The impact of major depressive disorder on glycaemic control in type 2 diabetes: a longitudinal cohort study using UK Biobank primary care records.

BACKGROUND: This study evaluates longitudinal associations between glycaemic control, measured by mean and within-patient variability of glycated haemaglobin (HbA1c) levels, and major depressive disorder (MDD) in individuals with type 2 diabetes (T2D), focusing on the timings of these diagnoses. METHODS: In UK Biobank, T2D was defined using self-report and linked health outcome data, then validated using polygenic scores. Repeated HbA1c measurements (mmol/mol) over the 10 years following T2D diagnosis were outcomes in mixed effects models, with disease duration included using restricted cubic splines. Four MDD exposures were considered: MDD diagnosis prior to T2D diagnosis (pre-T2D MDD), time between pre-T2D MDD diagnosis and T2D, new MDD diagnosis during follow-up (post-T2D MDD) and time since post-T2D MDD diagnosis. Models with and without covariate adjustment were considered. RESULTS: T2D diagnostic criteria were robustly associated with T2D polygenic scores. In 11,837 T2D cases (6.9 years median follow-up), pre-T2D MDD was associated with a 0.92 increase in HbA1c (95% CI: [0.00, 1.84]), but earlier pre-T2D MDD diagnosis correlated with lower HbA1c. These pre-T2D MDD effects became non-significant after covariate adjustment. Post-T2D MDD individuals demonstrated increasing HbA1c with years since MDD diagnosis ( ß = 0.51 , 95% CI: [0.17, 0.86]). Retrospectively, across study follow-up, within-patient variability in HbA1c was 1.16 (95% CI: 1.13-1.19) times higher in post-T2D MDD individuals. CONCLUSIONS: The timing of MDD diagnosis is important for understanding glycaemic control in T2D. Poorer control was observed in MDD diagnosed post-T2D, highlighting the importance of depression screening in T2D, and closer monitoring for individuals who develop MDD after T2D.

Exploring polygenic-environment and residual-environment interactions for depressive symptoms within the UK Biobank.

Substantial advances have been made in identifying genetic contributions to depression, but little is known about how the effect of genes can be modulated by the environment, creating a gene-environment interaction. Using multivariate reaction norm models (MRNMs) within the UK Biobank (N = 61294-91644), we investigate whether the polygenic and residual variance components of depressive symptoms are modulated by 17 a priori selected covariate traits-12 environmental variables and 5 biomarkers. MRNMs, a mixed-effects modelling approach, provide unbiased polygenic-covariate interaction estimates for a quantitative trait by controlling for outcome-covariate correlations and residual-covariate interactions. A continuous depressive symptom variable was the outcome in 17 MRNMs-one for each covariate trait. Each MRNM had a fixed-effects model (fixed effects included the covariate trait, demographic variables, and principal components) and a random effects model (where polygenic-covariate and residual-covariate interactions are modelled). Of the 17 selected covariates, 11 significantly modulate deviations in depressive symptoms through the modelled interactions, but no single interaction explains a large proportion of phenotypic variation. Results are dominated by residual-covariate interactions, suggesting that covariate traits (including neuroticism, childhood trauma, and BMI) typically interact with unmodelled variables, rather than a genome-wide polygenic component, to influence depressive symptoms. Only average sleep duration has a polygenic-covariate interaction explaining a demonstrably nonzero proportion of the variability in depressive symptoms. This effect is small, accounting for only 1.22% (95% confidence interval: [0.54, 1.89]) of variation. The presence of an interaction highlights a specific focus for intervention, but the negative results here indicate a limited contribution from polygenic-environment interactions.

Latent subtypes of manic and/or irritable episode symptoms in two population-based cohorts.

BACKGROUND: Mood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart. AIMS: To identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability. METHOD: We used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores. RESULTS: Five latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including 'minimally affected', 'inactive restless', active restless', 'focused creative' and 'extensively affected' individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment. CONCLUSIONS: Findings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Probing the Pharmacological Binding Sites of P-Glycoprotein Using Umbrella Sampling Simulations.

