Dysregulation of hepatic one-carbon metabolism in classical homocystinuria: Implications of redox-sensitive DHFR repression and tetrahydrofolate depletion for pathogenesis and treatment.

Cystathionine beta-synthase-deficient homocystinuria (HCU) is a life-threatening disorder of sulfur metabolism. HCU can be treated by using betaine to lower tissue and plasma levels of homocysteine (Hcy). Here, we show that mice with severely elevated Hcy and potentially deficient in the folate species tetrahydrofolate (THF) exhibit a very limited response to betaine indicating that THF plays a critical role in treatment efficacy. Analysis of a mouse model of HCU revealed a 10-fold increase in hepatic levels of 5-methyl -THF and a 30-fold accumulation of formiminoglutamic acid, consistent with a paucity of THF. Neither of these metabolite accumulations were reversed or ameliorated by betaine treatment. Hepatic expression of the THF-generating enzyme dihydrofolate reductase (DHFR) was significantly repressed in HCU mice and expression was not increased by betaine treatment but appears to be sensitive to cellular redox status. Expression of the DHFR reaction partner thymidylate synthase was also repressed and metabolomic analysis detected widespread alteration of hepatic histidine and glutamine metabolism. Many individuals with HCU exhibit endothelial dysfunction. DHFR plays a key role in nitric oxide (NO) generation due to its role in regenerating oxidized tetrahydrobiopterin, and we observed a significant decrease in plasma NOx (NO2 + NO3) levels in HCU mice. Additional impairment of NO generation may also come from the HCU-mediated induction of the 20-hydroxyeicosatetraenoic acid generating cytochrome CYP4A. Collectively, our data shows that HCU induces dysfunctional one-carbon metabolism with the potential to both impair betaine treatment and contribute to multiple aspects of pathogenesis in this disease.

Enzymatic cleaving of entangled DNA rings drives scale-dependent rheological trajectories.

DNA, which naturally occurs in linear, ring, and supercoiled topologies, frequently undergoes enzyme-driven topological conversion and fragmentation in vivo, enabling it to perform a variety of functions within the cell. In vitro, highly concentrated DNA polymers form entanglements that yield viscoelastic properties dependent on the topologies and lengths of the DNA. Enzyme-driven alterations of DNA size and shape therefore offer a means of designing active materials with programmable viscoelastic properties. Here, we incorporate multi-site restriction endonucleases into dense DNA solutions to linearize and fragment circular DNA molecules. We pair optical tweezers microrheology with differential dynamic microscopy and single-molecule tracking to measure the linear and nonlinear viscoelastic response and transport properties of entangled DNA solutions over a wide range of spatiotemporal scales throughout the course of enzymatic digestion. We show that, at short timescales, relative to the relaxation timescales of the polymers, digestion of these 'topologically-active' fluids initially causes an increase in elasticity and relaxation times followed by a gradual decrease. Conversely, for long timescales, linear viscoelastic moduli exhibit signatures of increasing elasticity. DNA diffusion, likewise, becomes increasingly slowed, in direct opposition to the short-time behavior. We hypothesize that this scale-dependent rheology arises from the population of small DNA fragments, which increases as digestion proceeds, driving self-association of larger fragments via depletion interactions, giving rise to slow relaxation modes of clusters of entangled chains, interspersed among shorter unentangled fragments. While these slow modes likely dominate at long times, they are presumably frozen out in the short-time limit, which instead probes the faster relaxation modes of the unentangled population.

Chirality Matters: Fine-Tuning of Novel Monoamine Reuptake Inhibitors Selectivity through Manipulation of Stereochemistry.

The high structural similarity, especially in transmembrane regions, of dopamine, norepinephrine, and serotonin transporters, as well as the lack of all crystal structures of human isoforms, make the specific targeting of individual transporters rather challenging. Ligand design itself is also rather limited, as many chemists, fully aware of the synthetic and analytical challenges, tend to modify lead compounds in a way that reduces the number of chiral centers and hence limits the potential chemical space of synthetic ligands. We have previously shown that increasing molecular complexity by introducing additional chiral centers ultimately leads to more selective and potent dopamine reuptake inhibitors. Herein, we significantly extend our structure-activity relationship of dopamine transporter-selective ligands and further demonstrate how stereoisomers of defined absolute configuration may fine-tune and direct the activity towards distinct targets. From the pool of active compounds, using the examples of stereoisomers 7h and 8h, we further showcase how in vitro activity significantly differs in in vivo drug efficacy experiments, calling for proper validation of individual stereoisomers in animal studies. Furthermore, by generating a large library of compounds with defined absolute configurations, we lay the groundwork for computational chemists to further optimize and rationally design specific monoamine transporter reuptake inhibitors.

