chronic Chagas patients.\n\nNeutropenia, reductions in platelet counts, and elevations in liver transaminases were all found to be exposure and thus dose dependent in patients with chronic Chagas disease. Clinically insignificant transient reductions in neutrophil and platelet counts consistent with these exposure-response relationships were observed in the g-HAT trials. In contrast, no evidence of hepatotoxicity was observed in the g-HAT trials.\n\nFexinidazole treatment results in a dose dependent liver toxicity and transient bone marrow suppression in Chagas disease. Regimens of shorter duration should be trialled for Chagas. The currently recommended regimen for sleeping sickness provides exposures within a satisfactory safety margin for bone marrow suppression and does not cause hepatotoxicity.

pharmacology and toxicology↗

bioRxiv2018

Machine learning for randomised controlled trials: identifying treatment effect heterogeneity with strict control of type I error

BackgroundIt is widely acknowledged that retrospective exploratory analyses of randomised controlled trials (RCTs) seeking to identify treatment effect heterogeneity (TEH) are prone to bias and false positives. Yet the increasing availability of multiple data modalities on subjects and the desire to learn all we can from trial participants motivates the inclusion of such analyses within RCTs. Coupled to this, widespread advances in AI and machine learning (ML) methods hold great potential to utilise such data to characterise subjects exhibiting heterogeneous treatment response.\n\nMethodsWe present new learning strategies for RCT ML discovery methods that ensure strict control of the false positive reporting rate at a pre-specified level. Our approach uses randomised data partitioning and statistical or ML based prediction on held-out data. This can test for both crossover and non-crossover TEH. The former is done via a two-sample hypothesis test measuring overall predictive performance of the ML method. The latter is done via stacking the ML predictors alongside a classical statistical model to formally test the added benefit of the ML algorithm. An adaptation of recent statistical theory allows for the construction of a valid aggregate p-value. This learning strategy is agnostic to the choice of ML method.\n\nResultsWe demonstrate our approach with a re-analysis of the SEAQUAMAT trial. We find no evidence for any crossover subgroup who would benefit from a change in treatment from the current standard-of-care, artesunate, but strong evidence for significant noncrossover TEH within the artesunate treatment group. We find that artesunate provides a differential benefit to patients with high numbers of circulating ring stage parasites.\n\nConclusionsOur ML approach combined with the use of computational notebooks and version control can improve the robustness and transparency of RCT exploratory analyses. The methods allow researchers to apply the latest ML techniques safe in the knowledge that any declared associations are statistically significant at a user defined level.

bioinformatics↗