The human multidrug transporter P-glycoprotein (P-gp) transports over 200 chemically diverse substrates, influencing their bioavailability and tissue distribution. Pharmacological studies have identified both competitive and noncompetitive P-gp substrates, but neither the precise location of the substrate binding sites, nor the basis of competitive and noncompetitive interactions has been fully characterized. Here, potential of mean force (PMF) calculations are used to identify the transport-competent minimum free energy binding locations of five compounds, Hoechst 33342, Rhodamine 123, paclitaxel, tariquidar, and verapamil to P-gp. Unrestrained molecular dynamics simulations were also performed to confirm the substrates were stable in the energy wells determined using the PMF calculations. All compounds had energy minima within the P-gp transmembrane (TM) pore. For Hoechst 33342 and Rhodamine 123, a second minimum outside the TM pore was also identified. Based on this and previous studies of nicardipine and morphine [ Subramanian et al. J. Chem. Inf. Model. 2015 , 55 , 1202 ], a general scheme that accounts for the observed noncompetitive and competitive substrate interactions with P-gp is proposed.

Transforming Summary Statistics from Logistic Regression to the Liability Scale: Application to Genetic and Environmental Risk Scores.

OBJECTIVE: Stratified medicine requires models of disease risk incorporating genetic and environmental factors. These may combine estimates from different studies, and the models must be easily updatable when new estimates become available. The logit scale is often used in genetic and environmental association studies; however, the liability scale is used for polygenic risk scores and measures of heritability, but combining parameters across studies requires a common scale for the estimates. METHODS: We present equations to approximate the relationship between univariate effect size estimates on the logit scale and the liability scale, allowing model parameters to be translated between scales. RESULTS: These equations are used to build a risk score on the liability scale, using effect size estimates originally estimated on the logit scale. Such a score can then be used in a joint effects model to estimate the risk of disease, and this is demonstrated for schizophrenia using a polygenic risk score and environmental risk factors. CONCLUSION: This straightforward method allows the conversion of model parameters between the logit and liability scales and may be a key tool to integrate risk estimates into a comprehensive risk model, particularly for joint models with environmental and genetic risk factors.

A potential new, stable state of the E-cadherin strand-swapped dimer in solution.

E-cadherin is a transmembrane glycoprotein that facilitates inter-cellular adhesion in the epithelium. The ectodomain of the native structure is comprised of five repeated immunoglobulin-like domains. All E-cadherin crystal structures show the protein in one of three alternative conformations: a monomer, a strand-swapped trans homodimer and the so-called X-dimer, which is proposed to be a kinetic intermediate to forming the strand-swapped trans homodimer. However, previous studies have indicated that even once the trans strand-swapped dimer is formed, the complex is highly dynamic and the E-cadherin monomers may reorient relative to each other. Here, molecular dynamics simulations have been used to investigate the stability and conformational flexibility of the human E-cadherin trans strand-swapped dimer. In four independent, 100 ns simulations, the dimer moved away from the starting structure and converged to a previously unreported structure, which we call the Y-dimer. The Y-dimer was present for over 90% of the combined simulation time, suggesting that it represents a stable conformation of the E-cadherin dimer in solution. The Y-dimer conformation is stabilised by interactions present in both the trans strand-swapped dimer and X-dimer crystal structures, as well as additional interactions not found in any E-cadherin dimer crystal structures. The Y-dimer represents a previously unreported, stable conformation of the human E-cadherin trans strand-swapped dimer and suggests that the available crystal structures do not fully capture the conformations that the human E-cadherin trans homodimer adopts in solution.

Understanding the accumulation of P-glycoprotein substrates within cells: The effect of cholesterol on membrane partitioning.

The apparent activity of the multidrug transporter P-glycoprotein (P-gp) is enhanced by the presence of cholesterol. Whether this is due to the direct effect of cholesterol on the activity of P-gp, its effect on the local concentration of substrate in the membrane, or its effect on the rate of entry of the drug into the cell, is unknown. In this study, molecular dynamics simulation techniques coupled with potential of mean force calculations have been used to investigate the role of cholesterol in the movement of four P-gp substrates across a POPC bilayer in the presence or absence of 10% cholesterol. The simulations suggest that the presence of cholesterol lowers the free energy associated with entering the middle of the bilayer in a substrate-specific manner. These findings suggest that P-gp substrates may preferentially accumulate in cholesterol-rich regions of the membrane, which may explain its enhanced transport activity.

Relative risk regression: reliable and flexible methods for log-binomial models.