Molecular disparities in colorectal cancers of White Americans, Alabama African Americans, and Oklahoma American Indians.

In the US, the majority of cancer samples analyzed are from white people, leading to biases in racial and ethnic treatment outcomes. Colorectal cancer (CRC) incidence and mortality rates are high in Alabama African Americans (AAs) and Oklahoma American Indians (AIs). We hypothesized that differences between racial groups may partially explain these disparities. Thus, we compared transcriptomic profiles of CRCs of Alabama AAs, Oklahoma AIs, and white people from both states. Compared to CRCs of white people, CRCs of AAs showed (a) higher expression of cytokines and vesicle trafficking toward modulated antitumor-immune activity, and (b) lower expression of the ID1/BMP/SMAD axis, IL22RA1, APOBEC3, and Mucins; and AIs had (c) higher expression of PTGS2/COX2 (an NSAID target/pro-oncogenic inflammation) and splicing regulators, and (d) lower tumor suppressor activities (e.g., TOB2, PCGF2, BAP1). Therefore, targeting strategies designed for white CRC patients may be less effective for AAs/AIs. These findings illustrate needs to develop optimized interventions to overcome racial CRC disparities.

Optical-Tweezers-integrating-Differential-Dynamic-Microscopy maps the spatiotemporal propagation of nonlinear strains in polymer blends and composites.

How local stresses propagate through polymeric fluids, and, more generally, how macromolecular dynamics give rise to viscoelasticity are open questions vital to wide-ranging scientific and industrial fields. Here, to unambiguously connect polymer dynamics to force response, and map the deformation fields that arise in macromolecular materials, we present Optical-Tweezers-integrating-Differential -Dynamic-Microscopy (OpTiDMM) that simultaneously imposes local strains, measures resistive forces, and analyzes the motion of the surrounding polymers. Our measurements with blends of ring and linear polymers (DNA) and their composites with stiff polymers (microtubules) uncover an unexpected resonant response, in which strain alignment, superdiffusivity, and elasticity are maximized when the strain rate is comparable to the entanglement rate. Microtubules suppress this resonance, while substantially increasing elastic storage, due to varying degrees to which the polymers buildup, stretch and flow along the strain path, and configurationally relax induced stress. More broadly, the rich multi-scale coupling of mechanics and dynamics afforded by OpTiDDM, empowers its interdisciplinary use to elucidate non-trivial phenomena that sculpt stress propagation dynamics-critical to commercial applications and cell mechanics alike.

Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

Synthesis and dopamine receptor binding of dihydrexidine and SKF 38393 catecholamine-based analogues.

Dopamine is an important neurotransmitter that regulates numerous essential functions, including cognition and voluntary movement. As such, it serves as an important scaffold for synthesis of novel analogues as part of drug development effort to obtain drugs for treatment of neurodegenerative diseases, such as Parkinson's disease. To that end, similarity search of the ZINC database based on two known dopamine-1 receptor (D1R) agonists, dihydrexidine (DHX) and SKF 38393, respectively, was used to predict novel chemical entities with potential binding to D1R. Three compounds that showed the highest similarity index were selected for synthesis and bioactivity profiling. All main synthesis products as well as the isolated intermediates, were properly characterized. The physico-chemical analyses were performed using HRESIMS, GC/MS, LC/MS with UV-Vis detection, and FTIR, 1H NMR and 13C NMR spectroscopy. Binding to D1 and D2 receptors and inhibition of dopamine reuptake via dopamine transporter were measured for the synthesized analogues of DHX and SKF 38393.

Pair-based likelihood approximations for stochastic epidemic models.

Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense that it is very computationally expensive to obtain. Although data-augmented Markov chain Monte Carlo (MCMC) methods provide a solution to this problem, employing a tractable augmented likelihood, such methods typically deteriorate in large populations due to poor mixing and increased computation time. Here, we describe a new approach that seeks to approximate the likelihood by exploiting the underlying structure of the epidemic model. Simulation study results show that this approach can be a serious competitor to data-augmented MCMC methods. Our approach can be applied to a wide variety of disease transmission models, and we provide examples with applications to the common cold, Ebola, and foot-and-mouth disease.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data.

Whole-genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such high-resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting.

Structure-Activity Relationships of Novel Thiazole-Based Modafinil Analogues Acting at Monoamine Transporters.

Atypical dopamine reuptake inhibitors, such as modafinil, are used for the treatment of sleeping disorders and investigated as potential therapeutics against cocaine addiction and for cognitive enhancement. Our continuous effort to find modafinil analogues with higher inhibitory activity on and selectivity toward the dopamine transporter (DAT) has previously led to the promising thiazole-containing derivatives CE-103, CE-111, CE-123, and CE-125. Here, we describe the synthesis and activity of a series of compounds based on these scaffolds, which resulted in several new selective DAT inhibitors and gave valuable insights into the structure-activity relationships. Introduction of the second chiral center and subsequent chiral separations provided all four stereoisomers, whereby the S-configuration on both generally exerted the highest activity and selectivity on DAT. The representative compound of this series was further characterized by in silico, in vitro, and in vivo studies that have demonstrated both safety and efficacy profile of this compound class.

Evaluating hospital infection control measures for antimicrobial-resistant pathogens using stochastic transmission models: Application to vancomycin-resistant enterococci in intensive care units.

Nosocomial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) are the cause of significant morbidity and mortality among hospital patients. It is important to be able to assess the efficacy of control measures using data on patient outcomes. In this paper, we describe methods for analysing such data using patient-level stochastic models which seek to describe the underlying unobserved process of transmission. The methods are applied to detailed longitudinal patient-level data on vancomycin-resistant Enterococci from a study in a US hospital with eight intensive care units (ICUs). The data comprise admission and discharge dates, dates and results of screening tests, and dates during which precautionary measures were in place for each patient during the study period. Results include estimates of the efficacy of the control measures, the proportion of unobserved patients colonized with vancomycin-resistant Enterococci, and the proportion of patients colonized on admission.

Training in multiple breath washout testing for respiratory physiotherapists.

INTRODUCTION: The development of multiple breath washout (MBW) testing in respiratory disease highlights the need for increased awareness amongst respiratory physiotherapists and a potential opportunity for professional development in the use of an important outcome measure for clinical trials. OBJECTIVES: To rationalise how MBW may be a useful assessment tool for respiratory physiotherapists and to describe a local MBW training and certification programme for physiotherapists. RESULTS: The respiratory Multidisciplinary Team in the Belfast Health and Social Care Trust (BHSCT) identified a need for MBW testing to be available to facilitate clinical research and assessment. A 2day training programme consisting of prereading preparation, self-directed learning, theory presentations, practical demonstrations and hands-on practice was developed and delivered. All participants underwent a certification process. CONCLUSION: We have demonstrated the successful training and certification of clinical and research physiotherapists and encourage other respiratory physiotherapists to consider MBW test training.

Modelling and Bayesian analysis of the Abakaliki smallpox data.

The celebrated Abakaliki smallpox data have appeared numerous times in the epidemic modelling literature, but in almost all cases only a specific subset of the data is considered. The only previous analysis of the full data set relied on approximation methods to derive a likelihood and did not assess model adequacy. The data themselves continue to be of interest due to concerns about the possible re-emergence of smallpox as a bioterrorism weapon. We present the first full Bayesian statistical analysis using data-augmentation Markov chain Monte Carlo methods which avoid the need for likelihood approximations and which yield a wider range of results than previous analyses. We also carry out model assessment using simulation-based methods. Our findings suggest that the outbreak was largely driven by the interaction structure of the population, and that the introduction of control measures was not the sole reason for the end of the epidemic. We also obtain quantitative estimates of key quantities including reproduction numbers.