Relative risks (RRs) are generally considered preferable to odds ratios in prospective studies. However, unlike logistic regression for odds ratios, the standard log-binomial model for RR regression does not respect the natural parameter constraints and is therefore often subject to numerical instability. In this paper, we develop a reliable and flexible method for fitting log-binomial models. We use an Expectation-Maximization (EM) algorithm where the multiplicative event probability is viewed as the joint probability for a collection of latent binary outcomes. This gives a simple iterative scheme that provides stable convergence to the maximum likelihood estimate. In addition to reliability, the method offers some flexible generalizations, including models with unspecified isotonic regression functions. We examine the method's performance using simulations and data analyses of the age-specific RR of mortality following heart attack. These analyses demonstrate the potential for numerical instability in RR regression and show how this can be overcome using the proposed approach. Source code to implement the method in R is provided as supplementary material available at Biostatistics online.

A tool for translating polygenic scores onto the absolute scale using summary statistics.

There is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. We also evaluate methods for estimating polygenic score AUC/R2 from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute ( https://opain.github.io/GenoPred/PRS_to_Abs_tool.html ). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.

The effects of oxidised phospholipids and cholesterol on the biophysical properties of POPC bilayers.

Oxidation of unsaturated membrane phospholipids by oxidative stress is associated with inflammation, infection, numerous diseases and neurodegenerative disorders. Lipid oxidation is observed in experimental samples when the parent lipid is exposed to oxidative stressors. The effect of phospholipid oxidation on the properties of biological membranes are still being explored, while low concentrations (0.1-2.0 mol%) of oxidised phospholipids are associated with disease states [1]. Previous computational studies have focused on the effect of high concentrations (~50 mol%) of oxidised phospholipids on binary lipid bilayers. This work systematically characterises the effect of lower concentrations (~10 mol%) of two oxidised lipid species, PoxnoPC (1-palmitoyl-2-(9'-oxo-nonanoyl)-sn-glycero-3-phosphocholine) or PazePC (1-palmitoyl-2-azelaoyl-sn-glycero-3-phosphocholine), on POPC/cholesterol and pure POPC bilayers. During µs atomistic simulations in pure POPC bilayers, PoxnoPC and PazePC reoriented their oxidised sn-2 acyl chains towards the solution, and PazePC adopted an extended conformation. The addition of 20 mol% cholesterol not only modulated the fluidity of the bilayers; it also modulated the flexibility of the PoxnoPC oxidised sn-2 tail, reducing bilayer disorder. In contrast, the addition of cholesterol had little effect on bilayers containing PazePC. Our studies show that the effect of oxidised lipids on the biophysical properties of a multicomponent bilayer cannot be intuitively extrapolated from a binary lipid system.

Lipid-Based Inhibitors Act Directly on GlyT2.

The endogenous lipids N-arachidonylglycine and oleoyl-l-carnitine are potential therapeutic leads in the treatment of chronic pain through their inhibition of the glycine transporter GlyT2. However, their mechanism of action is unknown. It has been hypothesized that these "bioactive" lipids either inhibit GlyT2 indirectly, by significantly perturbing the biophysical properties of the membrane, or directly, by binding directly to the transporter (either from a membrane-exposed or solvent-exposed binding site). Here, we used molecular dynamics simulations to study the effects of the lipids anandamide, N-arachidonylglycine, and oleoyl-l-carnitine on (a) the biophysical properties of the bilayer and (b) direct binding interactions with GlyT2. During the simulations, the biophysical properties of the bilayer itself, for example, the area per lipid, bilayer thickness, and order parameters, were not significantly altered by the presence or type of bioactive lipid, regardless of the presence of GlyT2. Our work, together with previous computational and experimental data, suggests that these acyl-inhibitors of GlyT2 inhibit the transporter by directly binding to it. However, these bioactive lipids bound to various parts of GlyT2 and did not prefer a single binding site during 4.5 µs of simulation. We postulate that the binding site is located at the solvent-exposed regions of GlyT2. Understanding the mechanism of action of these and related bioactive lipids is essential in effectively developing high-affinity GlyT2 inhibitors for the treatment of pain.

Is protein structure enough? A review of the role of lipids in SLC6 transporter function.