Multi-Institutional FASTQ File Exchange as a Means of Proficiency Testing for Next-Generation Sequencing Bioinformatics and Variant Interpretation.

Next-generation sequencing is becoming increasingly common in clinical laboratories worldwide and is revolutionizing clinical molecular testing. However, the large amounts of raw data produced by next-generation sequencing assays and the need for complex bioinformatics analyses present unique challenges. Proficiency testing in clinical laboratories has traditionally been designed to evaluate assays in their entirety; however, it can be alternatively applied to separate assay components. We developed and implemented a multi-institutional proficiency testing approach to directly assess custom bioinformatics and variant interpretation processes. Six clinical laboratories, all of which use the same commercial library preparation kit for next-generation sequencing analysis of tumor specimens, each submitted raw data (FASTQ files) from four samples. These 24 file sets were then deidentified and redistributed to five of the institutions for analysis and interpretation according to their clinically validated approach. Among the laboratories, there was a high rate of concordance in the calling of single-nucleotide variants, in particular those we considered clinically significant (100% concordance). However, there was significant discordance in the calling of clinically significant insertions/deletions, with only two of seven being called by all participating laboratories. Missed calls were addressed by each laboratory to improve their bioinformatics processes. Thus, through our alternative proficiency testing approach, we identified the bioinformatic detection of insertions/deletions as an area of particular concern for clinical laboratories performing next-generation sequencing testing.

Reconstructing transmission trees for communicable diseases using densely sampled genetic data.

Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation.

Transition of care for adolescents from paediatric services to adult health services.