SLC6 neurotransmitter transporters facilitate the Na+- and Cl--dependent uptake of amino acids and amino acid derivatives into cells. Disrupting transport leads to a range of neurological disorders. However, the SLC6 substrate transport mechanism is a topic of ongoing debate. Here, we review the prominent SLC6 substrate transport mechanisms through the lens of molecular dynamics simulations. SLC6 transporters are membrane proteins, yet their transport mechanism(s) have largely been studied without considering the impacts of synaptic lipid composition, or endogenous lipid modulators, on transporter structure and function. In this review, we highlight the importance of studying membrane transporters in an appropriate membrane model, and present opportunities for the community to glean understanding and insight into SLC6 transporter structure and function-in particular transport mechanism(s)-when both membrane lipids and endogenous lipid modulators are considered.

Mutation p.R356Q in the Collybistin Phosphoinositide Binding Site Is Associated With Mild Intellectual Disability.

The recruitment of inhibitory GABAA receptors to neuronal synapses requires a complex interplay between receptors, neuroligins, the scaffolding protein gephyrin and the GDP-GTP exchange factor collybistin (CB). Collybistin is regulated by protein-protein interactions at the N-terminal SH3 domain, which can bind neuroligins 2/4 and the GABAAR α2 subunit. Collybistin also harbors a RhoGEF domain which mediates interactions with gephyrin and catalyzes GDP-GTP exchange on Cdc42. Lastly, collybistin has a pleckstrin homology (PH) domain, which binds phosphoinositides, such as phosphatidylinositol 3-phosphate (PI3P/PtdIns3P) and phosphatidylinositol 4-monophosphate (PI4P/PtdIns4P). PI3P located in early/sorting endosomes has recently been shown to regulate the postsynaptic clustering of gephyrin and GABAA receptors and consequently the strength of inhibitory synapses in cultured hippocampal neurons. This process is disrupted by mutations in the collybistin gene (ARHGEF9), which cause X-linked intellectual disability (XLID) by a variety of mechanisms converging on disrupted gephyrin and GABAA receptor clustering at central synapses. Here we report a novel missense mutation (chrX:62875607C>T, p.R356Q) in ARHGEF9 that affects one of the two paired arginine residues in the PH domain that were predicted to be vital for binding phosphoinositides. Functional assays revealed that recombinant collybistin CB3SH3- R356Q was deficient in PI3P binding and was not able to translocate EGFP-gephyrin to submembrane microaggregates in an in vitro clustering assay. Expression of the PI3P-binding mutants CB3SH3- R356Q and CB3SH3- R356N/R357N in cultured hippocampal neurones revealed that the mutant proteins did not accumulate at inhibitory synapses, but instead resulted in a clear decrease in the overall number of synaptic gephyrin clusters compared to controls. Molecular dynamics simulations suggest that the p.R356Q substitution influences PI3P binding by altering the range of structural conformations adopted by collybistin. Taken together, these results suggest that the p.R356Q mutation in ARHGEF9 is the underlying cause of XLID in the probands, disrupting gephyrin clustering at inhibitory GABAergic synapses via loss of collybistin PH domain phosphoinositide binding.

Identification of an allosteric binding site on the human glycine transporter, GlyT2, for bioactive lipid analgesics.

The treatment of chronic pain is poorly managed by current analgesics, and there is a need for new classes of drugs. We recently developed a series of bioactive lipids that inhibit the human glycine transporter GlyT2 (SLC6A5) and provide analgesia in animal models of pain. Here, we have used functional analysis of mutant transporters combined with molecular dynamics simulations of lipid-transporter interactions to understand how these bioactive lipids interact with GlyT2. This study identifies a novel extracellular allosteric modulator site formed by a crevice between transmembrane domains 5, 7, and 8, and extracellular loop 4 of GlyT2. Knowledge of this site could be exploited further in the development of drugs to treat pain, and to identify other allosteric modulators of the SLC6 family of transporters.

Clinical, ultrasound and molecular biomarkers for early prediction of large for gestational age infants in nulliparous women: An international prospective cohort study.