BACKGROUND: There is evidence that the process of transition from paediatric (child) to adult health services is often associated with deterioration in the health of adolescents with chronic conditions.Transitional care is the term used to describe services that seek to bridge this care gap. It has been defined as 'the purposeful, planned movement of adolescents and young adults with chronic physical and medical conditions from child-centred to adult-oriented health care systems'. In order to develop appropriate services for adolescents, evidence of what works and what factors act as barriers and facilitators of effective interventions is needed. OBJECTIVES: To evaluate the effectiveness of interventions designed to improve the transition of care for adolescents from paediatric to adult health services. SEARCH METHODS: We searched The Cochrane Central Register of Controlled Trials 2015, Issue 1, (including the Cochrane Effective Practice and Organisation of Care Group Specialised Register), MEDLINE, EMBASE, PsycINFO, and Web of Knowledge to 19 June 2015. We also searched reference lists of included studies and relevant reviews, and contacted experts and study authors for additional studies. SELECTION CRITERIA: We considered randomised controlled trials (RCTs), controlled before- and after-studies (CBAs), and interrupted time-series studies (ITSs) that evaluated the effectiveness of any intervention (care model or clinical pathway), that aimed to improve the transition of care for adolescents from paediatric to adult health services. We considered adolescents with any chronic condition that required ongoing clinical care, who were leaving paediatric services and going on to receive services in adult healthcare units, and their families. Participating providers included all health professionals responsible for the care of young people. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data from included papers, assessed the risk of bias of each study, and assessed the certainty of the evidence for the main comparisons using GRADE. Discrepancies were resolved by discussion. Authors were contacted for missing data. We reported the findings of the studies as pre- and post-intervention means and calculated the unadjusted absolute change from baseline with 95% confidence intervals (CI). MAIN RESULTS: We included four RCTs (N = 238 participants) that explored: a two-day workshop-based transition preparation training for adolescents with spina bifida; a nurse-led, one-on-one, teaching session with the additional support of a 'health passport' for adolescents with heart disease; a web- and SMS-based educational intervention for adolescents with a range of different conditions; and a structured comprehensive transition programme with a transition co-ordinator for adolescents with type 1 diabetes.One study evaluating a one-on-one nurse-led intervention, and one evaluating a technology-based intervention suggested that these interventions may lead to slight improvements in transitional readiness and chronic disease self-management measured at six- to eight-month follow-ups (low certainty evidence). Results with the TRAQ self-management tool were: MD 0.20; 95% CI -0.16 to 0.56 and MD 0.43; 95% CI; -0.09 to 0.95; with the TRAQ self-advocacy tool: MD 0.37; 95% CI -0.06 to 0.80; and with the PAM tool were: MD 10; 95% CI 2.96 to 17.04. In contrast, transition-preparation training delivered via a two-day workshop for patients with spina bifida may lead to little or no difference in measures of self-care practice and general health behaviours when measured using the DSCPI-90©.Two studies evaluated the use of health services. One study evaluated a technology-based intervention and another a comprehensive transition programme; these interventions may lead to slightly more young people taking positive steps to initiate contact with health professionals themselves (Relative risk (RR): 4.87; 95% CI 0.24 to 98.12 and RR 1.50; 95% CI 0.32 to 6.94, respectively; low certainty evidence.Young people's knowledge of their disease may slightly improve with a nurse-led, one-on-one intervention to prepare young people for transition to an adult congenital heart programme (MD 14; 95% CI 2.67 to 25.33; one study; low certainty evidence).Disease-specific outcome measures were reported in two studies, both of which led to little or no difference in outcomes (low certainty evidence). One study found little or no difference between intervention and control groups. A second study found that follow-up HbA1c in young people with type 1 diabetes mellitus increased by 1.2% for each percentage increase in baseline HbA1c, independent of treatment group (1.2%; 95% CI 0.4 to 1.9; P = 0.01).Transition interventions may lead to little or no difference in well-being or quality of life as measured with the PARS III or PedsQ (two studies; low certainty evidence). Both the technology-based intervention and the two-day workshop for young people with spina bifida found little or no difference between intervention and control groups (MD 1.29; 95% CI -4.49 to 7.07). One study did not report the data.Four telephone support calls from a transition co-ordinator may lead to little or no difference in rates of transfer from paediatric to adult diabetes services (one study; low certainty evidence). At 12-month follow-up, there was little or no difference between groups of young people receiving usual care or a telephone support (RR 0.80; 95% CI 0.59 to 1.08)). They may slightly reduce the risk of disease-related hospital admissions at 12-month follow-up (RR 0.29; 95% CI 0.03 to 2.40). AUTHORS' CONCLUSIONS: The available evidence (four small studies; N = 238), covers a limited range of interventions developed to facilitate transition in a limited number of clinical conditions, with only four to 12 months follow-up. These follow-up periods may not be long enough for any changes to become apparent as transition is a lengthy process. There was evidence of improvement in patients' knowledge of their condition in one study, and improvements in self-efficacy and confidence in another, but since few studies were eligible for this review, and the overall certainty of the body of this evidence is low, no firm conclusions can be drawn about the effectiveness of the evaluated interventions. Further research is very likely to have an important impact on our confidence in the intervention effect and likely could change our conclusions. There is considerable scope for the rigorous evaluation of other models of transitional care, reporting on clinical outcomes with longer term follow-up.

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.

Stochastic epidemic models featuring contact tracing with delays.

This paper is concerned with a stochastic model for the spread of an SEIR (susceptible → exposed (=latent) → infective → removed) epidemic with a contact tracing scheme, in which removed individuals may name some of their infectious contacts, who are then removed if they have not been already after some tracing delay. The epidemic is analysed via an approximating, modified birth-death process, for which a type-reproduction number is derived in terms of unnamed individuals, that is shown to be infinite when the contact rate is sufficiently large. We obtain explicit results under the assumption of either constant or exponentially distributed infectious periods, including the epidemic extinction probability in the former case. Numerical illustrations show that, while the distributions of latent periods and delays have an effect on the spread of the epidemic, the assumption of whether the delays experienced by individuals infected by the same individual are of the same or independent length makes little difference.

Bayesian model choice for epidemic models with two levels of mixing.

This paper considers the problem of choosing between competing models for infectious disease final outcome data in a population that is partitioned into households. The epidemic models are stochastic individual-based transmission models of the susceptible-infective-removed type. The main focus is on various algorithms for the estimation of Bayes factors, of which a path sampling-based algorithm is seen to give the best results. We also explore theoretical properties in the case where the within-model prior distributions become increasingly uninformative, which show the need for caution when using Bayes factors as a model choice tool. A suitable form of deviance information criterion is also considered for comparison. The theory and methods are illustrated with both artificial data, and influenza data from the Tecumseh study of illness.