OBJECTIVE: To develop a prediction model for term infants born large for gestational age (LGA) by customised birthweight centiles. METHODS: International prospective cohort of nulliparous women with singleton pregnancy recruited to the Screening for Pregnancy Endpoints (SCOPE) study. LGA was defined as birthweight above the 90th customised centile, including adjustment for parity, ethnicity, maternal height and weight, fetal gender and gestational age. Clinical risk factors, ultrasound parameters and biomarkers at 14-16 or 19-21 weeks were combined into a prediction model for LGA infants at term using stepwise logistic regression in a training dataset. Prediction performance was assessed in a validation dataset using area under the Receiver Operating Characteristics curve (AUC) and detection rate at fixed false positive rates. RESULTS: The prevalence of LGA at term was 8.8% (n = 491/5628). Clinical and ultrasound factors selected in the prediction model for LGA infants were maternal birthweight, gestational weight gain between 14-16 and 19-21 weeks, and fetal abdominal circumference, head circumference and uterine artery Doppler resistance index at 19-21 weeks (AUC 0.67; 95%CI 0.63-0.71). Sensitivity of this model was 24% and 49% for a fixed false positive rate of 10% and 25%, respectively. The addition of biomarkers resulted in selection of random glucose, LDL-cholesterol, vascular endothelial growth factor receptor-1 (VEGFR1) and neutrophil gelatinase-associated lipocalin (NGAL), but with minimal improvement in model performance (AUC 0.69; 95%CI 0.65-0.73). Sensitivity of the full model was 26% and 50% for a fixed false positive rate of 10% and 25%, respectively. CONCLUSION: Prediction of LGA infants at term has limited diagnostic performance before 22 weeks but may have a role in contingency screening in later pregnancy.

Clinical and biochemical factors associated with preeclampsia in women with obesity.

OBJECTIVE: To compare early pregnancy clinical and biomarker risk factors for later development of preeclampsia between women with obesity (body mass index, BMI ≥30 kg/m2 ) and those with a normal BMI (20-25 kg/m2 ). METHODS: In 3,940 eligible nulliparous women from the Screening for Pregnancy Endpoints (SCOPE) study, a total of 53 biomarkers of glucose and lipid metabolism, placental function, and known markers of preeclampsia were measured at 14 to 16 weeks' gestation. Logistic regression was performed to identify clinical and biomarker risk factors for preeclampsia in women with and without obesity. RESULTS: Among 834 women with obesity and 3,106 with a normal BMI, 77 (9.2%) and 105 (3.4%) developed preeclampsia, respectively. In women with obesity, risk factors included a family history of thrombotic disease, low plasma placental growth factor, and higher uterine artery resistance index at 20 weeks. In women with a normal BMI, a family history of preeclampsia or gestational hypertension, mean arterial blood pressure, plasma endoglin and cystatin C, and uterine artery resistance index were associated with preeclampsia, while high fruit intake was protective. CONCLUSIONS: Women with obesity and a normal BMI have different early pregnancy clinical and biomarker risk factors for preeclampsia.

Reproductive fitness and genetic risk of psychiatric disorders in the general population.

The persistence of common, heritable psychiatric disorders that reduce reproductive fitness is an evolutionary paradox. Here, we investigate the selection pressures on sequence variants that predispose to schizophrenia, autism, bipolar disorder, major depression and attention deficit hyperactivity disorder (ADHD) using genomic data from 150,656 Icelanders, excluding those diagnosed with these psychiatric diseases. Polygenic risk of autism and ADHD is associated with number of children. Higher polygenic risk of autism is associated with fewer children and older age at first child whereas higher polygenic risk of ADHD is associated with having more children. We find no evidence for a selective advantage of a high polygenic risk of schizophrenia or bipolar disorder. Rare copy-number variants conferring moderate to high risk of psychiatric illness are associated with having fewer children and are under stronger negative selection pressure than common sequence variants.

Rare genetic variation in UNC13A may modify survival in amyotrophic lateral sclerosis.

Our objective was to identify whether rare genetic variation in amyotrophic lateral sclerosis (ALS) candidate survival genes modifies ALS survival. Candidate genes were selected based on evidence for modifying ALS survival. Each tail of the extreme 1.5% of survival was selected from the UK MND DNA Bank and all samples available underwent whole genome sequencing. A replication set from the Netherlands was used for validation. Sequences of candidate survival genes were extracted and variants passing quality control with a minor allele frequency ≤0.05 were selected for association testing. Analysis was by burden testing using SKAT. Candidate survival genes UNC13A, KIFAP3, and EPHA4 were tested for association in a UK sample comprising 25 short survivors and 25 long survivors. Results showed that only SNVs in UNC13A were associated with survival (p = 6.57 × 10-3). SNV rs10419420:G > A was found exclusively in long survivors (3/25) and rs4808092:G > A exclusively in short survivors (4/25). These findings were not replicated in a Dutch sample. In conclusion, population specific rare variants of UNC13A may modulate survival in ALS.